Quantifying the yield loss pattern in paddy crop within a native tree-based riverine agroforestry system in Chhattisgarh, Central India: Implications to food sustainability and climate smart farming

Quantifying the yield loss pattern in paddy crop within a native tree-based riverine agroforestry system in Chhattisgarh, Central India: Implications to food sustainability and climate smart farming | 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 Quantifying the yield loss pattern in paddy crop within a native tree-based riverine agroforestry system in Chhattisgarh, Central India: Implications to food sustainability and climate smart farming K. K. Chandra, Arun Kumar Shukla, Atul Bhardwaj, Rajesh Kumar, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7647772/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Riverine agroforestry is a most diverse, dynamic and widely adopted in tropical agroecosystem along river catchments contributes to climate resilient farming and restoring rivers flow. We estimated the yield loss in paddy cultivar MTU7029 grown with Acacia nilotica, Butea monosperma, Mangifera indica, Terminalia arjuna , and Terminalia tomentosa. Tree crop interaction study was performed for two crop cycle (year 2021 and 2022) covering entire grid points of 100km river stretches, utilizing the same farmer fields, crop variety, and tree species. Additionally, two other factors, namely stem diameter (10–40, 41–80, 81–120, and > 120 cm DBH) and tree density (10, 20, 40, 60 trees ha − 1 ) were taken into account for the most abundant tree species T. arjuna at grids 5, 8 and 10 of riverine ecosystem. For species specific interaction, 36 trees and diameter and density class assessment, 30 trees were employed from the farmer’s fields. Sample plots of 1m² size were established in standing crops at 2m, 8m, 15m, and 25m distances along a transect line from both sides of the tree, oriented in an east-west direction. Results showed a negative impact of trees on crop as Paddy yield reported 3.64 t ha − 1 at 2m and 5.52 t ha − 1 at 25m distance from the tree in crop field. Adverse effect of trees continued to decline paddy tiller and hills by 51% and 10.93% respectively in tree proximity. A large trees with big canopy M. indica and T. tomentosa were found most negative for crop yield than moderate to small canopy species A. nilotica, B. monosperma and T. arjuna . The large diameter trees led to the greater yield loss of 42.53% than the lower diameter trees. Farmland trees between 40–60 ha − 1 found to limit the yield by 66.45% when close to trees, however the yield loss was less with decreasing tree densities in the crop fields. The tree shade was observed to be the most influencing factor on yield loss of paddy than the other factors. Therefore, site specific tree selection based on morphological parameters such as height, canopy structure, and tree density up to 10–20 ha − 1 can promoted for the riverine agroecosystem. Regular canopy management and removal of exploitable diameter tree may also compensate the yield loss through intermediate income and yield loss reduction of paddy yield. Adoption of such practices may contribute towards climate resilient smart agroforestry practice expansion in riverine agroecosystem. Agroforestry Riverine ecosystem crop production Tree-crop Interaction biodiversity climate change Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Riverine agroforestry stands apart from conventional agroforestry systems because of its diverse biodiversity and greater number of trees, which help tackle global issues related to both productive and protective services. (Chandra and Singh, 2018 ; Rodenburg et al., 2022 ; Shukla et al., 2025a ). This ecosystem favours paddy production in South East Asia enabling high moisture regime and water stagnation adopted for high yielding paddy cultivars (Kumar et al., 2010 ; Bouman et al., 2007 ; Kumar et al., 2013 ; Thevs et al., 2019 ). However, paddy productivity in such areas has become challenging due to destruction in riverine habitat, mining activities, and unpredicted monsoon resulting to a drying rivers. Evolving a climate resilient self-sustaining farming systems need urgently to adopt smart agriculture to cope up the negative environmental externalities and food security (Wezel et al., 2020 ). Undoubtedly, tree integration at farmlands adjacent to rivers form a green belt, and boost ecosystem services (Sinclair and Hitinayake 2000 ), protect soil erosion through root binding and reducing the rain drop velocity through tree canopy (Zuazo and Pleguezuelo, 2008 ). Moreover, tree contributes to the efficient recycling of nutrients by adding mulches, leaf and root turnover (Maclean et al., 2003, Barrios et al., 2012, Karki et al., 2018 ). Thus tree based farming system approaches are often more effective in regulating, supporting and cultural ecosystem services (Pagella and Sinclaire, 2014) such as microclimate buffering, amelioration of soil properties and water infiltration and water table maintenance (Bayala et al., 2014 ; Fletcher et al., 2019 ; Sunderland et al., 2018 ; Ong and Kho 2015 ; Luedeling et al, 2016 ; Dawson et al., 2018 ; Chandra and Singh, 2018 ; Chandra et al., 2022 ; Shukla et al., 2025a ). The potential of agroforestry practices to mitigate climate change and increase carbon sequestration has also been widely studied (Kuyah et al., 2013 , Chandra et al., 2018; Rosenstock et al., 2014 Jose, 2009 ; Kumar et al., 2020 ; Rodenburg et al. 2022 ). However, the expansion of the agroforestry system in paddy dominated areas are limited and confined to some African and south eastern countries (Miyagawa et al., 2017 ; Rodenburg et al., 2022 ; Koch et al., 2025 ). The non-expansion of agroforestry believes to the yield loss of crop and income of farmers (Chandra, 2018 ; Chandra and Singh, 2018 , Pardon et al., 2025 ). In actuality, though, its reach is restricted to a few innovative, sizable farmers in the tropical region. This approach needs to spread as quickly as possible to all agricultural communities, regardless of land size and agroclimatic conditions, in the context of climate change and food security (FAO, 2020 ). The negative and direct effects of trees on crop output are proven to be a significant barrier to the growth of agroforestry (Rao et al., 1998 ). Allopathic effects (Xuan et al., 2004 ; Khaliq et al., 2012 ; Kumari et al., 2016 ), tree shadow (Roder and Maniphone, 1998 ), and crop-tree competition for resources were the main causes of this negativity in the past. Depending on the degree of competition for growth resources between the woody component and the related crops which may also be impacted by site conditions, trees can have a complimentary or competitive effect on crops. There are instances where agricultural crop production is said to be decreased with tree integration (Chemura et al., 2021 , Reuse et al., 2025). Large canopy shade on crops can place significant production restrictions on them, particularly in agroforestry systems based on Paddy. Because the benefits of agroforestry rely on site-specific responses of trees-crop systems and farming techniques, not all agroforestry treatments can be replicated elsewhere (Coe et al., 2014 ). These factors, along with unfavourable opinions held by peasant farmers, are impeding the advancement of agroforestry. As a result, scientific research is required to address the primary issues with Paddy agroforestry, such as the interactions between different system components and the vast geographical domains and timescales over which trees and crops interact at farmland (Smith et al., 2015 ; Rodenburg et al., 2022 ). Since the yield seems to be as the main indicator of quantitative performance of farmlands crops, its quantification to agroforestry models become a key to evaluate systems sustainability and food security. Efforts are being done to improve the self-sustenance in agriculture by equipping fields with the concept of neutral farming, organic farming and natural farming where the integration of perennial vegetation is increasingly advocating climate smart agriculture and sustainable farming (Garrity et al., 2010 ; Glover et al. 2012 ; Chandra, 2014 ; Rajesh Kumar et al., 2022 ; Muhie 2022 ). Such approaches needs to be propagated to save the soil health from the increasing effect of chemical fertilizers and pesticides in the cropping system (Angon et al., 2023 ; Terrer et al., 2021 ; Shinde et al., 2019 ). The high sensitivity of global warming and climate change results in drying out riverine habitat, unpredictable monsoons, and high input costs, making such Paddy-growing practices difficult (Rodenburg et al., 2022 ). Thus, in order to practise smart agriculture and prevent detrimental environmental externalities, a self-sustaining farming system must be developed quickly (Wezel et al., 2020 ). Although agroforestry is a tried-and-true method of agro-ecological intensification, Paddy is not as often planted with trees as other crops because it needs more direct sunshine and conditions of water stagnation (Garrity et al., 2010 ; Glover et al., 2012 ). Paddy reported less suitable to be intercropped with taller and more competitive species (Akanvou et al. 2001 ) and because paddy crop requires anaerobic soil condition, which are less favourable for many tree species (Kramer and Kozlowski, 1979). The regional differences that how and how many trees are integrated with paddy may be the other reason of a less obvious paddy in agroforestry. Worldwide, paddy stands out as a staple food for over half of the world's population (Khush, 2005 ). However, paddy faces numerous challenges, including the need for increased productivity (Peng et al., 2006 ) and the necessity of adopting sustainable agricultural practices to mitigate environmental degradation (Vermeulen et al., 2012 ). Farmers simply need to maintain the trees and take care of tending operations to lessen the tree canopy and ease of farm equipment movements during farming activities (Kosaka et al, 2006 ; Pham et al., 2015 ; Miyagawa et al., 2017 ; Chandra, 2018 ; Dumrongrojwatthana et al., 2020 ; Daum, 2023 ). The riverine Paddy agroforestry system, which stops soil degradation (Shinde et al., 2019 ) and protects Paddy crops from unfavourable local environments, is specifically used in the state of Chhattisgarh for agricultural intensification and multiple output (Bargali et al., 2009 ; Miyagawa et al., 2017 ; Watanabe et al., 2017). The farm produces more when the riverine trees are purposefully allowed to grow to the levees and floor (Chandra and Singh, 2018 ; Barrios et al., 2018 ; Nair, 2012 , Shukla et al., 2025a ). There are reports which alarm the negative impacts of trees on crops due to various reasons such as shadow of the tree, allelopathic effect, nutrient competitions, etc. (Kumari et al., 2016 ; Pardon et al., 2025 ), and more directly the shade of trees (Roder and Maniphone, 1998 ). Negative tree-crop interaction become more prominent especially when trees grows naturally than the well managed tree plantations. Therefore, all the interventions of agroforestry cannot be replicated everywhere as the agroforestry benefits varies on site specific responses of trees-crop system and farming contexts (Coe et al., 2014 ). Due to these reasons, the progress in agroforestry is held back concerning risk factors and negative perceptions of farmers towards agroforestry. To appraise the paddy based riverine agroforestry as adaptation measure to climate change, we shall take into account region-specific environmental conditions and the potential risks and benefits of trees in future farming (Rodenburg et al., 2022 ). The current study evaluated the effects of riverine agroforestry species on Paddy crop yield dynamics under farmer-field conditions. In riverine agroforestry practices along the Lilagar river in Chhattisgarh, tree species, diameter classes, and tree density (populations) were taken into consideration while quantifying the loss of Paddy yield due to tree cop interaction. Materials and methods Study site: The study area lies in the riverine area of the river Lilagar which is a tributary of the Sheonath and Mahanadi rivers of Chhattisgarh, India. It is located between latitudes 21°44' N to 22°01' N and longitudes 81°20' E to 81°40' E (Fig. 1 ). This river originates from the Udta dam in Korba district and ends at the Sheonath river at Deverghatta in Jangir Champa district. The total length of the river is 135 km, while its catchment area covers approximately 1538 km 2 . The second-third length of the river at higher elevations is occupied with natural forests, while its lower basin supports tree-based agroforestry systems and crop cultivation practices. The region has a tropical monsoon climate with an average annual rainfall of around 1200–1400 mm. The average temperature ranges from 22°C to 34°C. The communities that adjoin the river Lilagar rely on the river for their livelihoods and irrigation of crop fields. Sampling methods: The present study was carried out during April 2020 to December 2022 for 03 consecutive years and two crop cycles to consider multi season effects, comprising an extensive field survey along the entire river length in order to identify prevailing agroforestry practises. A total of 10 grid points were selected along the river at a 10 km interval, covering 100 km of river length and consisting of agroforestry and agriculture practises. Four sample plots were established randomly at each grid point of 1 ha each. Thus, a total of 4 ha is covered at each grid, and total 40 plots (40 ha) across river lengths. All the grids/ plot were laid out within 500m from the centre of the river in both the side. Five tree species A. nilotica (L.) Delile (S1), B. monosperma (Lam.) Taub. (S2), M. indica L. (S3), T. arjuna (Roxb.) Wight & Arn (S4), T. tomentosa Willd (S5) were selected randomly considering 3 grids for one species covering entire stretches of riverine ecosystem adjacent to the Lilagar river. Species selection was done based on their occurrence to different grids such as S1– from grids 5, 6, 9; S2–2, 3, 7; S3–4, 9, 10; S4–5, 8, 10 and S5 – grids 1, 8, 9. Tree species were available to farmer’s field and well maintained on levees in North South orientation at a distance between 5-6m apart. Each species experiment covers 3 grids, 12 sample plots (4 plots/grid), and 3 individual tree stems/plots (total 36 stem/species) were chosen. Tree- crop interaction effect was studied by laying out sample plots of 1m x 1m size in standing crop field cultivar MTU 7029 (Swarna) initiating from the stem at varying distances 2m, 8m, 15m, and 25m aliened in East-West direction of transect line (Fig. 2 ). Measurements of crop height, the number of tillers, and the number of paddy hills (m²) were recorded individually from each plot in species-specific grids. We adhered to the methodologies proposed by Khan and Ehrenreich ( 1994 ); Karki et al. ( 2018 ); and Asmamaw ( 2017 ) in their studies on tree-crop interactions. The paddy yield was determined by manually harvesting from the designated sample plots assigned for species specificity at various grid locations prior to the field harvest at maturity, which occurred between November 15th and 25th in 2021 and 2022. Following the analysis of data from the sample plots, the parameters of paddy crop growth and yield were converted to a per-hectare basis. Crop yield of sampled plots were dried out separately by keeping it in hot oven until constant weight. Ultimately, the findings were summarized by calculating the average values from the data collected over two years. The species T. arjuna was most adoptive in riverine ecosystem than the other species due to their ability to thrive under high moisture regime found abundant representing different diameter classes and density in paddy crop fields. Therefore, II and III rd investigations were conducted using this species only to evaluate the impact of different diameter classes and stem density (Stem counts) on paddy crop. Trees having diameter class of 10-40cm, 41–80 cm, 81–120 cm, and > 120 cm were marked at breast height and sample plots were taken close to the stem at 2m, 8m, 15m, and 25m aliened in East-West direction of transect line to quantify the yield loss pattern in paddy crop. Season of cropping, management practices and harvest timing were kept similar as mentioned in I st experiment. Similarly, to understand the varying density of T. arjuna stems on paddy yield was determined considering 2, 4, 8 and 12 trees per plot of size 2000m 2 representing 10, 20, 40 and 60 trees ha − 1 respectively. Tree specific plot ( T. arjuna ) for these two experiments were chosen from the grid 8 and 10 considering 6 plots (2 plots/grid), and 3 individual tree stems for each diameter and density classes. Total 30 stem were chosen separately for these two investigations. For density class experiment, the size of the T. arjuna trees selected were in the size between 71-120cm diameter with a mean of 98.5cm at BH. Further the impact of varying DBH and density on paddy were quantified by laying out sample plots of similar size and distances as mentioned in species specific tree-crop interactions. The crop parameters under study were also assessed at the same distances (2, 8, 15, and 25m) from the reference field that had no trees, which was managed in the same way as the field with trees, located 200m away. Measurement and data collection Tree height, diameter at breast height (DBH), height of the lowest branch (HLB), crown depth and crown area were measured twice in 2020 and 2021 and the average values were provided in figure. Tree height was measured with a clinometer, while crown depth measured by deducting the HBL from the total tree height. The crown area was measured using standard formula (πr 2 ). Statistical Analysis We used multivariate analysis to analyse the effect of the species, distance, diameter classes and tree populations and their interactions between variables on paddy tillers, hills and yield using state Pro software. To determine the key factors influencing yield and paddy attributes and quantitative interrelationships backwards stepwise regression analysis was applied using the criteria of probability of p < 0.05. Furthermore, Tukey’s Honestly Significant Difference (HSD) test was performed to determine significant differences between groups in the sample. Results and discussions The multivariate MANNOVA analysis of riverine agroforestry data shows that tree presence close and far from the crops varies significantly differed the growth and yield of paddy. The paddy yield showed significant difference to variables including tree species, diameter of trees, tree counts and its distances from the crop at P<0.05. 4. 1. Effect of tree species on paddy growth and production Tree morphological characteristics such as height, diameter, canopy depth and crown area and age differed species specifically and significantly ( Figure 3 ). Among all the trees occurring in riverine agroforestry, M. indica was tall, large in diameter with broader crown and aged. Other trees such as T. arjuna and T. tomentosa showed slighter smaller 27m height with a crown expansion of 235-240 m 2 area. B. monosperma and A. nilotica had small heighted and lower crown depth and crown area than the other species resulted to form shadow in a relatively small area on paddy field. Paddy field without any tree showed highest yield of 5.91 t ha -1 with the higher tillers and hill density as compared to the paddy field with trees (( Table 1 ). The paddy yield taken from different distances from the bund/ fields devoid trees was nonsignificant at P<0.05. We found a negative effect of various tree species in the paddy growth and yield at P<0.01, except tillers which rendered nonsignificant result. Number of paddy tillers declined towards the proximity of tree whereas, increased with the increasing distance from the tree. This decline was 24.22%, 8.75% and 4.67% at 2m, 8m and 15m as compared to 25m distance from the tree trunk ( Figure 4 ). When considering tree species M. indica had the greater effect in reducing the paddy tiller followed by T. arjuna , while the lowest impact was observed with B. monosperma . Similarly, the paddy hills were also negatively correlated with the increasing distances from the tree trunk. Results recorded the loss of 4 hills m 2 at the closest plot and 1.33 hills at the distance of 25m from the trees ( Table 2) . The loss was highest of 10.93% at 2m and 3.40% at farthest distance of the study. The presence of trees decreased the tillers and hills of paddy by 6.10 and 1.38% respectively as compared to paddy fields without trees (control plot). Species wise variation in the loss percentage of paddy hills was significant at P<0.01. Results indicate that M. indica and T. arjuna trees had greater effect on the hills than other species. Our study indicates the significantly negative impact of farm trees on paddy yields. The yield loss was highest towards the proximity of the trees. The grain yield of paddy ranged from 3.64 to 5.52 ton ha -1 in our study which were obtained from 2m and 25m plots far from the tree trunk respectively (Table 2) . On the other hand, grain yield reduced by 51.31% at 2m distance and 10.02% at 15m as compared to 25m distance from the tree trunk. Subsequently, the yield loss was 6.57% in fields with trees even far 25m from the trunk than the field devoid tree species. Significant variations on yield of paddy was observed due to presence of different tree species at varying distance from the tree trunk. Tree species M. indica could reduce the grain yield maximum by 72.96% followed by T. tomentosa (54.81%) and lowest of 43.35% at closest to crop. However, the grain yield losses were substantially lower with A. nilotica, T. arjuna and B. monosperma at 15 and 25m distances as compared to the same distances of other species of the study. The grain yield of paddy with M. indica was lowest (ranged 2.37 to 4.09 ton ha -1 ) and highest (4.34 to 6.23t ha -1 ) with B. monosperma at their closest and farthest plots of paddy fields. These data significantly differed at closure and far from the tree trunk which showed specific species variation in riverine agroforestry. Overall, the species such as A. nilotica , B. monosperma and T. arjuna exhibited less yield losses in paddy than the M. indica and T. tomentosa . Interactive effects of tree species and distance on paddy attributes under agroforestry practice was found nonsignificant at P<0.05. The correlation between different tree species and their distance from the crop showed positive correlation for all the attributes, however its impact observed highest in paddy yield. Thus it can be said that irrespective of the tree species, the distance factor between tree- paddy was more prominent and adverse for paddy yield ( Figure 5 ). As the large tree with larger crown such as M. indica , and T. tomentosa tend to shade on large area in paddy field found more deleterious than the tree species which had small crown and short height. Paddy requires strong sunlight for higher growth and yield than the other crops therefore, crop near tree line affected highest and its performed remain poor than the crop away from the tree or paddy crop with mono-cropping (Paula et al., 2018). Scordia et al. (2023) also compared Mediterranean agroforestry systems with treeless cropping systems and found that the majority of the studies identified shade as a cause of yield loss in field crops. Similar to our findings others also reported that the agroforestry of Eucalyptus tree had adverse effect on growth and yield attributes of crop due to its crown width and distance from the tree line (Khan et al., 2023; Koch et al., 2025). Under such conditions, the yield of the crop could be improved by manipulating the tree canopy, which further assist the crop growth component (Koch et al., 2025). Nadir et al., (2018) studied the relative performance of wheat crop with different shading intensities under Eucalyptus tree. Their findings highlights the decrease in photosynthetic rate, specific leaf weight and grain yield of wheat crop with the increase in shade duration (Yang et al., 2019). Moreover, Prasad et al. (2010) and Honfy et al. (2023) argued that the increase in grain yield with the increased distance from the tree line occurred due to increase in light intensity and decreased competition with the increased distance from the tree line between the tree roots. Bayala (2002) observed that the light competition is key for interactions between trees and crops. Tree species can reduce the amount of sunlight reaching crops and capture light depending upon tree factors viz. tree leaf area, leafing phenology, crown structure and crown management. The effect of Prosopis cineraria on yield of wheat crop due to distance has also been reported by Khan et al., (2023). This is the reason that the farmers pursued a regular tending operations to lesser the tree canopy and remove the exploitable diameter trees not for only the intermediate income from tree but also for the ease movements of farm equipment during farm activities (Koska et al, 2006; Pham et al., 2015; Miyagawa et al., 2017; Dumrongrojwatthana et al., 2020, Pardon et al., 2025, Koch et al., 2025). However, farmers have evolved management practices for reducing tree density, combining compatible species, and shoot pruning that contribute to control both above and below-ground competition (Namirembe et al., 2009). In riverine agroforestry, where copious amount of water is available to the tree species round the year, the tree flourish new leave and dense canopy round the year, therefore, tending and pruning operation become imperative to higher yield from the paddy crop. 4.2. Effect of diameter classes of trees The varying diameter of trees occurring on levees of the paddy field were evaluated considering four diameter classes including 20-40cm, 41-80cm, 81-120cm and above 120cm (Table 3) . Result reveals that the increase in tree diameter significantly reducing the paddy yields and its tiller, and hills. The higher tree diameter found to be more negative on paddy attributes than the less diameter levees trees. The mean tillers observed highest with tree diameter of 20-40cm and lowest with trees > 120cm diameter. In general, all the trees irrespective of diameter size had negative influence on the paddy yield parameters. However, the tiller loss was 19.07% with young trees with lesser diameter, whereas the losses increased to 26.48% with the largest diameter trees of the study ( Figure 6 ). Similarly, the highest hills per meter recorded 41.59 and lowest 35.71 for youngest and oldest tree diameter respectively representing 16.46% loss in hill population when comparing between young and old tree diameters. The same trends also continued in paddy yield which obtained 5.63t ha -1 with the presence of trees having 20-40cm diameter and 3.95t ha -1 with trees diameters >120cm. The relative yield losses estimated due to higher diameter trees was found to be 42.53% than the losses with the lowest tree diameter. Moreover, the distance from the tree found to impact highest to the paddy yield which recorded significant difference at P<0.01. This resulted that the mean yield losses may be expected up to 37% with trees presence near to trees which may be declined up to 10.1% with the increase of trees distance to the paddy crop. The DMR test also authenticated the influence of diameter classes on the paddy yield attributes which differentiated significantly at P <0.05. Besides the effect of diameter classes on paddy yield parameters, the paddy crop close to the tree stem rendered less tillers, hills and yields than the paddy crops with increasing distances from the trees. The tiller formations in paddy were 12.50 m 2 at 2m trees proximity and 15 tillers m 2 at 25 from tree distances. Similarly, the hills number was ranged 36.17 to 41.75 m 2 at closest and farthest plots of the study. Paddy production was highest of 5.65 t ha -1 and lowest of 4.17 t ha -1 at the farthest tree distance of 25m and 2m sample plots respectively under varying diameter classes of the trees. Two way interaction between diameter class and distance was found nonsignificant at P<0.05 which confirms that the tree and distance individually influence to the paddy yield when close to trees but its combined effect could not show any type of negative trends. Overall, the nonsignificant, negative correlation between DBH x tillers, DBH x hills of paddy, and DBH x yield of paddy (R2=0.0593, 0.4614 and 0.5064 respectively) ( Figure 7 ) also indicatives of the negative tree crop interface in paddy agroforestry systems. Reuse et al. (2025) and Pardon et al. (2018) observed that the tree height had a greater negative effect on crop yield in temperate climatic conditions. Other researchers also highlighted the light competition as a major factor playing a significant role and negatively influences the yields of annual crops (Pardon et al. 2018; Arenas-Corraliza et al. 2022) under agroforestry systems. The greater diameter tree develops large canopy with more height cast large shadow on crop for longer duration resulted to a higher yield loss at close proximity of the tree (Chauhan, et al. 2011). Crown shape of the trees and its size influence the degree of crop loss. Similar reports propounded by Kashyap et al. (2024) where paddy yield was decreased with the increase in tree diameter of Gmelina arborea plated in bund of the crop field. Thus need based regular pruning of tree branches can have positive impact in order to allowing more sunlight to paddy crop and substantially reduction to negative tree crop interaction. Luedeling et al., (2016) and Muthuri et al. (2009) also reported the effect of trees maize crop and indicated the benefits of pruning in reducing tree-crop competition. Moreover, harvesting of merchantable tree with greater diameter may be another solution for controlling the yield loss and economic benefit to the farmers. 4.3 Effect of tree stem counts on paddy attributes Number of trees on levees and their influences on tillers, hills and yield of paddy under riverine agroforestry practices are summarized ( Table 4) . The results showed statistically significant data for paddy attributes when analysed considering tree population at the field and distances at P<0.05. The mean number of tillers found highest 14.21 with the presence of two trees at 4m apart on levees while the lowest of 12.24 paddy tillers with the 12 trees occurring on levees of paddy agroforestry. The presence of tree augmented the tiller loss by 12.75% and 14.42% respectively for two and twelve trees maintaining at the farm levees ( Figure 8 ). However, variation observed in the paddy tiller population under varying tree populations and with the tree distances from the paddy crops. The tree distance influenced the paddy tiller and reduced it by 19.23% when two trees present at the levees, whereas the tillers decreased by 37.85% with 12 trees when compared between the paddy at 2m and 25m distances from the trees. Similarly, the yield loss declined significantly with the increase in distance and remained 3.54% at the distance of 15m than the farthest distance of the study. Similar trends were also followed by the number of hills and yield of paddy. The paddy hills decreased by 3% with lowest tree population which magnified furthers up to 8.27% with highest trees numbers. The paddy crop close to the trees showed 11.06% yield loss, however, the negative impact was negligible going far away from the trees. The yield losses found 56.67% with two trees and 66.45% yield loss with the highest tree population growing just near levees trees. However, the paddy yield loss ranged 65% and 13.6% at plots close and far from the trees respectively. This loss was quantified up to 2.15t ha -1 at closure plots than the crop growing at 25m distance from the trees. Two way ANOVA analysis showed nonsignificant results between tree number x distances. The negative correlation between the increasing number of trees and paddy tillers, hills and yield (R 2 - 0.039, 0.337 and 0.156 respectively) ( Figure 7 ) further prove the hypothesis that the increase in tree counts reduce the paddy yield through their growth attributes in riverine agroforestry practices. Our results concord with the findings of Khan and Chaudhry (2007) that indicated the increasing density of tree adversely affects the yield of crops under agroforestry. Vishwanath et al. (2000) and Kashyap et al. (2024) has evaluated the paddy yield under varying density of trees and reported that the higher stem number decline the paddy yield under tropical conditions. Reuse et al. (2025) also advocated the appropriate tree density based on observation that elative crop yields decrease with tree density. As tree density increases, the yield of arable crops can decrease (Honfy et al. 2023). On the other hand, more light penetration might be possible if trees are widely spaced with lower density. Conclusion The present investigation on tree-paddy interaction adjacent to river Lilager catchment where multiple tree species occurring naturally without any definite distance, and tree density in paddy crop fields clearly demonstrated the adverse impact on paddy growth characteristics and yield. Varying tree species had distinct impact on yield majorly based on crown size, and dimensions of the tree species. The shade effect of trees found to be the most important limiting factors for negative tree crop interaction. Larger the tree and denser the canopy causes more adversities on crop growth and yield aspects of paddy crop than the shorter and medium-small crown tree species. This factor can overcome with regular pruning of tree crown and harvest of exploitable diameter trees. It may became a regular source of farm income besides crop yield. In addition, higher tree density found to be deleterious for paddy production in a riverine ecosystem. The maintenance of appropriate tree counts between 10–20 ha − 1 to in paddy field allowing adequate sunlight and subsidise to reduce the yield loss. Conclusively, the selection of suitable indigenous tree species with small canopy size or vertical cylindrical canopy shape and their effective canopy management may maintain and or enhance the level the productivity and sustainability in paddy crop. Furthermore, more study need to conduct in a larger perspectives to authenticate and explore the species specific riverine agroforestry systems in other riverine area to confirm and encourage smart agriculture with nature based solutions. Declarations Acknowledgement: Thank to Dr. Rahul Bhadouria, Department of Environmental Sciences, Delhi University for statistical analysis of the manuscript data and Mr. Sudhir Ranjan, of Department of Forestry, wildlife & Environmental Sciences, Guru Ghasidas Vishwavidyalaya, for Preparation of Remote sensing based map of study site. Author contributions : Dr. K. K. Chandra- research supervision, Conceptualization research hypothesis, editing final manuscript, Dr. Atul Kumar Bhardwaj- Writing draft manuscript, Dr. Arun Kumar Shukla- Data collection, and field work, original draft preparation, Rajesh Kumar- graph preparation and formatting. Funding: No grant/ fund received for the research work. Data availability : No datasets were generated or analysed during the current study. Declarations Competing Interests: The authors declare no competing interests. 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Distance from levees (m) No. of Tillers (pl -1 ) No. of Paddy Hills (m 2 ) Crop yields (kg/ha) 2m 14.5±0.56b 40.50±1.48b 5467.2±288.42b 8m 16.5±0.56a 41.33±0.67b 5770±215.65ab 15m 14.67±0.33b 40.17±1.25a 6078.7±171.17ab 25m 16.83±0.54a 41.50±1.18a 6348.6±112.73a Mean 15.63±0.32 40.87±0.63 5916.1±118.64 F value 5.660 2.169 3.388 Significance (P) 0.006 (NS) 0.123 (NS) 0.038 (NS) Table 2 : Impact of different tree species on the Paddy crop growth and yields in riverine agroforestry practice prevailing along the river Lilagar catchments, Bilaspur, Chhattisgarh, India. (S1- A. nilotica , S2- B. monosperma , S3- M. indica , S4- T. arjuna S5- T. tomentosa ). ( The data shown are mean± SE of four replicates. Means within a row presented by different letters are significant at p ≤ 0.05 ). Tree Species Tree distance from the crop field (m) Mean 2m 8m 15m 25m Tiller (pl -1 ) S1 11.10±1.0b 14.00±0.58a 14.00±0.50a 13.67±1.45a 12.70±0.56 S2 13.67±0.33b 13.67±0.33b 15.67±0.33a 15.33±0.88ab 14.58±0.36 S3 11±0.58b 13.33±1.2ab 13.33±1.45ab 15±0.50a 13.17±0.6 S4 11.67±1.45b 13.33±1.2ab 13.67±0.67ab 14.67±0.88a 13.33±0.57 S5 12.67±0.88bc 14.33±0.67b 14.67±1.76b 16±0.58a 14.42±0.58 Mean 12.02 ±0.70 13.73 ±0.55 14.26 ±0.54 14.93 ±0.64 13.64 ±0.36 Hills (m 2 ) S1 34.00±1.73ab 34.00±1.0ab 38.0±2.52a 37±1.53ab 35.75±0.93 S2 38.33±0.67b 39.33±0.67b 40±0.58ab 43±1.73a 40.17±0.68 S3 30.67±2.6b 33.33±1.2ab 34±1.15ab 35.33±1.45a 33.33±0.89 S4 37.67±2.6c 39.33±2.19bc 41±3.61b 43±1.73a 40.25±1.27 S5 42.33±2.67ab 43.33±0.67ab 43.33±1.76ab 44.67±1.45a 43.42±0.80 Mean 36.66 ±2.03 37.86 ±1.20 39.26 ±1.70 40.60 ±1.61 38.584 ±0.77 Yield (kg ha-1) S1 3340.1±471b 3673±290b 4827.3±244.29a 5068±294a 4227±263.90 S2 4346.5±9.73c 5030.7±121.3bc 5722±156.21ab 6230.7±377.74a 5332.5±232.84 S3 2369.5±116.9b 2889.7±367.62b 3321.3±394.8ab 4098.3±244.01a 3169.7±230.25 S4 3950.1±439.36c 5241.7±236.1ab 5713±369.49ab 6115.3±155.43a 5255±280.9 S5 4234.7±63.14c 5334.7±59.7b 5503.6±182.05b 6087.4±172.44a 5290.1±209.87 Mean 3648.18 ±230 4433.96 ±188 5017.44 ±245.30 5519.44 ±233 4654.46 ±217 Table 3 : Diameter classes of T. arjuna impact on growth and yield of Paddy crop at varying distances from the tree base. (The data shown are mean± SE of four replicates. Means within a row presented by different letters are significant at p ≤ 0.05). Diameter Class Tree distance from the crop field (m) 2m 8m 15m 25m Mean Tiller (pl -1 ) 10-40cm 14±0.58c 14.67±0.33bc 15.67±0.33ab 16.67±0.33a 15.25±0.35 40-80cm 12.67±0.33bc 13±0.58b 14.67±0.33a 14.33±0.67a 13.67±0.33 80-120 12±0.58bc 12.67±0.33bc 13±0.58b 14.67±0.33a 13.08±0.36 > 120 11.33±0.33b 12.00±0.33ab 13±0.58ab 14.33±1.2a 12.66±0.38 Mean 12.50 ±0.33 13.08 ±0.35 14.00 ±0.45 15.00 ±0.50 13.66 ±0.36 Hills (m 2 ) 10-40cm 38.7±1.58c 40.33±0.88bc 42.67±0.88ab 44.67±1.33a 41.59±0.86 40-80cm 38±2.58b 42±1.80ab 42.67±0.33ab 43.00±1.15a 41.41±0.39 80-120 35.67±0.33c 37.33±1.33b 40.00±1.15a 41.33±0.67a 38.58±0.73 > 120 32.33±1.36b 34±1.00ab 36.33±1.67ab 38.00±2.00a 35.17±0.86 Mean 36.17 ±01.88 38.41 ±0.90 40.41 ±0.83 41.75 ±1.40 39.19 ±0.70 Yield (kg ha-1) 10-40cm 4690±193.13c 5443.3±114.65b 5760±119.06b 6650.9±198.53a 5636±228.26 40-80cm 5002±107.35b 5304.6±72.92b 5757.6±92.31a 5940.1±221.35a 5462.5±125.68 80-120 3651.9±90.81c 4198.9±194.94bc 4754.4±211.28ab 5282.4±169.2a 4471.9±179.95 > 120 3161.5±173.7c 3644.8±88.4bc 4267.1±187.36ab 4740.5±282.79a 3953.5±199.22 Mean 4176.35 ±166 4597.9 ±112.50 5134.77 ±152.0 5653.47 ±188.0 4890.62 ±167.0 Table 4 : Tree stem density of Paddy field and their effects on yield and other parameters in Paddy agroforestry existing along river Lilagar. (The data shown are mean± SE of four replicates. Means within a row represented by different letters are significant at p ≤ 0.05). Attribute No. of stem Tree distance from the crop field (m) 2m 8m 15m 25m Mean Tiller (pl -1 ) 2 stem 13±1a 13.67±0.88a 14.67±0.33ab 15.5±1a 14.21±0.37 4 stem 12±0.58b 13.50±0.88ab 14.00±0.67ab 15±0.40a 13.62±0.42 8 stem 12±1b 13.30±0.33ab 13.88±0.33a 14.00±0.67ab 13.29±0.39 12 stem 9.67±0.67b 12.67±1.45a 13.3±2.08a 13.33±1.2a 12.24±0.74 Mean 11.66 ±0.66 13.28 ±0.83 13.96 ±0.55 14.45 ±0.53 13.34 ±0.45 Hills (m 2 ) 2 stem 40±2.89ab 41.33±0.33ab 42.00±1.15a 42.33±2.19a 41.41±0.86 4 stem 37.33±1.2ab 38.67±0.88ab 39.33±1.2a 40.00±2a 38.83±0.66 8 stem 32.33±1.33b 34.67±1.88ab 37.67±0.88a 38.33±1.45a 35.75±0.88 12 stem 32±2b 33±2.65b 37.00±2a 36.67±2.73a 34.66±1.08 Mean 35.451 ±1.23 36.91 ±1.50 39.00 ±1.50 39.33 ±1.73 37.66 ±0.89 Yield (kg ha-1) 2 stem 4234±418.68b 5131.1±198.87b 5390±358.2ab 6633.6±649.05a 5347.2±318.79 4 stem 3155.3±400b 3796.7±343.05b 4973±322.06a 5388±283.04a 4053.42±306.02 8 stem 2998.3±150c 3622.1±132.99b 4510.1±341ab 5083.3±419.49a 4103.5±254.9 12 stem 2893.2±138b 3595.2±167.95b 4419±449a 4815.7±453.79a 3930.77±281.28 Mean 3320.2 ±265 4036.25 ±189.30 4823.02 ±340 5480.15 ±466 4414.90 ±270 Additional Declarations No competing interests reported. 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12:55:34","extension":"png","order_by":53,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":104160,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7647772/v1/4c9f21fb4433535a4811ac8c.png"},{"id":92948771,"identity":"131988d3-ef69-463f-98b3-312b14ecd112","added_by":"auto","created_at":"2025-10-07 12:55:38","extension":"png","order_by":54,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":409178,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7647772/v1/38e78527c284fb6baf1eb6c5.png"},{"id":92948721,"identity":"7db4ef4e-c17a-4bcf-a3ec-4b8f326049db","added_by":"auto","created_at":"2025-10-07 12:55:31","extension":"xml","order_by":55,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":200222,"visible":true,"origin":"","legend":"","description":"","filename":"dbbfa1db384147cbaeb3187da381c8421structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7647772/v1/65035ecd818fea4d2c90d61e.xml"},{"id":92948719,"identity":"483fe3e5-740b-4501-94e6-22336e143f89","added_by":"auto","created_at":"2025-10-07 12:55:30","extension":"html","order_by":56,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":219863,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7647772/v1/9e365e475268d5fc6446b57c.html"},{"id":92948828,"identity":"57441388-2b70-4baf-a2c5-0afbf97dc8e3","added_by":"auto","created_at":"2025-10-07 12:55:50","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":276440,"visible":true,"origin":"","legend":"\u003cp\u003eStudy map showing the Lilagar river (A) Catchment and drainage (B) Vegetation, and river drainage (C) False colour composite map.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7647772/v1/25200a0f932937cebd451e58.jpg"},{"id":92948799,"identity":"8fd23c8e-93f1-4656-a749-78d5fc3b9ab8","added_by":"auto","created_at":"2025-10-07 12:55:43","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":120290,"visible":true,"origin":"","legend":"\u003cp\u003eDiagrammatic representation of spatial arrangements in an agroforestry system, displaying tree line and sample plots at various distances from the tree and location of different grids along river stretches of Lilagar.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7647772/v1/c1ebaf1f27cb3c59ff0a52fb.jpg"},{"id":92948830,"identity":"689c5e53-bb66-4f14-b2b1-6245c69821a9","added_by":"auto","created_at":"2025-10-07 12:55:50","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":110390,"visible":true,"origin":"","legend":"\u003cp\u003eMorphological information of levees tree growing in Paddy based riverine agroforestry \u003cstrong\u003eA\u003c/strong\u003e: tree height (m), \u003cstrong\u003eB\u003c/strong\u003e: tree diameter at breast height (cm), \u003cstrong\u003eC\u003c/strong\u003e: height of lowest branch (m), \u003cstrong\u003eD\u003c/strong\u003e: crown depth (m), \u003cstrong\u003eE\u003c/strong\u003e: crown area (M\u003csup\u003e2\u003c/sup\u003e) and \u003cstrong\u003eF\u003c/strong\u003e: tree age (Years).\u0026nbsp; The line on figure bar shows ± SE of the mean data. Different letters above the bar represents the significant difference at p ≤ 0.05 as per DMR test. (DBH- Diameter at breast height, HLB- Height of lowest branch).\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7647772/v1/b490f3411e0e9d4ff54afa90.jpg"},{"id":92948805,"identity":"9f84b7f7-ee16-43c8-b921-6ff0cc055674","added_by":"auto","created_at":"2025-10-07 12:55:44","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":81670,"visible":true,"origin":"","legend":"\u003cp\u003eLoss pattern in Paddy attributes under the different tree species growing with Paddy crop in riverine agroforestry along Lilagar river, Bilaspur, Chhattisgarh, Central India. \u003cstrong\u003eA\u003c/strong\u003e. Tiller loss with tree distance from the tree, \u003cstrong\u003eB\u003c/strong\u003e. Tiller loss with different tree species (S1- \u003cem\u003eA. nilotica\u003c/em\u003e, S2- \u003cem\u003eB. monosperma\u003c/em\u003e, S3- \u003cem\u003eM. indica\u003c/em\u003e, S4- \u003cem\u003eT. arjuna\u003c/em\u003e S5- \u003cem\u003eT. tomentosa\u003c/em\u003e), \u003cstrong\u003eC\u003c/strong\u003e. Hill loss with tree distance from the crop, \u003cstrong\u003eD\u003c/strong\u003e. Hill loss with tree species, \u003cstrong\u003eE\u003c/strong\u003e. Paddy yield loss with tree distance from the crop, \u003cstrong\u003eF\u003c/strong\u003e. Paddy Yield loss with different tree species.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7647772/v1/bfc800d61740fd62a76376ee.jpg"},{"id":92948773,"identity":"8df1d9cf-86bb-4263-82ac-bbf4f66ff9c1","added_by":"auto","created_at":"2025-10-07 12:55:40","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":169033,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation Coefficient (R\u003csup\u003e2\u003c/sup\u003e) between \u003cstrong\u003eA.\u003c/strong\u003e tree species and Paddy tillers, \u003cstrong\u003eB\u003c/strong\u003e. tree species and Paddy hill and \u003cstrong\u003eC.\u003c/strong\u003e tree species and Paddy yield of riverine agroforestry under study. (\u003cstrong\u003eS1-\u003c/strong\u003e\u003cem\u003eA. nilotica\u003c/em\u003e, \u003cstrong\u003eS2\u003c/strong\u003e-\u003cem\u003eB. monosperma, \u003c/em\u003e\u003cstrong\u003eS3\u003c/strong\u003e- \u003cem\u003eM. indica, \u003c/em\u003e\u003cstrong\u003eS4\u003c/strong\u003e- \u003cem\u003eT. arjuna, \u003c/em\u003e\u003cstrong\u003eS5\u003c/strong\u003e- \u003cem\u003eT. tomentosa\u003c/em\u003e).\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7647772/v1/695c1652c5a5c282462d93d5.jpg"},{"id":92948779,"identity":"cd3cd5c3-5cdb-4d74-9372-76c98549d72d","added_by":"auto","created_at":"2025-10-07 12:55:41","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":76652,"visible":true,"origin":"","legend":"\u003cp\u003eLoss pattern in Paddy attributes under \u003cem\u003eT arjuna\u003c/em\u003e of different diameter classes occurring in a riverine agroforestry adjacent Lilagar river, Chhattisgarh, Central India. \u003cstrong\u003eA\u003c/strong\u003e. Tiller loss with tree distance from the crop, \u003cstrong\u003eB\u003c/strong\u003e. Tiller loss with different diameter classes, \u003cstrong\u003eC\u003c/strong\u003e. Hill loss with tree distance from the crop, \u003cstrong\u003eD\u003c/strong\u003e. Hill loss with diameter classes, \u003cstrong\u003eE\u003c/strong\u003e. Paddy yield loss with tree distance from the crop, \u003cstrong\u003eF\u003c/strong\u003e. Paddy Yield loss with different diameter classes.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7647772/v1/4fcf1c43cfd55e6ba39d2bd9.jpg"},{"id":92948717,"identity":"73deeaec-925f-4dc5-8af9-59b607f82af4","added_by":"auto","created_at":"2025-10-07 12:55:29","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":137380,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation Coefficient (R\u003csup\u003e2\u003c/sup\u003e) between \u003cstrong\u003eA.\u003c/strong\u003e Diameter classes x Paddy tillers, \u003cstrong\u003eB\u003c/strong\u003e. Diameter classes x Paddy hill and \u003cstrong\u003eC.\u003c/strong\u003e Diameter classes x Paddy yield \u003cstrong\u003eD.\u003c/strong\u003e Tree density x Paddy tillers, \u003cstrong\u003eE\u003c/strong\u003e. Tree density x Paddy hill and \u003cstrong\u003eF.\u003c/strong\u003e Tree density x Paddy yield in a riverine agroforestry under study.\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7647772/v1/b7c2b64fcf1495340fc36f4f.jpg"},{"id":92948685,"identity":"b4cce9d3-6d95-4ad3-b025-cb02d1b73d83","added_by":"auto","created_at":"2025-10-07 12:55:20","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":100065,"visible":true,"origin":"","legend":"\u003cp\u003eLoss pattern in Paddy attributes under the tree density (per 1000m\u003csup\u003e2\u003c/sup\u003e area) of \u003cem\u003eT arjuna\u003c/em\u003e growing with Paddy crop in riverine agroforestry along Lilagar river, Bilaspur, Chhattisgarh, Central India. \u003cstrong\u003eA\u003c/strong\u003e. Tiller loss with tree density, \u003cstrong\u003eB\u003c/strong\u003e. Tiller loss with tree density, \u003cstrong\u003eC\u003c/strong\u003e. Hill loss with tree distance from the crop, \u003cstrong\u003eD\u003c/strong\u003e. Hill loss with tree density, \u003cstrong\u003eE\u003c/strong\u003e. Paddy Yield loss with tree density \u003cstrong\u003eF\u003c/strong\u003e. Paddy yield loss with tree distance from the crop.\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7647772/v1/a917c2ed8fa4a1f92d59e494.jpg"},{"id":92948766,"identity":"705b8aba-6ec8-4554-b48a-705f743542a6","added_by":"auto","created_at":"2025-10-07 12:55:37","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":200509,"visible":true,"origin":"","legend":"\u003cp\u003eRiverine agroforestry system showing \u003cem\u003eT. arjuna \u003c/em\u003emaintained on farm levees in N-S orientation in paddy crop field adjacent to the river, Chhattisgarh, India.\u003c/p\u003e","description":"","filename":"9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7647772/v1/9436741dabcbbd41e3475281.jpg"},{"id":92950470,"identity":"ac2632cd-8955-4a11-8e06-994a5b86af13","added_by":"auto","created_at":"2025-10-07 13:06:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2559536,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7647772/v1/1de0f844-976a-448f-992a-312adbc404fe.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Quantifying the yield loss pattern in paddy crop within a native tree-based riverine agroforestry system in Chhattisgarh, Central India: Implications to food sustainability and climate smart farming","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRiverine agroforestry stands apart from conventional agroforestry systems because of its diverse biodiversity and greater number of trees, which help tackle global issues related to both productive and protective services. (Chandra and Singh, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Rodenburg et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Shukla et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e). This ecosystem favours paddy production in South East Asia enabling high moisture regime and water stagnation adopted for high yielding paddy cultivars (Kumar et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Bouman et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Kumar et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Thevs et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, paddy productivity in such areas has become challenging due to destruction in riverine habitat, mining activities, and unpredicted monsoon resulting to a drying rivers. Evolving a climate resilient self-sustaining farming systems need urgently to adopt smart agriculture to cope up the negative environmental externalities and food security (Wezel et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Undoubtedly, tree integration at farmlands adjacent to rivers form a green belt, and boost ecosystem services (Sinclair and Hitinayake \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), protect soil erosion through root binding and reducing the rain drop velocity through tree canopy (Zuazo and Pleguezuelo, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Moreover, tree contributes to the efficient recycling of nutrients by adding mulches, leaf and root turnover (Maclean et al., 2003, Barrios et al., 2012, Karki et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Thus tree based farming system approaches are often more effective in regulating, supporting and cultural ecosystem services (Pagella and Sinclaire, 2014) such as microclimate buffering, amelioration of soil properties and water infiltration and water table maintenance (Bayala et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Fletcher et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Sunderland et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ong and Kho \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Luedeling et al, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Dawson et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Chandra and Singh, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Chandra et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Shukla et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e). The potential of agroforestry practices to mitigate climate change and increase carbon sequestration has also been widely studied (Kuyah et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Chandra et al., 2018; Rosenstock et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2014\u003c/span\u003e Jose, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Kumar et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rodenburg et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, the expansion of the agroforestry system in paddy dominated areas are limited and confined to some African and south eastern countries (Miyagawa et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Rodenburg et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Koch et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The non-expansion of agroforestry believes to the yield loss of crop and income of farmers (Chandra, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Chandra and Singh, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Pardon et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn actuality, though, its reach is restricted to a few innovative, sizable farmers in the tropical region. This approach needs to spread as quickly as possible to all agricultural communities, regardless of land size and agroclimatic conditions, in the context of climate change and food security (FAO, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The negative and direct effects of trees on crop output are proven to be a significant barrier to the growth of agroforestry (Rao et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Allopathic effects (Xuan et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Khaliq et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Kumari et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), tree shadow (Roder and Maniphone, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), and crop-tree competition for resources were the main causes of this negativity in the past. Depending on the degree of competition for growth resources between the woody component and the related crops which may also be impacted by site conditions, trees can have a complimentary or competitive effect on crops. There are instances where agricultural crop production is said to be decreased with tree integration (Chemura et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Reuse et al., 2025). Large canopy shade on crops can place significant production restrictions on them, particularly in agroforestry systems based on Paddy. Because the benefits of agroforestry rely on site-specific responses of trees-crop systems and farming techniques, not all agroforestry treatments can be replicated elsewhere (Coe et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). These factors, along with unfavourable opinions held by peasant farmers, are impeding the advancement of agroforestry. As a result, scientific research is required to address the primary issues with Paddy agroforestry, such as the interactions between different system components and the vast geographical domains and timescales over which trees and crops interact at farmland (Smith et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Rodenburg et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Since the yield seems to be as the main indicator of quantitative performance of farmlands crops, its quantification to agroforestry models become a key to evaluate systems sustainability and food security. Efforts are being done to improve the self-sustenance in agriculture by equipping fields with the concept of neutral farming, organic farming and natural farming where the integration of perennial vegetation is increasingly advocating climate smart agriculture and sustainable farming (Garrity et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Glover et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Chandra, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Rajesh Kumar et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Muhie \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Such approaches needs to be propagated to save the soil health from the increasing effect of chemical fertilizers and pesticides in the cropping system (Angon et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Terrer et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Shinde et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe high sensitivity of global warming and climate change results in drying out riverine habitat, unpredictable monsoons, and high input costs, making such Paddy-growing practices difficult (Rodenburg et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Thus, in order to practise smart agriculture and prevent detrimental environmental externalities, a self-sustaining farming system must be developed quickly (Wezel et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Although agroforestry is a tried-and-true method of agro-ecological intensification, Paddy is not as often planted with trees as other crops because it needs more direct sunshine and conditions of water stagnation (Garrity et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Glover et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Paddy reported less suitable to be intercropped with taller and more competitive species (Akanvou et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) and because paddy crop requires anaerobic soil condition, which are less favourable for many tree species (Kramer and Kozlowski, 1979). The regional differences that how and how many trees are integrated with paddy may be the other reason of a less obvious paddy in agroforestry. Worldwide, paddy stands out as a staple food for over half of the world's population (Khush, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). However, paddy faces numerous challenges, including the need for increased productivity (Peng et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) and the necessity of adopting sustainable agricultural practices to mitigate environmental degradation (Vermeulen et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Farmers simply need to maintain the trees and take care of tending operations to lessen the tree canopy and ease of farm equipment movements during farming activities (Kosaka et al, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Pham et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Miyagawa et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Chandra, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Dumrongrojwatthana et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Daum, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe riverine Paddy agroforestry system, which stops soil degradation (Shinde et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and protects Paddy crops from unfavourable local environments, is specifically used in the state of Chhattisgarh for agricultural intensification and multiple output (Bargali et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Miyagawa et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Watanabe et al., 2017). The farm produces more when the riverine trees are purposefully allowed to grow to the levees and floor (Chandra and Singh, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Barrios et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Nair, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, Shukla et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e). There are reports which alarm the negative impacts of trees on crops due to various reasons such as shadow of the tree, allelopathic effect, nutrient competitions, etc. (Kumari et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Pardon et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and more directly the shade of trees (Roder and Maniphone, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Negative tree-crop interaction become more prominent especially when trees grows naturally than the well managed tree plantations. Therefore, all the interventions of agroforestry cannot be replicated everywhere as the agroforestry benefits varies on site specific responses of trees-crop system and farming contexts (Coe et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Due to these reasons, the progress in agroforestry is held back concerning risk factors and negative perceptions of farmers towards agroforestry. To appraise the paddy based riverine agroforestry as adaptation measure to climate change, we shall take into account region-specific environmental conditions and the potential risks and benefits of trees in future farming (Rodenburg et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The current study evaluated the effects of riverine agroforestry species on Paddy crop yield dynamics under farmer-field conditions. In riverine agroforestry practices along the Lilagar river in Chhattisgarh, tree species, diameter classes, and tree density (populations) were taken into consideration while quantifying the loss of Paddy yield due to tree cop interaction.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy site:\u003c/h2\u003e\u003cp\u003eThe study area lies in the riverine area of the river Lilagar which is a tributary of the Sheonath and Mahanadi rivers of Chhattisgarh, India. It is located between latitudes 21\u0026deg;44' N to 22\u0026deg;01' N and longitudes 81\u0026deg;20' E to 81\u0026deg;40' E (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This river originates from the Udta dam in Korba district and ends at the Sheonath river at Deverghatta in Jangir Champa district. The total length of the river is 135 km, while its catchment area covers approximately 1538 km\u003csup\u003e2\u003c/sup\u003e. The second-third length of the river at higher elevations is occupied with natural forests, while its lower basin supports tree-based agroforestry systems and crop cultivation practices. The region has a tropical monsoon climate with an average annual rainfall of around 1200\u0026ndash;1400 mm. The average temperature ranges from 22\u0026deg;C to 34\u0026deg;C. The communities that adjoin the river Lilagar rely on the river for their livelihoods and irrigation of crop fields.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSampling methods:\u003c/h3\u003e\n\u003cp\u003eThe present study was carried out during April 2020 to December 2022 for 03 consecutive years and two crop cycles to consider multi season effects, comprising an extensive field survey along the entire river length in order to identify prevailing agroforestry practises. A total of 10 grid points were selected along the river at a 10 km interval, covering 100 km of river length and consisting of agroforestry and agriculture practises. Four sample plots were established randomly at each grid point of 1 ha each. Thus, a total of 4 ha is covered at each grid, and total 40 plots (40 ha) across river lengths. All the grids/ plot were laid out within 500m from the centre of the river in both the side.\u003c/p\u003e\u003cp\u003eFive tree species \u003cem\u003eA. nilotica\u003c/em\u003e (L.) Delile (S1), \u003cem\u003eB. monosperma\u003c/em\u003e (Lam.) Taub. (S2), \u003cem\u003eM. indica\u003c/em\u003e L. (S3), \u003cem\u003eT. arjuna\u003c/em\u003e (Roxb.) Wight \u0026amp; Arn (S4), \u003cem\u003eT. tomentosa Willd\u003c/em\u003e (S5) were selected randomly considering 3 grids for one species covering entire stretches of riverine ecosystem adjacent to the Lilagar river. Species selection was done based on their occurrence to different grids such as S1\u0026ndash; from grids 5, 6, 9; S2\u0026ndash;2, 3, 7; S3\u0026ndash;4, 9, 10; S4\u0026ndash;5, 8, 10 and S5 \u0026ndash; grids 1, 8, 9. Tree species were available to farmer\u0026rsquo;s field and well maintained on levees in North South orientation at a distance between 5-6m apart. Each species experiment covers 3 grids, 12 sample plots (4 plots/grid), and 3 individual tree stems/plots (total 36 stem/species) were chosen. Tree- crop interaction effect was studied by laying out sample plots of 1m x 1m size in standing crop field cultivar MTU 7029 (Swarna) initiating from the stem at varying distances 2m, 8m, 15m, and 25m aliened in East-West direction of transect line (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Measurements of crop height, the number of tillers, and the number of paddy hills (m\u0026sup2;) were recorded individually from each plot in species-specific grids. We adhered to the methodologies proposed by Khan and Ehrenreich (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1994\u003c/span\u003e); Karki et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e); and Asmamaw (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) in their studies on tree-crop interactions. The paddy yield was determined by manually harvesting from the designated sample plots assigned for species specificity at various grid locations prior to the field harvest at maturity, which occurred between November 15th and 25th in 2021 and 2022. Following the analysis of data from the sample plots, the parameters of paddy crop growth and yield were converted to a per-hectare basis. Crop yield of sampled plots were dried out separately by keeping it in hot oven until constant weight. Ultimately, the findings were summarized by calculating the average values from the data collected over two years.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe species \u003cem\u003eT. arjuna\u003c/em\u003e was most adoptive in riverine ecosystem than the other species due to their ability to thrive under high moisture regime found abundant representing different diameter classes and density in paddy crop fields. Therefore, II and III\u003csup\u003erd\u003c/sup\u003e investigations were conducted using this species only to evaluate the impact of different diameter classes and stem density (Stem counts) on paddy crop. Trees having diameter class of 10-40cm, 41\u0026ndash;80 cm, 81\u0026ndash;120 cm, and \u0026gt;\u0026thinsp;120 cm were marked at breast height and sample plots were taken close to the stem at 2m, 8m, 15m, and 25m aliened in East-West direction of transect line to quantify the yield loss pattern in paddy crop. Season of cropping, management practices and harvest timing were kept similar as mentioned in I\u003csup\u003est\u003c/sup\u003e experiment. Similarly, to understand the varying density of \u003cem\u003eT. arjuna\u003c/em\u003e stems on paddy yield was determined considering 2, 4, 8 and 12 trees per plot of size 2000m\u003csup\u003e2\u003c/sup\u003e representing 10, 20, 40 and 60 trees ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e respectively. Tree specific plot (\u003cem\u003eT. arjuna\u003c/em\u003e) for these two experiments were chosen from the grid 8 and 10 considering 6 plots (2 plots/grid), and 3 individual tree stems for each diameter and density classes. Total 30 stem were chosen separately for these two investigations. For density class experiment, the size of the \u003cem\u003eT. arjuna\u003c/em\u003e trees selected were in the size between 71-120cm diameter with a mean of 98.5cm at BH. Further the impact of varying DBH and density on paddy were quantified by laying out sample plots of similar size and distances as mentioned in species specific tree-crop interactions. The crop parameters under study were also assessed at the same distances (2, 8, 15, and 25m) from the reference field that had no trees, which was managed in the same way as the field with trees, located 200m away.\u003c/p\u003e\n\u003ch3\u003eMeasurement and data collection\u003c/h3\u003e\n\u003cp\u003eTree height, diameter at breast height (DBH), height of the lowest branch (HLB), crown depth and crown area were measured twice in 2020 and 2021 and the average values were provided in figure. Tree height was measured with a clinometer, while crown depth measured by deducting the HBL from the total tree height. The crown area was measured using standard formula (πr\u003csup\u003e2\u003c/sup\u003e).\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eWe used multivariate analysis to analyse the effect of the species, distance, diameter classes and tree populations and their interactions between variables on paddy tillers, hills and yield using state Pro software. To determine the key factors influencing yield and paddy attributes and quantitative interrelationships backwards stepwise regression analysis was applied using the criteria of probability of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Furthermore, Tukey\u0026rsquo;s Honestly Significant Difference (HSD) test was performed to determine significant differences between groups in the sample.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results and discussions","content":"\u003cp\u003eThe multivariate MANNOVA analysis of riverine agroforestry data shows that tree presence close and far from the crops varies significantly differed the growth and yield of paddy. The paddy yield showed significant difference to variables including tree species, diameter of trees, tree counts and its distances from the crop at P\u0026lt;0.05.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. 1. Effect of tree species on paddy growth and production\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTree morphological characteristics such as height, diameter, canopy depth and crown area and age differed species specifically and significantly (\u003cstrong\u003eFigure 3\u003c/strong\u003e). Among all the trees occurring in riverine agroforestry, \u003cem\u003eM. indica\u003c/em\u003e was tall, large in diameter with broader crown and aged. Other trees such as \u003cem\u003eT. arjuna\u003c/em\u003e and \u003cem\u003eT. tomentosa\u003c/em\u003e showed slighter smaller 27m height with a crown expansion of 235-240 m\u003csup\u003e2\u003c/sup\u003e area. \u003cem\u003eB. monosperma\u0026nbsp;\u003c/em\u003eand \u003cem\u003eA. nilotica\u003c/em\u003e had small heighted and lower crown depth and crown area than the other species resulted to form shadow in a relatively small area on paddy field. Paddy field without any tree showed highest yield of 5.91 t ha\u003csup\u003e-1\u003c/sup\u003e with the higher tillers and hill density as compared to the paddy field with trees ((\u003cstrong\u003eTable 1\u003c/strong\u003e). The paddy yield taken from different distances from the bund/ fields devoid trees was nonsignificant at P\u0026lt;0.05.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe found a negative effect of various tree species in the paddy growth and yield at P\u0026lt;0.01, except tillers which rendered nonsignificant result. Number of paddy tillers declined towards the proximity of tree whereas, increased with the increasing distance from the tree. This decline was 24.22%, 8.75% and 4.67% at 2m, 8m and 15m as compared to 25m distance from the tree trunk (\u003cstrong\u003eFigure 4\u003c/strong\u003e). When considering tree species \u003cem\u003eM. indica\u003c/em\u003e had the greater effect in reducing the paddy tiller followed by \u003cem\u003eT. arjuna\u003c/em\u003e, while the lowest impact was observed with \u003cem\u003eB. monosperma\u003c/em\u003e. Similarly, the paddy hills were also negatively correlated with the increasing distances from the tree trunk. Results recorded the loss of 4 hills m\u003csup\u003e2\u003c/sup\u003e at the closest plot and 1.33 hills at the distance of 25m from the trees (\u003cstrong\u003eTable 2)\u003c/strong\u003e. The loss was highest of 10.93% at 2m and 3.40% at farthest distance of the study. The presence of trees decreased the tillers and hills of paddy by 6.10 and 1.38% respectively as compared to paddy fields without trees (control plot). \u0026nbsp;Species wise variation in the loss percentage of paddy hills was significant at P\u0026lt;0.01. Results indicate that \u003cem\u003eM. indica\u0026nbsp;\u003c/em\u003eand \u003cem\u003eT. arjuna\u0026nbsp;\u003c/em\u003etrees had greater effect on the hills than other species.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur study indicates the significantly negative impact of farm trees on paddy yields. The yield loss was highest towards the proximity of the trees. The grain yield of paddy ranged from 3.64 to 5.52 ton ha\u003csup\u003e-1\u003c/sup\u003e in our study which were obtained from 2m and 25m plots far from the tree trunk respectively \u003cstrong\u003e(Table 2)\u003c/strong\u003e. On the other hand, grain yield reduced by 51.31% at 2m distance and 10.02% at 15m as compared to 25m distance from the tree trunk. Subsequently, the yield loss was 6.57% in fields with trees even far 25m from the trunk than the field devoid tree species. Significant variations on yield of paddy was observed due to presence of different tree species at varying distance from the tree trunk. Tree species \u003cem\u003eM. indica\u003c/em\u003e could reduce the grain yield maximum by 72.96% followed by \u003cem\u003eT. tomentosa\u003c/em\u003e (54.81%) and lowest of 43.35% at closest to crop. However, the grain yield losses were substantially lower with \u003cem\u003eA. nilotica, T. arjuna\u003c/em\u003e and \u003cem\u003eB. monosperma\u003c/em\u003e at 15 and 25m distances as compared to the same distances of other species of the study. \u0026nbsp;The grain yield of paddy with \u003cem\u003eM. indica\u003c/em\u003e was lowest (ranged 2.37 to 4.09 ton ha\u003csup\u003e-1\u003c/sup\u003e) and highest (4.34 to 6.23t ha\u003csup\u003e-1\u003c/sup\u003e) with \u003cem\u003eB. monosperma\u003c/em\u003e at their closest and farthest plots of paddy fields. These data significantly differed at closure and far from the tree trunk which showed specific species variation in riverine agroforestry. Overall, the species such as \u003cem\u003eA. nilotica\u003c/em\u003e, \u003cem\u003eB. monosperma\u003c/em\u003e and \u003cem\u003eT. arjuna\u003c/em\u003e exhibited less yield losses in paddy than the \u003cem\u003eM. indica\u003c/em\u003e and \u003cem\u003eT. tomentosa\u003c/em\u003e. \u0026nbsp;Interactive effects of tree species and distance on paddy attributes under agroforestry practice was found nonsignificant at P\u0026lt;0.05. The correlation between different tree species and their distance from the crop showed positive correlation for all the attributes, however its impact observed highest in paddy yield. Thus it can be said that irrespective of the tree species, the distance factor between tree- paddy was more prominent and adverse for paddy yield (\u003cstrong\u003eFigure 5\u003c/strong\u003e). As the large tree with larger crown such as \u003cem\u003eM. indica\u003c/em\u003e, and \u003cem\u003eT. tomentosa\u003c/em\u003e tend to shade on large area in paddy field found more deleterious than the tree species which had small crown and short height.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePaddy requires strong sunlight for higher growth and yield than the other crops therefore, crop near tree line affected highest and its performed remain poor than the crop away from the tree or paddy crop with mono-cropping (Paula et al., 2018). \u0026nbsp;Scordia et al. (2023) also compared Mediterranean agroforestry systems with treeless cropping systems and found that the majority of the studies identified shade as a cause of yield loss in field crops. Similar to our findings others also reported that the agroforestry of \u003cem\u003eEucalyptus\u003c/em\u003e tree had adverse effect on growth and yield attributes of crop due to its crown width and distance from the tree line (Khan et al., 2023; Koch et al., 2025). Under such conditions, the yield of the crop could be improved by manipulating the tree canopy, which further assist the crop growth component (Koch et al., 2025). Nadir et al., (2018) studied the relative performance of wheat crop with different shading intensities under \u003cem\u003eEucalyptus\u003c/em\u003e tree. Their findings highlights the decrease in photosynthetic rate, specific leaf weight and grain yield of wheat crop with the increase in shade duration (Yang et al., 2019). Moreover, Prasad et al. (2010) and Honfy et al. (2023) argued that the increase in grain yield with the increased distance from the tree line occurred due to increase in light intensity and decreased competition with the increased distance from the tree line between the tree roots. Bayala (2002) observed that the light competition is key for interactions between trees and crops. Tree species can reduce the amount of sunlight reaching crops and capture light depending upon tree factors viz. tree leaf area, leafing phenology, crown structure and crown management. The effect of \u003cem\u003eProsopis cineraria\u003c/em\u003e on yield of wheat crop due to distance has also been reported by Khan et al., (2023). This is the reason that the farmers pursued a regular tending operations to lesser the tree canopy and remove the exploitable diameter trees not for only the intermediate income from tree but also for the ease movements of farm equipment during farm activities (Koska et al, 2006; Pham et al., 2015; Miyagawa et al., 2017; Dumrongrojwatthana et al., 2020, Pardon et al., 2025, Koch et al., 2025). However,\u0026nbsp;farmers have evolved management practices for reducing tree density, combining compatible species, and shoot pruning that contribute to control both above and below-ground competition\u0026nbsp;(Namirembe et al., 2009).\u0026nbsp;In riverine\u0026nbsp;agroforestry, where copious amount of water is available to the tree species round the year, the tree flourish new leave and dense canopy round the year, therefore, tending and pruning operation become imperative to higher yield from the paddy crop.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2. Effect of diameter classes of trees\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe varying diameter of trees occurring on levees of the paddy field were evaluated considering four diameter classes including 20-40cm, 41-80cm, 81-120cm and above 120cm \u003cstrong\u003e(Table 3)\u003c/strong\u003e. Result reveals that the increase in tree diameter significantly reducing the paddy yields and its tiller, and hills. The higher tree diameter found to be more negative on paddy attributes than the less diameter levees trees. The mean tillers observed highest with tree diameter of 20-40cm and lowest with trees \u0026gt; 120cm diameter. In general, all the trees irrespective of diameter size had negative influence on the paddy yield parameters. However, the tiller loss was 19.07% with young trees with lesser diameter, whereas the losses increased to 26.48% with the largest diameter trees of the study (\u003cstrong\u003eFigure 6\u003c/strong\u003e). Similarly, the highest hills per meter recorded 41.59 and lowest 35.71 for youngest and oldest tree diameter respectively representing 16.46% loss in hill population when comparing between young and old tree diameters. The same trends also continued in paddy yield which obtained 5.63t ha\u003csup\u003e-1\u0026nbsp;\u003c/sup\u003ewith the presence of trees having 20-40cm diameter and 3.95t ha\u003csup\u003e-1\u003c/sup\u003e with trees diameters \u0026gt;120cm. \u0026nbsp;The relative yield losses estimated due to higher diameter trees was found to be 42.53% than the losses with the lowest tree diameter. Moreover, the distance from the tree found to impact highest to the paddy yield which recorded significant difference at P\u0026lt;0.01. This resulted that the mean yield losses may be expected up to 37% with trees presence near to trees which may be declined up to 10.1% with the increase of trees distance to the paddy crop. The DMR test also authenticated the influence of diameter classes on the paddy yield attributes which differentiated significantly at P \u0026lt;0.05. Besides the effect of diameter classes on paddy yield parameters, the paddy crop close to the tree stem rendered less tillers, hills and yields than the paddy crops with increasing distances from the trees. The tiller formations in paddy were 12.50 m\u003csup\u003e2\u003c/sup\u003e at 2m trees proximity and 15 tillers m\u003csup\u003e2\u003c/sup\u003e at 25 from tree distances. Similarly, the hills number was ranged 36.17 to 41.75 m\u003csup\u003e2\u003c/sup\u003e at closest and farthest plots of the study. Paddy production was highest of 5.65 t ha\u003csup\u003e-1\u003c/sup\u003e and lowest of 4.17 t ha\u003csup\u003e-1\u003c/sup\u003e at the farthest tree distance of 25m and 2m sample plots respectively under varying diameter classes of the trees. Two way interaction between diameter class and distance was found nonsignificant at P\u0026lt;0.05 which confirms that the tree and distance individually influence to the paddy yield when close to trees but its combined effect could not show any type of negative trends. \u0026nbsp;Overall, the nonsignificant, negative correlation between DBH x tillers, DBH x hills of paddy, and DBH x yield of paddy (R2=0.0593, 0.4614 and 0.5064 respectively) (\u003cstrong\u003eFigure 7\u003c/strong\u003e) also indicatives of the negative tree crop interface in paddy agroforestry systems. Reuse et al. (2025) and Pardon et al. (2018) observed that the tree height had a greater negative effect on crop yield in temperate climatic conditions. Other researchers also highlighted the light competition as a major factor playing a significant role and negatively influences the yields of annual crops (Pardon et al. 2018; Arenas-Corraliza et al. 2022) under agroforestry systems. The greater diameter tree develops large canopy with more height cast large shadow on crop for longer duration resulted to a higher yield loss at close proximity of the tree (Chauhan, et al. 2011). Crown shape of the trees and its size influence the degree of crop loss. Similar reports propounded by Kashyap et al. (2024) where paddy yield was decreased with the increase in tree diameter of \u003cem\u003eGmelina arborea\u003c/em\u003e plated in bund of the crop field. Thus need based regular pruning of tree branches can have positive impact in order to allowing more sunlight to paddy crop and substantially reduction to negative tree crop interaction. Luedeling et al., (2016) and Muthuri et al. (2009) also reported the effect of trees maize crop and indicated the benefits of pruning in reducing tree-crop competition. Moreover, harvesting of merchantable tree with greater diameter may be another solution for controlling the yield loss and economic benefit to the farmers. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3 Effect of tree stem counts on paddy attributes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNumber of trees on levees and their influences on tillers, hills and yield of paddy under riverine agroforestry practices are summarized (\u003cstrong\u003eTable 4)\u003c/strong\u003e. The results showed statistically significant data for paddy attributes when analysed considering tree population at the field and distances at P\u0026lt;0.05. The mean number of tillers found highest 14.21 with the presence of two trees at 4m apart on levees while the lowest of 12.24 paddy tillers with the 12 trees occurring on levees of paddy agroforestry. The presence of tree augmented the tiller loss by 12.75% and 14.42% respectively for two and twelve trees maintaining at the farm levees (\u003cstrong\u003eFigure 8\u003c/strong\u003e). \u0026nbsp;However, variation observed in the paddy tiller population under varying tree populations and with the tree distances from the paddy crops. The tree distance influenced the paddy tiller and reduced it by 19.23% when two trees present at the levees, whereas the tillers decreased by 37.85% with 12 trees when compared between the paddy at 2m and 25m distances from the trees. Similarly, the yield loss declined significantly with the increase in distance and remained 3.54% at the distance of 15m than the farthest distance of the study. \u0026nbsp; Similar trends were also followed by the number of hills and yield of paddy. The paddy hills decreased by 3% with lowest tree population which magnified furthers up to 8.27% with highest trees numbers. The paddy crop close to the trees showed 11.06% yield loss, however, the negative impact was negligible going far away from the trees. The yield losses found 56.67% with two trees and 66.45% yield loss with the highest tree population growing just near levees trees. \u0026nbsp;However, the paddy yield loss ranged 65% and 13.6% at plots close and far from the trees respectively. This loss was quantified up to 2.15t ha\u003csup\u003e-1\u003c/sup\u003e at closure plots than the crop growing at 25m distance from the trees. Two way ANOVA analysis showed nonsignificant results between tree number x distances. The negative correlation between the increasing number of trees and paddy tillers, hills and yield (R\u003csup\u003e2\u003c/sup\u003e- 0.039, 0.337 and 0.156 respectively) (\u003cstrong\u003eFigure 7\u003c/strong\u003e) further prove the hypothesis that the increase in tree counts reduce the paddy yield through their growth attributes in riverine agroforestry practices. Our results concord with the findings of Khan and Chaudhry (2007) that indicated the increasing density of tree adversely affects the yield of crops under agroforestry. Vishwanath et al. \u0026nbsp;(2000) and Kashyap et al. (2024) has evaluated the paddy yield under varying density of trees and reported that the higher stem number decline the paddy yield under tropical conditions. Reuse et al. (2025) also advocated the appropriate tree density based on observation that elative crop yields decrease with tree density. As tree density increases, the yield of arable crops can decrease (Honfy et al. 2023). On the other hand, more light penetration might be possible if trees are widely spaced with lower density.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe present investigation on tree-paddy interaction adjacent to river Lilager catchment where multiple tree species occurring naturally without any definite distance, and tree density in paddy crop fields clearly demonstrated the adverse impact on paddy growth characteristics and yield. Varying tree species had distinct impact on yield majorly based on crown size, and dimensions of the tree species. The shade effect of trees found to be the most important limiting factors for negative tree crop interaction. Larger the tree and denser the canopy causes more adversities on crop growth and yield aspects of paddy crop than the shorter and medium-small crown tree species. This factor can overcome with regular pruning of tree crown and harvest of exploitable diameter trees. It may became a regular source of farm income besides crop yield. In addition, higher tree density found to be deleterious for paddy production in a riverine ecosystem. The maintenance of appropriate tree counts between 10\u0026ndash;20 ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e to in paddy field allowing adequate sunlight and subsidise to reduce the yield loss. Conclusively, the selection of suitable indigenous tree species with small canopy size or vertical cylindrical canopy shape and their effective canopy management may maintain and or enhance the level the productivity and sustainability in paddy crop. Furthermore, more study need to conduct in a larger perspectives to authenticate and explore the species specific riverine agroforestry systems in other riverine area to confirm and encourage smart agriculture with nature based solutions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement:\u003c/strong\u003e Thank to Dr. Rahul Bhadouria, Department of Environmental Sciences, Delhi University for statistical analysis of the manuscript data and Mr. Sudhir Ranjan, of Department of Forestry, wildlife \u0026amp; Environmental Sciences, Guru Ghasidas Vishwavidyalaya, for Preparation of Remote sensing based map of study site. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Author contributions\u003c/strong\u003e: Dr. K. K. Chandra- research supervision, Conceptualization research hypothesis, editing final manuscript, Dr. Atul Kumar Bhardwaj- Writing draft manuscript, Dr. Arun Kumar Shukla- Data collection, and field work, original draft preparation, Rajesh Kumar- graph preparation and formatting.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e No grant/ fund received for the research work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e: No datasets were generated or analysed during the current study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations Competing Interests:\u003c/strong\u003e The authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAkanvou R, Bastiaans MJ, Kropff J, Goudriaan M (2001) Becker Characterization of growth, nitrogen accumulation and competitive ability of six tropical legumes for potential use in intercropping systems J Agron Crop Sci 187:111-120\u003c/li\u003e\n\u003cli\u003eAngon PB, Anjum N, Akter M, Shreejana KC, Suma RP, Jannat S (2023) An Overview of the Impact of Tillage and Cropping Systems on Soil Health in Agricultural Practices. 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Agrofor Syst 50: 157-177. DOI: 10.1023/A:1006486912126\u003c/li\u003e\n\u003cli\u003eWangpakapattanawong P, Finlayson I, Oborn JM, Roshetko F, Sinclair K, Shono S, Borelli A, Hillbrand M Conigliaro (2017) Agroforestry in Rice-production Landscapes in Southeast Asia: a Practical Manual. Food and Agriculture Organization of the United Nations Regional Office for Asia and the Pacific; World Agroforestry Centre (ICRAF) Southeast Asia Regional Program,, Bangkok, Thailand; Bogor, Indonesia (2017)\u003c/li\u003e\n\u003cli\u003eWezel A, Herren BG, Kerr RB et al (2020) Agroecological principles and elements and their implications for transitioning to sustainable food systems. A review. Agron. Sustain. Dev. 40: 40. https://doi.org/10.1007/s13593-020-00646-z\u003c/li\u003e\n\u003cli\u003eXuan TD, Eiji T, Hiroyuki T , Mitsuhiro T, Khanh TM, Chung IM (2004) Evaluation on phytotoxicity of neem (\u003cem\u003eAzadirachta indica.\u003c/em\u003e A. Juss) to crops and weeds. Crop Prot 23: 335-345.\u003c/li\u003e\n\u003cli\u003eYang T, Duan ZP, Zhu Y, Gan YW, Wang BJ, Hao XD, Xu WL, Zhang W, Li LH (2019) Effects of distance from a tree line on photosynthetic characteristics and yield of wheat in a jujube tree/wheat. Agroforestry system 93:1545\u0026ndash;1555 https://doi.org/10.1007/s10457-018-0267-\u003c/li\u003e\n\u003cli\u003eZuazo VHD, Pleguezuelo CRR (2008) Soil-erosion and runoff prevention by plant covers. A review. Agronomy for Sustainable Development, Springer, 2008, 28 (1): pp.65-86. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable-1\u003c/strong\u003e: Growth and yield parameters of Paddy crop in a field devoid of any tree. The data shown are mean\u0026plusmn; SE of four replicates. (\u003cem\u003eMeans within a row represented by different letters are significant at p \u0026le; 0.05\u003c/em\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: 128px;\"\u003e\n \u003cp\u003eDistance from levees (m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eNo. of Tillers (pl\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eNo. of Paddy Hills (m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eCrop yields (kg/ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e2m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e14.5\u0026plusmn;0.56b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e40.50\u0026plusmn;1.48b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e5467.2\u0026plusmn;288.42b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e8m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e16.5\u0026plusmn;0.56a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e41.33\u0026plusmn;0.67b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e5770\u0026plusmn;215.65ab\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e15m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e14.67\u0026plusmn;0.33b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e40.17\u0026plusmn;1.25a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e6078.7\u0026plusmn;171.17ab\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e25m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e16.83\u0026plusmn;0.54a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e41.50\u0026plusmn;1.18a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e6348.6\u0026plusmn;112.73a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e15.63\u0026plusmn;0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e40.87\u0026plusmn;0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e5916.1\u0026plusmn;118.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eF value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e5.660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e2.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e3.388\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eSignificance (P)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0.006 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e0.123 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e0.038 (NS)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e: Impact of different tree species on the Paddy crop growth and yields in riverine agroforestry practice prevailing along the river Lilagar catchments, Bilaspur, Chhattisgarh, India. (S1- \u003cem\u003eA. nilotica\u003c/em\u003e, S2- \u003cem\u003eB. monosperma\u003c/em\u003e, S3- \u003cem\u003eM. indica\u003c/em\u003e, S4- \u003cem\u003eT. arjuna\u003c/em\u003e S5- \u003cem\u003eT. tomentosa\u003c/em\u003e). \u003cem\u003e(\u003c/em\u003e\u003cem\u003eThe data shown are mean\u0026plusmn; SE of four replicates. Means within a row presented by different letters are significant at p \u0026le; 0.05\u003c/em\u003e).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"586\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTree Species\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 422px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTree distance from the crop field (m)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2m\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8m\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e15m\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e25m\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 488px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTiller (pl\u003csup\u003e-1\u003c/sup\u003e)\u003c/strong\u003e\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eS1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e11.10\u0026plusmn;1.0b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e14.00\u0026plusmn;0.58a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e14.00\u0026plusmn;0.50a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e13.67\u0026plusmn;1.45a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e12.70\u0026plusmn;0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eS2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e13.67\u0026plusmn;0.33b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e13.67\u0026plusmn;0.33b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e15.67\u0026plusmn;0.33a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e15.33\u0026plusmn;0.88ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e14.58\u0026plusmn;0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eS3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e11\u0026plusmn;0.58b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e13.33\u0026plusmn;1.2ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e13.33\u0026plusmn;1.45ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e15\u0026plusmn;0.50a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e13.17\u0026plusmn;0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eS4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e11.67\u0026plusmn;1.45b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e13.33\u0026plusmn;1.2ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e13.67\u0026plusmn;0.67ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e14.67\u0026plusmn;0.88a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e13.33\u0026plusmn;0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eS5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e12.67\u0026plusmn;0.88bc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e14.33\u0026plusmn;0.67b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e14.67\u0026plusmn;1.76b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e16\u0026plusmn;0.58a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e14.42\u0026plusmn;0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e12.02\u003c/strong\u003e\u0026plusmn;0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e13.73\u003c/strong\u003e\u0026plusmn;0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e14.26\u003c/strong\u003e\u0026plusmn;0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e14.93\u003c/strong\u003e\u0026plusmn;0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e13.64\u003c/strong\u003e\u0026plusmn;0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 488px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHills (m\u003csup\u003e2\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eS1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e34.00\u0026plusmn;1.73ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e34.00\u0026plusmn;1.0ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e38.0\u0026plusmn;2.52a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e37\u0026plusmn;1.53ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e35.75\u0026plusmn;0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eS2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e38.33\u0026plusmn;0.67b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e39.33\u0026plusmn;0.67b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e40\u0026plusmn;0.58ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e43\u0026plusmn;1.73a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e40.17\u0026plusmn;0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eS3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e30.67\u0026plusmn;2.6b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e33.33\u0026plusmn;1.2ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e34\u0026plusmn;1.15ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e35.33\u0026plusmn;1.45a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e33.33\u0026plusmn;0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eS4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e37.67\u0026plusmn;2.6c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e39.33\u0026plusmn;2.19bc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e41\u0026plusmn;3.61b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e43\u0026plusmn;1.73a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e40.25\u0026plusmn;1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eS5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e42.33\u0026plusmn;2.67ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e43.33\u0026plusmn;0.67ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e43.33\u0026plusmn;1.76ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e44.67\u0026plusmn;1.45a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e43.42\u0026plusmn;0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e36.66\u003c/strong\u003e\u0026plusmn;2.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e37.86\u003c/strong\u003e\u0026plusmn;1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e39.26\u003c/strong\u003e\u0026plusmn;1.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e40.60\u003c/strong\u003e\u0026plusmn;1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e38.584\u003c/strong\u003e\u0026plusmn;0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 488px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYield (kg ha-1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eS1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e3340.1\u0026plusmn;471b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e3673\u0026plusmn;290b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e4827.3\u0026plusmn;244.29a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e5068\u0026plusmn;294a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e4227\u0026plusmn;263.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eS2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e4346.5\u0026plusmn;9.73c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e5030.7\u0026plusmn;121.3bc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e5722\u0026plusmn;156.21ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e6230.7\u0026plusmn;377.74a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e5332.5\u0026plusmn;232.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eS3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e2369.5\u0026plusmn;116.9b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e2889.7\u0026plusmn;367.62b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e3321.3\u0026plusmn;394.8ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e4098.3\u0026plusmn;244.01a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e3169.7\u0026plusmn;230.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eS4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e3950.1\u0026plusmn;439.36c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e5241.7\u0026plusmn;236.1ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e5713\u0026plusmn;369.49ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e6115.3\u0026plusmn;155.43a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e5255\u0026plusmn;280.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eS5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e4234.7\u0026plusmn;63.14c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e5334.7\u0026plusmn;59.7b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e5503.6\u0026plusmn;182.05b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e6087.4\u0026plusmn;172.44a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e5290.1\u0026plusmn;209.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3648.18\u003c/strong\u003e\u0026plusmn;230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4433.96\u003c/strong\u003e\u0026plusmn;188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5017.44\u003c/strong\u003e\u0026plusmn;245.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5519.44\u003c/strong\u003e\u0026plusmn;233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4654.46\u003c/strong\u003e\u0026plusmn;217\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e: Diameter classes of \u003cem\u003eT. arjuna\u003c/em\u003e impact on growth and yield of Paddy crop at varying distances from the tree base. (The data shown are mean\u0026plusmn; SE of four replicates. Means within a row presented by different letters are significant at p \u0026le; 0.05).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"591\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiameter Class\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 515px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTree distance from the crop field (m)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2m\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8m\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e15m\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e25m\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 496px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTiller (pl\u003csup\u003e-1\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e10-40cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e14\u0026plusmn;0.58c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e14.67\u0026plusmn;0.33bc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e15.67\u0026plusmn;0.33ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e16.67\u0026plusmn;0.33a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e15.25\u0026plusmn;0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e40-80cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e12.67\u0026plusmn;0.33bc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e13\u0026plusmn;0.58b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e14.67\u0026plusmn;0.33a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e14.33\u0026plusmn;0.67a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e13.67\u0026plusmn;0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e80-120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e12\u0026plusmn;0.58bc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e12.67\u0026plusmn;0.33bc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e13\u0026plusmn;0.58b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e14.67\u0026plusmn;0.33a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e13.08\u0026plusmn;0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026gt; 120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e11.33\u0026plusmn;0.33b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e12.00\u0026plusmn;0.33ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e13\u0026plusmn;0.58ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e14.33\u0026plusmn;1.2a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e12.66\u0026plusmn;0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e12.50\u003c/strong\u003e\u0026plusmn;0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e13.08\u003c/strong\u003e\u0026plusmn;0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e14.00\u003c/strong\u003e\u0026plusmn;0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e15.00\u003c/strong\u003e\u0026plusmn;0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e13.66\u003c/strong\u003e\u0026plusmn;0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 496px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHills (m\u003csup\u003e2\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e10-40cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e38.7\u0026plusmn;1.58c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e40.33\u0026plusmn;0.88bc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e42.67\u0026plusmn;0.88ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e44.67\u0026plusmn;1.33a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e41.59\u0026plusmn;0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e40-80cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e38\u0026plusmn;2.58b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e42\u0026plusmn;1.80ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e42.67\u0026plusmn;0.33ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e43.00\u0026plusmn;1.15a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e41.41\u0026plusmn;0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e80-120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e35.67\u0026plusmn;0.33c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e37.33\u0026plusmn;1.33b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e40.00\u0026plusmn;1.15a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e41.33\u0026plusmn;0.67a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e38.58\u0026plusmn;0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026gt; 120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e32.33\u0026plusmn;1.36b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e34\u0026plusmn;1.00ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e36.33\u0026plusmn;1.67ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e38.00\u0026plusmn;2.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e35.17\u0026plusmn;0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e36.17\u003c/strong\u003e\u0026plusmn;01.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e38.41\u003c/strong\u003e\u0026plusmn;0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e40.41\u003c/strong\u003e\u0026plusmn;0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e41.75\u003c/strong\u003e\u0026plusmn;1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e39.19\u003c/strong\u003e\u0026plusmn;0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 496px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYield (kg ha-1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e10-40cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e4690\u0026plusmn;193.13c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e5443.3\u0026plusmn;114.65b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e5760\u0026plusmn;119.06b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e6650.9\u0026plusmn;198.53a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e5636\u0026plusmn;228.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e40-80cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e5002\u0026plusmn;107.35b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e5304.6\u0026plusmn;72.92b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e5757.6\u0026plusmn;92.31a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e5940.1\u0026plusmn;221.35a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e5462.5\u0026plusmn;125.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e80-120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e3651.9\u0026plusmn;90.81c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e4198.9\u0026plusmn;194.94bc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4754.4\u0026plusmn;211.28ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e5282.4\u0026plusmn;169.2a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e4471.9\u0026plusmn;179.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026gt; 120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e3161.5\u0026plusmn;173.7c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e3644.8\u0026plusmn;88.4bc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4267.1\u0026plusmn;187.36ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e4740.5\u0026plusmn;282.79a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e3953.5\u0026plusmn;199.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4176.35\u003c/strong\u003e\u0026plusmn;166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4597.9\u003c/strong\u003e\u0026plusmn;112.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5134.77\u003c/strong\u003e\u0026plusmn;152.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5653.47\u003c/strong\u003e\u0026plusmn;188.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4890.62\u003c/strong\u003e\u0026plusmn;167.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e: Tree stem density of Paddy field and their effects on yield and other parameters in Paddy agroforestry existing along river Lilagar. (The data shown are mean\u0026plusmn; SE of four replicates. Means within a row represented by different letters are significant at p \u0026le; 0.05).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"650\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAttribute\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo. of stem\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 501px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTree distance from the crop field (m)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2m\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8m\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e15m\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e25m\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eTiller (pl\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2 stem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e13\u0026plusmn;1a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e13.67\u0026plusmn;0.88a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e14.67\u0026plusmn;0.33ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e15.5\u0026plusmn;1a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e14.21\u0026plusmn;0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e4 stem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e12\u0026plusmn;0.58b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e13.50\u0026plusmn;0.88ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e14.00\u0026plusmn;0.67ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e15\u0026plusmn;0.40a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e13.62\u0026plusmn;0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e8 stem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e12\u0026plusmn;1b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e13.30\u0026plusmn;0.33ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e13.88\u0026plusmn;0.33a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e14.00\u0026plusmn;0.67ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e13.29\u0026plusmn;0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e12 stem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e9.67\u0026plusmn;0.67b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e12.67\u0026plusmn;1.45a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e13.3\u0026plusmn;2.08a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e13.33\u0026plusmn;1.2a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e12.24\u0026plusmn;0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e11.66\u003c/strong\u003e\u003cstrong\u003e\u0026plusmn;0.66\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e13.28\u003c/strong\u003e\u003cstrong\u003e\u0026plusmn;0.83\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e13.96\u003c/strong\u003e\u003cstrong\u003e\u0026plusmn;0.55\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e14.45\u003c/strong\u003e\u003cstrong\u003e\u0026plusmn;0.53\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e13.34\u003c/strong\u003e\u003cstrong\u003e\u0026plusmn;0.45\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eHills (m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2 stem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e40\u0026plusmn;2.89ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e41.33\u0026plusmn;0.33ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e42.00\u0026plusmn;1.15a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e42.33\u0026plusmn;2.19a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e41.41\u0026plusmn;0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e4 stem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e37.33\u0026plusmn;1.2ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e38.67\u0026plusmn;0.88ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e39.33\u0026plusmn;1.2a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e40.00\u0026plusmn;2a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e38.83\u0026plusmn;0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e8 stem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e32.33\u0026plusmn;1.33b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e34.67\u0026plusmn;1.88ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e37.67\u0026plusmn;0.88a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e38.33\u0026plusmn;1.45a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e35.75\u0026plusmn;0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e12 stem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e32\u0026plusmn;2b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e33\u0026plusmn;2.65b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e37.00\u0026plusmn;2a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e36.67\u0026plusmn;2.73a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e34.66\u0026plusmn;1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e35.451\u003c/strong\u003e\u003cstrong\u003e\u0026plusmn;1.23\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e36.91\u003c/strong\u003e\u003cstrong\u003e\u0026plusmn;1.50\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e39.00\u003c/strong\u003e\u003cstrong\u003e\u0026plusmn;1.50\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e39.33\u003c/strong\u003e\u003cstrong\u003e\u0026plusmn;1.73\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e37.66\u003c/strong\u003e\u003cstrong\u003e\u0026plusmn;0.89\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eYield (kg ha-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2 stem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e4234\u0026plusmn;418.68b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e5131.1\u0026plusmn;198.87b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e5390\u0026plusmn;358.2ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e6633.6\u0026plusmn;649.05a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e5347.2\u0026plusmn;318.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e4 stem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e3155.3\u0026plusmn;400b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e3796.7\u0026plusmn;343.05b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e4973\u0026plusmn;322.06a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e5388\u0026plusmn;283.04a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e4053.42\u0026plusmn;306.02\u003c/p\u003e\n \u003c/td\u003e\n 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\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e12 stem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e2893.2\u0026plusmn;138b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e3595.2\u0026plusmn;167.95b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e4419\u0026plusmn;449a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e4815.7\u0026plusmn;453.79a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e3930.77\u0026plusmn;281.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3320.2\u003c/strong\u003e\u003cstrong\u003e\u0026plusmn;265\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4036.25\u003c/strong\u003e\u003cstrong\u003e\u0026plusmn;189.30\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4823.02\u003c/strong\u003e\u003cstrong\u003e\u0026plusmn;340\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5480.15\u003c/strong\u003e\u003cstrong\u003e\u0026plusmn;466\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4414.90\u003c/strong\u003e\u003cstrong\u003e\u0026plusmn;270\u003c/strong\u003e\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":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"agroforestry-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agfo","sideBox":"Learn more about [Agroforestry Systems](http://link.springer.com/journal/10457)","snPcode":"10457","submissionUrl":"https://submission.nature.com/new-submission/10457/3","title":"Agroforestry Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Agroforestry, Riverine ecosystem, crop production, Tree-crop Interaction, biodiversity, climate change","lastPublishedDoi":"10.21203/rs.3.rs-7647772/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7647772/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRiverine agroforestry is a most diverse, dynamic and widely adopted in tropical agroecosystem along river catchments contributes to climate resilient farming and restoring rivers flow. We estimated the yield loss in paddy cultivar MTU7029 grown with \u003cem\u003eAcacia nilotica, Butea monosperma, Mangifera indica, Terminalia arjuna\u003c/em\u003e, and \u003cem\u003eTerminalia tomentosa.\u003c/em\u003e Tree crop interaction study was performed for two crop cycle (year 2021 and 2022) covering entire grid points of 100km river stretches, utilizing the same farmer fields, crop variety, and tree species. Additionally, two other factors, namely stem diameter (10\u0026ndash;40, 41\u0026ndash;80, 81\u0026ndash;120, and \u0026gt;\u0026thinsp;120 cm DBH) and tree density (10, 20, 40, 60 trees ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) were taken into account for the most abundant tree species \u003cem\u003eT. arjuna\u003c/em\u003e at grids 5, 8 and 10 of riverine ecosystem. For species specific interaction, 36 trees and diameter and density class assessment, 30 trees were employed from the farmer\u0026rsquo;s fields. Sample plots of 1m\u0026sup2; size were established in standing crops at 2m, 8m, 15m, and 25m distances along a transect line from both sides of the tree, oriented in an east-west direction. Results showed a negative impact of trees on crop as Paddy yield reported 3.64 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at 2m and 5.52 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at 25m distance from the tree in crop field. Adverse effect of trees continued to decline paddy tiller and hills by 51% and 10.93% respectively in tree proximity. A large trees with big canopy \u003cem\u003eM. indica\u003c/em\u003e and \u003cem\u003eT. tomentosa\u003c/em\u003e were found most negative for crop yield than moderate to small canopy species \u003cem\u003eA. nilotica, B. monosperma\u003c/em\u003e and \u003cem\u003eT. arjuna\u003c/em\u003e. The large diameter trees led to the greater yield loss of 42.53% than the lower diameter trees. Farmland trees between 40\u0026ndash;60 ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e found to limit the yield by 66.45% when close to trees, however the yield loss was less with decreasing tree densities in the crop fields. The tree shade was observed to be the most influencing factor on yield loss of paddy than the other factors. Therefore, site specific tree selection based on morphological parameters such as height, canopy structure, and tree density up to 10\u0026ndash;20 ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e can promoted for the riverine agroecosystem. Regular canopy management and removal of exploitable diameter tree may also compensate the yield loss through intermediate income and yield loss reduction of paddy yield. Adoption of such practices may contribute towards climate resilient smart agroforestry practice expansion in riverine agroecosystem.\u003c/p\u003e","manuscriptTitle":"Quantifying the yield loss pattern in paddy crop within a native tree-based riverine agroforestry system in Chhattisgarh, Central India: Implications to food sustainability and climate smart farming","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-07 12:32:22","doi":"10.21203/rs.3.rs-7647772/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-13T13:14:52+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-12T18:44:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"96298824873228509493051699331897694532","date":"2025-11-10T08:35:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-10T16:27:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"118374834401491126156994531330314463222","date":"2025-09-27T11:11:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-23T01:08:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-19T10:24:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-19T04:37:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Agroforestry Systems","date":"2025-09-18T09:13:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"agroforestry-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agfo","sideBox":"Learn more about [Agroforestry Systems](http://link.springer.com/journal/10457)","snPcode":"10457","submissionUrl":"https://submission.nature.com/new-submission/10457/3","title":"Agroforestry Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d1bad59a-fed7-449d-aa19-ec4f951b18ae","owner":[],"postedDate":"October 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-28T08:08:56+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-07 12:32:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7647772","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7647772","identity":"rs-7647772","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}