Cumulative Impact of Herbicides and Tillage on the Soil Microbiome, Fungal Diversity and Crop Productivity under Conservation Agriculture

Cumulative Impact of Herbicides and Tillage on the Soil Microbiome, Fungal Diversity and Crop Productivity under Conservation Agriculture | 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 Cumulative Impact of Herbicides and Tillage on the Soil Microbiome, Fungal Diversity and Crop Productivity under Conservation Agriculture Knight Nthebere, Ram Prakash Tata, Padmaja Bhimireddy, Jayasree Gudapati, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3967847/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In a diversified cropping system, the tillage methods and weed management practices significantly influence the soil microbiome, which affects crop productivity. The synergetic impacts of such practices on the soil microbiome in association with yield under cotton–maize –Sesbania rostrata rotation with CA have not been extensively explored thus far in southern India. Therefore, a 4-year CA experiment was undertaken to investigate the impact of tillage and weed management on the soil microbiome and fungal diversity at 30 DAS and on the tasselling of maize and crop yield and to identify sustainable tillage and weed management practices that can provide nature-based solutions. The three tillage practices used were T 1 : CT(C)-CT(M)-fallow (N Sr ), T 2 : CT(C)-ZT(M)-ZT( Sr ) and T 3 : ZT+R(C)-ZT+R(M)-ZT+R( Sr ), and the following weed control tactics were used: W 1 -chemical weed control, W 2 -chemical (herbicide) rotation, W 3 - integrated weed management (IWM) and the W 4 -non-weeded control. Rhizosphere soil and rhizoplane samples were collected from the respective plots at 30 DAS after herbicide application and tasselling. Analysis of the microbial population and enzyme and microbial activities, viz ., soil basal respiration (SBR), metabolic quotient (qCO 2 ), microbial quotient (qMB), and soil microbial biomass carbon (SMBC) and nitrogen (SMBN), was performed following standard procedures. rRNA gene sequencing of 18S rRNA was performed with rhizosphere soil and rhizoplane fungi isolated at tasselling. The yield was recorded at harvest. The salient findings indicated a decrease in enzyme activity, microbial population, and microbial activity at the initial stage (30 DAS) due to the impact of herbicides, which subsequently increased in response to tasselling, except for qCO 2, which decreased. These biological properties were greater in the T 3 treatment and nonweeded control followed by IWM, except for qCO 2, which showed a decreasing trend relative to T 1 and T 2 and W 1 and W 2 at both sampling stages of maize. K yield (KY) and system yield (SY) were greater in the T 3 , IWM, and herbicide-treated plots (W 1 and W 2 ) than in the T 1 , T 2 and nonweeded control plots. Talaromyces flavus , a beneficial rhizosphere soil inhabitant, was identified in T 3 in combination with the IWM. Considering both crop productivity and soil biological assessment, T 3 and IWM were considered the best treatment combinations among all the other treatments with SY (4453 kg ha -1 ). These findings signify the importance of adopting reduced tillage (T 3 ) and IWM for farmers while striving for nature-based solutions. Agroecology Biological Assessment Soil Health Fungal Diversity Conservation Agriculture Nature-Based Solution Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The world's population is expected to increase to approximately 10 billion by 2050, challenging farmers to intensify production while meeting food demand—in a scenario of modest economic growth—by approximately 50% relative to 2013 [ 1 ]. These impending crises for food production are likely to cause a considerable shift to industrial farming practices. Commercial agricultural norms are associated with intensive tillage, the use of synthetic chemical fertilizers, and the use of agrochemicals, all of which have negative impacts on the quality of soil resources and biodiversity required to promote soil biological activities. Globally, approximately 10 hectares of land used for agricultural production are instantly depleted as a result of various degradation processes caused by urbanization-related agricultural systems [ 2 ]. Approximately 120 million hectares of cultivable land are regarded as degraded in India [ 3 ], which is a considerable opportunity for sustainable food production [ 4 ]. Thus, an increase in food production must always be bolstered up by a sustainable agricultural system to sustain soil resources and facilitate soil biological processes [ 5 ]. In this view, conservation agriculture (CA) is gaining momentum as a sustainable and eco-friendly production system meant to augment the soil biological functions of agroecosystems with little mechanical activity and rational utilization of chemical inputs. Soil microorganisms play an essential role in driving soil biological processes [ 6 ], which is essential for maintaining soil quality, agricultural sustainability and multiple ecosystem functions. Ecosystem functions are controlled by rhizosphere soil microorganisms and frequently employ function-based metrics such as soil basal respiration, decomposition of soil organic matter (SOM), soil microbial activities and extracellular enzyme activity [ 7 ]. The constituents of rhizosphere soil microorganisms and function-based metrics are strongly influenced by similar edaphic properties; thus, suitable agricultural management practices, such as irrigation, tillage, crop diversification and weed management practices, can allow rhizosphere soil microorganisms to perform different ecological functions [ 8 – 9 ]. Rhizosphere soil microorganisms and microbial activities change rapidly with changes in soil management practices and environmental conditions with short turnover [ 10 ] and can be used as early indicators of soil health and crop yield improvement. Soil enzyme activity depends upon different abiotic factors, viz ., soil pH, moisture content, oxygen availability and soil texture [ 11 ]. These properties are subject to change depending on the intensity of tillage, weed control practices, and diverse crop species being implemented and consequently have a significant impact on soil microbial composition and enzyme activities [ 12 ]. Conventional agricultural systems with intensive tillage practices decrease the activities of soil microbes and enzymes, change microbial diversity, and delay nutrient cycling, consequently reducing the stability or resilience of the soil functional status [ 13 ]. By altering the SOM content, cropping systems could also shift the balance of rhizosphere soil enzymes, microbial activities and populations toward biodiversity and function [ 14 ]. A number of quantitative evaluations have been accepted universally for assessing changes in soil functional activity. For instance, the SMBC, SMBN, microbial quotient (qMB), soil basal respiration (SR) and metabolic quotient (qCO 2 ) have been broadly utilized as indicators of soil biological status [ 15 ]. qCO 2 constitutes the metabolic level of soil microorganisms, in which greater values indicate greater stress conditions; however, a rise could also indicate an input of easily degradable carbon that activates microbial activity at times [ 16 ]. The qCO 2 is based on the concept of Odum’s ecosystem succession theory, which is increasingly being applied as an indicator of ecosystem development (where it declines) and of disturbance (where it theoretically increases) [ 17 ]. Thus, the activities of soil enzymes and soil basal respiration (SBR) serve as determinants of the intensity of soil biogeochemical processes. Insights into how microbial activities and population ripening occur in response to various agricultural management practices and turmoil are essential for identifying the best agricultural practices that can augment and sustain soil resources and crop yield [ 18 ]. Soil enzyme activity is deemed to be indicative of specific biochemical reaction processes of whole-soil microbial activities that occur during SOM mineralization and is also an important indicator of soil health, pollution and ecological restoration with a short turnover time [ 19 – 20 ]. Microbial responses in trials have often been assessed by soil enzyme activity [ 21 ]. The introduction of new-generation selective herbicides and shortage of manual labour available for manual weeding have resulted in significant increases in preemergence and postemergence herbicide use in maize. However, herbicides are known to have significant negative or positive effects on soil microbial activities, population and diversity, which in turn impact soil processes [ 22 ]. A better understanding of the impact of herbicides on soil enzyme dynamics, the functional diversity of soil microorganisms and fungal diversity in ecosystems could provide a unique opportunity for an integrated biological assessment of soils due to their crucial role in several soil biological activities, ease of measurement, and rapid response to changes in soil management. To date, several studies have explored the influence of tillage on soil enzyme activity, microbial activities and population dynamics in combination with CA [ 23 ]; however, the direct effects of different tillage practices and weed management practices on biological parameters at different crop growth stages and on fungal diversity at various zone levels (in the soil rhizosphere and rhizoplane) and on how crop productivity relates to soil functional metrics and biodiversity have not been extensively investigated under a diversified crop rotation system (cotton-maize- Sesbania rostrata ) in the southern region of India. Insights into fungal diversity in response to CA practices under various tillage practices and weed management practices in a diversified crop rotation can aid in identifying different pathogenic and beneficial fungal species (to facilitate the breakdown of SOM, stabilize carbon and nutrients for soil health maintenance, promote plant growth, increase crop yield, restore ecosystems and inhibit pathogens). Thus, agricultural techniques such as tillage, crop residue management, and crop rotation can influence the diversity and activity of microorganisms such as fungi [ 24 ]. Cereal-based cropping systems are common practices in southern regions of India, while maize yield and productivity decline monotonically under continuous intensive tillage systems and correspond to the deterioration of soil physicochemical properties and a decrease in soil biological activities [11; 25]. Along with ZT, the diversified crop rotation and retention of crop remains use precrop effects, which lead to enhanced biological diversity and increased crop yield over continuous cropping of a single cereal-based crop(s) under intensive tillage with crop residue removal [ 26 – 27 ]. Thus, a reduction in tillage intensity with continuous retention of previous crop residues and the integration of chemical and cultural weed control practices in CA under a diversified cropping system may be a solution for reducing soil degradation processes and the risk of agricultural production while improving soil functional metrics and rhizosphere soil microbial populations, which can have direct positive effects on crop productivity. Therefore, the present study was performed with the following objectives: to investigate the synergetic effects of different tillage practices and weed management practices on soil microbial and enzyme activities, microbial population and diversity at various sampling stages of maize crops, i.e. , 30 DAS and tasselling stage; to determine the maize grain yield and system yield in terms of cotton equivalent yield (CEY) in a 4-year CA (8th crop cycle) experiment under a cotton–maize –Sesbania rostrata cropping system; and to identify a suitable tillage practice and weed management option that can reduce perturbations in soil, enhance soil biological activities and harbour beneficial fungal diversity species, reduce the metabolic quotient, boost maize productivity and system CEY. Materials and methodologies Details and characterization of the experimental area This current field study was undertaken at the College Farm, PJTSAU, Southern Telangana Zone of India, under the All India Coordinated Research Project (AICRP) on Weed Management implemented from 2020 in the monsoon, winter and summer seasons under cotton ( Gossypium hirsutum ), maize ( Zea mays ), and green manure ( Sesbania rostrata ) rotations, respectively. The experiment continued from 2020 until 2023, and soil samples were collected for analysis of soil parameters and recording of yield after the winter maize crop was harvested from 2022-23 (the fourth year of the 8th crop cycle). The field trial is located at 160 18' 17" N latitude and 780 25' 38" E longitude. The zone is a dryland zone with approximately 708 mm of mean annual rainfall [ 28 ]. Extreme heat and humidity occur during the summer months (March to fortnight of June), when the mean temperature is 30°C. Maximum temperatures often exceed 42°C from April to May. December and January are extreme winter months with the lowest temperatures dropping as low as 10°C occasionally. Rainfall surpasses 75% due to the southwest monsoon and occurs between June and September [ 28 ]. Weather during crop development Meteorological observations taken during crop development from the station situated at the Institute of Agricultural Research (IAR), Rajendranagar, on a weekly basis are presented in Fig. 2 . Soil characteristics The soil of the study area is classified as follows: Inceptisol , sandy clay loam in texture; red chalk in colour; slightly alkaline (7.82) in soil pH as a result of available lime concretion beneath the horizon; 1.23 Mg m-3 in bulk density; nonsaline (0.33 dS m-₁); medium range in soil organic carbon (6.50 g kg-₁); low range in available soil nitrogen (220.90 kg ha-₁); medium range in available soil phosphorus (22.40 g kg-₁); and high range in available soil potassium (408.75 kg ha-₁) at the soil surface (0–15 cm) at the initiation of the experiment. Design of the experiment and treatment details A conservation agriculture (CA) experiment was conducted in accordance with a split plot design with three tillage (s) practices in the main plots, as shown in Table 1 ; four weed management options were used in the subplot treatments, as detailed in Table 2 ; and combinations of tillage and weed management were replicated thrice. For T 1 , which was subjected to conventional tillage, the plots were prepared by ploughing two times, followed by rotovating and seeding. In T 2, during zero tillage (ZT), no-tillage of the soil was performed; i.e. , seeding was performed directly by opening the soil followed by surface soil sealing, and in T 3 , there was zero tillage (ZT) + residue retention (R) or no tillage of the soil. The preceding crop (cotton and Sesbania rostrata ) residues were shredded, retained, and incorporated into the soil, and seeding was performed directly by opening the soil, accompanied by soil surface sealing with mulch from crop residues (Table 1 ). The weed management strategies used included the following: W 1 : chemical weed control, W 2 : chemical (herbicide) rotation, W 3 : chemical weed control and power + 1 hand weeding (IWM) and W 4 : non-weeded control, as fully described in Table 2 . No tillage operations or weed management were performed prior to sowing summer green manure ( Sesbania rostrata ), as it was cultivated for up to 45 days with the intention of retaining and incorporating its residues into the soil in T 3 . No green manure ( Sesbania rostrata ) was sown in the T 1 plots; i.e. , the T 1 plots were fallowed during the summer season. Table 1 Annotation of tillage treatments with crop diversification in the main plots Tillage (s) Seasons Monsoon Winter Summer T 1 : CT (C) – CT (M) – Fallow (N Sr ) T 2 : CT (C) – ZT (M) – ZT ( Sr ) T 3 : ZT + R (C) – ZT + R (M) – ZT + R ( Sr ) CT(C) = conventional tillage (cotton), CT(C) = conventional tillage (maize), Fallow (N Sr ) = Fallow, No Sesbania rostrata , ZT (M) = zero tillage (maize), ZT ( Sr ) = Sesbania rostrata , ZT + R (C) = zero tillage (cotton) + residue retention, ZT + R (M) = zero tillage (Maize) + residue retention, ZT + R ( Sr ) = zero tillage ( Sesbania rostrata ) + residue retention. Table 2 Weed management (WM) in subtreatments and interaction with tillage (T) in the main treatments Monsoon (Cotton) Winter (Maize) W 1 : Chemical Weed Control W 2 : Herbicide Rotation (Every year) W 3 : IWM W 4 : Non-weeded Control W 1 : Chemical Weed Control W 2 : Herbicide Rotation (Every year) W 3 : IWM W 4 : Non-weeded Control T 1 Diuron preemergen -ce application PE 0.75 kg/ha fb tank mix application of pyrithiobac -sodium 62.5 g/ha + quiza- lofop-ethyl 50 g/ha as PoE (Postemergen- ce application) (2–3 weed leaf stage) fb directed spray (interrow) of paraquat 0.5 kg/ha at 50–55 DAS. Diuron PE 0.75 kg/ha fb tank mix application of pyrithiobac-sodium 62.5 g/ha + quizalofop-ethyl 50 g/ha as PoE (2–3 weed leaf stage) fb directed spray (interrow) of paraquat 0.5 kg/ha at 50–55 DAS. rotated with Pendimethalin 1 kg ha -1 fb tank mix application of pyrithiobac-sodium 62.5 g/ha + quiza- lofop ethyl 50 g/ha as PoE (2–3 weed leaf stage) fb directed spray (interrow) of paraquat 24% SL 0.5 kg/ha at 50–55 DAS. Atrazine 1.0 kg/ha + paraquat 600 g/ha PE fb tembotrione 120 g/ha at 20–25 DAS as PoE (T 2 , T 3 ). Atrazine 1.0 kg/ha PE fb tembotrione 120 g/ha at 20–25 DAS at PoE (T 1) . rotated with Atrazine 1.0 kg/ha + paraquat 600 g/ha PE fb halosulfuron- methyl 67.5 g/ha at 20–25 DAS as PoE (T 2 , T 3 ). Atrazine 1.0 kg/ha PE fb halo-sulfuron methyl 67.5 g/ha at 20–25 DAS as PoE (T 1 ). T 2 Diuron PE 0.75 kg/ha fb mechanical brush cutter twice at 25 and 60 DAS. One hand weeding was done after the critical period of crop-weed competit-ion, i.e., between 45–50 days after sowing) Atrazine 1.0 kg/ha + paraquat 600 g/ha PE fb tembo-trione 120 g/ha at 20–25 DAS as PoE (T 2, T 3 ). Atrazine 1 kg ha -1 PE fb tembo-trione 120 g/ha at 20–25 DAS as PoE (T 1 ). Tembotri- one 120 g/ha Atrazine 50% WP 0.5 kg/ha as Early post- emergenc e) EPoE fb brush cutter at 40 DAS One hand weeding was done after the critical period of crop-weed competiti- on, i.e., between 45–50 days after sowing. T 3 T 1 = conventional tillage (CT) – conventional tillage (CT) – Fallow, T 2 = conventional tillage (CT) – zero tillage (ZT) – zero tillage (ZT), T 3 = zero tillage (ZT) + R (residue retention) – zero tillage (ZT) + R (residue retention) – zero tillage (ZT) + R (residue retention), IWM = integration of chemical weed control and power + 1 hand weeding. Sowing and fertilizer application The DHM 117 maize seeds were seeded at 60 cm between the rows and 25 cm between the rows, with a net field plot size of 41.3 m2 in 10 rows for each plot. Prior to seeding, the experimental plots were ploughed two times, accompanied by rotovating and levelling with manual raking in T 1 ; these plots were conventionally tilled plots, while the maize seeds were dibbled with no-till in ZT plots. The quantity of the maize seeds utilized for sowing was 20 kg ha-₁. The crop was thinned in the portions of the plots with a high crop population, and the gaps were filled where the seeds did not emerge 13 days after seed emergence. The crop was typically developed and advanced with supplemental irrigation because the amount of rainfall received during the crop developmental period was limited. Advocated dose fertilizers (ADFs) for N:P:K (200:60:50 kg ha-₁) were supplied to raise the crop through urea, di-ammonium phosphate (DAP) and muriate potash (MOP). Urea and DAP were applied thrice—as basal, at knee height and during the maize tasselling period. Sampling and standard analytical procedures Soil physicochemical properties Composite soil samples were randomly collected in triplicate from each treatment plot at a depth of 0–15 after the maize crop was harvested during the 8th crop cycle in April 2023. These collected soil samples were air-dried well under shade, processed through a wooden hammer, passed through a 0.5 mm sieve, and analysed for soil organic carbon by following standard methods described by Walkley and Black [ 29 ]. For the analysis of soil pH, a 2 mm sieve was used to sieve the soil samples, and analysis was performed according to Jackson [ 30 ]. Soil microbial population and microbial and enzyme activities Sampling of rhizosphere soil was performed at two growth stages of maize crops (8th crop cycle) in 2022-23 during the experiment: the first, after preemergence, early postemergence and postemergence application of herbicides in chemical weed control (W 1 ) and herbicide rotation weed management (W 2 ) plots at 30 DAS in the maize crop; the second, at the tasselling stage. Composite samples were collected from the respective plots in polythene bags with zippers, taken to the laboratory, passed through a 2 mm sieve and analysed on the same day as collection from the field. The functional activity was measured in terms of soil microbial activities related to the soil microbial population, soil organic matter and nitrogen cycling. The soil water content was determined according to Monteiro and Frighetto [ 31 ], and this information was utilized in calculating the evaluated parameters. Soil microbial activity Soil basal respiration (SBR) was measured in a closed jar incubated for 24 hours at 26°C [ 32 ]. The CO 2 released was trapped in NaOH and determined by HCl titration. The results are reported as milligrams of CO 2 released per kilogram of soil per hour (Eq. 1). where Vb is the volume of HCl consumed in the blank (ml); Vs is the volume of HCl consumed in the test sample (ml); M is the HCl molarity; 6 is the equivalent factor (1 ml of 0.5 N HCl is equivalent to 6 mg C-CO 2 in the NaOH solution); ds is the weight of dry soil; and t is the time of incubation. Total soil microbial biomass carbon (SMBC) was determined by following the procedure of fumigation extraction [ 33 – 34 ], in which the soil was fumigated with chloroform-free ethanol in a desiccator. Overnight fumigation (24 hours) of chloroform was performed to kill the organisms in the soil samples, after which the amount of readily oxidizable C in the sample was measured through standard chemical procedures. The SMBC values are given as the carbon content of fumigated soil minus that of nonfumigated soil; all the values are divided by the proportion of microbial C evolved (K EC ). A value of 0.25 ± 0.05 was used for kc in the SMBC calculation, representing the efficiency of the extraction of soil microbial biomass carbon (Eq. 2). where EC f is mg of C per kilogram of fumigated soil, EC nf is mg of C per kilogram of nonfumigated soil, and K EC is part of the microbial C evolved (0.25 ± 0.05). Soil microbial biomass nitrogen (SMBN) was determined by the CH 3 Cl fumigation-extraction technique as described by Brookes et al. [ 35 ] and Amato and Ladd [ 36 ]. Aliquots of fresh soil (5 g) were fumigated in a glass desiccator with ethanol-free CH 3 Cl vapour for 24 hrs at 21°C. Both fumigated and unfumigated soil samples were extracted with 0.5 ml of 0.5 mol/L K 2 SO 4 for 30 min before gravimetric titration through ashless Whatman filter paper. The total dissolved N in the extracts was measured by persulfate digestion followed by NO 3 − determination (VCl 3 /Griess reaction). The difference in total dissolved nitrogen (TDN) between the fumigated and unfumigated soil extracts was attributed to the release (flush) of N from the lysed microbial cells. The calculation of SMBN was performed according to Eq. 2. A correction factor for SMBN (K EN = 0.45) was applied for incomplete extraction of microbial N [ 37 ]. The metabolic quotient (qCO2), the ratio between SBR and SMBC [ 38 ], was employed to obtain the efficiency of substrate consumption by microorganisms as a stress indicator when the microbial biomass was affected. The microbial quotient (MBC:SOC) was the ratio of the MBC to the SOC [ 39 ]. Soil enzymatic activity Dehydrogenase activity (DHA) was assayed according to Casida et al. [ 40 ], and the red colour of triphenyl formazan (TPF) was determined via spectrophotometry (λ = 485 nm). Fluorescein diacetate (FDA) activity was estimated according to Green et al. [ 41 ], and the amount of greenish-yellow fluorescein was measured via spectrophotometry at a wavelength of 490 nm. The urease activity was determined by quantifying the rate of release of NH 4 + from the hydrolysis of urea as described by Tabatabai and Bremner [ 42 ]. The activity of urease was subsequently calculated and expressed as µg of NH 4 + released g − 1 soil h − 1 as described by Tabatabai and Bremner [ 42 ]. The β-galactosidase and phosphatase activities were estimated according to Eivazi and Tabatabai [ 43 ] and Tabatabai and Bremner [ 44 ]. After the appropriate incubation time for each enzyme (60 min for β-galactosidase and phosphatase), their respective substrates (ρ-nitrophenyl-β-D-galactopyranoside and ρ-nitrophenyl-phosphate) were hydrolysed into the yellow colour ρ -nitrophenol, which was determined by spectrophotometry (λ = 420 nm and λ = 405 nm, respectively). Rhizosphere soil and rhizoplane microbial population Functional culturable groups of rhizosphere soil microorganisms, viz ., Azotobacter , Azospirillum , and total fungi, were assessed. Rhizosphere Azotobacter and the total fungal population were evaluated following the protocols described in Albino and Andrade [ 45 ]. A colony counter was used to count the colonies that formed after 7 days of incubation in a BOD incubator at 30°C for Azotobacter and 3–5 days at 25°C in a BOD incubator for the total fungal population. The population density was estimated as colony forming units (CFU) per gram of dry soil (Eq. 3) [ 46 ]. For enumeration of the rhizosphere soil Azospirillum population, rhizosphere soil samples (0.1 ml aliquots) were inoculated into semisolid nitrogen-free bromothymol blue malate medium (Nfb) according to Döbereiner and Day (1976) and incubated in a BOD incubator for 3–4 days at 30°C until the pellicles formed in tubes containing Nfb medium and 0.1 ml of rhizosphere soil sample aliquot. Azospirillum abundance was estimated by the most likely number (MPN) table (s), which was transformed to the logarithm of the most likely number per gram of soil (log MPN.g -1 ) suggested by Alexander [ 47 ], Woomer et al. [ 48 ] and Cochran [ 49 ] and expressed as colony-forming units (CFU g -1 ) of soil on a dry weight basis, and the others (rhizosphere soil Azotobacter and total fungi) were transformed and expressed as the logarithm of colony-forming units per gram of soil (log CFU.g -1 soil). For enumeration of the rhizoplane microbial population, root samples were collected from the plants by removing the plants, separating the roots from the plants through cutting with the help of a knife, and removing the rhizosphere soil. The roots were collected in a polythene zip cover. Using a pair of scissors, the roots were separated, and one gram of the roots was transferred to 100 ml of sterile distilled water and washed thoroughly using a rotary mixer. One milliliter from 100 ml of the sample was transferred into 10 ml of saline blanks, and serial dilutions were made for each treatment following the same methodology employed for enumeration of the soil microbial population. The samples were incubated in a BOD incubator for 3–5 days at 25°C with dilutions of up to 10 4 . Eq. 3 [ 46 ] was used for calculation, after which the results were transformed and are expressed as the logarithm of colony-forming units per gram of roots (log CFU.g -1 roots). Fungal Diversity Isolation criteria and purification rRNA gene sequencing of 18S was performed on fungal colonies obtained at the tasselling stage from the maize crop 2022-23. Before identification, the fungal colonies were incubated for approximately 10‒12 days on rose bengal solid agar media at 25°C to allow sporulation to occur. Based on the colour of the spores formed, classification was performed, and 8 representative plates of all 12 treatment combinations were selected for purification to obtain pure fungal strains based on the abundance of the same number of spores. These colonies, which were predominant on plates and represented the treatment combinations, were picked and cultured in potato dextrose (PDA) solid agar medium for 5 days to allow the growth of pure strains of fungal species. The 8 pure fungal strains were subjected to sequencing to identify the fungal species present in the different treatment combinations for tillage and weed management. Deoxyribonucleic acid (DNA) extraction and polymerase chain reaction (PCR) amplification of 18S gene partial sequencing Deoxyribonucleic acid (DNA) extraction was performed by picking sample and isolating genomic DNA from those samples (pure fungal strains). The DNA was placed in a mortar and homogenized with 1 ml of extraction buffer, after which the homogenate was transferred to a 2 ml-microfuge tube. An equal volume of phenol:chloroform:isoamyl alcohol (25:24:1) was added to the tubes, which were mixed well by gently shaking the tubes. The tubes were centrifuged at room temperature for 15 min at 14,000 rpm. The upper aqueous phase was collected in a new tube, and an equal volume of chloroform:isoamyl alcohol (24:1) was added and mixed. The upper aqueous phase obtained after centrifuging at room temperature for 10 min at 14,000 rpm was transferred to a new tube. The DNA was precipitated from the solution by adding 0.1 volume of 3.0 M sodium acetate (pH 7.0) and 0.7 volume of isopropanol. After 15 minutes of incubation at room temperature, the tubes were centrifuged at 4°C for 15 minutes at 14,000 rpm. The DNA pellet was washed twice with 70% ethanol and then very briefly with 100% ethanol and air-dried. The DNA was dissolved in TE (10 mM Tris-Cl [pH 8.0], 1 mM EDTA). To remove ribonucleic acid (RNA), 5 µl of DNAse or free RNAse A (10 mg ml − 1 ) was added to the DNA. After extraction of the total DNA, different quantities of DNA extracted from the treatment samples (154, 155, 169, 170, 175, 179 and 198 ng µl − 1 ) were subjected to polymerase chain reaction (PCR) amplification of the 18 s gene according to Baldoni et al. [ 50 ] along with 10 pM of each primer mixture (High-Fidelity DNA Polymerase, 0.5 mM dNTPs, 3.2 mM MgCl 2 and PCR enzyme buffer cycling condition) (PCR Clean Kit). The PCR cycling and amplification conditions are presented in Table 3 . The sequences of primers used are listed in Table 4 . The aligned sequence data of the samples are included in the supplementary data. The PCR products were sequenced bidirectionally according to Staden et al. [ 51 ], and the sequence mix used was as follows: 10 µl of sequencing mixture, 4 µl of Big Dye Terminator-ready Reaction Mix, 1 µl of template (100 ng ul-1), 2 µl of primer (10 pmol λ-1), 3 µl of milliquoise water and 25 cycles of PCR conditions. The mixture was subjected to initial denaturation at 96°C for 5 minutes, denaturation at 96°C for 30 seconds, hybridization at 50°C for 30 seconds and elongation at 60°C for 1.30 minutes. Data analysis and identification The data were analysed by using a sequencing machine (ABI 3130 genetic analyser), a chemistry cycle sequencing kit (big dye terminator version 3.1”, a polymer and a capillary array (POP_7 pol capillary array) with a BDTv3-KB-Denovo_v 5.2 protocol and a sequence scape_ v 5.2 software reaction plate (Applied Biosystem Micro Amp Optical 96-Well Reaction plate). Identification was performed by using the system software aligner to align the sequences, and a comparative search of GenBank sequences in the National Centre for Biotechnology Information (NCBI) was carried out using the BLASTn tool to identify the organisms and their closest neighbours. The phylogenetic tree builder used sequences aligned with the system software aligner, and a distance matrix was generated using the Jukes–Cantor corrected distance model. When generating the distance matrix, only alignment model positions were used; alignment inserts were ignored, and the minimum comparable position was 200. The tree was created using a neighbor with an alphabet size of 4 and a length of 1000. The consensus sequence was deposited in the GenBank in the NCBI database to obtain accession numbers of identified organisms from the type material. Multiple sequence alignment and phylogenetic tree construction The default parameters were used for the Mega11 Version 11.0.13 and clustalW algorithm, which included pairwise alignment with a gap opening penalty of 15, a penalty of 6.06, multiple alignments with a gap opening penalty of 15 and a gap extension penalty of 6.06. Additionally, the default parameters included a matrix with a DNA weight matrix (IUB transition), a weight of 0.50, and a negative matrix with an off-delay divergent cut-off of 30. For construction of the phylogenetic tree, the neighbour joining test of the phylogenetic method and bootstrap method were used, and 1000 bootstrap replicates were used; moreover, the nucleotide model/method substitution type included 17 nucleotide sequences. The evolutionary distances were computed using the maximum composite likelihood method [ 52 ]. The codon positions included were 1st + 2nd + 3rd + noncoding. The maximum composite likelihood substitution included transitions + transversion rates among sites – uniform rate pattern among lineages – same (homogeneous) gap/missing data treatment in pairwise deletion. There was a total of 2,043 positions in the final dataset. Table 3 Details of the PCR cycling and amplification conditions Cycling Conditions Initial Denaturation 3 minutes at 94°C 30 Cycles Denaturation 1 minute at 94°C Annealing 1 minute at 50°C Extension 2 minutes at 72°C Final Extension 7 minutes at 72°C PCR Amplification conditions Volume DNA 1 ul 18 s Forward Primer 2 ul 18 s Reverse Primer 2 ul dNTPs (2.5 mM each) 4 ul 10X Taq DNA polymerase Assay Buffer 10 ul Taq DNA Polymerase Enzyme (3U ml -1 ) 1 ul Water 30 ul Total reaction volume 50 ul Table 4 Primer details - The polymerase chain reaction (PCR) product size was ~ 2 kb S. No Oligo Name Sequence (5`à 3`) Tm (°C) GC- Content 1 18 sForward TCCTGAGGGAAACTTCG 47 52.94% 2 18 s Reverse ACCCGCTGAACTTAAGC 47 52.94% Crop Productivity The grain yield for maize in each net plot was recorded by weighing the sun-dried produce before threshing, and the yield was expressed in kg ha − 1 . Similarly, the maize stover in the net plot area was cut, and the sun-dried weight was expressed in kg ha − 1 . Cotton was the first crop, followed by maize and Sesbania rostrata in the cropping system; therefore, the system yield was computed in terms of the cotton equivalent yield (CEY) (the monsoon seed cotton yield after the 4th year used in the calculation is attached as supplementary data) using Eq. 4, as follows: Statistical analysis The data were analysed statistically by applying the analysis of variance technique following the ANOVA for split plot design as suggested by [ 53 ]. The significance of critical differences in the treatment means was calculated at the 5 percent level of probability. Turkey’s test was also used for ranking microbial activity treatment means because of the significance of the differences at the 5% probability level. Results Soil pH and soil organic carbon Soil physico-chemical attributes were not significantly influenced by tillage or weed management options except for soil organic carbon (SOC), which was significantly impacted by the different tillage practices. The interaction effects (tillage and weed management) on the soil pH, EC and SOC were not significant (Table 5 ). The ZT + R(C)-ZT + R(M)-ZT + R( Sr ) treatment had a significantly greater SOC (7.92 g kg-₁) than did the CT(C)-CT(M)-Fallow(N Sr ) and CT(C)-ZT(M)-ZT( Sr ) treatments. Overall, the SOC contents were greater in all the treatments than in the initial treatment (6.5 g kg-₁). The soil pH was slightly alkaline, with a decrease observed across all the treatments over the initial soil pH range (Table 5 ). Table 5 Impact of tillage and weed management options on soil pH and soil organic carbon (SOC) after harvest of winter maize (8th crop cycle). Treatments pH SOC (g kg-₁) 0–15 cm 0–15 cm Tillage practices Initial (s) 7.82 6.50 T 1 : CT(C)-CT(M)-Fallow (N Sr ) 7.15 6.71 T 2 : CT(C)-ZT(M)-ZT( Sr ) 7.14 7.26 T 3 : ZT + R(C)-ZT + R(C)-ZT + R(Sr) 7.04 7.92 SE(m)± 0.05 0.15 CD(P = 0.05) NS 0.60 Weed management options W 1 - Chemical weed control 7.11 7.29 W 2 - chemical (herbicide) rotation 7.09 7.30 W 3 - IWM 7.13 7.34 W 4 - Nonweeded control 7.11 7.25 SE(m)± 0.08 0.19 CD(P = 0.05) Ns Ns Interactions (TxW) CD(P = 0.05) Ns Ns CT = conventional tillage, ZT = zero tillage; R = crop residue retention; IWM = chemical weed control + power and 1 hand weeding; C = cotton; M = maize; S r = Sesbania rostrata ; CD (P = 0.05) = critical difference at the 5% probability level; Ns = nonsignificant; SE(m) = standard error of the mean. Soil Microbial Activity Tillage and weed management practices exerted a significant influence on overall soil microbiological activity at all sampling stages (at 30 DAS after the application of herbicides and during the tasselling stage of maize). The soil microbial activity indices (SMAIs) included soil microbial biomass carbon (SMBC), microbial biomass nitrogen (SMBN), soil basal respiration (SBR), the microbial quotient (qMB) and the metabolic quotient (qCO 2 ) (Fig. 3 a, b, c, d, e and 4 a, b, c, d, e). These SMAIs were significantly promoted and increased by the adoption of ZT + R(C)-ZT + R(M)-ZT + R( Sr ) at both sampling stages of the crop relative to CT(C)-CT(M)-Fallow(N Sr ) and CT(C)-ZT(M)-ZT( Sr ), but not qCO 2 . Among weed practices, a significant increase in SMAIs was observed with non-weeded control and the combination of chemical weed control and power + 1-hand weeding (IWM) at both sampling stages. The herbicides applied at 30 DAS to maize via chemical weed control and chemical (herbicidal) rotation resulted in a significant reduction in the SMAIs, which later increased until the tasselling stage of the crop. The qCO 2 values were significantly lower in the ZT + R(C)-ZT + R(M)-ZT + R( Sr ) treatment than in the CT(C)-CT(M)-Fallow (N Sr ) and CT(C)-ZT(M)-ZT( Sr ) treatments at both stages of the crop. With respect to weed management practices, the qCO 2 values were significantly lower in the non-weeded control and IWM plots than in the herbicide-treated plots. There were no significant treatment interaction effects on the SMAIs observed during either period of sampling (Fig. 3 a, b, c, d, e and 4 a, b, c, d, e). At 30 DAS, for maize, the SMBC, SMBN, SBR, and qMB were significantly greater (7.52% and 26.27%, 11.01% and 28.90%, 0.64% and 17.60%, 15.15% and 15.16%, respectively) under ZT + R(C)-ZT + R(M)-ZT + R( Sr ) than under CT(C)-ZT(M)-ZT( Sr ) and CT(C)-CT(M)-Fallow (N Sr ). Among weed management options, after the application of preemergence (PE), early postemergence (EPoE) and postemergence (PoE) herbicides at 30 DAS, 21.41–21.72% and 2.93–3.23% of SMBC, 20.00-21.40% and 14.21%-15.71% of SMBN, 13.73–23.16% and 9.21–19.70% of SBR, and 8.11–21.62% and 9.09–15.91% of qMB were greater under nonweeded control and IWM, respectively, than under chemical (herbicide) rotation and chemical weed control (Fig. 3 a, b, c, d, e). During the same sampling period (30 DAS), the ZT + R(C)-ZT + R(M)-ZT + R( Sr ) treatment resulted in significant reductions in the qCO2 concentration compared with that in the CT(C)-ZT(M)-ZT( Sr ) and CT(C)-CT(M)-Fallow(N Sr ) groups. There were also significant decreases of 20.24%, 32.60%, 11.23% and 24.99% in the qCO2 concentration in the non-weeded control and IWM treatment groups, respectively, compared to those in the chemical (herbicide) rotation and chemical weed control groups (Fig. 3 a, b, c, d, e). At the tasselling stage, there was an overall progressive increase in the soil microbial activity indices (SMAIs) due to the advancement of the crop. The trends in the SMAIs were similar to those observed at 30 DAS. Among all the tillage practices, ZT + R(C)-ZT + R(M)-ZT + R( Sr ) had 10.92% and 26.64% SMBC, 5.53% and 19.04% SMBN, 1.88% and 9.18% SBR, and 2.27% and 13.64% qMB higher than those of CT(C)-ZT(M)-ZT( Sr ) and CT(C)-CT(M)-Fallow(N Sr ), respectively (Fig. 4 a, b, c, d, e). With respect to weed management choices, higher percentages of SMBC (10.83–16.11% and 14.46–19.54%), SMBN (11.80- 12.94% and 8.29–9.47%), SBR (5.36–9.67% and 1.58–6.06%), and qMB (9.09–15.91% and 14.89–21.28%) were observed under IWM and non-weeded control, respectively, than under chemical weed control and chemical (herbicide) rotation. During the same stage of the crop (tasselling), the trends in qCO 2 were similar to those at 30 DAS, with a further significant decrease observed under ZT + R(C)-ZT + R(M)-ZT + R( Sr ) relative to CT(C)-ZT(M)-ZT( Sr ) and CT(C)-CT(M)-Fallow(N Sr ). The non-weeded control and IWM also facilitated a considerable decrease in qCO 2 during crop growth compared to chemical (herbicide) rotation and chemical weed control (Fig. 4 a, b, c, d, e). Soil Enzyme Activities The addition of ZT + R(C)-ZT + R(M)-ZT + R( Sr ) and the nonweeded control, as well as the combination of chemical weed control and power + 1 hand weeding (IWM), improved the activities of rhizosphere soil dehydrogenase (DHA), urease (SUA), alkaline and acid phosphatase (AlP and AcP), fluorescein diacetate (FDA) and β-galactosidase (β-GaA), which are involved in soil carbon (C), nitrogen (C) and phosphorus (P) cycling. This improvement in soil enzyme activity in the ZT + R(C)-ZT + R(M)-ZT + R( Sr ), nonweeded control and IWM treatment groups was observed at both sampling stages, during which the activity significantly increased continuously with crop progression. Herbicides were applied at 30 DAS; after PE, EPoE, and PoE resulted in a massive decrease in the activity of the soil enzymes, which subsequently returned to their initial levels at the tasselling stage (Table 6 a, b, c). Rhizosphere soil enzyme activity at 30 DAS in maize in the CT(C)-CT(M)-Fallow (N Sr ) and CT(C)-ZT(M)-ZT( Sr ) treatments was significantly lower than that in the ZT + R(C)-ZT + R(M)-ZT + R( Sr ) treatment. The DHA, SUA, AlP, AcP, FDA, and β-GaA concentrations were 16.88% and 31.87%, 16.58% and 27.87%, 11.35% and 22.44%, 8.24% and 23.85%, and 12.35% and 19.77%, 9.44% and 16.87% greater, respectively, in the ZT + R(C)-ZT + R(M)-ZT + R( Sr ) plots than in the CT(C)-ZT(M)-ZT( Sr ) and CT(C)-CT(M)-Fallow (N Sr ) plots (Table 6 a, b, c). Among the weed management options, DHA, SUA, AlP, AcP, FDA, and β-GaA had 17.76–18.68%, 30.28–31.06%, 10.24–11.79% and 25.51–26.79%, 7.37–9.29% and 18.80-20.48%, 2.41–3.81% and 21.12–22.26%, 4.31–4.88% and 24.26–24.71%, 3.89–5.18% and 24.82–25.83% greater under IWM and non-weeded control, respectively, than under chemical weed control and chemical (herbicide) rotation at the same sampling (30 DAS) (Table 6 a, b, c). At the tasselling stage, the activities of all the rhizosphere soil enzymes exhibited trends similar to those observed under the weed management options and tillage practices at 30 DAS. Enzyme activities increased significantly irrespective of the treatment, and tillage was the main factor influencing these activities. During the crop growth development period (tasselling), for DHA, SUA, AlP, AcP, FDA, and β-GaA, the percentages were 10.85% and 20.61%, 10.19% and 15.67%, 15.77% and 28.56%, 5.23% and 23.24%, 21.17% and 35.97%, and 16.71% and 32.88% greater, respectively, under ZT + R(C)-ZT + M)-ZT + R( Sr ) than under CT(C)-ZT(M)-ZT( Sr ) and CT(C)-CT(M)-Fallow (N Sr ) (Table 6 a, b, c). With regard to weed management choices, for DHA, SUA, AlP, AcP, FDA, and β-GaA, 16.30-16.96% and 20.55–21.18%, 3.95–9.52% and 7.39–12.76%, 12.03–16.10% and 21.52–25.15%, 4.63–5.79% and 8.00-9.09%, 12.64–17.04% and 15.02–19.30%, 5.55–7.89% and 10.53–12.74% higher under IWM and non-weeded control, respectively, than chemical (herbicide rotation) and chemical weed control at the tasselling stage (Table 6 a, b, c). Tillage practices (main treatments) and weed management options interaction effects on DHA was 19.09–25.99%, 9.20-31.97%, 16.87–39.04%, SUA was 7.18–19.25%, 14.32–32.72%, 29.59–32.17% AlP was 7.34–16.98%, 13.22–22.34%, 26.37–34.90%, AcP was 16.04–22.84%, 15.02–18.57%, 27.52–28.01%, FDA was 8.71–19.76%, 21.65–28.80%, 27.41–33.96%, β-GaA was 20.87–26.22%, 26.24–28.48%, 18.21–24.62% higher under ZT + R(C)-ZT + R(M)-ZT + R( Sr ) in combination with non-weeded control, CT(C)-ZT(M)-ZT( Sr ) on interaction with non-weeded control, CT(C)-CT(M)-Fallow (N Sr ) in combination with non-weeded control over ZT + R(C)-ZT + R(M)-ZT + R( Sr ) in combination with IWM and chemical weed control or chemical (herbicide) rotation, CT-ZT-ZT on interaction with IWM and chemical weed control or chemical (herbicide) rotation, CT(C)-CT(M)-Fallow(N Sr ) coupled with IWM and chemical weed control or herbicide rotation, respectively observed at 30 DAS of the crop (Table 6 a, b, c). A progressive increase in overall rhizosphere soil enzyme activity was observed at the tasselling stage, and the treatment interaction effects (trends) on the activities of various enzymes appeared to be the same as those observed at 30 DAS, with significantly greater enzyme activity observed in the ZT + R(C)-ZT + R(M)-ZT + R( Sr ) treatment in combination with the non-weeded control and IWM relative to all the other treatment combinations (Table 6 a, b, c). Table 6 a Impact of tillage practices and weed management options on rhizosphere soil dehydrogenase (µg TPF. g − 1 dry soil. day − 1 ) and urease (µg NH 4 + - N. g − 1 dry soil. 2 hr − 1 ) activity on two different maize plants growth stages. Treatment Soil dehydrogenase activity Soil urease activity Tillage WM 30 DAS Tasselling 30 DAS Tasselling T 1 : CT(C)-CT(M)-Fallow (N Sr ) W 1 21.50 43.22 31.79 65.37 W 2 21.90 48.10 33.00 68.76 W 3 29.32 56.24 32.06 70.84 W 4 35.27 62.81 46.87 77.00 T 2 : CT(C)-ZT(M)-ZT( Sr ) W 1 27.35 51.67 34.87 67.47 W 2 27.71 49.84 35.07 74.43 W 3 36.50 66.98 44.41 78.05 W 4 40.20 67.71 51.83 80.33 T 3 : ZT + R(C)-ZT + R(C)-ZT + R(Sr) W 1 37.00 63.89 48.03 79.69 W 2 35.28 62.11 44.63 82.41 W 3 38.57 68.01 51.30 85.99 W 4 47.67 70.94 55.27 86.28 Tillage (Main plots) T 1 : CT(C)-CT(M)-Fallow (N Sr ) 27.00 52.59 35.93 70.49 T 2 : CT(C)-ZT(M)-ZT( Sr ) 32.94 59.05 41.55 75.07 T 3 : ZT + R(C)-ZT + R(C)-ZT + R(Sr) 39.63 66.24 49.81 83.59 Weed Management (Subplots) W 1 - Chemical weed control 28.62 52.93 38.23 70.84 W 2 - chemical (herbicide) rotation 28.30 53.35 37.57 75.20 W 3 - IWM 34.80 63.74 42.59 78.29 W 4 - Non-weeded control 41.05 67.15 51.32 81.20 SE(m)± CD (P = 0.05) SE(m) ± CD (P = 0.05) SE(m) ± CD (P = 0.05) SE(m) ± CD (P = 0.05) Tillage 1.83 6.29 1.24 4.99 0.79 3.10 1.23 4.85 Weed Management 0.08 0.45 0.35 1.87 0.84 2.50 0.61 1.81 Interactions W at same level of T 2.02 6.48 1.02 11.07 1.46 4.33 1.06 3.14 T at same level of W 0.31 1.36 1.00 11.09 1.49 4.42 1.54 4.56 CT = conventional tillage, ZT = zero tillage; R = crop residue retention; IWM = chemical weed control + power and 1 hand weeding; WM = weed management; C = cotton; M = maize; S r = Sesbania rostrata ; CD (P = 0.05) = critical difference at the 5% probability level; Ns = nonsignificant; SE(m) = standard error of the mean. Table 6 b : Impact of tillage practices and weed management options on rhizosphere soil acid and alkaline phosphatase activity (µg. p -Nitrophenol. g − 1 dry soil. hr − 1 ) at two different maize growth stages. Treatment Acid phosphatase activity Alkaline phosphatase activity Tillage WM 30 DAS Tasselling 30 DAS Tasselling T 1 : CT(C)-CT(M)-Fallow (N Sr ) W 1 45.54 122.75 117.18 221.05 W 2 46.14 123.12 120.63 226.27 W 3 45.85 129.64 132.52 241.76 W 4 63.26 130.51 179.99 251.70 T 2 : CT(C)-ZT(M)-ZT( Sr ) W 1 56.42 148.85 143.27 260.44 W 2 57.35 153.19 146.79 277.31 W 3 58.88 157.89 160.09 275.21 W 4 69.29 164.89 184.48 296.25 T 3 : ZT + R(C)-ZT + R(C)-ZT + R(Sr) W 1 63.17 162.56 176.55 306.33 W 2 59.25 152.59 160.50 300.42 W 3 64.47 167.72 179.13 337.04 W 4 76.79 176.40 193.32 373.14 Tillage (Main plots) T 1 : CT(C)-CT(M)-Fallow (N Sr ) 50.20 126.51 137.58 235.20 T 2 : CT(C)-ZT(M)-ZT( Sr ) 60.49 156.20 157.25 277.30 T 3 : ZT + R(C)-ZT + R(C)-ZT + R(Sr) 65.92 164.82 177.38 329.23 Weed Management (Subplots) W 1 - Chemical weed control 55.04 144.72 145.66 262.61 W 2 - chemical (herbicide) rotation 54.25 142.97 142.64 268.00 W 3 - IWM 56.40 151.75 157.25 284.67 W 4 - Non-weeded control 69.78 157.27 179.38 307.03 SE(m)± CD (P = 0.05) SE(m) ± CD (P = 0.05) SE(m) ± CD (P = 0.05) SE(m) ± CD (P = 0.05) Tillage 0.80 3.15 2.34 9.18 3.06 12.02 6.38 25.04 Weed Management 0.66 1.96 1.43 4.24 3.07 9.13 4.56 13.56 Interactions W at same level of T 1.14 3.39 2.47 7.35 5.32 15.81 7.9 23.48 T at same level of W 1.27 3.78 3.17 9.42 5.53 16.44 9.36 27.79 CT = conventional tillage, ZT = zero tillage; R = crop residue retention; IWM = chemical weed control + power and 1 hand weeding; WM = weed management; C = cotton; M = maize; S r = Sesbania rostrata ; CD (P = 0.05) = critical difference at the 5% probability level; SE(m) = standard error of the mean. Table 6 c : Impact of tillage practices and weed management options on rhizosphere soil fluorescein diacetate (µg. fluorescein. g − 1 dry soil. 3 h−1 ) and β-galactosidase (nmol p -nitrophenol. g − 1 dry soil. hr − 1 ) activity at two different maize growth stages. Treatment Fluorescein di-acetate activity β-galactosidase activity Tillage WM 30 DAS Tasselling 30 DAS Tasselling T 1 : CT(C)-CT(M)-Fallow (N Sr ) W 1 118.91 149.97 118.25 152.92 W 2 130.70 172.35 120.58 159.79 W 3 120.94 186.95 128.31 168.79 W 4 180.06 190.21 156.88 187.71 T 2 : CT(C)-ZT(M)-ZT( Sr ) W 1 132.83 199.73 128.74 192.67 W 2 135.92 204.54 129.38 206.47 W 3 146.18 220.52 132.76 210.06 W 4 186.57 236.42 180.00 221.15 T 3 : ZT + R(C)-ZT + R(C)-ZT + R(Sr) W 1 169.96 240.19 148.76 243.49 W 2 152.56 244.31 140.50 237.73 W 3 173.57 303.62 150.70 249.57 W 4 190.14 304.35 190.44 266.22 Tillage (Main plots) T 1 : CT(C)-CT(M)-Fallow (N Sr ) 137.65 174.87 131.01 167.30 T 2 : CT(C)-ZT(M)-ZT( Sr ) 150.38 215.30 142.72 207.59 T 3 : ZT + R(C)-ZT + R(C)-ZT + R(Sr) 171.56 273.12 157.60 249.25 Weed Management (Subplots) W 1 - Chemical weed control 140.57 196.63 131.92 196.36 W 2 - chemical (herbicide) rotation 139.73 207.07 130.15 201.33 W 3 - IWM 146.90 237.03 137.26 213.17 W 4 - Non-weeded control 185.59 243.66 175.48 225.03 SE(m)± CD (P = 0.05) SE(m) ± CD (P = 0.05) SE(m) ± CD (P = 0.05) SE(m) ± CD (P = 0.05) Tillage 2.88 11.30 4.17 16.38 0.96 3.76 3.01 11.82 Weed Management 3.02 8.97 4.37 12.97 1.62 4.81 3.08 9.14 Interactions W at same level of T 5.23 15.53 7.56 22.46 2.80 8.33 5.33 15.84 T at same level of W 5.36 15.94 7.76 23.07 2.61 7.75 5.51 16.38 CT = conventional tillage, ZT = zero tillage; R = crop residue retention; IWM = chemical weed control + power and 1 hand weeding; WM = weed management; C = cotton; M = maize; S r = Sesbania rostrata ; CD (P = 0.05) = critical difference at the 5% probability level; SE(m) = standard error of the mean. Microbial population Rhizosphere soil microbial and rhizoplane fungal counts were significantly influenced by different tillage practices and weed management practices, and the interaction effects of the tillage and weed management practices on the soil microbial and rhizoplane fungal populations were significant at both sampling stages (30 DAS and tasselling) (Table 7 a and b). The spraying of the herbicides either in rotation or repeatedly every other year, such as preemergence (PE), early postemergence (EPoE) and postemergence (PoE), at 30 DAS suppressed the growth and population of the microorganisms. Among all weed management options, at 30 DAS, the rhizosphere soils of Azotobacter (Azot) , Azospirillum (Azosp) , and total fungal (TF) and total fungal (RF) rhizoplane populations were 0.44–0.66% and 3.62–3.84%, 1.40–1.63% and 4.51–4.74%, 0.47-070% and 3.63–3.85%, 1.79–2.04% and 6.55–6.80% greater under IWM and nonweeded control, respectively, than under chemical weed control and chemical (herbicide) rotation (Table 7 a, and b). In terms of all the different tillage systems, the rhizosphere soil Azot , Azosp , TF, and rhizoplane TF populations were 1.51% and 2.81%, 1.60% and 3.43%, 1.61% and 2.75%, and 3.69% and 6.39% greater under ZT + R(C)-ZT + R(M)-ZT + R( Sr ) than under CT(C)-ZT(M)-ZT( Sr ) and CT(C)-CT(M)-Fallow (N Sr ), respectively, at 30 DAS (Table 7 a, and b). The population of rhizosphere soil microorganisms and rhizoplane total fungi (TF) increased significantly during the tasselling period of the crop in all the treatments, and tillage was the principal factor influencing the progressive increase in the microbial population. At that growth stage, for maize crops (tasselling), the populations of rhizosphere soil Azot , Azosp , TF, and rhizoplane TF were 1.20% and 1.80%, 1.21% and 2.23%, 2.38% and 4.75%, and 3.12 and 4.45% greater, respectively, in the ZT + R(C)-ZT + R(M)-ZT + R( Sr ) treatment than in the CT(C)-ZT(M)-ZT( Sr ) and CT(C)-CT(M)-Fallow (N Sr ) treatments (Table 7 a, and b). A significant difference with a continuous increase in the overall microbial population was observed for all weed management options, possibly due to microorganism recovery from herbicidal injury at tasselling. The patterns of the growth of both the rhizosphere soil and rhizoplane microbial counts at that crop growth stage (tasselling) resembled the trends observed at 30 DAS. During the initial stage of crop development (30 DAS), the interaction effects of the various treatments (tillage and weed management) on the rhizosphere soil Azotobacter ( Azot ) were 1.91–3.39%, 3.62–4.26%, and 3.88–4.31%; Azospirillum ( Azosp ) counts were 2.90–4.24%, 2.71–4.30%, and 4.32–6.36%; total fungal (TF) counts were 2.68–4.25%, 2.51–3.20%, and 3.89–4.35%; rhizoplane total fungal (TF) counts were 2.64–3.84%, 3.41–7.56%, and 8.33–9.31%; and the results were superior to those of the ZT + R(C)-ZT + R( Sr ) treatment in combination with the non-weeded control, CT(C)-ZT(M)-ZT( Sr ) combined with the non-weeded control, and CT(C)-CT(M)-Fallow (N Sr ) coupled with the non-weeded control over the ZT + R(C)-ZT + R(M)-ZT + R(Sr) At the tasselling stage of the crop, all microbial counts increased further, regardless of the treatment combination. Among all the treatment interactions, at tasselling of maize, ZT + R(C)-ZT + R(M)-ZT + R( Sr ) in combination with the unweeded control had a significantly greater rhizosphere soil microbial and rhizoplane fungal population, which was closely and statistically followed by the interaction of ZT + R(C)-ZT + R(M)-ZT + R( Sr ) and IWM in comparison with all the other treatment combinations (Table 7 a and b). The fungal population observations indicated that the rhizosphere soil fungal counts were greater than the rhizoplane fungal counts during both sampling periods (30 DAS and tasselling) in the maize crop (Table 7 a and b). Table 7 a : Impact of tillage practices and weed management options on rhizosphere soil Azotobacter and Azospirillum populations (log CFU g − 1 soil) at two different maize growth stages. Treatment Azotobacter population Azospirillum population Tillage WM 30 DAS Tasselling 30 DAS Tasselling T 1 : CT(C)-CT(M)-Fallow (N Sr ) W 1 4.44 4.89 4.12 4.79 W 2 4.46 4.91 4.13 4.81 W 3 4.45 4.92 4.21 4.85 W 4 4.64 4.93 4.40 4.86 T 2 : CT(C)-ZT(M)-ZT( Sr ) W 1 4.50 4.94 4.23 4.87 W 2 4.51 4.95 4.24 4.88 W 3 4.53 4.96 4.30 4.89 W 4 4.70 4.98 4.42 4.89 T 3 : ZT + R(C)-ZT + R(C)-ZT + R(Sr) W 1 4.62 5.01 4.34 4.90 W 2 4.56 5.03 4.29 4.93 W 3 4.63 5.05 4.35 4.95 W 4 4.72 5.08 4.48 4.99 Tillage (Main plots) T 1 : CT(C)-CT(M)-Fallow (N Sr ) 4.50 4.91 4.22 4.83 T 2 : CT(C)-ZT(M)-ZT( Sr ) 4.56 4.94 4.30 4.88 T 3 : ZT + R(C)-ZT + R(C)-ZT + R(Sr) 4.63 5.00 4.37 4.94 Weed Management (Subplots) W 1 - Chemical weed control 4.52 4.91 4.23 4.85 W 2 - chemical (herbicide) rotation 4.51 4.94 4.22 4.87 W 3 - IWM 4.54 4.95 4.29 4.89 W 4 - Non-weeded control 4.69 5.00 4.43 4.91 SE(m)± CD (P = 0.05) SE(m) ± CD (P = 0.05) SE(m) ± CD (P = 0.05) SE(m) ± CD (P = 0.05) Tillage 0.004 0.015 0.004 0.014 0.004 0.016 0.003 0.012 Weed Management 0.003 0.008 0.001 0.002 0.003 0.010 0.001 0.003 Interactions W at same level of T 0.005 0.014 0.001 0.004 0.006 0.017 0.002 0.005 T at same level of W 0.006 0.016 0.004 0.011 0.006 0.019 0.003 0.010 CT = conventional tillage, ZT = zero tillage; R = crop residue retention; IWM = chemical weed control + power and 1 hand weeding; WM = weed management; C = cotton; M = maize; S r = Sesbania rostrata ; CD (P = 0.05) = critical difference at the 5% probability level; SE(m) = standard error of the mean. The table mean values are in log CFU g -1 soil from log transformation of exponential (10 3 ) values from CFU g -1 soil (oven dry basis) taken from plate counts. Table 7 b : Impact of tillage practices and weed management options on the total fungal population of the rhizosphere soil and rhizoplane at two different maize growth stages. Treatment Rhizosphere soil total fungal population (log CFU g − 1 soil) Rhizoplane total fungal Population (log CFU g − 1 roots) Tillage WM 30 DAS Tasselling 30 DAS Tasselling T 1 : CT(C)-CT(M)-Fallow (N Sr ) W 1 4.18 4.38 3.70 4.21 W 2 4.20 4.39 3.71 4.24 W 3 4.19 4.42 3.74 4.30 W 4 4.37 4.45 4.08 4.39 T 2 : CT(C)-ZT(M)-ZT( Sr ) W 1 4.24 4.46 3.79 4.31 W 2 4.25 4.49 3.81 4.32 W 3 4.27 4.51 3.96 4.35 W 4 4.38 4.61 4.10 4.41 T 3 : ZT + R(C)-ZT + R(C)-ZT + R(Sr) W 1 4.34 4.60 4.05 4.43 W 2 4.28 4.61 4.01 4.45 W 3 4.35 4.64 4.06 4.49 W 4 4.47 4.68 4.17 4.58 Tillage (Main plots) T 1 : CT(C)-CT(M)-Fallow (N Sr ) 4.24 4.41 3.81 4.29 T 2 : CT(C)-ZT(M)-ZT( Sr ) 4.29 4.52 3.92 4.35 T 3 : ZT + R(C)-ZT + R(C)-ZT + R(Sr) 4.36 4.63 4.07 4.49 Weed Management (Subplots) W 1 - Chemical weed control 4.25 4.48 3.85 4.32 W 2 - chemical (herbicide) rotation 4.24 4.50 3.84 4.34 W 3 - IWM 4.27 4.52 3.92 4.38 W 4 - Non-weeded control 4.41 4.59 4.12 4.46 SE(m)± CD (P = 0.05) SE(m) ± CD (P = 0.05) SE(m) ± CD (P = 0.05) SE(m) ± CD (P = 0.05) Tillage 0.003 0.014 0.006 0.024 0.007 0.029 0.005 0.022 Weed Management 0.003 0.007 0.001 0.004 0.004 0.013 0.002 0.006 Interactions W at same level of T 0.004 0.013 0.003 0.008 0.008 0.023 0.003 0.010 T at same level of W 0.005 0.015 0.007 0.019 0.010 0.030 0.006 0.018 CT = conventional tillage, ZT = zero tillage; R = crop residue retention; IWM = chemical weed control + power and 1 hand weeding; WM = weed management; C = cotton; M = maize; S r = Sesbania rostrata ; CD (P = 0.05) = critical difference at the 5% probability level; SE(m) = standard error of the mean. The table mean values are in log CFU g -1 soil/roots from log transformation of exponential (10 3 ) values from CFU g -1 soil (oven dry basis)/roots taken from plate counts. Fungal Diversity Sub-culturing of the fungi from the culture plates was performed prior to sequencing to purify the fungal strains, as depicted in Fig. 5 , and agarose gel electrophoresis images of total deoxyribonucleic acid (DNA) and polymerase chain reaction (PCR) products of the 18s rRNa gene are shown in Fig. 6 a, b and c. The fungi were identified based on nucleotide sequence homology of the 18 s rRNA gene are presented in Table 8 . The results of 18S rRNA gene sequencing indicated that Talaromyces flavus var. flavus (5-PJTSAU-KNIGHT-23) was identified under the T 3 : ZT + R(C)-ZT + R(M)-ZT + R( Sr ) and W 3 : IWM combinations (T 3 W 3 ) and T 2 : CT(C)-ZT(M)-ZT( Sr ) on interaction with IWM (T 2 W 3 ). The other species of rhizosphere soil fungal and rhizoplane fungal isolates, viz ., Aspergillus niger, Penicillium limosum, Aspergillus terreus, Apiospora serenensis, Zasmidium cellare , and Ochraceocephala foeniculi , were identified under the T 1 : CT(C)-CT(M)-Fallow (N Sr ), T 2 : CT(C)-ZT(M)-ZT( Sr ) and T 3 : ZT + R(C)-ZT + R(M)-ZT + R( Sr ) tillage (main treatments) in combination with the W 1 : chemical weed control, W 2 : chemical (herbicide) rotation and W 4 : non-weeded control (sub-treatments) (Table 8 ). The isolate ID (8-PJTSAU-KNIGHT), which was isolated abundantly from the rhizoplane zone across all the tillage and weed management treatments, was identified as Penicillium limosum . Phylogenetic tree(s) of all the 8 identified fungal species and multiple sequence alignments (MSAs) of the data are included in the supplementary data. Table 8 Impact of tillage and weed management practices on fungal diversity at the tasselling stage of winter maize. Rhizosphere soil fungal microbe (s) S.NO Isolate ID Treatment combination Fungal name Identity (%) Accession numbers 1 1-PJTSAU-KNIGHT-23 T 1 W 1 Aspergillus niger 100.00% PP177339 1-PJTSAU-KNIGHT-23 T 1 W 2 Aspergillus niger 1-PJTSAU-KNIGHT-23 T 1 W 4 Aspergillus niger 2 2-PJTSAU-KNIGHT-23 T 1 W 3 Aspergillus niger 100.00% PP177340 3 3-PJTSAU-KNIGHT-23 T 2 W 1 Aspergillus terrus 99.33% PP177341 4 4-PJTSAU-KNIGHT-23 T 2 W 2 Apiospora serenensis 98.47% PP177342 4-PJTSAU-KNIGHT-23 T 3 W 2 5 5-PJTSAU-KNIGHT-23 T 2 W 3 Taloromyces flavus var. flavus 99.63% PP177343 5-PJTSAU-KNIGHT-23 T 3 W 3 6 6-PJTSAU-KNIGHT-23 T 2 W 4 Zasmidium cellare 100.00% PP177344 7 7-PJTSAU-KNIGHT-23 T 3 W 1 Penicillium limosum 99.88% PP177345 7-PJTSAU-KNIGHT-23 T 3 W 4 Rhizoplane fungal microbe (s) 8 8-PJTSAU-KNIGHT-23 Abundant in all T & W combinations Ochraceocephala foeniculi 96.55% PP177346 Main treatments : T 1 = CT(C)-CT(M)-Fallow (N Sr ); T 2 = CT(C)-ZT(M)-ZT( Sr ); T 3 = ZT + R(C)-ZT + R(M)-ZT + R( Sr ); Sub-treatments : W 1 = chemical weed control; W 2 = chemical (herbicide) rotation; W 3 = chemical weed control and power + 1 hand weeding; W 4 = non-weeded control. T = Tillage; W = Weed Management, CT = Conventional Tillage, ZT = Zero Tillage; C = Cotton; M = Maize; S r = Sesbania rostrata . The details of the entire sequence along with accession numbers can be accessed through the following link : https://submit.ncbi.nlm.nih.gov/subs/?search=SUB14162715 Crop productivity Maize Grain Yield and System Productivity (Cotton Equivalent Yield) Tillage and weed management practices exerted significant influences on maize grain yield (kernel yield) and system productivity in terms of cotton equivalent yield (CEY) (Table 9 ). The treatment interaction effects on CEY were significant and not significant for kernel yield (KY) (Table 9 ). A significantly greater KY (6801 kg ha − 1 ) was recorded in the ZT + R(C)-ZT + R(M)-ZT + R( Sr ) treatment, while a lower KY (6014 kg ha − 1 ) was observed in the CT(C)-CT(M)-Fallow(N Sr ) treatment. The adoption of chemical weed control and chemical (herbicide) rotation resulted in significantly greater KY (7245 kg ha − 1 and 7324 kg ha − 1 ), followed by chemical weed control and power + 1 hand weeding (IWM) with a KY of 6722 kg ha − 1 . A significantly lower KY (4099 kg ha − 1 ) was exhibited by the non-weeded control (Table 9 ). The maize grain yield, which was recorded from different tillage-weed management treatment combinations, was converted into cotton equivalent yield (CEY) considering the monitory equivalence. The winter CEY was subsequently added to the monsoon cotton yield of the 4th year to arrive at the cotton equivalent yield of the cotton–maize system (system CEY) for the 4th year. The data on the system CEY are presented in Table 9 . ZT + R(C)-ZT + R(M)-ZT + R( Sr ) exhibited a significantly greater CEY (3775 kg ha − 1 ) than did CT(C)-ZT(M)-ZT( Sr ) and CT(C)-CT(M)-Fallow(N Sr ), with a CEY of 3517 kg ha − 1 3328 kg ha − 1 (Table 9 ). Among the weed management strategies, IWM had a significantly greater system CEY (4157 kg ha − 1 ) than chemical (herbicide) rotation, chemical weed control or non-weeded control, with system CEYs of 4065 kg ha − 1 , 4018 kg ha − 1 and 1921 kg ha − 1 , respectively (Fig. 6 a). Based on the tillage and weed management interactions, in combination with the IWM, ZT + R(C)-ZT + R(M)-ZT + R( Sr ) had a significantly greater CEY (4453 kg ha − 1 ), and the lowest CEY values (1767 kg ha − 1 and 1848 kg ha − 1 , respectively) were observed with CT(C)-ZT(M)-ZT( Sr ) in combination with the non-weeded control and CT(C)-CT(M)-Fallow(N Sr ) in combination with the non-weeded control in comparison with all the other treatment combinations (Table 9 ). The CT(C)-CT(M)-Fallow(N Sr ) and all weed management combinations were also associated with a lower system CEY (Table 9 ). Table 9 The grain yield of maize and the system yield in terms of system cotton equivalent yield (CEY) were influenced by tillage practices and weed management (WM) options after the 4th year in the 8th crop cycle under conservation agriculture. Treatment Interaction kernel yield (kg ha-₁) System (CEY) (kg ha-₁) Tillage WM T 1 : CT(C)-CT(M)-Fallow (N Sr ) W 1 6822 3756 W 2 6854 3801 W 3 6354 3908 W 4 4025 1848 W 1 7133 4005 T 2 : CT(C)-ZT(M)-ZT( Sr ) W 2 7662 4187 W 3 6558 4109 W 4 3559 1767 T 3 : ZT + R(C)- ZT + R(M)- ZT + R( Sr ) W 1 7780 4292 W 2 7456 4206 W 3 7253 4453 W 4 4713 2157 Tillage practices T 1 : CT(C)-CT(M)-Fallow (N Sr ) 6014 3328 T 2 : CT(C)-ZT(M)-ZT( Sr ) 6228 3517 T 3 : ZT + R(C)-ZT + R(C)-ZT + R(Sr) 6801 3775 Weed Management options W 1 - Chemical weed control 7245 4018 W 2 -chemical (herbicide rotation) 7324 4065 W 3 - IWM 6722 4157 W 4 - Non -weeded control 4099 1921 SE(m)± CD(P = 0.05) SE(m)± CD(P = 0.05) Tillage 144.83 568.66 18.69 73.38 Weed Management 126.98 377.28 40.29 119.71 Interactions W at same level of T 219.94 Ns 69.79 207.35 T at same level of W 239.28 Ns 63.26 187.96 CT = conventional tillage, ZT = zero tillage; R = crop residue retention; IWM = chemical weed control + power and 1 hand weeding; C = cotton; M = maize; S r = Sesbania rostrata ; CD (P = 0.05) = critical difference at the 5% probability level; Ns = nonsignificant; SE(m) = standard error of the mean. Discussion Soil pH and soil organic carbon Among all the other soil factors, tillage and weed management strategies contribute to the alteration of soil organic carbon (SOC). The SOC concentration in the soil surface soil under no-till conditions with at least 30% maintenance of crop debris is less prone to depletion due to lower soil disturbance and cumulative crop residue, thus yielding a greater SOC content [ 54 ]. This greater SOC content exhibited by ZT + R(C)-ZT + R(M)-ZT + R( Sr ) could be associated with the continuous adoption of ZT, accrual retention and incorporation of the preceding cotton and Sesbania crops into the soil for consecutive years, which resulted in soil aggregation enhancement and shielded the soil against SOC loss. Similar results were presented by Bitew et al. [ 55 ] for a CA-based maize–legume cropping sequence. In zones where soil and weather conditions are conducive to the production of biomass and where adverse crop yield effects are unnoticed, CA practices result in greater quantities of SOC than CT-managed systems, especially in the topsoil. CT transposes the soil, displaces the soil clods, and exposes SOM to wetting-drying phenomena, resulting in a reduction in SOC content [ 56 ]. The reduction in SOC levels observed under CT(C)-CT(M)-Fallow(N Sr ) could be the result of the continuous removal of crop leaves, and primary and secondary tillage are employed for ploughing, which disturbs soil aggregation and promotes susceptibility to erosion. Thus, CA-based practices such as zero tillage (ZT) are directly associated with the maintenance of crop residues and nutrient management, which in turn impacts SOC accumulation and dynamics under diversified cropping systems. In contrast to CT, conservation tillage practices, which retain crop debris, tend to stabilize soil pH conditions and elevate SOC, which is conducive to increasing the soil microbial composition through the continuous disposal of crop residues. Soil microbial activity The potential influence of several biogeocenotic services in the soil environment can be expressed to a certain degree by the activities of microbes; thus, the soil basal respiration (SBR), metabolic quotient (qCO 2 ) and microbial quotient (qMB) were employed to perform an extensive assessment of microbial activity. The results of the present investigation demonstrated that zero tillage (ZT) with retention of crop residues resulted in significantly greater microbial activity (Table 2 a, b, c), probably due to the ample additive-free materials drawn from the crops, which can become vital components for rapid metabolic reactions to external sources of carbon and slow down the figure(s) of the metabolic quotient (qCO 2 ), thereby facilitating the utilization of large quantities of additive-free substrates by microbiomes for proliferation rather than for respiration utilization purposes. The increased qMB under ZT + R(C)-ZT + R(M)-ZT + R( Sr) apparently indicated the efficiency of soil microbes in utilizing sources of carbon materials for survival and growth as opposed to tillage practices, in which continuous crop residue remains (CT(C)-ZT(M)-ZT( Sr ) and CT(C)-CT(M)-Fallow(N Sr ), which can exert a small impact on soil microbes to a certain extent on biological oxygen requirements, might equilibrate the proportion of carbon dioxide released by microbes to microbial biomass. The observed results also suggest that soil microbial biomass carbon (SMBC) and nitrogen (SMBN), SBR and qMB increase as crops advance, particularly with the incorporation of nonchemical cultural weed control practices and the adoption of no-till practices along with the continuous retention of previous crop residues. This increase can be attributed to the most active and reproductive stage of the crop in which the rhizosphere begins to become enriched with specific microorganisms and nutrients necessary to perform activities related to SOM cycling. Thus, the increases in SMBN, SMBC, SBR and qMB might also be the result of root proliferation, exudation and crop litterfall, which serve as substrates for the activity of microorganisms and favourable biophysical climates created for microbes under ZT, promoting better soil functional diversity. Additionally, the availability of energy and nutrient resources and the limited oxidation of soil organic carbon, favoured by the prevalence of soil microorganisms, likely contributed to this observed enhancement. Koné et al. [ 57 ] reported that soil microorganisms that in turn may improve biological activity in the rhizosphere include more plants with extensive rooting systems and more well-spread root hairs [ 58 ], and these characteristics are found to contribute to the exudation of vast amounts of organic compounds and consequently promote an increase in SMBC and SMBN [ 59 ]. These results are supported by Chaudhari et al. (2020), who observed the combination of zero tillage + residue incorporation with inter-cultivation (IC) + hand weeding (HW) at 15, 30 and 45 DAS as the most suitable strategy for sustaining the greatest soil microbial biomass. Less soil disturbance under conservation tillage and crop residue retention/incorporation tend to improve aggregation in soil and SMBC, possibly due to a rise in SOC [ 60 – 61 ]. Hazarika et al. [ 62 ] also indicated that, compared with those in conventionally tilled soil, the SMBN content in the till and/or reduced tillage systems was greater, which is also consistent with the results of this study. Conventional tillage disrupts soil aggregates, exposes the soil and increases the susceptibility to erosion, and herbicides may stimulate or activate the soil microbial biomass. In the present study, intensive tillage and herbicide spraying caused a drastic decrease in SMBC after preemergence and after the emergence of herbicides at 30 DAS, possibly due to increased soil disturbance and herbicide inhibition. Modak et al. [ 63 ] also reported a reduction in SMBC after herbicide application owing to the adverse effects of herbicides on the microbial population, while in treatments without the involvement of herbicides, more SMBC and SMBN were recorded relative to those in treatments with herbicide components. Similarly, Pertile et al. [ 64 ] observed a significant reduction in SMBC and SMBN following the application of flumioxazin and imazethapyr herbicides compared to those in the control. The positive response of conservation tillage practices compared to conventional tillage systems was probably due to higher levels of C substrates available for microorganism growth, as well as better soil physical conditions and greater water retention due to altered land configurations and applied residues [ 65 ]. Soil disturbance as a result of intensive tillage had a significant impact on increasing the mean qCO2. Likewise, the application of herbicides before and after emergence greatly increased the mean qCO2 value in this study. The lower metabolic quotient (qCO 2 ) values observed under ZT, which leaves crop residue in the soil, could be an indicator of the lower energy requirements of microorganisms. Similarly, Engell et al. [ 66 ] reported lower qCO 2 values. These findings indicate a low demand for energy maintenance by the microbial community. This discovery is in accordance with the meta-analysis of Zuber and Villamil [ 67 ] under similar soil field conditions as in the present study and indicated that sandy clay loam soils have lower qCO 2 values under NT than under CT, although the impact of tillage was found to be low in soils with very fine particles. Low values of qCO 2 are an indication of conducive conditions for the predominance of microbial activity [ 66 ]. The results of the present study on qCO 2 are also supported by Jiang et al . [ 68 ], who reported a significant increase in qCO2 at 0–20 cm soil depth under a conventional tillage system. Similarly, in the study of Aziz et al . [ 69 ], the qCO2 was up to 50% greater under conventional tillage than under no-tillage. A lower qCO2 reflected improved physiological conditions resulting from amended organic matter, while a higher qCO2 indicated soil degradation under intensive land use [ 70 ]. On the other hand, a rise in qCO2 might not only be ascribed to microbial stress but also be interpreted as a positive priming on the decomposition of the labile soil organic carbon pool following the addition of readily degradable carbon substrates to the soil [ 16 ]. In the present investigation, higher qCO 2 was associated with low values of SMBC in conventionally tilled plots and herbicide-treated plots and is likely to reflect stress and poor conditions related to physical soil disturbance. Weed management involving herbicides was found to increase qCO 2, indicating stress or disorder, probably due to the detrimental effects of applied herbicides on the soil microbial population. In accordance with the findings of the present study, Pertile et al . [ 64 ] observed an increase in the metabolic quotient during the first 15 days of herbicide application, although their study was conducted under soil incubation conditions, indicating an initial negative effect of the herbicides on soil microorganisms. Since the application of chemical compounds in the soil requires an adaptation of soil microbial biomass that uses their reserves to degrade these compounds, C from microbial biomass ultimately becomes lost, thus increasing qCO 2 . Rhizosphere soil enzyme activity Maintenance of crop leftovers in zero tillage (ZT) plots, cultural weed control tactics and SOC preservation in conservation agriculture positively influence biomass production and activated soil microbes by modifying the provision of the substrate. Rhizosphere soil enzymes such as dehydrogenase (DHA), fluorescein diacetate (FDA), β-galactosidase (β-GaA), alkaline phosphatase (AlA) and acid phosphatase (AcA) play essential roles in the breakdown of carbon in the soil [ 71 ], and urease (SUA) plays a role in the hydrolysis of urea. The present investigation clearly indicated that ZT + R(C)-ZT + R(M)-ZT + R( Sr ) and cultural weed control methods (nongweeded control and IWM) are likely to encourage the metabolic reactions necessary for the development of biotic microorganisms, which ultimately advance the activity of urea hydrolysis and carbon (C) cycling, i.e. , greater succession of C and nitrogen (N), which can aid in the accrual of microbial biomass [ 72 ]. The functional groups of culturable microbes are often interlinked with C and N cycling activities and are related to rhizosphere soil enzymes [ 73 ], demonstrating that alterations in SOC fractions can become the chief driver of soil microorganism constituents [ 74 ]. Since rhizosphere soil enzymes are secreted by specific groups of microbes, the diversity of crops plays a key role in enhancing the activity of enzymes, which agrees with the results of this study in which ZT + R(C)-ZT + R(M)-ZT + R( Sr ) (zero tillage with diverse crop residues retained in the soil surface), nonweeded control and IWM treatments resulted in increased activity of rhizosphere enzymes. The diversification of crops (cotton-maize- Sesbania rostrata ) in rotation resulted in a significant modification in the activity of enzymes, probably due to the greater variety of crops having greater distinct litterfall and rhizosphere exudation; thus, the ZT + R(C)-ZT + R(M)-ZT + R( Sr ), nonweeded control and IWM treatments under these diverse cropping systems have shown a large influence on rhizosphere soil functional diversity. The addition of crop residues through crop residue retention and ZT resulted in greater soil enzyme activity than intensive tillage with continuous crop residue removal. The activity of overall enzymes increased significantly with crop growth advancement, possibly due to the secretion of beneficial nutrients, the decomposition of organic substrates and herbicide degradation. It can be inferred that the significant reductions in the rhizosphere soil DHA, FDA, SUA, AlA, AcA, and β-GaA concentrations were more pronounced when herbicides were applied for weed management. The order of reduction in the activity of these enzymes was chemical (herbicide) rotation followed by chemical weed control and IWM rotation (W 1 > W 2 > W 3 ) at 30 DAS. The higher rhizosphere soil enzyme activities observed in the ZT + R(C)-ZT + R(M)-ZT + R( Sr ) treatment group relative to those in the CT(C)-CT(M)-Fallow (N Sr ) or CT(C)-ZT(M)-ZT( Sr ) treatment groups at 30 DAS could be associated with the partial inhibition of preemergence herbicides reaching the soil. This inhibition is likely a result of the presence of crop residues (cotton crop residues utilized for maize) on the soil surface in ZT + R(C)-ZT + R(M)-ZT + R( Sr ). These results concur with those of Priya et al. [ 75 ], who observed that herbicides significantly inhibited soil DHA after application at 15 DAS, although their study was conducted under soil incubation conditions. Modak et al . [ 63 ] also recorded the maximum values of DHA under two weed management treatments without the use of herbicides, viz ., weedy checks, hoeing and weeding twice. The findings of Varsha et al. [ 76 ] on SUA support the observations obtained in the present study, in which SUA decreased following herbicide application, and the activity returned to normal over time. This might be due to the herbicide effect on microbial population stabilized after time or the herbicide themselves adsorbed irreversibly on soil colloids with an increase in time, resulting in decreased inhibition. Similarly, Raj et al. [ 77 ] revealed a reduction in acid (AcA) and alkaline (AlA) phosphatase activity in all herbicide treatment plots 15 days after herbicide application, but 45 days after herbicide application, increases in AcA and AlA were observed. This difference might be due to the change in the species composition of the soil microorganisms and variation in the availability of the organic substrate. The results of Madsen et al . [ 78 ] also concur with those of the present investigation, in which the highest FDA was recorded in systems where the soil was covered with winter crop residues due to added organic matter (OM) and the lowest FDA occurred in conventional systems (systems without crop cover and treated with herbicides) due to the very low input of OM and usage of herbicides and pesticides. Shahid et al . [ 79 ] also reported the adverse effects of herbicide application on the secretion of β-GaA. Thus, it may be deduced that, from the results of this study, herbicide treatments resulted in a significant decrease in rhizospheric soil enzyme activity in comparison with that in the untreated plots (non-weeded control plots) of soil samples. Water plays an essential and complex role in the activity of enzymes. The activity of the enzymes involved in SOM cycling and urea hydrolysis assessed in this study increased with increasing soil water content up to near field capacity, followed by a decreasing trend thereafter [ 80 ]. In the case of our experiment, the soil water content was maintained at the field capacity level through supplemental irrigation; thus, the enzyme activity increased throughout the treatment. Savant et al. [ 80 ] also observed a greater rate of urea hydrolysis in soil at field capacity than in wetted soils after 24 h of incubation. Several studies have indicated that soil DHA is significantly influenced by water content and decreases with decreasing soil humidity [ 81 ]. In addition, DHA reached higher values at lower soil water potentials [ 81 ]. In the winter season, there was a low rainfall amount; thus, a lower oxygen diffusion rate and redox potential were observed; however, the field moisture level were maintained with supplemental irrigation, and the relative humidity also increased, which could be the reason for the higher DHA observed in this study irrespective of the treatments. Wang et al. [ 82 ] observed a reduction in SUA due to flooding as a result of a high amount of rainfall, which was probably due to an increase in metal ions under reduced conditions, which decreased SUA. Therefore, during the sampling periods in winter, there was an increase in humidity and no flooding conditions due to scant rainfall; therefore, irrigation was provided for the development of the crop and for retention of the field moisture content, which might be the reason for obtaining higher SUA levels irrespective of the treatment. Microbial population Soil organic matter (SOM) is a crucial driver of microbial population size and diversity and can affect microbial counts [ 83 – 84 ]. Similarly, the rhizosphere soils of Azospirillum (Azosp) , Azotobacter ( Azot ), and total fungal (TF) populations were greater where crop residues were retained, probably due to the greater addition of organic matter through crop residues and the reduced tillage and access to a steady source of organic carbon to support the microbial population compared to conventional tillage (CT). A decreased disturbance of soil favours the formation and stabilization of macroaggregates to improve and protect habitats for microbial populations. Zero-till (ZT) increases soil aggregation by reducing soil disturbance and increasing SOM and possibly increasing the growth of microbes that bind soil particles and microaggregates together [ 85 ]. Nitrogen-free bacterial fixers require aerobic environments to obtain resources for N 2 fixation [ 86 ]. Several workers have also reported that the change from conventional to zero tillage alters the distribution of SOM only along the soil profile [ 87 – 88 ]. The observations recorded in this study on rhizosphere soil microbial and rhizoplane fungal populations also indicated that the counts increased significantly with crop advancement due to the progressive mineralization of litterfall from crops, root exudates and added crop residues and the increased availability of organic substrates, which serve as food and help increase the population of nitrogen-fixing microorganisms [ 89 ]. Singh et al. [ 90 ] also reported the stimulation of Azotobacter spp. in upper soil layers under minimum tillage, which was attributed to increased availability of nutrients and root proliferation. Our results concur with those of Verma et al . [ 83 ], who observed an increasing trend in the prevalence of Azospirillum with an increase in the organic carbon concentration from 0.2- 1.0%. The findings of this study are consistent with those of Bashan et al . [ 91 ], who reported that SOM had a definite role in the survival of Azospirillum strains. Azospirillum species tend to move toward locations where O 2 is ideal for metabolism (at low O 2 concentrations) [ 92 ]. Therefore, an increase in the Azospirillum population with an increase in moisture content (maintained through supplemental irrigation prior to collection of rhizosphere soil samples) was observed in the present study. The same results were reported by Belaid et al. [ 93 ], who observed that when the moisture content increased, the Azospirillum population increased over time. Rhizoplane fungi inhabit the root systems of crops and are connected directly to plant metabolism. Higher counts of Rhizoplane total fungi were also detected in ZT plants than in the other strains, which incorporated the residues, probably due to the release of organic exudates and increased plant nutrient absorption and exchange with the root system of the crops. Tillage operations incorporate crop residues, prepare seed beds, alleviate compaction, improve nutrient mineralization, and reduce weeds, pests and pathogens [ 94 – 95 ]. Conventional tillage (ploughing) continually exposes deeper soil to wet–dry and freeze–thaw cycles at the surface, thus increasing macroaggregate turnover, disrupting the existing pore network and ultimately favouring soil erosion [ 10 ]. These alterations in soil structure have direct effects on the physical habitat of microbes, with fungi often being detrimentally affected through the destruction of their hyphal network. Increased structural diversity of root systems and changes in SOM and nutrient inputs through plant litter and rhizodeposition can alter porosity, aeration and aggregate stability, providing more diverse niches for microbes [ 96 ]. Odunfa [ 97 ] noted that some fungi need specific nutrient substances for growth and hence are host specific. Oyeyiola [ 98 ] also noted several fungal microflora populations in the rhizoplane but not in the Okro ( Abelmoschus esculentus ) crop. The application of herbicides before emergence drastically reduced the population of rhizosphere soil microbes compared to that after emergence at 30 DAS, which could be due to the direct application of herbicides to the soil rhizosphere. However, the decrease in population after postemergence herbicide application was not large due to leaf foliage and weed emergence; moreover, the herbicides applied might not have fully reached the soil. The population of rhizosphere soil microbes was greater in the non-weeded control (no herbicide application), probably due to soil coverage by the weeds. Tapas et al . [ 99 ] also reported that one of the reasons for the less harmful effects of postemergence herbicides on microorganisms may be the greater foliage of crops at 22 DAS (at the time of herbicide application), which covers the soil surface, resulting in a decrease in and absorption of applied herbicides in the soil. In contrast, the deposition of preemergence herbicides in the soil is relatively greater than that in the exposed bare soil surface at the time of preemergence herbicide spraying. Furthermore, they noted that the effects of herbicides on the soil microflora are normally most severe immediately after their application. The application of postemergence herbicides (fenoxaprop-p-ethyl and ethoxy sulfuron), which were sprayed at 20 days after crop emergence, did not suppress the growth of microorganisms such as nitrogen-free bacteria, which was visualized by their respective populations at 20, 30 and 50 DAS in rhizosphere soil and weed control practices. Hand weeding was found to be most appropriate for microbial population increase in soil, followed by herbicide application [ 99 ], and these findings support the present investigation. Similarly, Barman et al. [ 100 ] reported that the Azotobacter count decreased in response to herbicide treatments compared to that in the control group and that Azotobacter could not reach this level, indicating increased susceptibility to the herbicide. Konstantinovic et al . reported similar inhibitory effects of other preemergence herbicides, viz ., alachlor and atrazine, on the Azotobacter count [ 101 ]. The results of this study also indicated that various herbicides strongly inhibited the growth of fungi. The inhibition of fungal mycelial growth by herbicide application was consistent with the findings of previous studies [ 102 – 103 ]. Similarly, Eze [ 104 ] reported that glyphosate, paraquat, atrazine and linuron were more effective at inhibiting the mycelial growth of all the rhizoplane fungi screened than was primextra. Fungal Diversity A Vast fungal diversity has been interlinked with plant systems, viz ., epiphytic, endophytic and rhizosphere fungi. All these fungi, in association with plant systems, play essential roles in plant growth, crop yield and soil health improvement[ 105 ]. Agricultural techniques such as tillage, crop residue management through retention and incorporation into the soil, and crop rotation influence the physical and chemical properties of the soil inhabited by microorganisms such as fungi, thus affecting their abundance, diversity, and activity [ 24 ]. The adoption of zero tillage (ZT) and conservation tillage (ZT + crop residue retention) with the integration of chemical weed control and power + 1-hand weeding (IWM) as soil management practices tends to harbour beneficial fungal species while improving and maintaining soil health and quality in the long run. Talaromyces flavus var. flavus was identified under CT(C)-ZT(M)-ZT( Sr ) and ZT + R(C)-ZT + R(M)-ZT + R( Sr ) in combination with chemical weed control and power + 1 hand weeding (IWM) (T 2 W 3 and T 3 W 3 ) in the rhizosphere soil; these plants have been newly reported as beneficial fungal species inhabiting the soil (soil stabilizer) and plant growth-promoting fungi (PGPF) with high potential to inhibit other pathogenic fungal species (biocontrol agents) while benefiting the plant and the soil [ 106 – 110 ]. However, the CT(C)-CT(M)-Fallow(N Sr ), CT(C)-ZT(M)-ZT( Sr ) and ZT + R(C)-ZT + R(M)-ZT + R( Sr ) treatments in combination with chemical (herbicide) rotation and chemical weed control generally produced pathogenic fungal species ( Aspergillus niger, Penicillin limosum, Aspergillus terreus, Apiospora serenensis, Zasmidium cellare, and Ochraceocephala foeniculi ), which were previously reported to have adverse effects on soil health and productivity. It is evident that in combination with crop residue removal, crop residue removal occurs in combination with nonweed control and herbicide treatment in plots, and the application of herbicides or no-weed control, regardless of the tillage combination, caused changes and the production of pathogenic fungal microbes, which agrees with the results of Bhardwaj et al . [ 111 ] on the impact of herbicides in irrigated tropical rice fields, in which predominant pathogenic fungi ( Humicola, Nigrospora , Paramyrothecium, Mariannaea, Ceratobasidium, Funneliformis, Aspergillus, Pseudorhypophila, and Lecythophora ) were identified with unweeded control and herbicide-treated plots, indicating the adverse effects of herbicides and high weed density populations on microbial dynamics. These results on fungal diversity signify the importance of conservation tillage and minimum tillage coupled with IWM under conservation agricultural practices compared to conventional tillage in combination with herbicides and hand weed removal only during critical periods of weed competition. Crop productivity Maize Grain Yield and System Productivity (Cotton Equivalent Yield) The improved growth/development of crops and increased yield largely rely on tillage practices, as these practices play a crucial role in determining the development of a crop's rooting system, the soil volume explored by roots for moisture and nutrients, the availability of air, and the regulation of soil temperature, among other factors. The importance of crop-weed interactions in determining the competition faced by crop plants for light, moisture and space is well established. Confined root growth leads to decreased nutrient uptake and poor crop growth [ 112 ]. The meta-data analysis of ZT with residue retention indicated that the effect on crop yields in comparison with that in CT was inconsistent and was impacted substantially by cropping system, aridity index, crop residue maintenance, ZT duration, and weed management strategy [ 113 ]. In the present investigation, the maize grain and harvest indices were greater in the ZT + R(C)-ZT + R(M)-ZT + R( Sr ) treatment than in the other tillage methods. This superior performance can be interconnected with the development of robust, deep-rooted systems in crops facilitated by the practice of zero tillage. The implementation of ZT is thought to augment the nutrient absorption capacity of crops, thereby fostering their physiological growth and overall development. Furthermore, the preservation of crop residues on the soil surface under the ZT + R(C)-ZT + R(M)-ZT + R( Sr ) treatment likely contributed to the enhanced retention and availability of soil moisture. This aspect is especially crucial during the posttasselling stage of the maize crop, which coincided with a warm period from mid-March to May. Given the limited moisture conditions during this period, supplemental irrigation was applied to ensure optimal soil moisture levels throughout crop development. Research by You et al. [ 114 ] also indicated that short-term reduced tillage (rotary-till and no-till) and residue incorporation enhanced soil properties; spring maize grain yield; growth and attributes; and increased root biomass and shoot ratio. Furthermore, the interaction of tillage and residue treatments can increase crop biomass and yield [ 115 – 116 ]. A number of previous studies conducted on short-term conservation tillage have not paid full attention to how yield can be improved. Long-term conventional tillage always hinders root growth and the root-to-shoot ratio [ 117 ]. No-till enhances root biomass and shoot biomass, regulates the shoot-to-root ratio and increases yield in comparison with plough-till and rotary-till [ 118 – 119 ]. Residue incorporation can also enhance crop biomass and yield due to enhanced soil buffering capacity [ 120 – 121 ]. The postemergence tank-mix combination of atrazine and tembotrione herbicides was applied at recommended rates in chemical weed control and chemical (herbicide) rotation, which resulted in effective weed control and no phytotoxicity. The absence of phytotoxic effects suggests the efficacy and safety of the combination of tembotrione and atrazine for weed management, which contributes to improved crop performance. Poor crop performance was also observed under non-weeded control, which was ultimately reflected in yield. This could be due to the high weed density at the critical crop growth stage, which outcompeted the crop for available moisture, nutrients, light and rooting space. Ganapathi et al . [ 122 ] also recorded higher kernel and harvest indices and lower weed dry weight with IWM than with the use of only advocated herbicides and non-weeded treatments due to less weed infestation. Similar results were obtained by Kumar et al. [ 123 ], who reported that the application of preemergence herbicide followed by one rotary hoeing at 35 DAS led to increased grain yield. The results of Ahmad et al . [ 124 ] concur with the findings of the present investigation, who noted that nicosulfuron application and one-way weeding with a hoe at 15 DAS led to greater kernel yield, whereas the lowest kernel yield was obtained from the non-weeded control. In the present study, there was an increase in corn yield and system CEY when employing zero tillage with crop residue retention (ZT + R(C)-ZT + R(M)-ZT + R( Sr )) and IWM, chemical weed control and chemical (herbicide) rotation. This improvement could be attributed to the synergistic effects of efficient weed management achieved through the use of chemical and cultural mechanical control tactics, as well as moisture and nutrient preservation facilitated by no-till practices that retain crop residues. These results are supported by Ahmad et al . [ 124 ], who deduced that maize can flourish when cultivated under zero tillage either with the application of atrazine or glyphosate or with hand weeding (HW) at 40 DAS as an alternative to manual weeding in spring seasons to attain higher grain yield. Treatment performance was assessed on the basis of system cotton equivalent yield (CEY) and maize grain yield, microbial population and fungal diversity, and microbial and enzyme activities. The winter maize grain yield recorded from the different tillage weed management treatment combinations was converted into cotton equivalent yield (CEY) considering the minority equivalence. The winter CEY was then added to the monsoon cotton yield in the 4 years to arrive at the cotton equivalent yield of the cotton–maize system (system CEY) for the fourth year. The system CEY and enzyme activities were used to evaluate the different tillage practices, weed management options and various tillage-weed management treatment combinations to identify a combination of remunerative tillage, weed management and tillage-weed management practices with a relatively higher system (CEY), maize grain yield, microbial and enzyme activities, microbial population and fungal diversity. These data are presented in figure (s) 3a, b, c, 4a, b, c, 7a-h and Tables 7 a, b, 8 and 9 . Tillage and weed management had significant effects on the soil microbiological properties and system yield (SY) in terms of cotton equivalent (CEY). Even though ZT + R(C)-ZT + R(M)-ZT + R( Sr ) in combination with the non-weeded control (T 3 W 4 ) had higher microbial and enzyme activities, diverse groups of pathogenic fungal species and microbial populations, except for the metabolic quotient, were lower than those in the other treatment combinations, but the crop productivity in the ZT + R(M)-ZT + R(M)-ZT + R(M)-ZT + R(Sr) treatment combination was significantly lower than that in the other treatment combinations. In combination with all weed management methods, CT had lower microbial and enzyme activities, diverse groups of pathogenic fungal species, and microbial populations, but not metabolic quotients, which were greater under these treatment combinations. However, the crop productivity in the CT treatment combined with all weed management options was greater than that in the ZT + R(C)-ZT + R(M)-ZT + R( Sr ) treatment combined with the non-weeded control, which indicates increased productivity but poor soil health, as indicated by the soil biological attributes. The SY in terms of CEY was greater in the ZT + R(C)-ZT + R(M)-ZT + R( Sr ) treatment in combination with the IWM, which indicated that the adoption of ZT + R(C)-ZT + R(M)-ZT + R( Sr ) in combination with IWM practices can maintain the status of soil microbiological attributes at higher levels, harbour beneficial fungal species and increase farmer productivity. Therefore, implementing zero tillage with the retention of crop residues in CA together with IWM aids in improving soil health and can optimize productivity for farmers in cotton–maize –Sesbania rostrata cropping systems. Conclusion On the basis of four years of investigation into the cumulative effects of tillage and weed management options on microbial activity and population, fungal diversity, enzyme activities, and crop productivity under conservation agriculture (CA),the following conclusions can be drawn:conservation tillage in combination with nonweeded control (only one-way weeding at the critical period of weed competition) and the combination of chemical weed control and power + 1-hand weeding (IWM) significantly enhanced soil enzymatic and microbial activities and a microbial population and decreased metabolic quotient (qCO 2 ), whereas conventionally tilled (CT) and chemically treated plots resulted in a drastic reduction in soil enzymatic and microbial activities and in the microbial population and increased qCO 2 during both the sampling period (30 DAS and tasselling) during maize growth. ZT with and without crop residue incorporation in combination with IWM harboured beneficial soil inhabitant fungal species, Talaromyces flavus (a soil stabilizer, plant growth promoter, and soil pathogenic fungal inhibitor), while CT, which interacts with overall weed management, led to the production of pathogenic fungal species identified at the tasselling stage of maize. The maize grain yield and system yield in terms of cotton equivalent yield (CEY) were greater under ZT, which included the retention of crop leftovers; IWM, chemical weed control; and chemical (herbicidal) rotation plots than under CT, crop residue removal; and non-weeded control treatments. There was no significant effect of the combination of treatment on maize grain yield (P=0.05). ZT with crop residue maintenance (ZT+R(C)-ZT+R(M)-ZT+R( Sr )) in combination with IWM had a significantly greater system CEY (4453 kg ha -1 ), followed by ZT+R in combination with chemical weed control and chemical (herbicide) rotation, with system CEYs of 4292 kg ha -1 and 4206 kg ha -1 , respectively. Among the tillage practices, ZT+R(C)-ZT+R(M)-ZT+R( Sr ) had a greater system CEY than did CT(C)-CT(M)-Fallow(N Sr ) and CT(C)-ZT(M)-ZT( Sr ). With regard to weed management options, IWM had higher system CEY. The SY in terms of CEY was greater in the ZT+R(C)-ZT+R(M)-ZT+R( Sr ) treatment in combination with IWM, whichindicated that the adoption of conservation tillage with IWM practices augments important soil microbiological attributes, harbours beneficial fungal species and provides good productivity to farmers in the long-term. Even though the interaction of ZT+R(C)-ZT+R(M)-ZT+R( Sr ) with non-weeded control had a positive effect on increasing soil microbiological parameters and activities, crop productivity was very low; therefore, the adoption of ZT+R(C)-ZT+R(M)-ZT+R( Sr ) in CA along with IWM helps improve soil health and can optimize productivity in a long-term cotton-maize- Sesbania rostrata cropping system. Declarations Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. 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Aliso: J Syst Floristic Bot 3(2):183–197 Naraghi L, Heydari A, Rezaee S et al (2021) Biocontrol agent Talaromyces flavus stimulates the growth of cotton and potato. J Plant Growth Regul 31:471–477 Madi NS, Harvey LM, Mehlert A et al (1997) Synthesis of two distinct exopolysaccharide fractions by cultures of the polymorphic fungus Aureobasidium pullulans . Carbohydr Polym 32(3–4):307–314 Stosz SK, Fravel DR, Roberts DP (1996) In vitro analysis of the role of glucose oxidase from Talaromyces flavus in biocontrol of the plant pathogen Verticillium dahliae . Appl Environ Microbiol 62(9):3183–3186 Bashyal M, Aggarwal R (2023) Talaromyces flavus : An Important Rhizospheric Inhabitant. Detection, Diagnosis and Management of Soil-borne Phytopathogens. Springer Nature, Singapore, pp 269–282 Bhardwaj L, Reddy B, Nath AJ et al (2024) Influence of herbicide on rhizospheric microbial communities and soil properties in irrigated tropical rice field. Ecol Ind 158:111534 Kumar R, Singh RS, Jaidev et al (2022) Conservation system and weed control measure on yield 1054 and soil health in wheat. Biennial conference of the Indian society of weed science on Doubling former’ income by ( The role of 1055 weed science MPUA and T, Udaipur, India. 2017 Pittelkow CM, Linquist BA, Lundy ME et al (2015) When does no-till yield more? A global meta-analysis. Field crops Res 183:156–168 You D, Tian P, Sui P et al (2016) Short-term effects of tillage and residue on spring maize yield through regulating root-shoot ratio in Northeast China. Sci Rep 7(1):13314 Abdullah AS (2014) Minimum tillage and residue management increase soil water content, soil organic matter and canola seed yield and seed oil content in the semiarid areas of Northern Iraq. Soil Tillage Res 144:150–155 Radicetti E, Mancinelli R, Moscetti R et al (2016) Management of winter cover crop residues under different tillage conditions affects nitrogen utilization efficiency and yield of eggplant ( Solanum melanogena L.) in Mediterranean environment. Soil Tillage Res 155:329–338 Plaza-Bonilla D, Álvaro-Fuentes J, Hansen NC et al (2014) Winter cereal root growth and aboveground–belowground biomass ratios as affected by site and tillage system in dryland Mediterranean conditions. Plant Soil 374:925–939 Jin YH, Zhou DW, Jiang SC (2010) Comparison of soil water content and corn yield in furrow and conventional ridge sown systems in a semiarid region of China. Agric Water Manage 97(2):326–332 He J, Li H, Kuhn NJ et al (2010) Effect of ridge tillage, no-tillage, and conventional tillage on soil temperature, water use, and crop performance in cold and semiarid areas in Northeast China. Soil Res 48(8):737–744 Getahun GT, Munkholm LJ, Schjønning P (2016) The influence of clay-to-carbon ratio on soil physical properties in a humid sandy loam soil with contrasting tillage and residue management. Geoderma 264:94–102 Rusinamhodzi L, Corbeels M, Van Wijk MT et al (2011) A meta-analysis of long-term effects of conservation agriculture on maize grain yield under rain-fed conditions. Agron Sustain Dev 31:657–673 Ganapathi S, Dhanapal G, Thimmegowda M et al (2022) Studies on the Effects of Different Tillage and Weed Management Approaches on Weed and Growth Parameters in Maize Crops and Its Influence on Yield. Mysore J Agricultural Sci. ; 56(2) Kumar BN, Babalad HB (2018) Soil organic carbon, carbon sequestration, soil microbial biomass carbon and nitrogen and soil enzymatic activity as influenced by conservation agriculture in pigeon pea and soybean intercropping system. Int J Curr Microbiol Appl Sci 7(3):323–333 Ahmad H, Shafi M, Liaqat W et al (2018) Effect of tillage practices and weed control methods on yield and yield components of maize. Middle East J Agricultural Res 7(1):175–181 Additional Declarations The authors declare no competing interests. Supplementary Files MSA.pdf Multiple Sequence Alignment Phylogenetictreesoffungalmicroorganisms.doc Phylogenetic trees of 8 Fungal species ResearchHighlightsKnight.doc Research highlights SupplementarydataJournal.doc Aligned sequence of data during 18s gene partial sequencing and cotton seed yield in table used to compute system yield Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3967847","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":273546229,"identity":"7dcbe6c0-6336-4fa2-9910-517a8b87faae","order_by":0,"name":"Knight Nthebere","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYBACCQYGNgbGBiCLHUQYWBCjhRmqhecASIsEKVokEqC2EgKSs/uPPfi4wyafX/L51Q0/CiQY+Nu7E/BqkZY5zG4480ya5czZOWU3e4AOkzhzdgNeLXISyWzSvG2HDQxu56Td4AFqMZDIJULL37b/BvY3z6Td/EOMFmmQFsa2AwYGEuzHbhNli+SMZDPJ3jPJBhJncthuyxhI8BD0i8SNxGcSP3fYGfC3H392880fGzn+9l78WpAAjwGYJFY5CLA/IEX1KBgFo2AUjCAAABbyQ38EgeDtAAAAAElFTkSuQmCC","orcid":"","institution":"Professor Jayashankar Telangana State Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Knight","middleName":"","lastName":"Nthebere","suffix":""},{"id":273546276,"identity":"ace8d9e7-8191-4b89-8d39-85d1a4632b0c","order_by":1,"name":"Ram Prakash Tata","email":"","orcid":"","institution":"Professor Jayashankar Telangana State Agricultural 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19:44:49","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-3967847/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3967847/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51364528,"identity":"61aab33e-c59f-4f5e-9d57-c42a91ff5f13","added_by":"auto","created_at":"2024-02-20 09:39:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":177369,"visible":true,"origin":"","legend":"\u003cp\u003eSatellite view of the experimental field (36 plots inside demarcated with yellow lines)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3967847/v1/2c88378eeaa8d442ce7ad60a.png"},{"id":51364970,"identity":"afaa6834-1bd9-4dfc-9838-d61d733ba240","added_by":"auto","created_at":"2024-02-20 09:47:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":54559,"visible":true,"origin":"","legend":"\u003cp\u003eWeekly base mean meteorological observations during maize development\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3967847/v1/6dfcb603aa3f749d8a5e6649.png"},{"id":51364531,"identity":"b7586ff1-b6cf-47de-badb-9d7efd04f785","added_by":"auto","created_at":"2024-02-20 09:39:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":34733,"visible":true,"origin":"","legend":"\u003cp\u003eImpact of tillage practices and weed management options on soil microbial activity, viz., soil microbial biomass carbon-SMBC \u003cstrong\u003e(a)\u003c/strong\u003e, soil microbial biomass nitrogen-SMBN\u003cstrong\u003e (b)\u003c/strong\u003e,\u003cstrong\u003e \u003c/strong\u003esoil basal respiration-SBR\u003cstrong\u003e (c)\u003c/strong\u003e,\u003cstrong\u003e \u003c/strong\u003emetabolic quotient-qCO\u003csub\u003e2\u003c/sub\u003e\u003cstrong\u003e (d)\u003c/strong\u003e,\u003cstrong\u003e and \u003c/strong\u003emicrobial quotient-qMB\u003cstrong\u003e (e), \u003c/strong\u003eat 30 days after sowing (DAS) on the maize crop (8\u003csup\u003eth\u003c/sup\u003e crop cycle). Means with distinct symbols demonstrate significant differences between the treatments at the 5% probability level (Tukey’s test), and means with the same symbols indicate no significant differences among the treatment means at the 5% probability level. Refer to Tables 1 and 2 for treatment details.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3967847/v1/26d537fd0a5fd45b52bb9e41.png"},{"id":51364971,"identity":"7ed3a844-2eb9-4259-94a5-4abf3ae41639","added_by":"auto","created_at":"2024-02-20 09:47:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":36537,"visible":true,"origin":"","legend":"\u003cp\u003eImpact of tillage practices and weed management options on soil microbial activity, viz., soil microbial biomass carbon-SMBC \u003cstrong\u003e(a)\u003c/strong\u003e, soil microbial biomass nitrogen-SMBN\u003cstrong\u003e (b)\u003c/strong\u003e,\u003cstrong\u003e \u003c/strong\u003esoil basal respiration-SBR\u003cstrong\u003e (c)\u003c/strong\u003e,\u003cstrong\u003e \u003c/strong\u003emetabolic quotient-qCO\u003csub\u003e2\u003c/sub\u003e\u003cstrong\u003e (d)\u003c/strong\u003e,\u003cstrong\u003e and \u003c/strong\u003emicrobial quotient-qMB\u003cstrong\u003e (e), \u003c/strong\u003eat the 30-tasselling stage of the maize crop (8\u003csup\u003eth\u003c/sup\u003e crop cycle). Means with distinct symbols demonstrate significant differences between the treatments at the 5% probability level (Tukey’s test), and means with the same symbols indicate no significant differences among the treatment means at the 5% probability level. Refer to Tables 1 and 2 for treatment details.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3967847/v1/a9ac292ca2055fa0d992e3e8.png"},{"id":51364533,"identity":"3cc9c315-882d-441b-bd0e-1001e6caf11e","added_by":"auto","created_at":"2024-02-20 09:39:44","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":528654,"visible":true,"origin":"","legend":"\u003cp\u003eTreatments of various cultured rhizosphere soil and rhizoplane fungal species grown on PDA from the colonies enumerated at the tasselling stage of maize. RS: rhizosphere; RP: rhizoplane; T: tillage; W: weed management; T\u003csub\u003e1\u003c/sub\u003e = CT(C)-CT(M)-Fallow (N\u003cem\u003eSr\u003c/em\u003e); T\u003csub\u003e2\u003c/sub\u003e = CT(C)-ZT(M)-ZT(\u003cem\u003eSr\u003c/em\u003e); T\u003csub\u003e3\u003c/sub\u003e = ZT+R(C)-ZT+R(M)-ZT+R(\u003cem\u003eSr\u003c/em\u003e);\u003cstrong\u003e \u003c/strong\u003eW\u003csub\u003e1 \u003c/sub\u003e= chemical weed control; C= cotton; M= maize; \u003cem\u003eS\u003c/em\u003er= \u003cem\u003eSesbania rostrate\u003c/em\u003e; W\u003csub\u003e2\u003c/sub\u003e = chemical (herbicide) rotation; W\u003csub\u003e3\u003c/sub\u003e = integration of chemical weed control and power + 1 hand weeding (IWM); W\u003csub\u003e4 \u003c/sub\u003e= nonweeded control. T= tillage; TW=tillage and weed management treatment interaction.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3967847/v1/20e93f42758ee0d25c17a50c.png"},{"id":51364536,"identity":"eaa6cfed-c29f-456e-8c08-d170e9fe60ad","added_by":"auto","created_at":"2024-02-20 09:39:44","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":106221,"visible":true,"origin":"","legend":"\u003cp\u003eAgarose gel electrophoresis of genomic DNA extracted from isolated fungi\u003c/p\u003e\n\u003cp\u003eAgarose gel electrophoresis of the PCR-amplified product (18S rRNA gene).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-3967847/v1/f9d4b880b0cb90e1f6aa7dc1.png"},{"id":51365222,"identity":"5b4d5ed4-a672-4fa4-809f-f4a51f6d759f","added_by":"auto","created_at":"2024-02-20 09:55:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2461580,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3967847/v1/222ca038-19e1-492b-975c-d167d8ea9e94.pdf"},{"id":51364535,"identity":"4686fb95-bce6-4bc1-8b59-0b26c4ccbc93","added_by":"auto","created_at":"2024-02-20 09:39:44","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":267903,"visible":true,"origin":"","legend":"\u003cp\u003eMultiple Sequence Alignment\u003c/p\u003e","description":"","filename":"MSA.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3967847/v1/ce76bf909fc9c11c6812039c.pdf"},{"id":51364530,"identity":"df196d4a-acb7-4133-9b1e-e58d53bb1386","added_by":"auto","created_at":"2024-02-20 09:39:44","extension":"doc","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":770560,"visible":true,"origin":"","legend":"\u003cp\u003ePhylogenetic trees of 8 Fungal species\u003c/p\u003e","description":"","filename":"Phylogenetictreesoffungalmicroorganisms.doc","url":"https://assets-eu.researchsquare.com/files/rs-3967847/v1/1d63b950eb9f7209769bc5bc.doc"},{"id":51364527,"identity":"6484f623-6d6d-4494-b331-727f6b300061","added_by":"auto","created_at":"2024-02-20 09:39:44","extension":"doc","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":30720,"visible":true,"origin":"","legend":"\u003cp\u003eResearch highlights\u003c/p\u003e","description":"","filename":"ResearchHighlightsKnight.doc","url":"https://assets-eu.researchsquare.com/files/rs-3967847/v1/f336ab1c8da9ccaaea6a80d9.doc"},{"id":51364534,"identity":"28e9f1a6-f921-4d0c-9e07-aa182bc2ee17","added_by":"auto","created_at":"2024-02-20 09:39:44","extension":"doc","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":69120,"visible":true,"origin":"","legend":"\u003cp\u003eAligned sequence of data during 18s gene partial sequencing and cotton seed yield in table used to compute system yield\u0026nbsp;\u003c/p\u003e","description":"","filename":"SupplementarydataJournal.doc","url":"https://assets-eu.researchsquare.com/files/rs-3967847/v1/5978be6b5f13fbdaf2ae3ee0.doc"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eCumulative Impact of Herbicides and Tillage on the Soil Microbiome, Fungal Diversity and Crop Productivity under Conservation Agriculture\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe world's population is expected to increase to approximately 10\u0026nbsp;billion by 2050, challenging farmers to intensify production while meeting food demand\u0026mdash;in a scenario of modest economic growth\u0026mdash;by approximately 50% relative to 2013 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. These impending crises for food production are likely to cause a considerable shift to industrial farming practices. Commercial agricultural norms are associated with intensive tillage, the use of synthetic chemical fertilizers, and the use of agrochemicals, all of which have negative impacts on the quality of soil resources and biodiversity required to promote soil biological activities. Globally, approximately 10 hectares of land used for agricultural production are instantly depleted as a result of various degradation processes caused by urbanization-related agricultural systems [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Approximately 120\u0026nbsp;million hectares of cultivable land are regarded as degraded in India [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], which is a considerable opportunity for sustainable food production [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Thus, an increase in food production must always be bolstered up by a sustainable agricultural system to sustain soil resources and facilitate soil biological processes [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In this view, conservation agriculture (CA) is gaining momentum as a sustainable and eco-friendly production system meant to augment the soil biological functions of agroecosystems with little mechanical activity and rational utilization of chemical inputs.\u003c/p\u003e \u003cp\u003eSoil microorganisms play an essential role in driving soil biological processes [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], which is essential for maintaining soil quality, agricultural sustainability and multiple ecosystem functions. Ecosystem functions are controlled by rhizosphere soil microorganisms and frequently employ function-based metrics such as soil basal respiration, decomposition of soil organic matter (SOM), soil microbial activities and extracellular enzyme activity [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The constituents of rhizosphere soil microorganisms and function-based metrics are strongly influenced by similar edaphic properties; thus, suitable agricultural management practices, such as irrigation, tillage, crop diversification and weed management practices, can allow rhizosphere soil microorganisms to perform different ecological functions [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Rhizosphere soil microorganisms and microbial activities change rapidly with changes in soil management practices and environmental conditions with short turnover [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and can be used as early indicators of soil health and crop yield improvement. Soil enzyme activity depends upon different abiotic factors, \u003cem\u003eviz\u003c/em\u003e., soil pH, moisture content, oxygen availability and soil texture [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These properties are subject to change depending on the intensity of tillage, weed control practices, and diverse crop species being implemented and consequently have a significant impact on soil microbial composition and enzyme activities [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Conventional agricultural systems with intensive tillage practices decrease the activities of soil microbes and enzymes, change microbial diversity, and delay nutrient cycling, consequently reducing the stability or resilience of the soil functional status [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. By altering the SOM content, cropping systems could also shift the balance of rhizosphere soil enzymes, microbial activities and populations toward biodiversity and function [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA number of quantitative evaluations have been accepted universally for assessing changes in soil functional activity. For instance, the SMBC, SMBN, microbial quotient (qMB), soil basal respiration (SR) and metabolic quotient (qCO\u003csub\u003e2\u003c/sub\u003e) have been broadly utilized as indicators of soil biological status [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. qCO\u003csub\u003e2\u003c/sub\u003e constitutes the metabolic level of soil microorganisms, in which greater values indicate greater stress conditions; however, a rise could also indicate an input of easily degradable carbon that activates microbial activity at times [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The qCO\u003csub\u003e2\u003c/sub\u003e is based on the concept of Odum\u0026rsquo;s ecosystem succession theory, which is increasingly being applied as an indicator of ecosystem development (where it declines) and of disturbance (where it theoretically increases) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Thus, the activities of soil enzymes and soil basal respiration (SBR) serve as determinants of the intensity of soil biogeochemical processes. Insights into how microbial activities and population ripening occur in response to various agricultural management practices and turmoil are essential for identifying the best agricultural practices that can augment and sustain soil resources and crop yield [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Soil enzyme activity is deemed to be indicative of specific biochemical reaction processes of whole-soil microbial activities that occur during SOM mineralization and is also an important indicator of soil health, pollution and ecological restoration with a short turnover time [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Microbial responses in trials have often been assessed by soil enzyme activity [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The introduction of new-generation selective herbicides and shortage of manual labour available for manual weeding have resulted in significant increases in preemergence and postemergence herbicide use in maize. However, herbicides are known to have significant negative or positive effects on soil microbial activities, population and diversity, which in turn impact soil processes [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. A better understanding of the impact of herbicides on soil enzyme dynamics, the functional diversity of soil microorganisms and fungal diversity in ecosystems could provide a unique opportunity for an integrated biological assessment of soils due to their crucial role in several soil biological activities, ease of measurement, and rapid response to changes in soil management. To date, several studies have explored the influence of tillage on soil enzyme activity, microbial activities and population dynamics in combination with CA [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]; however, the direct effects of different tillage practices and weed management practices on biological parameters at different crop growth stages and on fungal diversity at various zone levels (in the soil rhizosphere and rhizoplane) and on how crop productivity relates to soil functional metrics and biodiversity have not been extensively investigated under a diversified crop rotation system (cotton-maize-\u003cem\u003eSesbania rostrata\u003c/em\u003e) in the southern region of India. Insights into fungal diversity in response to CA practices under various tillage practices and weed management practices in a diversified crop rotation can aid in identifying different pathogenic and beneficial fungal species (to facilitate the breakdown of SOM, stabilize carbon and nutrients for soil health maintenance, promote plant growth, increase crop yield, restore ecosystems and inhibit pathogens). Thus, agricultural techniques such as tillage, crop residue management, and crop rotation can influence the diversity and activity of microorganisms such as fungi [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCereal-based cropping systems are common practices in southern regions of India, while maize yield and productivity decline monotonically under continuous intensive tillage systems and correspond to the deterioration of soil physicochemical properties and a decrease in soil biological activities [11; 25]. Along with ZT, the diversified crop rotation and retention of crop remains use precrop effects, which lead to enhanced biological diversity and increased crop yield over continuous cropping of a single cereal-based crop(s) under intensive tillage with crop residue removal [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Thus, a reduction in tillage intensity with continuous retention of previous crop residues and the integration of chemical and cultural weed control practices in CA under a diversified cropping system may be a solution for reducing soil degradation processes and the risk of agricultural production while improving soil functional metrics and rhizosphere soil microbial populations, which can have direct positive effects on crop productivity. Therefore, the present study was performed with the following objectives: to investigate the synergetic effects of different tillage practices and weed management practices on soil microbial and enzyme activities, microbial population and diversity at various sampling stages of maize crops, \u003cem\u003ei.e.\u003c/em\u003e, 30 DAS and tasselling stage; to determine the maize grain yield and system yield in terms of cotton equivalent yield (CEY) in a 4-year CA (8th crop cycle) experiment under a cotton\u0026ndash;maize\u003cem\u003e\u0026ndash;Sesbania rostrata\u003c/em\u003e cropping system; and to identify a suitable tillage practice and weed management option that can reduce perturbations in soil, enhance soil biological activities and harbour beneficial fungal diversity species, reduce the metabolic quotient, boost maize productivity and system CEY.\u003c/p\u003e"},{"header":"Materials and methodologies","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eDetails and characterization of the experimental area\u003c/h2\u003e\n \u003cp\u003eThis current field study was undertaken at the College Farm, PJTSAU, Southern Telangana Zone of India, under the All India Coordinated Research Project (AICRP) on Weed Management implemented from 2020 in the monsoon, winter and summer seasons under cotton (\u003cem\u003eGossypium hirsutum\u003c/em\u003e), maize (\u003cem\u003eZea mays\u003c/em\u003e), and green manure (\u003cem\u003eSesbania rostrata\u003c/em\u003e) rotations, respectively. The experiment continued from 2020 until 2023, and soil samples were collected for analysis of soil parameters and recording of yield after the winter maize crop was harvested from 2022-23 (the fourth year of the 8th crop cycle). The field trial is located at 160 18\u0026apos; 17\u0026quot; N latitude and 780 25\u0026apos; 38\u0026quot; E longitude. The zone is a dryland zone with approximately 708 mm of mean annual rainfall [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]. Extreme heat and humidity occur during the summer months (March to fortnight of June), when the mean temperature is 30\u0026deg;C. Maximum temperatures often exceed 42\u0026deg;C from April to May. December and January are extreme winter months with the lowest temperatures dropping as low as 10\u0026deg;C occasionally. Rainfall surpasses 75% due to the southwest monsoon and occurs between June and September [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eWeather during crop\u003c/strong\u003e \u003cstrong\u003edevelopment\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eMeteorological observations taken during crop development from the station situated at the Institute of Agricultural Research (IAR), Rajendranagar, on a weekly basis are presented in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003eSoil characteristics\u003c/h2\u003e\n \u003cp\u003eThe soil of the study area is classified as follows: \u003cem\u003eInceptisol\u003c/em\u003e, sandy clay loam in texture; red chalk in colour; slightly alkaline (7.82) in soil pH as a result of available lime concretion beneath the horizon; 1.23 Mg m-3 in bulk density; nonsaline (0.33 dS m-₁); medium range in soil organic carbon (6.50 g kg-₁); low range in available soil nitrogen (220.90 kg ha-₁); medium range in available soil phosphorus (22.40 g kg-₁); and high range in available soil potassium (408.75 kg ha-₁) at the soil surface (0\u0026ndash;15 cm) at the initiation of the experiment.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eDesign of the experiment and treatment details\u003c/h2\u003e\n \u003cp\u003eA conservation agriculture (CA) experiment was conducted in accordance with a split plot design with three tillage (s) practices in the main plots, as shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e; four weed management options were used in the subplot treatments, as detailed in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e; and combinations of tillage and weed management were replicated thrice. For T\u003csub\u003e1\u003c/sub\u003e, which was subjected to conventional tillage, the plots were prepared by ploughing two times, followed by rotovating and seeding. In T\u003csub\u003e2,\u003c/sub\u003e during zero tillage (ZT), no-tillage of the soil was performed; \u003cem\u003ei.e.\u003c/em\u003e, seeding was performed directly by opening the soil followed by surface soil sealing, and in T\u003csub\u003e3\u003c/sub\u003e, there was zero tillage (ZT)\u0026thinsp;+\u0026thinsp;residue retention (R) or no tillage of the soil. The preceding crop (cotton and \u003cem\u003eSesbania rostrata\u003c/em\u003e) residues were shredded, retained, and incorporated into the soil, and seeding was performed directly by opening the soil, accompanied by soil surface sealing with mulch from crop residues (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The weed management strategies used included the following: W\u003csub\u003e1\u003c/sub\u003e: chemical weed control, W\u003csub\u003e2\u003c/sub\u003e: chemical (herbicide) rotation, W\u003csub\u003e3\u003c/sub\u003e: chemical weed control and power\u0026thinsp;+\u0026thinsp;1 hand weeding (IWM) and W\u003csub\u003e4\u003c/sub\u003e: non-weeded control, as fully described in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. No tillage operations or weed management were performed prior to sowing summer green manure (\u003cem\u003eSesbania rostrata\u003c/em\u003e), as it was cultivated for up to 45 days with the intention of retaining and incorporating its residues into the soil in T\u003csub\u003e3\u003c/sub\u003e. No green manure (\u003cem\u003eSesbania rostrata\u003c/em\u003e) was sown in the T\u003csub\u003e1\u003c/sub\u003e plots; \u003cem\u003ei.e.\u003c/em\u003e, the T\u003csub\u003e1\u003c/sub\u003e plots were fallowed during the summer season.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAnnotation of tillage treatments with crop diversification in the main plots\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eTillage (s)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eSeasons\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMonsoon\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWinter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSummer\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003csub\u003e1\u003c/sub\u003e :\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCT (C) \u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCT (M) \u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFallow (N\u003cem\u003eSr\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003csub\u003e2\u003c/sub\u003e :\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCT (C) \u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZT (M) \u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZT (\u003cem\u003eSr\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003csub\u003e3\u003c/sub\u003e :\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZT\u0026thinsp;+\u0026thinsp;R (C) \u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZT\u0026thinsp;+\u0026thinsp;R (M) \u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZT\u0026thinsp;+\u0026thinsp;R (\u003cem\u003eSr\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eCT(C)\u0026thinsp;=\u0026thinsp;conventional tillage (cotton), CT(C)\u0026thinsp;=\u0026thinsp;conventional tillage (maize), Fallow (N\u003cem\u003eSr\u003c/em\u003e)\u0026thinsp;=\u0026thinsp;Fallow, No \u003cem\u003eSesbania rostrata\u003c/em\u003e, ZT (M)\u0026thinsp;=\u0026thinsp;zero tillage (maize), ZT (\u003cem\u003eSr\u003c/em\u003e)\u0026thinsp;=\u0026thinsp;\u003cem\u003eSesbania rostrata\u003c/em\u003e, ZT\u0026thinsp;+\u0026thinsp;R (C)\u0026thinsp;=\u0026thinsp;zero tillage (cotton)\u0026thinsp;+\u0026thinsp;residue retention, ZT\u0026thinsp;+\u0026thinsp;R (M)\u0026thinsp;=\u0026thinsp;zero tillage (Maize)\u0026thinsp;+\u0026thinsp;residue retention, ZT\u0026thinsp;+\u0026thinsp;R (\u003cem\u003eSr\u003c/em\u003e)\u0026thinsp;=\u0026thinsp;zero tillage (\u003cem\u003eSesbania rostrata\u003c/em\u003e)\u0026thinsp;+\u0026thinsp;residue retention.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eWeed management (WM) in subtreatments and interaction with tillage (T) in the main treatments\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eMonsoon (Cotton)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eWinter (Maize)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e1\u003c/sub\u003e:\u003c/p\u003e\n \u003cp\u003eChemical Weed Control\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e2\u003c/sub\u003e: Herbicide Rotation (Every year)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e3\u003c/sub\u003e:\u003c/p\u003e\n \u003cp\u003eIWM\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e4\u003c/sub\u003e:\u003c/p\u003e\n \u003cp\u003eNon-weeded Control\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e1\u003c/sub\u003e:\u003c/p\u003e\n \u003cp\u003eChemical Weed Control\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e2\u003c/sub\u003e: Herbicide Rotation\u003c/p\u003e\n \u003cp\u003e(Every year)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e3\u003c/sub\u003e: IWM\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e4\u003c/sub\u003e:\u003c/p\u003e\n \u003cp\u003eNon-weeded Control\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eDiuron\u003c/p\u003e\n \u003cp\u003epreemergen\u003c/p\u003e\n \u003cp\u003e-ce application PE 0.75 kg/ha fb tank mix application of pyrithiobac\u003c/p\u003e\n \u003cp\u003e-sodium 62.5 g/ha\u0026thinsp;+\u0026thinsp;quiza- lofop-ethyl 50 g/ha as PoE (Postemergen- ce application) (2\u0026ndash;3 weed leaf stage) \u003cem\u003efb\u003c/em\u003e directed spray (interrow) of paraquat 0.5\u003c/p\u003e\n \u003cp\u003ekg/ha at 50\u0026ndash;55 DAS.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eDiuron PE 0.75\u003c/p\u003e\n \u003cp\u003ekg/ha \u003cem\u003efb\u003c/em\u003e tank mix application of pyrithiobac-sodium\u003c/p\u003e\n \u003cp\u003e62.5 g/ha\u0026thinsp;+\u0026thinsp;quizalofop-ethyl 50 g/ha as PoE (2\u0026ndash;3 weed leaf stage) \u003cem\u003efb\u003c/em\u003e directed spray (interrow) of paraquat 0.5 kg/ha at 50\u0026ndash;55 DAS.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003erotated with\u003c/strong\u003e Pendimethalin 1 kg ha\u003csup\u003e-1\u003c/sup\u003e fb tank mix application of pyrithiobac-sodium 62.5 g/ha\u0026thinsp;+\u0026thinsp;quiza- lofop ethyl 50 g/ha as PoE (2\u0026ndash;3 weed leaf stage) \u003cem\u003efb\u003c/em\u003e directed spray (interrow) of paraquat 24% SL 0.5 kg/ha at\u003c/p\u003e\n \u003cp\u003e50\u0026ndash;55 DAS.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eAtrazine 1.0 kg/ha\u003c/p\u003e\n \u003cp\u003e+ paraquat 600 g/ha PE \u003cem\u003efb\u003c/em\u003e tembotrione 120 g/ha at 20\u0026ndash;25 DAS as PoE (T\u003csub\u003e2\u003c/sub\u003e, T\u003csub\u003e3\u003c/sub\u003e). Atrazine 1.0 kg/ha PE \u003cem\u003efb\u003c/em\u003e tembotrione 120 g/ha at 20\u0026ndash;25 DAS at PoE (T\u003csub\u003e1)\u003c/sub\u003e.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003erotated with\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eAtrazine 1.0 kg/ha\u003c/p\u003e\n \u003cp\u003e+ paraquat 600 g/ha PE \u003cem\u003efb\u003c/em\u003e halosulfuron- methyl 67.5 g/ha at 20\u0026ndash;25 DAS as PoE (T\u003csub\u003e2\u003c/sub\u003e, T\u003csub\u003e3\u003c/sub\u003e). Atrazine 1.0 kg/ha PE \u003cem\u003efb\u003c/em\u003e halo-sulfuron methyl 67.5 g/ha at 20\u0026ndash;25 DAS as PoE (T\u003csub\u003e1\u003c/sub\u003e).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiuron PE 0.75\u003c/p\u003e\n \u003cp\u003ekg/ha \u003cem\u003efb\u003c/em\u003e mechanical brush cutter twice at 25\u003c/p\u003e\n \u003cp\u003eand 60 DAS.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eOne hand weeding was done after the critical period of crop-weed competit-ion, i.e., between 45\u0026ndash;50 days after sowing)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAtrazine 1.0 kg/ha\u0026thinsp;+\u0026thinsp;paraquat 600 g/ha PE \u003cem\u003efb\u003c/em\u003e tembo-trione 120 g/ha at\u003c/p\u003e\n \u003cp\u003e20\u0026ndash;25 DAS\u003c/p\u003e\n \u003cp\u003eas PoE (T\u003csub\u003e2,\u003c/sub\u003e T\u003csub\u003e3\u003c/sub\u003e). Atrazine 1 kg ha\u003csup\u003e-1\u003c/sup\u003e PE \u003cem\u003efb\u003c/em\u003e tembo-trione 120 g/ha at 20\u0026ndash;25 DAS as PoE (T\u003csub\u003e1\u003c/sub\u003e).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eTembotri- one\u003c/p\u003e\n \u003cp\u003e120 g/ha Atrazine 50% WP\u003c/p\u003e\n \u003cp\u003e0.5 kg/ha as Early post- emergenc\u003c/p\u003e\n \u003cp\u003ee) EPoE \u003cem\u003efb\u003c/em\u003e brush cutter at 40 DAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eOne hand weeding was done after the critical period of crop-weed competiti- on, i.e., between 45\u0026ndash;50 days after sowing.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eT\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;conventional tillage (CT) \u0026ndash; conventional tillage (CT) \u0026ndash; Fallow, T\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;conventional tillage (CT) \u0026ndash; zero tillage (ZT) \u0026ndash; zero tillage (ZT), T\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;zero tillage (ZT)\u0026thinsp;+\u0026thinsp;R (residue retention) \u0026ndash; zero tillage (ZT)\u0026thinsp;+\u0026thinsp;R (residue retention) \u0026ndash; zero tillage (ZT)\u0026thinsp;+\u0026thinsp;R (residue retention), IWM\u0026thinsp;=\u0026thinsp;integration of chemical weed control and power\u0026thinsp;+\u0026thinsp;1 hand weeding.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003eSowing and fertilizer application\u003c/h2\u003e\n \u003cp\u003eThe DHM 117 maize seeds were seeded at 60 cm between the rows and 25 cm between the rows, with a net field plot size of 41.3 m2 in 10 rows for each plot. Prior to seeding, the experimental plots were ploughed two times, accompanied by rotovating and levelling with manual raking in T\u003csub\u003e1\u003c/sub\u003e; these plots were conventionally tilled plots, while the maize seeds were dibbled with no-till in ZT plots. The quantity of the maize seeds utilized for sowing was 20 kg ha-₁. The crop was thinned in the portions of the plots with a high crop population, and the gaps were filled where the seeds did not emerge 13 days after seed emergence. The crop was typically developed and advanced with supplemental irrigation because the amount of rainfall received during the crop developmental period was limited. Advocated dose fertilizers (ADFs) for N:P:K (200:60:50 kg ha-₁) were supplied to raise the crop through urea, di-ammonium phosphate (DAP) and muriate potash (MOP). Urea and DAP were applied thrice\u0026mdash;as basal, at knee height and during the maize tasselling period.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eSampling and standard analytical procedures\u003c/h2\u003e\n \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\n \u003ch2\u003eSoil physicochemical properties\u003c/h2\u003e\n \u003cp\u003eComposite soil samples were randomly collected in triplicate from each treatment plot at a depth of 0\u0026ndash;15 after the maize crop was harvested during the 8th crop cycle in April 2023. These collected soil samples were air-dried well under shade, processed through a wooden hammer, passed through a 0.5 mm sieve, and analysed for soil organic carbon by following standard methods described by Walkley and Black [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]. For the analysis of soil pH, a 2 mm sieve was used to sieve the soil samples, and analysis was performed according to Jackson [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eSoil microbial population and microbial and enzyme activities\u003c/h2\u003e\n \u003cp\u003eSampling of rhizosphere soil was performed at two growth stages of maize crops (8th crop cycle) in 2022-23 during the experiment: the first, after preemergence, early postemergence and postemergence application of herbicides in chemical weed control (W\u003csub\u003e1\u003c/sub\u003e) and herbicide rotation weed management (W\u003csub\u003e2\u003c/sub\u003e) plots at 30 DAS in the maize crop; the second, at the tasselling stage. Composite samples were collected from the respective plots in polythene bags with zippers, taken to the laboratory, passed through a 2 mm sieve and analysed on the same day as collection from the field. The functional activity was measured in terms of soil microbial activities related to the soil microbial population, soil organic matter and nitrogen cycling. The soil water content was determined according to Monteiro and Frighetto [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e], and this information was utilized in calculating the evaluated parameters.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eSoil microbial activity\u003c/h2\u003e\n \u003cp\u003eSoil basal respiration (SBR) was measured in a closed jar incubated for 24 hours at 26\u0026deg;C [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e]. The CO\u003csub\u003e2\u003c/sub\u003e released was trapped in NaOH and determined by HCl titration. The results are reported as milligrams of CO\u003csub\u003e2\u003c/sub\u003e released per kilogram of soil per hour (Eq. 1).\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n \u003cp\u003ewhere Vb is the volume of HCl consumed in the blank (ml); Vs is the volume of HCl consumed in the test sample (ml); M is the HCl molarity; 6 is the equivalent factor (1 ml of 0.5 N HCl is equivalent to 6 mg C-CO\u003csub\u003e2\u003c/sub\u003e in the NaOH solution); ds is the weight of dry soil; and t is the time of incubation.\u003c/p\u003e\n \u003cp\u003eTotal soil microbial biomass carbon (SMBC) was determined by following the procedure of fumigation extraction [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e], in which the soil was fumigated with chloroform-free ethanol in a desiccator. Overnight fumigation (24 hours) of chloroform was performed to kill the organisms in the soil samples, after which the amount of readily oxidizable C in the sample was measured through standard chemical procedures. The SMBC values are given as the carbon content of fumigated soil minus that of nonfumigated soil; all the values are divided by the proportion of microbial C evolved (K\u003csub\u003e\u003cem\u003eEC\u003c/em\u003e\u003c/sub\u003e). A value of 0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05 was used for kc in the SMBC calculation, representing the efficiency of the extraction of soil microbial biomass carbon (Eq.\u0026nbsp;2).\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n \u003cp\u003ewhere EC\u003cem\u003ef\u003c/em\u003e is mg of C per kilogram of fumigated soil, EC\u003cem\u003enf\u003c/em\u003e is mg of C per kilogram of nonfumigated soil, and K\u003csub\u003e\u003cem\u003eEC\u003c/em\u003e\u003c/sub\u003e is part of the microbial C evolved (0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cp\u003eSoil microbial biomass nitrogen (SMBN) was determined by the CH\u003csub\u003e3\u003c/sub\u003eCl fumigation-extraction technique as described by Brookes et al. [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e] and Amato and Ladd [\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e]. Aliquots of fresh soil (5 g) were fumigated in a glass desiccator with ethanol-free CH\u003csub\u003e3\u003c/sub\u003eCl vapour for 24 hrs at 21\u0026deg;C. Both fumigated and unfumigated soil samples were extracted with 0.5 ml of 0.5 mol/L K\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e for 30 min before gravimetric titration through ashless Whatman filter paper. The total dissolved N in the extracts was measured by persulfate digestion followed by NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e determination (VCl\u003csub\u003e3\u003c/sub\u003e/Griess reaction). The difference in total dissolved nitrogen (TDN) between the fumigated and unfumigated soil extracts was attributed to the release (flush) of N from the lysed microbial cells. The calculation of SMBN was performed according to Eq.\u0026nbsp;2. A correction factor for SMBN (K\u003csub\u003eEN\u003c/sub\u003e = 0.45) was applied for incomplete extraction of microbial N [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e]. The metabolic quotient (qCO2), the ratio between SBR and SMBC [\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e], was employed to obtain the efficiency of substrate consumption by microorganisms as a stress indicator when the microbial biomass was affected. The microbial quotient (MBC:SOC) was the ratio of the MBC to the SOC [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eSoil enzymatic activity\u003c/h2\u003e\n \u003cp\u003eDehydrogenase activity (DHA) was assayed according to Casida et al. [\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e], and the red colour of triphenyl formazan (TPF) was determined via spectrophotometry (\u0026lambda;\u0026thinsp;=\u0026thinsp;485 nm). Fluorescein diacetate (FDA) activity was estimated according to Green et al. [\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e], and the amount of greenish-yellow fluorescein was measured via spectrophotometry at a wavelength of 490 nm. The urease activity was determined by quantifying the rate of release of NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e from the hydrolysis of urea as described by Tabatabai and Bremner [\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e]. The activity of urease was subsequently calculated and expressed as \u0026micro;g of NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e released g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e soil h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e as described by Tabatabai and Bremner [\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e]. The \u0026beta;-galactosidase and phosphatase activities were estimated according to Eivazi and Tabatabai [\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e] and Tabatabai and Bremner [\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e]. After the appropriate incubation time for each enzyme (60 min for \u0026beta;-galactosidase and phosphatase), their respective substrates (\u0026rho;-nitrophenyl-\u0026beta;-D-galactopyranoside and \u0026rho;-nitrophenyl-phosphate) were hydrolysed into the yellow colour \u003cem\u003e\u0026rho;\u003c/em\u003e-nitrophenol, which was determined by spectrophotometry (\u0026lambda;\u0026thinsp;=\u0026thinsp;420 nm and \u0026lambda;\u0026thinsp;=\u0026thinsp;405 nm, respectively).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eRhizosphere soil and rhizoplane microbial population\u003c/h2\u003e\n \u003cp\u003eFunctional culturable groups of rhizosphere soil microorganisms, \u003cem\u003eviz\u003c/em\u003e., \u003cem\u003eAzotobacter\u003c/em\u003e, \u003cem\u003eAzospirillum\u003c/em\u003e, and total fungi, were assessed. Rhizosphere \u003cem\u003eAzotobacter\u003c/em\u003e and the total fungal population were evaluated following the protocols described in Albino and Andrade [\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e]. A colony counter was used to count the colonies that formed after 7 days of incubation in a BOD incubator at 30\u0026deg;C for \u003cem\u003eAzotobacter\u003c/em\u003e and 3\u0026ndash;5 days at 25\u0026deg;C in a BOD incubator for the total fungal population. The population density was estimated as colony forming units (CFU) per gram of dry soil (Eq. 3) [\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e]. For enumeration of the rhizosphere soil \u003cem\u003eAzospirillum\u003c/em\u003e population, rhizosphere soil samples (0.1 ml aliquots) were inoculated into semisolid nitrogen-free bromothymol blue malate medium (Nfb) according to D\u0026ouml;bereiner and Day (1976) and incubated in a BOD incubator for 3\u0026ndash;4 days at 30\u0026deg;C until the pellicles formed in tubes containing Nfb medium and 0.1 ml of rhizosphere soil sample aliquot. \u003cem\u003eAzospirillum\u003c/em\u003e abundance was estimated by the most likely number (MPN) table (s), which was transformed to the logarithm of the most likely number per gram of soil (log MPN.g\u003csup\u003e-1\u003c/sup\u003e) suggested by Alexander [\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e], Woomer et al. [\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e] and Cochran [\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e] and expressed as colony-forming units (CFU g\u003csup\u003e-1\u003c/sup\u003e) of soil on a dry weight basis, and the others (rhizosphere soil \u003cem\u003eAzotobacter\u003c/em\u003e and total fungi) were transformed and expressed as the logarithm of colony-forming units per gram of soil (log CFU.g\u003csup\u003e-1\u003c/sup\u003e soil).\u003c/p\u003e\n \u003cp\u003eFor enumeration of the rhizoplane microbial population, root samples were collected from the plants by removing the plants, separating the roots from the plants through cutting with the help of a knife, and removing the rhizosphere soil. The roots were collected in a polythene zip cover. Using a pair of scissors, the roots were separated, and one gram of the roots was transferred to 100 ml of sterile distilled water and washed thoroughly using a rotary mixer. One milliliter from 100 ml of the sample was transferred into 10 ml of saline blanks, and serial dilutions were made for each treatment following the same methodology employed for enumeration of the soil microbial population. The samples were incubated in a BOD incubator for 3\u0026ndash;5 days at 25\u0026deg;C with dilutions of up to 10\u003csup\u003e4\u003c/sup\u003e. Eq.\u0026nbsp;3 [\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e] was used for calculation, after which the results were transformed and are expressed as the logarithm of colony-forming units per gram of roots (log CFU.g\u003csup\u003e-1\u003c/sup\u003e roots).\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eFungal Diversity\u003c/h2\u003e\n \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\n \u003ch2\u003eIsolation criteria and purification\u003c/h2\u003e\n \u003cp\u003erRNA gene sequencing of 18S was performed on fungal colonies obtained at the tasselling stage from the maize crop 2022-23. Before identification, the fungal colonies were incubated for approximately 10‒12 days on rose bengal solid agar media at 25\u0026deg;C to allow sporulation to occur. Based on the colour of the spores formed, classification was performed, and 8 representative plates of all 12 treatment combinations were selected for purification to obtain pure fungal strains based on the abundance of the same number of spores. These colonies, which were predominant on plates and represented the treatment combinations, were picked and cultured in potato dextrose (PDA) solid agar medium for 5 days to allow the growth of pure strains of fungal species. The 8 pure fungal strains were subjected to sequencing to identify the fungal species present in the different treatment combinations for tillage and weed management.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eDeoxyribonucleic acid (DNA) extraction and polymerase chain reaction (PCR) amplification of 18S gene partial sequencing\u003c/h2\u003e\n \u003cp\u003eDeoxyribonucleic acid (DNA) extraction was performed by picking sample and isolating genomic DNA from those samples (pure fungal strains). The DNA was placed in a mortar and homogenized with 1 ml of extraction buffer, after which the homogenate was transferred to a 2 ml-microfuge tube.\u003c/p\u003e\n \u003cp\u003eAn equal volume of phenol:chloroform:isoamyl alcohol (25:24:1) was added to the tubes, which were mixed well by gently shaking the tubes. The tubes were centrifuged at room temperature for 15 min at 14,000 rpm. The upper aqueous phase was collected in a new tube, and an equal volume of chloroform:isoamyl alcohol (24:1) was added and mixed. The upper aqueous phase obtained after centrifuging at room temperature for 10 min at 14,000 rpm was transferred to a new tube. The DNA was precipitated from the solution by adding 0.1 volume of 3.0 M sodium acetate (pH 7.0) and 0.7 volume of isopropanol. After 15 minutes of incubation at room temperature, the tubes were centrifuged at 4\u0026deg;C for 15 minutes at 14,000 rpm. The DNA pellet was washed twice with 70% ethanol and then very briefly with 100% ethanol and air-dried. The DNA was dissolved in TE (10 mM Tris-Cl [pH 8.0], 1 mM EDTA). To remove ribonucleic acid (RNA), 5 \u0026micro;l of DNAse or free RNAse A (10 mg ml\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) was added to the DNA.\u003c/p\u003e\n \u003cp\u003eAfter extraction of the total DNA, different quantities of DNA extracted from the treatment samples (154, 155, 169, 170, 175, 179 and 198 ng \u0026micro;l\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) were subjected to polymerase chain reaction (PCR) amplification of the 18 s gene according to Baldoni et al. [\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e] along with \u003cem\u003e10 pM\u003c/em\u003e of each primer mixture (High-Fidelity DNA Polymerase, 0.5 mM dNTPs, 3.2 mM MgCl\u003csub\u003e2\u003c/sub\u003e and PCR enzyme buffer cycling condition) (PCR Clean Kit). The PCR cycling and amplification conditions are presented in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. The sequences of primers used are listed in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. The aligned sequence data of the samples are included in the supplementary data. The PCR products were sequenced bidirectionally according to Staden et al. [\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e], and the sequence mix used was as follows: 10 \u0026micro;l of sequencing mixture, 4 \u0026micro;l of Big Dye Terminator-ready Reaction Mix, 1 \u0026micro;l of template (100 ng ul-1), 2 \u0026micro;l of primer (10 pmol \u0026lambda;-1), 3 \u0026micro;l of milliquoise water and 25 cycles of PCR conditions. The mixture was subjected to initial denaturation at 96\u0026deg;C for 5 minutes, denaturation at 96\u0026deg;C for 30 seconds, hybridization at 50\u0026deg;C for 30 seconds and elongation at 60\u0026deg;C for 1.30 minutes.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eData analysis and identification\u003c/h2\u003e\n \u003cp\u003eThe data were analysed by using a sequencing machine (ABI 3130 genetic analyser), a chemistry cycle sequencing kit (big dye terminator version 3.1\u0026rdquo;, a polymer and a capillary array (POP_7 pol capillary array) with a BDTv3-KB-Denovo_v 5.2 protocol and a sequence scape_ v 5.2 software reaction plate (Applied Biosystem Micro Amp Optical 96-Well Reaction plate). Identification was performed by using the system software aligner to align the sequences, and a comparative search of GenBank sequences in the National Centre for Biotechnology Information (NCBI) was carried out using the BLASTn tool to identify the organisms and their closest neighbours. The phylogenetic tree builder used sequences aligned with the system software aligner, and a distance matrix was generated using the Jukes\u0026ndash;Cantor corrected distance model. When generating the distance matrix, only alignment model positions were used; alignment inserts were ignored, and the minimum comparable position was 200. The tree was created using a neighbor with an alphabet size of 4 and a length of 1000. The consensus sequence was deposited in the GenBank in the NCBI database to obtain accession numbers of identified organisms from the type material.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eMultiple sequence alignment and phylogenetic tree construction\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003eThe default parameters were used for the\u003c/strong\u003e Mega11 Version 11.0.13 and clustalW algorithm, which included pairwise alignment with a gap opening penalty of 15, a penalty of 6.06, multiple alignments with a gap opening penalty of 15 and a gap extension penalty of 6.06. Additionally, the default parameters included a matrix with a DNA weight matrix (IUB transition), a weight of 0.50, and a negative matrix with an off-delay divergent cut-off of 30.\u003c/p\u003e\n \u003cp\u003eFor construction of the phylogenetic tree, the neighbour joining test of the phylogenetic method and bootstrap method were used, and 1000 bootstrap replicates were used; moreover, the nucleotide model/method substitution type included 17 nucleotide sequences. The evolutionary distances were computed using the maximum composite likelihood method [\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e]. The codon positions included were 1st\u0026thinsp;+\u0026thinsp;2nd\u0026thinsp;+\u0026thinsp;3rd\u0026thinsp;+\u0026thinsp;noncoding. The maximum composite likelihood substitution included transitions\u0026thinsp;+\u0026thinsp;transversion rates among sites \u0026ndash; uniform rate pattern among lineages \u0026ndash; same (homogeneous) gap/missing data treatment in pairwise deletion. There was a total of 2,043 positions in the final dataset.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDetails of the PCR cycling and amplification conditions\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eCycling Conditions\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInitial Denaturation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 minutes at 94\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003e30 Cycles\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDenaturation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 minute at 94\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnnealing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 minute at 50\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExtension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 minutes at 72\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinal Extension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 minutes at 72\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ePCR Amplification conditions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eVolume\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 ul\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e18 s Forward Primer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 ul\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e18 s Reverse Primer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 ul\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003edNTPs (2.5 mM each)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 ul\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e10X Taq DNA polymerase Assay Buffer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 ul\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTaq DNA Polymerase Enzyme (3U ml\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 ul\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eWater\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 ul\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTotal reaction volume\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50 ul\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePrimer details - The polymerase chain reaction (PCR) product size was ~\u0026thinsp;2 kb\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS. No\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOligo Name\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSequence (5`\u0026agrave; 3`)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTm (\u0026deg;C)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGC- Content\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 sForward\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTCCTGAGGGAAACTTCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.94%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 s Reverse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eACCCGCTGAACTTAAGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.94%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003eCrop Productivity\u003c/h2\u003e\n \u003cp\u003eThe grain yield for maize in each net plot was recorded by weighing the sun-dried produce before threshing, and the yield was expressed in kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Similarly, the maize stover in the net plot area was cut, and the sun-dried weight was expressed in kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Cotton was the first crop, followed by maize and \u003cem\u003eSesbania rostrata\u003c/em\u003e in the cropping system; therefore, the system yield was computed in terms of the cotton equivalent yield (CEY) (the monsoon seed cotton yield after the 4th year used in the calculation is attached as supplementary data) using Eq. 4, as follows:\u003c/p\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eThe data were analysed statistically by applying the analysis of variance technique following the ANOVA for split plot design as suggested by [\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e]. The significance of critical differences in the treatment means was calculated at the 5 percent level of probability. Turkey\u0026rsquo;s test was also used for ranking microbial activity treatment means because of the significance of the differences at the 5% probability level.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n \u003ch2\u003eSoil pH and soil organic carbon\u003c/h2\u003e\n \u003cp\u003eSoil physico-chemical attributes were not significantly influenced by tillage or weed management options except for soil organic carbon (SOC), which was significantly impacted by the different tillage practices. The interaction effects (tillage and weed management) on the soil pH, EC and SOC were not significant (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). The ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(M)-ZT\u0026thinsp;+\u0026thinsp;R(\u003cem\u003eSr\u003c/em\u003e) treatment had a significantly greater SOC (7.92 g kg-₁) than did the CT(C)-CT(M)-Fallow(N\u003cem\u003eSr\u003c/em\u003e) and CT(C)-ZT(M)-ZT(\u003cem\u003eSr\u003c/em\u003e) treatments. Overall, the SOC contents were greater in all the treatments than in the initial treatment (6.5 g kg-₁). The soil pH was slightly alkaline, with a decrease observed across all the treatments over the initial soil pH range (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eImpact of tillage and weed management options on soil pH and soil organic carbon (SOC) after harvest of winter maize (8th crop cycle).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTreatments\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSOC (g kg-₁)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;15 cm\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;15 cm\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTillage practices\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInitial (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003csub\u003e1\u003c/sub\u003e: CT(C)-CT(M)-Fallow (N\u003cem\u003eSr\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003csub\u003e2\u003c/sub\u003e: CT(C)-ZT(M)-ZT(\u003cem\u003eSr\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003csub\u003e3\u003c/sub\u003e: ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(Sr)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE(m)\u0026plusmn;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD(P\u0026thinsp;=\u0026thinsp;0.05)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeed management options\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e1\u003c/sub\u003e- Chemical weed control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e2\u003c/sub\u003e- chemical (herbicide) rotation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e3\u003c/sub\u003e- IWM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e4\u003c/sub\u003e- Nonweeded control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE(m)\u0026plusmn;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD(P\u0026thinsp;=\u0026thinsp;0.05)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInteractions (TxW) CD(P\u0026thinsp;=\u0026thinsp;0.05)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eCT\u0026thinsp;=\u0026thinsp;conventional tillage, ZT\u0026thinsp;=\u0026thinsp;zero tillage; R\u0026thinsp;=\u0026thinsp;crop residue retention; IWM\u0026thinsp;=\u0026thinsp;chemical weed control\u0026thinsp;+\u0026thinsp;power and 1 hand weeding; C\u0026thinsp;=\u0026thinsp;cotton; M\u0026thinsp;=\u0026thinsp;maize; \u003cem\u003eS\u003c/em\u003er\u0026thinsp;=\u0026thinsp;\u003cem\u003eSesbania rostrata\u003c/em\u003e; CD (P\u0026thinsp;=\u0026thinsp;0.05)\u0026thinsp;=\u0026thinsp;critical difference at the 5% probability level; Ns\u0026thinsp;=\u0026thinsp;nonsignificant; SE(m)\u0026thinsp;=\u0026thinsp;standard error of the mean.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\n \u003ch2\u003eSoil Microbial Activity\u003c/h2\u003e\n \u003cp\u003eTillage and weed management practices exerted a significant influence on overall soil microbiological activity at all sampling stages (at 30 DAS after the application of herbicides and during the tasselling stage of maize). The soil microbial activity indices (SMAIs) included soil microbial biomass carbon (SMBC), microbial biomass nitrogen (SMBN), soil basal respiration (SBR), the microbial quotient (qMB) and the metabolic quotient (qCO\u003csub\u003e2\u003c/sub\u003e) (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea, b, c, d, e and \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea, b, c, d, e). These SMAIs were significantly promoted and increased by the adoption of ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(M)-ZT\u0026thinsp;+\u0026thinsp;R(\u003cem\u003eSr\u003c/em\u003e) at both sampling stages of the crop relative to CT(C)-CT(M)-Fallow(N\u003cem\u003eSr\u003c/em\u003e) and CT(C)-ZT(M)-ZT(\u003cem\u003eSr\u003c/em\u003e), but not qCO\u003csub\u003e2\u003c/sub\u003e. Among weed practices, a significant increase in SMAIs was observed with non-weeded control and the combination of chemical weed control and power\u0026thinsp;+\u0026thinsp;1-hand weeding (IWM) at both sampling stages. The herbicides applied at 30 DAS to maize via chemical weed control and chemical (herbicidal) rotation resulted in a significant reduction in the SMAIs, which later increased until the tasselling stage of the crop. The qCO\u003csub\u003e2\u003c/sub\u003e values were significantly lower in the ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(M)-ZT\u0026thinsp;+\u0026thinsp;R(\u003cem\u003eSr\u003c/em\u003e) treatment than in the CT(C)-CT(M)-Fallow (N\u003cem\u003eSr\u003c/em\u003e) and CT(C)-ZT(M)-ZT(\u003cem\u003eSr\u003c/em\u003e) treatments at both stages of the crop. With respect to weed management practices, the qCO\u003csub\u003e2\u003c/sub\u003e values were significantly lower in the non-weeded control and IWM plots than in the herbicide-treated plots. There were no significant treatment interaction effects on the SMAIs observed during either period of sampling (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea, b, c, d, e and \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea, b, c, d, e).\u003c/p\u003e\n \u003cp\u003eAt 30 DAS, for maize, the SMBC, SMBN, SBR, and qMB were significantly greater (7.52% and 26.27%, 11.01% and 28.90%, 0.64% and 17.60%, 15.15% and 15.16%, respectively) under ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(M)-ZT\u0026thinsp;+\u0026thinsp;R(\u003cem\u003eSr\u003c/em\u003e) than under CT(C)-ZT(M)-ZT(\u003cem\u003eSr\u003c/em\u003e) and CT(C)-CT(M)-Fallow (N\u003cem\u003eSr\u003c/em\u003e). Among weed management options, after the application of preemergence (PE), early postemergence (EPoE) and postemergence (PoE) herbicides at 30 DAS, 21.41\u0026ndash;21.72% and 2.93\u0026ndash;3.23% of SMBC, 20.00-21.40% and 14.21%-15.71% of SMBN, 13.73\u0026ndash;23.16% and 9.21\u0026ndash;19.70% of SBR, and 8.11\u0026ndash;21.62% and 9.09\u0026ndash;15.91% of qMB were greater under nonweeded control and IWM, respectively, than under chemical (herbicide) rotation and chemical weed control (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea, b, c, d, e). During the same sampling period (30 DAS), the ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(M)-ZT\u0026thinsp;+\u0026thinsp;R(\u003cem\u003eSr\u003c/em\u003e) treatment resulted in significant reductions in the qCO2 concentration compared with that in the CT(C)-ZT(M)-ZT(\u003cem\u003eSr\u003c/em\u003e) and CT(C)-CT(M)-Fallow(N\u003cem\u003eSr\u003c/em\u003e) groups. There were also significant decreases of 20.24%, 32.60%, 11.23% and 24.99% in the qCO2 concentration in the non-weeded control and IWM treatment groups, respectively, compared to those in the chemical (herbicide) rotation and chemical weed control groups (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea, b, c, d, e).\u003c/p\u003e\n \u003cp\u003eAt the tasselling stage, there was an overall progressive increase in the soil microbial activity indices (SMAIs) due to the advancement of the crop. The trends in the SMAIs were similar to those observed at 30 DAS. Among all the tillage practices, ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(M)-ZT\u0026thinsp;+\u0026thinsp;R(\u003cem\u003eSr\u003c/em\u003e) had 10.92% and 26.64% SMBC, 5.53% and 19.04% SMBN, 1.88% and 9.18% SBR, and 2.27% and 13.64% qMB higher than those of CT(C)-ZT(M)-ZT(\u003cem\u003eSr\u003c/em\u003e) and CT(C)-CT(M)-Fallow(N\u003cem\u003eSr\u003c/em\u003e), respectively (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea, b, c, d, e). With respect to weed management choices, higher percentages of SMBC (10.83\u0026ndash;16.11% and 14.46\u0026ndash;19.54%), SMBN (11.80- 12.94% and 8.29\u0026ndash;9.47%), SBR (5.36\u0026ndash;9.67% and 1.58\u0026ndash;6.06%), and qMB (9.09\u0026ndash;15.91% and 14.89\u0026ndash;21.28%) were observed under IWM and non-weeded control, respectively, than under chemical weed control and chemical (herbicide) rotation. During the same stage of the crop (tasselling), the trends in qCO\u003csub\u003e2\u003c/sub\u003e were similar to those at 30 DAS, with a further significant decrease observed under ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(M)-ZT\u0026thinsp;+\u0026thinsp;R(\u003cem\u003eSr\u003c/em\u003e) relative to CT(C)-ZT(M)-ZT(\u003cem\u003eSr\u003c/em\u003e) and CT(C)-CT(M)-Fallow(N\u003cem\u003eSr\u003c/em\u003e). The non-weeded control and IWM also facilitated a considerable decrease in qCO\u003csub\u003e2\u003c/sub\u003e during crop growth compared to chemical (herbicide) rotation and chemical weed control (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea, b, c, d, e).\u003c/p\u003e\n \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\n \u003ch2\u003eSoil Enzyme Activities\u003c/h2\u003e\n \u003cp\u003eThe addition of ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(M)-ZT\u0026thinsp;+\u0026thinsp;R(\u003cem\u003eSr\u003c/em\u003e) and the nonweeded control, as well as the combination of chemical weed control and power\u0026thinsp;+\u0026thinsp;1 hand weeding (IWM), improved the activities of rhizosphere soil dehydrogenase (DHA), urease (SUA), alkaline and acid phosphatase (AlP and AcP), fluorescein diacetate (FDA) and \u0026beta;-galactosidase (\u0026beta;-GaA), which are involved in soil carbon (C), nitrogen (C) and phosphorus (P) cycling. This improvement in soil enzyme activity in the ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(M)-ZT\u0026thinsp;+\u0026thinsp;R(\u003cem\u003eSr\u003c/em\u003e), nonweeded control and IWM treatment groups was observed at both sampling stages, during which the activity significantly increased continuously with crop progression. Herbicides were applied at 30 DAS; after PE, EPoE, and PoE resulted in a massive decrease in the activity of the soil enzymes, which subsequently returned to their initial levels at the tasselling stage (Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea, b, c).\u003c/p\u003e\n \u003cp\u003eRhizosphere soil enzyme activity at 30 DAS in maize in the CT(C)-CT(M)-Fallow (N\u003cem\u003eSr\u003c/em\u003e) and CT(C)-ZT(M)-ZT(\u003cem\u003eSr\u003c/em\u003e) treatments was significantly lower than that in the ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(M)-ZT\u0026thinsp;+\u0026thinsp;R(\u003cem\u003eSr\u003c/em\u003e) treatment. The DHA, SUA, AlP, AcP, FDA, and \u0026beta;-GaA concentrations were 16.88% and 31.87%, 16.58% and 27.87%, 11.35% and 22.44%, 8.24% and 23.85%, and 12.35% and 19.77%, 9.44% and 16.87% greater, respectively, in the ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(M)-ZT\u0026thinsp;+\u0026thinsp;R(\u003cem\u003eSr\u003c/em\u003e) plots than in the CT(C)-ZT(M)-ZT(\u003cem\u003eSr\u003c/em\u003e) and CT(C)-CT(M)-Fallow (N\u003cem\u003eSr\u003c/em\u003e) plots (Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea, b, c). Among the weed management options, DHA, SUA, AlP, AcP, FDA, and \u0026beta;-GaA had 17.76\u0026ndash;18.68%, 30.28\u0026ndash;31.06%, 10.24\u0026ndash;11.79% and 25.51\u0026ndash;26.79%, 7.37\u0026ndash;9.29% and 18.80-20.48%, 2.41\u0026ndash;3.81% and 21.12\u0026ndash;22.26%, 4.31\u0026ndash;4.88% and 24.26\u0026ndash;24.71%, 3.89\u0026ndash;5.18% and 24.82\u0026ndash;25.83% greater under IWM and non-weeded control, respectively, than under chemical weed control and chemical (herbicide) rotation at the same sampling (30 DAS) (Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea, b, c).\u003c/p\u003e\n \u003cp\u003eAt the tasselling stage, the activities of all the rhizosphere soil enzymes exhibited trends similar to those observed under the weed management options and tillage practices at 30 DAS. Enzyme activities increased significantly irrespective of the treatment, and tillage was the main factor influencing these activities. During the crop growth development period (tasselling), for DHA, SUA, AlP, AcP, FDA, and \u0026beta;-GaA, the percentages were 10.85% and 20.61%, 10.19% and 15.67%, 15.77% and 28.56%, 5.23% and 23.24%, 21.17% and 35.97%, and 16.71% and 32.88% greater, respectively, under ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;M)-ZT\u0026thinsp;+\u0026thinsp;R(\u003cem\u003eSr\u003c/em\u003e) than under CT(C)-ZT(M)-ZT(\u003cem\u003eSr\u003c/em\u003e) and CT(C)-CT(M)-Fallow (N\u003cem\u003eSr\u003c/em\u003e) (Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea, b, c). With regard to weed management choices, for DHA, SUA, AlP, AcP, FDA, and \u0026beta;-GaA, 16.30-16.96% and 20.55\u0026ndash;21.18%, 3.95\u0026ndash;9.52% and 7.39\u0026ndash;12.76%, 12.03\u0026ndash;16.10% and 21.52\u0026ndash;25.15%, 4.63\u0026ndash;5.79% and 8.00-9.09%, 12.64\u0026ndash;17.04% and 15.02\u0026ndash;19.30%, 5.55\u0026ndash;7.89% and 10.53\u0026ndash;12.74% higher under IWM and non-weeded control, respectively, than chemical (herbicide rotation) and chemical weed control at the tasselling stage (Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea, b, c).\u003c/p\u003e\n \u003cp\u003eTillage practices (main treatments) and weed management options interaction effects on DHA was 19.09\u0026ndash;25.99%, 9.20-31.97%, 16.87\u0026ndash;39.04%, SUA was 7.18\u0026ndash;19.25%, 14.32\u0026ndash;32.72%, 29.59\u0026ndash;32.17% AlP was 7.34\u0026ndash;16.98%, 13.22\u0026ndash;22.34%, 26.37\u0026ndash;34.90%, AcP was 16.04\u0026ndash;22.84%, 15.02\u0026ndash;18.57%, 27.52\u0026ndash;28.01%, FDA was 8.71\u0026ndash;19.76%, 21.65\u0026ndash;28.80%, 27.41\u0026ndash;33.96%, \u0026beta;-GaA was 20.87\u0026ndash;26.22%, 26.24\u0026ndash;28.48%, 18.21\u0026ndash;24.62% higher under ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(M)-ZT\u0026thinsp;+\u0026thinsp;R(\u003cem\u003eSr\u003c/em\u003e) in combination with non-weeded control, CT(C)-ZT(M)-ZT(\u003cem\u003eSr\u003c/em\u003e) on interaction with non-weeded control, CT(C)-CT(M)-Fallow (N\u003cem\u003eSr\u003c/em\u003e) in combination with non-weeded control over ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(M)-ZT\u0026thinsp;+\u0026thinsp;R(\u003cem\u003eSr\u003c/em\u003e) in combination with IWM and chemical weed control or chemical (herbicide) rotation, CT-ZT-ZT on interaction with IWM and chemical weed control or chemical (herbicide) rotation, CT(C)-CT(M)-Fallow(N\u003cem\u003eSr\u003c/em\u003e) coupled with IWM and chemical weed control or herbicide rotation, respectively observed at 30 DAS of the crop (Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea, b, c). A progressive increase in overall rhizosphere soil enzyme activity was observed at the tasselling stage, and the treatment interaction effects (trends) on the activities of various enzymes appeared to be the same as those observed at 30 DAS, with significantly greater enzyme activity observed in the ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(M)-ZT\u0026thinsp;+\u0026thinsp;R(\u003cem\u003eSr\u003c/em\u003e) treatment in combination with the non-weeded control and IWM relative to all the other treatment combinations (Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea, b, c).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea\u0026nbsp;\u003c/strong\u003eImpact of tillage practices and weed management options on rhizosphere soil dehydrogenase\u003c/p\u003e\n \u003cp\u003e(\u0026micro;g TPF. g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e dry soil. day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and urease (\u0026micro;g NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e- N. g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e dry soil. 2 hr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) activity on two different maize plants\u003c/p\u003e\n \u003cp\u003egrowth stages.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tabb\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTreatment\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eSoil dehydrogenase activity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eSoil urease activity\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTillage\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWM\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e30 DAS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTasselling\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e30 DAS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTasselling\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eT\u003csub\u003e1\u003c/sub\u003e: CT(C)-CT(M)-Fallow (N\u003cem\u003eSr\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e43.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e31.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e65.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e48.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e33.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e68.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e56.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e32.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e70.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e62.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e46.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e77.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eT\u003csub\u003e2\u003c/sub\u003e: CT(C)-ZT(M)-ZT(\u003cem\u003eSr\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e51.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e34.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e67.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e49.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e35.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e74.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e66.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e44.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e78.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e67.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e51.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e80.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eT\u003csub\u003e3\u003c/sub\u003e: ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(Sr)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e63.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e48.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e79.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e62.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e44.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e82.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e68.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e51.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e85.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e70.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e55.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e86.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003eTillage (Main plots)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003csub\u003e1\u003c/sub\u003e: CT(C)-CT(M)-Fallow (N\u003cem\u003eSr\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e27.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e52.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e35.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e70.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003csub\u003e2\u003c/sub\u003e: CT(C)-ZT(M)-ZT(\u003cem\u003eSr\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e32.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e59.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e41.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e75.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003csub\u003e3\u003c/sub\u003e: ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(Sr)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e39.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e66.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e49.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e83.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeed Management (Subplots)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e1\u003c/sub\u003e- Chemical weed control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e28.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e52.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e38.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e70.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e2\u003c/sub\u003e- chemical (herbicide) rotation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e28.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e53.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e37.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e75.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e3\u003c/sub\u003e- IWM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e34.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e63.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e42.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e78.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e4\u003c/sub\u003e- Non-weeded control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e41.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e67.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e51.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e81.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE(m)\u0026plusmn;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(P\u0026thinsp;=\u0026thinsp;0.05)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE(m) \u0026plusmn;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(P\u0026thinsp;=\u0026thinsp;0.05)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE(m) \u0026plusmn;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(P\u0026thinsp;=\u0026thinsp;0.05)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE(m) \u0026plusmn;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(P\u0026thinsp;=\u0026thinsp;0.05)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTillage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeed Management\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003eInteractions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eW at same level of T\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eT at same level of W\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eCT\u0026thinsp;=\u0026thinsp;conventional tillage, ZT\u0026thinsp;=\u0026thinsp;zero tillage; R\u0026thinsp;=\u0026thinsp;crop residue retention; IWM\u0026thinsp;=\u0026thinsp;chemical weed control\u0026thinsp;+\u0026thinsp;power and 1 hand weeding; WM\u0026thinsp;=\u0026thinsp;weed management; C\u0026thinsp;=\u0026thinsp;cotton; M\u0026thinsp;=\u0026thinsp;maize; \u003cem\u003eS\u003c/em\u003er\u0026thinsp;=\u0026thinsp;\u003cem\u003eSesbania rostrata\u003c/em\u003e; CD (P\u0026thinsp;=\u0026thinsp;0.05)\u0026thinsp;=\u0026thinsp;critical difference at the 5% probability level; Ns\u0026thinsp;=\u0026thinsp;nonsignificant; SE(m)\u0026thinsp;=\u0026thinsp;standard error of the mean.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eb\u003c/strong\u003e: Impact of tillage practices and weed management options on rhizosphere soil acid and alkaline phosphatase activity (\u0026micro;g. \u003cem\u003ep\u003c/em\u003e-Nitrophenol. g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e dry soil. hr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) at two different maize growth stages.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTreatment\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eAcid phosphatase activity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eAlkaline phosphatase activity\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTillage\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWM\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e30 DAS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTasselling\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e30 DAS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTasselling\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eT\u003csub\u003e1\u003c/sub\u003e: CT(C)-CT(M)-Fallow (N\u003cem\u003eSr\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e122.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e117.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e221.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e123.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e120.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e226.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e129.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e132.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e241.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e130.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e179.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e251.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eT\u003csub\u003e2\u003c/sub\u003e: CT(C)-ZT(M)-ZT(\u003cem\u003eSr\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e148.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e143.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e260.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e153.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e146.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e277.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e157.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e160.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e275.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e164.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e184.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e296.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eT\u003csub\u003e3\u003c/sub\u003e: ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(Sr)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e162.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e176.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e306.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e152.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e160.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e300.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e167.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e179.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e337.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e176.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e193.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e373.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003eTillage (Main plots)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003csub\u003e1\u003c/sub\u003e: CT(C)-CT(M)-Fallow (N\u003cem\u003eSr\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e50.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e126.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e137.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e235.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003csub\u003e2\u003c/sub\u003e: CT(C)-ZT(M)-ZT(\u003cem\u003eSr\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e60.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e156.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e157.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e277.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003csub\u003e3\u003c/sub\u003e: ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(Sr)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e65.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e164.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e177.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e329.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeed Management (Subplots)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e1\u003c/sub\u003e- Chemical weed control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e55.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e144.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e145.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e262.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e2\u003c/sub\u003e- chemical (herbicide) rotation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e54.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e142.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e142.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e268.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e3\u003c/sub\u003e- IWM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e56.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e151.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e157.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e284.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e4\u003c/sub\u003e- Non-weeded control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e69.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e157.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e179.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e307.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE(m)\u0026plusmn;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(P\u0026thinsp;=\u0026thinsp;0.05)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE(m) \u0026plusmn;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(P\u0026thinsp;=\u0026thinsp;0.05)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE(m) \u0026plusmn;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(P\u0026thinsp;=\u0026thinsp;0.05)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE(m) \u0026plusmn;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(P\u0026thinsp;=\u0026thinsp;0.05)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTillage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeed Management\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003eInteractions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eW at same level of T\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eT at same level of W\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eCT\u0026thinsp;=\u0026thinsp;conventional tillage, ZT\u0026thinsp;=\u0026thinsp;zero tillage; R\u0026thinsp;=\u0026thinsp;crop residue retention; IWM\u0026thinsp;=\u0026thinsp;chemical weed control\u0026thinsp;+\u0026thinsp;power and 1 hand weeding; WM\u0026thinsp;=\u0026thinsp;weed management; C\u0026thinsp;=\u0026thinsp;cotton; M\u0026thinsp;=\u0026thinsp;maize; \u003cem\u003eS\u003c/em\u003er\u0026thinsp;=\u0026thinsp;\u003cem\u003eSesbania rostrata\u003c/em\u003e; CD (P\u0026thinsp;=\u0026thinsp;0.05)\u0026thinsp;=\u0026thinsp;critical difference at the 5% probability level; SE(m)\u0026thinsp;=\u0026thinsp;standard error of the mean.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003ec\u003c/strong\u003e: Impact of tillage practices and weed management options on rhizosphere soil fluorescein diacetate (\u0026micro;g. fluorescein. g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e dry soil.\u003csup\u003e3 h\u0026minus;1\u003c/sup\u003e) and \u0026beta;-galactosidase (nmol \u003cem\u003ep\u003c/em\u003e-nitrophenol. g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e dry soil. hr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) activity at two different maize growth stages.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTreatment\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eFluorescein di-acetate activity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026beta;-galactosidase activity\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTillage\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWM\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e30 DAS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTasselling\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e30 DAS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTasselling\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eT\u003csub\u003e1\u003c/sub\u003e: CT(C)-CT(M)-Fallow (N\u003cem\u003eSr\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e149.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e118.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e152.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e130.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e172.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e120.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e159.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e186.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e128.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e168.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e180.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e190.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e156.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e187.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eT\u003csub\u003e2\u003c/sub\u003e: CT(C)-ZT(M)-ZT(\u003cem\u003eSr\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e132.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e199.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e128.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e192.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e135.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e204.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e129.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e206.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e146.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e220.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e132.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e210.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e186.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e236.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e180.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e221.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eT\u003csub\u003e3\u003c/sub\u003e: ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(Sr)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e169.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e240.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e148.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e243.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e152.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e244.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e140.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e237.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e173.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e303.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e150.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e249.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e190.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e304.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e190.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e266.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003eTillage (Main plots)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003csub\u003e1\u003c/sub\u003e: CT(C)-CT(M)-Fallow (N\u003cem\u003eSr\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e137.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e174.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e131.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e167.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003csub\u003e2\u003c/sub\u003e: CT(C)-ZT(M)-ZT(\u003cem\u003eSr\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e150.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e215.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e142.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e207.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003csub\u003e3\u003c/sub\u003e: ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(Sr)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e171.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e273.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e157.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e249.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeed Management (Subplots)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e1\u003c/sub\u003e- Chemical weed control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e140.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e196.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e131.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e196.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e2\u003c/sub\u003e- chemical (herbicide) rotation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e139.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e207.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e130.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e201.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e3\u003c/sub\u003e- IWM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e146.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e237.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e137.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e213.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e4\u003c/sub\u003e- Non-weeded control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e185.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e243.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e175.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e225.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE(m)\u0026plusmn;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(P\u0026thinsp;=\u0026thinsp;0.05)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE(m) \u0026plusmn;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(P\u0026thinsp;=\u0026thinsp;0.05)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE(m) \u0026plusmn;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(P\u0026thinsp;=\u0026thinsp;0.05)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE(m) \u0026plusmn;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(P\u0026thinsp;=\u0026thinsp;0.05)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTillage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeed Management\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003eInteractions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eW at same level of T\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eT at same level of W\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eCT\u0026thinsp;=\u0026thinsp;conventional tillage, ZT\u0026thinsp;=\u0026thinsp;zero tillage; R\u0026thinsp;=\u0026thinsp;crop residue retention; IWM\u0026thinsp;=\u0026thinsp;chemical weed control\u0026thinsp;+\u0026thinsp;power and 1 hand weeding; WM\u0026thinsp;=\u0026thinsp;weed management; C\u0026thinsp;=\u0026thinsp;cotton; M\u0026thinsp;=\u0026thinsp;maize; \u003cem\u003eS\u003c/em\u003er\u0026thinsp;=\u0026thinsp;\u003cem\u003eSesbania rostrata\u003c/em\u003e; CD (P\u0026thinsp;=\u0026thinsp;0.05)\u0026thinsp;=\u0026thinsp;critical difference at the 5% probability level; SE(m)\u0026thinsp;=\u0026thinsp;standard error of the mean.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\n \u003ch2\u003eMicrobial population\u003c/h2\u003e\n \u003cp\u003eRhizosphere soil microbial and rhizoplane fungal counts were significantly influenced by different tillage practices and weed management practices, and the interaction effects of the tillage and weed management practices on the soil microbial and rhizoplane fungal populations were significant at both sampling stages (30 DAS and tasselling) (Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ea and b).\u003c/p\u003e\n \u003cp\u003eThe spraying of the herbicides either in rotation or repeatedly every other year, such as preemergence (PE), early postemergence (EPoE) and postemergence (PoE), at 30 DAS suppressed the growth and population of the microorganisms. Among all weed management options, at 30 DAS, the rhizosphere soils of \u003cem\u003eAzotobacter (Azot)\u003c/em\u003e, \u003cem\u003eAzospirillum (Azosp)\u003c/em\u003e, and total fungal (TF) and total fungal (RF) rhizoplane populations were 0.44\u0026ndash;0.66% and 3.62\u0026ndash;3.84%, 1.40\u0026ndash;1.63% and 4.51\u0026ndash;4.74%, 0.47-070% and 3.63\u0026ndash;3.85%, 1.79\u0026ndash;2.04% and 6.55\u0026ndash;6.80% greater under IWM and nonweeded control, respectively, than under chemical weed control and chemical (herbicide) rotation (Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ea, and b). In terms of all the different tillage systems, the rhizosphere soil \u003cem\u003eAzot\u003c/em\u003e, \u003cem\u003eAzosp\u003c/em\u003e, TF, and rhizoplane TF populations were 1.51% and 2.81%, 1.60% and 3.43%, 1.61% and 2.75%, and 3.69% and 6.39% greater under ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(M)-ZT\u0026thinsp;+\u0026thinsp;R(\u003cem\u003eSr\u003c/em\u003e) than under CT(C)-ZT(M)-ZT(\u003cem\u003eSr\u003c/em\u003e) and CT(C)-CT(M)-Fallow (N\u003cem\u003eSr\u003c/em\u003e), respectively, at 30 DAS (Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ea, and b). The population of rhizosphere soil microorganisms and rhizoplane total fungi (TF) increased significantly during the tasselling period of the crop in all the treatments, and tillage was the principal factor influencing the progressive increase in the microbial population. At that growth stage, for maize crops (tasselling), the populations of rhizosphere soil \u003cem\u003eAzot\u003c/em\u003e, \u003cem\u003eAzosp\u003c/em\u003e, TF, and rhizoplane TF were 1.20% and 1.80%, 1.21% and 2.23%, 2.38% and 4.75%, and 3.12 and 4.45% greater, respectively, in the ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(M)-ZT\u0026thinsp;+\u0026thinsp;R(\u003cem\u003eSr\u003c/em\u003e) treatment than in the CT(C)-ZT(M)-ZT(\u003cem\u003eSr\u003c/em\u003e) and CT(C)-CT(M)-Fallow (N\u003cem\u003eSr\u003c/em\u003e) treatments (Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ea, and b). A significant difference with a continuous increase in the overall microbial population was observed for all weed management options, possibly due to microorganism recovery from herbicidal injury at tasselling. The patterns of the growth of both the rhizosphere soil and rhizoplane microbial counts at that crop growth stage (tasselling) resembled the trends observed at 30 DAS.\u003c/p\u003e\n \u003cp\u003eDuring the initial stage of crop development (30 DAS), the interaction effects of the various treatments (tillage and weed management) on the rhizosphere soil \u003cem\u003eAzotobacter\u003c/em\u003e (\u003cem\u003eAzot\u003c/em\u003e) were 1.91\u0026ndash;3.39%, 3.62\u0026ndash;4.26%, and 3.88\u0026ndash;4.31%; \u003cem\u003eAzospirillum\u003c/em\u003e (\u003cem\u003eAzosp\u003c/em\u003e) counts were 2.90\u0026ndash;4.24%, 2.71\u0026ndash;4.30%, and 4.32\u0026ndash;6.36%; total fungal (TF) counts were 2.68\u0026ndash;4.25%, 2.51\u0026ndash;3.20%, and 3.89\u0026ndash;4.35%; rhizoplane total fungal (TF) counts were 2.64\u0026ndash;3.84%, 3.41\u0026ndash;7.56%, and 8.33\u0026ndash;9.31%; and the results were superior to those of the ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(\u003cem\u003eSr\u003c/em\u003e) treatment in combination with the non-weeded control, CT(C)-ZT(M)-ZT(\u003cem\u003eSr\u003c/em\u003e) combined with the non-weeded control, and CT(C)-CT(M)-Fallow (N\u003cem\u003eSr\u003c/em\u003e) coupled with the non-weeded control over the ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(M)-ZT\u0026thinsp;+\u0026thinsp;R(Sr) At the tasselling stage of the crop, all microbial counts increased further, regardless of the treatment combination. Among all the treatment interactions, at tasselling of maize, ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(M)-ZT\u0026thinsp;+\u0026thinsp;R(\u003cem\u003eSr\u003c/em\u003e) in combination with the unweeded control had a significantly greater rhizosphere soil microbial and rhizoplane fungal population, which was closely and statistically followed by the interaction of ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(M)-ZT\u0026thinsp;+\u0026thinsp;R(\u003cem\u003eSr\u003c/em\u003e) and IWM in comparison with all the other treatment combinations (Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ea and b).\u003c/p\u003e\n \u003cp\u003eThe fungal population observations indicated that the rhizosphere soil fungal counts were greater than the rhizoplane fungal counts during both sampling periods (30 DAS and tasselling) in the maize crop (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ea and b).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e: Impact of tillage practices and weed management options on rhizosphere soil \u003cem\u003eAzotobacter\u003c/em\u003e and \u003cem\u003eAzospirillum\u003c/em\u003e populations (log CFU g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e soil) at two different maize growth stages.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTreatment\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003eAzotobacter\u003c/em\u003e population\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cem\u003eAzospirillum\u003c/em\u003e population\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTillage\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWM\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e30 DAS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTasselling\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e30 DAS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTasselling\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eT\u003csub\u003e1\u003c/sub\u003e: CT(C)-CT(M)-Fallow (N\u003cem\u003eSr\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eT\u003csub\u003e2\u003c/sub\u003e: CT(C)-ZT(M)-ZT(\u003cem\u003eSr\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eT\u003csub\u003e3\u003c/sub\u003e: ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(Sr)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003eTillage (Main plots)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003csub\u003e1\u003c/sub\u003e: CT(C)-CT(M)-Fallow (N\u003cem\u003eSr\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003csub\u003e2\u003c/sub\u003e: CT(C)-ZT(M)-ZT(\u003cem\u003eSr\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003csub\u003e3\u003c/sub\u003e: ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(Sr)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeed Management (Subplots)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e1\u003c/sub\u003e- Chemical weed control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e2\u003c/sub\u003e- chemical (herbicide) rotation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e3\u003c/sub\u003e- IWM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e4\u003c/sub\u003e- Non-weeded control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE(m)\u0026plusmn;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(P\u0026thinsp;=\u0026thinsp;0.05)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE(m) \u0026plusmn;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(P\u0026thinsp;=\u0026thinsp;0.05)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE(m) \u0026plusmn;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(P\u0026thinsp;=\u0026thinsp;0.05)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE(m) \u0026plusmn;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(P\u0026thinsp;=\u0026thinsp;0.05)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTillage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeed Management\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003eInteractions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eW at same level of T\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eT at same level of W\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eCT\u0026thinsp;=\u0026thinsp;conventional tillage, ZT\u0026thinsp;=\u0026thinsp;zero tillage; R\u0026thinsp;=\u0026thinsp;crop residue retention; IWM\u0026thinsp;=\u0026thinsp;chemical weed control\u0026thinsp;+\u0026thinsp;power and 1 hand weeding; WM\u0026thinsp;=\u0026thinsp;weed management; C\u0026thinsp;=\u0026thinsp;cotton; M\u0026thinsp;=\u0026thinsp;maize; \u003cem\u003eS\u003c/em\u003er\u0026thinsp;=\u0026thinsp;\u003cem\u003eSesbania rostrata\u003c/em\u003e; CD (P\u0026thinsp;=\u0026thinsp;0.05)\u0026thinsp;=\u0026thinsp;critical difference at the 5% probability level; SE(m)\u0026thinsp;=\u0026thinsp;standard error of the mean.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eThe table mean values are in log CFU g\u003c/strong\u003e \u003csup\u003e\u0026nbsp;\u003cstrong\u003e-1\u003c/strong\u003e\u0026nbsp;\u003c/sup\u003e \u003cstrong\u003esoil from log transformation of exponential (10\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e) values from CFU g\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e-1\u003c/strong\u003e\u003c/sup\u003e \u003cstrong\u003esoil (oven dry basis) taken from plate counts.\u003c/strong\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab9\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eb\u003c/strong\u003e: Impact of tillage practices and weed management options on the total fungal population of the rhizosphere soil and rhizoplane at two different maize growth stages.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTreatment\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eRhizosphere soil total fungal population (log CFU g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e soil)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eRhizoplane total fungal\u003c/p\u003e\n \u003cp\u003ePopulation (log CFU g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e roots)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTillage\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWM\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e30 DAS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTasselling\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e30 DAS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTasselling\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eT\u003csub\u003e1\u003c/sub\u003e: CT(C)-CT(M)-Fallow (N\u003cem\u003eSr\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eT\u003csub\u003e2\u003c/sub\u003e: CT(C)-ZT(M)-ZT(\u003cem\u003eSr\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eT\u003csub\u003e3\u003c/sub\u003e: ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(Sr)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003eTillage (Main plots)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003csub\u003e1\u003c/sub\u003e: CT(C)-CT(M)-Fallow (N\u003cem\u003eSr\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003csub\u003e2\u003c/sub\u003e: CT(C)-ZT(M)-ZT(\u003cem\u003eSr\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003csub\u003e3\u003c/sub\u003e: ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(Sr)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeed Management (Subplots)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e1\u003c/sub\u003e- Chemical weed control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e2\u003c/sub\u003e- chemical (herbicide) rotation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e3\u003c/sub\u003e- IWM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e4\u003c/sub\u003e- Non-weeded control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE(m)\u0026plusmn;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(P\u0026thinsp;=\u0026thinsp;0.05)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE(m) \u0026plusmn;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(P\u0026thinsp;=\u0026thinsp;0.05)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE(m) \u0026plusmn;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(P\u0026thinsp;=\u0026thinsp;0.05)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE(m) \u0026plusmn;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(P\u0026thinsp;=\u0026thinsp;0.05)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTillage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeed Management\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003eInteractions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eW at same level of T\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eT at same level of W\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eCT\u0026thinsp;=\u0026thinsp;conventional tillage, ZT\u0026thinsp;=\u0026thinsp;zero tillage; R\u0026thinsp;=\u0026thinsp;crop residue retention; IWM\u0026thinsp;=\u0026thinsp;chemical weed control\u0026thinsp;+\u0026thinsp;power and 1 hand weeding; WM\u0026thinsp;=\u0026thinsp;weed management; C\u0026thinsp;=\u0026thinsp;cotton; M\u0026thinsp;=\u0026thinsp;maize; \u003cem\u003eS\u003c/em\u003er\u0026thinsp;=\u0026thinsp;\u003cem\u003eSesbania rostrata\u003c/em\u003e; CD (P\u0026thinsp;=\u0026thinsp;0.05)\u0026thinsp;=\u0026thinsp;critical difference at the 5% probability level; SE(m)\u0026thinsp;=\u0026thinsp;standard error of the mean.\u003c/p\u003e\n \u003cp\u003eThe table mean values are in log CFU g\u003csup\u003e-1\u003c/sup\u003e soil/roots from log transformation of exponential (10\u003csup\u003e3\u003c/sup\u003e) values from CFU g\u003csup\u003e-1\u003c/sup\u003e soil (oven dry basis)/roots taken from plate counts.\u003c/p\u003e\n \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\n \u003ch2\u003eFungal Diversity\u003c/h2\u003e\n \u003cp\u003eSub-culturing of the fungi from the culture plates was performed prior to sequencing to purify the fungal strains, as depicted in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, and agarose gel electrophoresis images of total deoxyribonucleic acid (DNA) and polymerase chain reaction (PCR) products of the 18s rRNa gene are shown in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea, b and c. The fungi were identified based on nucleotide sequence homology of the 18 s rRNA gene \u003cstrong\u003eare\u003c/strong\u003e presented in Table \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e. The results of 18S rRNA gene sequencing indicated that \u003cem\u003eTalaromyces flavus var. flavus\u003c/em\u003e (5-PJTSAU-KNIGHT-23) was identified under the T\u003csub\u003e3\u003c/sub\u003e: ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(M)-ZT\u0026thinsp;+\u0026thinsp;R(\u003cem\u003eSr\u003c/em\u003e) and W\u003csub\u003e3\u003c/sub\u003e: IWM combinations (T\u003csub\u003e3\u003c/sub\u003eW\u003csub\u003e3\u003c/sub\u003e) and T\u003csub\u003e2\u003c/sub\u003e: CT(C)-ZT(M)-ZT(\u003cem\u003eSr\u003c/em\u003e) on interaction with IWM (T\u003csub\u003e2\u003c/sub\u003eW\u003csub\u003e3\u003c/sub\u003e). The other species of rhizosphere soil fungal and rhizoplane fungal isolates, \u003cem\u003eviz\u003c/em\u003e., \u003cem\u003eAspergillus niger, Penicillium limosum, Aspergillus terreus, Apiospora serenensis, Zasmidium cellare\u003c/em\u003e, and \u003cem\u003eOchraceocephala foeniculi\u003c/em\u003e, were identified under the T\u003csub\u003e1\u003c/sub\u003e: CT(C)-CT(M)-Fallow (N\u003cem\u003eSr\u003c/em\u003e), T\u003csub\u003e2\u003c/sub\u003e: CT(C)-ZT(M)-ZT(\u003cem\u003eSr\u003c/em\u003e) and T\u003csub\u003e3\u003c/sub\u003e: ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(M)-ZT\u0026thinsp;+\u0026thinsp;R(\u003cem\u003eSr\u003c/em\u003e) tillage (main treatments) in combination with the W\u003csub\u003e1\u003c/sub\u003e: chemical weed control, W\u003csub\u003e2\u003c/sub\u003e: chemical (herbicide) rotation and W\u003csub\u003e4\u003c/sub\u003e: non-weeded control (sub-treatments) (Table \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e). The isolate ID (8-PJTSAU-KNIGHT), which was isolated abundantly from the rhizoplane zone across all the tillage and weed management treatments, was identified as \u003cem\u003ePenicillium limosum\u003c/em\u003e. Phylogenetic tree(s) of all the 8 identified fungal species and multiple sequence alignments (MSAs) of the data are included in the supplementary data.\u003c/p\u003e\n \u003ctable id=\"Tab10\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eImpact of tillage and weed management practices on fungal diversity at the tasselling stage of winter maize.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eRhizosphere soil fungal microbe (s)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS.NO\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIsolate ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTreatment combination\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFungal name\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIdentity\u003c/p\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAccession\u003c/p\u003e\n \u003cp\u003enumbers\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1-PJTSAU-KNIGHT-23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003csub\u003e1\u003c/sub\u003eW\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAspergillus niger\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e100.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003ePP177339\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1-PJTSAU-KNIGHT-23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003csub\u003e1\u003c/sub\u003eW\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAspergillus niger\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1-PJTSAU-KNIGHT-23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003csub\u003e1\u003c/sub\u003eW\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAspergillus niger\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2-PJTSAU-KNIGHT-23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003csub\u003e1\u003c/sub\u003eW\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAspergillus niger\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePP177340\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3-PJTSAU-KNIGHT-23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003csub\u003e2\u003c/sub\u003eW\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAspergillus terrus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.33%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePP177341\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4-PJTSAU-KNIGHT-23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003csub\u003e2\u003c/sub\u003eW\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eApiospora serenensis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e98.47%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePP177342\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4-PJTSAU-KNIGHT-23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003csub\u003e3\u003c/sub\u003eW\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5-PJTSAU-KNIGHT-23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003csub\u003e2\u003c/sub\u003eW\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eTaloromyces flavus var. flavus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e99.63%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePP177343\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5-PJTSAU-KNIGHT-23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003csub\u003e3\u003c/sub\u003eW\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6-PJTSAU-KNIGHT-23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003csub\u003e2\u003c/sub\u003eW\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eZasmidium cellare\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePP177344\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7-PJTSAU-KNIGHT-23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003csub\u003e3\u003c/sub\u003eW\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003ePenicillium limosum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e99.88%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePP177345\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7-PJTSAU-KNIGHT-23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003csub\u003e3\u003c/sub\u003eW\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eRhizoplane fungal microbe (s)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8-PJTSAU-KNIGHT-23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbundant in all T \u0026amp; W combinations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eOchraceocephala foeniculi\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.55%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePP177346\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\u003cbr\u003e\u003cstrong\u003eMain treatments\u003c/strong\u003e: T\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;CT(C)-CT(M)-Fallow (N\u003cem\u003eSr\u003c/em\u003e); T\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;CT(C)-ZT(M)-ZT(\u003cem\u003eSr\u003c/em\u003e); T\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(M)-ZT\u0026thinsp;+\u0026thinsp;R(\u003cem\u003eSr\u003c/em\u003e); \u003cstrong\u003eSub-treatments\u003c/strong\u003e: W\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;chemical weed control; W\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;chemical (herbicide) rotation; W\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;chemical weed control and power\u0026thinsp;+\u0026thinsp;1 hand weeding; W\u003csub\u003e4\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;non-weeded control. T\u0026thinsp;=\u0026thinsp;Tillage; W\u0026thinsp;=\u0026thinsp;Weed Management, CT\u0026thinsp;=\u0026thinsp;Conventional Tillage, ZT\u0026thinsp;=\u0026thinsp;Zero Tillage; C\u0026thinsp;=\u0026thinsp;Cotton; M\u0026thinsp;=\u0026thinsp;Maize; \u003cem\u003eS\u003c/em\u003er\u0026thinsp;=\u0026thinsp;\u003cem\u003eSesbania rostrata\u003c/em\u003e.\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\n \u003cp\u003e\u003cstrong\u003eThe details of the entire sequence along with accession numbers can be accessed through the following link\u003c/strong\u003e:\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u0026nbsp;\u003cspan class=\"RefSource\"\u003ehttps://submit.ncbi.nlm.nih.gov/subs/?search=SUB14162715\u003c/span\u003e \u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\n \u003ch2\u003eCrop productivity\u003c/h2\u003e\n \u003cdiv id=\"Sec30\" class=\"Section3\"\u003e\n \u003ch2\u003eMaize Grain Yield and System Productivity (Cotton Equivalent Yield)\u003c/h2\u003e\n \u003cp\u003eTillage and weed management practices exerted significant influences on maize grain yield (kernel yield) and system productivity in terms of cotton equivalent yield (CEY) (Table \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e). The treatment interaction effects on CEY were significant and not significant for kernel yield (KY) (Table \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eA significantly greater KY (6801 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) was recorded in the ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(M)-ZT\u0026thinsp;+\u0026thinsp;R(\u003cem\u003eSr\u003c/em\u003e) treatment, while a lower KY (6014 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) was observed in the CT(C)-CT(M)-Fallow(N\u003cem\u003eSr\u003c/em\u003e) treatment. The adoption of chemical weed control and chemical (herbicide) rotation resulted in significantly greater KY (7245 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 7324 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), followed by chemical weed control and power\u0026thinsp;+\u0026thinsp;1 hand weeding (IWM) with a KY of 6722 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. A significantly lower KY (4099 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) was exhibited by the non-weeded control (Table \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe maize grain yield, which was recorded from different tillage-weed management treatment combinations, was converted into cotton equivalent yield (CEY) considering the monitory equivalence. The winter CEY was subsequently added to the monsoon cotton yield of the 4th year to arrive at the cotton equivalent yield of the cotton\u0026ndash;maize system (system CEY) for the 4th year. The data on the system CEY are presented in Table \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(M)-ZT\u0026thinsp;+\u0026thinsp;R(\u003cem\u003eSr\u003c/em\u003e) exhibited a significantly greater CEY (3775 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) than did CT(C)-ZT(M)-ZT(\u003cem\u003eSr\u003c/em\u003e) and CT(C)-CT(M)-Fallow(N\u003cem\u003eSr\u003c/em\u003e), with a CEY of 3517 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e 3328 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (Table \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e). Among the weed management strategies, IWM had a significantly greater system CEY (4157 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) than chemical (herbicide) rotation, chemical weed control or non-weeded control, with system CEYs of 4065 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 4018 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 1921 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea). Based on the tillage and weed management interactions, in combination with the IWM, ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(M)-ZT\u0026thinsp;+\u0026thinsp;R(\u003cem\u003eSr\u003c/em\u003e) had a significantly greater CEY (4453 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), and the lowest CEY values (1767 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 1848 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively) were observed with CT(C)-ZT(M)-ZT(\u003cem\u003eSr\u003c/em\u003e) in combination with the non-weeded control and CT(C)-CT(M)-Fallow(N\u003cem\u003eSr\u003c/em\u003e) in combination with the non-weeded control in comparison with all the other treatment combinations (Table \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e). The CT(C)-CT(M)-Fallow(N\u003cem\u003eSr\u003c/em\u003e) and all weed management combinations were also associated with a lower system CEY (Table \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab11\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe grain yield of maize and the system yield in terms of system cotton equivalent yield (CEY) were influenced by tillage practices and weed management (WM) options after the 4th year in the 8th crop cycle under conservation agriculture.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTreatment Interaction\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003ekernel yield\u003c/p\u003e\n \u003cp\u003e(kg ha-₁)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\" rowspan=\"2\"\u003e\n \u003cp\u003eSystem (CEY)\u003c/p\u003e\n \u003cp\u003e(kg ha-₁)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTillage\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eWM\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\" rowspan=\"5\"\u003e\n \u003cp\u003eT\u003csub\u003e1\u003c/sub\u003e: CT(C)-CT(M)-Fallow (N\u003cem\u003eSr\u003c/em\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eW\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6822\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e3756\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eW\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6854\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e3801\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eW\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6354\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e3908\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eW\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4025\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1848\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eW\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e7133\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e4005\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eT\u003csub\u003e2\u003c/sub\u003e: CT(C)-ZT(M)-ZT(\u003cem\u003eSr\u003c/em\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eW\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e7662\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e4187\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eW\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6558\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e4109\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eW\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3559\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1767\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eT\u003csub\u003e3\u003c/sub\u003e: ZT\u0026thinsp;+\u0026thinsp;R(C)- ZT\u0026thinsp;+\u0026thinsp;R(M)- ZT\u0026thinsp;+\u0026thinsp;R(\u003cem\u003eSr\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eW\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e7780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e4292\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eW\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e7456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e4206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eW\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e7253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e4453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eW\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e2157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003e\u003cstrong\u003eTillage practices\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003csub\u003e1\u003c/sub\u003e: CT(C)-CT(M)-Fallow (N\u003cem\u003eSr\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e6014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e3328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003csub\u003e2\u003c/sub\u003e: CT(C)-ZT(M)-ZT(\u003cem\u003eSr\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e6228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e3517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003csub\u003e3\u003c/sub\u003e: ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(Sr)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e6801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e3775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeed Management options\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e1\u003c/sub\u003e- Chemical weed control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e7245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e4018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e2\u003c/sub\u003e-chemical (herbicide rotation)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e7324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e4065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e3\u003c/sub\u003e- IWM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e6722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e4157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003csub\u003e4\u003c/sub\u003e- Non -weeded control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e4099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE(m)\u0026plusmn;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD(P\u0026thinsp;=\u0026thinsp;0.05)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE(m)\u0026plusmn;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD(P\u0026thinsp;=\u0026thinsp;0.05)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTillage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e144.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e568.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e18.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeed Management\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e126.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e377.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e40.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003e\u003cstrong\u003eInteractions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eW at same level of T\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e219.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e69.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e207.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eT at same level of W\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e239.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e63.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e187.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eCT\u0026thinsp;=\u0026thinsp;conventional tillage, ZT\u0026thinsp;=\u0026thinsp;zero tillage; R\u0026thinsp;=\u0026thinsp;crop residue retention; IWM\u0026thinsp;=\u0026thinsp;chemical weed control\u0026thinsp;+\u0026thinsp;power and 1 hand weeding; C\u0026thinsp;=\u0026thinsp;cotton; M\u0026thinsp;=\u0026thinsp;maize; \u003cem\u003eS\u003c/em\u003er\u0026thinsp;=\u0026thinsp;\u003cem\u003eSesbania rostrata\u003c/em\u003e; CD (P\u0026thinsp;=\u0026thinsp;0.05)\u0026thinsp;=\u0026thinsp;critical difference at the 5% probability level; Ns\u0026thinsp;=\u0026thinsp;nonsignificant; SE(m)\u0026thinsp;=\u0026thinsp;standard error of the mean.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003eSoil pH and soil organic carbon\u003c/h2\u003e \u003cp\u003eAmong all the other soil factors, tillage and weed management strategies contribute to the alteration of soil organic carbon (SOC). The SOC concentration in the soil surface soil under no-till conditions with at least 30% maintenance of crop debris is less prone to depletion due to lower soil disturbance and cumulative crop residue, thus yielding a greater SOC content [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. This greater SOC content exhibited by ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(M)-ZT\u0026thinsp;+\u0026thinsp;R(\u003cem\u003eSr\u003c/em\u003e) could be associated with the continuous adoption of ZT, accrual retention and incorporation of the preceding cotton and \u003cem\u003eSesbania\u003c/em\u003e crops into the soil for consecutive years, which resulted in soil aggregation enhancement and shielded the soil against SOC loss. Similar results were presented by Bitew et al. [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] for a CA-based maize\u0026ndash;legume cropping sequence. In zones where soil and weather conditions are conducive to the production of biomass and where adverse crop yield effects are unnoticed, CA practices result in greater quantities of SOC than CT-managed systems, especially in the topsoil. CT transposes the soil, displaces the soil clods, and exposes SOM to wetting-drying phenomena, resulting in a reduction in SOC content [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. The reduction in SOC levels observed under CT(C)-CT(M)-Fallow(N\u003cem\u003eSr\u003c/em\u003e) could be the result of the continuous removal of crop leaves, and primary and secondary tillage are employed for ploughing, which disturbs soil aggregation and promotes susceptibility to erosion. Thus, CA-based practices such as zero tillage (ZT) are directly associated with the maintenance of crop residues and nutrient management, which in turn impacts SOC accumulation and dynamics under diversified cropping systems. In contrast to CT, conservation tillage practices, which retain crop debris, tend to stabilize soil pH conditions and elevate SOC, which is conducive to increasing the soil microbial composition through the continuous disposal of crop residues.\u003c/p\u003e \u003cdiv id=\"Sec33\" class=\"Section3\"\u003e \u003ch2\u003eSoil microbial activity\u003c/h2\u003e \u003cp\u003eThe potential influence of several biogeocenotic services in the soil environment can be expressed to a certain degree by the activities of microbes; thus, the soil basal respiration (SBR), metabolic quotient (qCO\u003csub\u003e2\u003c/sub\u003e) and microbial quotient (qMB) were employed to perform an extensive assessment of microbial activity. The results of the present investigation demonstrated that zero tillage (ZT) with retention of crop residues resulted in significantly greater microbial activity (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, b, c), probably due to the ample additive-free materials drawn from the crops, which can become vital components for rapid metabolic reactions to external sources of carbon and slow down the figure(s) of the metabolic quotient (qCO\u003csub\u003e2\u003c/sub\u003e), thereby facilitating the utilization of large quantities of additive-free substrates by microbiomes for proliferation rather than for respiration utilization purposes. The increased qMB under ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(M)-ZT\u0026thinsp;+\u0026thinsp;R(\u003cem\u003eSr)\u003c/em\u003e apparently indicated the efficiency of soil microbes in utilizing sources of carbon materials for survival and growth as opposed to tillage practices, in which continuous crop residue remains (CT(C)-ZT(M)-ZT(\u003cem\u003eSr\u003c/em\u003e) and CT(C)-CT(M)-Fallow(N\u003cem\u003eSr\u003c/em\u003e), which can exert a small impact on soil microbes to a certain extent on biological oxygen requirements, might equilibrate the proportion of carbon dioxide released by microbes to microbial biomass.\u003c/p\u003e \u003cp\u003eThe observed results also suggest that soil microbial biomass carbon (SMBC) and nitrogen (SMBN), SBR and qMB increase as crops advance, particularly with the incorporation of nonchemical cultural weed control practices and the adoption of no-till practices along with the continuous retention of previous crop residues. This increase can be attributed to the most active and reproductive stage of the crop in which the rhizosphere begins to become enriched with specific microorganisms and nutrients necessary to perform activities related to SOM cycling. Thus, the increases in SMBN, SMBC, SBR and qMB might also be the result of root proliferation, exudation and crop litterfall, which serve as substrates for the activity of microorganisms and favourable biophysical climates created for microbes under ZT, promoting better soil functional diversity. Additionally, the availability of energy and nutrient resources and the limited oxidation of soil organic carbon, favoured by the prevalence of soil microorganisms, likely contributed to this observed enhancement. Kon\u0026eacute; et al. [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e] reported that soil microorganisms that in turn may improve biological activity in the rhizosphere include more plants with extensive rooting systems and more well-spread root hairs [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], and these characteristics are found to contribute to the exudation of vast amounts of organic compounds and consequently promote an increase in SMBC and SMBN [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. These results are supported by Chaudhari et al. (2020), who observed the combination of zero tillage\u0026thinsp;+\u0026thinsp;residue incorporation with inter-cultivation (IC)\u0026thinsp;+\u0026thinsp;hand weeding (HW) at 15, 30 and 45 DAS as the most suitable strategy for sustaining the greatest soil microbial biomass. Less soil disturbance under conservation tillage and crop residue retention/incorporation tend to improve aggregation in soil and SMBC, possibly due to a rise in SOC [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Hazarika et al. [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e] also indicated that, compared with those in conventionally tilled soil, the SMBN content in the till and/or reduced tillage systems was greater, which is also consistent with the results of this study.\u003c/p\u003e \u003cp\u003eConventional tillage disrupts soil aggregates, exposes the soil and increases the susceptibility to erosion, and herbicides may stimulate or activate the soil microbial biomass. In the present study, intensive tillage and herbicide spraying caused a drastic decrease in SMBC after preemergence and after the emergence of herbicides at 30 DAS, possibly due to increased soil disturbance and herbicide inhibition. Modak et al. [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e] also reported a reduction in SMBC after herbicide application owing to the adverse effects of herbicides on the microbial population, while in treatments without the involvement of herbicides, more SMBC and SMBN were recorded relative to those in treatments with herbicide components. Similarly, Pertile et al. [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e] observed a significant reduction in SMBC and SMBN following the application of flumioxazin and imazethapyr herbicides compared to those in the control. The positive response of conservation tillage practices compared to conventional tillage systems was probably due to higher levels of C substrates available for microorganism growth, as well as better soil physical conditions and greater water retention due to altered land configurations and applied residues [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSoil disturbance as a result of intensive tillage had a significant impact on increasing the mean qCO2. Likewise, the application of herbicides before and after emergence greatly increased the mean qCO2 value in this study. The lower metabolic quotient (qCO\u003csub\u003e2\u003c/sub\u003e) values observed under ZT, which leaves crop residue in the soil, could be an indicator of the lower energy requirements of microorganisms. Similarly, Engell et al. [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e] reported lower qCO\u003csub\u003e2\u003c/sub\u003e values. These findings indicate a low demand for energy maintenance by the microbial community. This discovery is in accordance with the meta-analysis of Zuber and Villamil [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e] under similar soil field conditions as in the present study and indicated that sandy clay loam soils have lower qCO\u003csub\u003e2\u003c/sub\u003e values under NT than under CT, although the impact of tillage was found to be low in soils with very fine particles. Low values of qCO\u003csub\u003e2\u003c/sub\u003e are an indication of conducive conditions for the predominance of microbial activity [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe results of the present study on qCO\u003csub\u003e2\u003c/sub\u003e are also supported by Jiang \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e], who reported a significant increase in qCO2 at 0\u0026ndash;20 cm soil depth under a conventional tillage system. Similarly, in the study of Aziz \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e], the qCO2 was up to 50% greater under conventional tillage than under no-tillage. A lower qCO2 reflected improved physiological conditions resulting from amended organic matter, while a higher qCO2 indicated soil degradation under intensive land use [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. On the other hand, a rise in qCO2 might not only be ascribed to microbial stress but also be interpreted as a positive priming on the decomposition of the labile soil organic carbon pool following the addition of readily degradable carbon substrates to the soil [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In the present investigation, higher qCO\u003csub\u003e2\u003c/sub\u003e was associated with low values of SMBC in conventionally tilled plots and herbicide-treated plots and is likely to reflect stress and poor conditions related to physical soil disturbance.\u003c/p\u003e \u003cp\u003eWeed management involving herbicides was found to increase qCO\u003csub\u003e2,\u003c/sub\u003e indicating stress or disorder, probably due to the detrimental effects of applied herbicides on the soil microbial population. In accordance with the findings of the present study, Pertile \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e] observed an increase in the metabolic quotient during the first 15 days of herbicide application, although their study was conducted under soil incubation conditions, indicating an initial negative effect of the herbicides on soil microorganisms. Since the application of chemical compounds in the soil requires an adaptation of soil microbial biomass that uses their reserves to degrade these compounds, C from microbial biomass ultimately becomes lost, thus increasing qCO\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section3\"\u003e \u003ch2\u003eRhizosphere soil enzyme activity\u003c/h2\u003e \u003cp\u003eMaintenance of crop leftovers in zero tillage (ZT) plots, cultural weed control tactics and SOC preservation in conservation agriculture positively influence biomass production and activated soil microbes by modifying the provision of the substrate. Rhizosphere soil enzymes such as dehydrogenase (DHA), fluorescein diacetate (FDA), β-galactosidase (β-GaA), alkaline phosphatase (AlA) and acid phosphatase (AcA) play essential roles in the breakdown of carbon in the soil [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e], and urease (SUA) plays a role in the hydrolysis of urea. The present investigation clearly indicated that ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(M)-ZT\u0026thinsp;+\u0026thinsp;R(\u003cem\u003eSr\u003c/em\u003e) and cultural weed control methods (nongweeded control and IWM) are likely to encourage the metabolic reactions necessary for the development of biotic microorganisms, which ultimately advance the activity of urea hydrolysis and carbon (C) cycling, \u003cem\u003ei.e.\u003c/em\u003e, greater succession of C and nitrogen (N), which can aid in the accrual of microbial biomass [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe functional groups of culturable microbes are often interlinked with C and N cycling activities and are related to rhizosphere soil enzymes [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e], demonstrating that alterations in SOC fractions can become the chief driver of soil microorganism constituents [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. Since rhizosphere soil enzymes are secreted by specific groups of microbes, the diversity of crops plays a key role in enhancing the activity of enzymes, which agrees with the results of this study in which ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(M)-ZT\u0026thinsp;+\u0026thinsp;R(\u003cem\u003eSr\u003c/em\u003e) (zero tillage with diverse crop residues retained in the soil surface), nonweeded control and IWM treatments resulted in increased activity of rhizosphere enzymes. The diversification of crops (cotton-maize-\u003cem\u003eSesbania rostrata\u003c/em\u003e) in rotation resulted in a significant modification in the activity of enzymes, probably due to the greater variety of crops having greater distinct litterfall and rhizosphere exudation; thus, the ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(M)-ZT\u0026thinsp;+\u0026thinsp;R(\u003cem\u003eSr\u003c/em\u003e), nonweeded control and IWM treatments under these diverse cropping systems have shown a large influence on rhizosphere soil functional diversity. The addition of crop residues through crop residue retention and ZT resulted in greater soil enzyme activity than intensive tillage with continuous crop residue removal. The activity of overall enzymes increased significantly with crop growth advancement, possibly due to the secretion of beneficial nutrients, the decomposition of organic substrates and herbicide degradation.\u003c/p\u003e \u003cp\u003eIt can be inferred that the significant reductions in the rhizosphere soil DHA, FDA, SUA, AlA, AcA, and β-GaA concentrations were more pronounced when herbicides were applied for weed management. The order of reduction in the activity of these enzymes was chemical (herbicide) rotation followed by chemical weed control and IWM rotation (W\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;\u0026gt;\u0026thinsp;W\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;\u0026gt;\u0026thinsp;W\u003csub\u003e3\u003c/sub\u003e) at 30 DAS. The higher rhizosphere soil enzyme activities observed in the ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(M)-ZT\u0026thinsp;+\u0026thinsp;R(\u003cem\u003eSr\u003c/em\u003e) treatment group relative to those in the CT(C)-CT(M)-Fallow (N\u003cem\u003eSr\u003c/em\u003e) or CT(C)-ZT(M)-ZT(\u003cem\u003eSr\u003c/em\u003e) treatment groups at 30 DAS could be associated with the partial inhibition of preemergence herbicides reaching the soil. This inhibition is likely a result of the presence of crop residues (cotton crop residues utilized for maize) on the soil surface in ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(M)-ZT\u0026thinsp;+\u0026thinsp;R(\u003cem\u003eSr\u003c/em\u003e). These results concur with those of Priya \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e], who observed that herbicides significantly inhibited soil DHA after application at 15 DAS, although their study was conducted under soil incubation conditions. Modak \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e] also recorded the maximum values of DHA under two weed management treatments without the use of herbicides, \u003cem\u003eviz\u003c/em\u003e., weedy checks, hoeing and weeding twice. The findings of Varsha et al. [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e] on SUA support the observations obtained in the present study, in which SUA decreased following herbicide application, and the activity returned to normal over time. This might be due to the herbicide effect on microbial population stabilized after time or the herbicide themselves adsorbed irreversibly on soil colloids with an increase in time, resulting in decreased inhibition. Similarly, Raj et al. [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e] revealed a reduction in acid (AcA) and alkaline (AlA) phosphatase activity in all herbicide treatment plots 15 days after herbicide application, but 45 days after herbicide application, increases in AcA and AlA were observed. This difference might be due to the change in the species composition of the soil microorganisms and variation in the availability of the organic substrate. The results of Madsen \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e] also concur with those of the present investigation, in which the highest FDA was recorded in systems where the soil was covered with winter crop residues due to added organic matter (OM) and the lowest FDA occurred in conventional systems (systems without crop cover and treated with herbicides) due to the very low input of OM and usage of herbicides and pesticides. Shahid \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e] also reported the adverse effects of herbicide application on the secretion of β-GaA. Thus, it may be deduced that, from the results of this study, herbicide treatments resulted in a significant decrease in rhizospheric soil enzyme activity in comparison with that in the untreated plots (non-weeded control plots) of soil samples.\u003c/p\u003e \u003cp\u003eWater plays an essential and complex role in the activity of enzymes. The activity of the enzymes involved in SOM cycling and urea hydrolysis assessed in this study increased with increasing soil water content up to near field capacity, followed by a decreasing trend thereafter [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. In the case of our experiment, the soil water content was maintained at the field capacity level through supplemental irrigation; thus, the enzyme activity increased throughout the treatment. Savant \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e] also observed a greater rate of urea hydrolysis in soil at field capacity than in wetted soils after 24 h of incubation. Several studies have indicated that soil DHA is significantly influenced by water content and decreases with decreasing soil humidity [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. In addition, DHA reached higher values at lower soil water potentials [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. In the winter season, there was a low rainfall amount; thus, a lower oxygen diffusion rate and redox potential were observed; however, the field moisture level were maintained with supplemental irrigation, and the relative humidity also increased, which could be the reason for the higher DHA observed in this study irrespective of the treatments. Wang \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e] observed a reduction in SUA due to flooding as a result of a high amount of rainfall, which was probably due to an increase in metal ions under reduced conditions, which decreased SUA. Therefore, during the sampling periods in winter, there was an increase in humidity and no flooding conditions due to scant rainfall; therefore, irrigation was provided for the development of the crop and for retention of the field moisture content, which might be the reason for obtaining higher SUA levels irrespective of the treatment.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eMicrobial population\u003c/h3\u003e\n\u003cp\u003eSoil organic matter (SOM) is a crucial driver of microbial population size and diversity and can affect microbial counts [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e]. Similarly, the rhizosphere soils of \u003cem\u003eAzospirillum (Azosp)\u003c/em\u003e, \u003cem\u003eAzotobacter\u003c/em\u003e (\u003cem\u003eAzot\u003c/em\u003e), and total fungal (TF) populations were greater where crop residues were retained, probably due to the greater addition of organic matter through crop residues and the reduced tillage and access to a steady source of organic carbon to support the microbial population compared to conventional tillage (CT). A decreased disturbance of soil favours the formation and stabilization of macroaggregates to improve and protect habitats for microbial populations. Zero-till (ZT) increases soil aggregation by reducing soil disturbance and increasing SOM and possibly increasing the growth of microbes that bind soil particles and microaggregates together [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e]. Nitrogen-free bacterial fixers require aerobic environments to obtain resources for N\u003csub\u003e2\u003c/sub\u003e fixation [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e]. Several workers have also reported that the change from conventional to zero tillage alters the distribution of SOM only along the soil profile [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]. The observations recorded in this study on rhizosphere soil microbial and rhizoplane fungal populations also indicated that the counts increased significantly with crop advancement due to the progressive mineralization of litterfall from crops, root exudates and added crop residues and the increased availability of organic substrates, which serve as food and help increase the population of nitrogen-fixing microorganisms [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e]. Singh \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e] also reported the stimulation of \u003cem\u003eAzotobacter\u003c/em\u003e spp. in upper soil layers under minimum tillage, which was attributed to increased availability of nutrients and root proliferation. Our results concur with those of Verma \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e], who observed an increasing trend in the prevalence of \u003cem\u003eAzospirillum\u003c/em\u003e with an increase in the organic carbon concentration from 0.2- 1.0%. The findings of this study are consistent with those of Bashan \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e], who reported that SOM had a definite role in the survival of \u003cem\u003eAzospirillum\u003c/em\u003e strains.\u003c/p\u003e \u003cp\u003e \u003cem\u003eAzospirillum\u003c/em\u003e species tend to move toward locations where O\u003csub\u003e2\u003c/sub\u003e is ideal for metabolism (at low O\u003csub\u003e2\u003c/sub\u003e concentrations) [\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e]. Therefore, an increase in the \u003cem\u003eAzospirillum\u003c/em\u003e population with an increase in moisture content (maintained through supplemental irrigation prior to collection of rhizosphere soil samples) was observed in the present study. The same results were reported by Belaid \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e], who observed that when the moisture content increased, the \u003cem\u003eAzospirillum\u003c/em\u003e population increased over time.\u003c/p\u003e \u003cp\u003eRhizoplane fungi inhabit the root systems of crops and are connected directly to plant metabolism. Higher counts of Rhizoplane total fungi were also detected in ZT plants than in the other strains, which incorporated the residues, probably due to the release of organic exudates and increased plant nutrient absorption and exchange with the root system of the crops. Tillage operations incorporate crop residues, prepare seed beds, alleviate compaction, improve nutrient mineralization, and reduce weeds, pests and pathogens [\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e]. Conventional tillage (ploughing) continually exposes deeper soil to wet\u0026ndash;dry and freeze\u0026ndash;thaw cycles at the surface, thus increasing macroaggregate turnover, disrupting the existing pore network and ultimately favouring soil erosion [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. These alterations in soil structure have direct effects on the physical habitat of microbes, with fungi often being detrimentally affected through the destruction of their hyphal network. Increased structural diversity of root systems and changes in SOM and nutrient inputs through plant litter and rhizodeposition can alter porosity, aeration and aggregate stability, providing more diverse niches for microbes [\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e]. Odunfa [\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e] noted that some fungi need specific nutrient substances for growth and hence are host specific. Oyeyiola [\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e] also noted several fungal microflora populations in the rhizoplane but not in the Okro (\u003cem\u003eAbelmoschus esculentus\u003c/em\u003e) crop.\u003c/p\u003e \u003cp\u003eThe application of herbicides before emergence drastically reduced the population of rhizosphere soil microbes compared to that after emergence at 30 DAS, which could be due to the direct application of herbicides to the soil rhizosphere. However, the decrease in population after postemergence herbicide application was not large due to leaf foliage and weed emergence; moreover, the herbicides applied might not have fully reached the soil. The population of rhizosphere soil microbes was greater in the non-weeded control (no herbicide application), probably due to soil coverage by the weeds. Tapas \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e] also reported that one of the reasons for the less harmful effects of postemergence herbicides on microorganisms may be the greater foliage of crops at 22 DAS (at the time of herbicide application), which covers the soil surface, resulting in a decrease in and absorption of applied herbicides in the soil. In contrast, the deposition of preemergence herbicides in the soil is relatively greater than that in the exposed bare soil surface at the time of preemergence herbicide spraying. Furthermore, they noted that the effects of herbicides on the soil microflora are normally most severe immediately after their application.\u003c/p\u003e \u003cp\u003eThe application of postemergence herbicides (fenoxaprop-p-ethyl and ethoxy sulfuron), which were sprayed at 20 days after crop emergence, did not suppress the growth of microorganisms such as nitrogen-free bacteria, which was visualized by their respective populations at 20, 30 and 50 DAS in rhizosphere soil and weed control practices. Hand weeding was found to be most appropriate for microbial population increase in soil, followed by herbicide application [\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e], and these findings support the present investigation. Similarly, Barman \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e] reported that the \u003cem\u003eAzotobacter\u003c/em\u003e count decreased in response to herbicide treatments compared to that in the control group and that Azotobacter could not reach this level, indicating increased susceptibility to the herbicide. Konstantinovic \u003cem\u003eet al\u003c/em\u003e. reported similar inhibitory effects of other preemergence herbicides, \u003cem\u003eviz\u003c/em\u003e., alachlor and atrazine, on the \u003cem\u003eAzotobacter\u003c/em\u003e count [\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e]. The results of this study also indicated that various herbicides strongly inhibited the growth of fungi. The inhibition of fungal mycelial growth by herbicide application was consistent with the findings of previous studies [\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e]. Similarly, Eze [\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e] reported that glyphosate, paraquat, atrazine and linuron were more effective at inhibiting the mycelial growth of all the rhizoplane fungi screened than was primextra.\u003c/p\u003e\n\u003ch3\u003eFungal Diversity\u003c/h3\u003e\n\u003cp\u003eA Vast fungal diversity has been interlinked with plant systems, \u003cem\u003eviz\u003c/em\u003e., epiphytic, endophytic and rhizosphere fungi. All these fungi, in association with plant systems, play essential roles in plant growth, crop yield and soil health improvement[\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e]. Agricultural techniques such as tillage, crop residue management through retention and incorporation into the soil, and crop rotation influence the physical and chemical properties of the soil inhabited by microorganisms such as fungi, thus affecting their abundance, diversity, and activity [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The adoption of zero tillage (ZT) and conservation tillage (ZT\u0026thinsp;+\u0026thinsp;crop residue retention) with the integration of chemical weed control and power\u0026thinsp;+\u0026thinsp;1-hand weeding (IWM) as soil management practices tends to harbour beneficial fungal species while improving and maintaining soil health and quality in the long run. \u003cem\u003eTalaromyces flavus var. flavus\u003c/em\u003e was identified under CT(C)-ZT(M)-ZT(\u003cem\u003eSr\u003c/em\u003e) and ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(M)-ZT\u0026thinsp;+\u0026thinsp;R(\u003cem\u003eSr\u003c/em\u003e) in combination with chemical weed control and power\u0026thinsp;+\u0026thinsp;1 hand weeding (IWM) (T\u003csub\u003e2\u003c/sub\u003eW\u003csub\u003e3\u003c/sub\u003e and T\u003csub\u003e3\u003c/sub\u003eW\u003csub\u003e3\u003c/sub\u003e) in the rhizosphere soil; these plants have been newly reported as beneficial fungal species inhabiting the soil (soil stabilizer) and plant growth-promoting fungi (PGPF) with high potential to inhibit other pathogenic fungal species (biocontrol agents) while benefiting the plant and the soil [\u003cspan additionalcitationids=\"CR107 CR108 CR109\" citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e]. However, the CT(C)-CT(M)-Fallow(N\u003cem\u003eSr\u003c/em\u003e), CT(C)-ZT(M)-ZT(\u003cem\u003eSr\u003c/em\u003e) and ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(M)-ZT\u0026thinsp;+\u0026thinsp;R(\u003cem\u003eSr\u003c/em\u003e) treatments in combination with chemical (herbicide) rotation and chemical weed control generally produced pathogenic fungal species (\u003cem\u003eAspergillus niger, Penicillin limosum, Aspergillus terreus, Apiospora serenensis, Zasmidium cellare, and Ochraceocephala foeniculi\u003c/em\u003e), which were previously reported to have adverse effects on soil health and productivity.\u003c/p\u003e \u003cp\u003eIt is evident that in combination with crop residue removal, crop residue removal occurs in combination with nonweed control and herbicide treatment in plots, and the application of herbicides or no-weed control, regardless of the tillage combination, caused changes and the production of pathogenic fungal microbes, which agrees with the results of Bhardwaj \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e] on the impact of herbicides in irrigated tropical rice fields, in which predominant pathogenic fungi (\u003cem\u003eHumicola, Nigrospora\u003c/em\u003e, \u003cem\u003eParamyrothecium, Mariannaea, Ceratobasidium, Funneliformis, Aspergillus, Pseudorhypophila, and Lecythophora\u003c/em\u003e) were identified with unweeded control and herbicide-treated plots, indicating the adverse effects of herbicides and high weed density populations on microbial dynamics.\u003c/p\u003e \u003cp\u003eThese results on fungal diversity signify the importance of conservation tillage and minimum tillage coupled with IWM under conservation agricultural practices compared to conventional tillage in combination with herbicides and hand weed removal only during critical periods of weed competition.\u003c/p\u003e \u003cdiv id=\"Sec37\" class=\"Section2\"\u003e \u003ch2\u003eCrop productivity\u003c/h2\u003e \u003cdiv id=\"Sec38\" class=\"Section3\"\u003e \u003ch2\u003eMaize Grain Yield and System Productivity (Cotton Equivalent Yield)\u003c/h2\u003e \u003cp\u003eThe improved growth/development of crops and increased yield largely rely on tillage practices, as these practices play a crucial role in determining the development of a crop's rooting system, the soil volume explored by roots for moisture and nutrients, the availability of air, and the regulation of soil temperature, among other factors. The importance of crop-weed interactions in determining the competition faced by crop plants for light, moisture and space is well established. Confined root growth leads to decreased nutrient uptake and poor crop growth [\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e]. The meta-data analysis of ZT with residue retention indicated that the effect on crop yields in comparison with that in CT was inconsistent and was impacted substantially by cropping system, aridity index, crop residue maintenance, ZT duration, and weed management strategy [\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e]. In the present investigation, the maize grain and harvest indices were greater in the ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(M)-ZT\u0026thinsp;+\u0026thinsp;R(\u003cem\u003eSr\u003c/em\u003e) treatment than in the other tillage methods. This superior performance can be interconnected with the development of robust, deep-rooted systems in crops facilitated by the practice of zero tillage.\u003c/p\u003e \u003cp\u003eThe implementation of ZT is thought to augment the nutrient absorption capacity of crops, thereby fostering their physiological growth and overall development. Furthermore, the preservation of crop residues on the soil surface under the ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(M)-ZT\u0026thinsp;+\u0026thinsp;R(\u003cem\u003eSr\u003c/em\u003e) treatment likely contributed to the enhanced retention and availability of soil moisture. This aspect is especially crucial during the posttasselling stage of the maize crop, which coincided with a warm period from mid-March to May. Given the limited moisture conditions during this period, supplemental irrigation was applied to ensure optimal soil moisture levels throughout crop development. Research by You et al. [\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e] also indicated that short-term reduced tillage (rotary-till and no-till) and residue incorporation enhanced soil properties; spring maize grain yield; growth and attributes; and increased root biomass and shoot ratio. Furthermore, the interaction of tillage and residue treatments can increase crop biomass and yield [\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e]. A number of previous studies conducted on short-term conservation tillage have not paid full attention to how yield can be improved.\u003c/p\u003e \u003cp\u003eLong-term conventional tillage always hinders root growth and the root-to-shoot ratio [\u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e117\u003c/span\u003e]. No-till enhances root biomass and shoot biomass, regulates the shoot-to-root ratio and increases yield in comparison with plough-till and rotary-till [\u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e118\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e119\u003c/span\u003e]. Residue incorporation can also enhance crop biomass and yield due to enhanced soil buffering capacity [\u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e120\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e121\u003c/span\u003e]. The postemergence tank-mix combination of atrazine and tembotrione herbicides was applied at recommended rates in chemical weed control and chemical (herbicide) rotation, which resulted in effective weed control and no phytotoxicity. The absence of phytotoxic effects suggests the efficacy and safety of the combination of tembotrione and atrazine for weed management, which contributes to improved crop performance. Poor crop performance was also observed under non-weeded control, which was ultimately reflected in yield. This could be due to the high weed density at the critical crop growth stage, which outcompeted the crop for available moisture, nutrients, light and rooting space. Ganapathi \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e122\u003c/span\u003e] also recorded higher kernel and harvest indices and lower weed dry weight with IWM than with the use of only advocated herbicides and non-weeded treatments due to less weed infestation. Similar results were obtained by Kumar et al. [\u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e123\u003c/span\u003e], who reported that the application of preemergence herbicide followed by one rotary hoeing at 35 DAS led to increased grain yield. The results of Ahmad \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e124\u003c/span\u003e] concur with the findings of the present investigation, who noted that nicosulfuron application and one-way weeding with a hoe at 15 DAS led to greater kernel yield, whereas the lowest kernel yield was obtained from the non-weeded control. In the present study, there was an increase in corn yield and system CEY when employing zero tillage with crop residue retention (ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(M)-ZT\u0026thinsp;+\u0026thinsp;R(\u003cem\u003eSr\u003c/em\u003e)) and IWM, chemical weed control and chemical (herbicide) rotation. This improvement could be attributed to the synergistic effects of efficient weed management achieved through the use of chemical and cultural mechanical control tactics, as well as moisture and nutrient preservation facilitated by no-till practices that retain crop residues. These results are supported by Ahmad \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e124\u003c/span\u003e], who deduced that maize can flourish when cultivated under zero tillage either with the application of atrazine or glyphosate or with hand weeding (HW) at 40 DAS as an alternative to manual weeding in spring seasons to attain higher grain yield.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTreatment performance was assessed on the basis of system cotton equivalent yield (CEY) and maize grain yield, microbial population and fungal diversity, and microbial and enzyme activities.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe winter maize grain yield recorded from the different tillage weed management treatment combinations was converted into cotton equivalent yield (CEY) considering the minority equivalence. The winter CEY was then added to the monsoon cotton yield in the 4 years to arrive at the cotton equivalent yield of the cotton\u0026ndash;maize system (system CEY) for the fourth year. The system CEY and enzyme activities were used to evaluate the different tillage practices, weed management options and various tillage-weed management treatment combinations to identify a combination of remunerative tillage, weed management and tillage-weed management practices with a relatively higher system (CEY), maize grain yield, microbial and enzyme activities, microbial population and fungal diversity. These data are presented in figure (s) 3a, b, c, 4a, b, c, 7a-h and Tables \u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e7\u003c/span\u003ea, b, \u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e8\u003c/span\u003e and \u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eTillage and weed management had significant effects on the soil microbiological properties and system yield (SY) in terms of cotton equivalent (CEY). Even though ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(M)-ZT\u0026thinsp;+\u0026thinsp;R(\u003cem\u003eSr\u003c/em\u003e) in combination with the non-weeded control (T\u003csub\u003e3\u003c/sub\u003eW\u003csub\u003e4\u003c/sub\u003e) had higher microbial and enzyme activities, diverse groups of pathogenic fungal species and microbial populations, except for the metabolic quotient, were lower than those in the other treatment combinations, but the crop productivity in the ZT\u0026thinsp;+\u0026thinsp;R(M)-ZT\u0026thinsp;+\u0026thinsp;R(M)-ZT\u0026thinsp;+\u0026thinsp;R(M)-ZT\u0026thinsp;+\u0026thinsp;R(Sr) treatment combination was significantly lower than that in the other treatment combinations. In combination with all weed management methods, CT had lower microbial and enzyme activities, diverse groups of pathogenic fungal species, and microbial populations, but not metabolic quotients, which were greater under these treatment combinations. However, the crop productivity in the CT treatment combined with all weed management options was greater than that in the ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(M)-ZT\u0026thinsp;+\u0026thinsp;R(\u003cem\u003eSr\u003c/em\u003e) treatment combined with the non-weeded control, which indicates increased productivity but poor soil health, as indicated by the soil biological attributes. The SY in terms of CEY was greater in the ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(M)-ZT\u0026thinsp;+\u0026thinsp;R(\u003cem\u003eSr\u003c/em\u003e) treatment in combination with the IWM, which indicated that the adoption of ZT\u0026thinsp;+\u0026thinsp;R(C)-ZT\u0026thinsp;+\u0026thinsp;R(M)-ZT\u0026thinsp;+\u0026thinsp;R(\u003cem\u003eSr\u003c/em\u003e) in combination with IWM practices can maintain the status of soil microbiological attributes at higher levels, harbour beneficial fungal species and increase farmer productivity. Therefore, implementing zero tillage with the retention of crop residues in CA together with IWM aids in improving soil health and can optimize productivity for farmers in cotton\u0026ndash;maize\u003cem\u003e\u0026ndash;Sesbania rostrata\u003c/em\u003e cropping systems.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOn the basis of four years of investigation into the cumulative effects of tillage and weed management options on microbial activity and population, fungal diversity, enzyme activities, and crop productivity under conservation agriculture (CA),the following conclusions can be drawn:conservation tillage in combination with nonweeded control (only one-way weeding at the critical period of weed competition) and the combination of chemical weed control and power + 1-hand weeding (IWM) significantly enhanced soil enzymatic and microbial activities and a microbial population and decreased metabolic quotient (qCO\u003csub\u003e2\u003c/sub\u003e), whereas conventionally tilled (CT) and chemically treated plots resulted in a drastic reduction in soil enzymatic and microbial activities and in the microbial population and increased qCO\u003csub\u003e2\u003c/sub\u003e during both the sampling period (30 DAS and tasselling) during maize growth. ZT with and without crop residue incorporation in combination with IWM harboured beneficial soil inhabitant fungal species, \u003cem\u003eTalaromyces flavus\u003c/em\u003e (a soil stabilizer, plant growth promoter, and soil pathogenic fungal inhibitor), while CT, which interacts with overall weed management, led to the production of pathogenic fungal species identified at the tasselling stage of maize. The maize grain yield and system yield in terms of cotton equivalent yield (CEY) were greater under ZT, which included the retention of crop leftovers; IWM, chemical weed control; and chemical (herbicidal) rotation plots than under CT, crop residue removal; and non-weeded control treatments. There was no significant effect of the combination of treatment on maize grain yield (P=0.05). ZT with crop residue maintenance (ZT+R(C)-ZT+R(M)-ZT+R(\u003cem\u003eSr\u003c/em\u003e)) in combination with IWM had a significantly greater system CEY (4453 kg ha\u003csup\u003e-1\u003c/sup\u003e), followed by ZT+R in combination with chemical weed control and chemical (herbicide) rotation, with system CEYs of 4292 kg ha\u003csup\u003e-1\u003c/sup\u003e and 4206 kg ha\u003csup\u003e-1\u003c/sup\u003e, respectively. Among the tillage practices, ZT+R(C)-ZT+R(M)-ZT+R(\u003cem\u003eSr\u003c/em\u003e) had a greater system CEY than did CT(C)-CT(M)-Fallow(N\u003cem\u003eSr\u003c/em\u003e) and CT(C)-ZT(M)-ZT(\u003cem\u003eSr\u003c/em\u003e). With regard to weed management options, IWM had higher system CEY. The SY in terms of CEY\u0026nbsp;was\u0026nbsp;greater in the\u0026nbsp;ZT+R(C)-ZT+R(M)-ZT+R(\u003cem\u003eSr\u003c/em\u003e) treatment in combination with\u0026nbsp;IWM,\u0026nbsp;whichindicated that\u0026nbsp;the\u0026nbsp;adoption\u0026nbsp;of\u0026nbsp;conservation tillage\u0026nbsp;with\u0026nbsp;IWM\u0026nbsp;practices\u0026nbsp;augments important soil microbiological attributes, harbours beneficial fungal species\u0026nbsp;and provides good\u0026nbsp;productivity\u0026nbsp;to\u0026nbsp;farmers in the long-term.\u0026nbsp;Even though the interaction of\u0026nbsp;ZT+R(C)-ZT+R(M)-ZT+R(\u003cem\u003eSr\u003c/em\u003e) with non-weeded control had a positive effect on increasing soil microbiological parameters and activities, crop productivity was very low; therefore, the\u0026nbsp;adoption of\u0026nbsp;ZT+R(C)-ZT+R(M)-ZT+R(\u003cem\u003eSr\u003c/em\u003e) in CA\u0026nbsp;along\u0026nbsp;with IWM\u0026nbsp;helps\u0026nbsp;improve\u0026nbsp;soil\u0026nbsp;health\u0026nbsp;and\u0026nbsp;can\u0026nbsp;optimize\u0026nbsp;productivity in a long-term cotton-maize-\u003cem\u003eSesbania rostrata\u003c/em\u003e cropping system.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eInstitutional\u0026nbsp;Review\u0026nbsp;Board\u0026nbsp;Statement:\u003c/strong\u003e Not\u0026nbsp;applicable.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed\u0026nbsp;Consent\u0026nbsp;Statement:\u003c/strong\u003e Not\u0026nbsp;applicable.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData\u0026nbsp;availability statement:\u0026nbsp;\u003c/strong\u003eAvailable upon request\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e The\u0026nbsp;authors\u0026nbsp;are\u0026nbsp;extremely\u0026nbsp;thankful to\u0026nbsp;the All-India\u0026nbsp;Coordinated\u0026nbsp;Research\u0026nbsp;Project (AICRP)\u0026nbsp;on\u0026nbsp;weed\u0026nbsp;management\u0026nbsp;for\u0026nbsp;the\u0026nbsp;financial\u0026nbsp;sponsorship\u0026nbsp;received\u0026nbsp;for the implementation and execution of this ongoing conservation agriculture experiment carried out at a college farm, Professor Jayashankar\u0026nbsp;Telangana\u0026nbsp;State\u0026nbsp;Agricultural\u0026nbsp;University\u0026nbsp;(PJTSAU),\u0026nbsp;Rajendranagar,\u0026nbsp;and\u0026nbsp;Telangana (India)\u0026nbsp;under the\u0026nbsp;aegis\u0026nbsp;of\u0026nbsp;“All\u0026nbsp;India\u0026nbsp;Coordinated\u0026nbsp;Research\u0026nbsp;Project\u0026nbsp;on\u0026nbsp;Long-Term\u0026nbsp;Experiments.”\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts\u0026nbsp;of\u0026nbsp;interest:\u0026nbsp;\u003c/strong\u003eThe authors declare that no conflicts of interest exist.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMekouar MA (2018) Food and Agriculture Organization of the United Nation (FAO). 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Sci Rep 7(1):13314\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbdullah AS (2014) Minimum tillage and residue management increase soil water content, soil organic matter and canola seed yield and seed oil content in the semiarid areas of Northern Iraq. Soil Tillage Res 144:150\u0026ndash;155\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRadicetti E, Mancinelli R, Moscetti R et al (2016) Management of winter cover crop residues under different tillage conditions affects nitrogen utilization efficiency and yield of eggplant (\u003cem\u003eSolanum melanogena\u003c/em\u003e L.) in Mediterranean environment. Soil Tillage Res 155:329\u0026ndash;338\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePlaza-Bonilla D, \u0026Aacute;lvaro-Fuentes J, Hansen NC et al (2014) Winter cereal root growth and aboveground\u0026ndash;belowground biomass ratios as affected by site and tillage system in dryland Mediterranean conditions. 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Int J Curr Microbiol Appl Sci 7(3):323\u0026ndash;333\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmad H, Shafi M, Liaqat W et al (2018) Effect of tillage practices and weed control methods on yield and yield components of maize. Middle East J Agricultural Res 7(1):175\u0026ndash;181\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"c6775f80-b063-495f-9a88-9e0876c654c4","identifier":"10.13039/501100001503","name":"Indian Council of Agricultural Research","awardNumber":"Not Applicable","order_by":0}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"ICAR- All India Coordinated Research Project (AICRP) on Weed Management","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Biological Assessment, Soil Health, Fungal Diversity, Conservation Agriculture, Nature-Based Solution","lastPublishedDoi":"10.21203/rs.3.rs-3967847/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3967847/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn a diversified cropping system, the tillage methods and weed management practices significantly influence the soil microbiome, which affects crop productivity. The synergetic impacts of such practices on the soil microbiome in association with yield under cotton–maize\u003cem\u003e–Sesbania rostrata\u003c/em\u003e rotation with CA have not been extensively explored thus far in southern India. Therefore, a 4-year CA experiment was undertaken to investigate the impact of tillage and weed management on the soil microbiome and fungal diversity at 30 DAS and on the tasselling of maize and crop yield and to identify sustainable tillage and weed management practices that can provide nature-based solutions. The three tillage practices used were \u003cstrong\u003eT\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/sub\u003e: CT(C)-CT(M)-fallow (N\u003cem\u003eSr\u003c/em\u003e), \u003cstrong\u003eT\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e: CT(C)-ZT(M)-ZT(\u003cem\u003eSr\u003c/em\u003e) and \u003cstrong\u003eT\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/sub\u003e: ZT+R(C)-ZT+R(M)-ZT+R(\u003cem\u003eSr\u003c/em\u003e), and the following weed control tactics were used: \u003cstrong\u003eW\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/sub\u003e-chemical weed control, \u003cstrong\u003eW\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e-chemical (herbicide) rotation, \u003cstrong\u003eW\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/sub\u003e- integrated weed management (IWM) and the \u003cstrong\u003eW\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/sub\u003e-non-weeded control. Rhizosphere soil and rhizoplane samples were collected from the respective plots at 30 DAS after herbicide application and tasselling. Analysis of the microbial population and enzyme and microbial activities, \u003cem\u003eviz\u003c/em\u003e., soil basal respiration (SBR), metabolic quotient (qCO\u003csub\u003e2\u003c/sub\u003e), microbial quotient (qMB), and soil microbial biomass carbon (SMBC) and nitrogen (SMBN), was performed following standard procedures. rRNA gene sequencing of 18S rRNA was performed with rhizosphere soil and rhizoplane fungi isolated at tasselling. The yield was recorded at harvest. The salient findings indicated a decrease in enzyme activity, microbial population, and microbial activity at the initial stage (30 DAS) due to the impact of herbicides, which subsequently increased in response to tasselling, except for qCO\u003csub\u003e2, \u003c/sub\u003ewhich decreased. These biological properties were greater in the T\u003csub\u003e3\u003c/sub\u003e treatment and nonweeded control followed by IWM, except for qCO\u003csub\u003e2, \u003c/sub\u003ewhich showed a decreasing trend relative to T\u003csub\u003e1\u003c/sub\u003e and T\u003csub\u003e2\u003c/sub\u003e and W\u003csub\u003e1\u003c/sub\u003e and W\u003csub\u003e2\u003c/sub\u003e at both sampling stages of maize. K yield (KY) and system yield (SY) were greater in the T\u003csub\u003e3\u003c/sub\u003e, IWM, and herbicide-treated plots (W\u003csub\u003e1\u003c/sub\u003e and W\u003csub\u003e2\u003c/sub\u003e) than in the T\u003csub\u003e1\u003c/sub\u003e, T\u003csub\u003e2\u003c/sub\u003e and nonweeded control plots. \u003cem\u003eTalaromyces flavus\u003c/em\u003e, a beneficial rhizosphere soil inhabitant, was identified in T\u003csub\u003e3\u003c/sub\u003e in combination with the IWM. Considering both crop productivity and soil biological assessment, T\u003csub\u003e3\u003c/sub\u003e and IWM were considered the best treatment combinations among all the other treatments with SY (4453 kg ha\u003csup\u003e-1\u003c/sup\u003e). These findings signify the importance of adopting reduced tillage (T\u003csub\u003e3\u003c/sub\u003e) and IWM for farmers while striving for nature-based solutions.\u003c/p\u003e","manuscriptTitle":"Cumulative Impact of Herbicides and Tillage on the Soil Microbiome, Fungal Diversity and Crop Productivity under Conservation Agriculture","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-20 09:39:39","doi":"10.21203/rs.3.rs-3967847/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c27a95b9-d433-4d37-ad17-55e6b5ba65e7","owner":[],"postedDate":"February 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":28826271,"name":"Agroecology"}],"tags":[],"updatedAt":"2024-02-20T09:39:39+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-20 09:39:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3967847","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3967847","identity":"rs-3967847","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}