Spatial variability in soil physicochemical properties across forest disturbances in the different forest divisions of Jharkhand, India

Spatial variability in soil physicochemical properties across forest disturbances in the different forest divisions of Jharkhand, India | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spatial variability in soil physicochemical properties across forest disturbances in the different forest divisions of Jharkhand, India Shambhu Nath Mishra, S. Sivaranjani, Pramod Chandra Lakra, Shachi Pandey, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6357879/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 18 You are reading this latest preprint version Abstract Forests provide a wide range of ecosystem processes and services, including net primary production, climate regulation, water regulation, and nutrient cycling. However, forest ecosystems face immense pressure from various human-induced and natural disturbances, which significantly contribute to their degradation. The restoration of forests is a critical global concern, particularly in Jharkhand, India, where forests are highly vulnerable to mining activities and land degradation. The restoration and maintenance of forests are required which cannot be comprehended without understanding of soil. In this context, the present study was conducted to analyse the physicochemical properties of forest soils under forest disturbances across all forest divisions of Jharkhand. Stratification was conducted by grouping areas based on forest type (dense/moderately dense forests and open/scrub/degraded forests) in 31 forest divisions. Within each stratum, random sampling points were selected for each division. Soil samples were collected at three depths: 0–30 cm, 30–60 cm, and 60–90 cm. The collected soil samples were analysed for 12 soil parameters including, basic parameters (pH, EC and Organic Carbon), major nutrients (Available Nitrogen (AN), Available phosphorus (AP) and Exchangeable Potassium (AK)), secondary nutrients (Available Sulphur (AS)) and micronutrients (Available.) Zinc (Zn), boron (B), iron (Fe), manganese (Mn) and copper (Cu)). The result of two-way ANOVA showed a significant ( P MDF > DF and 0–30 > 30–60 > 60–90 cm at the disturbance and soil depth respectively. The correlation matrix among soil parameters recorded a positive relation between Fe with Mn (0.975 P < 0.001 ), EC with pH (0.243 P < 0.001) and AK to AP (0.221 P < 0.001). The calculation of recommended dose of fertilizers revealed that most forest divisions in Jharkhand require additional NPK, except for Bokaro, Chatra South, Deoghar, Giridih East, Giridih West, Jamtara, Koderma, Medininagar, Sahibganj, and Saraikela divisions. This research identifies nutrient deficiencies in the soil and provides recommendations for calculating fertilizer doses to support sustainable management practices and enhance plantation success. Soil nutrients Forest disturbance Land restoration Nutrient recommendation plantation Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Soil is considered an essential natural resource that sustains vegetation and microbial ecosystems by providing nutrients and water. Composed of minerals, water, air, and organic matter, soil supports the origin and maintenance of living ecosystems. In forest ecosystems, soil regulates species distribution, the production of forest goods, wildlife habitats, species richness, and diversity (Schoonover and Crim 2015). Covering over 30% of the Earth's surface, forests provide numerous benefits to human society, fundamentally supported by forest soils (FAO 2010). However, over time, the balance between forest and soil systems has been disrupted by various natural and human activities. Forest soils develop from the interaction of climate, organisms, and geological parent materials, varying by topographic locations (Hillel and Hatfield 2005) are characterized by diverse soil-dwelling organisms, the recycling of organic materials, substantial litter layers, and deeply rooted trees. Forest soils enhance the plantations resilience against natural disasters by providing physical support, regulating water and gas exchange, and supplying 24 of the 26 essential elements needed by plants and animals. The soil health is the capacity of soil to function as a dynamic, living ecosystem that supports humans, animals, and plants and collective properties of forest soils that sustain forest ecosystem functioning (Lehmann et al., 2020 ). Soil health plays a crucial role in shaping agricultural policies, meeting stakeholder needs, and ensuring sustainable supply chain management. However, over time, forest soil health has faced degradation due to deforestation, disturbances such as harvesting and fires, climate change and acid rain. Forest soils face challenges, including acidification and biodiversity loss, necessitating effective monitoring (Wellbrock et al. , 2024). Deforestation depletes soil organic carbon (Hosseini et al. , 2024; Nave et al. , 2024), while climate change accelerates decomposition of organic matter (Amante and Wedajo 2024), and reduce soil moisture (Nigussie et al. , 2024). Soil health indicators, such as nutrient levels and microbial activity, deteriorate following forest conversion, emphasizing the negative effects on soil quality (Kooch et al. , 2024). Soil ecosystem services declined by 60% between 1950 and 2010 (León et al. , 2014) with 33% of the Earth's land surface impacted by some form of soil degradation (Bini 2009 ). In arid and semi-arid regions, desertification a specific form of land degradation affects around 41% of the continental area (Sterk et al., 2001 ). Carbon emissions from forest degradation are estimated to range between 40% and 212% of those from deforestation globally (Baccini et al ., 2017). The FAO, 2022 warned that 90% of the Earth's topsoil could be at risk by 2050 with higher degradation rate in tropical regions. Tropical forests accounting for 45% of the global forest cover are rapidly declining at 3.7 million hectares annually (Weisse et al., 2021 ). In India, dry and moist deciduous forests dominate 58% of the country's total forest area but face deforestation, agricultural intensification, mining, climate change, logging, and forest fires (Kumar et al., 2022 ; Shukla et al., 2022 ; Jakhar and Punia 2022 ; Thakur et al., 2024 ). Jharkhand tropical moist deciduous and tropical dry deciduous forests suffer from overexploitation (Kumar and Saikia 2020 ), anthropogenic disturbances such as grazing, browsing, removal of ground cover for grass collection, and the cutting and lopping of trees (Sagar and Singh 2003; Sanji et al. , 2020; Kumar and Saikia 2020 ). Mining, particularly in Jharia coalfield, depletes vegetation and topsoil making it prone to soil erosion (Singh et al. , 2016; Kumar et al. , 2019; Rai and Paul 2011). These anthropogenic actions have significantly degraded soil quality, leading to further forest ecosystem deterioration (Sanji et al. , 2020) therefore, protecting forest soil health is crucial for sustaining future forest resources. Study on forest nutrition in tropical forests within developing countries has been relatively slow (Alvarado 2015) in compare to studies on forest nutrition that began in Cuba (1970), Brazil (1980), and Central America (Herrero 2001). In India, studies highlighted the role of forest soil health in conserving degraded forest ecosystems (Lehmann et al., 2020 ; Shao et al., 2020 ; Salam 2024 ), importance of soil health in restoring and sustainably managing forest ecosystems (Wani and Singh 2021; Shukla et al. , 2020; Kavya et al. , 2020; Patra et al. , 2017) and soil nutrient availability and forest soil quality (Pal et al. , 2013; Panwar et al. , 2011; Kaur et al. , 2021). Essential forest ecosystem services such as oxygen production, carbon sequestration, biodiversity and land management rely on the availability of soil nutrients (Grau et al. , 2017; Binkley and Fisher 2019; Brockerhoff et al. , 2017). While studies in Jharkhand have examined soil carbon and nutrient accumulation (Roy et al., 2010 ), forest soil health (Frost et al., 2019 ; Kumari et al., 2024 ), the relationship between biodiversity and soil properties in Sal forests (Mishra et al., 2024 ), and the assessment of soil and vegetation health (Patra et al., 2015 ; Ahmad et al., 2018 ; Kumar et al., 2024 ), a comprehensive study on forest soil health across all forest divisions remains absent. Jharkhand's undulating plateau, drought-prone landscape (Bhattacharyya et al. , 2013) with anthropogenic activities, adversely affects key soil functions. Despite covering approximately 29.81% of the state, forests which host a diverse range of vegetation types, including dry peninsular Sal forests, northern dry mixed deciduous forests, moist peninsular Sal forests, dry deciduous scrubland, and dry bamboo brakes (FSI 2023) face degradation due to anthropogenic disturbances, high temperatures and heavy rainfall. The Forest Survey of India (2021) reports only 3.26% of Jharkhand's total forest as very dense forest, indicating urgent need for forest ecosystem restoration. Around 40% of Jharkhand's population comprises tribal communities relies on forests for their livelihoods (Census of India 2011). Effective forest management required assessing soil nutrient status across different forest conditions. The study aims to analyze the physicochemical properties of forest soils under varying vegetation types and adjacent degraded lands across all forest divisions, to evaluate the interactions between soil nutrients and their interrelationships, and identify nutrient deficiencies and propose sustainable management practices. It emphasizes the importance of soil as a critical component of forest restoration, serving as the initial step toward improving productivity and restoring degraded forest ecosystems. Understanding forest soil health is key to restoring degraded forests and securing livelihood for resource-poor communities. Material and methods Study area The present study was conducted in Jharkhand, lies between latitudes 22°00'–24°37'N and longitudes 83°15'–87°01'E. The state spans a total geographical area of 79,714 km², which accounts for approximately 2.4% of India’s total geographical area. The elevation of the region ranges from 6 m to 1366 m above mean sea level. The climate of the state has a humid sub-tropical to tropical wet and dry tropical in nature from north to south-east. The state has received average moderate rainfall (950 to 1300 mm), and temperatures vary from 6ºC to 47ºC in winter and summer, respectively (Climate of Jharkhand 2013). According to Champion and Seth (1968), the forests of Jharkhand are classified under Sub-group 5B, namely the Northern Tropical Dry Deciduous Forest. The Sub-Types and other sub-types of other Sub-group are: (i) 5B/Cle) 5B/C1-Northern dry Sal bearing forest (e.g., Dry Peninsular Sal Forest); (ii) 5B/C2-Northern Tropical dry mixed deciduous forest; (iii) 5B/Dsl-Dry deciduous scrub forest; (iv) 5B/E2-Boswellia forest; (v) 5B/E9-Dry bamboo brakes; (vi) 8A/C3-Central Indian sub-tropical Hill Forest. From ancient times, the state of Jharkhand has had a special relationship with forest. Jharkhand has a total of 23,765.78 km 2 forest cover which represent about 29.81% of the total geographical area of the state out of which, 2,635.35 km 2 (3.31%) is cover with very dense forest, followed by 9,640.99 km 2 (12.09%) moderate dense and 11,489.44 km 2 (14.41%) open forest (FSI, 2023). According to soil taxonomym Jharkhand’s soils are classified into three orders, Entisols, Inceptisols, and Alfisols, based on their morphological, physical, and chemical characteristics. Soils found in hilly areas, such as flat-topped hills, foothills, uplands, and low-lying land, belong to the Alfisols order due to the presence of argillic strata in the subsoils while, soils of hill slopes and hill slope valleys are categorized under the Inceptisols order (FAO, 2007). The state falls within agro-climatic zone 7, the Eastern Plateaus & Hills Region and is divided into these sub-zones i.e . The Central and Northeastern Plateau Sub-Zone (Zone IV), the Western Plateau Sub-Zone (Zone V), and the Southeastern Plateau Sub-Zone (Zone VI). The state's important tree species include Shorea robusta Gaertner f., Madhuca longifolia var., Adina cordifolia (Roxb.) Brandis, Diospyros tomentosa and Buchanania cochinchinensis (Lour.) Almeida. As per Census (2011), the population of Jharkhand state has 32,988,134 of which male and female are 16,930,315 and 16,057,819 respectively. The state is home to 30 different tribal groups, comprising about 40% of the total population. The prominent indigenous tribal communities ( Adivasis ) include the Santhals, Mundas, Oraons, Hos, Kharia, Bhumij, Birhors, who have a deep connection with nature. Their day-to-day sustenance depends on forest resources like, fruits, roots of some plants, flowers, edible mushrooms, tubers of some plant fuel wood. They also depend on medicinal herbs for traditional healing practices and preserve the sacred groove in the vicinity of the forest known as Sarna . Methodology Sampling The state forest is divided into 31 territorial forest divisions (Table 1). The forest cover within these divisions is categorized based on canopy cover, as very dense forest (VDF), moderately dense forest (MDF), open forest (OF), scrub forest (SF), and non-forest (NF), following the FSI (2023) protocol. Soil samples were collected from these forest divisions to analysed various physicochemical parameters. A uniform sampling protocol was prescribed for collection of soil samples from all forest divisions of the state. A 5×5 km grid-based Remote Sensing and GIS approach was adopted to determine the sampling points which varied depending on the specific strata covering five major forest/vegetation classes, sixteen forest types, (as per Champion and Seth’s classification) including detailed forest division-specific data. The other data including names, geographical coordinates, elevation, slope, aspect, hill-shade, and soil classification (as per FAO) were considered. Special attention was given to the changeability of degraded lands, such as open forests and scrub forests, to simplify future afforestation actions by the forest departments in these areas. Within each stratum, random sampling points were selected for each division. Soil samples were collected at three depths: 0–30 cm, 30–60 cm, and 60–90 cm. In cases where it was not feasible to dig or auger beyond a certain depth, the reasons were recorded in the field data sheet for future reference. Sample preparation A total of 261 soil samples were collected from 87 soil sampling points across four types of forest areas, at three depths (0–30 cm, 30–60 cm, and 60–90 cm). Among these samples, very dense and moderately dense forest were categorised as non-degraded forest (NDF), open forest were considered as moderately-degraded forest (MDF), while scrub forest and no forest zone have recognised as the degraded forest (DF). The soil samples were dried in the shade followed by hand crushing to remove gravels and plant residues. The air-dried soil samples that had passed through a 2.0 mm sieve were analysed for 12 soil parameters including, basic parameters (pH, EC and Organic Carbon), major nutrients (Available Nitrogen (N), Available phosphorus (P) and Exchangeable Potassium (K)), secondary nutrients (Available Sulphur (S)) and micronutrients (Available.) Zinc (Zn), boron (B), iron (Fe), manganese (Mn) and copper (Cu)). Analysis The prepared soil samples were used to estimate soil physicochemical properties. The pH and Electrical Conductivity (EC) were measured using the potentiometric method. Soil organic carbon content was analysed using the wet digestion method (Walkley and Black 1934). For major nutrient analysis: Available nitrogen (AN) was determined through the alkaline permanganate method (Subbiah and Asija 1956) using an automated Kjeldahl Nitrogen Analyzer equipped with a digestion unit and accessories. While available phosphate (AP) was analysed using Olsen’s and Bray’s method (1954) with a UV Spectrophotometer. Available potassium (AP) was measured using the Neutral Normal Ammonium Acetate method (Stanford and English, 1949) with a Flame Photometer. In case of secondary nutrients, available sulphur (AS) was estimated through the Turbidimetric method (Hot Water, CaCl₂, or Phosphate method) (Chesnin and Yien 1950) using an Atomic Absorption Spectrophotometer with appropriate lamps, N₂O and acetylene gas cylinders, regulators, and other accessories. However, for micronutrients (Available.) Zinc (Zn), iron (Fe), manganese (Mn) and copper (Cu)) analysis, DTPA (diethylene triaminepentaacetic acid) method (Lindsay and Norvell 1978) were used through Atomic Absorption Spectrophotometer with suitable lamps, N2O & Acetylene Gas Cylinders, regulators etc. Although for Boran estimation, Hot-water Soluble Boron method (Gupta 1967) was used through Atomic Absorption Spectrophotometer. Statistical analysis The original data was described descriptively, involving the computation of mean and standard deviation. The soil physico-chemical parameters were compared using a two-way ANOVA across different forest disturbance classes (NDF, MDF and DF) and soil depth (0-30, 30-60 and 60-90 cm). Duncan's multiple range test (DMRT) was additionally utilized to evaluate differences among several groups of variables. Pearson's correlation coefficients were performed among soil physico-chemical parameters to calculated the characteristics link of these parameters. Furthermore, principal component analysis (PCA) was applied to discern disparities and determine the extent to which the total variance of soil physico-chemical parameters contributed to the overall variation. All experimental data underwent processing and analysis using SPSS version 23 and GraphPad Prism version 6. The PCA was performed using Minitab version 19 software package. Result The forests of Jharkhand are primarily of two types: tropical dry deciduous forest and tropical moist deciduous forest. These forests are further divided into different forest divisions based on their characteristics or properties, such as non-degraded, medium-degraded, and degraded forests. According to the Forest Department of Jharkhand, the state forest is divided into 31 territorial forest divisions. Soil samples from these divisions were collected to analyse various physicochemical parameters. Variability of soil pH, EC and organic carbon in different forest divisions In the present study, the physicochemical properties of forest soil have significantly affected by forest disturbances (NDF MDF and DF) and soil depth (0–30, 30-60 and 60-90 cm). Two-way ANOVA showed a significant ( P <0.05 ) variation in forest pH, EC and OC at disturbance as well as soil depth factors among 31 forest division of Jharkhand state (Table 1). In case of the forest disturbances, the highest pH was recorded in the NDF forest for Garhwa north (6.36±0.42) followed by Ranchi (6.31±0.22), Medininagar (6.22±0.37) and Hazaribagh West (6.16±0.44) forest division at 0-30 cm depth. However, the lowest pH was reported at Saraikera (4.75±0.39) at 60-90 soil depth followed by Jamtara (4.88±0.51), Kolhan (4.89±0.33) and Simdega (4.99±0.65) at 30-60 cm depth. In general, the trends of soil pH for non-degraded forest class were usually decreases from increasing soil depth for all the forest divisions. In respect to the MDF classes, Hazaribagh East (6.72±3) reported largest pH at 60-90 cm, which decrease from Garhwa north (6.50±0.29), Ranchi (6.31±0.20) to Saranda (6.29±0.46) at 0-30 cm depth. Whereas, lowest pH evaluated for Saraikera (4.43±0.28) at 0-30 cm, Giridih West (4.71±0.55) and Giridih East (4.73±0.43) at 60-90 cm. Similarly for DF, pH ranges from 6.92 to 4.77 for all forest divisions, which gradually decreases from increase of the soil depth (Table 1). The two-way ANOVA analysis revealed, change in soil pH occurs due to forest disturbances were recorded in Dhalbhum, Giridih East, Latehar, Saraikera forest divisions. Whereas, variation due to soil depths were recorded in Deoghar, Dhanbad, Garhwa north, Giridih East, Giridih West, Jamtara, Koderma, Medininagar, Ranchi . On the other hand, only Giridih East, Gumla and Hazaribagh West Forest divisions had reported significantly changes in soil pH by forest disturbance × soil depth at ( P < 0.001 ), ( P < 0.01 ) and ( P < 0.05 ) (Table 1). Similar to the soil pH, the soil EC were also shown a variable result at forest disturbances as well as soil depth in different forest divisions. The highest EC for NDF was reported for Dhanbad (173.40±55 dS/m) followed by Ranchi (119.91±47 dS/m) and Simdega (106.63±51 dS/m) for 0-30 cm. This indicates high soil conductivity, possibly due to increased salinity or ion concentration. Whereas, lowest EC recorded for Deoghar (22.86±3 dS/m) followed by Lohardaga (22.91±10 dS/m) Dumka (32.84±14 dS/m) and forest divisions for 60-90 cm soil depths. In MDF, the most EC observed in Gumla (136.27±74 dS/m), which consequently decreases to Saranda (106.19±41 dS/m), Dhanbad (97.54±81 dS/m) and Sahibganj (92.86±16 dS/m) forest divisions for 0-30 cm. A general decreasing trend in EC was observed across all forest divisions as soil depth increased, from shallow to deeper layers. Additionally, in the case of forest disturbance, EC also decreased from the non-degraded forest class to the degraded forest class across all soil depths in most forest divisions (Table 1). Further, the distribution of OC in case of forest disturbances, the largest OC (%) reported for Sahibganj in DF (1.65±0.40 %) which decreased to NDF (1.41±0.85 %) and MDF (1.13±0.40%) forest classes at 0-30 soil depth. The second largest OC were recorded for Bokaro in DF ( 1.64±0.28%) that similarly declined to NDF (1.31±0.23%) forest classes at 0-30 cm depth. On the other hand, the lowest OC obtained for Ranchi in DF (0.09±0.08%) with subsequent decreased in NDF (0.15±0.10%) and MDF (0.23±0.13%) forest classes. This trend was continued for Gumla, where minimum OC recorded in DF (0.09±0.05%) with decreased in NDF (0.19±0.19%) and MDF (0.20±0.12%) forest classes. In most of the cases, the amount of OC generally decreases with increase of the soil depth for all forest classes in most of the forest divisions. In this investigation, 9 forest divisions has recorded (>1%) of organic carbon for nondegraded forest classes, while 10 forest divisions in medium degraded forest and 8 forest divisions for degraded forest divisions. There was seven forest division including Chatra North, Chatra South, Dhalbhum, HZB East, Kolhan, Latehar, Ranchi, which OC content significantly altered through forest disturbance. On the other hand, 12 forest divisions such as Deoghar, Dhalbhum, Dhanbad, Dumka, Giridih East, Godda, Gumla, HZB East, Jamtara, Khunti, Koderma and Ranchi had affected via. soil depth. Only Bokaro forest divisions significantly ( P < 0.05 ) altered by disturbance × soil depth. Variability of soil NPK in different forest divisions NPK fertilizers are important because they contain three essential nutrients for plant growth and health: nitrogen (N), phosphorus (P), and potassium (K). These nutrients can be partly supplied by the soil and should be partly added with organic manures and fertilizers. The NPK analysis indicating that these nutrients shown variation at forest disturbances and soil depth. The forest divisions including, Bokaro, Chatra South, Deovghar Dhalbhum, Dhanbad, Garhwa North, Giridih East, Gumla, Hazaribagh East, Hazaribagh West, Jamtara, Koderma, Lohardaga, Ranchi, Saraikera, Sahibganj shown variation in AN due to forest disturbances at ( P < 0.001 ), ( P < 0.01 ) and ( P < 0.05 ). On the other hand, Bokaro, Chatra North, Chatra South, Deovghar, Dhalbhum, Dhanbad, Giridih East, Godda, Giridih West, Gumla, Hazaribagh East, Hazaribagh West, Koderma, Kolhan, Pakur, Ranchi, Saraikera forest divisions recorded variation in AN via. soil depth. Similarly for AP, only Chatra South, Chatra North, Dhalbhum, Kolhan and Pakur, Porhat forest divisions observed changes by forest disturbances. However, Chatra South, Chatra North, Deovghar, Dhanbad, Giridih East, Giridih West, Gumla, Hazaribagh East, Hazaribagh West, Jamtara, Khunti, Koderma, Kolhan, Lohardaga, Medininagar, Ramgarh, Ranchi and Saranda reported differences through depth. Further, AK, recorded non-significant changes for forest disturbances in all forest divisions except Chatra South, Dhalbhum, Garhwa North, Giridih East, Hazaribagh East, Hazaribagh West, Latehar, Pakur, Saraikera Saranda and Sahibganj forest divisions. Whereas, significant changes for Giridih West, Gumla, Hazaribagh East, Hazaribagh West, Lohardaga, Medininagar and Ranchi divisions (Fig. 2). In case of NDF, the highest AN reported for Deoghar (776.24±130 kg/ha) followed by Hazaribagh West (578.48±56 kg/ha), Khunti (515.70±57 kg/ha) and Chatra South (508.17±53) at 0-30 cm depth. Whereas lowest AN recorded in Pakur (44.21±6 kg/ha) followed by Kolhan (59.20±10 kg/ha), Saraikera (76.81±17 kg/ha) and Lohardaga (77.26±27 kg/ha) at 60-90 cm depth. A general trend finds, where AN decrease with increasing the soil depth (Fig. 2). When we analysed the soil of MDF, maximum AN reported for Ramgarh (692.99±91 kg/ha) followed by Hazaribagh West (446.04±68 kg/ha) and Khunti (422.94±139 kg/ha) at upper depth of soil, while minimum value obtained for Lohardaga (43.84±7 kg/ha) 30-60 cm depth followed by Pakur (59.84±12 kg/ha), Chaibasa (63.36±14 kg/ha), Saraikera, Kolhan (67.09±11 kg/ha) and (96.92±17 kg/ha) lower most depth. A similar pattern of AN decrease from the upper to lower soil depths was also observed for DF, however the decrease became more pronounced with increasing soil depth in degraded forest. Where maximum AN was recorded in Bokaro (798.70±94 kg/ha), while the minimum in Dhalbhum (29.84±0.9 kg/ha). Further, the value of AP varied from 1.83 to 58.13 kg/ha for NDF in all forest divisions, where the highest AP reported for Koderma (58.13±4 kg/ha) with other forest divisions including, Chatra South (49.77±4 kg/ha), Medininagar (44.34±7 kg/ha), Deoghar (41.32±4 kg/ha) and Giridih East (40.85±2 kg/ha). This trend of AP was also continued for MDF classes in the same forest divisions (Fig. 2). On other hands, for DF classes more AP obtained for Koderma (54.11±2 kg/ha) with sequential decrease in Deoghar (41.32±4 kg/ha), Giridih East (35.64±0.73 kg/ha) and Giridih West (32.79±2 kg/ha) at upper depth of soil. Whereas very less AP recorded for Dhanbad (0.54±0.23 kg/ha), Gumla (2.93 ±0.98 kg/ha), Saranda (3.0±0.2 kg/ha), Ranchi (4.07± 0.42 kg/ha) and Hazaribagh West (5.00±0.12 kg/ha) in lower most soil depth (Fig. 2). Additionally for AK, the NDF class exhibited the highest AK in Chatra South (1028.33±159 kg/ha), Simdega (398.30±99 kg/ha), Jamtara (386.94±144 kg/ha), Garhwa South (345.14±110 kg/ha) and Sahibganj (337.21±81 kg/ha) at a depth of 30-60 cm. Although, a lower AK content obtained in Pakur (70.84±17 kg/ha), Lohardaga (75.18±15 kg/ha), Gumla (81.09±13 kg/ha), Hazaribagh West (83.57±18 kg/ha) and Giridih West (84.52±18 kg/ha) at lower soil depth. The MDF forest classes record a lowest AK for Hazaribagh West (62.29±10 kg/ha), Giridih East (64.14±14 kg/ha), Ramgargh (75.64± 14 kg/ha) and Hazaribagh East (86.46±20 kg/ha) at lower depth of forest soil. Just reverse to these, Chatra South (1115.52±168 kg/ha), Simdega (323.33±96 kg/ha), Chatra North (299.79±39 kg/ha) and Garhwa North (257.66±68 kg/ha), reported highest AK value at upper depth of the soil. In FD classes, largest AK content exhibited for Simdega (609.28±64 kg/ha), SBG (488.32±75 kg/ha), Bokaro (394.54±198 kg/ha) and Ranchi (355.15±11 1kg/ha) forest divisions. Although smallest AK investigated for Dhanbad (37.93±3 kg/ha), Ramgarh (42.63±10 kg/ha), Giridih East (47.65±2 kg/ha) and Hazaribagh Weste (62.29±10 kg/ha) forest divisions (Fig. 2). Variation in soil micronutrients content in different forest divisions The investigation of the micronutrients summarized that largest copper content recorded in Chaibasa (6.83±0.65 ppm), Saraikera (6.00±0.36 ppm), Lohardaga (5.86±0.81 ppm) and Khunti (4.64±0.54 ppm) forest divisions for NDF classes. Similarly, for the MDF classes, the copper content was highest in the Saraikera (7.24±0.42 ppm), Chaibasa (7.22±0.64 ppm), Lohardaga (6.42±0.57 ppm), and Medininagar (5.65±0.46 ppm) forest divisions. This trend persisted for the DF classes within the same forest divisions at the upper soil depth (Fig. 3). However, the lowest concentration for copper were recorded for Gumla (0.34±0.08 ppm), Ramgarh (0.39±0.03 ppm), Giridih East (0.41±0.10 ppm) and Hazaribagh East (0.50±0.18 ppm) in NDF, as well as Koderma (0.38±0.10 ppm), Hazaribagh West (0.49±0.10 ppm), Gumla (0.55±0.11 ppm) for MDF. Similarly for Zinc content, Lohardaga (4.59±1.15) and Giridih East (4.28±0.91 ppm) recorded most concentration for upper 0-30 cm depth and Dumka (0.06±0.01 ppm) and Gumla (0.13±0.03 ppm) reported least concentration for NDF at 60-90 cm depth. On the other hand, Giridih East (3.53±1.24 ppm) at 60-90 cm depth, Dhanbad (3.06±0.54 ppm) and Lohardaga (3.04±0.040 ppm) at 0-30 cm depth in MDF, while Lohardaga (5.23±1.11 ppm), Giridih East (4.59±0.70 ppm) and Jamtara (3.89±0.88 ppm) reported highest Zinc for DF classes. However, Dumka and Ranchi (0.15±0.04 ppm), Saranda (0.16±0.05 ppm), Gumla (0.22±0.01 ppm) for MDF and SBG (0.02±0.01 ppm), Gumla (0.11±0.05 ppm), Ranchi (0.13±0.02 ppm) reported smallest Zinc concentration at DF classes in lower soil depth. In Case Mn concentration, SBG (32.01±11.59 ppm), Dhanbad (30.58±0.79 ppm) and Simdega (20.88±2.15 ppm) recorded most concentration for NDF which consequently decreased for MDF and DF for all soil depth. The concentration of Mn was also decrease from upper soil depth to lower soil depth. These trends were also continued for Iron, Boran and Sulphur (Fig. 3). Discussions The study of forest soil health in Jharkhand, a state rich in biodiversity and forest cover, typically involves analysing various soil parameters that impact vegetation growth and maintain ecological balance. This research is crucial for ensuring ecosystem sustainability, particularly in forested regions like Jharkhand, India. Impact of forest disturbances on soil physico-chemical characteristics The study revealed that the pH values of soils across three forest classes (NDF, MDF, DF) ranged from 6.92 ± 0.42 to 4.73 ± 0.43, indicating slightly acidic conditions. Forest soils are typically acidic due to the breakdown of organic matter and minerals over time. The presence of carbonic acid, primarily derived from decaying plant tissues (both aboveground and belowground) and root exudates, contributes to these acidic conditions (Perween et al. , 2019). Similar acidic soil was also recorded by (Kumar et al., 2021 ) in different forest divisions of sol forest in Jharkhand. Further, the EC were varied from 173.40 ± 55 to 22.87 ± 3 dS/m, which subsequently tends to decline with increase of the forest disturbances and soil depth. Upon comparing this result with those of other authors, it is slightly lower than the EC range (207.08 to 116.66 dS/m) reported by Pandey et al., ( 2021 ) in the Jumar watershed of Jharkhand. A decrease in electrical conductivity (EC) is associated with fewer salts in the soil, indicating that the soil is neutral to slightly saline (Smith and Doran 2015 ). Experimental results have shown that soil EC is directly proportional to nutrient concentration and inversely proportional to soil depth (Othaman et al., 2020 ). Therefore, disturbed forests may exhibit lower soil EC due to poor vegetation cover and increased nutrient leaching (Mgelwa et al., 2024 ). Despite these the state of Jharkhand is a mineral-rich soils, where some areas, particularly those in Dhanbad, Ranchi, and Simdega forest divisions, show higher EC due to the influence of parent rock material and intensive mineralization, which contribute to a higher ionic content (Frost et al., 2019 ). In contrast, areas with sandy soils, such as Devgarh, Dumka, and Lohardaga, tend to have lower EC because sand has a reduced ion-retention capacity (Alam et al., 2020 ) (Table 1 ). The highest OC obtained for the degraded forest class in Sahibganj (1.65 ± 0.40%) can be attributed to several interrelated factors, including microbial activity, soil structure, and the effects of reforestation (Ke et al., 2023 ). For example, coniferous and mixed forests, under disturbed conditions, exhibited a higher proportion of macroaggregates, which contributed to greater soil organic carbon (SOC) accumulation (Ke et al., 2023 ). A similar study by Das and Maiti ( 2016 ) reported high carbon accumulation in reclaimed coalmine forest soils in the Jharia coalfields, Jharkhand. In contrast, higher OC levels in NDF and MDF forest classes support the theory that soil organic carbon content is primarily influenced by the balance between carbon incorporation and decomposition rates in the ecosystem (Huang and Song 2010 ; Deng and Shangguan 2017 ; Tiwari et al. , 2019). A list of studies has confirmed that soil organic carbon content decreases rapidly when natural vegetation cover is reduced due to disturbances such as deforestation, land use changes, and urbanization (Mehta et al., 2008 ; Van and Olsson 2011; Sharma et al. , 2022; Mayer et al., 2024 ). Vegetation cover can influence soil characteristics, with species-specific effects on the quantity and quality of plant litter. Natural forest ecosystems typically exhibit higher annual carbon input compared to degraded ones (Huang and Song, 2010 ). As a result, NDF and MDF forests contain more organic carbon than degraded forest systems. Our results were also justifiable with findings of Mgelwa et al., ( 2024 ), where forest disturbances significantly reduced soil OC, EC, and pH by 52%, 50%, and 98%, respectively, during the 21st century. Additionally, Ahirwal et al., ( 2021 ) finding that reported a decline in SOC stocks of 84% in afforested mined soils and 50% in agricultural soils compare to natural forest in Jharkhand. Effect of forest disturbances on soil NPK The NPK content of most forest divisions in Jharkhand state is influenced by both forest disturbances and soil depth (Fig. 2 ). The decline in NPK content with increasing disturbances in tropical deciduous forests can be attributed to several interrelated factors. Disturbances such as deforestation, extreme weather events, forest fires, and anthropogenic pressures disrupt nutrient cycling, resulting in nutrient depletion in both the soil and vegetation (Roy et al., 2010 ; Ahirwal et al., 2021 ; Sharma et al., 2023 ). These disturbances reduce nutrient cycling (Gautam and Mandal 2018 ), lead to a significant loss in biomass (31.63%) (Thakrey et al., 2022 ), and decrease overall species diversity (Thakrey et al., 2022 ), all of which affect the availability of essential nutrients like NPK. However, higher NPK levels in the surface layers of soil across different forest divisions are attributed to the accumulation and decomposition of litterfall, with the subsequent nutrient release pattern influenced by climatic factors (Bhalawe et al., 2013 ). These factors significantly affect the distribution of soil NPK content in relation to disturbance and soil depth. Similar studies have also reported significant changes in soil NPK content with disturbance and soil depth (Mehta et al., 2008 ; Bhattacharyya et al ., 2015; Tiwari et al ., 2019; Shankar and Garkoti 2024 ). The largest AN value for Deoghar, Hazaribagh West, Khunti, and Ramgarh in NDF and MDF at upper soil depths indicate a nutrient-rich forest cover. Non-degraded forests typically have higher soil organic matter, which enhances nitrogen retention and availability. This organic matter acts as a reservoir for nitrogen, supporting microbial activity and nutrient cycling (Wang et al., 2010 ). Factors such as moisture, water-holding capacity, organic carbon, and organic matter also contribute to supporting forest AN in the forest ecosystem (Kumar et al., 2021 ). In non-degraded forests, the presence of diverse plant species and healthy microbial communities promotes effective nitrogen utilization and minimizes leaching losses (Mo et al., 2003 ). These factors explain the healthy AN observed in the Deoghar, Hazaribagh West, Khunti, and Ramgarh forest divisions. An enhanced AN in the Khunti, Hazaribagh, and Ramgarh regions was also reported by Kumar et al., ( 2021 ) for Jharkhand forests. Similarly for high AP in the non-degraded forests of Koderma, Chatra South, Giridih East, and Medininagar at the upper soil horizon indicates the influence of weathering processes and the retention capabilities of natural forest ecosystems. Weathering transforms primary minerals into more soluble forms of phosphorus, particularly in the upper soil horizons (Tuyishime, 2022 ). Additionally, the decomposition of organic residues from plant material releases phosphorus in forms that are more accessible to plants, and humic substances can bind phosphorus, enhancing its availability for vegetation uptake (Izhar et al ., 2020). This increased availability of nutrients, such as NPK, in the upper soil layers of non-degraded forests can be attributed to the mineralization of leaf litter on the forest floor. Mineralized leaf litter releases AN and AP more rapidly compared to other nutrients (Uma et al., 2011 ). On the other hand, the higher potassium (K) content in the upper forest soil layers results from the decomposition of litterfall and the solubilization of insoluble K forms present in the soil due to organic decomposition products (Naik 2014 ). Naik ( 2014 ) and Pandey et al., ( 2020 ) also observed that the availability of nitrogen (N), phosphorus (P), and potassium (K) was higher in surface layers than in subsurface horizons under various tree species, with availability decreasing gradually with depth. Effect of forest disturbances on soil micronutrients Micronutrients play a critical role in forest ecosystems as they serve as key indicators of ecosystem health and stability. Soil micronutrient concentrations are essential for plant growth, development, high productivity, and maintaining balance in soil chemistry (Shepherd and Oliverio 2024 ). Across all forest classes (NDF, MDF, and DF) a significant increase was observed in the levels of DTPA-extractable micronutrients, with the relative distribution following the trend: NDF > MDF > DF (Fig. 3 ). However, the content of DTPA-extractable micronutrients decreased with increasing soil depth, indicating a negative impact as the soil profile deepened (Fig. 3 ). The influence of forest class on micronutrient content showed significantly higher levels in NDF compared to MDF and DF. This could be attributed to the higher organic matter content in NDF soils, driven by greater litter fall and root biomass, which improved soil aeration. Enhanced aeration likely prevented the oxidation and precipitation of micronutrients in bound forms while supplying chelating agents that increased micronutrient solubility and availability (Saha et al., 2019 ; Dhaliwal and Dhaliwal 2019 ). Our findings have aligned with the results of previous studies (Maini et al ., 2022; Tiwari et al ., 2019; Dhaliwal et al., 2023 ). Similarly, other studies have reported increased micronutrient content in tree-based systems and non-degraded forests, which could also be linked to exogenous carbon inputs from litter, root biomass, root exudates, and above-ground biomass. These inputs lower soil pH and redox potential, thereby enhancing the availability of micronutrients in the soil (Maini et al ., 2022; Mandal and Dhaliwal 2018 ). A similar study reported by Dhaliwal et al., ( 2023 ) in northwestern India, where a significant decline in micronutrient content observed with increasing soil depth. This trend was also supported by Singh and Sharma ( 2012 ), who observed similar decreases in micronutrient levels under plantation soils. Corelation matrix of soil physio-chemical characteristics The correlation matrix among the soil physico-chemical properties was prepared and shown in the (Fig. 4 ), where most of the major soil chemical property values shows good correlation. The soil Fe content has positively correlated with Mn (0.975 P < 0.001 ), EC with pH (0.243 P < 0.001 ) and AK to AP (0.221 P < 0.001 ). Soil EC has a weak positive relationship with OC (0.155 P < 0.001 ) and AK (0.140 P < 0.001 ). where soil OC shown positive association with AK (0.146 P < 0.001 ) and B (0.104 P < 0.001 ). The AN recorded a very weak positive relation with AP (0.128 P < 0.001) and AK (0.122 P < 0.001 ) while negative association with Cu (-0.144 P < 0.001 ) and Zn (-0.141 P < 0.001 ). A very strong positive correlation between Mn and Fe is observed due to the uneven distribution of Mn in forest soil. The highest concentration of Mn is typically found in the topmost soil layers, whereas Fe tends to be distributed more evenly under similar conditions. In deciduous forests, higher pH levels and lower dissolved organic carbon (DOC) enhance the retention of Mn and Fe oxides, thereby promoting their association (Rotter et al., 2017 ). Both Fe and Mn share similar biogeochemical cycling processes, such as weathering and precipitation as oxides. Sequential extraction studies reveal that Fe and Mn are often present in reducible and oxidizable fractions, which suggests their co-occurrence in soil profiles (Walna et al., 2010 ). Similar findings have also been reported by Zaitsev et al ( 2020 ) and Dhaliwal et al., ( 2023 ). The positive relationship between electrical conductivity (EC) and soil pH in forest soils can be explained by the concentration of hydrogen ions. A higher concentration of hydrogen ions in the soil leads to an increased EC. Consequently, low soil pH, caused by a large number of hydrogen ions, may promote higher soil EC (Aizat et al., 2014 ). Additionally, EC in forest soils is positively correlated with organic acids. Organic acids can dissolve minerals containing potassium and exchangeable calcium, which further contributes to the EC of soil systems (Osman and Osman 2013 ). This explains the positive correlation of EC with OC and AK, as supported by various studies (Osman and Osman 2013 ; Kim and Park 2013; Mazur et al ., 2023). The positive correlation between AN and both available potassium AK and AP in forest soil can be attributed to several interconnected mechanisms involving nutrient cycling and microbial activity. An increase in nitrogen availability often stimulates microbial biomass, which enhances the cycling of phosphorus and potassium, indicating a synergistic effect on nutrient dynamics (Zhu et al., 2015 ). Moreover, nitrogen fertilization can alter soil pH, influencing the solubility and availability of other nutrients (Karklina and Stola 2019). Similar positive correlations among macronutrients (AN, AP, OC, and AK) have also been recorded in forest ecosystems in Jharkhand (Naik 2014 ; Kumar and Saikia 2021 ; Kumar et al., 2024 ). Calculation of recommended doses of fertilizer to diagnose forest soil fertility Following investigation of the soil physicochemical properties across 31 forest divisions in Jharkhand state, we calculated the recommended doses of fertilizers in NPK (Nitrogen, Phosphorus, and Potassium) form to address soil fertility constraints at the forest division level. These recommendations were made for three types of fertilizers: inorganic fertilizers, organic farmyard manure (FYM), and vermicompost, to meet the NPK requirements. The recommended fertilizer doses were determined based on the standard values of various soil parameters, which were calculated as the average values for NDF each forest division. The evaluation revealed that all forest divisions in Jharkhand state require additional AN and AK, except Bokaro and Sahibganj for AN, and Chatra South and Saraikela for AK. However, seven forest divisions (Chatra South, Deoghar, Giridih East, Giridih West, Jamtara, Koderma, and Medininagar) do not require additional AP, unlike the other divisions. For inorganic fertilizer recommendations, the highest urea dose for nitrogen supplementation was suggested for Lohardaga (446.3 kg/ha), followed by Chaibasa (355.3 kg/ha) and Pakur (354.9 kg/ha), while the lowest dose was recommended for Ramgarh (81.9 kg/ha). Similarly, for phosphorus supplementation, the maximum dose of Single Super Phosphate (SSP) was suggested for Saranda (89.4 kg/ha), and the minimum for Khunti (20.4 kg/ha). For potassium supplementation, the highest potassium oxide (K₂O) doses were proposed for Hazaribagh East (170.0 kg/ha), Giridih West (160.9 kg/ha), and Hazaribagh East (157.9 kg/ha), while the lowest doses were suggested for Simdega (3.7 kg/ha), Jamtara (22.1 kg/ha), and Garhwa South (27.9 kg/ha). Regarding organic FYM recommendations for nitrogen requirements, the highest FYM dose was suggested for Lohardaga (41,056 kg/ha), and the lowest for Ramgarh (7,538 kg/ha). For phosphorus and potassium requirements, the largest FYM doses were recommended for Chaibasa (65.37 kg/ha) and Lohardaga (205.28 kg/ha), respectively, while the smallest doses were recommended for Ramgarh (15.08 kg/ha for phosphorus and 37.69 kg/ha for potassium). Conclusions Our findings indicate that forest disturbance classes and soil depths significantly influence the physico-chemical properties of forest soils across different forest divisions in Jharkhand, India. Most soil parameters recorded their maximum and minimum values in NDF and at lower soil depths, respectively. The higher nutrient content in NDF soils compared to MDF and DF can be attributed to greater litter return and minimal anthropogenic interference. A sequential decline in soil nutrients with increasing soil depth (30–60 cm and 60–90 cm) is likely due to reduced organic matter, moisture content, and the diversity of active microflora at deeper levels. Similarly, the NDF class and the 0–30 cm soil depth exhibited the highest concentrations of DTPA-extractable Cu, Zn, Fe, Mn, B, and S, which were significantly higher than those observed in other forest classes and depths. Correlation analysis showed that EC positively correlated with pH, OC and AK, while AN had a positive relationship with AP and AK. These interactions indicate that the presence and interaction of one nutrient can influence the uptake and utilization of another. The recommended dose of fertilizer analysis revealed that most forest divisions in Jharkhand require additional NPK, except for Bokaro, Chatra South, Deoghar, Giridih East, Giridih West, Jamtara, Koderma, Medininagar, Sahibganj, and Saraikela divisions. These findings serve as a guide for soil health improvement layout design in the restoration and reconstruction of dry tropical forests, and they suggest that forest disturbances and soil depth should be specifically considered when assessing the impact of management strategies on forest and soil quality This study provides valuable insights into the relationship between vegetation and soil quality parameters. The nutrient recommendations derived from soil tests, both organic and inorganic, offer useful guidance for stakeholders and foresters in planning new plantations and promoting sustainable management practices. This information will assist State Forest Departments (SFDs) in managing forests sustainably and aid forest managers and officials in developing long-term action plans for conservation and management. Furthermore, plantation growers can use this data to identify suitable areas for tree planting based on soil quality, benefiting rural communities. The methodologies and results of this study are adaptable and can be applied to other regions facing similar ecological challenges, supporting global efforts in sustainable forest management and the restoration of degraded ecosystems. Declarations Author Contribution Authors’ contribution: 1 and 6- conceptualized, designed, coordinated the study, participatedin data collection, analysis, interpretation and drafting and finalization of manuscript, 2 to 5conceptualized, design, and finalized the manuscript. All authors read and approved thefinal manuscript. Acknowledgement The authors show their gratitude to the Ministry of Environment, Forest & Climate Change, Compensatory Afforestation Fund Management and Planning Authority (CAMPA) for providing financial support. We are thankful for the generous support, encouragement and motivation by DG, ICFRE, Dehradun, Director, ICFRE-FRI, Dehradun, Director, ICFRE-IFP, Ranchi. We are thankful to PCCF (HoFF), DFOs of all forest divisions of Jharkhand SFD. Thanks to Sh. Satish Kumar, Dr. Rahul Kumar, Sh. Tulsi Mandal, Miss Pragya and other support officials, India Meteorological Department Ranchi, Google Earth Engine (GEE) and USGS and FSI, Dehradun. References Ahirwal, J., Kumari, S., Singh, A. K., Kumar, A., & Maiti, S. K. (2021). Changes in soil properties and carbon fluxes following afforestation and agriculture in tropical forest. Ecological indicators, 123, 107354. Ahmad, F., Uddin, M. M., & Goparaju, L. (2018). An evaluation of vegetation health and the socioeconomic dimension of the vulnerability of Jharkhand state of India in climate change scenarios and their likely impact: A geospatial approach. Environmental & Socio-economic Studies , 6 (4), 39-47. Aizat, A. M., Roslan, M. K., Sulaiman, W. N. A., & Karam, D. S. (2014). The relationship between soil pH and selected soil properties in 48 years logged-over forest. International journal of environmental sciences, 4(6), 1129-1140. Alam, S., Hammoda, H., Khan, F., Enazi, R., & Goktepe, I. (2020). Electrical Conductivity, pH, Organic Matter and Texture of Selected Soils Around the Qatar University Campus. Research in Agriculture Livestock and Fisheries, 7, 403-409. Ali, S., Khan, S. M., Ahmad, Z., Siddiq, Z., Ullah, A., Yoo, S., ... & Raposo, A. (2023). Carbon sequestration potential of different forest types in Pakistan and its role in regulating services for public health. Frontiers in Public Health , 10 , 1064586. Amarasinghe, A., Chen, C., Van Zwieten, L., & Rashti, M. R. (2023). The role of edaphic variables and management practices in regulating soil microbial resilience to drought-A meta-analysis. Science of The Total Environment , 169544. Baccini A, Walker W, Carvahlo L, Farina M, Sulla-Menashe D and Houghton R 2017 Tropical forests are a net carbon source based on new measurements of gain and loss Science 358 230–4. Banerjee, S., & Van Der Heijden, M. G. (2023). Soil microbiomes and one health. Nature Reviews Microbiology , 21 (1), 6-20. Bar-On, Y. M., Phillips, R., & Milo, R. (2018). The biomass distribution on Earth. Proceedings of the National Academy of Sciences , 115 (25), 6506-6511. Behera, S. K., Mishra, S., Singh, S. N., Kumar, M., Chaudhary, L. B., Jain, M. K., ... & Pandey, N. (2019). Assessment of essential nutrients in soil with litter chemistry of tropical deciduous forest, India. International Journal of Plant and Environment, 5(01), 40-46. Bhaduri, A., Chatterjee, S., Bakuli, K., Hazra, D., & Pandey, S. (2019). Nutrient cycling and metabolic activity of soil microbes in pristine forests in comparison to a monoculture. Vegetos , 32 (3), 324-332. Bhalawe, S., Nayak, D., Kukadia, M. U., & Gayakvad, P. (2013). Leaf litter decomposition pattern of trees. The Bioscan, 8(4), 1135-1140. Bhavana Dixit, B. D., & Rashmi Agarwal, R. A. (2013). Dynamics of soil nutrients and ecto-mycorrhizal symbionts in disturbed and undisturbed stands of Tropical Dry Deciduous Forest of Central India. Bini, C. (2009). Soil: A precious natural resource. In Conservation of natural resources (pp. 1-48). Nova Science. Blanco-Canqui, H., Lal, R., Blanco-Canqui, H., & Lal, R. (2008). Soil resilience and conservation. Principles of soil conservation and management , 425-447. Chavan BL, Rasal GB (2010). Sequestered standing carbon stock in selective tree species grown in University campus at Aurangabad, Maharashtra, India. Int J Eng Sci Technol. (2010) 2:3003–7. Chesin, L. and Yien, C.H. (1950). Turbidimetric determination of available sulphates. In Soil Science Society of America, Proceedings, 15, 149-151. Das, R., & Maiti, S. K. (2016). Importance of carbon fractionation for the estimation of carbon sequestration in reclaimed coalmine soils—A case study from Jharia coalfields, Jharkhand, India. Ecological Engineering , 90 , 135-140. Deng, L., Shangguan, Z.P., 2017. Afforestation drives soil carbon and nitrogen changes in China. Land Degrad. Dev. 28, 151–165. Dhaliwal, M. K., & Dhaliwal, S. S. (2019). Impact of manure and fertilizers on chemical fractions of Zn and Cu in soil under rice-wheat cropping system. Journal of the Indian Society of Soil Science, 67(1), 85-91. Dhaliwal, S. S., Sharma, V., Shukla, A. K., Kaur, J., Gupta, R. K., Verma, V., ... & Singh, P. (2023). Interactive effect of land use systems on depth-wise soil properties and micronutrients minerals in North-Western, India. Heliyon, 9(2). Foster, N. W., & Bhatti, J. S. (2006). Forest ecosystems: nutrient cycling. Encyclopedia of soil science, 718721. Frost, P. S., van Es, H. M., Rossiter, D. G., Hobbs, P. R., & Pingali, P. L. (2019). Soil health characterization in smallholder agricultural catchments in India. Applied Soil Ecology , 138 , 171-180. Frost, P. S., van Es, H. M., Rossiter, D. G., Hobbs, P. R., & Pingali, P. L. (2019). Soil health characterization in smallholder agricultural catchments in India. Applied Soil Ecology, 138, 171-180. Gautam, T. P., & Mandal, T. N. (2018). Storage and flux of nutrients in disturbed and undisturbed tropical moist forest of Eastern Nepal. International Journal of Forestry Research, 2018(1), 8516321. Gupta, U.C. (1967). A simplified method for determining hot-watersoluble boron in podzol soils. Soil Science, 103(6): 424-428. Huang, J., Song, C., 2010. Effects of land use on soil water soluble organic C and microbial biomass C concentrations in the Sanjiang Plain in Northeast China. Acta Agric. Scand. Sect. B Soil Plant Sci. 60 (2), 182–188. Izhar Shafi, M., Adnan, M., Fahad, S., Wahid, F., Khan, A., Yue, Z., ... & Datta, R. (2020). Application of single superphosphate with humic acid improves the growth, yield and phosphorus uptake of wheat (Triticum aestivum L.) in calcareous soil. Agronomy, 10(9), 1224. Jakhar, S., & Punia, H. (2022). Conceptualizing Different Forest Degradation Drivers with Special Reference to India. International Journal of Environment and Climate Change , 12 (11), 3184-3200. Kārkliņa, I., & Stola, J. (2019). Impact of forest soil enrichment with nitrogen fertilizer on throughfall and soil water chemical properties. Research for Rural Development, 1. Ke, Y., Li, H., Luo, T., Chen, B., Wang, Q., Jiang, X., & Liu, W. (2023). Reforestation Increases the Aggregate Organic Carbon Concentration Induced by Soil Microorganisms in a Degraded Red Soil, Subtropical China. Microorganisms, 11(8), 2008. Kim, H. N., & Park, J. H. (2024). Monitoring of soil EC for the prediction of soil nutrient regime under different soil water and organic matter contents. Applied Biological Chemistry, 67(1), 1. Kindermann G, Obersteiner M, Sohngen B, Sathaye J, Andrasko K, Rametsteiner E, et al. Global cost estimates of reducing carbon emissions through avoided deforestation. Proc Nat Acad Sci. (2008) 105:10302–7. doi: 10.1073/pnas.0710616105. Kumar, A., Bharose, R., Kumar, R., & Sen, P (2024). Assessment of physico-chemical properties of soil and carbon sequestration of natural sal (Shorea robusta) forest under different regimes of Jharkhand. International Journal of Research in Agronomy; 7(8): 317-324 Kumar, A., Bharose, R., Kumar, R., & Sen, P. (2021). Assessment of physico-chemical properties of soil and carbon sequestration of natural sal (Shorea robusta) forest under different regimes of Jharkhand. Annals of Experimental Biology, 9 (3): 1-6. Kumar, A., Bharose, R., Kumar, R., & Sen, P. (2024). Assessment of physico-chemical properties of soil and carbon sequestration of natural sal (Shorea robusta) forest under different regimes of Jharkhand. Kumar, A., Bharose, R., Kumar, R., & Sen, P. (2024). Assessment of physico-chemical properties of soil and carbon sequestration of natural sal (Shorea robusta) forest under different regimes of Jharkhand. International Journal of Research in Agronomy 2024; 7(8): 317-324. Kumar, R., & Saikia, P. (2020). Forest resources of Jharkhand, Eastern India: socio-economic and bio-ecological perspectives. Socio-economic and Eco-biological Dimensions in Resource use and Conservation: Strategies for Sustainability, 61-101. Kumar, R., & Saikia, P. (2021). Spatio-temporal soil nutrient dynamics and plant species diversity in selected Sal forests of Ranchi, Eastern India. Vegetos , 34 (1), 235-248. Kumar, R., Kumar, A., & Saikia, P. (2022). Deforestation and forests degradation impacts on the environment. In Environmental Degradation: Challenges and Strategies for Mitigation (pp. 19-46). Cham: Springer International Publishing. Kumari, A., Thomas, T., Swaroop, N., Mohanta, S. R., & Thomas, A. (2024). Assessment of Soil Health and Soil Quality of Bokaro, District, Jharkhand, India. International Journal of Plant & Soil Science , 36 (6), 642-651. Lal, R. (2005). Forest soils and carbon sequestration. Forest ecology and management , 220 (1-3), 242-258. Lamb, D., Erskine, P. D., & Parrotta, J. A. (2005). Restoration of degraded tropical forest landscapes. Science , 310 (5754), 1628-1632. Latehar and Hazaribagh District of Jharkhand. International Journal of Chemical Studies; SP6: 625-628. Lehmann, J., Bossio, D. A., Kögel-Knabner, I., & Rillig, M. C. (2020). The concept and future prospects of soil health. Nature Reviews Earth & Environment , 1 (10), 544-553. León, J. D., & Osorio, N. W. (2014). Role of litter turnover in soil quality in tropical degraded lands of Colombia. The Scientific World Journal, 2014(1), 693981. Levasseur, P. A., Aherne, J., Basiliko, N., & Watmough, S. A. (2024). Organic matter, carbon, and nitrogen relationships of regreened forest soils in an industrially impacted landscape. Soil Research , 62 (5). Lindsay, W.L. and Norvell, W. (1978). Development of a DTPA soil test for zinc, iron, manganese, and copper. Soil science society of America journal, 42(3): 421-428. Liu, J., Chen, J., Chen, G., Guo, J., & Li, Y. (2020). Enzyme stoichiometry indicates the variation of microbial nutrient requirements at different soil depths in subtropical forests. PLoS One, 15(2), e0220599. Maini, A., Sharma, V., & Sharma, S. (2020). Assessment of soil carbon and biochemical indicators of soil quality under rainfed land use systems in North Eastern region of Punjab, India. Carbon Management, 11(2), 169-182. Malhi YM, Brown S. Forests, carbon and global climate. Philosophical Trans Royal Soc London Series A Math Phys Eng Sci. (2002) 360:1567–91. doi: 10.1098/rsta.2002.1020. Mandal, A., & Dhaliwal, S. S. (2018, November). Distribution of micronutrients in soils under different land-uses in south-western plains of Punjab. In 83rd Annual Convention of ISSS (pp. 27-30). Mayer, M., Baltensweiler, A., James, J., Rigling, A., & Hagedorn, F. (2024). A global synthesis and conceptualization of the magnitude and duration of soil carbon losses in response to forest disturbances. Global Ecology and Biogeography , 33 (1), 141-150. Mazur, P., Gozdowski, D., & Wnuk, A. (2022). Relationships between soil electrical conductivity and sentinel-2-derived NDVI with pH and content of selected nutrients. Agronomy, 12(2), 354. Mehta, V. K., Sullivan, P. J., Walter, M. T., Krishnaswamy, J., & DeGloria, S. D. (2008). Impacts of disturbance on soil properties in a dry tropical forest in Southern India. Ecohydrology: Ecosystems, Land and Water Process Interactions, Ecohydrogeomorphology , 1 (2), 161-175. Mehta, V. K., Sullivan, P. J., Walter, M. T., Krishnaswamy, J., & DeGloria, S. D. (2008). Impacts of disturbance on soil properties in a dry tropical forest in Southern India. Ecohydrology: Ecosystems, Land and Water Process Interactions, Ecohydrogeomorphology, 1(2), 161-175. Mgelwa, A. S., Ngaba, M. J. Y., Hu, B., Gurmesa, G. A., Mwakaje, A. G., Nyemeck, M. P. B., ... & Rennenberg, H. (2024). Meta-analysis of 21st century studies shows that deforestation induces profound changes in soil characteristics, particularly soil organic carbon accumulation. Forest Ecosystems, 100257. Mishra, S. N., Kumar, R., & Panwar, V. P. (2024). Biodiversity and Soil Properties of Saranda Sal Forest: Implications for Conservation and Management. International Journal of Plant & Soil Science , 36 (7), 326-337. Mo, J., Brown, S., Peng, S., & Kong, G. (2003). Nitrogen availability in disturbed, rehabilitated and mature forests of tropical China. Forest Ecology and Management, 175(1-3), 573-583. Naik, S. K. (2014). Distribution of nitrogen, phosphorus, potassium and Zinc content in mango grown acidic soils of Jharkhand. The Ecoscan, 8(1&2), 135-139. Osman, K. T., & Osman, K. T. (2013). Chemical Properties of Forest Soils. Forest Soils: Properties and Management, 45-61. Othaman, N. N., Isa, M. N., Ismail, R. C., Ahmad, M. I., & Hui, C. K. (2020). Factors that affect soil electrical conductivity (EC) based system for smart farming application. In AIP conference proceedings (Vol. 2203, No. 1). AIP Publishing. Pandey, H. P., Pokhrel, S., Pandey, P., Pokhrel, N. P., & Paudel, G. (2020). Effect of primary soil nutrients (NPK) on forests' biomass production and SOC density of Nepalese forests. Proceedings of the International Academy of Ecology and Environmental Sciences, 10(3), 86-96. Pandey, S., Kumari, N., & Prasad, S. P. (2021). Soil quality and pollution assessment around Jumar watershed of Jharkhand, India. Arabian Journal of Geosciences, 14, 1-20. Patra, A. K., Lenka, N. K., & Biswas, A. K. (2015). Soil health assessment and management. Indian J Fertil , 11 , 16-27. Raj, M., Lal, K., Satdev, Kumari, P., Kumari, S., Dubey, V. K., ... & Kumar, S. (2024). Potential nutrient cycling and management in agroforestry. In Agroforestry to Combat Global Challenges: Current Prospects and Future Challenges (pp. 71-92). Singapore: Springer Nature Singapore. Rotter, P., Kuta, J., Vácha, R., & Sáňka, M. (2017). The role of Mn and Fe oxides in risk elements retention in soils under different forest types. Roy, P. K., Samal, N. R., Roy, M. B., & Mazumdar, A. (2010). Soil carbon and nutrient accumulation under forest plantations in Jharkhand state of India. CLEAN–Soil, Air, Water , 38 (8), 706-712. Roy, P. K., Samal, N. R., Roy, M. B., & Mazumdar, A. (2010). Soil carbon and nutrient accumulation under forest plantations in Jharkhand state of India. CLEAN–Soil, Air, Water, 38(8), 706-712. Saha, S., Saha, B., Seth, T., Dasgupta, S., Ray, M., Pal, B., ... & Hazra, G. (2019). Micronutrients availability in soil–plant system in response to long-term integrated nutrient management under rice–wheat cropping system. Journal of Soil Science and Plant Nutrition, 19, 712-724. Salam, A. (2024). Internet of things for sustainable forestry. In Internet of Things for sustainable community development: Wireless communications, sensing, and systems (pp. 147-181). Cham: Springer International Publishing. Shankar, A., & Garkoti, S. C. (2024). Influence of forest types on soil physicochemical and biological characteristics of associated agroecosystems in the central Himalaya. Science of The Total Environment, 906, 167731. Shao, G., Ai, J., Sun, Q., Hou, L., & Dong, Y. (2020). Soil quality assessment under different forest types in the Mount Tai, central Eastern China. Ecological Indicators, 115, 106439. Sharma, A., Patel, S. K., & Singh, G. S. (2023). Variation in species composition, structural diversity, and regeneration along disturbances in tropical dry forest of Northern India. Journal of Asia-Pacific Biodiversity , 16 (1), 83-95. Shepherd, R. M., & Oliverio, A. M. (2024). Micronutrients modulate the structure and function of soil bacterial communities. Soil Biology and Biochemistry, 192, 109384. Shukla, J., Dhyani, S., Pujari, P., Mishra, A., & Verma, P. (2022). Impact of agriculture intensification on forest degradation and tree carbon stock; promoting multi‐criteria optimization for restoration in Central India. Land Degradation & Development , 33 (16), 3103-3117. Singh, B., & Sharma, K. N. (2012). Depthwise distribution of soil organic carbon and nutrients under some tree species after seventeen years of plantation. Journal of the Indian Society of Soil Science , 60 (3), 198-203. Smith, J. L. and Doran, J. W. (2015) ‘Measurement and use of pH andelectrical conductivity for soil quality analysis’, pp. 169–185. Stanford, G. and L. English. (1949). Use of flame photometer in rapid soil test for K and Ca. Agronomy. J., 41: 446-447. Sterk, G., Riksen, M. J. P. M., & Goossens, D. (2001). Dryland degradation by wind erosion and its control. Annals of arid Zone , 40 (3), 351-368. Subbiah, B.V. and Asija. G.L. (1956). A rapid procedure for the estimation of available nitrogen in soils. Current Science, 25: 259-260. Thakrey, M., Singh, L., Jhariya, M. K., Tomar, A., Singh, A. K., & Toppo, S. (2022). Impact of disturbance on biomass, carbon, and nitrogen storage in vegetation and on soil properties of tropical dry deciduous forest in Chhattisgarh, India. Land Degradation & Development, 33(11), 1810-1820. Thakur, T. K., Swamy, S. L., Thakur, A., Mishra, A., Bakshi, S., Kumar, A., ... & Kumar, R. (2024). Land cover changes and carbon dynamics in Central India's dry tropical forests: A 25-year assessment and nature-based eco-restoration approaches. Journal of Environmental Management , 351 , 119809. Tuyishime, M. (2022). Phosphorus chemistry in managed forest soils: effects of weathering and wood ash fertilisation (No. 2022: 75). Uma, M., Saravanan, T. S., & Rajendran, K. (2011). Litter production and nutrient dynamics of casuarinas equisetifolia in farm forestry plantation of southern India. The Bioscan, 6(3), 525-528. Van Miegroet, H., & Olsson, M. (2011). Ecosystem disturbance and soil organic carbon–a review. Soil carbon in sensitive European ecosystems: From science to land management, 85-117. Walkley A, Black IA (1934) An examination of the Degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method. Soil sci 37:29–38. Walna, B., Spychalski, W., & Ibragimow, A. (2010). Fractionation of iron and manganese in the horizons of a nutrient-poor forest soil profile using the sequential extraction method. Polish Journal of Environmental Studies , 19 (5), 1029-1037. Wang, F., Li, Z., Xia, H., Zou, B., Li, N., Liu, J., & Zhu, W. (2010). Effects of nitrogen-fixing and non-nitrogen-fixing tree species on soil properties and nitrogen transformation during forest restoration in southern China. Soil Science & Plant Nutrition, 56(2), 297-306. Weisse, M., Goldman, E., & Carter, S. (2021). Forest Pulse: The Latest on the World’s Forests. World Resources Institute. Available at https://research. wri. org/gfr/forest-pulse. Zaitsev, G. A., Dubrovina, O. A., & Shainurov, R. I. (2020). Iron and manganese migration in “soil–plant” system in Scots pine stands in conditions of contamination by the steel plant’s emissions. Scientific Reports, 10(1), 11025. Zeba Perween, Pradeep Kumar Thakur, Nikita Kumari, Rashmi Khusboo Minz, Rachna Sunanda Kachhap and Kamlesh Pandey (2019). Soil nutrients and fertility status of forest in Zhu, F., Lu, X., Liu, L., & Mo, J. (2015). Phosphate addition enhanced soil inorganic nutrients to a large extent in three tropical forests. Scientific Reports , 5 (1), 7923. Tables Table 1. Different Forest Divisions of Jharkhand, India. S.No Forest Divisions S.No. Forest Divisions 1 Bokaro 17 Jamtara 2 Chaibasa 18 Khunti 3 Chatra North 19 Koderma 4 Chatra South 20 Kolhan 5 Deoghar 21 Latehar 6 Dhalbhum 22 Lohardaga 7 Dhanbad 22 Medininagar 8 Dumka 23 Pakur 9 Garhwa North 24 Porahat 10 Garhwa South 25 Ramgarh 11 Giridih East 26 Ranchi 12 Giridih West 27 Sahibganj 13 Godda 28 Saraikera 14 Gumla 30 Saranda 15 Hazaribagh East 31 Simdega 16 Hazaribagh west Table 2. Distribution of pH, EC and OC of different forest classes in various depth for 31 forest divisions of Jharkhand, India Parameters→ pH Electrical Conductivity (dS/m) Organic Carbon (%) Forest class→ Depth NDF MDF DF NDF MDF DF NDF MDF DF Forest divisions ↓ Bokaro 0-30 5.25±0.36Aa _ 5.41±0.32Aa 38.60±12.18Aa _ 31.47±3.21Aa 1.31±0.23Aa _ 1.64±0.28Aa 30-60 5.06±0.36Aa _ 5.34±0.34Aa 28.03±10.43Aa _ 27.50±3.77Aa 1.11±0.19Aa _ 1.36±0.14Aa 60-90 5.07±0.46Aa _ 5.20±0.44Aa 29.35±12.27Aa _ 22.73±5.63Aa 1.07±0.24Ba _ 1.35±0.30Ba Two-way ANOVA FC: df=2, F=1.43 Dt: df=2, F=0.52 FC×Dt: df= 4, F=0.09 FC: df=2, F=1.34 Dt: df=2, F=1.80 FC ×Dt: df=4, F=0.267 FC: df=2, F=9.82* Dt: df=2, F=3.42* FC×Dt: df= 4, F=0.05* Chaibasa 0-30 5.65±0.49Aa 5.45±0.33Aa 5.68±0.12Aa 71.67±53Aa 60.00±29Aa 40.0±5Aa 0.59±0.32Aab 0.76±0.15Ab 0.45±0.2Aa 30-60 5.57±0.44Aa 5.32±0.29Aa 5.65±0.09Aa 55.00±33Aa 55.00±22Aa 40.0±11Aa 0.54±0.31Aab 0.72±0.13Ab 0.43±0.5Aa 60-90 5.46±0.47Aa 5.33±0.35Aa 5.62±0.5Aa 46.67±18Aa 42.50±18Aa 30.0±9Aa 0.48±0.30Aab 0.69±0.15Ab 0.41±0.6Aa Two-way ANOVA FC: df=2, F=1.66 Dt: df=2, F=0.17 FC ×Dt: df= 4, F=0.05 FC: df=2, F=0.64 Dt: df=2, F=057 FC ×Dt: df= 4, F=0.08 FC: df=2, F=4.65 Dt: df=2, F=0.19 FC ×Dt: df= 4, F=0.02 Chatra North 0-30 5.92±0.31Aa 5.89±0.55Aa 6.34±0.13Aa 85.22±17Bab 71.47±14Ba 113.05±33Bb 0.38±0.08Aa 0.53±0.13Ab 0.51±0.31Ab 30-60 5.82±0.37Aa 5.68±0.64Aa 5.86±0.03Aa 65.14±13Aab 60.33±9Aa 58.48±12Ab 0.35±0.07Aa 0.50±0.13Ab 0.48±0.31Ab 60-90 5.76±0.29Aa 5.59±0.55Aa 5.73±0.46Aa 66.06±18Abab 50.17±16Aa 63.42±15Ab 0.32±0.07Aa 0.48±0.12Ab 0.46±0.24Ab Two-way ANOVA FC: df=2, F=1.29 Dt: df=2, F=2.61 FC ×Dt: df= 4, F=0.35 FC: df= 2, F=4.18* Dt: df=2, F=14.5*** FC ×Dt: df= 4, F=2.30 FC: df=2, F=3.51* Dt: df=2, F=0.33 FC ×Dt: df= 4, F=0.01 Chatra South 0-30 5.37±0.62Aab 5.34±0.52Aa 6.01±0.23Ab 85.12±55Aa 76.75±38Aa 61.50±3Aa 0.72±0.28Aa 1.26±0.28Ab 0.55±0.22Aa 30-60 5.50±0.45Aab 5.15±0.06Aa 5.73±0.48Ab 70.51±33Aa 42.40±0.06Aa 58.83±9Aa 0.63±0.24Aa 0.95±0.24Ab 0.47±0.19Aa 60-90 5.49±0.62Aab 5.04±0.30Aa 5.60±0.50Ab 56.78±22Aa 35.70±6Aa 61.53±23Aa 0.58±0.22Aa 0.87±0.52Ab 0.42±0.19Aa Two-way ANOVA FC: df=2, F=3.03 Dt: df=2, F=0.38 FC ×Dt: df= 4, F=046 FC: df=2, F=0.97 Dt: df=2, F=1.33 FC ×Dt: df= 4, F=0.38 FC: df=2, F=9.94*** Dt: df=2, F=2.28 FC ×Dt: df= 4, F=0.28 Deoghar 0-30 5.60±0.55Ba 5.71±0.44Ba 5.61±0.47Ba 27.22±4Aa 75.15±33Aab 55.38±35Ab 0.30±0.14Ba 0.47±0.23Bb 0.41±0.17Bab 30-60 5.25±0.35Aa 5.32±039Aa 5.30±0.61Aa 23.12±3Aa 59.38±44Aab 49.03±29Ab 0.21±0.14Aa 0.29±0.12Ab 0.29±0.19Aab 60-90 5.11±0.35Aa 5.28±0.44Aa 5.34±0.37Aa 22.86±3Aa 43.62±41Aab 43.23±29Ab 0.13±0.08Aa 0.29±0.18Ab 0.28±0.19Aab Two-way ANOVA FC: df=2, F=0.30 Dt: df=2, F=3.35* FC ×Dt: df=4, F=0.08 FC: df=2, F=3.70* Dt: df=2, F=0.67 FC ×Dt: df= 4, F=0.20 FC: df=2, F=3.03 Dt: df=2, F=3.89* FC ×Dt: df= 4, F=0.16 Dhalbhum 0-30 5.48±0.22Aa 5.51±0.27Aa 5.83±0.49Ab 44.29±18Aa 52.50±18Aa 45.0±12Aa 0.94±0.30Bb 1.51±0.36ABc 0.62±0.17Aa 30-60 5.45±0.22Aa 5.45±0.25Aa 5.74±0.44Ab 30.0±5Aa 47.50±15Aa 40.0±11Aa 0.84±0.24Bb 1.13±0.12ABc 0.58±0.16Aa 60-90 5.42±0.28Aa 5.41±0.25Aa 5.65±0.38Ab 36.43±15Aa 40.0±14Aa 32.50±9Aa 0.80±0.25Bb 0.99±0.07ABc 0.53±0.16Aa Two-way ANOVA FC: df=2, F=3.78* Dt: df=2, F=0.47 FC ×Dt: df= 4, F=0.05 FC: df=2, F=1.92 Dt: df=2, F=2.34 FC ×Dt: df= 4, F=0.49 FC: df=2, F=22*** Dt: df=2, F=4.21* FC ×Dt: df= 4, F=1.17 Dhanbad 0-30 6.10±0.23Ba 5.88±0.29Ba 5.99±0.32Ba 173.40±55Aa 97.54±81Aa 136.73±64Aa 1.24±0.17Ba 1.15±0.22Ba 1.19±0.20Ba 30-60 5.76±0.36Aa 5.48±0.30Aa 5.91±0.18Aa 130.80±40Aa 78.09±81Aa 64.60±29Aa 1.08±0.13Aba 1.03±0.21Aba 1.02±0.18ABa 60-90 5.53±0.36Aa 5.33±0.31Aa 5.55±0.50Aa 118.54±44Aa 83.81±44Aa 42.80±4Aa 0.99±0.36Aa 0.95±0.28Aa 0.89±0.15Aa Two-way ANOVA FC: df=2, F=3.18 Dt: df=2, F=7.99* FC ×Dt: df= 4, F=0.28 FC: df=2, F=1.77 Dt: df=2, F=1.37 FC ×Dt: df= 4, F=0.27 FC: df=2, F=0.31 Dt: df=2, F=3.60* FC ×Dt: df= 4, F=0.05 Dumka 0-30 5.75±0.28Ba 5.56±0.23Ba 5.80±0.25Ba 38.79±18Ba 54.14±19Bab 58.60±21Bb 1.03±0.32Ba 0.80±0.38Ba 0.87±0.29Ba 30-60 5.59±0.35ABa 5.66±0.18ABa 5.58±0.15ABa 31.39±7Aaa 38.16±4Aab 43.43±13Ab 0.77±0.10Aa 0.51±0.39Aa 0.60±0.18Aa 60-90 5.48±0.26Aa 5.53±0.18Aa 5.50±0.32Aa 32.84±14Aa 38.35±13Aab 34.78±9Ab 0.68±0.30Aa 0.58±0.31Aa 0.60±0.32Aa Two-way ANOVA FC: df=2, F=0.10 Dt: df=2, F=2.20 FC ×Dt: df= 4, F=0.62 FC: df=2, F=2.95 Dt: df=2, F=4.84* FC ×Dt: df= 4, F=0.56 FC: df=2, F=1.99 Dt: df=2, F=4.47* FC ×Dt: df= 4, F=0.13 Garhwa north 0-30 6.36±0.42Ba 6.50±0.29Ba 6.33±0.48Ba 106.37±53Aa 84.35±12Aa 97.88±44Aa 0.46±0.18Bab 0.44±0.23Bb 0.30±0.10Ba 30-60 6.13±0.34Aa 5.67±0.13Aa 5.75±0.48Aa 84.67±52Aa 69.13±6Aa 74.18±45Aa 0.27±0.13Aab 0.36±0.14Ab 0.21±0.10Aa 60-90 5.74±0.54Aa 5.92±0.27Aa 5.76±0.36Aa 63.28±15Aa 68.70±10Aa 77.43±42Aa 0.25±0.14Aab 0.37±0.24Ab 0.21±0.11Aa Two-way ANOVA FC: df=2, F=0.42 Dt: df=2, F=9.77*** FC ×Dt: df= 4, F=1.01 FC: df=2, F=0.30 Dt: df=2, F=1.87 FC ×Dt: df= 4, F=0.24 FC: df=2, F=3.09 Dt: df=2, F=2.84 FC ×Dt: df= 4, F=0.38 Garhwa South 0-30 5.76±0.53Aa 6.22±0.55Aa 5.79±0.52Aa 61.46±16Aa 61.36±8Aa 84.25±63Aa 0.49±0.25Aab 0.38±.16Aa 0.61±0.18Ab 30-60 5.59±0.58Aa 5.80±0.33Aa 5.28±0.49Aa 52.93±16Aa 52.44±15Aa 64.33±20Aa 0.38±0.30Aab 0.27±.15Aa 0.55±0.17Ab 60-90 5.51±0.68Aa 5.64±0.40Aa 5.39±0.43Aa 54.27±17Aa 59.03±20Aa 65.19±33Aa 0.37±0.24Aab 0.25±.11Aa 0.42±0.30Ab Two-way ANOVA FC: df=2, F=1.63 Dt: df=2, F=1.72 FC ×Dt: df= 4, F=0.20 FC: df=2, F=1.04 Dt: df=2, F=0.79 FC ×Dt: df= 4, F=0.11 FC: df=2, F=2.37 Dt: df=2, F=1.14 FC ×Dt: df= 4, F=0.07 Giridih East 0-30 5.81±0.42Cb 5.63±0.34Cab 5.13±0.38Ca 65.99±27Ba 51.50±10Ba 48.58±10Ba 0.61±0.22Ba 0.45±0.10Ba 0.48±0.08Ba 30-60 5.33±0.35Bb 5.31±0.28Bab 5.04±0.39Ba 42.37±14Aa 40.91±9Aa 43.79±9Aa 0.44±0.16Aa 0.36±0.5Aa 0.46±0.6Aa 60-90 5.17±0.40Ab 4.73±0.43Aab 4.87±0.48Aa 37.96±10Aa 30.71±14Aa 39.95±6Aa 0.34±0.20Aa 0.23±0.12Aa 0.37±0.12Aa Two-way ANOVA FC: df=2, F=4.02* Dt: df=2, F=8.06** FC ×Dt: df= 4, F=0.93* FC: df=2, F=1.15 Dt: df=2, F=4.45* FC ×Dt: df= 4, F=0.66 FC: df=2, F=2.47 Dt: df=2, F=5.62* FC ×Dt: df= 4, F=0.38 Giridih West 0-30 5.65±0.39Ba 5.61±0.30Ba 5.39±0.39Ba 63.26±33Aa 60.82±15Aa 58.83±31Aa 0.59±0.21Ba 0.56±0.34Ba 0.48±0.09Ba 30-60 5.38±0.36Ba 5.47±0.35Ba 5.25±0.40Ba 38.05±11Aa 56.84±17Aa 41.33±10Aa 0.42±0.22ABa 0.44±0.27ABa 0.41±0.4ABa 60-90 5.27±0.49Aa 4.71±0.55Aa 4.94±0.39Aa 45.94±23Aa 46.75±21Aa 37.70±10Aa 0.47±0.20Aa 0.30±0.13Aa 0.32±0.11Aa Two-way ANOVA FC: df=2, F=1.23 Dt: df=2, F=6.25** FC ×Dt: df= 4, F=0.97 FC: df=2, F=0.53 Dt: df=2, F=2.55 FC ×Dt: df= 4, F=0.42 FC: df=2, F=0.72 Dt: df=2, F=2.40 FC ×Dt: df= 4, F=0.35 Godda 0-30 5.74±0.48Aa 5.71±0.70Aa 6.12±0.13Aa 63.0±22Ba 51.11±18Ba 60.0±14Ba 1.18±0.40Ba 0.83±0.34Ba 0.94±0.13Ba 30-60 5.64±0.50Aa 5.48±0.60Aa 6.02±0.20Aa 54.0±23ABa 45.56±18ABa 50.0±12ABa 0.71±0.13Aa 0.69±0.24Aa 0.81±0.11Aa 60-90 5.53±0.48Aa 5.39±0.59Aa 5.98±0.30Aa 42.0±21Aa 37.78±16Aa 50.0±10Aa 0.59±0.12Aa 0.57±0.11Aa 0.50±0.11Aa Two-way ANOVA FC: df=2, F=1.07 Dt: df=2, F=0.25 FC ×Dt: df= 4, F=0.03 FC: df=2, F=0.96 Dt: df=2, F=0.99 FC ×Dt: df= 4, F=0.09 FC: df=2, F=1.16 Dt: df=2, F=4.40* FC ×Dt: df= 4, F=0.85 Gumla 0-30 6.04±0.29Ca 6.27±0.40Cb 6.09±0.37Ca 68.35±55Ba 136.27±74Bb 55.98±14Ba 0.86±0.59Ba 0.93±0.39Ba 0.47±0.27Ba 30-60 5.69±0.35Ba 5.91±0.40Bb 5.71±0.15Ba 49.29±25Aa 71.59±36Ab 36.32±5Aa 0.61±0.61Ba 0.72±0.38Ba 0.32±0.23Ba 60-90 5.10±0.66Aa 5.73±0.32Ab 4.77±0.87Aa 44.19±27Aa 66.95±34Ab 31.17±5Aa 0.19±0.19Aa 0.20±0.12Aa 0.09±0.05Aa Two-way ANOVA FC: df=2, F=3.71* Dt: df=2, F=14.58*** FC ×Dt: df= 4, F=0.96* FC: df=2, F=5.46** Dt: df=2, F=3.68* FC ×Dt: df= 4, F=0.68 FC: df=2, F=1.95 Dt: df=2, F=7.31** FC ×Dt: df= 4, F=0.21 HZB East 0-30 6.06±063Aa 6.11±0.25Aa 5.83±0.61Aa 44.68±12Ba 37.90±7Ba 45.80±24Ba 1.04±0.29Bab 0.97±0.18Bb 0.83±0.19Ba 30-60 5.59±0.51Aa 5.40±0.38Aa 5.63±0.36Aa 36.96±13Aa 29.13±5Aa 29.73±10Aa 0.77±0.10Aab 0.93±0.20Ab 0.76±0.14Aa 60-90 5.25±0.32Aa 6.72±3Aa 5.32±0.22Aa 27.00±3Aa 26.75±8Aa 34.28±19Aa 0.71±0.14Aab 0.89±0.15Ab 0.70±0.12Aa Two-way ANOVA FC: df=2, F=0.96 Dt: df=2, F=0.78 FC ×Dt: df= 4, F=1.12 FC: df=2, F=0.59 Dt: df=2, F=3.71* FC ×Dt: df= 4, F=039 FC: df=2, F=3.37* Dt: df=2, F=4.23* FC ×Dt: df= 4, F=0.93 HZB West 0-30 6.16±0.44Ba 6.09±0.45Ba 6.52±0.11Bb 47.72±17Ba 46.40±23Ba 99.90±20Bb 1.06±0.20Ba 0.94±0.28Ba 0.90±0.23Ba 30-60 5.75±0.41Aa 5.41±0.26Aa 6.25±0.15Ab 36.46±11Aa 33.11±16Aa 80.90±12Ab 0.86±0.12ABa 0.89±0.29ABa 0.74±0.31ABa 60-90 5.45±0.35Aa 5.37±0.30Aa 5.87±0.23Ab 28.48±4Aa 30.82±14Aa 39.0±14Ab 0.78±0.15Aa 0.82±0.29Aa 0.63±0.25Aa Two-way ANOVA FC: df=2, F=3.70* Dt: df=2, F=6.31** FC ×Dt: df= 4, F=0.43* FC: df=2, F=7.92** Dt: df=2, F=8.07** FC ×Dt: df= 4, F=1.18 FC: df=2, F=0.58 Dt: df=2, F=1.88 FC ×Dt: df= 4, F=0.43 Jamtara 0-30 5.59±0.55Ba 5.41±0.21Ba 5.52±0.51Ba 75.08±52Ba 61.74±29Ba 46.20±5Ba 1.01±0.06Ca 1.12±0.15Ca 1.11±0.30Ca 30-60 5.10±0.46Aa 5.04±0.28Aa 5.23±0.43Aa 47.60±15Aa 42.97±12Aa 45.23±6Aa 0.86±0.17Ba 0.94±0.16Ba 0.94±0.27Ba 60-90 4.88±0.51Aa 4.82±0.31Aa 4.94±0.40Aa 42.46±14Aa 38.35±5Aa 38.13±5Aa 0.79±0.21Aa 0.73±0.27Aa 0.74±0.11Aa Two-way ANOVA FC: df=2, F=0.49 Dt: df=2, F=8.54** FC ×Dt: df= 4, F=0.07 FC: df=2, F=0.85 Dt: df=2, F=3.13 FC ×Dt: df= 4, F=0.39 FC: df=2, F=0.28 Dt: df=2, F=9.91*** FC ×Dt: df= 4, F=0.35 Khunti 0-30 5.67±0.58Aa 5.45±0.41Aa 5.82±0.77Aa 81.67±43Ba 90.61±25Ba 146.17±54Ba 0.58±0.22Cb 0.54±0.19Cab 0.31±0.17Ca 30-60 5.31±0.31Aa 5.30±0.32Aa 5.73±0.75Aa 64.61±28ABa 68.89±31ABa 84.66±53ABa 0.38±0.12Bb 0.25±0.16Bab 0.26±0.13Ba 60-90 5.23±0.28Aa 5.17±0.22Aa 5.59±0.87Aa 51.52±29Aa 61.22±27Aa 63.98±34Aa 0.22±0.11Ab 0.17±0.12Aab 0.15±0.07Aa Two-way ANOVA FC: df=2, F=2.07 Dt: df=2, F=1.45 FC ×Dt: df= 4, F=0.11 FC: df=2, F=1.84 Dt: df=2, F=4.37* FC ×Dt: df= 4, F=0.53 FC: df=2, F=2.98 Dt: df=2, F=11.84*** FC ×Dt: df= 4, F=0.78 Koderma 0-30 5.81±0.38Ba 6.17±0.24Ba 5.83±0.19Ba 75.87±59Ba 83.74±51Ba 67.18±4Ba 0.89±0.13Bab 0.95±0.21Bb 0.84±0.04Ba 30-60 5.48±0.66Ba 5.80±0.41Ba 5.66±0.38Ba 56.58±49ABa 65.46±31ABa 55.05±8ABa 0.80±0.11ABab 0.85±0.18ABb 0.76±0.07ABa 60-90 5.25±0.51Aa 5.48±0.43Aa 5.19±0.45Aa 36.88±22Aa 46.17±31Aa 37.53±11Aa 0.76±0.10Aab 0.80±0.19Ab 0.68±0.04Aa Two-way ANOVA FC: df=2, F=2.23 Dt: df=2, F=7.95** FC ×Dt: df= 4, F=0.13 FC: df=2, F=0.33 Dt: df=2, F=2.82 FC ×Dt: df= 4, F=0.02 FC: df=2, F=2.24 Dt: df=2, F=4.13* FC ×Dt: df= 4, F=0.04 Kolhan 0-30 5.33±0.52Aa 5.18±0.50Aa 5.80±1.5Ab 61.67±20Ba 45.71±20Ba 60.0±02Ba 0.87±0.16Bb 0.80±0.16Bb 1.18±0.03Ba 30-60 4.89±0.33Aa 4.98±0.57Aa 5.75±1.4Ab 49.67±19ABa 38.29±10ABa 47.50±10ABa 0.82±0.14ABb 0.76±0.15ABb 1.12±0.06ABa 60-90 5.04±0.65Aa 4.88±0.65Aa 5.48±1.2Ab 43.67±20Aa 32.86±09Aa 40.0±14Aa 0.74±0.15Ab 0.71±0.18Ab 1.01±0.02Aa Two-way ANOVA FC: df=2, F=2.33 Dt: df=2, F=0.63 FC ×Dt: df= 4, F=0.12 FC: df=2, F=2.92 Dt: df=2, F=2.86 FC ×Dt: df= 4, F=0.06 FC: df=2, F=12.49*** Dt: df=2, F=2.21 FC ×Dt: df= 4, F=0.08 Latehar 0-30 5.74±0.40Aa 5.50±0.61Aa 5.77±0.08Aa 80.02±41Aa 55.43±26Aa 47.60±13Aa 0.87±0.44Bb 0.91±0.38Bb 0.39±0.14Ba 30-60 5.43±0.55Aa 5.12±0.62Aa 5.56±0.23Aa 58.54±23Aa 45.80±26Aa 36.55±0.09Aa 0.66±0.18Abb 0.63±0.15Abb 0.33±0.09ABa 60-90 5.25±0.58Aa 5.10±0.49Aa 5.53±0.21Aa 51.64±23Aa 39.27±23Aa 29.50±1.5Aa 0.48±0.41Ab 0.54±0.19Ab 0.25±0.06Aa Two-way ANOVA FC: df=2, F=32.93*** Dt: df=2, F=1.21 FC ×Dt: df= 4, F=1.45 FC: df=2, F=2.31 Dt: df=2, F=1.37 FC ×Dt: df= 4, F=0.08 FC: df=2, F=4.65* Dt: df=2, F=3.06 FC ×Dt: df= 4, F=0.22 Lohardaga 0-30 5.33±0.17Ba 5.52±0.32Ba 5.37±0.10Ba 40.06±23Bb 34.29±14Bb 15.05±2Ba 0.92±0.85Aa 0.46±0.26Aa 0.61±0.34Aa 30-60 5.13±0.28Aa 5.21±0.24Aa 4.94±0.31Aa 27.63±15Ab 22.28±3Ab 12.95±3Aa 0.59±0.72Aa 0.25±0.07Aa 0.45±0.14Aa 60-90 5.02±0.30Aa 5.20±0.34Aa 5.05±0.65Aa 22.61±10Ab 22.88±10Ab 12.66±0.42Aa 0.51±0.75Aa 0.29±0.14Aa 0.42±0.31Aa Two-way ANOVA FC: df=2, F=1.66 Dt: df=2, F=4.61 FC ×Dt: df= 4, F=0.16 FC: df=2, F=5.13 Dt: df=2, F=2.67 FC ×Dt: df= 4, F=0.41 FC: df=2, F=2.26 Dt: df=2, F=1.19 FC ×Dt: df= 4, F=0.11 Medininagar 0-30 6.22±0.37Ba 6.34±0.85Ba _ 80.63±46Ba 89.89±38Ba _ 0.62±0.30Aa 0.50±0.17Aa _ 30-60 5.66±0.24ABa 6.16±0.75ABa _ 57.35±24ABa 72.02±24ABa _ 0.56±026Aa 0.46±0.16Aa _ 60-90 5.48±0.20Aa 5.74±0.77Aa _ 51.44±29Aa 60.99±16Aa _ 0.52±0.27Aa 0.39±0.14Aa _ Two-way ANOVA FC: df=2, F=2.25 Dt: df=2, F=3.86* FC ×Dt: df= 4, F=3.11 FC: df=2, F=1.38 Dt: df=2, F=3.29 FC ×Dt: df= 4, F=0.04 FC: df=2, F=3.03 Dt: df=2, F=0.76 FC ×Dt: df= 4, F=0.03 Pakur 0-30 5.67±0.55Aa 5.63±0.48Aa _ 85.71±47Aa 82.50±57Aa _ 0.80±0.31Aa 0.96±0.24Aa _ 30-60 5.63±0.55Aa 5.58±0.49Aa _ 74.29±35Aa 67.50±47Aa _ 0.75±0.30Aa 0.86±0.22Aa _ 60-90 5.40±0.46Aa 5.48±0.52Aa _ 68.57±39Aa 60.63±41Aa _ 0.67±0.23Aa 0.81±0.21Aa _ Two-way ANOVA FC: df=2, F=0.02 Dt: df=2, F=0.70 FC ×Dt: df= 4, F=0.70 FC: df=2, F=0.19 Dt: df=2, F=0.71 FC ×Dt: df= 4, F=0.01 FC: df=2, F=3.37 Dt: df=2, F=1.14 FC ×Dt: df= 4, F=0.03 Porhat 0-30 5.36±0.61Ba 5.35±0.44Ba 5.77±0.36Ba 48.75±12Ba 70.00±26Bb 76.67±23Bb 1.04±0.30Aa 1.03±0.40Aa 1.00±0.46Aa 30-60 5.04±0.52ABa 4.97±0.54ABa 5.69±0.37ABa 40.00±14ABa 58.33±23ABb 70.00±17ABb 0.92±0.30Aa 0.99±0.40Aa 0.89±31Aa 60-90 5.05±0.54Aa 4.78±0.46Aa 4.84±0.55Aa 36.25±13Aa 51.67±19Ab 56.67±15Ab 0.88±0.32Aa 0.95±0.40Aa 0.84±0.28Aa Two-way ANOVA FC: df=2, F=1.77 Dt: df=2, F=4.78 FC ×Dt: df= 4, F=0.82 FC: df=2, F=8.94** Dt: df=2, F=3.12 FC ×Dt: df= 4, F=0.11 FC: df=2, F=0.18 Dt: df=2, F=0.53 FC ×Dt: df= 4, F=0.04 Ramgarh 0-30 5.67±0.74Ba 5.85±0.51Ba 5.89±0.31Ba 38.31±21Aa 26.93±17Aa 23.74±5Aa 0.62±0.27Aa 0.52±0.15Aa 0.60±0.43Aa 30-60 5.49±0.51ABa 5.52±0.64ABa 5.59±0.37ABa 30.10±18Aa 21.32±16Aa 19.28±6Aa 0.55±0.28Aa 0.43±0.15Aa 0.54±0.44Aa 60-90 5.26±0.54Aa 5.42±0.64Aa 5.18±0.22Aa 26.09±15Aa 17.89±16Aa 16.71±8Aa 0.50±0.29Aa 0.37±0.14Aa 0.42±0.34Aa Two-way ANOVA FC: df=2, F=0.20 Dt: df=2, F=2.75 FC ×Dt: df= 4, F=0.12 FC: df=2, F=2.10 Dt: df=2, F=1.13 FC ×Dt: df= 4, F=0.03 FC: df=2, F=0.90 Dt: df=2, F=1.13 FC ×Dt: df= 4, F=0.03 Ranchi 0-30 6.31±0.22Ca 6.31±0.20Ca 6.32±0.42Ca 119.91±47Bb 58.15±22Ba 81.61±48Ba 0.49±0.21Cab 0.53±0.19Cb 0.39±0.28Ca 30-60 5.70±0.41Ba 5.98±0.46Ba 5.65±0.43Ba 101.66±51ABb 44.77±4ABa 44.94±27ABa 0.32±0.21Bab 0.43±0.21Bb 0.15±0.14Ba 60-90 5.18±0.54Aa 5.47±.47Aa 4.97±0.66Aa 63.20±42Ab 39.75±4Aa 41.08±26Aa 0.15±0.10Aab 0.23±0.13Ab 0.09±0.08Aa Two-way ANOVA FC: df=2, F=1.24 Dt: df=2, F=19.79*** FC ×Dt: df= 4, F=0.36 FC: df=2, F=8.04** Dt: df=2, F=4.19* FC ×Dt: df= 4, F=0.60 FC: df=2, F=3.56* Dt: df=2, F=4.19* FC ×Dt: df= 4, F=0.24 Saraikera 0-30 4.82±0.39Aab 4.55±0.32Aa 5.00±0.18Ab 55.71±24Aa 75.00±49Aa 50.0±14Aa 0.89±0.30Aa 1.07±0.58Aa 0.79±0.23Aa 30-60 4.78±0.38Aab 4.50±0.32Aa 4.95±0.18Ab 50.00±20Aa 68.33±47Aa 45.0±7Aa 0.83±0.28Aa 0.90±0.41Aa 0.76±0.21Aa 60-90 4.75±0.39Aab 4.43±0.28Aa 4.77±0.17Ab 42.14±19Aa 61.67±48Aa 40.0±14Aa 0.80±0.30Aa 0.82±0.82Aa 0.70±.017Aa Two-way ANOVA FC: df=2, F=4.92* Dt: df=2, F=0.48 FC ×Dt: df= 4, F=0.05 FC: df=2, F=1.81 Dt: df=2, F=0.35 FC ×Dt: df= 4, F=0.01 FC: df=2, F=0.61 Dt: df=2, F=0.44 FC ×Dt: df= 4, F=0.09 Saranda 0-30 6.16±0.53Aa 6.29±0.46Aa 6.95±0.36Ab 85.85±32Aa 106.19±41Aa 148.30±30Ab 1.18±0.51Bb 1.04±0.29Bab 0.89±0.19Ba 30-60 6.08±0.54Aa 5.97±0.46Aa 6.67±0.25Ab 80.74±27Aa 84.03±51Aa 144.50±25Ab 1.07±0.48ABb 0.69±0.27ABab 0.57±0.22ABa 60-90 5.99±0.62Aa 5.78±0.49Aa 6.40±0.12Ab 65.37±23Aa 80.16±43Aa 120.70±24Ab 0.96±0.53Ab 0.62±0.33Aab 0.36±0.23Aa Two-way ANOVA FC: df=2, F=2.21 Dt: df=2, F=1.11 FC ×Dt: df= 4, F=0.20 FC: df=2, F=1.12 Dt: df=2, F= FC ×Dt: df= 4, F=0.69 FC: df=2, F=3.32 Dt: df=2, F=1.70 FC ×Dt: df= 4, F=0.20 Sahibganj 0-30 5.17±0.27Aa 5.23±0.48Aa 5.62±0.43Aa 94.29±60Aa 92.86±16Aa 90.0±16Aa 1.41±0.85Ba 1.13±0.40Ba 1.65±0.40Ba 30-60 5.09±0.27Aa 5.16±0.49Aa 5.55±0.44Aa 74.29±25Aa 82.86±16Aa 90.0±12Aa 0.97±0.23ABa 0.95±0.28ABa 1.15±0.22ABa 60-90 5.04±0.27Aa 5.13±0.50Aa 5.46±0.45Aa 64.29±24Aa 77.14±13Aa 80.0±19Aa 0.89±0.19Aa 0.93±0.36Aa 1.02±0.11Aa Two-way ANOVA FC: df=2, F=1.66 Dt: df=2, F=1.90 FC ×Dt: df= 4, F=0.01 FC: df=2, F=0.30 Dt: df=2, F=0.64 FC ×Dt: df= 4, F=0.13 FC: df=2, F=0.60 Dt: df=2, F=2.08 FC ×Dt: df= 4, F=0.32 Simdega 0-30 5.49±0.66Ba 5.38±0.53Ba 5.42±0.42Ba 106.63±51Ba 82.58±32Ba 106.10±20Ba 1.0±0.22Ba 1.09±0.48Ba 1.09±030Ba 30-60 5.34±0.67ABa 4.98±0.44ABa 5.25±0.36ABa 75.53±40ABa 65.59±19ABa 95.10±19ABa 0.94±0.23ABa 0.86±0.31ABa 0.96±0.21ABa 60-90 4.99±0.65Aa 4.85±0.42Aa 5.05±0.23Aa 60.96±24Aa 55.89±10Aa 85.20±18Aa 0.78±0.17Aa 0.76±0.33Aa 0.70±0.41Aa Two-way ANOVA FC: df=2, F=0.71 Dt: df=2, F=1.16 FC ×Dt: df= 4, F=0.10 FC: df=2, F=1.37 Dt: df=2, F=1.56 FC ×Dt: df= 4, F=0.19 FC: df=2, F=0.01 Dt: df=2, F=1.98 FC ×Dt: df= 4, F=0.19 Different capital letters designate significant differences at P < 0.05 at forest class (Non-degraded, Medium Degraded and Degraded Forest) for each depth; different lowercase letters indicate significant differences at P < 0.05 among soil depth at a same forest class followed by Duncan’s MRT test. FC Forest class, Dt soil depth df degree of freedom, *P < 0.05, **P<0.01, ***P<0.001 Table 3. Recommendation of NPK fertilizer to diagnose forest soil fertility related constraints, which calculated with the help of standard procedures S.N. Name of Forest divisions Inorganic Fertilizer Recommendation Organic FYM Recommendation Vermicompost Recommendation Urea for Nitrogen (kg/ha) SSP for Phosphorus (kg/ha) K2O for Potassium (kg/ha) For Nitrogen (kg/ha) For Phosphorus (kg/ha) For Potassium (kg/ha) For Nitrogen (kg/ha) For Phosphorus (kg/ha) For Potassium (kg/ha) 1 Bokaro -- 49.5 62.4 -- -- -- -- -- -- 2 Chaibasa 355.3 67.0 65.1 32686 65.37 163.43 10214.4 71.50 81.72 3 Chatra North 291.6 72.3 112.6 26830 53.66 134.15 8384.4 58.69 67.08 4 Chatra South 275.8 -- -- 25372 50.74 126.86 7928.8 55.50 63.43 5 Deoghar 202.0 -- 52.2 18580 37.16 92.90 5806.3 40.64 46.45 6 Dhalbhum 289.6 44.8 117.0 26640 53.28 133.20 8325.0 58.28 66.60 7 Dhanbad 242.1 63.5 136.6 22274 44.55 111.37 6960.6 48.72 55.69 8 Dumka 129.2 -- 73.4 11882 23.76 59.41 3713.1 25.99 29.71 9 Garhwa North 272.1 48.5 44.1 25030 50.06 125.15 7821.9 54.75 62.58 10 Garhwa South 272.1 69.8 27.9 25030 50.06 125.15 7821.9 54.75 62.58 11 Giridih East 194.9 -- 103.5 20728 35.86 89.65 6477.5 45.34 51.82 12 Giridih West 225.3 -- 160.9 17930 41.46 103.64 5603.1 39.22 44.83 13 Godda 241.8 50.1 84.7 22246 44.49 111.23 6951.9 48.66 55.62 14 Gumla 82.4 78.9 84.7 7578 15.16 37.89 2368.1 16.58 18.95 15 HZB East 154.7 72.0 170.0 14236 28.47 71.18 4448.8 31.14 35.59 16 HZB West 208.5 66.1 157.9 19182 38.36 95.91 5994.4 41.96 47.96 17 Jamtara 173.7 -- 22.1 15978 31.96 79.89 4993.1 34.95 39.95 18 Khunti 84.2 20.4 66.1 7746 15.49 38.73 2420.6 16.94 19.37 19 Koderma 239.5 -- 70.3 22036 44.07 110.18 6886.3 48.20 55.09 20 Kolhan 349.0 40.2 106.1 32108 64.22 160.54 10033.8 70.24 80.27 21 Latehar 273.6 51.0 45.7 25168 50.34 125.84 7865.0 55.06 62.92 22 Lohardaga 446.3 69.6 92.5 41056 82.11 205.28 12830.0 89.81 102.64 23 Medininagar 88.7 -- 58.8 8160 16.32 40.80 2550.0 17.85 20.40 24 Pakur 354.9 54.2 89.8 32650 65.30 163.25 10203.1 71.42 81.63 25 Porahat 245.7 34.3 101.7 22606 45.21 113.03 7064.4 49.45 56.52 26 Ramgarh 81.9 16.1 92.2 7538 15.08 37.69 2355.6 16.49 18.85 27 Ranchi 251.5 59.6 63.6 23138 46.28 115.69 7230.6 50.61 57.85 28 Sahibganj -- 47.25 61.7 -- -- -- -- -- -- 29 Saranda 129.8 89.4 45.9 11940 23.88 59.70 3731.3 26.12 29.85 30 Simdega 231.8 65.1 3.7 21324 42.65 106.62 6663.8 46.65 53.31 31 Saraikera 251.7 60.1 -- 23152 46.30 115.76 7235.0 50.65 57.88 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 16 May, 2025 Reviews received at journal 16 May, 2025 Reviews received at journal 14 May, 2025 Reviews received at journal 11 May, 2025 Reviews received at journal 09 May, 2025 Reviews received at journal 02 May, 2025 Reviewers agreed at journal 01 May, 2025 Reviewers agreed at journal 01 May, 2025 Reviewers agreed at journal 30 Apr, 2025 Reviewers agreed at journal 29 Apr, 2025 Reviewers agreed at journal 29 Apr, 2025 Reviewers agreed at journal 29 Apr, 2025 Reviewers agreed at journal 29 Apr, 2025 Reviewers agreed at journal 29 Apr, 2025 Reviewers invited by journal 29 Apr, 2025 Editor assigned by journal 25 Apr, 2025 Submission checks completed at journal 25 Apr, 2025 First submitted to journal 02 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Sivaranjani","email":"","orcid":"","institution":"Govind Ballabh Pant National Institute of Himalayan Environment'(NIHE), North-East, Regional Centre, Chandranagar (Near Neni Hyundai Service), Itanagar, Arunachal Pradesh-791113.","correspondingAuthor":false,"prefix":"","firstName":"S.","middleName":"","lastName":"Sivaranjani","suffix":""},{"id":437209045,"identity":"5f560183-061e-424f-b655-b2c9f671d979","order_by":2,"name":"Pramod Chandra Lakra","email":"","orcid":"","institution":"Maharashtra Forest Department","correspondingAuthor":false,"prefix":"","firstName":"Pramod","middleName":"Chandra","lastName":"Lakra","suffix":""},{"id":437209047,"identity":"c2402d41-703a-4a56-9f12-002daa2fe79d","order_by":3,"name":"Shachi Pandey","email":"","orcid":"","institution":"Forestry and Climate Change Advisor (RECAP4NDC), Deutsche Gesellschaft fur Internationale Zusammenarbeit (GIZ)","correspondingAuthor":false,"prefix":"","firstName":"Shachi","middleName":"","lastName":"Pandey","suffix":""},{"id":437209051,"identity":"17b605d6-69fe-4c81-97a1-9df45ee8577d","order_by":4,"name":"Sanoj Kumar Patel","email":"","orcid":"","institution":"ICFRE- Forest Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Sanoj","middleName":"Kumar","lastName":"Patel","suffix":""},{"id":437209052,"identity":"92edfb94-1b4d-4d96-b805-0658459e2a4b","order_by":5,"name":"Vijender Pal Panwar","email":"","orcid":"","institution":"ICFRE- Forest Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Vijender","middleName":"Pal","lastName":"Panwar","suffix":""}],"badges":[],"createdAt":"2025-04-02 05:53:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6357879/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6357879/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80116794,"identity":"36b3971b-ce16-402c-9568-f70da079ff03","added_by":"auto","created_at":"2025-04-08 06:35:27","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":241122,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a). Study area map with respect to India\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(b). Different strata including five major forest classes, sixteen forest types, elevation, slope, aspect, hill-shade, 5x5km grid and soil classification map were laid over study area\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(c). Sampling points for soil collection from all forest divisions of Jharkhand\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6357879/v1/97be0eef77c4a290a342f63b.jpg"},{"id":80116793,"identity":"8ae77a04-398f-40b7-b55d-63a1972f42d8","added_by":"auto","created_at":"2025-04-08 06:35:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":959613,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a). Distribution of Soil Available Nitrogen Phosphate and potassium in different forest class at various soil depth of Bokaro, Chaibasa Chatra North Chatra South Deoghar Forest divisions. Different capital letters designate significant differences at \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eP \u0026lt; 0.05\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e at forest class (Non-degraded, Medium Degraded and Degraded Forest) for each depth; different lowercase letters indicate significant differences at \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eP \u0026lt; 0.05\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eamong soil depth at a same forest class followed by Duncan’s MRT test\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(b). Distribution of Soil Available Nitrogen Phosphate and potassium in different forest class at various soil depth of Dhalbhum, Dhanbad, Dumka, Garhwa North, Garhwa South, Giridih East, Giridih West and Godda Gumlaforest divisions. Different capital letters designate significant differences at \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eP \u0026lt; 0.05\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e at forest class (Non-degraded, Medium Degraded and Degraded Forest) for each depth; different lowercase letters indicate significant differences at \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eP \u0026lt; 0.05\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e among soil depth at a same forest class followed by Duncan’s MRT test.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(c). Distribution of Soil Available Nitrogen Phosphate and potassium in different forest class at various soil depth of Hazaribagh East, Hazaribagh west Jamtara, Khunti, Koderma, Kolhan, Latehar, Lohardaga, Medininagar, Pakur, Poraha tforest divisions. Different capital letters designate significant differences at P \u0026lt; 0.05 at forest class (Non-degraded, Medium Degraded and Degraded Forest) for each depth; different lowercase letters indicate significant differences at P \u0026lt; 0.05 among soil depth at a same forest class followed by Duncan’s MRT test.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(d). Distribution of Soil Available Nitrogen Phosphate and potassium in different forest class at various soil depth of Ramgarh, Ranchi, Sahibganj, Saraikera Saranda and Simdega forest divisions. Different capital letters designate significant differences at \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eP \u0026lt; 0.05\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e at forest class (Non-degraded, Medium Degraded and Degraded Forest) for each depth; different lowercase letters indicate significant differences at \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eP \u0026lt; 0.05\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e among soil depth at a same forest class followed by Duncan’s MRT test.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6357879/v1/f7c226a37cb8bb187a4e6a52.png"},{"id":80117372,"identity":"9ea76af8-aef5-48c2-98c2-e3bbf4f9f02f","added_by":"auto","created_at":"2025-04-08 06:43:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1088849,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a). Distribution of Soil Available Copper, Zinc, Manganese, Iron, Boran and Sulphur in different forest class at various soil depth of Bokaro, Chaibasa Chatra North Chatra South Deoghar forest divisions. Different capital letters designate significant differences at \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eP \u0026lt; 0.05\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e at forest class (Non-degraded, Medium Degraded and Degraded Forest) for each depth; different lowercase letters indicate significant differences at \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eP \u0026lt; 0.05\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e among soil depth at a same forest class followed by Duncan’s MRT test.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(b). Distribution of Soil Available Copper, Zinc, Manganese, Iron, Boran and Sulphur in different forest class at various soil depth of Dhalbhum, Dhanbad, Dumka, Garhwa North, Garhwa South, Giridih East, Giridih West and Godda Gumlaforest divisions. Different capital letters designate significant differences at P \u0026lt; 0.05 at forest class (Non-degraded, Medium Degraded and Degraded Forest) for each depth; different lowercase letters indicate significant differences at P \u0026lt; 0.05 among soil depth at a same forest class followed by Duncan’s MRT test.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(c). Distribution of Soil Available Copper, Zinc, Manganese, Iron, Boran and Sulphur in different forest class at various soil depth of Hazaribagh East, Hazaribagh west Jamtara, Khunti, Koderma, Kolhan, Latehar, Lohardaga, Medininagar, Pakur, Poraha tforest divisions. Different capital letters designate significant differences at P \u0026lt; 0.05 at forest class (Non-degraded, Medium Degraded and Degraded Forest) for each depth; different lowercase letters indicate significant differences at P \u0026lt; 0.05 among soil depth at a same forest class followed by Duncan’s MRT test.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(d). Distribution of Soil Available Copper, Zinc, Manganese, Iron, Boran and Sulphur in different forest class at various soil depth of Ramgarh, Ranchi, Sahibganj, Saraikera Saranda and Simdega forest divisions. Different capital letters designate significant differences at P \u0026lt; 0.05 at forest class (Non-degraded, Medium Degraded and Degraded Forest) for each depth; different lowercase letters indicate significant differences at P \u0026lt; 0.05 among soil depth at a same forest class followed by Duncan’s MRT test.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6357879/v1/3d14ddd29deafa0fc310a4df.png"},{"id":80117373,"identity":"917a5c75-abd7-46f8-b67b-e4639de5a503","added_by":"auto","created_at":"2025-04-08 06:43:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":312468,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePearson Corelation between soil physicochemical properties of Jharkhand Forest soil.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003e*P \u0026lt; 0.05, **P\u0026lt;0.01, ***P\u0026lt;0.001\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEC electrical conductivity; OC Organic carbon, AN Available Nitrogen, AP Available Phosphate, AK Available Potassium, Cu Copper, Zn Zinc, Mn Manganese, Fe Iron, B Boran, S Sulphur.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6357879/v1/0018b2033fac83ef2fc91807.png"},{"id":80118617,"identity":"0277ffca-ce4b-486a-911f-6efebecb5ee3","added_by":"auto","created_at":"2025-04-08 06:59:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6871271,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6357879/v1/9931240c-5c89-4e81-94b6-cc1d52b0719a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatial variability in soil physicochemical properties across forest disturbances in the different forest divisions of Jharkhand, India","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSoil is considered an essential natural resource that sustains vegetation and microbial ecosystems by providing nutrients and water. Composed of minerals, water, air, and organic matter, soil supports the origin and maintenance of living ecosystems. In forest ecosystems, soil regulates species distribution, the production of forest goods, wildlife habitats, species richness, and diversity (Schoonover and Crim 2015). Covering over 30% of the Earth's surface, forests provide numerous benefits to human society, fundamentally supported by forest soils (FAO 2010). However, over time, the balance between forest and soil systems has been disrupted by various natural and human activities. Forest soils develop from the interaction of climate, organisms, and geological parent materials, varying by topographic locations (Hillel and Hatfield 2005) are characterized by diverse soil-dwelling organisms, the recycling of organic materials, substantial litter layers, and deeply rooted trees. Forest soils enhance the plantations resilience against natural disasters by providing physical support, regulating water and gas exchange, and supplying 24 of the 26 essential elements needed by plants and animals. The soil health is the capacity of soil to function as a dynamic, living ecosystem that supports humans, animals, and plants and collective properties of forest soils that sustain forest ecosystem functioning (Lehmann et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Soil health plays a crucial role in shaping agricultural policies, meeting stakeholder needs, and ensuring sustainable supply chain management. However, over time, forest soil health has faced degradation due to deforestation, disturbances such as harvesting and fires, climate change and acid rain.\u003c/p\u003e \u003cp\u003eForest soils face challenges, including acidification and biodiversity loss, necessitating effective monitoring (Wellbrock \u003cem\u003eet al.\u003c/em\u003e, 2024). Deforestation depletes soil organic carbon (Hosseini \u003cem\u003eet al.\u003c/em\u003e, 2024; Nave \u003cem\u003eet al.\u003c/em\u003e, 2024), while climate change accelerates decomposition of organic matter (Amante and Wedajo 2024), and reduce soil moisture (Nigussie \u003cem\u003eet al.\u003c/em\u003e, 2024). Soil health indicators, such as nutrient levels and microbial activity, deteriorate following forest conversion, emphasizing the negative effects on soil quality (Kooch \u003cem\u003eet al.\u003c/em\u003e, 2024). Soil ecosystem services declined by 60% between 1950 and 2010 (Le\u0026oacute;n \u003cem\u003eet al.\u003c/em\u003e, 2014) with 33% of the Earth's land surface impacted by some form of soil degradation (Bini \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). In arid and semi-arid regions, desertification a specific form of land degradation affects around 41% of the continental area (Sterk et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Carbon emissions from forest degradation are estimated to range between 40% and 212% of those from deforestation globally (Baccini \u003cem\u003eet al\u003c/em\u003e., 2017). The FAO, 2022 warned that 90% of the Earth's topsoil could be at risk by 2050 with higher degradation rate in tropical regions.\u003c/p\u003e \u003cp\u003eTropical forests accounting for 45% of the global forest cover are rapidly declining at 3.7\u0026nbsp;million hectares annually (Weisse et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In India, dry and moist deciduous forests dominate 58% of the country's total forest area but face deforestation, agricultural intensification, mining, climate change, logging, and forest fires (Kumar et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Shukla et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Jakhar and Punia \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Thakur et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Jharkhand tropical moist deciduous and tropical dry deciduous forests suffer from overexploitation (Kumar and Saikia \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), anthropogenic disturbances such as grazing, browsing, removal of ground cover for grass collection, and the cutting and lopping of trees (Sagar and Singh 2003; Sanji \u003cem\u003eet al.\u003c/em\u003e, 2020; Kumar and Saikia \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Mining, particularly in Jharia coalfield, depletes vegetation and topsoil making it prone to soil erosion (Singh \u003cem\u003eet al.\u003c/em\u003e, 2016; Kumar \u003cem\u003eet al.\u003c/em\u003e, 2019; Rai and Paul 2011). These anthropogenic actions have significantly degraded soil quality, leading to further forest ecosystem deterioration (Sanji \u003cem\u003eet al.\u003c/em\u003e, 2020) therefore, protecting forest soil health is crucial for sustaining future forest resources.\u003c/p\u003e \u003cp\u003eStudy on forest nutrition in tropical forests within developing countries has been relatively slow (Alvarado 2015) in compare to studies on forest nutrition that began in Cuba (1970), Brazil (1980), and Central America (Herrero 2001). In India, studies highlighted the role of forest soil health in conserving degraded forest ecosystems (Lehmann et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Shao et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Salam \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), importance of soil health in restoring and sustainably managing forest ecosystems (Wani and Singh 2021; Shukla \u003cem\u003eet al.\u003c/em\u003e, 2020; Kavya \u003cem\u003eet al.\u003c/em\u003e, 2020; Patra \u003cem\u003eet al.\u003c/em\u003e, 2017) and soil nutrient availability and forest soil quality (Pal \u003cem\u003eet al.\u003c/em\u003e, 2013; Panwar \u003cem\u003eet al.\u003c/em\u003e, 2011; Kaur \u003cem\u003eet al.\u003c/em\u003e, 2021). Essential forest ecosystem services such as oxygen production, carbon sequestration, biodiversity and land management rely on the availability of soil nutrients (Grau \u003cem\u003eet al.\u003c/em\u003e, 2017; Binkley and Fisher 2019; Brockerhoff \u003cem\u003eet al.\u003c/em\u003e, 2017). While studies in Jharkhand have examined soil carbon and nutrient accumulation (Roy et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), forest soil health (Frost et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kumari et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), the relationship between biodiversity and soil properties in Sal forests (Mishra et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and the assessment of soil and vegetation health (Patra et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Ahmad et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kumar et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), a comprehensive study on forest soil health across all forest divisions remains absent.\u003c/p\u003e \u003cp\u003eJharkhand's undulating plateau, drought-prone landscape (Bhattacharyya \u003cem\u003eet al.\u003c/em\u003e, 2013) with anthropogenic activities, adversely affects key soil functions. Despite covering approximately 29.81% of the state, forests which host a diverse range of vegetation types, including dry peninsular Sal forests, northern dry mixed deciduous forests, moist peninsular Sal forests, dry deciduous scrubland, and dry bamboo brakes (FSI 2023) face degradation due to anthropogenic disturbances, high temperatures and heavy rainfall. The Forest Survey of India (2021) reports only 3.26% of Jharkhand's total forest as very dense forest, indicating urgent need for forest ecosystem restoration. Around 40% of Jharkhand's population comprises tribal communities relies on forests for their livelihoods (Census of India 2011).\u003c/p\u003e \u003cp\u003eEffective forest management required assessing soil nutrient status across different forest conditions. The study aims to analyze the physicochemical properties of forest soils under varying vegetation types and adjacent degraded lands across all forest divisions, to evaluate the interactions between soil nutrients and their interrelationships, and identify nutrient deficiencies and propose sustainable management practices. It emphasizes the importance of soil as a critical component of forest restoration, serving as the initial step toward improving productivity and restoring degraded forest ecosystems. Understanding forest soil health is key to restoring degraded forests and securing livelihood for resource-poor communities.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003e\u003cstrong\u003eStudy area\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study was conducted in Jharkhand, lies between latitudes 22\u0026deg;00\u0026apos;\u0026ndash;24\u0026deg;37\u0026apos;N and longitudes 83\u0026deg;15\u0026apos;\u0026ndash;87\u0026deg;01\u0026apos;E. The state spans a total geographical area of 79,714 km\u0026sup2;, which accounts for approximately 2.4% of India\u0026rsquo;s total geographical area. The elevation of the region ranges from 6 m to 1366 m above mean sea level. The climate of the state has a humid sub-tropical to tropical wet and dry tropical in nature from north to south-east. The state has received average moderate rainfall (950 to 1300 mm), and temperatures vary from 6\u0026ordm;C to 47\u0026ordm;C in winter and summer, respectively (Climate of Jharkhand 2013). According to Champion and Seth (1968), the forests of Jharkhand are classified under Sub-group 5B, namely the Northern Tropical Dry Deciduous Forest. The Sub-Types and other sub-types of other Sub-group are: (i) 5B/Cle) 5B/C1-Northern dry Sal bearing forest (e.g., Dry Peninsular Sal Forest); (ii) 5B/C2-Northern Tropical dry mixed deciduous forest; (iii) 5B/Dsl-Dry deciduous scrub forest; (iv) 5B/E2-Boswellia forest; (v) 5B/E9-Dry bamboo brakes; (vi) 8A/C3-Central Indian sub-tropical Hill Forest. From ancient times, the state of Jharkhand has had a special relationship with forest. Jharkhand has a total of 23,765.78 km\u003csup\u003e2\u003c/sup\u003e forest cover which represent about 29.81% of the total geographical area of the state out of which, 2,635.35 km\u003csup\u003e2\u003c/sup\u003e (3.31%) is cover with very dense forest, followed by 9,640.99 km\u003csup\u003e2\u003c/sup\u003e (12.09%) moderate dense and 11,489.44 km\u003csup\u003e2\u003c/sup\u003e (14.41%) open forest (FSI, 2023).\u003c/p\u003e\n\u003cp\u003eAccording to soil taxonomym Jharkhand\u0026rsquo;s soils are classified into three orders, Entisols, Inceptisols, and Alfisols, based on their morphological, physical, and chemical characteristics. \u0026nbsp;Soils found in hilly areas, such as flat-topped hills, foothills, uplands, and low-lying land, belong to the Alfisols order due to the presence of argillic strata in the subsoils while, soils of hill slopes and hill slope valleys are categorized under the Inceptisols order (FAO, 2007). The state falls within agro-climatic zone 7, the Eastern Plateaus \u0026amp; Hills Region and is divided into these sub-zones \u003cem\u003ei.e\u003c/em\u003e. The Central and Northeastern Plateau Sub-Zone (Zone IV), the Western Plateau Sub-Zone (Zone V), and the Southeastern Plateau Sub-Zone (Zone VI). The state\u0026apos;s important tree species include \u003cem\u003eShorea robusta\u003c/em\u003e Gaertner f., \u003cem\u003eMadhuca longifolia\u003c/em\u003e var., \u003cem\u003eAdina cordifolia\u003c/em\u003e (Roxb.) Brandis, \u003cem\u003eDiospyros tomentosa\u003c/em\u003e and \u003cem\u003eBuchanania cochinchinensis\u003c/em\u003e (Lour.) Almeida.\u003c/p\u003e\n\u003cp\u003eAs per Census (2011), the population of Jharkhand state has\u0026nbsp;32,988,134 of which male and female are 16,930,315 and 16,057,819 respectively. The state is home to 30 different tribal groups, comprising about 40% of the total population. The prominent indigenous tribal communities (\u003cem\u003eAdivasis\u003c/em\u003e) include the Santhals, Mundas, Oraons, Hos, Kharia, Bhumij, Birhors, who have a deep connection with nature. Their day-to-day sustenance depends on forest resources like, fruits, roots of some plants, flowers, edible mushrooms, tubers of some plant fuel wood. They also depend on medicinal herbs for traditional healing practices and preserve the sacred groove in the vicinity of the forest known as \u003cem\u003eSarna\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethodology\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSampling\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe state forest is divided into 31 territorial forest divisions (Table 1). The forest cover within these divisions is categorized based on canopy cover, as very dense forest (VDF), moderately dense forest (MDF), open forest (OF), scrub forest (SF), and non-forest (NF), following the FSI (2023) protocol. Soil samples were collected from these forest divisions to analysed various physicochemical parameters. A uniform sampling protocol was prescribed for collection of soil samples from all forest divisions of the state. A 5\u0026times;5 km grid-based Remote Sensing and GIS approach was adopted to determine the sampling points which varied depending on the specific strata covering five major forest/vegetation classes, sixteen forest types, (as per Champion and Seth\u0026rsquo;s classification) including detailed forest division-specific data. \u0026nbsp;The other data including names, geographical coordinates, elevation, slope, aspect, hill-shade, and soil classification (as per FAO) were considered. Special attention was given to the changeability of degraded lands, such as open forests and scrub forests, to simplify future afforestation actions by the forest departments in these areas. Within each stratum, random sampling points were selected for each division. Soil samples were collected at three depths: 0\u0026ndash;30 cm, 30\u0026ndash;60 cm, and 60\u0026ndash;90 cm. In cases where it was not feasible to dig or auger beyond a certain depth, the reasons were recorded in the field data sheet for future reference.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample preparation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 261 soil samples were collected from 87 soil sampling points across four types of forest areas, at three depths (0\u0026ndash;30 cm, 30\u0026ndash;60 cm, and 60\u0026ndash;90 cm).\u0026nbsp;Among these samples, very dense and moderately dense forest were categorised as non-degraded forest (NDF), open forest were considered as moderately-degraded forest (MDF), while scrub forest and no forest zone have recognised as the degraded forest (DF). The soil samples were dried in the shade followed by hand crushing to remove gravels and plant residues. The air-dried soil samples that had passed through a 2.0 mm sieve were analysed for 12 soil parameters including, basic parameters (pH, EC and Organic Carbon), major nutrients (Available Nitrogen (N), Available phosphorus (P) and Exchangeable Potassium (K)), secondary nutrients (Available Sulphur (S)) and micronutrients (Available.) Zinc (Zn), boron (B), iron (Fe), manganese (Mn) and copper (Cu)). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe prepared soil samples were used to estimate soil physicochemical properties. The pH and Electrical Conductivity (EC) were measured using the potentiometric method. Soil organic carbon content was analysed using the wet digestion method (Walkley and Black 1934). For major nutrient analysis: Available nitrogen (AN) was determined through the alkaline permanganate method (Subbiah and Asija 1956) using an automated Kjeldahl Nitrogen Analyzer equipped with a digestion unit and accessories. While available phosphate (AP) was analysed using Olsen\u0026rsquo;s and Bray\u0026rsquo;s method (1954) with a UV Spectrophotometer. Available potassium (AP) was measured using the Neutral Normal Ammonium Acetate method (Stanford and English, 1949) with a Flame Photometer.\u0026nbsp;In case of\u0026nbsp;secondary nutrients, available sulphur (AS) was estimated through the Turbidimetric method (Hot Water, CaCl₂, or Phosphate method) (Chesnin and Yien 1950) using an Atomic Absorption Spectrophotometer with appropriate lamps, N₂O and acetylene gas cylinders, regulators, and other accessories.\u0026nbsp;However, for micronutrients (Available.) Zinc (Zn), iron (Fe), manganese (Mn) and copper (Cu)) analysis, DTPA (diethylene triaminepentaacetic acid) method (Lindsay and Norvell 1978) were used through Atomic Absorption Spectrophotometer with suitable lamps, N2O \u0026amp; Acetylene Gas Cylinders, regulators etc. Although for Boran estimation, Hot-water Soluble Boron method (Gupta 1967) was used through Atomic Absorption Spectrophotometer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original data was described descriptively, involving the computation of mean and standard deviation. The soil physico-chemical parameters were compared using a two-way ANOVA across different forest disturbance classes (NDF, MDF and DF) and soil depth (0-30, 30-60 and 60-90 cm). Duncan\u0026apos;s multiple range test (DMRT) was additionally utilized to evaluate differences among several groups of variables. Pearson\u0026apos;s correlation coefficients were performed among soil physico-chemical parameters to calculated the characteristics link of these parameters. Furthermore, principal component analysis (PCA) was applied to discern disparities and determine the extent to which the total variance of soil physico-chemical parameters contributed to the overall variation. All experimental data underwent processing and analysis using SPSS version 23 and GraphPad Prism version 6. The PCA was performed using Minitab version 19 software package.\u003c/p\u003e"},{"header":"Result","content":"\u003cp\u003eThe forests of Jharkhand are primarily of two types: tropical dry deciduous forest and tropical moist deciduous forest. These forests are further divided into different forest divisions based on their characteristics or properties, such as non-degraded, medium-degraded, and degraded forests. According to the Forest Department of Jharkhand, the state forest is divided into 31 territorial forest divisions. Soil samples from these divisions were collected to analyse various physicochemical parameters.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVariability of soil pH, EC and organic carbon in different forest divisions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the present study, the physicochemical properties of forest soil have significantly affected by forest disturbances (NDF MDF and DF) and soil depth (0\u0026ndash;30, 30-60 and 60-90 cm). Two-way ANOVA showed a significant (\u003cem\u003eP \u0026lt;0.05\u003c/em\u003e) variation in forest pH, EC and OC at disturbance as well as soil depth factors among 31 forest division of Jharkhand state (Table 1). In case of the forest disturbances, the highest pH was recorded in the NDF forest for Garhwa north (6.36\u0026plusmn;0.42) followed by Ranchi (6.31\u0026plusmn;0.22),\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eMedininagar\u0026nbsp;(6.22\u0026plusmn;0.37) and Hazaribagh West (6.16\u0026plusmn;0.44) forest division at 0-30 cm depth. However, the lowest pH was reported at\u0026nbsp;Saraikera\u0026nbsp;(4.75\u0026plusmn;0.39) at 60-90 soil depth followed by\u0026nbsp;Jamtara\u0026nbsp;(4.88\u0026plusmn;0.51),\u0026nbsp;Kolhan\u0026nbsp;(4.89\u0026plusmn;0.33) and\u0026nbsp;Simdega\u0026nbsp;(4.99\u0026plusmn;0.65) at 30-60 cm depth. In general, the trends of soil pH for non-degraded forest class were usually decreases from increasing soil depth for all the forest divisions. In respect to the MDF classes, Hazaribagh East (6.72\u0026plusmn;3) reported largest pH at 60-90 cm, which decrease from\u0026nbsp;Garhwa north\u0026nbsp;(6.50\u0026plusmn;0.29), Ranchi (6.31\u0026plusmn;0.20) to\u0026nbsp;Saranda\u0026nbsp;(6.29\u0026plusmn;0.46) at 0-30 cm depth. Whereas, lowest pH evaluated for Saraikera (4.43\u0026plusmn;0.28) at 0-30 cm,\u0026nbsp;Giridih West\u0026nbsp;(4.71\u0026plusmn;0.55) and\u0026nbsp;Giridih East\u0026nbsp;(4.73\u0026plusmn;0.43) at\u0026nbsp;60-90 cm. Similarly for DF, pH ranges from 6.92 to 4.77 for all forest divisions, which gradually decreases from increase of the soil depth (Table 1). The two-way ANOVA analysis revealed, change in soil pH occurs due to forest disturbances were recorded in\u0026nbsp;Dhalbhum, Giridih East,\u0026nbsp;Latehar, Saraikera forest divisions.\u0026nbsp;Whereas, variation due to soil depths were recorded in Deoghar, Dhanbad, Garhwa north, Giridih East, Giridih West, Jamtara, Koderma, Medininagar, Ranchi\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eOn the other hand, only Giridih East, Gumla and Hazaribagh West Forest divisions had reported significantly changes in soil pH by forest disturbance \u0026times; soil depth at (\u003cem\u003eP \u0026lt; 0.001\u003c/em\u003e), (\u003cem\u003eP \u0026lt; 0.01\u003c/em\u003e) and (\u003cem\u003eP \u0026lt; 0.05\u003c/em\u003e) (Table 1). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSimilar to the soil pH, the soil EC were also shown a variable result at forest disturbances as well as soil depth\u0026nbsp;in different forest divisions. The highest EC for NDF was reported for Dhanbad (173.40\u0026plusmn;55\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003edS/m) followed by Ranchi (119.91\u0026plusmn;47 dS/m) and Simdega (106.63\u0026plusmn;51 dS/m) for 0-30 cm. This indicates high soil conductivity, possibly due to increased salinity or ion concentration. Whereas, lowest EC recorded for Deoghar (22.86\u0026plusmn;3 dS/m) followed by\u0026nbsp;Lohardaga (22.91\u0026plusmn;10 dS/m) Dumka (32.84\u0026plusmn;14 dS/m) and forest divisions for 60-90 cm soil depths. In MDF, the most EC observed in Gumla (136.27\u0026plusmn;74 dS/m), which consequently decreases to Saranda (106.19\u0026plusmn;41 dS/m), Dhanbad (97.54\u0026plusmn;81 dS/m) and Sahibganj\u0026nbsp;(92.86\u0026plusmn;16\u0026nbsp;dS/m) forest divisions for 0-30 cm.\u0026nbsp;A general decreasing trend in EC was observed across all forest divisions as soil depth increased, from shallow to deeper layers. Additionally, in the case of forest disturbance, EC also decreased from the non-degraded forest class to the degraded forest class across all soil depths in most forest divisions (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurther, the distribution of OC in case of forest disturbances, the largest OC (%) reported for Sahibganj in DF (1.65\u0026plusmn;0.40 %) which decreased to NDF (1.41\u0026plusmn;0.85 %) and MDF (1.13\u0026plusmn;0.40%) forest classes at 0-30 soil depth. The second largest OC were recorded for\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eBokaro in DF\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(\u003c/strong\u003e1.64\u0026plusmn;0.28%) that similarly declined to NDF (1.31\u0026plusmn;0.23%) forest classes at 0-30 cm depth. On the other hand, the lowest OC obtained for Ranchi in DF (0.09\u0026plusmn;0.08%) with subsequent decreased in NDF (0.15\u0026plusmn;0.10%) and MDF (0.23\u0026plusmn;0.13%) forest classes. This trend was continued for Gumla, where minimum OC recorded in DF (0.09\u0026plusmn;0.05%) with decreased in NDF (0.19\u0026plusmn;0.19%) and MDF (0.20\u0026plusmn;0.12%) forest classes. In most of the cases, the amount of OC generally decreases with increase of the soil depth for all forest classes in most of the forest divisions. In this investigation, 9 forest divisions has recorded (\u0026gt;1%) of organic carbon for nondegraded forest classes, while 10 forest divisions in medium degraded forest and 8 forest divisions for degraded forest divisions. There was seven forest division including Chatra North, Chatra South, Dhalbhum, HZB East, Kolhan, Latehar, Ranchi, which OC content significantly altered through forest disturbance. On the other hand, 12 forest divisions such as Deoghar, Dhalbhum, Dhanbad, Dumka, Giridih East, Godda, Gumla, HZB East, Jamtara, Khunti, Koderma and Ranchi had affected via. soil depth. Only Bokaro forest divisions significantly (\u003cem\u003eP \u0026lt; 0.05\u003c/em\u003e) altered by disturbance \u0026times; soil depth. \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVariability of soil NPK in different forest divisions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNPK fertilizers are important because they contain three essential nutrients for plant growth and health: nitrogen (N), phosphorus (P), and potassium (K). These nutrients can be partly supplied by the soil and should be partly added with organic manures and fertilizers. The NPK analysis indicating that these nutrients shown variation at forest disturbances and soil depth. The forest divisions including, Bokaro, Chatra South, Deovghar Dhalbhum, Dhanbad, Garhwa North, Giridih East, Gumla, Hazaribagh East, Hazaribagh West, Jamtara, Koderma, Lohardaga, Ranchi, Saraikera, Sahibganj shown variation in AN due to forest disturbances\u0026nbsp;at (\u003cem\u003eP \u0026lt; 0.001\u003c/em\u003e), (\u003cem\u003eP \u0026lt; 0.01\u003c/em\u003e) and (\u003cem\u003eP \u0026lt; 0.05\u003c/em\u003e). On the other hand, Bokaro, Chatra North, Chatra South, Deovghar, Dhalbhum, Dhanbad, Giridih East, Godda, Giridih West, Gumla, Hazaribagh East, Hazaribagh West, Koderma, Kolhan, Pakur, Ranchi, Saraikera forest divisions recorded variation in AN via. soil depth. Similarly for AP, only Chatra South, Chatra North, Dhalbhum, Kolhan and Pakur, Porhat forest divisions observed changes by forest disturbances. However, Chatra South, Chatra North, Deovghar, Dhanbad, Giridih East, Giridih West, Gumla, Hazaribagh East, Hazaribagh West, Jamtara, Khunti, Koderma, Kolhan,\u0026nbsp;Lohardaga, Medininagar, Ramgarh, Ranchi and Saranda reported differences through depth. Further, AK, recorded non-significant changes for forest disturbances in all forest divisions except Chatra South, Dhalbhum, Garhwa North, Giridih East, Hazaribagh East, Hazaribagh West, Latehar, Pakur, Saraikera Saranda and Sahibganj forest divisions. Whereas, significant changes for Giridih West, Gumla, Hazaribagh East, Hazaribagh West, Lohardaga, Medininagar and Ranchi divisions (Fig. 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn case of NDF, the highest AN reported for Deoghar (776.24\u0026plusmn;130 kg/ha) followed by Hazaribagh West (578.48\u0026plusmn;56 kg/ha), Khunti (515.70\u0026plusmn;57 kg/ha) and Chatra South (508.17\u0026plusmn;53) at 0-30 cm depth. Whereas lowest AN recorded in Pakur (44.21\u0026plusmn;6 kg/ha) followed by Kolhan (59.20\u0026plusmn;10 kg/ha), Saraikera (76.81\u0026plusmn;17 kg/ha) and Lohardaga (77.26\u0026plusmn;27 kg/ha) at 60-90 cm depth. A general trend finds, where AN decrease with increasing the soil depth (Fig. 2). When we analysed the soil of MDF, maximum AN reported for \u0026nbsp;Ramgarh (692.99\u0026plusmn;91 kg/ha) followed by Hazaribagh West (446.04\u0026plusmn;68 kg/ha) and Khunti (422.94\u0026plusmn;139 kg/ha) at upper depth of soil, while minimum value obtained for Lohardaga (43.84\u0026plusmn;7 kg/ha) 30-60 cm depth followed by Pakur (59.84\u0026plusmn;12 kg/ha), Chaibasa (63.36\u0026plusmn;14 kg/ha), Saraikera, Kolhan (67.09\u0026plusmn;11 kg/ha) and (96.92\u0026plusmn;17 kg/ha) lower most depth. A similar pattern of AN decrease from the upper to lower soil depths was also observed for DF, however the decrease became more pronounced with increasing soil depth in degraded forest. Where maximum AN was recorded in Bokaro (798.70\u0026plusmn;94 kg/ha), while the minimum in Dhalbhum (29.84\u0026plusmn;0.9 kg/ha).\u003c/p\u003e\n\u003cp\u003eFurther, the value of AP varied from 1.83 to 58.13 kg/ha for NDF in all forest divisions, where the highest AP reported for Koderma (58.13\u0026plusmn;4 kg/ha) with other forest divisions including, Chatra South (49.77\u0026plusmn;4 kg/ha), Medininagar (44.34\u0026plusmn;7 kg/ha),\u0026nbsp;Deoghar (41.32\u0026plusmn;4 kg/ha) and Giridih East (40.85\u0026plusmn;2 kg/ha). This trend of AP was also continued for MDF classes in the same forest divisions (Fig. 2). On other hands, for DF classes more AP obtained for Koderma (54.11\u0026plusmn;2 kg/ha) with sequential decrease in Deoghar (41.32\u0026plusmn;4 kg/ha), Giridih East (35.64\u0026plusmn;0.73 kg/ha) and Giridih West (32.79\u0026plusmn;2 kg/ha) at upper depth of soil. Whereas very less AP recorded for\u0026nbsp;Dhanbad (0.54\u0026plusmn;0.23 kg/ha), Gumla (2.93 \u0026plusmn;0.98 kg/ha), Saranda (3.0\u0026plusmn;0.2 kg/ha), Ranchi (4.07\u0026plusmn; 0.42 kg/ha) and Hazaribagh West (5.00\u0026plusmn;0.12 kg/ha) in lower most soil depth (Fig. 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdditionally for AK, the NDF class exhibited the highest AK in Chatra South (1028.33\u0026plusmn;159 kg/ha), Simdega (398.30\u0026plusmn;99 kg/ha), Jamtara (386.94\u0026plusmn;144 kg/ha), Garhwa South (345.14\u0026plusmn;110 kg/ha) and Sahibganj (337.21\u0026plusmn;81 kg/ha) at a depth of 30-60 cm. Although, a lower AK content obtained in Pakur (70.84\u0026plusmn;17 kg/ha), Lohardaga (75.18\u0026plusmn;15 kg/ha), Gumla (81.09\u0026plusmn;13 kg/ha), Hazaribagh West (83.57\u0026plusmn;18 kg/ha) and Giridih West (84.52\u0026plusmn;18 kg/ha) at lower soil depth. The MDF forest classes record a lowest AK for Hazaribagh West (62.29\u0026plusmn;10 kg/ha), Giridih East (64.14\u0026plusmn;14 kg/ha), Ramgargh (75.64\u0026plusmn; 14 kg/ha) and Hazaribagh East (86.46\u0026plusmn;20 kg/ha) at lower depth of forest soil. Just reverse to these, Chatra South (1115.52\u0026plusmn;168 kg/ha), Simdega (323.33\u0026plusmn;96 kg/ha), Chatra North (299.79\u0026plusmn;39 kg/ha) and Garhwa North (257.66\u0026plusmn;68 kg/ha), reported highest AK value at upper depth of the soil. In FD classes, largest AK content exhibited for Simdega (609.28\u0026plusmn;64 kg/ha), SBG (488.32\u0026plusmn;75 kg/ha), Bokaro (394.54\u0026plusmn;198 kg/ha) and Ranchi (355.15\u0026plusmn;11 1kg/ha) forest divisions. \u0026nbsp;Although smallest AK investigated for Dhanbad (37.93\u0026plusmn;3 kg/ha), Ramgarh (42.63\u0026plusmn;10 kg/ha), Giridih East (47.65\u0026plusmn;2 kg/ha) and Hazaribagh Weste (62.29\u0026plusmn;10 kg/ha) forest divisions (Fig. 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVariation in soil micronutrients content in different forest divisions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe investigation of the micronutrients summarized that largest copper content recorded in Chaibasa (6.83\u0026plusmn;0.65 ppm), Saraikera (6.00\u0026plusmn;0.36 ppm), Lohardaga (5.86\u0026plusmn;0.81 ppm) and Khunti (4.64\u0026plusmn;0.54 ppm) forest divisions for NDF classes. Similarly, for the MDF classes, the copper content was highest in the Saraikera (7.24\u0026plusmn;0.42 ppm), Chaibasa (7.22\u0026plusmn;0.64 ppm), Lohardaga (6.42\u0026plusmn;0.57 ppm), and Medininagar (5.65\u0026plusmn;0.46 ppm) forest divisions. This trend persisted for the DF classes within the same forest divisions at the upper soil depth (Fig. 3). However, the lowest concentration for copper were recorded for Gumla (0.34\u0026plusmn;0.08 ppm), Ramgarh (0.39\u0026plusmn;0.03 ppm), Giridih East (0.41\u0026plusmn;0.10 ppm) and Hazaribagh East (0.50\u0026plusmn;0.18 ppm) in NDF, as well as Koderma (0.38\u0026plusmn;0.10 ppm), Hazaribagh West (0.49\u0026plusmn;0.10 ppm), Gumla (0.55\u0026plusmn;0.11 ppm) for MDF. Similarly for Zinc content, Lohardaga (4.59\u0026plusmn;1.15) and Giridih East (4.28\u0026plusmn;0.91 ppm) recorded most concentration for upper 0-30 cm depth and Dumka (0.06\u0026plusmn;0.01 ppm) and Gumla (0.13\u0026plusmn;0.03 ppm) reported least concentration for NDF at 60-90 cm depth. On the other hand, Giridih East (3.53\u0026plusmn;1.24 ppm) at 60-90 cm depth, Dhanbad (3.06\u0026plusmn;0.54 ppm) and Lohardaga (3.04\u0026plusmn;0.040 ppm) at 0-30 cm depth in MDF, while Lohardaga (5.23\u0026plusmn;1.11 ppm), Giridih East (4.59\u0026plusmn;0.70 ppm) and Jamtara (3.89\u0026plusmn;0.88 ppm) reported highest Zinc for DF classes. However, Dumka and Ranchi (0.15\u0026plusmn;0.04 ppm), Saranda (0.16\u0026plusmn;0.05 ppm), Gumla (0.22\u0026plusmn;0.01 ppm) for MDF and SBG (0.02\u0026plusmn;0.01 ppm), Gumla (0.11\u0026plusmn;0.05 ppm), Ranchi (0.13\u0026plusmn;0.02 ppm) reported smallest Zinc concentration at DF classes in lower soil depth. \u0026nbsp;In Case Mn concentration, SBG (32.01\u0026plusmn;11.59 ppm), Dhanbad (30.58\u0026plusmn;0.79 ppm) and Simdega (20.88\u0026plusmn;2.15 ppm) recorded most concentration for NDF which consequently decreased for MDF and DF for all soil depth. The concentration of Mn was also decrease from upper soil depth to lower soil depth. These trends were also continued for Iron, Boran and Sulphur (Fig. 3).\u003c/p\u003e"},{"header":"Discussions","content":"\u003cp\u003eThe study of forest soil health in Jharkhand, a state rich in biodiversity and forest cover, typically involves analysing various soil parameters that impact vegetation growth and maintain ecological balance. This research is crucial for ensuring ecosystem sustainability, particularly in forested regions like Jharkhand, India.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eImpact of forest disturbances on soil physico-chemical characteristics\u003c/h2\u003e \u003cp\u003eThe study revealed that the pH values of soils across three forest classes (NDF, MDF, DF) ranged from 6.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42 to 4.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43, indicating slightly acidic conditions. Forest soils are typically acidic due to the breakdown of organic matter and minerals over time. The presence of carbonic acid, primarily derived from decaying plant tissues (both aboveground and belowground) and root exudates, contributes to these acidic conditions (Perween \u003cem\u003eet al.\u003c/em\u003e, 2019). Similar acidic soil was also recorded by (Kumar et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) in different forest divisions of sol forest in Jharkhand. Further, the EC were varied from 173.40\u0026thinsp;\u0026plusmn;\u0026thinsp;55 to 22.87\u0026thinsp;\u0026plusmn;\u0026thinsp;3 dS/m, which subsequently tends to decline with increase of the forest disturbances and soil depth. Upon comparing this result with those of other authors, it is slightly lower than the EC range (207.08 to 116.66 dS/m) reported by Pandey et al., (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) in the Jumar watershed of Jharkhand. A decrease in electrical conductivity (EC) is associated with fewer salts in the soil, indicating that the soil is neutral to slightly saline (Smith and Doran \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Experimental results have shown that soil EC is directly proportional to nutrient concentration and inversely proportional to soil depth (Othaman et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, disturbed forests may exhibit lower soil EC due to poor vegetation cover and increased nutrient leaching (Mgelwa et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Despite these the state of Jharkhand is a mineral-rich soils, where some areas, particularly those in Dhanbad, Ranchi, and Simdega forest divisions, show higher EC due to the influence of parent rock material and intensive mineralization, which contribute to a higher ionic content (Frost et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In contrast, areas with sandy soils, such as Devgarh, Dumka, and Lohardaga, tend to have lower EC because sand has a reduced ion-retention capacity (Alam et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe highest OC obtained for the degraded forest class in Sahibganj (1.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40%) can be attributed to several interrelated factors, including microbial activity, soil structure, and the effects of reforestation (Ke et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). For example, coniferous and mixed forests, under disturbed conditions, exhibited a higher proportion of macroaggregates, which contributed to greater soil organic carbon (SOC) accumulation (Ke et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). A similar study by Das and Maiti (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) reported high carbon accumulation in reclaimed coalmine forest soils in the Jharia coalfields, Jharkhand. In contrast, higher OC levels in NDF and MDF forest classes support the theory that soil organic carbon content is primarily influenced by the balance between carbon incorporation and decomposition rates in the ecosystem (Huang and Song \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Deng and Shangguan \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Tiwari \u003cem\u003eet al.\u003c/em\u003e, 2019). A list of studies has confirmed that soil organic carbon content decreases rapidly when natural vegetation cover is reduced due to disturbances such as deforestation, land use changes, and urbanization (Mehta et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Van and Olsson 2011; Sharma \u003cem\u003eet al.\u003c/em\u003e, 2022; Mayer et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Vegetation cover can influence soil characteristics, with species-specific effects on the quantity and quality of plant litter. Natural forest ecosystems typically exhibit higher annual carbon input compared to degraded ones (Huang and Song, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). As a result, NDF and MDF forests contain more organic carbon than degraded forest systems. Our results were also justifiable with findings of Mgelwa et al., (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), where forest disturbances significantly reduced soil OC, EC, and pH by 52%, 50%, and 98%, respectively, during the 21st century. Additionally, Ahirwal et al., (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) finding that reported a decline in SOC stocks of 84% in afforested mined soils and 50% in agricultural soils compare to natural forest in Jharkhand.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eEffect of forest disturbances on soil NPK\u003c/h2\u003e \u003cp\u003eThe NPK content of most forest divisions in Jharkhand state is influenced by both forest disturbances and soil depth (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The decline in NPK content with increasing disturbances in tropical deciduous forests can be attributed to several interrelated factors. Disturbances such as deforestation, extreme weather events, forest fires, and anthropogenic pressures disrupt nutrient cycling, resulting in nutrient depletion in both the soil and vegetation (Roy et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Ahirwal et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sharma et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These disturbances reduce nutrient cycling (Gautam and Mandal \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), lead to a significant loss in biomass (31.63%) (Thakrey et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and decrease overall species diversity (Thakrey et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), all of which affect the availability of essential nutrients like NPK. However, higher NPK levels in the surface layers of soil across different forest divisions are attributed to the accumulation and decomposition of litterfall, with the subsequent nutrient release pattern influenced by climatic factors (Bhalawe et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). These factors significantly affect the distribution of soil NPK content in relation to disturbance and soil depth. Similar studies have also reported significant changes in soil NPK content with disturbance and soil depth (Mehta et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Bhattacharyya \u003cem\u003eet al\u003c/em\u003e., 2015; Tiwari \u003cem\u003eet al\u003c/em\u003e., 2019; Shankar and Garkoti \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe largest AN value for Deoghar, Hazaribagh West, Khunti, and Ramgarh in NDF and MDF at upper soil depths indicate a nutrient-rich forest cover. Non-degraded forests typically have higher soil organic matter, which enhances nitrogen retention and availability. This organic matter acts as a reservoir for nitrogen, supporting microbial activity and nutrient cycling (Wang et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Factors such as moisture, water-holding capacity, organic carbon, and organic matter also contribute to supporting forest AN in the forest ecosystem (Kumar et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In non-degraded forests, the presence of diverse plant species and healthy microbial communities promotes effective nitrogen utilization and minimizes leaching losses (Mo et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). These factors explain the healthy AN observed in the Deoghar, Hazaribagh West, Khunti, and Ramgarh forest divisions. An enhanced AN in the Khunti, Hazaribagh, and Ramgarh regions was also reported by Kumar et al., (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) for Jharkhand forests.\u003c/p\u003e \u003cp\u003eSimilarly for high AP in the non-degraded forests of Koderma, Chatra South, Giridih East, and Medininagar at the upper soil horizon indicates the influence of weathering processes and the retention capabilities of natural forest ecosystems. Weathering transforms primary minerals into more soluble forms of phosphorus, particularly in the upper soil horizons (Tuyishime, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additionally, the decomposition of organic residues from plant material releases phosphorus in forms that are more accessible to plants, and humic substances can bind phosphorus, enhancing its availability for vegetation uptake (Izhar \u003cem\u003eet al\u003c/em\u003e., 2020). This increased availability of nutrients, such as NPK, in the upper soil layers of non-degraded forests can be attributed to the mineralization of leaf litter on the forest floor. Mineralized leaf litter releases AN and AP more rapidly compared to other nutrients (Uma et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). On the other hand, the higher potassium (K) content in the upper forest soil layers results from the decomposition of litterfall and the solubilization of insoluble K forms present in the soil due to organic decomposition products (Naik \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Naik (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and Pandey et al., (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) also observed that the availability of nitrogen (N), phosphorus (P), and potassium (K) was higher in surface layers than in subsurface horizons under various tree species, with availability decreasing gradually with depth.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eEffect of forest disturbances on soil micronutrients\u003c/h2\u003e \u003cp\u003eMicronutrients play a critical role in forest ecosystems as they serve as key indicators of ecosystem health and stability. Soil micronutrient concentrations are essential for plant growth, development, high productivity, and maintaining balance in soil chemistry (Shepherd and Oliverio \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Across all forest classes (NDF, MDF, and DF) a significant increase was observed in the levels of DTPA-extractable micronutrients, with the relative distribution following the trend: NDF\u0026thinsp;\u0026gt;\u0026thinsp;MDF\u0026thinsp;\u0026gt;\u0026thinsp;DF (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e3\u003c/span\u003e). However, the content of DTPA-extractable micronutrients decreased with increasing soil depth, indicating a negative impact as the soil profile deepened (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The influence of forest class on micronutrient content showed significantly higher levels in NDF compared to MDF and DF. This could be attributed to the higher organic matter content in NDF soils, driven by greater litter fall and root biomass, which improved soil aeration. Enhanced aeration likely prevented the oxidation and precipitation of micronutrients in bound forms while supplying chelating agents that increased micronutrient solubility and availability (Saha et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Dhaliwal and Dhaliwal \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Our findings have aligned with the results of previous studies (Maini \u003cem\u003eet al\u003c/em\u003e., 2022; Tiwari \u003cem\u003eet al\u003c/em\u003e., 2019; Dhaliwal et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Similarly, other studies have reported increased micronutrient content in tree-based systems and non-degraded forests, which could also be linked to exogenous carbon inputs from litter, root biomass, root exudates, and above-ground biomass. These inputs lower soil pH and redox potential, thereby enhancing the availability of micronutrients in the soil (Maini \u003cem\u003eet al\u003c/em\u003e., 2022; Mandal and Dhaliwal \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). A similar study reported by Dhaliwal et al., (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) in northwestern India, where a significant decline in micronutrient content observed with increasing soil depth. This trend was also supported by Singh and Sharma (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), who observed similar decreases in micronutrient levels under plantation soils.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eCorelation matrix of soil physio-chemical characteristics\u003c/h2\u003e \u003cp\u003eThe correlation matrix among the soil physico-chemical properties was prepared and shown in the (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e4\u003c/span\u003e), where most of the major soil chemical property values shows good correlation. The soil Fe content has positively correlated with Mn (0.975 \u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e), EC with pH (0.243 \u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e) and AK to AP (0.221 \u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e). Soil EC has a weak positive relationship with OC (0.155 \u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e) and AK (0.140 \u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e). where soil OC shown positive association with AK (0.146 \u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e) and B (0.104 \u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e). The AN recorded a very weak positive relation with AP (0.128 \u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/em\u003e and AK (0.122 \u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e) while negative association with Cu (-0.144 \u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e) and Zn (-0.141 \u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e). A very strong positive correlation between Mn and Fe is observed due to the uneven distribution of Mn in forest soil. The highest concentration of Mn is typically found in the topmost soil layers, whereas Fe tends to be distributed more evenly under similar conditions. In deciduous forests, higher pH levels and lower dissolved organic carbon (DOC) enhance the retention of Mn and Fe oxides, thereby promoting their association (Rotter et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Both Fe and Mn share similar biogeochemical cycling processes, such as weathering and precipitation as oxides. Sequential extraction studies reveal that Fe and Mn are often present in reducible and oxidizable fractions, which suggests their co-occurrence in soil profiles (Walna et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Similar findings have also been reported by Zaitsev et al (\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Dhaliwal et al., (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe positive relationship between electrical conductivity (EC) and soil pH in forest soils can be explained by the concentration of hydrogen ions. A higher concentration of hydrogen ions in the soil leads to an increased EC. Consequently, low soil pH, caused by a large number of hydrogen ions, may promote higher soil EC (Aizat et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Additionally, EC in forest soils is positively correlated with organic acids. Organic acids can dissolve minerals containing potassium and exchangeable calcium, which further contributes to the EC of soil systems (Osman and Osman \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This explains the positive correlation of EC with OC and AK, as supported by various studies (Osman and Osman \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Kim and Park 2013; Mazur \u003cem\u003eet al\u003c/em\u003e., 2023). The positive correlation between AN and both available potassium AK and AP in forest soil can be attributed to several interconnected mechanisms involving nutrient cycling and microbial activity. An increase in nitrogen availability often stimulates microbial biomass, which enhances the cycling of phosphorus and potassium, indicating a synergistic effect on nutrient dynamics (Zhu et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Moreover, nitrogen fertilization can alter soil pH, influencing the solubility and availability of other nutrients (Karklina and Stola 2019). Similar positive correlations among macronutrients (AN, AP, OC, and AK) have also been recorded in forest ecosystems in Jharkhand (Naik \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Kumar and Saikia \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kumar et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eCalculation of recommended doses of fertilizer to diagnose forest soil fertility\u003c/h2\u003e \u003cp\u003eFollowing investigation of the soil physicochemical properties across 31 forest divisions in Jharkhand state, we calculated the recommended doses of fertilizers in NPK (Nitrogen, Phosphorus, and Potassium) form to address soil fertility constraints at the forest division level. These recommendations were made for three types of fertilizers: inorganic fertilizers, organic farmyard manure (FYM), and vermicompost, to meet the NPK requirements. The recommended fertilizer doses were determined based on the standard values of various soil parameters, which were calculated as the average values for NDF each forest division. The evaluation revealed that all forest divisions in Jharkhand state require additional AN and AK, except Bokaro and Sahibganj for AN, and Chatra South and Saraikela for AK. However, seven forest divisions (Chatra South, Deoghar, Giridih East, Giridih West, Jamtara, Koderma, and Medininagar) do not require additional AP, unlike the other divisions. For inorganic fertilizer recommendations, the highest urea dose for nitrogen supplementation was suggested for Lohardaga (446.3 kg/ha), followed by Chaibasa (355.3 kg/ha) and Pakur (354.9 kg/ha), while the lowest dose was recommended for Ramgarh (81.9 kg/ha). Similarly, for phosphorus supplementation, the maximum dose of Single Super Phosphate (SSP) was suggested for Saranda (89.4 kg/ha), and the minimum for Khunti (20.4 kg/ha). For potassium supplementation, the highest potassium oxide (K₂O) doses were proposed for Hazaribagh East (170.0 kg/ha), Giridih West (160.9 kg/ha), and Hazaribagh East (157.9 kg/ha), while the lowest doses were suggested for Simdega (3.7 kg/ha), Jamtara (22.1 kg/ha), and Garhwa South (27.9 kg/ha). Regarding organic FYM recommendations for nitrogen requirements, the highest FYM dose was suggested for Lohardaga (41,056 kg/ha), and the lowest for Ramgarh (7,538 kg/ha). For phosphorus and potassium requirements, the largest FYM doses were recommended for Chaibasa (65.37 kg/ha) and Lohardaga (205.28 kg/ha), respectively, while the smallest doses were recommended for Ramgarh (15.08 kg/ha for phosphorus and 37.69 kg/ha for potassium).\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur findings indicate that forest disturbance classes and soil depths significantly influence the physico-chemical properties of forest soils across different forest divisions in Jharkhand, India. Most soil parameters recorded their maximum and minimum values in NDF and at lower soil depths, respectively. The higher nutrient content in NDF soils compared to MDF and DF can be attributed to greater litter return and minimal anthropogenic interference. A sequential decline in soil nutrients with increasing soil depth (30\u0026ndash;60 cm and 60\u0026ndash;90 cm) is likely due to reduced organic matter, moisture content, and the diversity of active microflora at deeper levels. Similarly, the NDF class and the 0\u0026ndash;30 cm soil depth exhibited the highest concentrations of DTPA-extractable Cu, Zn, Fe, Mn, B, and S, which were significantly higher than those observed in other forest classes and depths. Correlation analysis showed that EC positively correlated with pH, OC and AK, while AN had a positive relationship with AP and AK. These interactions indicate that the presence and interaction of one nutrient can influence the uptake and utilization of another. The recommended dose of fertilizer analysis revealed that most forest divisions in Jharkhand require additional NPK, except for Bokaro, Chatra South, Deoghar, Giridih East, Giridih West, Jamtara, Koderma, Medininagar, Sahibganj, and Saraikela divisions. These findings serve as a guide for soil health improvement layout design in the restoration and reconstruction of dry tropical forests, and they suggest that forest disturbances and soil depth should be specifically considered when assessing the impact of management strategies on forest and soil quality\u003c/p\u003e \u003cp\u003eThis study provides valuable insights into the relationship between vegetation and soil quality parameters. The nutrient recommendations derived from soil tests, both organic and inorganic, offer useful guidance for stakeholders and foresters in planning new plantations and promoting sustainable management practices. This information will assist State Forest Departments (SFDs) in managing forests sustainably and aid forest managers and officials in developing long-term action plans for conservation and management. Furthermore, plantation growers can use this data to identify suitable areas for tree planting based on soil quality, benefiting rural communities. The methodologies and results of this study are adaptable and can be applied to other regions facing similar ecological challenges, supporting global efforts in sustainable forest management and the restoration of degraded ecosystems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthors\u0026rsquo; contribution: 1 and 6- conceptualized, designed, coordinated the study, participatedin data collection, analysis, interpretation and drafting and finalization of manuscript, 2 to 5conceptualized, design, and finalized the manuscript. All authors read and approved thefinal manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors show their gratitude to the Ministry of Environment, Forest \u0026amp; Climate Change, Compensatory Afforestation Fund Management and Planning Authority (CAMPA) for providing financial support. We are thankful for the generous support, encouragement and motivation by DG, ICFRE, Dehradun, Director, ICFRE-FRI, Dehradun, Director, ICFRE-IFP, Ranchi. We are thankful to PCCF (HoFF), DFOs of all forest divisions of Jharkhand SFD. Thanks to Sh. Satish Kumar, Dr. Rahul Kumar, Sh. Tulsi Mandal, Miss Pragya and other support officials, India Meteorological Department Ranchi, Google Earth Engine (GEE) and USGS and FSI, Dehradun.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAhirwal, J., Kumari, S., Singh, A. K., Kumar, A., \u0026amp; Maiti, S. K. (2021). Changes in soil properties and carbon fluxes following afforestation and agriculture in tropical forest. Ecological indicators, 123, 107354.\u003c/li\u003e\n \u003cli\u003eAhmad, F., Uddin, M. M., \u0026amp; Goparaju, L. (2018). An evaluation of vegetation health and the socioeconomic dimension of the vulnerability of Jharkhand state of India in climate change scenarios and their likely impact: A geospatial approach. \u003cem\u003eEnvironmental \u0026amp; Socio-economic Studies\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(4), 39-47.\u003c/li\u003e\n \u003cli\u003eAizat, A. M., Roslan, M. K., Sulaiman, W. N. A., \u0026amp; Karam, D. S. (2014). The relationship between soil pH and selected soil properties in 48 years logged-over forest. International journal of environmental sciences, 4(6), 1129-1140.\u003c/li\u003e\n \u003cli\u003eAlam, S., Hammoda, H., Khan, F., Enazi, R., \u0026amp; Goktepe, I. (2020). Electrical Conductivity, pH, Organic Matter and Texture of Selected Soils Around the Qatar University Campus. Research in Agriculture Livestock and Fisheries, 7, 403-409.\u003c/li\u003e\n \u003cli\u003eAli, S., Khan, S. M., Ahmad, Z., Siddiq, Z., Ullah, A., Yoo, S., ... \u0026amp; Raposo, A. (2023). Carbon sequestration potential of different forest types in Pakistan and its role in regulating services for public health. \u003cem\u003eFrontiers in Public Health\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e, 1064586.\u003c/li\u003e\n \u003cli\u003eAmarasinghe, A., Chen, C., Van Zwieten, L., \u0026amp; Rashti, M. R. (2023). The role of edaphic variables and management practices in regulating soil microbial resilience to drought-A meta-analysis. \u003cem\u003eScience of The Total Environment\u003c/em\u003e, 169544.\u003c/li\u003e\n \u003cli\u003eBaccini A, Walker W, Carvahlo L, Farina M, Sulla-Menashe D and Houghton R 2017 Tropical forests are a net carbon source based on new measurements of gain and loss Science 358 230\u0026ndash;4.\u003c/li\u003e\n \u003cli\u003eBanerjee, S., \u0026amp; Van Der Heijden, M. G. (2023). Soil microbiomes and one health. \u003cem\u003eNature Reviews Microbiology\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(1), 6-20.\u003c/li\u003e\n \u003cli\u003eBar-On, Y. M., Phillips, R., \u0026amp; Milo, R. (2018). The biomass distribution on Earth. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e, \u003cem\u003e115\u003c/em\u003e(25), 6506-6511.\u003c/li\u003e\n \u003cli\u003eBehera, S. K., Mishra, S., Singh, S. N., Kumar, M., Chaudhary, L. B., Jain, M. K., ... \u0026amp; Pandey, N. (2019). Assessment of essential nutrients in soil with litter chemistry of tropical deciduous forest, India. International Journal of Plant and Environment, 5(01), 40-46.\u003c/li\u003e\n \u003cli\u003eBhaduri, A., Chatterjee, S., Bakuli, K., Hazra, D., \u0026amp; Pandey, S. (2019). Nutrient cycling and metabolic activity of soil microbes in pristine forests in comparison to a monoculture. \u003cem\u003eVegetos\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(3), 324-332.\u003c/li\u003e\n \u003cli\u003eBhalawe, S., Nayak, D., Kukadia, M. U., \u0026amp; Gayakvad, P. (2013). Leaf litter decomposition pattern of trees. The Bioscan, 8(4), 1135-1140.\u003c/li\u003e\n \u003cli\u003eBhavana Dixit, B. D., \u0026amp; Rashmi Agarwal, R. A. (2013). Dynamics of soil nutrients and ecto-mycorrhizal symbionts in disturbed and undisturbed stands of Tropical Dry Deciduous Forest of Central India.\u003c/li\u003e\n \u003cli\u003eBini, C. (2009). Soil: A precious natural resource. In \u003cem\u003eConservation of natural resources\u003c/em\u003e (pp. 1-48). Nova Science.\u003c/li\u003e\n \u003cli\u003eBlanco-Canqui, H., Lal, R., Blanco-Canqui, H., \u0026amp; Lal, R. (2008). Soil resilience and conservation. \u003cem\u003ePrinciples of soil conservation and management\u003c/em\u003e, 425-447.\u003c/li\u003e\n \u003cli\u003eChavan BL, Rasal GB (2010). Sequestered standing carbon stock in selective tree species grown in University campus at Aurangabad, Maharashtra, India. Int J Eng Sci Technol. (2010) 2:3003\u0026ndash;7.\u003c/li\u003e\n \u003cli\u003eChesin, L. and Yien, C.H. (1950). Turbidimetric determination of available sulphates. In Soil Science Society of America, Proceedings, 15, 149-151.\u003c/li\u003e\n \u003cli\u003eDas, R., \u0026amp; Maiti, S. K. (2016). Importance of carbon fractionation for the estimation of carbon sequestration in reclaimed coalmine soils\u0026mdash;A case study from Jharia coalfields, Jharkhand, India. \u003cem\u003eEcological Engineering\u003c/em\u003e, \u003cem\u003e90\u003c/em\u003e, 135-140.\u003c/li\u003e\n \u003cli\u003eDeng, L., Shangguan, Z.P., 2017. Afforestation drives soil carbon and nitrogen changes in China. Land Degrad. Dev. 28, 151\u0026ndash;165.\u003c/li\u003e\n \u003cli\u003eDhaliwal, M. K., \u0026amp; Dhaliwal, S. S. (2019). Impact of manure and fertilizers on chemical fractions of Zn and Cu in soil under rice-wheat cropping system. Journal of the Indian Society of Soil Science, 67(1), 85-91.\u003c/li\u003e\n \u003cli\u003eDhaliwal, S. S., Sharma, V., Shukla, A. K., Kaur, J., Gupta, R. K., Verma, V., ... \u0026amp; Singh, P. (2023). Interactive effect of land use systems on depth-wise soil properties and micronutrients minerals in North-Western, India. Heliyon, 9(2).\u003c/li\u003e\n \u003cli\u003eFoster, N. W., \u0026amp; Bhatti, J. S. (2006). Forest ecosystems: nutrient cycling. Encyclopedia of soil science, 718721.\u003c/li\u003e\n \u003cli\u003eFrost, P. S., van Es, H. M., Rossiter, D. G., Hobbs, P. R., \u0026amp; Pingali, P. L. (2019). Soil health characterization in smallholder agricultural catchments in India. \u003cem\u003eApplied Soil Ecology\u003c/em\u003e, \u003cem\u003e138\u003c/em\u003e, 171-180.\u003c/li\u003e\n \u003cli\u003eFrost, P. S., van Es, H. M., Rossiter, D. G., Hobbs, P. R., \u0026amp; Pingali, P. L. (2019). Soil health characterization in smallholder agricultural catchments in India. Applied Soil Ecology, 138, 171-180.\u003c/li\u003e\n \u003cli\u003eGautam, T. P., \u0026amp; Mandal, T. N. (2018). Storage and flux of nutrients in disturbed and undisturbed tropical moist forest of Eastern Nepal. International Journal of Forestry Research, 2018(1), 8516321.\u003c/li\u003e\n \u003cli\u003eGupta, U.C. (1967). A simplified method for determining hot-watersoluble boron in podzol soils. Soil Science, 103(6): 424-428.\u003c/li\u003e\n \u003cli\u003eHuang, J., Song, C., 2010. Effects of land use on soil water soluble organic C and microbial biomass C concentrations in the Sanjiang Plain in Northeast China. Acta Agric. Scand. Sect. B Soil Plant Sci. 60 (2), 182\u0026ndash;188.\u003c/li\u003e\n \u003cli\u003eIzhar Shafi, M., Adnan, M., Fahad, S., Wahid, F., Khan, A., Yue, Z., ... \u0026amp; Datta, R. (2020). Application of single superphosphate with humic acid improves the growth, yield and phosphorus uptake of wheat (Triticum aestivum L.) in calcareous soil. Agronomy, 10(9), 1224.\u003c/li\u003e\n \u003cli\u003eJakhar, S., \u0026amp; Punia, H. (2022). Conceptualizing Different Forest Degradation Drivers with Special Reference to India. \u003cem\u003eInternational Journal of Environment and Climate Change\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(11), 3184-3200.\u003c/li\u003e\n \u003cli\u003eKārkliņa, I., \u0026amp; Stola, J. (2019). Impact of forest soil enrichment with nitrogen fertilizer on throughfall and soil water chemical properties. Research for Rural Development, 1.\u003c/li\u003e\n \u003cli\u003eKe, Y., Li, H., Luo, T., Chen, B., Wang, Q., Jiang, X., \u0026amp; Liu, W. (2023). Reforestation Increases the Aggregate Organic Carbon Concentration Induced by Soil Microorganisms in a Degraded Red Soil, Subtropical China. Microorganisms, 11(8), 2008.\u003c/li\u003e\n \u003cli\u003eKim, H. N., \u0026amp; Park, J. H. (2024). Monitoring of soil EC for the prediction of soil nutrient regime under different soil water and organic matter contents. Applied Biological Chemistry, 67(1), 1.\u003c/li\u003e\n \u003cli\u003eKindermann G, Obersteiner M, Sohngen B, Sathaye J, Andrasko K, Rametsteiner E, et al. Global cost estimates of reducing carbon emissions through avoided deforestation. Proc Nat Acad Sci. (2008) 105:10302\u0026ndash;7. doi: 10.1073/pnas.0710616105.\u003c/li\u003e\n \u003cli\u003eKumar, A., Bharose, R., Kumar, R., \u0026amp; Sen, P (2024). Assessment of physico-chemical properties of soil and carbon sequestration of natural sal (Shorea robusta) forest under different regimes of Jharkhand.\u0026nbsp;International Journal of Research in Agronomy; 7(8): 317-324\u003c/li\u003e\n \u003cli\u003eKumar,\u0026nbsp;A., Bharose, R., Kumar, R., \u0026amp; Sen, P. (2021). Assessment of physico-chemical properties of soil and carbon sequestration of natural sal (Shorea robusta) forest under different regimes of Jharkhand.\u0026nbsp;Annals of Experimental Biology, 9 (3): 1-6.\u003c/li\u003e\n \u003cli\u003eKumar, A., Bharose, R., Kumar, R., \u0026amp; Sen, P. (2024). Assessment of physico-chemical properties of soil and carbon sequestration of natural sal (Shorea robusta) forest under different regimes of Jharkhand.\u003c/li\u003e\n \u003cli\u003eKumar, A., Bharose, R., Kumar, R., \u0026amp; Sen, P. (2024). Assessment of physico-chemical properties of soil and carbon sequestration of natural sal (Shorea robusta) forest under different regimes of Jharkhand.\u0026nbsp;\u003cem\u003eInternational Journal of Research in Agronomy\u003c/em\u003e 2024; 7(8): 317-324.\u003c/li\u003e\n \u003cli\u003eKumar, R., \u0026amp; Saikia, P. (2020). Forest resources of Jharkhand, Eastern India: socio-economic and bio-ecological perspectives. Socio-economic and Eco-biological Dimensions in Resource use and Conservation: Strategies for Sustainability, 61-101.\u003c/li\u003e\n \u003cli\u003eKumar, R., \u0026amp; Saikia, P. (2021). Spatio-temporal soil nutrient dynamics and plant species diversity in selected Sal forests of Ranchi, Eastern India. \u003cem\u003eVegetos\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e(1), 235-248.\u003c/li\u003e\n \u003cli\u003eKumar, R., Kumar, A., \u0026amp; Saikia, P. (2022). Deforestation and forests degradation impacts on the environment. In Environmental Degradation: Challenges and Strategies for Mitigation (pp. 19-46). Cham: Springer International Publishing.\u003c/li\u003e\n \u003cli\u003eKumari, A., Thomas, T., Swaroop, N., Mohanta, S. R., \u0026amp; Thomas, A. (2024). Assessment of Soil Health and Soil Quality of Bokaro, District, Jharkhand, India. \u003cem\u003eInternational Journal of Plant \u0026amp; Soil Science\u003c/em\u003e, \u003cem\u003e36\u003c/em\u003e(6), 642-651.\u003c/li\u003e\n \u003cli\u003eLal, R. (2005). Forest soils and carbon sequestration. \u003cem\u003eForest ecology and management\u003c/em\u003e, \u003cem\u003e220\u003c/em\u003e(1-3), 242-258.\u003c/li\u003e\n \u003cli\u003eLamb, D., Erskine, P. D., \u0026amp; Parrotta, J. A. (2005). Restoration of degraded tropical forest landscapes. \u003cem\u003eScience\u003c/em\u003e, \u003cem\u003e310\u003c/em\u003e(5754), 1628-1632.\u003c/li\u003e\n \u003cli\u003eLatehar and Hazaribagh District of Jharkhand.\u0026nbsp;International Journal of Chemical Studies; SP6: 625-628.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLehmann, J., Bossio, D. A., K\u0026ouml;gel-Knabner, I., \u0026amp; Rillig, M. C. (2020). The concept and future prospects of soil health. \u003cem\u003eNature Reviews Earth \u0026amp; Environment\u003c/em\u003e, \u003cem\u003e1\u003c/em\u003e(10), 544-553.\u003c/li\u003e\n \u003cli\u003eLe\u0026oacute;n, J. D., \u0026amp; Osorio, N. W. (2014). Role of litter turnover in soil quality in tropical degraded lands of Colombia. The Scientific World Journal, 2014(1), 693981.\u003c/li\u003e\n \u003cli\u003eLevasseur, P. A., Aherne, J., Basiliko, N., \u0026amp; Watmough, S. A. (2024). Organic matter, carbon, and nitrogen relationships of regreened forest soils in an industrially impacted landscape. \u003cem\u003eSoil Research\u003c/em\u003e, \u003cem\u003e62\u003c/em\u003e(5).\u003c/li\u003e\n \u003cli\u003eLindsay, W.L. and Norvell, W. (1978). Development of a DTPA soil test for zinc, iron, manganese, and copper. Soil science society of America journal, 42(3): 421-428.\u003c/li\u003e\n \u003cli\u003eLiu, J., Chen, J., Chen, G., Guo, J., \u0026amp; Li, Y. (2020). Enzyme stoichiometry indicates the variation of microbial nutrient requirements at different soil depths in subtropical forests. PLoS One, 15(2), e0220599.\u003c/li\u003e\n \u003cli\u003eMaini, A., Sharma, V., \u0026amp; Sharma, S. (2020). Assessment of soil carbon and biochemical indicators of soil quality under rainfed land use systems in North Eastern region of Punjab, India. Carbon Management, 11(2), 169-182.\u003c/li\u003e\n \u003cli\u003eMalhi YM, Brown S. Forests, carbon and global climate. \u003cem\u003ePhilosophical Trans Royal Soc London Series A Math Phys Eng Sci.\u003c/em\u003e (2002) 360:1567\u0026ndash;91. doi: 10.1098/rsta.2002.1020.\u003c/li\u003e\n \u003cli\u003eMandal, A., \u0026amp; Dhaliwal, S. S. (2018, November). Distribution of micronutrients in soils under different land-uses in south-western plains of Punjab. In \u003cem\u003e83rd Annual Convention of ISSS\u003c/em\u003e (pp. 27-30).\u003c/li\u003e\n \u003cli\u003eMayer, M., Baltensweiler, A., James, J., Rigling, A., \u0026amp; Hagedorn, F. (2024). A global synthesis and conceptualization of the magnitude and duration of soil carbon losses in response to forest disturbances. \u003cem\u003eGlobal Ecology and Biogeography\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e(1), 141-150.\u003c/li\u003e\n \u003cli\u003eMazur, P., Gozdowski, D., \u0026amp; Wnuk, A. (2022). Relationships between soil electrical conductivity and sentinel-2-derived NDVI with pH and content of selected nutrients. Agronomy, 12(2), 354.\u003c/li\u003e\n \u003cli\u003eMehta, V. K., Sullivan, P. J., Walter, M. T., Krishnaswamy, J., \u0026amp; DeGloria, S. D. (2008). Impacts of disturbance on soil properties in a dry tropical forest in Southern India. \u003cem\u003eEcohydrology: Ecosystems, Land and Water Process Interactions, Ecohydrogeomorphology\u003c/em\u003e, \u003cem\u003e1\u003c/em\u003e(2), 161-175.\u003c/li\u003e\n \u003cli\u003eMehta, V. K., Sullivan, P. J., Walter, M. T., Krishnaswamy, J., \u0026amp; DeGloria, S. D. (2008). Impacts of disturbance on soil properties in a dry tropical forest in Southern India. Ecohydrology: Ecosystems, Land and Water Process Interactions, Ecohydrogeomorphology, 1(2), 161-175.\u003c/li\u003e\n \u003cli\u003eMgelwa, A. S., Ngaba, M. J. Y., Hu, B., Gurmesa, G. A., Mwakaje, A. G., Nyemeck, M. P. B., ... \u0026amp; Rennenberg, H. (2024). Meta-analysis of 21st century studies shows that deforestation induces profound changes in soil characteristics, particularly soil organic carbon accumulation. Forest Ecosystems, 100257.\u003c/li\u003e\n \u003cli\u003eMishra, S. N., Kumar, R., \u0026amp; Panwar, V. P. (2024). Biodiversity and Soil Properties of Saranda Sal Forest: Implications for Conservation and Management. \u003cem\u003eInternational Journal of Plant \u0026amp; Soil Science\u003c/em\u003e, \u003cem\u003e36\u003c/em\u003e(7), 326-337.\u003c/li\u003e\n \u003cli\u003eMo, J., Brown, S., Peng, S., \u0026amp; Kong, G. (2003). Nitrogen availability in disturbed, rehabilitated and mature forests of tropical China. Forest Ecology and Management, 175(1-3), 573-583.\u003c/li\u003e\n \u003cli\u003eNaik, S. K. (2014). Distribution of nitrogen, phosphorus, potassium and Zinc content in mango grown acidic soils of Jharkhand. The Ecoscan, 8(1\u0026amp;2), 135-139.\u003c/li\u003e\n \u003cli\u003eOsman, K. T., \u0026amp; Osman, K. T. (2013). Chemical Properties of Forest Soils. Forest Soils: Properties and Management, 45-61.\u003c/li\u003e\n \u003cli\u003eOthaman, N. N., Isa, M. N., Ismail, R. C., Ahmad, M. I., \u0026amp; Hui, C. K. (2020). Factors that affect soil electrical conductivity (EC) based system for smart farming application. In AIP conference proceedings (Vol. 2203, No. 1). AIP Publishing.\u003c/li\u003e\n \u003cli\u003ePandey, H. P., Pokhrel, S., Pandey, P., Pokhrel, N. P., \u0026amp; Paudel, G. (2020). Effect of primary soil nutrients (NPK) on forests\u0026apos; biomass production and SOC density of Nepalese forests. Proceedings of the International Academy of Ecology and Environmental Sciences, 10(3), 86-96.\u003c/li\u003e\n \u003cli\u003ePandey, S., Kumari, N., \u0026amp; Prasad, S. P. (2021). Soil quality and pollution assessment around Jumar watershed of Jharkhand, India. Arabian Journal of Geosciences, 14, 1-20.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003ePatra, A. K., Lenka, N. K., \u0026amp; Biswas, A. K. (2015). Soil health assessment and management. \u003cem\u003eIndian J Fertil\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e, 16-27.\u003c/li\u003e\n \u003cli\u003eRaj, M., Lal, K., Satdev, Kumari, P., Kumari, S., Dubey, V. K., ... \u0026amp; Kumar, S. (2024). Potential nutrient cycling and management in agroforestry. In \u003cem\u003eAgroforestry to Combat Global Challenges: Current Prospects and Future Challenges\u003c/em\u003e (pp. 71-92). Singapore: Springer Nature Singapore.\u003c/li\u003e\n \u003cli\u003eRotter, P., Kuta, J., V\u0026aacute;cha, R., \u0026amp; S\u0026aacute;ňka, M. (2017). The role of Mn and Fe oxides in risk elements retention in soils under different forest types.\u003c/li\u003e\n \u003cli\u003eRoy, P. K., Samal, N. R., Roy, M. B., \u0026amp; Mazumdar, A. (2010). Soil carbon and nutrient accumulation under forest plantations in Jharkhand state of India. \u003cem\u003eCLEAN\u0026ndash;Soil, Air, Water\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e(8), 706-712.\u003c/li\u003e\n \u003cli\u003eRoy, P. K., Samal, N. R., Roy, M. B., \u0026amp; Mazumdar, A. (2010). Soil carbon and nutrient accumulation under forest plantations in Jharkhand state of India. CLEAN\u0026ndash;Soil, Air, Water, 38(8), 706-712.\u003c/li\u003e\n \u003cli\u003eSaha, S., Saha, B., Seth, T., Dasgupta, S., Ray, M., Pal, B., ... \u0026amp; Hazra, G. (2019). Micronutrients availability in soil\u0026ndash;plant system in response to long-term integrated nutrient management under rice\u0026ndash;wheat cropping system. Journal of Soil Science and Plant Nutrition, 19, 712-724.\u003c/li\u003e\n \u003cli\u003eSalam, A. (2024). Internet of things for sustainable forestry. In \u003cem\u003eInternet of Things for sustainable community development: Wireless communications, sensing, and systems\u003c/em\u003e (pp. 147-181). Cham: Springer International Publishing.\u003c/li\u003e\n \u003cli\u003eShankar, A., \u0026amp; Garkoti, S. C. (2024). Influence of forest types on soil physicochemical and biological characteristics of associated agroecosystems in the central Himalaya. Science of The Total Environment, 906, 167731.\u003c/li\u003e\n \u003cli\u003eShao, G., Ai, J., Sun, Q., Hou, L., \u0026amp; Dong, Y. (2020). Soil quality assessment under different forest types in the Mount Tai, central Eastern China. Ecological Indicators, 115, 106439.\u003c/li\u003e\n \u003cli\u003eSharma, A., Patel, S. K., \u0026amp; Singh, G. S. (2023). Variation in species composition, structural diversity, and regeneration along disturbances in tropical dry forest of Northern India. \u003cem\u003eJournal of Asia-Pacific Biodiversity\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(1), 83-95.\u003c/li\u003e\n \u003cli\u003eShepherd, R. M., \u0026amp; Oliverio, A. M. (2024). Micronutrients modulate the structure and function of soil bacterial communities. Soil Biology and Biochemistry, 192, 109384.\u003c/li\u003e\n \u003cli\u003eShukla, J., Dhyani, S., Pujari, P., Mishra, A., \u0026amp; Verma, P. (2022). Impact of agriculture intensification on forest degradation and tree carbon stock; promoting multi‐criteria optimization for restoration in Central India. \u003cem\u003eLand Degradation \u0026amp; Development\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e(16), 3103-3117.\u003c/li\u003e\n \u003cli\u003eSingh, B., \u0026amp; Sharma, K. N. (2012). Depthwise distribution of soil organic carbon and nutrients under some tree species after seventeen years of plantation. \u003cem\u003eJournal of the Indian Society of Soil Science\u003c/em\u003e, \u003cem\u003e60\u003c/em\u003e(3), 198-203.\u003c/li\u003e\n \u003cli\u003eSmith, J. L. and Doran, J. W. (2015) \u0026lsquo;Measurement and use of pH andelectrical conductivity for soil quality analysis\u0026rsquo;, pp. 169\u0026ndash;185.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eStanford, G. and L. English. (1949). Use of flame photometer in rapid soil test for K and Ca. Agronomy. J., 41: 446-447.\u003c/li\u003e\n \u003cli\u003eSterk, G., Riksen, M. J. P. M., \u0026amp; Goossens, D. (2001). Dryland degradation by wind erosion and its control. \u003cem\u003eAnnals of arid Zone\u003c/em\u003e, \u003cem\u003e40\u003c/em\u003e(3), 351-368.\u003c/li\u003e\n \u003cli\u003eSubbiah, B.V. and Asija. G.L. (1956). A rapid procedure for the estimation of available nitrogen in soils. \u003cem\u003eCurrent Science,\u003c/em\u003e 25: 259-260.\u003c/li\u003e\n \u003cli\u003eThakrey, M., Singh, L., Jhariya, M. K., Tomar, A., Singh, A. K., \u0026amp; Toppo, S. (2022). Impact of disturbance on biomass, carbon, and nitrogen storage in vegetation and on soil properties of tropical dry deciduous forest in Chhattisgarh, India. Land Degradation \u0026amp; Development, 33(11), 1810-1820.\u003c/li\u003e\n \u003cli\u003eThakur, T. K., Swamy, S. L., Thakur, A., Mishra, A., Bakshi, S., Kumar, A., ... \u0026amp; Kumar, R. (2024). Land cover changes and carbon dynamics in Central India\u0026apos;s dry tropical forests: A 25-year assessment and nature-based eco-restoration approaches. \u003cem\u003eJournal of Environmental Management\u003c/em\u003e, \u003cem\u003e351\u003c/em\u003e, 119809.\u003c/li\u003e\n \u003cli\u003eTuyishime, M. (2022). Phosphorus chemistry in managed forest soils: effects of weathering and wood ash fertilisation (No. 2022: 75).\u003c/li\u003e\n \u003cli\u003eUma, M., Saravanan, T. S., \u0026amp; Rajendran, K. (2011). Litter production and nutrient dynamics of casuarinas equisetifolia in farm forestry plantation of southern India. The Bioscan, 6(3), 525-528.\u003c/li\u003e\n \u003cli\u003eVan Miegroet, H., \u0026amp; Olsson, M. (2011). Ecosystem disturbance and soil organic carbon\u0026ndash;a review. Soil carbon in sensitive European ecosystems: From science to land management, 85-117.\u003c/li\u003e\n \u003cli\u003eWalkley A, Black IA (1934) An examination of the Degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method. Soil sci 37:29\u0026ndash;38.\u003c/li\u003e\n \u003cli\u003eWalna, B., Spychalski, W., \u0026amp; Ibragimow, A. (2010). Fractionation of iron and manganese in the horizons of a nutrient-poor forest soil profile using the sequential extraction method. \u003cem\u003ePolish Journal of Environmental Studies\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(5), 1029-1037.\u003c/li\u003e\n \u003cli\u003eWang, F., Li, Z., Xia, H., Zou, B., Li, N., Liu, J., \u0026amp; Zhu, W. (2010). Effects of nitrogen-fixing and non-nitrogen-fixing tree species on soil properties and nitrogen transformation during forest restoration in southern China. Soil Science \u0026amp; Plant Nutrition, 56(2), 297-306.\u003c/li\u003e\n \u003cli\u003eWeisse, M., Goldman, E., \u0026amp; Carter, S. (2021). Forest Pulse: The Latest on the World\u0026rsquo;s Forests. World Resources Institute. Available at https://research. wri. org/gfr/forest-pulse.\u003c/li\u003e\n \u003cli\u003eZaitsev, G. A., Dubrovina, O. A., \u0026amp; Shainurov, R. I. (2020). Iron and manganese migration in \u0026ldquo;soil\u0026ndash;plant\u0026rdquo; system in Scots pine stands in conditions of contamination by the steel plant\u0026rsquo;s emissions. Scientific Reports, 10(1), 11025.\u003c/li\u003e\n \u003cli\u003eZeba Perween, Pradeep Kumar Thakur, Nikita Kumari, Rashmi Khusboo Minz, Rachna Sunanda Kachhap and Kamlesh Pandey (2019). Soil nutrients and fertility status of forest in\u003c/li\u003e\n \u003cli\u003eZhu, F., Lu, X., Liu, L., \u0026amp; Mo, J. (2015). Phosphate addition enhanced soil inorganic nutrients to a large extent in three tropical forests. \u003cem\u003eScientific Reports\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(1), 7923.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Different Forest Divisions of Jharkhand, India.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"368\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eS.No\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eForest Divisions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eS.No.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eForest Divisions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eBokaro\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eJamtara\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eChaibasa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eKhunti\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eChatra North\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eKoderma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eChatra South\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eKolhan\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eDeoghar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eLatehar\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eDhalbhum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eLohardaga\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eDhanbad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eMedininagar\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eDumka\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003ePakur\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eGarhwa North\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003ePorahat\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eGarhwa South\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eRamgarh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eGiridih East\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eRanchi\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eGiridih West\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eSahibganj\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eGodda\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eSaraikera\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eGumla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eSaranda\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eHazaribagh East\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eSimdega\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eHazaribagh west\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Distribution of pH, EC and OC of different forest classes in various depth for 31 forest divisions of Jharkhand, India\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"1072\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameters\u0026rarr;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 300px;\"\u003e\n \u003cp\u003e\u003cstrong\u003epH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 300px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eElectrical Conductivity (dS/m)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 315px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOrganic Carbon (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eForest class\u0026rarr;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDepth\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNDF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMDF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNDF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMDF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNDF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMDF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eForest divisions \u0026nbsp;\u0026darr;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBokaro\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.25\u0026plusmn;0.36Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.41\u0026plusmn;0.32Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e38.60\u0026plusmn;12.18Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e31.47\u0026plusmn;3.21Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e1.31\u0026plusmn;0.23Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1.64\u0026plusmn;0.28Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e30-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.06\u0026plusmn;0.36Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.34\u0026plusmn;0.34Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e28.03\u0026plusmn;10.43Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e27.50\u0026plusmn;3.77Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e1.11\u0026plusmn;0.19Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1.36\u0026plusmn;0.14Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e60-90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.07\u0026plusmn;0.46Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.20\u0026plusmn;0.44Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e29.35\u0026plusmn;12.27Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e22.73\u0026plusmn;5.63Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e1.07\u0026plusmn;0.24Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1.35\u0026plusmn;0.30Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eTwo-way ANOVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC: df=2, F=1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eDt: df=2, F=0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC\u0026times;Dt: df= 4, F=0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eFC: df=2, F=1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eDt: df=2, F=1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df=4, F=0.267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eFC: df=2, F=9.82*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eDt: df=2, F=3.42*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC\u0026times;Dt: df= 4, F=0.05*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChaibasa\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.65\u0026plusmn;0.49Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.45\u0026plusmn;0.33Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.68\u0026plusmn;0.12Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e71.67\u0026plusmn;53Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e60.00\u0026plusmn;29Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e40.0\u0026plusmn;5Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.59\u0026plusmn;0.32Aab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.76\u0026plusmn;0.15Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.45\u0026plusmn;0.2Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e30-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.57\u0026plusmn;0.44Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.32\u0026plusmn;0.29Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.65\u0026plusmn;0.09Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e55.00\u0026plusmn;33Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e55.00\u0026plusmn;22Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e40.0\u0026plusmn;11Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.54\u0026plusmn;0.31Aab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.72\u0026plusmn;0.13Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.43\u0026plusmn;0.5Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e60-90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.46\u0026plusmn;0.47Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.33\u0026plusmn;0.35Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.62\u0026plusmn;0.5Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e46.67\u0026plusmn;18Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e42.50\u0026plusmn;18Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e30.0\u0026plusmn;9Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.48\u0026plusmn;0.30Aab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.69\u0026plusmn;0.15Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.41\u0026plusmn;0.6Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eTwo-way ANOVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC: df=2, F=1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eDt: df=2, F=0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eFC: df=2, F=0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eDt: df=2, F=057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eFC: df=2, F=4.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eDt: df=2, F=0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChatra North\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.92\u0026plusmn;0.31Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.89\u0026plusmn;0.55Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e6.34\u0026plusmn;0.13Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e85.22\u0026plusmn;17Bab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e71.47\u0026plusmn;14Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e113.05\u0026plusmn;33Bb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.38\u0026plusmn;0.08Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.53\u0026plusmn;0.13Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.51\u0026plusmn;0.31Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e30-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.82\u0026plusmn;0.37Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.68\u0026plusmn;0.64Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.86\u0026plusmn;0.03Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e65.14\u0026plusmn;13Aab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e60.33\u0026plusmn;9Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e58.48\u0026plusmn;12Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.35\u0026plusmn;0.07Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.50\u0026plusmn;0.13Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.48\u0026plusmn;0.31Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e60-90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.76\u0026plusmn;0.29Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.59\u0026plusmn;0.55Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.73\u0026plusmn;0.46Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e66.06\u0026plusmn;18Abab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e50.17\u0026plusmn;16Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e63.42\u0026plusmn;15Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.32\u0026plusmn;0.07Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.48\u0026plusmn;0.12Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.46\u0026plusmn;0.24Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eTwo-way ANOVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC: df=2, F=1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eDt: df=2, F=2.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eFC: df= 2, F=4.18*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eDt: df=2, F=14.5***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=2.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eFC: df=2, F=3.51*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eDt: df=2, F=0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChatra South\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.37\u0026plusmn;0.62Aab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.34\u0026plusmn;0.52Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e6.01\u0026plusmn;0.23Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e85.12\u0026plusmn;55Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e76.75\u0026plusmn;38Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e61.50\u0026plusmn;3Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.72\u0026plusmn;0.28Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e1.26\u0026plusmn;0.28Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.55\u0026plusmn;0.22Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e30-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.50\u0026plusmn;0.45Aab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.15\u0026plusmn;0.06Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.73\u0026plusmn;0.48Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e70.51\u0026plusmn;33Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e42.40\u0026plusmn;0.06Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e58.83\u0026plusmn;9Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.63\u0026plusmn;0.24Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.95\u0026plusmn;0.24Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.47\u0026plusmn;0.19Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e60-90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.49\u0026plusmn;0.62Aab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.04\u0026plusmn;0.30Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.60\u0026plusmn;0.50Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e56.78\u0026plusmn;22Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e35.70\u0026plusmn;6Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e61.53\u0026plusmn;23Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.58\u0026plusmn;0.22Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.87\u0026plusmn;0.52Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.42\u0026plusmn;0.19Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eTwo-way ANOVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC: df=2, F=3.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eDt: df=2, F=0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eFC: df=2, F=0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eDt: df=2, F=1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eFC: df=2, F=9.94***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eDt: df=2, F=2.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDeoghar\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.60\u0026plusmn;0.55Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.71\u0026plusmn;0.44Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.61\u0026plusmn;0.47Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e27.22\u0026plusmn;4Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e75.15\u0026plusmn;33Aab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e55.38\u0026plusmn;35Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.30\u0026plusmn;0.14Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.47\u0026plusmn;0.23Bb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.41\u0026plusmn;0.17Bab\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e30-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.25\u0026plusmn;0.35Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.32\u0026plusmn;039Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.30\u0026plusmn;0.61Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e23.12\u0026plusmn;3Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e59.38\u0026plusmn;44Aab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e49.03\u0026plusmn;29Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.21\u0026plusmn;0.14Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.29\u0026plusmn;0.12Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.29\u0026plusmn;0.19Aab\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e60-90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.11\u0026plusmn;0.35Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.28\u0026plusmn;0.44Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.34\u0026plusmn;0.37Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e22.86\u0026plusmn;3Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e43.62\u0026plusmn;41Aab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e43.23\u0026plusmn;29Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.13\u0026plusmn;0.08Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.29\u0026plusmn;0.18Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.28\u0026plusmn;0.19Aab\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eTwo-way ANOVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC: df=2, F=0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eDt: df=2, F=3.35*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df=4, F=0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eFC: df=2, F=3.70*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eDt: df=2, F=0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eFC: df=2, F=3.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eDt: df=2, F=3.89*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDhalbhum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.48\u0026plusmn;0.22Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.51\u0026plusmn;0.27Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.83\u0026plusmn;0.49Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e44.29\u0026plusmn;18Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e52.50\u0026plusmn;18Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e45.0\u0026plusmn;12Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.94\u0026plusmn;0.30Bb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e1.51\u0026plusmn;0.36ABc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.62\u0026plusmn;0.17Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e30-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.45\u0026plusmn;0.22Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.45\u0026plusmn;0.25Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.74\u0026plusmn;0.44Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e30.0\u0026plusmn;5Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e47.50\u0026plusmn;15Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e40.0\u0026plusmn;11Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.84\u0026plusmn;0.24Bb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e1.13\u0026plusmn;0.12ABc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.58\u0026plusmn;0.16Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e60-90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.42\u0026plusmn;0.28Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.41\u0026plusmn;0.25Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.65\u0026plusmn;0.38Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e36.43\u0026plusmn;15Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e40.0\u0026plusmn;14Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e32.50\u0026plusmn;9Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.80\u0026plusmn;0.25Bb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.99\u0026plusmn;0.07ABc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.53\u0026plusmn;0.16Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eTwo-way ANOVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC: df=2, F=3.78*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eDt: df=2, F=0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eFC: df=2, F=1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eDt: df=2, F=2.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eFC: df=2, F=22***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eDt: df=2, F=4.21*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDhanbad\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e6.10\u0026plusmn;0.23Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.88\u0026plusmn;0.29Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.99\u0026plusmn;0.32Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e173.40\u0026plusmn;55Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e97.54\u0026plusmn;81Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e136.73\u0026plusmn;64Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e1.24\u0026plusmn;0.17Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e1.15\u0026plusmn;0.22Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1.19\u0026plusmn;0.20Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e30-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.76\u0026plusmn;0.36Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.48\u0026plusmn;0.30Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.91\u0026plusmn;0.18Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e130.80\u0026plusmn;40Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e78.09\u0026plusmn;81Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e64.60\u0026plusmn;29Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e1.08\u0026plusmn;0.13Aba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e1.03\u0026plusmn;0.21Aba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1.02\u0026plusmn;0.18ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e60-90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.53\u0026plusmn;0.36Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.33\u0026plusmn;0.31Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.55\u0026plusmn;0.50Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e118.54\u0026plusmn;44Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e83.81\u0026plusmn;44Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e42.80\u0026plusmn;4Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.99\u0026plusmn;0.36Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.95\u0026plusmn;0.28Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.89\u0026plusmn;0.15Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eTwo-way ANOVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC: df=2, F=3.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eDt: df=2, F=7.99*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eFC: df=2, F=1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eDt: df=2, F=1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eFC: df=2, F=0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eDt: df=2, F=3.60*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDumka\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.75\u0026plusmn;0.28Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.56\u0026plusmn;0.23Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.80\u0026plusmn;0.25Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e38.79\u0026plusmn;18Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e54.14\u0026plusmn;19Bab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e58.60\u0026plusmn;21Bb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e1.03\u0026plusmn;0.32Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.80\u0026plusmn;0.38Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.87\u0026plusmn;0.29Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e30-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.59\u0026plusmn;0.35ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.66\u0026plusmn;0.18ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.58\u0026plusmn;0.15ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e31.39\u0026plusmn;7Aaa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e38.16\u0026plusmn;4Aab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e43.43\u0026plusmn;13Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.77\u0026plusmn;0.10Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.51\u0026plusmn;0.39Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.60\u0026plusmn;0.18Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e60-90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.48\u0026plusmn;0.26Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.53\u0026plusmn;0.18Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.50\u0026plusmn;0.32Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e32.84\u0026plusmn;14Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e38.35\u0026plusmn;13Aab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e34.78\u0026plusmn;9Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.68\u0026plusmn;0.30Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.58\u0026plusmn;0.31Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.60\u0026plusmn;0.32Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eTwo-way ANOVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC: df=2, F=0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eDt: df=2, F=2.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eFC: df=2, F=2.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eDt: df=2, F=4.84*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eFC: df=2, F=1.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eDt: df=2, F=4.47*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGarhwa north\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e6.36\u0026plusmn;0.42Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e6.50\u0026plusmn;0.29Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e6.33\u0026plusmn;0.48Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e106.37\u0026plusmn;53Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e84.35\u0026plusmn;12Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e97.88\u0026plusmn;44Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.46\u0026plusmn;0.18Bab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.44\u0026plusmn;0.23Bb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.30\u0026plusmn;0.10Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e30-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e6.13\u0026plusmn;0.34Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.67\u0026plusmn;0.13Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.75\u0026plusmn;0.48Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e84.67\u0026plusmn;52Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e69.13\u0026plusmn;6Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e74.18\u0026plusmn;45Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.27\u0026plusmn;0.13Aab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.36\u0026plusmn;0.14Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.21\u0026plusmn;0.10Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e60-90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.74\u0026plusmn;0.54Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.92\u0026plusmn;0.27Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.76\u0026plusmn;0.36Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e63.28\u0026plusmn;15Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e68.70\u0026plusmn;10Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e77.43\u0026plusmn;42Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.25\u0026plusmn;0.14Aab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.37\u0026plusmn;0.24Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.21\u0026plusmn;0.11Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eTwo-way ANOVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC: df=2, F=0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eDt: df=2, F=9.77***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eFC: df=2, F=0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eDt: df=2, F=1.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eFC: df=2, F=3.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eDt: df=2, F=2.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGarhwa South\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.76\u0026plusmn;0.53Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e6.22\u0026plusmn;0.55Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.79\u0026plusmn;0.52Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e61.46\u0026plusmn;16Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e61.36\u0026plusmn;8Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e84.25\u0026plusmn;63Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.49\u0026plusmn;0.25Aab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.38\u0026plusmn;.16Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.61\u0026plusmn;0.18Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e30-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.59\u0026plusmn;0.58Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.80\u0026plusmn;0.33Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.28\u0026plusmn;0.49Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e52.93\u0026plusmn;16Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e52.44\u0026plusmn;15Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e64.33\u0026plusmn;20Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.38\u0026plusmn;0.30Aab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.27\u0026plusmn;.15Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.55\u0026plusmn;0.17Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e60-90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.51\u0026plusmn;0.68Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.64\u0026plusmn;0.40Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.39\u0026plusmn;0.43Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e54.27\u0026plusmn;17Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e59.03\u0026plusmn;20Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e65.19\u0026plusmn;33Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.37\u0026plusmn;0.24Aab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.25\u0026plusmn;.11Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.42\u0026plusmn;0.30Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eTwo-way ANOVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC: df=2, F=1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eDt: df=2, F=1.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eFC: df=2, F=1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eDt: df=2, F=0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eFC: df=2, F=2.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eDt: df=2, F=1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGiridih East\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.81\u0026plusmn;0.42Cb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.63\u0026plusmn;0.34Cab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.13\u0026plusmn;0.38Ca\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e65.99\u0026plusmn;27Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e51.50\u0026plusmn;10Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e48.58\u0026plusmn;10Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.61\u0026plusmn;0.22Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.45\u0026plusmn;0.10Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.48\u0026plusmn;0.08Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e30-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.33\u0026plusmn;0.35Bb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.31\u0026plusmn;0.28Bab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.04\u0026plusmn;0.39Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e42.37\u0026plusmn;14Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e40.91\u0026plusmn;9Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e43.79\u0026plusmn;9Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.44\u0026plusmn;0.16Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.36\u0026plusmn;0.5Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.46\u0026plusmn;0.6Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e60-90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.17\u0026plusmn;0.40Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e4.73\u0026plusmn;0.43Aab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e4.87\u0026plusmn;0.48Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e37.96\u0026plusmn;10Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e30.71\u0026plusmn;14Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e39.95\u0026plusmn;6Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.34\u0026plusmn;0.20Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.23\u0026plusmn;0.12Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.37\u0026plusmn;0.12Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eTwo-way ANOVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC: df=2, F=4.02*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eDt: df=2, F=8.06**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.93*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eFC: df=2, F=1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eDt: df=2, F=4.45*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eFC: df=2, F=2.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eDt: df=2, F=5.62*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGiridih West\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.65\u0026plusmn;0.39Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.61\u0026plusmn;0.30Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.39\u0026plusmn;0.39Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e63.26\u0026plusmn;33Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e60.82\u0026plusmn;15Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e58.83\u0026plusmn;31Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.59\u0026plusmn;0.21Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.56\u0026plusmn;0.34Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.48\u0026plusmn;0.09Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e30-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.38\u0026plusmn;0.36Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.47\u0026plusmn;0.35Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.25\u0026plusmn;0.40Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e38.05\u0026plusmn;11Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e56.84\u0026plusmn;17Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e41.33\u0026plusmn;10Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.42\u0026plusmn;0.22ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.44\u0026plusmn;0.27ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.41\u0026plusmn;0.4ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e60-90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.27\u0026plusmn;0.49Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e4.71\u0026plusmn;0.55Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e4.94\u0026plusmn;0.39Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e45.94\u0026plusmn;23Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e46.75\u0026plusmn;21Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e37.70\u0026plusmn;10Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.47\u0026plusmn;0.20Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.30\u0026plusmn;0.13Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.32\u0026plusmn;0.11Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eTwo-way ANOVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC: df=2, F=1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eDt: df=2, F=6.25**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eFC: df=2, F=0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eDt: df=2, F=2.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eFC: df=2, F=0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eDt: df=2, F=2.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGodda\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.74\u0026plusmn;0.48Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.71\u0026plusmn;0.70Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e6.12\u0026plusmn;0.13Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e63.0\u0026plusmn;22Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e51.11\u0026plusmn;18Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e60.0\u0026plusmn;14Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e1.18\u0026plusmn;0.40Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.83\u0026plusmn;0.34Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.94\u0026plusmn;0.13Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e30-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.64\u0026plusmn;0.50Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.48\u0026plusmn;0.60Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e6.02\u0026plusmn;0.20Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e54.0\u0026plusmn;23ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e45.56\u0026plusmn;18ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e50.0\u0026plusmn;12ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.71\u0026plusmn;0.13Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.69\u0026plusmn;0.24Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.81\u0026plusmn;0.11Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e60-90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.53\u0026plusmn;0.48Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.39\u0026plusmn;0.59Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.98\u0026plusmn;0.30Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e42.0\u0026plusmn;21Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e37.78\u0026plusmn;16Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e50.0\u0026plusmn;10Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.59\u0026plusmn;0.12Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.57\u0026plusmn;0.11Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.50\u0026plusmn;0.11Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eTwo-way ANOVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC: df=2, F=1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eDt: df=2, F=0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eFC: df=2, F=0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eDt: df=2, F=0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eFC: df=2, F=1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eDt: df=2, F=4.40*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGumla\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e6.04\u0026plusmn;0.29Ca\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e6.27\u0026plusmn;0.40Cb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e6.09\u0026plusmn;0.37Ca\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e68.35\u0026plusmn;55Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e136.27\u0026plusmn;74Bb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e55.98\u0026plusmn;14Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.86\u0026plusmn;0.59Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.93\u0026plusmn;0.39Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.47\u0026plusmn;0.27Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e30-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.69\u0026plusmn;0.35Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.91\u0026plusmn;0.40Bb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.71\u0026plusmn;0.15Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e49.29\u0026plusmn;25Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e71.59\u0026plusmn;36Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e36.32\u0026plusmn;5Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.61\u0026plusmn;0.61Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.72\u0026plusmn;0.38Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.32\u0026plusmn;0.23Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e60-90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.10\u0026plusmn;0.66Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.73\u0026plusmn;0.32Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e4.77\u0026plusmn;0.87Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e44.19\u0026plusmn;27Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e66.95\u0026plusmn;34Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e31.17\u0026plusmn;5Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.19\u0026plusmn;0.19Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.20\u0026plusmn;0.12Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.09\u0026plusmn;0.05Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eTwo-way ANOVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC: df=2, F=3.71*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eDt: df=2, F=14.58***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.96*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eFC: df=2, F=5.46**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eDt: df=2, F=3.68*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eFC: df=2, F=1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eDt: df=2, F=7.31**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHZB East\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e6.06\u0026plusmn;063Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e6.11\u0026plusmn;0.25Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.83\u0026plusmn;0.61Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e44.68\u0026plusmn;12Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e37.90\u0026plusmn;7Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e45.80\u0026plusmn;24Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e1.04\u0026plusmn;0.29Bab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.97\u0026plusmn;0.18Bb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.83\u0026plusmn;0.19Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e30-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.59\u0026plusmn;0.51Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.40\u0026plusmn;0.38Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.63\u0026plusmn;0.36Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e36.96\u0026plusmn;13Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e29.13\u0026plusmn;5Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e29.73\u0026plusmn;10Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.77\u0026plusmn;0.10Aab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.93\u0026plusmn;0.20Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.76\u0026plusmn;0.14Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e60-90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.25\u0026plusmn;0.32Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e6.72\u0026plusmn;3Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.32\u0026plusmn;0.22Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e27.00\u0026plusmn;3Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e26.75\u0026plusmn;8Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e34.28\u0026plusmn;19Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.71\u0026plusmn;0.14Aab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.89\u0026plusmn;0.15Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.70\u0026plusmn;0.12Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eTwo-way ANOVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC: df=2, F=0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eDt: df=2, F=0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eFC: df=2, F=0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eDt: df=2, F=3.71*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eFC: df=2, F=3.37*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eDt: df=2, F=4.23*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHZB West\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e6.16\u0026plusmn;0.44Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e6.09\u0026plusmn;0.45Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e6.52\u0026plusmn;0.11Bb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e47.72\u0026plusmn;17Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e46.40\u0026plusmn;23Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e99.90\u0026plusmn;20Bb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e1.06\u0026plusmn;0.20Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.94\u0026plusmn;0.28Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.90\u0026plusmn;0.23Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e30-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.75\u0026plusmn;0.41Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.41\u0026plusmn;0.26Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e6.25\u0026plusmn;0.15Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e36.46\u0026plusmn;11Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e33.11\u0026plusmn;16Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e80.90\u0026plusmn;12Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.86\u0026plusmn;0.12ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.89\u0026plusmn;0.29ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.74\u0026plusmn;0.31ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e60-90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.45\u0026plusmn;0.35Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.37\u0026plusmn;0.30Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.87\u0026plusmn;0.23Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e28.48\u0026plusmn;4Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e30.82\u0026plusmn;14Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e39.0\u0026plusmn;14Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.78\u0026plusmn;0.15Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.82\u0026plusmn;0.29Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.63\u0026plusmn;0.25Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eTwo-way ANOVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC: df=2, F=3.70*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eDt: df=2, F=6.31**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.43*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eFC: df=2, F=7.92**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eDt: df=2, F=8.07**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eFC: df=2, F=0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eDt: df=2, F=1.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eJamtara\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.59\u0026plusmn;0.55Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.41\u0026plusmn;0.21Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.52\u0026plusmn;0.51Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e75.08\u0026plusmn;52Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e61.74\u0026plusmn;29Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e46.20\u0026plusmn;5Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e1.01\u0026plusmn;0.06Ca\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e1.12\u0026plusmn;0.15Ca\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1.11\u0026plusmn;0.30Ca\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e30-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.10\u0026plusmn;0.46Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.04\u0026plusmn;0.28Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.23\u0026plusmn;0.43Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e47.60\u0026plusmn;15Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e42.97\u0026plusmn;12Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e45.23\u0026plusmn;6Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.86\u0026plusmn;0.17Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.94\u0026plusmn;0.16Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.94\u0026plusmn;0.27Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e60-90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e4.88\u0026plusmn;0.51Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e4.82\u0026plusmn;0.31Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e4.94\u0026plusmn;0.40Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e42.46\u0026plusmn;14Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e38.35\u0026plusmn;5Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e38.13\u0026plusmn;5Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.79\u0026plusmn;0.21Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.73\u0026plusmn;0.27Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.74\u0026plusmn;0.11Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eTwo-way ANOVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC: df=2, F=0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eDt: df=2, F=8.54**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eFC: df=2, F=0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eDt: df=2, F=3.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eFC: df=2, F=0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eDt: df=2, F=9.91***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKhunti\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.67\u0026plusmn;0.58Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.45\u0026plusmn;0.41Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.82\u0026plusmn;0.77Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e81.67\u0026plusmn;43Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e90.61\u0026plusmn;25Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e146.17\u0026plusmn;54Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.58\u0026plusmn;0.22Cb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.54\u0026plusmn;0.19Cab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.31\u0026plusmn;0.17Ca\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e30-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.31\u0026plusmn;0.31Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.30\u0026plusmn;0.32Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.73\u0026plusmn;0.75Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e64.61\u0026plusmn;28ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e68.89\u0026plusmn;31ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e84.66\u0026plusmn;53ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.38\u0026plusmn;0.12Bb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.25\u0026plusmn;0.16Bab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.26\u0026plusmn;0.13Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e60-90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.23\u0026plusmn;0.28Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.17\u0026plusmn;0.22Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.59\u0026plusmn;0.87Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e51.52\u0026plusmn;29Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e61.22\u0026plusmn;27Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e63.98\u0026plusmn;34Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.22\u0026plusmn;0.11Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.17\u0026plusmn;0.12Aab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.15\u0026plusmn;0.07Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eTwo-way ANOVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC: df=2, F=2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eDt: df=2, F=1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eFC: df=2, F=1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eDt: df=2, F=4.37*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eFC: df=2, F=2.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eDt: df=2, F=11.84***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKoderma\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.81\u0026plusmn;0.38Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e6.17\u0026plusmn;0.24Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.83\u0026plusmn;0.19Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e75.87\u0026plusmn;59Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e83.74\u0026plusmn;51Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e67.18\u0026plusmn;4Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.89\u0026plusmn;0.13Bab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.95\u0026plusmn;0.21Bb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.84\u0026plusmn;0.04Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e30-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.48\u0026plusmn;0.66Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.80\u0026plusmn;0.41Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.66\u0026plusmn;0.38Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e56.58\u0026plusmn;49ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e65.46\u0026plusmn;31ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e55.05\u0026plusmn;8ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.80\u0026plusmn;0.11ABab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.85\u0026plusmn;0.18ABb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.76\u0026plusmn;0.07ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e60-90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.25\u0026plusmn;0.51Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.48\u0026plusmn;0.43Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.19\u0026plusmn;0.45Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e36.88\u0026plusmn;22Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e46.17\u0026plusmn;31Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e37.53\u0026plusmn;11Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.76\u0026plusmn;0.10Aab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.80\u0026plusmn;0.19Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.68\u0026plusmn;0.04Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eTwo-way ANOVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC: df=2, F=2.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eDt: df=2, F=7.95**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eFC: df=2, F=0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eDt: df=2, F=2.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eFC: df=2, F=2.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eDt: df=2, F=4.13*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKolhan\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.33\u0026plusmn;0.52Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.18\u0026plusmn;0.50Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.80\u0026plusmn;1.5Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e61.67\u0026plusmn;20Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e45.71\u0026plusmn;20Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e60.0\u0026plusmn;02Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.87\u0026plusmn;0.16Bb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.80\u0026plusmn;0.16Bb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1.18\u0026plusmn;0.03Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e30-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e4.89\u0026plusmn;0.33Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e4.98\u0026plusmn;0.57Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.75\u0026plusmn;1.4Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e49.67\u0026plusmn;19ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e38.29\u0026plusmn;10ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e47.50\u0026plusmn;10ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.82\u0026plusmn;0.14ABb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.76\u0026plusmn;0.15ABb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1.12\u0026plusmn;0.06ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e60-90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.04\u0026plusmn;0.65Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e4.88\u0026plusmn;0.65Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.48\u0026plusmn;1.2Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e43.67\u0026plusmn;20Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e32.86\u0026plusmn;09Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e40.0\u0026plusmn;14Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.74\u0026plusmn;0.15Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.71\u0026plusmn;0.18Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1.01\u0026plusmn;0.02Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eTwo-way ANOVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC: df=2, F=2.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eDt: df=2, F=0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eFC: df=2, F=2.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eDt: df=2, F=2.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eFC: df=2, F=12.49***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eDt: df=2, F=2.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLatehar\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.74\u0026plusmn;0.40Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.50\u0026plusmn;0.61Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.77\u0026plusmn;0.08Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e80.02\u0026plusmn;41Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e55.43\u0026plusmn;26Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e47.60\u0026plusmn;13Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.87\u0026plusmn;0.44Bb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.91\u0026plusmn;0.38Bb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.39\u0026plusmn;0.14Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e30-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.43\u0026plusmn;0.55Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.12\u0026plusmn;0.62Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.56\u0026plusmn;0.23Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e58.54\u0026plusmn;23Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e45.80\u0026plusmn;26Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e36.55\u0026plusmn;0.09Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.66\u0026plusmn;0.18Abb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.63\u0026plusmn;0.15Abb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.33\u0026plusmn;0.09ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e60-90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.25\u0026plusmn;0.58Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.10\u0026plusmn;0.49Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.53\u0026plusmn;0.21Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e51.64\u0026plusmn;23Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e39.27\u0026plusmn;23Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e29.50\u0026plusmn;1.5Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.48\u0026plusmn;0.41Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.54\u0026plusmn;0.19Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.25\u0026plusmn;0.06Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eTwo-way ANOVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC: df=2, F=32.93***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eDt: df=2, F=1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eFC: df=2, F=2.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eDt: df=2, F=1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eFC: df=2, F=4.65*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eDt: df=2, F=3.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLohardaga\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.33\u0026plusmn;0.17Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.52\u0026plusmn;0.32Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.37\u0026plusmn;0.10Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e40.06\u0026plusmn;23Bb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e34.29\u0026plusmn;14Bb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e15.05\u0026plusmn;2Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.92\u0026plusmn;0.85Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.46\u0026plusmn;0.26Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.61\u0026plusmn;0.34Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e30-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.13\u0026plusmn;0.28Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.21\u0026plusmn;0.24Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e4.94\u0026plusmn;0.31Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e27.63\u0026plusmn;15Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e22.28\u0026plusmn;3Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e12.95\u0026plusmn;3Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.59\u0026plusmn;0.72Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.25\u0026plusmn;0.07Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.45\u0026plusmn;0.14Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e60-90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.02\u0026plusmn;0.30Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.20\u0026plusmn;0.34Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.05\u0026plusmn;0.65Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e22.61\u0026plusmn;10Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e22.88\u0026plusmn;10Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e12.66\u0026plusmn;0.42Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.51\u0026plusmn;0.75Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.29\u0026plusmn;0.14Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.42\u0026plusmn;0.31Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eTwo-way ANOVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC: df=2, F=1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eDt: df=2, F=4.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eFC: df=2, F=5.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eDt: df=2, F=2.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eFC: df=2, F=2.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eDt: df=2, F=1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedininagar\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e6.22\u0026plusmn;0.37Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e6.34\u0026plusmn;0.85Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e80.63\u0026plusmn;46Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e89.89\u0026plusmn;38Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.62\u0026plusmn;0.30Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.50\u0026plusmn;0.17Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e30-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.66\u0026plusmn;0.24ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e6.16\u0026plusmn;0.75ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e57.35\u0026plusmn;24ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e72.02\u0026plusmn;24ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.56\u0026plusmn;026Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.46\u0026plusmn;0.16Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e60-90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.48\u0026plusmn;0.20Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.74\u0026plusmn;0.77Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e51.44\u0026plusmn;29Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e60.99\u0026plusmn;16Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.52\u0026plusmn;0.27Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.39\u0026plusmn;0.14Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eTwo-way ANOVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC: df=2, F=2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eDt: df=2, F=3.86*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=3.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eFC: df=2, F=1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eDt: df=2, F=3.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eFC: df=2, F=3.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eDt: df=2, F=0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePakur\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.67\u0026plusmn;0.55Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.63\u0026plusmn;0.48Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e85.71\u0026plusmn;47Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e82.50\u0026plusmn;57Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.80\u0026plusmn;0.31Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.96\u0026plusmn;0.24Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e30-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.63\u0026plusmn;0.55Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.58\u0026plusmn;0.49Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e74.29\u0026plusmn;35Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e67.50\u0026plusmn;47Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.75\u0026plusmn;0.30Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.86\u0026plusmn;0.22Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e60-90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.40\u0026plusmn;0.46Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.48\u0026plusmn;0.52Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e68.57\u0026plusmn;39Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e60.63\u0026plusmn;41Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.67\u0026plusmn;0.23Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.81\u0026plusmn;0.21Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eTwo-way ANOVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC: df=2, F=0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eDt: df=2, F=0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eFC: df=2, F=0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eDt: df=2, F=0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eFC: df=2, F=3.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eDt: df=2, F=1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePorhat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.36\u0026plusmn;0.61Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.35\u0026plusmn;0.44Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.77\u0026plusmn;0.36Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e48.75\u0026plusmn;12Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e70.00\u0026plusmn;26Bb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e76.67\u0026plusmn;23Bb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e1.04\u0026plusmn;0.30Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e1.03\u0026plusmn;0.40Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1.00\u0026plusmn;0.46Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e30-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.04\u0026plusmn;0.52ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e4.97\u0026plusmn;0.54ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.69\u0026plusmn;0.37ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e40.00\u0026plusmn;14ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e58.33\u0026plusmn;23ABb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e70.00\u0026plusmn;17ABb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.92\u0026plusmn;0.30Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.99\u0026plusmn;0.40Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.89\u0026plusmn;31Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e60-90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.05\u0026plusmn;0.54Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e4.78\u0026plusmn;0.46Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e4.84\u0026plusmn;0.55Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e36.25\u0026plusmn;13Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e51.67\u0026plusmn;19Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e56.67\u0026plusmn;15Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.88\u0026plusmn;0.32Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.95\u0026plusmn;0.40Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.84\u0026plusmn;0.28Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eTwo-way ANOVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC: df=2, F=1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eDt: df=2, F=4.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eFC: df=2, F=8.94**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eDt: df=2, F=3.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eFC: df=2, F=0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eDt: df=2, F=0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRamgarh\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.67\u0026plusmn;0.74Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.85\u0026plusmn;0.51Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.89\u0026plusmn;0.31Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e38.31\u0026plusmn;21Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e26.93\u0026plusmn;17Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e23.74\u0026plusmn;5Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.62\u0026plusmn;0.27Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.52\u0026plusmn;0.15Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.60\u0026plusmn;0.43Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e30-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.49\u0026plusmn;0.51ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.52\u0026plusmn;0.64ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.59\u0026plusmn;0.37ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e30.10\u0026plusmn;18Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e21.32\u0026plusmn;16Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e19.28\u0026plusmn;6Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.55\u0026plusmn;0.28Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.43\u0026plusmn;0.15Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.54\u0026plusmn;0.44Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e60-90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.26\u0026plusmn;0.54Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.42\u0026plusmn;0.64Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.18\u0026plusmn;0.22Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e26.09\u0026plusmn;15Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e17.89\u0026plusmn;16Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e16.71\u0026plusmn;8Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.50\u0026plusmn;0.29Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.37\u0026plusmn;0.14Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.42\u0026plusmn;0.34Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eTwo-way ANOVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC: df=2, F=0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eDt: df=2, F=2.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eFC: df=2, F=2.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eDt: df=2, F=1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eFC: df=2, F=0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eDt: df=2, F=1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRanchi\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e6.31\u0026plusmn;0.22Ca\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e6.31\u0026plusmn;0.20Ca\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e6.32\u0026plusmn;0.42Ca\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e119.91\u0026plusmn;47Bb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e58.15\u0026plusmn;22Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e81.61\u0026plusmn;48Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.49\u0026plusmn;0.21Cab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.53\u0026plusmn;0.19Cb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.39\u0026plusmn;0.28Ca\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e30-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.70\u0026plusmn;0.41Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.98\u0026plusmn;0.46Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.65\u0026plusmn;0.43Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e101.66\u0026plusmn;51ABb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e44.77\u0026plusmn;4ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e44.94\u0026plusmn;27ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.32\u0026plusmn;0.21Bab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.43\u0026plusmn;0.21Bb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.15\u0026plusmn;0.14Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e60-90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.18\u0026plusmn;0.54Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.47\u0026plusmn;.47Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e4.97\u0026plusmn;0.66Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e63.20\u0026plusmn;42Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e39.75\u0026plusmn;4Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e41.08\u0026plusmn;26Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.15\u0026plusmn;0.10Aab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.23\u0026plusmn;0.13Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.09\u0026plusmn;0.08Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eTwo-way ANOVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC: df=2, F=1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eDt: df=2, F=19.79***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eFC: df=2, F=8.04**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eDt: df=2, F=4.19*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eFC: df=2, F=3.56*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eDt: df=2, F=4.19*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSaraikera\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e4.82\u0026plusmn;0.39Aab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e4.55\u0026plusmn;0.32Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.00\u0026plusmn;0.18Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e55.71\u0026plusmn;24Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e75.00\u0026plusmn;49Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e50.0\u0026plusmn;14Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.89\u0026plusmn;0.30Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e1.07\u0026plusmn;0.58Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.79\u0026plusmn;0.23Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e30-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e4.78\u0026plusmn;0.38Aab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e4.50\u0026plusmn;0.32Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e4.95\u0026plusmn;0.18Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e50.00\u0026plusmn;20Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e68.33\u0026plusmn;47Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e45.0\u0026plusmn;7Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.83\u0026plusmn;0.28Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.90\u0026plusmn;0.41Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.76\u0026plusmn;0.21Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e60-90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e4.75\u0026plusmn;0.39Aab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e4.43\u0026plusmn;0.28Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e4.77\u0026plusmn;0.17Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e42.14\u0026plusmn;19Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e61.67\u0026plusmn;48Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e40.0\u0026plusmn;14Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.80\u0026plusmn;0.30Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.82\u0026plusmn;0.82Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.70\u0026plusmn;.017Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eTwo-way ANOVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC: df=2, F=4.92*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eDt: df=2, F=0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eFC: df=2, F=1.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eDt: df=2, F=0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eFC: df=2, F=0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eDt: df=2, F=0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSaranda\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e6.16\u0026plusmn;0.53Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e6.29\u0026plusmn;0.46Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e6.95\u0026plusmn;0.36Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e85.85\u0026plusmn;32Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e106.19\u0026plusmn;41Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e148.30\u0026plusmn;30Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e1.18\u0026plusmn;0.51Bb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e1.04\u0026plusmn;0.29Bab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.89\u0026plusmn;0.19Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e30-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e6.08\u0026plusmn;0.54Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.97\u0026plusmn;0.46Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e6.67\u0026plusmn;0.25Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e80.74\u0026plusmn;27Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e84.03\u0026plusmn;51Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e144.50\u0026plusmn;25Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e1.07\u0026plusmn;0.48ABb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.69\u0026plusmn;0.27ABab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.57\u0026plusmn;0.22ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e60-90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.99\u0026plusmn;0.62Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.78\u0026plusmn;0.49Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e6.40\u0026plusmn;0.12Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e65.37\u0026plusmn;23Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e80.16\u0026plusmn;43Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e120.70\u0026plusmn;24Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.96\u0026plusmn;0.53Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.62\u0026plusmn;0.33Aab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.36\u0026plusmn;0.23Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eTwo-way ANOVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC: df=2, F=2.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eDt: df=2, F=1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eFC: df=2, F=1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eDt: df=2, F=\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eFC: df=2, F=3.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eDt: df=2, F=1.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSahibganj\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.17\u0026plusmn;0.27Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.23\u0026plusmn;0.48Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.62\u0026plusmn;0.43Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e94.29\u0026plusmn;60Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e92.86\u0026plusmn;16Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e90.0\u0026plusmn;16Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e1.41\u0026plusmn;0.85Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e1.13\u0026plusmn;0.40Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1.65\u0026plusmn;0.40Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e30-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.09\u0026plusmn;0.27Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.16\u0026plusmn;0.49Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.55\u0026plusmn;0.44Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e74.29\u0026plusmn;25Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e82.86\u0026plusmn;16Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e90.0\u0026plusmn;12Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.97\u0026plusmn;0.23ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.95\u0026plusmn;0.28ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1.15\u0026plusmn;0.22ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e60-90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.04\u0026plusmn;0.27Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.13\u0026plusmn;0.50Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.46\u0026plusmn;0.45Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e64.29\u0026plusmn;24Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e77.14\u0026plusmn;13Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e80.0\u0026plusmn;19Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.89\u0026plusmn;0.19Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.93\u0026plusmn;0.36Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1.02\u0026plusmn;0.11Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eTwo-way ANOVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC: df=2, F=1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eDt: df=2, F=1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eFC: df=2, F=0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eDt: df=2, F=0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eFC: df=2, F=0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eDt: df=2, F=2.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSimdega\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.49\u0026plusmn;0.66Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.38\u0026plusmn;0.53Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.42\u0026plusmn;0.42Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e106.63\u0026plusmn;51Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e82.58\u0026plusmn;32Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e106.10\u0026plusmn;20Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e1.0\u0026plusmn;0.22Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e1.09\u0026plusmn;0.48Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1.09\u0026plusmn;030Ba\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e30-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.34\u0026plusmn;0.67ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e4.98\u0026plusmn;0.44ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.25\u0026plusmn;0.36ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e75.53\u0026plusmn;40ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e65.59\u0026plusmn;19ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e95.10\u0026plusmn;19ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.94\u0026plusmn;0.23ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.86\u0026plusmn;0.31ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.96\u0026plusmn;0.21ABa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e60-90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e4.99\u0026plusmn;0.65Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e4.85\u0026plusmn;0.42Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.05\u0026plusmn;0.23Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e60.96\u0026plusmn;24Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e55.89\u0026plusmn;10Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e85.20\u0026plusmn;18Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.78\u0026plusmn;0.17Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.76\u0026plusmn;0.33Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.70\u0026plusmn;0.41Aa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eTwo-way ANOVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC: df=2, F=0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eDt: df=2, F=1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eFC: df=2, F=1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eDt: df=2, F=1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eFC: df=2, F=0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eDt: df=2, F=1.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFC \u0026times;Dt: df= 4, F=0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;Different capital letters designate significant differences at \u003cem\u003eP \u0026lt; 0.05\u003c/em\u003e at forest class (Non-degraded, Medium Degraded and Degraded Forest) for each depth; different lowercase letters indicate significant differences at \u003cem\u003eP \u0026lt; 0.05\u003c/em\u003e among soil depth at a same forest class followed by Duncan\u0026rsquo;s MRT test.\u003c/p\u003e\n\u003cp\u003eFC Forest class, Dt soil depth df degree of freedom, \u003cem\u003e*P \u0026lt; 0.05, **P\u0026lt;0.01, ***P\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Recommendation of NPK fertilizer to diagnose forest soil fertility related constraints, which calculated with the help of standard procedures\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eS.N.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eName of Forest divisions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInorganic Fertilizer Recommendation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOrganic FYM\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eRecommendation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 256px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVermicompost\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eRecommendation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrea for Nitrogen (kg/ha)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSP for Phosphorus\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(kg/ha)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eK2O\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003efor Potassium\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(kg/ha)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFor Nitrogen\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(kg/ha)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFor Phosphorus\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(kg/ha)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFor Potassium\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(kg/ha)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFor Nitrogen\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(kg/ha)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFor Phosphorus\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(kg/ha)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFor Potassium\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(kg/ha)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eBokaro\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e49.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e62.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eChaibasa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e355.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e67.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e65.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e32686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e65.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e163.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e10214.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e71.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e81.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eChatra North\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e291.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e72.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e112.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e26830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e53.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e134.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e8384.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e58.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e67.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eChatra South\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e275.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e25372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e50.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e126.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e7928.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e55.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e63.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eDeoghar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e202.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e52.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e18580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e37.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e92.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e5806.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e40.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e46.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eDhalbhum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e289.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e44.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e117.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e26640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e53.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e133.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e8325.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e58.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e66.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eDhanbad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e242.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e63.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e136.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e22274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e44.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e111.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e6960.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e48.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e55.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eDumka\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e129.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e73.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e11882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e23.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e59.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e3713.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e25.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e29.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eGarhwa North\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e272.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e48.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e44.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e25030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e50.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e125.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e7821.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e54.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e62.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eGarhwa South\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e272.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e69.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e27.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e25030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e50.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e125.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e7821.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e54.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e62.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eGiridih East\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e194.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e103.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e20728\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e35.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e89.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e6477.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e45.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e51.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eGiridih West\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e225.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e160.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e17930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e41.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e103.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e5603.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e39.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e44.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eGodda\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e241.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e50.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e84.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e22246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e44.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e111.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e6951.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e48.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e55.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eGumla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e82.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e78.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e84.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e7578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e15.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e37.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e2368.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e16.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e18.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eHZB East\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e154.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e72.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e170.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e14236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e28.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e71.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e4448.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e31.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e35.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eHZB West\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e208.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e66.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e157.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e19182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e38.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e95.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e5994.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e41.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e47.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eJamtara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e173.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e22.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e15978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e31.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e79.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e4993.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e34.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e39.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eKhunti\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e84.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e20.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e66.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e7746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e15.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e38.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e2420.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e16.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e19.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eKoderma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e239.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e70.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e22036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e44.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e110.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e6886.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e48.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e55.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eKolhan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e349.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e40.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e106.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e32108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e64.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e160.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e10033.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e70.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e80.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eLatehar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e273.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e51.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e45.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e25168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e50.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e125.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e7865.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e55.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e62.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eLohardaga\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e446.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e69.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e92.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e41056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e82.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e205.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e12830.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e89.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e102.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eMedininagar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e88.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e58.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e8160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e16.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e40.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e2550.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e17.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e20.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003ePakur\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e354.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e54.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e89.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e32650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e65.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e163.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e10203.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e71.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e81.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003ePorahat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e245.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e34.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e101.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e22606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e45.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e113.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e7064.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e49.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e56.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eRamgarh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e81.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e16.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e92.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e7538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e15.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e37.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e2355.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e16.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e18.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eRanchi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e251.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e59.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e63.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e23138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e46.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e115.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e7230.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e50.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e57.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eSahibganj\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e47.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e61.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eSaranda\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e129.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e89.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e45.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e11940\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e23.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e59.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e3731.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e26.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e29.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eSimdega\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e231.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e65.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e21324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e42.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e106.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e6663.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e46.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e53.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eSaraikera\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e251.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e60.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e23152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e46.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e115.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e7235.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e50.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e57.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-soil","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Soil](https://link.springer.com/journal/44378)","snPcode":"44378","submissionUrl":"https://submission.nature.com/new-submission/44378/3","title":"Discover Soil","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Soil nutrients, Forest disturbance, Land restoration, Nutrient recommendation, plantation","lastPublishedDoi":"10.21203/rs.3.rs-6357879/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6357879/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eForests provide a wide range of ecosystem processes and services, including net primary production, climate regulation, water regulation, and nutrient cycling. However, forest ecosystems face immense pressure from various human-induced and natural disturbances, which significantly contribute to their degradation. The restoration of forests is a critical global concern, particularly in Jharkhand, India, where forests are highly vulnerable to mining activities and land degradation. The restoration and maintenance of forests are required which cannot be comprehended without understanding of soil. In this context, the present study was conducted to analyse the physicochemical properties of forest soils under forest disturbances across all forest divisions of Jharkhand. Stratification was conducted by grouping areas based on forest type (dense/moderately dense forests and open/scrub/degraded forests) in 31 forest divisions. Within each stratum, random sampling points were selected for each division. Soil samples were collected at three depths: 0\u0026ndash;30 cm, 30\u0026ndash;60 cm, and 60\u0026ndash;90 cm. The collected soil samples were analysed for 12 soil parameters including, basic parameters (pH, EC and Organic Carbon), major nutrients (Available Nitrogen (AN), Available phosphorus (AP) and Exchangeable Potassium (AK)), secondary nutrients (Available Sulphur (AS)) and micronutrients (Available.) Zinc (Zn), boron (B), iron (Fe), manganese (Mn) and copper (Cu)). The result of two-way ANOVA showed a significant (\u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e) variation in forest pH, EC and OC at disturbance as well as soil depth factors among 31 forest divisions of Jharkhand state. All the 12 soil parameters recorded a decreasing trend from NDF\u0026thinsp;\u0026gt;\u0026thinsp;MDF\u0026thinsp;\u0026gt;\u0026thinsp;DF and 0\u0026ndash;30\u0026thinsp;\u0026gt;\u0026thinsp;30\u0026ndash;60\u0026thinsp;\u0026gt;\u0026thinsp;60\u0026ndash;90 cm at the disturbance and soil depth respectively. The correlation matrix among soil parameters recorded a positive relation between Fe with Mn (0.975 \u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e), EC with pH (0.243 P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and AK to AP (0.221 P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The calculation of recommended dose of fertilizers revealed that most forest divisions in Jharkhand require additional NPK, except for Bokaro, Chatra South, Deoghar, Giridih East, Giridih West, Jamtara, Koderma, Medininagar, Sahibganj, and Saraikela divisions. This research identifies nutrient deficiencies in the soil and provides recommendations for calculating fertilizer doses to support sustainable management practices and enhance plantation success.\u003c/p\u003e","manuscriptTitle":"Spatial variability in soil physicochemical properties across forest disturbances in the different forest divisions of Jharkhand, India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-08 06:35:22","doi":"10.21203/rs.3.rs-6357879/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-16T06:46:59+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-16T06:46:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-14T12:25:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-11T05:19:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-09T08:07:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-02T11:33:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"27756828957337899909948367793383107292","date":"2025-05-01T10:22:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"195429893253570322503436185587396370131","date":"2025-05-01T09:19:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"189481276568370059003098286436787142766","date":"2025-04-30T04:22:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"329209362689175493001571076007422095755","date":"2025-04-29T17:01:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"306753126451400184035771664269453116199","date":"2025-04-29T10:04:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"69076146232309791813705040861673679127","date":"2025-04-29T09:44:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"81866057591543815622946169764637802761","date":"2025-04-29T09:29:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"326032679940714184124407968886038819884","date":"2025-04-29T09:06:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-29T08:57:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-25T12:58:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-25T12:56:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Soil","date":"2025-04-02T05:49:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-soil","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Soil](https://link.springer.com/journal/44378)","snPcode":"44378","submissionUrl":"https://submission.nature.com/new-submission/44378/3","title":"Discover Soil","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"832526d9-4aff-4759-94aa-9cda4f94c63b","owner":[],"postedDate":"April 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-07-15T07:38:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-08 06:35:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6357879","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6357879","identity":"rs-6357879","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}