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Introduction

Conservation agriculture (CA) is attaining momentum as a sustainable and eco-friendly production system meant to augment soil biological functions of the agro-ecosystem with little mechanical practices and rational utilization of chemical inputs. Cereal-based cropping systems are common practices in southern regions of India, while maize yield and productivity decline monotonically under continuous intensive tillage, corresponding to the decrease in soil biological activities, increased vulnerability to pests and diseases, and decreased diversity and abundance of microbial communities in the soil (soil biodiversity)^1,2. Along with zero tillage (ZT), the diversified crop rotation and retention of crop remains use pre-crop effects, which lead to enhanced biological diversity and crop yield³. Thus, a reduction in tillage intensity with continuous retention of crop residues and the integration of chemical and cultural weed control practices in CA under a diversified cropping system⁴ may be a solution for reducing soil degradation processes and the risk of agricultural production, while improving soil functional metrics and rhizosphere soil microbial populations⁵. Soil microorganisms play an essential part as drivers of soil biological processes⁶, which is a gain for maintenance of soil quality, agricultural sustainability and multiple ecosystem functions⁷. Ecosystem functions controlled by rhizosphere soil microorganisms frequently employed function-based metrics such as soil basal respiration, decomposition of soil organic matter (SOM), soil microbial activities and extracellular enzyme activity⁸. The constituent of rhizosphere microorganisms and function-based metrics are highly influenced by similar changing edaphic properties, thus, a suitable agricultural management practices such as irrigation, tillage, crop diversification and weed management practices can allow rhizosphere soil microorganisms to perform their different ecological functions⁹. Soil microorganisms and microbial activities change rapidly with any change in soil management practices and environmental conditions with a short turnover¹⁰, and can be used as early indicators for soil health and crop yield improvement. Soil enzyme activity depends upon different abiotic factors, viz., soil pH, moisture content, oxygen availability and soil texture etc¹. These properties are subject to change depending on the intensity of tillage, weed control practices, diverse crop species being implemented, and consequently have a significant impact on soil microbial composition and enzyme activities⁹. Conventional agricultural systems with intensive tillage practice decline the activities of soil microbes, enzyme, change microbial diversity, shatter nutrient cycling and consequently reduce stability or resilience of soil functional status⁹.

Microbial quotient (qMB), soil basal respiration (SR), metabolic quotient (qCO₂), soil microbial biomass carbon and nitrogen (SMBC and SMBN) have been broadly utilized as indicators of soil biological status¹⁰. The qCO₂ 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)¹¹. Soil enzyme activity is deemed to be an indicative of specific biochemical reaction processes of the whole soil microbial activities that occur in SOM mineralization, and also an important indicator of soil health and ecological restoration with a short turn-over time¹².

CA based technology such as zero tillage (ZT), retention of the crop residues and crop species diversification are used as an alternative for machine intensive tillage practices in the semi-arid zone of southern India and across the globe. This minimizes the labour and machinery while increasing productivity, utilizing all the resources efficiently and improving the soil health¹³. Thus, adoption of CA along with Sesbania rostrata can be a restorative measure with less C: N to promote rapid soil microbial mineralization of nutrients (conversion of organic forms of nutrients in plants residues into inorganic forms by microorganisms) in cotton-maize-Sesbania rostrata cropping system¹⁴ and reduction in soil erosion by engaging farm round the year. Weeds are one of the major problems in CA as it provides conducive conditions for perennial weeds in most of the cropping system¹⁴. Traditionally, farmers in southern India manage the weeds in maize crop by pre-emergence herbicide application followed by inter-cultivation and manual weeding. Whereas in cotton crop, weed management is mostly by inter-cultivation or pre-emergence herbicide application followed by inter-cultivation with cattle drawn/ tractor drawn implements. The introduction of new generation selective herbicides and shortage of labour for manual weeding have resulted in a significant increase in pre-emergence and post-emergence herbicide use in these crops. Even though weed control through the application of the herbicides is widely accepted and effective¹⁵, the extensive use of herbicides, however, poses a significant negative or positive effect on soil microbial activities, population and diversity which in turn impact soil processes¹⁵.

A better understanding of herbicides impact on soil enzymes dynamics, functional diversity of soil microorganisms in the ecosystem could provide a unique opportunity for an integrated biological assessment of soils due to their crucial role in several soil biological activities, and rapid response to the changes in soil management. To date, several studies have already explored the influence of tillage and overall impact of the herbicides on soil enzyme activity, microbial activities and population dynamics with CA in different agro-ecological regions¹⁶. However, the research that examines the direct effects of herbicides, how the timing of herbicides application (i.e., at different crop growth stages) in combination with different tillage practices on biological parameters and fungal diversity at various zone levels (rhizosphere and rhizoplane), and crop productivity relates to soil functional metrics and biodiversity have not been extensively investigated under Cotton-Maize-Sesbania rostrata rotation system in the semi-arid region of Telangana. 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 a novel pathogenic and beneficial fungal species. Hence, the present investigation was performed 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 tasseling (60 DAS) and to determine the maize grain and system yield in terms of cotton equivalent yield (CEY) after 3rd year with CA under cotton–maize–Sesbania rostrata cropping system.

Materials and methods

Details of the experiment and weather during the development of the crop

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. The experiment was implemented from 2020 in the monsoon, winter and summer seasons under cotton (Gossypium hirsutum), maize (Zea mays), and green manure (Sesbania rostrata) rotations, respectively. This study continued from 2020 until 2023, and soil samples were collected at 30 DAS and 60 DAS of maize for analysis of enzyme activity, microbial population and activity, and other soil parameters (Soil pH and soil organic carbon at 60 DAS). The yield of winter maize was recorded after harvest in 2022-23. The field trial is located at 16⁰ 18’ 17” N latitude and 78⁰ 25’ 38” E longitude. Meteorological observations during the crop development from the station situated at the Institute of Agricultural Research (IAR), Rajendranagar on weekly basis are presented in Supplementary Fig. 1.

Design of the experiment and treatment details

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 used in the sub-plot treatments are detailed in Table 2. The combinations of tillage and weed management were replicated thrice. The selection of these treatment combinations in a CA practice was considered to better assess their effects on soil biological attributes, fungal diversity and the yield of maize in southern Telangana, India. For T₁, which was subjected to conventional tillage, the plots were prepared by ploughing two times, followed by rotovating and seeding. In T₂, no-till of the soil (Zero tillage- ZT) i.e., seeding was done directly by opening the soil followed by surface soil sealing, and in T₃, there was ZT (cotton) + Sesbania rostrata residues (SrR) in monsoon – ZT (maize) + cotton residues (CR) in winter – ZT (Sesbania rostrata) + maize stubbles (MS) (i.e., Sesbania rostrata was sown adjacent to maize stubbles) in summer. The succeeding crops (cotton and Sesbania) residues were shredded and retained (as surface mulch), and seeding was performed directly by opening the soil, accompanied by surface sealing with mulch from crop residues (Table 1). The cumulative mean annual input of organic biomass/residues estimated from cotton and Sesbania rostrata retained in T₃ plots, since the year 2020–2023, was about 200.0 to 240.0 Mg ha-₁¹⁷. The weed management strategies used included the following: W₁: chemical weed control, W₂: herbicide rotation, W₃: integrated weed management (IWM) and W₄: single hand-weeded control, as fully described in Table 2. No tillage operations or weed management were implemented prior to sowing of summer Sesbania rostrata, as it was cultivated up to 45 days to be retained and cover the soil in T₃. There was no Sesbania rostrata sown in the T₁ plots; i.e., the 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
T₁ :	CT (C) –	CT (M) –	Fallow (NSr)
T₂ :	CT (C) –	ZT (M) –	ZT (Sr)
T₃ :	ZT(C) + SrR –	ZT (M) + CR –	ZT (Sr) + MS

CT(C) = conventional tillage (cotton), ZT(M) = zero tillage (maize), Fallow (NSr) = Fallow(No Sesbania rostrata), ZT(Sr) = zero tillage (Sesbania rostrata), ZT(C) + SrR = zero tillage (cotton) + Sesbania rostrata residues, ZT (M) + CR = zero tillag (Maize) + cotton residues, ZT (Sr) + MS = zero tillage (Sesbania rostrata) + maize stubbles.

Table 2. Weed management (W) in sub-treatments and interaction with tillage (T) in main treatments.

	Monsoon (Cotton)				Winter (Maize)
	W₁: Chemical Weed Control	W₂: Herbicide Rotation (Alternative year)	W₃: IWM	W₄: Single hand- weeded Control	W₁: Chemical Weed Control	W₂: Herbicide Rotation (Alternative year)	W₃: IWM	W₄: Single hand- weeded Control
T₁	Diuron Pre-emergence (PE) application 0.75 kg/ha fb tank mix application of pyrithiobacsodium 62.5 g/ha + quizalofop-ethyl 50 g/ha as PoE (Post-emergen ce application) (2–3 weed leaf stage) fb directed spray (inter-row) of paraquat 0.5 kg/ha at 50–55 DAS (vegetative to flowering stage).	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 to 3 weed leaf stage) fb directed spray (inter-row) of paraquat 0.5 kg/ha at 50–55 DAS. rotated with Pendimethalin 1 kg ha^-1fb 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 (inter-row) of paraquat 24% SL 0.5 kg/ha at 55 DAS (vegetative to flowering stage).	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 weed competition i.e. between 45–50 days after sowing).	Atrazine 1.0 kg/ha + paraquat 600 g/ha PE fb tembotrione 120 g/ha at 20–25 DAS as PoE (T_2, T₃). Atrazine 1 kg ha^-1 PE fb tembotrione 120 g/ha at 20–25 DAS as PoE (T₁).	Atrazine 1.0 kg/ha + paraquat 600 g/ha PE fb tembotrione 120 g/ha at 20–25 DAS as PoE (T₂, T₃). Atrazine 1.0 kg/ha PE fb tembotrione 120 g/ha at 20–25 DAS at PoE (T₁₎. 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 (T₂, T₃). Atrazine 1.0 kg/ha PE fb halosulfuron methyl 67.5 g/ha at 20–25 DAS as PoE (T₁).	Tembotrione 120 g/ha & Atrazine 50% WP 0.5 kg/ha both applied as early post-emergence) EPoE fb brush cutter at 40 DAS.	One hand weeding was done after the critical period of crop weed competition i.e. between 45–50 days after sowing).
T₂
T₃

T₁ = conventional tillage (cotton) – conventional tillage (maize) – Fallow (No Sesbania rostrata), T₂ = conventional tillage (cotton) – zero tillage (maize) – zero tillage (Sesbania rostrata), T₃ = zero tillage (cotton) + Sesbania rostrata residues (SrR) – zero tillage (maize) + cotton residues (CR) – zero tillage (Sesbania rostrata) + maize stubbles (MS), IWM = integrated weed management. Herbicides were applied uniformly across tillage treatments.

Crop management

Sowing and fertilizer application during the crop (s) development

The experimental particulars and attributes of crop varieties are shown in Table supplementary 1 and 2, respectively. Before sowing of the crops (cotton and maize), the field was plowed twice followed by rotovation and levelling field operators in conventionally tilled (T₁) plots, whereas in no-till (ZT) plots, seeds dibbling was performed. The cotton and maize crops were thinned in the portions of the plots with high crop population and gap filled where seeds did not emerge 13 and 10 days, respectively after seed emergence. For Sesbania, sowing was done directly in solid rows (30 cm spacing) between the maize stubbles in the T₂ and T₃ treatments without any tillage operations. Conversely, the CT (T₁) plots were fallowed during summer i.e., there was no Sesbania in such plots. This distinction in management practices reflects the specific treatments applied to each plot in the experimental design. The crops particularly cotton and maize were raised in accordance with recommended dose of fertilizers (RDFs); the N: P: K (120-60-60 kg ha^− 1) were applied in the form of urea, di-ammonium phosphate (DAP) and muriate of potash (MOP) for cotton. The recommended dose of phosphorus (RDP) was applied in the form of DAP as basal after cotton emergence in T₁, T₂ and T₃. Urea were applied at 30 DAS, flowering stage and square formation stages of cotton in equal splits; the N: P: K (200:60:50 kg ha-₁) were supplied through urea, DAP and MOP, respectively to raise maize crop. Application of urea and DAP were split thrice as basal, at knee height and maize tasseling period. No fertilizer application during growth and development of Sesbania rostrate. Both the crops (cotton and maize) were fully developed following cultural practices and typically advanced with rainfall in monsoon during cotton and supplemental irrigation in winter during maize. At 30 days after sowing (DAS), Sesbania rostrata was knock-down and removed in the T₂ while in the T₃, shrub master was used to shred and retain Sesbania as surface mulch to the soil. The details on the dates of sowing and harvesting for each crop are presented in supplementary Table 3.

Sampling and standard analytical procedures

Soil physico-chemical properties

Composite soil samples were randomly collected in triplicate from each treatment plot at a depth of 0–15 cm⁶ before the start of the experiment in 2020 and at 60 days after sowing (DAS) of maize in the 5th crop cycle in 2023. These collected soil samples were air-dried under shade, processed through a wooden hammer, and passed through 0.5 and 2 mm sieve for analysis of soil properties before the start of the experiment (2020) by following standard methods (Table 3).

Soil characteristics of the experimental site

The soil characterization with respect to distinct soil characteristics was achieved prior to the experiment i.e., before implementation and imposition of the treatments in 36 plots in 2020 through collection of 36 surface soil samples (0–15 cm soil depth) in triplicates (Table 3). These surface soil samples were all inter-mixed, processed and analyzed for parameters duly following the standard protocols and methods as shown in Table 3.

Table 3. Soil characterization in the 0–15 cm soil depth before the start of the experiment (2020), the methods and references used for each soil parameter analysed.

S.No	Soil property	Soil test value	Method	Reference
1	Taxonomic categorization	Inceptisol	-	Soil Survey Staff¹⁸
2	Mechanical separates
	Sand (%)	66.00	Hydrometer method	Bouyoucos¹⁹
	SD	0.30
	SE (m)±	0.05
	CD(P = 0.05)	NS
	Silt (%)	12.60
	SD	0.06
	SE (m)±	0.01
	CD(P = 0.05)	NS
	Clay (%)	21.40
	SD	0.42
	SE (m)±	0.07
	CD (P = 0.05)	NS
	Textural class	Sandy clay loam
3	Slightly alkaline in soil reaction ( pH)	7.82	Soil: water suspension (1: 2.5)	Jackson²⁰
	SD	0.30
	SE (m)±	0.05
	CD (P = 0.05)	NS
4	Non- saline in electrical conductivity (dS m-₁)	0.33
	SD	0.06
	SE (m)±	0.01
	CD (P = 0.05)	NS
5	Bulk density (Mg m^− 3)	1.23	Core sampler	Blake and Hartge²¹
	SD	0.24
	SE (m)±	0.04
	CD(P = 0.05)	NS
6	Medium in soil organic carbon (g kg^− 1)	6.50	Wet oxidation	Walkley and Black²²
	SD	1.32
	SE (m)±	0.22
	CD (P = 0.05)	NS
7	Low in available soil nitrogen (kg ha-₁)	220.90	Alkaline KMnO₄	Subbiah and Asija²³
	SD	6.66
	SE (m)±	1.11
	CD (P = 0.05)	NS
8	Medium in available soil phosphorus (g kg-₁)	22.40	Olsen	Olsen et al.²⁴.
	SD	2.40
	SE (m)±	0.40
	CD (P = 0.05)	NS
9	High in available soil potassium (kg ha-₁)	408.75	1 N Neutral ammonium acetate	Jackson²⁰
	SD	3.60
	SE (m)±	0.60
	CD (P = 0.05)	NS

Soil microbial population, microbial and enzyme activities

Rhizosphere soil sampling was done at two growth stages of maize (5th crop cycle) in 2022–2023: the first, at 30 days after sowing (DAS) of maize i.e., after pre-emergence, early post-emergence and post-emergence application of herbicides in W₁ and W₂ sub-plots and the second, at tasselling stage (60 DAS) of maize. Composite samples were collected in respective plots at depth of 0–15 cm, consistent across the treatments, by pulling the maize plant (s) with the roots, followed by shaking the roots as to collect 1gram of rhizosphere soil (to exclude bulk soil to avoid contamination) in polythene zip bags, then taken to the laboratory, passed through 2 mm sieve and analysed on the same day as collected from the field. For rhizoplane, sampling was done by collecting the 1gram of maize roots in polythene zip bags, after removing the rhizosphere soil. The functional activity was measured in terms of microbial activities related to microbial population and organic matter cycling. Soil water content was determined according to Wu et al.²⁵. , and the information was used in calculating the evaluated parameters.

Soil microbial activity

Soil basal respiration (SBR) was measured according to Da-Silva et al.²⁶. Soil microbial biomass carbon (SMBC) was determined by fumigation extraction method²⁷ and Soil microbial biomass nitrogen (SMBN) by the CH₃Cl fumigation-extraction technique²⁸. The metabolic quotient (qCO₂), the ratio between SBR and SMBC²⁹, was employed to obtain the efficiency of substrate consumption by microorganisms as a stress indicator when the SMBC is affected. Microbial quotient was the ratio of MBC to SOC³⁰.

Soil enzymatic activity

Dehydrogenase activity (DHA) was assayed according to Casida et al.³¹. and red coloured of Triphenyl formazan (TPF) was read in spectrophotometry (λ = 485 nm). Fluorescein di-acetate activity (FDA) was estimated according to Green et al.³². and the bright yellow-greenish fluorescein was measured by spectrophotometry at λ = 490 nm. The soil urease activity (SUA) was determined according to Tabatabai and Bremner³³. The β-galactosidase (β-GaA) and phosphatase activity were estimated according to Eivazi and Tabatabai³⁴ and Tabatabai and Bremner³⁵ and all 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, total fungi were evaluated by following the protocols described by Albino and Andrade³⁶. The population was calculated according to Schmidt and Caldwell³⁷. Azospirillum population was enumerated through inoculation into semi-solid nitrogen-free bromothymol blue malate medium (Nfb) according to Dobereiner et al.³⁸ and estimated by most probable number (MPN) method³⁹. Enumeration of rhizoplane fungal population was determined according to Turnbull et al.⁴⁰. and Schmidt and Caldwell³⁷.

Fungal diversity

Isolation criteria and purification

The 18s rRNA gene sequencing for the current study was done with fungal colonies obtained at tasseling stage of maize, 2022-23. Prior to identification, prolonged incubation of about 10–12 days of the fungal colonies grown on Rose Bengal solid agar medium at 25^oC, was done as to allow occurrence of the sporulation. Based on the colour of the spores formed, classification was done and 8 plates representatives of all 12 treatment combinations were selected for purification as to obtain pure fungal strains based on the abundance of the same number of the spores. These predominant spores (colonies) in plates, representing the treatment combinations were picked and cultured in potato dextrose (PDA) solid agar medium for 5 days to allow the growth of fungal pure strains. These 8 pure strains of fungi were sent to Biokart Sequencing Centre as to identify fungal species present in different combinations of tillage and weed management.

Deoxyribonucleic acid (DNA) extraction and polymerase chain reaction (PCR) amplification of 18s gene partial sequencing

Deoxyribonucleic acid (DNA) extraction was done according to⁴¹ by picking up the sample and isolated genomic DNA from those samples (pure fungal strains), placing in a mortar, homogenizing with 1 ml of extraction buffer. The homogenate was transferred to a 2 ml-microfuge tube. An equal volume of phenol: chloroform: isoamly alcohol (25:24:1) was added to the tubes and mixed well by gentle shaking. 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: isoamly 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 3.0M Sodium acetate pH 7.0 and 0.7 volume of isopropanol. After 15 min of incubation at room temperature, the tubes were centrifuged at 4^oC for 15 min 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 (Tris-Cl 10 mM pH 8.0, EDTA 1 mM). To remove RNA, 5 µl of DNAse, free RNAse A (10 mg ml^− 1) was added to the DNA⁴².

After extraction of the total DNA, polymerase chain reaction (PCR) amplification of 18s gene was done according to Baldoni et al.⁴³. with 10pM of each primer composition of TAQ Master mix (High-Fidelity DNA Polymerase, 0.5mM dNTPs, 3.2mM MgCl₂ and PCR enzyme buffer cycling condition) (PCR clean kit). The PCR cycling and amplification conditions are in Table supplementary 4, primer details in Table 4 and sequencing mix composition, PCR and amplification conditions (Table supplementary 4). The aligned sequence data is in supplementary Fig. 2. The PCR product was sequenced bi-directionally⁴³.

Data analysis and identification

Data was analysed by using sequencing machine: ABI 3130 genetic analyser, chemistry cycle sequencing kit: big dye terminator version 3.1” polymer and capillary array: POP_7 pol capillary array with BDTv3-KB-Denovo_v 5.2 protocol and sequence scape_ v 5.2 software reaction plate: Applied Biosystem Micro Amp Optical 96-Well Reaction plate. Identification was done by using the system software aligner to align the sequences and a comparative search of GenBank sequences in National Centre for Biotechnology Information (NCBI) was carried out using the BLASTn tool to identify the organisms and their closest neighbours. The sequences aligned with system software aligner were used for construction of phylogenetic trees (supplementary Fig. 2) 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 Weighbor with alphabet size 4 and length size of 1000.The consensus sequence was deposited to the Gen Bank in NCBI to obtain accession numbers of identified organisms from type material.

Multiple sequence alignment and phylogenetic tree

The multiple sequence alignment and phylogenetic tree were constructed using Mega11 Version 11.0.13⁴⁴ and clustal W algorithm according to Saitou and Nei⁴⁵. The evolutionary distances were computed using the maximum composite likelihood method⁴⁶. Codon positions included were 1st + 2nd + 3rd + non-coding and accounted for 2043 positions in the final dataset.

Table 4. The primer Details - The polymerase chain reaction (PCR) product size ~ 2 kb.

S. No	Oligo Name	Sequence (5`à 3`)	Tm (°C)	GC- Content
1	18sForward	TCCTGAGGGAAACTTCG	47	52.94%
2	18s Reverse	ACCCGCTGAACTTAAGC	47	52.94%

Crop productivity

Grain yield for maize in each net plot was recorded by weighing air-dried produce at 14% moisture content before threshing and expressed in kg ha^− 1. The system yield was computed in terms of cotton equivalent yield (monsoon seed cotton yield used in the calculation after 3rd year of the experiment) (Table supplementary 5), using the Eq. 1, as under:

Statistical analysis

The data were analyzed statistically applying the analysis of variance technique dully following the ANOVA for split plot design as suggested by Panse and Sukhatme⁴⁷. Critical difference for examining the treatment means for their significance was calculated at 5 per cent probability level. Tukey’s HSD test was used, to distinct the treatment means at 5% probability level. Pearson’s correlation coefficients and principal component analysis (PCA) were performed in SQI CAL software (a tool for soil health assessment) developed by Mohanty⁴⁸ to ascertain the relationship among the soil microbial population, enzyme and microbial activity at both sampling stages, and yields (kernel and system cotton equivalent yield), and to identify the selected variables from respective PCs that could be considered for improving soil health and assessing the soil quality as influenced by conservation agricultural management practices (tillage and weed management). This SQI CAL tool (https://nishantsinha51.shinyapps.io/SQICAL/ accessed on 28 October 2020) performed principal component analysis (PCA) for extracting minimum datasets from measured soil parameters. The PCA linearly reduces the data dimensionality while limiting the loss of information by creating uncorrelated principal components (PCs). The principal components (PCs) encompass contributions from all soil variables, and arranged such that the first few PCs capture the majority of the variance from the original dataset. The PCs that received high eigenvalues were assumed to best represent the variation in the system. Therefore, only the PCs with eigenvalues > 1 were considered in this tool. Under a particular PC, each variable was given a factor loading that represents the variable contribution to the particular PC. Only the variables with high factor loadings were retained from each PC. When multiple variables were retained under a single PC, a multivariate correlation analysis was performed to determine the relation among them. If the highly loaded factors were not correlated, then each variable was deemed important. Further, the weights were assigned based on the variance explained by each PC. This variance percentage, standardized to unity, provided the weight for variables chosen under a given PC. Eigen values and PCA contribution were generated from the data calculated by the PCA. Eigen values greater than one were selected and arranged separately. Based on eigen values and factor loadings on each principal component (PC) estimated by PCA and the correlation between the analyzed soil parameters and variable selection from calculated PCA (+/of 10%).

Results

Soil pH and soil organic carbon (SOC)

Adoption of different tillage practices exerted a significant impact on SOC. The ZT(C) + SrR-ZT(M) + CR-ZT(Sr) + MS was observed with a significantly higher SOC (Table 5). Overall, SOC contents were higher in all the treatments relative to the initial SOC value (6.5 g kg-₁). Soil pH was slightly alkaline with a drop-off, notable across all the treatments over the initial soil pH value (Table 4).

Table 5. Impact of tillage and weed management options on soil pH and soil organic carbon (SOC) at 60 days after sowing (DAS) of winter maize (after 3rd year).

Treatment		pH	SOC (g kg-₁)
Tillage	WM	0–15 cm
T₁: CT(C)-CT(M)-Fallow (NSr)	W₁	7.37	6.50
	W₂	7.02	6.53
	W₃	7.11	6.88
	W₄	7.12	6.93
T₂: CT(C)-ZT(M)-ZT(Sr)	W₁	7.17	7.15
	W₂	7.11	7.17
	W₃	7.16	7.25
	W₄	7.11	7.28
T₃: ZT(C) + SrR-ZT(M) + CR-ZT(Sr) + MS	W₁	6.79	7.56
	W₂	7.14	7.80
	W₃	7.12	7.97
	W₄	7.11	8.35
Tillage (Main plots)
T₁: CT(C)-CT(M)-Fallow (NSr)	7.16		6.71
T₂: CT(C)-ZT(M)-ZT(Sr)	7.14		7.21
T₃: ZT(C) + SrR-ZT(M) + CR-ZT(Sr) + MS	7.04		7.92
Weed Management (Subplots)
W₁- Chemical weed control	7.11		7.07
W₂- Herbicide rotation	7.09		7.17
W₃- IWM	7.13		7.37
W₄- Single hand-weeded control	7.11		7.52
	SE(m)±	CD (P = 0.05)	SE(m) ±	CD(P = 0.05)
Tillage	0.14	NS	0.15	0.57
Weed Management	0.13	NS	0.19	NS
W at same level of T	0.23	NS	0.24	NS
T at same level of W	0.25	NS	0.25	NS

T₁ = conventional tillage (cotton) – conventional tillage (maize) – Fallow (No Sesbania rostrata), T₂ = conventional tillage (cotton) – zero tillage (maize) – zero tillage (Sesbania rostrata), T₃ = zero tillage (cotton) + Sesbania rostrata residues (SrR)– zero tillage (maize) + cotton residues (CR) – zero tillage (Sesbania rostrata) + maize stubbles (MS), T = TillIage, W = Weed management, IWM = Integrated weed management, CD (P = 0.05) = critical difference at 5% probability level, NS = non-significant, SE(m) = standard error of the mean.

Soil microbial activity

Soil microbial activity indices (SMAIs) viz., soil microbial biomass carbon (SMBC), microbial biomass nitrogen (SMBN), soil basal respiration (SBR), microbial quotient (qMB) and metabolic quotient (qCO₂) were significantly increased by the adoption of ZT(C) + SrR- ZT(M) + CR-ZT(Sr) + MS and Single hand-weeded control combinations, except qCO₂ values which were significantly lower in such combination at both sampling stages of maize (Figs. 1 and 2). The pre-emergence (PE), early post emergence (EPoE) and post-emergence (PoE) application of herbicides, at 30 DAS of maize in the Chemical weed control (W₁) and Herbicide rotation (W₂) subplots with response to different tillage practices resulted in a significant reduction of SMAIs, which later increased until tasseling phase (60 DAS) of the crop. The reduction in SMBC, SMBN, SBR, qMB was 38.91% and 43.53%, 44.72% and 48.37%, 25.90% and 28.49%, 31.07% and 35.77% in the W₂ and W₁ in combination with T₁: CT(C)-CT(M)-Fallow(NSr), respectively relative to Single hand-weeded control with T₃: ZT(C) + SrR- ZT(M) + CR-ZT(Sr) + MS at 30 DAS. These drastic decreases on SMBC, SMBN, SBR, qMB observed at 30 DAS, increased by 24.67–27.37%, 66.40-68.41%, 50.40-52.13%, 24.79–27.22 in the W₂ and W₁ combined with T₁, respectively at the tasseling.

Soil enzyme activity and microbial population

Adoption of ZT(C) + SrR-ZT(M) + CR-ZT(Sr) + MS and Single hand-weeded control, followed by IWM significantly improved the activities of enzymes (Figs. 3 and 4) at both sampling stages i.e., 30 DAS and Tasseling stage (60 DAS). Herbicides applied in W₁ and W₂ sub-plots, at 30 DAS of the crop i.e., after PE, EPoE, PoE, resulted in a massive decrease in the activity of enzymes (Fig. 3), which later regained at tasseling stage of maize (Fig. 4).

[See PDF for image]

Fig. 1

Impact of tillage practices and weed management options of soil microbial activity viz., soil microbial biomass carbon-SMBC, soil microbial biomass nitrogen-SMBN, soil basal respiration-SBR, metabolic quotient-qCO₂ and microbial quotient-qMB at 30 days after sowing (DAS) of maize (5th crop cycle). Means having distinct letters demonstrate significant variances between the treatments at 5% probability level (Tukey’s HSD test) and means having the same letters indicate no significant variances among the treatment means at 5% probability level. Refer to Tables 1 and 2 for treatment details.

[See PDF for image]

Fig. 2

Impact of tillage practices and weed management options of soil microbial activity viz., soil microbial biomass carbon-SMBC, soil microbial biomass nitrogen-SMBN, soil basal respiration-SBR, metabolic quotient-qCO₂ and microbial quotient-qMB at tasseling stage (60 DAS) of maize (5th crop cycle). Means having distinct letters demonstrate significant variances between the treatments at 5% probability level (Tukey’s HSD test) and means having the same letters indicate no significant variances among the treatment means at 5% probability level. Refer to Tables 1 and 2 for treatment details.

[See PDF for image]

Fig. 3

Impact of tillage practices and weed management options on soil enzyme activity viz., dehydrogenase activity (DHA), soil urease activity (SUA), acid phosphatase activity (AcPA), alkaline phosphatase activity (AlPA), fluorescein di-acetate (FDA) and β-galactosidase activity (β-GaA) at 30 DAS of maize (5th crop cycle). Means having distinct letters demonstrate significant variances between the treatments at 5% probability level (Tukey’s HSD test) and means having the same letters indicate no significant variances among the treatment means at 5% probability level. Refer to Tables 1 and 2 for treatment details.

[See PDF for image]

Fig. 4

Impact of tillage practices and weed management options on soil enzyme activity viz., dehydrogenase activity (DHA), soil urease activity (SUA), acid phosphatase activity (AcPA), alkaline phosphatase activity (AlPA), fluorescein di-acetate (FDA) and β-galactosidase activity (β-GaA) at tasseling stage (60 DAS) of maize (5th crop cycle). Means having distinct letters demonstrate significant variances between the treatments at 5% probability level (Tukey’s HSD test) and means having the same letters indicate no significant variances among the treatment means at 5% probability level. Refer to Tables 1 and 2 for treatment details.

The reduction in dehydrogenase activity (DHA), soil urease activity (SUA), acid phosphatase activity (AcPA), alkaline phosphatase activity (AlPA), fluorescein di-acetate activity (FDA), β-galactosidase activity (β-GaA) was 32.20% and 39.08%, 20.31% and 24.24%, 30.20% and 30.41%, 39.36% and 40.76%, 43.37% and 50.72%, 39.98% and 42.56% in the W₂ and W₁ in combination with T₁: CT(C)-CT(M)-Fallow(NSr), respectively relative to Single hand-weeded control with T₃: ZT(C) + SrR- ZT(M) + CR-ZT(Sr) + MS at 30 DAS. These significant decreases on DHA, SUA, AcPA, AlPA, FDA, β-GaA notable at 30 DAS of maize, increased by 50.25–54.47%, 51.37–52.01%, 62.52–62.90%, 46.69–46.98%, 20.71–24.17%, 40.94–42.04% in the W₂ and W₁ combined with T₁, respectively at the tasseling. The trends on soil microbial population were similar to enzyme activities (Table supplementary 6a and 6b).

Fungal diversity

The sub-culturing of the fungi from the culture plates was done prior to sequencing to purify the fungal strains, (supplementary Fig. 3), and agarose gel electrophoresis images of total Deoxyribonucleic acid (DNA) and polymerase chain reaction (PCR) amplified product of 18s rRNA gene are in supplementary Fig. 4a, b and c. The fungi were identified based on nucleotide sequence homology of 18s rRNA gene (Table 6). The results indicated that Talaromyces flavus var. flavus (5-PJTSAU-KNIGHT-23) was identified under T₃: ZT(C) + SrR-ZT(M) + CR-ZT(Sr) + MS and W₃: IWM (T₃W₃) treatment combinations, and T₂: CT(C)-ZT(M)-ZT(Sr) on interaction with IWM (T₂W₃). The other species of rhizosphere soil fungal and rhizoplane fungal isolates viz., Aspergillus niger, Penicillin limosum, Aspergillus terreus, Apiospora serenensis, Zasmidium cellare, and Ochraceocephala foeniculi were identified under T₁: CT(C)-CT(M)-Fallow (NSr), T₂: CT(C)-ZT(M)-ZT(Sr) and T₃: ZT(C) + SrR-ZT(M) + CR-ZT(Sr) + MS in combination with W₁: chemical weed control, W₂: herbicide rotation and W₄: single hand-weeded control (Table 6). The isolate ID (8-PJTSAU-KNIGHT), isolated abundantly from rhizoplane zone across all the tillage practices and weed management options combinations, was identified as Penicillium limosum. Phylogenetic tree(s) of all the 8 identified fungal species (supplementary Fig. 5) and multiple sequence alignments (MSA) of data (Supplementary Fig. 6a, b, c, d, e).

Table 6. Impact of tillage and weed management practices on fungal diversity at tasselling stage of winter maize.

S.NO	Isolate ID	Treatment combination	Fungal name	Identity (%)	Accession numbers
Rhizosphere soil fungal microbe (s)
1	1-PJTSAU-KNIGHT-23	T₁W₁	Aspergillus niger	100.00%	PP177339
	1-PJTSAU-KNIGHT-23	T₁W₂	Aspergillus niger
	1-PJTSAU-KNIGHT-23	T₁W₄	Aspergillus niger
2	2-PJTSAU-KNIGHT-23	T₁W₃	Aspergillus niger	100.00%	PP177340
3	3-PJTSAU-KNIGHT-23	T₂W₁	Aspergillus terrus	99.33%	PP177341
4	4-PJTSAU-KNIGHT-23	T₂W₂	Apiospora serenensis	98.47%	PP177342
4	4-PJTSAU-KNIGHT-23	T₃W₂	Apiospora serenensis	98.47%	PP177342
5	5-PJTSAU-KNIGHT-23	T₂W₃	Taloromyces flavus	99.63%	PP177343
5	5-PJTSAU-KNIGHT-23	T₃W₃	Taloromyces flavus	99.63%	PP177343
6	6-PJTSAU-KNIGHT-23	T₂W₄	Zasmidium cellare	100.00%	PP177344
7	7-PJTSAU-KNIGHT-23	T₃W₁	Penicillium limosum	99.88%	PP177345
7	7-PJTSAU-KNIGHT-23	T₃W₄	Penicillium limosum	99.88%	PP177345
Rhizoplane fungal microbe (s)
8	8-PJTSAU-KNIGHT-23	Abundant in all T & W combinations	Ochraceocephala foeniculi	96.55%	PP177346

Main treatments: T₁ = conventional tillage (cotton) – conventional tillage (maize) – Fallow (No Sesbania rostrata), T₂ = conventional tillage (cotton) – zero tillage (maize) – zero tillage (Sesbania rostrata), T₃ = zero tillage (cotton) + Sesbania rostrata residues (SrR) – zero tillage (maize) + cotton residues (CR) – zero tillage (Sesbania rostrata) + maize stubbles (MS); Sub treatments: W₁ = Chemical weed control; W₂ = Herbicide rotation; W₃ = Integrated Weed Management (IWM); W₄ = Single hand-weeded Control. T = Tillage; W = Weed Management.

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 and system cotton equivalent yield

The data on the effect of various systems on different tillage practices and weed management on kernel yield (KY) and system cotton equivalent yield (CEY) is given in Table 7. A significantly greater KY (6801 kg ha^− 1) was observed with the ZT(C) + SrR-ZT(M) + CR-ZT(Sr) + MS while a lower KY (6014 kg ha^− 1) was notable under the CT(C)-CT(M)-Fallow (NSr). The adoption of chemical weed control and herbicide rotation resulted in significantly greater KY (7245 kg ha^− 1 and 7324 kg ha^− 1, respectively), followed by IWM (6722 kg ha^− 1). In contrast, the KY was significantly lower KY (4099 kg ha^− 1) with Single hand-weeded control. The maize grain yield obtained from different tillage- weed management 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 3rd year to arrive at the CEY of the cotton– maize system (system CEY) after 3 years. The ZT(C) + SrR-ZT(M) + CR-ZT(Sr) + MS exhibited a significantly higher CEY (3775 kg ha^− 1) relative to other tillage practices examined (Table 7). Among the weed management strategies, integrated weed management (IWM) had a significantly greater system CEY (4157 kg ha^− 1) compared to the rest of the weed management practices examined. Based on the tillage and weed management combinations, the ZT(C) + SrR-ZT(M) + CR-ZT(Sr) + MS with IWM, recorded significantly higher CEY (4453 kg ha^− 1), while the combination of CT(C)-ZT(M)-ZT(Sr), CT(C)-CT(M)-Fallow(NSr) and ZT(C) + SrR-ZT(M) + CR-ZT(Sr) + MS with Single hand-weeded control resulted in the lowest CEY values (1848 kg ha^− 1, 1767 kg ha^− 1 and 2157, respectively).

Table 7. Kernel yield of maize and system yield in terms of system cotton equivalent yield (CEY) as influenced by tillage practices and weed management (WM) options after 3rd year.

Treatment Interaction	WM	kernel yield (kg ha-₁)	System (CEY) (kg ha-₁)
Tillage	WM	kernel yield (kg ha-₁)	System (CEY) (kg ha-₁)
T₁: CT(C)-CT(M)-Fallow (NSr)	W₁	6822	3756
	W₂	6854	3801
	W₃	6354	3908
	W₄	4025	1848
	W₁	7133	4005
T₂: CT(C)-ZT(M)-ZT(Sr)	W₂	7662	4187
	W₃	6558	4109
	W₄	3559	1767
T₃: ZT(C) + SrR-ZT(M) + CR-ZT(Sr) + MS	W₁	7780	4292
	W₂	7456	4206
	W₃	7253	4453
	W₄	4713	2157
Tillage practices
T₁: CT(C)-CT(M)-Fallow (NSr)	6014		3328
T₂: CT(C)-ZT(M)-ZT(Sr)	6228		3517
T₃: ZT(C) + SrR-ZT(M) + CR-ZT(Sr) + MS	6801		3775
Weed Management options
W₁- Chemical weed control	7245		4018
W₂- Herbicide rotation	7324		4065
W₃- IWM	6722		4157
W₄- Single hand-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

T₁ = conventional tillage (cotton) – conventional tillage (maize) – Fallow (No Sesbania rostrata), T₂ = conventional tillage (cotton) – zero tillage (maize) – zero tillage (Sesbania rostrata), T₃ = zero tillage (cotton) + Sesbania rostrata residues (SrR)– zero tillage (maize) + cotton residues (CR) – zero tillage (Sesbania rostrata) + maize stubbles (MS), T = TillIage, W = Weed management, IWM = Integrated weed management, CD (P = 0.05) = critical difference at 5% probability level, NS = non-significant, SE(m) = standard error of the mean.

Correlation among various soil biological attributes and crop yield

Correlation among the various soil biological properties at both sampling stages i.e., 30 DAS and tasseling stages of maize and yield (kernel and system CEY) is presented in Table 8. The data demonstrates that the soil biological atttributes, viz. Azotobacter population, Azospirillum count, soil fungal count, rhizoplane fungal count, soil dehydrogenase, phosphatase (acid and alkaline), urease, fluorescein di-acetate and β-galactosidase activity, and microbial quotient, soil basal respiration, microbial biomass carbon and nitrogen were significantly correlated with each other at both sampling phases. However, no significant correlation was observed between yield (kernel and system CEY) and all the soil biological attributes at both sampling stages. All the soil biological parameters were significantly correlated with each other except metabolic quotient, which showed a significant negative correlation with the overall soil biological attributes.

Table 8. Correlation among various soil biological parameters (microbial population, enzyme activity and microbial activity), and yields (kernel and system cotton equivalent yield).

	SMBN.30	SBR.30	qMB.30	qCO₂.30	SMBC.60	SMBN.60	SBR.60	qMB.60	qCO₂.60	DHA.30	SUA.30	AcPA.30	AlPA.30	FDA.30	β-GaA.30
SMBN.30	1
SBR.30	0.821**	1
qMB.30	0.897**	0.915	1
qCO2.30	-0.938**	-0.746**	-0.796**	1
SMBC.60	0.924**	0.905**	0.915**	-0.904**	1
SMBN.60	0.802**	0.761**	0.713**	-0.794**	0.761**	1
SBR.60	0.784**	0.869**	0.769**	-0.713**	0.811**	0.617**	1
qMB.60	0.820**	0.865**	0.847**	-0.858**	0.964**	0.674**	0.777**	1
qCO2.60	-0.876**	-0.923**	-0.956**	0.813**	-0.932**	-0.776**	-0.758**	-0.871**	1
DHA.30	0.876**	0.865**	0.840**	-0.908**	0.899**	0.721**	0.821**	0.885**	-0.799**	1
SUA.30	0.843**	0.815**	0.865**	-0.848**	0.829**	0.619**	0.708**	0.788**	-0.780**	0.948**	1
AcPA.30	0.839**	0.898**	0.915**	-0.768**	0.858**	0.624**	0.795**	0.830**	-0.828**	0.926**	0.934**	1
AlPA.30	0.797**	0.842**	0.863**	-0.809**	0.865**	0.558**	0.726**	0.873**	-0.787**	0.947**	0.964**	0.959**	1
FDA.30	0.746**	0.744**	0.799**	-0.739**	0.740**	0.478**	0.629**	0.728**	-0.675**	0.888**	0.963**	0.932**	0.963**	1
β-GaA.30	0.804**	0.854**	0.805**	-0.810**	0.876**	0.610**	0.780**	0.887**	-0.748**	0.955**	0.902**	0.944**	0.963**	0.907**	1
DHA.60	0.802**	0.814**	0.779**	-0.888**	0.862**	0.707**	0.722**	0.861**	-0.750**	0.961**	0.928**	0.837**	0.915**	0.852**	0.897**
SUA.60	0.917**	0.820**	0.875**	-0.929**	0.911**	0.653**	0.796**	0.865**	-0.843**	0.916**	0.921**	0.846**	0.889**	0.840**	0.842**
AcPA.60	0.877**	0.950**	0.935**	-0.806**	0.918**	0.821**	0.810**	0.843**	-0.974**	0.833**	0.794**	0.856**	0.796**	0.692**	0.789**
AlPA.60	0.985**	0.860**	0.929**	-0.920**	0.940**	0.794**	0.813**	0.852**	-0.919**	0.885**	0.847**	0.876**	0.826**	0.760**	0.828**
FDA.60	0.986**	0.832**	0.910**	-0.943**	0.946**	0.820**	0.739**	0.846**	-0.906**	0.866**	0.844**	0.822**	0.806**	0.741**	0.799**
β-GaA.60	0.939**	0.874**	0.954**	-0.909**	0.944**	0.757**	0.786**	0.891**	-0.963**	0.874**	0.858**	0.868**	0.848**	0.764**	0.804**
AZOT.30	0.781**	0.794**	0.822**	-0.758**	0.773**	0.533**	0.681**	0.755**	-0.717**	0.910**	0.960**	0.961**	0.966**	0.990**	0.935**
AZOSP.30	0.775**	0.829**	0.808**	-0.784**	0.833**	0.555**	0.755**	0.850**	-0.722**	0.956**	0.939**	0.958**	0.983**	0.952**	0.985**
FUNGI.30	0.811**	0.784**	0.834**	-0.771**	0.791**	0.544**	0.721**	0.768**	-0.709**	0.921**	0.947**	0.970**	0.958**	0.973**	0.942**
RPFUNGI.30	0.800**	0.803**	0.848**	-0.824**	0.826**	0.544**	0.713**	0.828**	-0.761**	0.946**	0.984**	0.937**	0.983**	0.966**	0.920**
AZOT.60	0.978**	0.799**	0.908**	-0.945**	0.915**	0.789**	0.742**	0.835**	-0.908**	0.850**	0.829**	0.811**	0.785**	0.723**	0.756**
AZOSP.60	0.967**	0.868**	0.918**	-0.927**	0.974**	0.798**	0.822**	0.921**	-0.919**	0.885**	0.812**	0.845**	0.817**	0.714**	0.830**
FUNGI.60	0.936**	0.896**	0.931**	-0.926**	0.939**	0.800**	0.777**	0.892**	-0.947**	0.897**	0.880**	0.873**	0.865**	0.799**	0.836**
RPFUNGI.30	0.954**	0.815**	0.895**	-0.949**	0.933**	0.711**	0.771**	0.898**	-0.850**	0.943**	0.913**	0.903**	0.909**	0.856**	0.897**
CEY	-0.168^NS	-0.276 ^NS	-0.166^NS	0.138^NS	-0.12 ^NS	-0.020 ^NS	-0.299^NS	-0.164^NS	-0.007^NS	-0.446^NS	-0.465^NS	-0.523^NS	-0.491^NS	-0.614^NS	-0.556 ^NS
KY	-0.161 ^NS	-0.292 ^NS	-0.131^NS	0.167^NS	-0.14 ^NS	-0.07 ^NS	-0.291^NS	-0.181^NS	-0.017 ^NS	-0.469^NS	-0.471^NS	-0.484^NS	-0.487^NS	-0.590^NS	-0.565^NS

	DHA.60	SUA.60	AcPA.60	AlPA.60	FDA.60	β-GaA.60	AZOT.30	AZOSP.30	FUNGI.30	RPFUNGI.30	AZOT.60	AZOSP.60	FUNGI.60	RPFUNGI.30	CEY
SMBN.30
SBR.30
qMB.30
qCO2.30
SMBC.60
SMBN.60
SBR.60
qMB.60
qCO2.60
DHA.30
SUA.30
AcPA.30
AlPA.30
FDA.30
β-GaA.30
DHA.60	1
SUA.60	0.905**	1
AcPA.60	0.768**	0.818**	1
AlPA.60	0.793**	0.904**	0.928**	1
FDA.60	0.824**	0.922**	0.899**	0.976**	1
β-GaA.60	0.812**	0.922**	0.939**	0.970**	0.949**	1
AZOT.30	0.848**	0.840**	0.746**	0.802**	0.768**	0.794**	1
AZOSP.30	0.905**	0.848**	0.753**	0.798**	0.764**	0.792**	0.966**	1
FUNGI.30	0.839**	0.837**	0.745**	0.832**	0.786**	0.803**	0.983**	0.968**	1
RPFUNGI.30	0.923**	0.911**	0.756**	0.813**	0.796**	0.844**	0.960**	0.963**	0.949**	1
AZOT.60	0.785**	0.919**	0.878**	0.976**	0.973**	0.973**	0.752**	0.739**	0.773**	0.799**	1
AZOSP.60	0.820**	0.910**	0.898**	0.970**	0.963**	0.959**	0.751**	0.794**	0.787**	0.803**	0.966**	1
FUNGI.60	0.855**	0.937**	0.929**	0.953**	0.950**	0.978**	0.830**	0.820**	0.815**	0.861**	0.960**	0.946**	1
RPFUNGI.30	0.879**	0.946**	0.832**	0.952**	0.941**	0.941**	0.875**	0.892**	0.900**	0.913**	0.953**	0.958**	0.941**	1
CEY	-0.363^NS	-0.223^NS	-0.084^NS	-0.159^NS	-0.089^NS	-0.076 ^NS	-0.625 ^NS	-0.609^NS	-0.615^NS	-0.496^NS	-0.066 ^NS	-0.117 ^NS	-0.162 ^NS	-0.302 ^NS	1
KY	-0.435^NS	-0.240^NS	-0.083^NS	-0.133^NS	-0.101^NS	-0.047 ^NS	-0.596 ^NS	-0.606^NS	-0.573^NS	-0.489^NS	-0.041^NS	-0.111^NS	-0.156^NS	-0.283^NS	0.970^NS

**Correlation is significant at 0.01 level. SMBC. 30= Soil microbial biomass carbon at 30 days after sowing (DAS), SMBN. 30= Soil microbial biomass nitrogen at 30 DAS, qMB. 30= microbial quotient at 30 DAS, qCO₂. 30= metabolic quotient at 30 DAS, SBR. 30= soil basal respiration at 30 DAS, SMBC. 60= Soil microbial biomass carbon at 60 days after sowing (DAS), SMBN. 60= Soil microbial biomass nitrogen at 60 DAS, qMB. 60= microbial quotient at 60 DAS, qCO₂. 60= metcbolic quotient at 60 DAS, SBR. 60= soil basal respiration at 30 DAS, DHA. 30= Dehydrogenase activity at 30 days after sowing (DAS), SUA. 30= Soil urease activity at 30 DAS, AcPA. 30= Acid phosphatase activity at 30 DAS, AlPA. 30= Acid phosphatase activity at 30 DAS, FDA. 30= Fluorescein di-acetate activity at 30 DAS, β-GaA= β-galactosidase activity at 30 DAS, DHA. 60= Dehydrogenase activity at 60 DAS, SUA. 60= Soil urease activity at 60 DAS, AcPA. 60= Acid phosphatase activity at 60 DAS, AlPA. 60= Acid phosphatase activity at 60 DAS, FDA. 60= Fluorescein.

di-acetate activity at 60 DAS, β-GaA= β-galactosidase activity at 60 DAS, AZOT. 30= Azotobacter population at 30 DAS, Azosp. 30=Azospirillum population at 30 DAS, Fungi. 30= soil fungal population at 30 DAS, RPFungi. 30= Rhizoplane fungal population at 30 DAS, AZOT. 60= Azotobacter population at 60 DAS, Azosp. 60=Azospirillum population at 60 DAS, Fungi. 60= soil fungal population at 60 DAS, RPFungi. 60= Rhizoplane fungal population at 60 DAS, CEY= System cotton equivalent yield, KY= Kernel yield.

Principal component analysis (PCA) and variable selection

PCA was used to analyse β-galactosidase activity, microbial quotient, dehydrogenase activity, soil urease activity, acid phosphatase activity, alkaline phosphatase activity, fluorescein di-acetate activity, Azotobacter population, Azospirillum population, rhizosphere fungal population, rhizoplane fungal population, metabolic quotient, soil basal respiration, soil microbial biomass carbon and nitrogen as to ascertain their contribution for improving soil health and quality at 30 days after sowing (DAS) and 60 DAS of winter maize inclusive of soil organic carbon as well as soil pH at this stage (60 DAS). The PCA clustered all the observations into 11 principal components (PCs) at 30 and 60 DAS of maize (Table Supplementary 4a and 4b). At 30 DAS and 60 DAS of the crop, PC 1 contributed 89.60% and 83.41% of explained variances, respectively, suggesting overfitting as validated by the scree plots (Fig. 5 and Fig. 6). The Eigen values of 12.54 and 13.35 were also higher at 30 DAS and 60 DAS of the crop, respectively (Table Supplementary 7a and 7b).

[See PDF for image]

Fig. 5

Scree plot at 30 days after sowing (DAS) period.

[See PDF for image]

Fig. 6

Scree plot at 60 days after sowing (DAS) period.

PC 1 was the only PC with Eigen value of more than 1 in comparison with the other PCs at both stages (30 and 60 DAS) of maize (Table Supplementary 4a and 4b). Therefore, PC 1 was regarded as the best PC for further analysis at both crop growth stages. The weight values for respective PCs was 1 because both variable and cumulative variable percent were 89.60% and 83.41% at 30 and 60 DAS of maize, respectively for PC 1. As numerous data sets are dependent, the indicators or variables were selected based on PCA and correlation among the soil parameters. These selected variables from PC 1 at 30 DAS of the crop were β-galactosidase activity, microbial quotient, dehydrogenase activity, soil urease activity, acid phosphatase activity, alkaline phosphatase activity, fluorescein di-acetate activity, Azotobacter population, Azospirillum population, rhizosphere fungal population and rhizoplane fungal population at 30 DAS and all the variables selected from PC 1 at 30 DAS including soil organic carbon at 60 DAS of the crop, based on their higher factor loading scores (Table Supplementary 4a and 4b).

Discussions

Soil pH and soil organic carbon

Among all other soil factors, tillage contributed significantly in the alteration of soil organic carbon (SOC). The SOC concentration in the soil surface under zero tillage (ZT) with maintenance of the crop debris was higher. This greater SOC exhibited by ZT(C) + SrR-ZT(M) + CR-ZT(Sr) + MS (Table 4) could be associated with continuous adoption of ZT due to lower soil disturbance, cumulative retention of the previous Sesbania, cotton residues and maize stubbles for consecutive years, which likely resulted in soil aggregate enhancement, protection of the soil against SOC loss. In zones where soil and weather conditions are conducive for the production of biomass, and where adverse crop yield effects are unnoticed, then CA practices demonstrate greater quantity of SOC comparative to conventionally tilled (CT) managed systems, particularly in the top soil⁴⁹. The reduction in SOC levels observed under CT(C)-CT(M)-Fallow (NSr) (Table 5), could be linked with the primary and secondary tillage implements employed during ploughing which might have disturbed the soil aggregation. Thus, CA-based practices such as ZT are directly associated with the maintenance of crop residues, which in turn influence SOC accumulation and dynamics conducive for soil microbial composition under diversified cropping systems.

Soil microbial activity

The efficiency of soil microbes utilizes more sources of carbon materials for their survival and growth in the ZT(C) + SrR-ZT(M) + CR-ZT(Sr) + MS, which exert a small impact on soil microbes to a certain extent on biological oxygen requirement, which might equilibrate the proportion of carbon-dioxide released by microbes to microbial biomass, thus increasing the qMB⁵⁰. The observed results suggest that soil microbial biomass carbon (SMBC) and nitrogen (SMBN), SBR, qMB, enzyme activities and microbial population increased as the crop advanced, being significantly higher under the adoption of conservation tillage (ZT + crop residue retention) and single hand-weeded control followed by integrated weed management (Fig. 2). This increase is attributed to the decomposition of organic substrates, herbicides degradation, active and reproductive stage of the crop in which the rhizosphere begins to get enriched with specific microorganisms and secretion of beneficial nutrients necessary to perform the activities related to SOM cycling. The increase might also be the result of root proliferation, exudation, crop litter fall and conducive bio-physical environment created for microbes which serves as substrates to enhance their activity under ZT(C) + SrR-ZT(M) + CR-ZT(Sr) + MS, promoting better soil functional diversity. The availability of energy and nutrient resources and the limited oxidation of SOC, favored by the prevalence of soil microorganisms, likely contributed to this observed enhancement. These results of this present investigation are supported by Chaudhari et al.⁵¹ who observed that the combination of zero tillage + residue retention and inter-cultivation (IC) + hand weeding (HW) is the most suitable strategy for sustenance of greatest soil microbial biomass. Similarly, Lin et al.⁵². investigated the effects of biodegradable mulching on grain yield of wheat and microbial biomass carbon, which aligns with the focus of the study on sustainable agricultural practices.

Conventional tillage (CT) disrupts the soil aggregates, exposes the soil and makes it more prone to erosion. Herbicides may stimulate or activate the soil microbial biomass. In this experiment, conventional tillage practices and herbicides-treated plots have caused a drastic decrease on soil biological indicators except for qCO₂, observed after pre-emergence, early post emergence, post emergence application of herbicides at 30 DAS of maize. This could be associated with more soil disturbance due to intensive cultivation and herbicides inhibiting factor. Soil disturbance as a result of intensive tillage may have 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 (qCO₂) values observed under ZT, which retain crop residues in the soil, could be an indicator of the lower energy requirements of microorganisms. However, the reported qCO₂ in the study is within the threshold for healthy agricultural soils (between 0.5 and 2.0 mg CO₂–C g^–1 MBC h^–1)⁵³, not indicating any disorder due to the application of tillage practices and herbicides. This could be due to short-term conservation agricultural practice and the quick degradation of the herbicides applied. Engell et al.⁵⁴. reported lower qCO₂ values with the adoption of ZT. 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 Villamil⁵⁵ under similar soil field conditions as in the present study, who have indicated that sandy clay loam soils experience lower qCO₂ values under NT, although the impact of tillage was found to be low in soils with very fine particles. Low values of qCO₂ are an indication of conducive conditions for the predominance of microbial activity⁵⁴. A lower qCO₂ is a reflection of improved physiological conditions resulting from amended organic matter, while higher qCO₂ is an indication of soil degradation under intensive land use⁵⁵. On the other hand, a rise in qCO₂ might not only be ascribed to microbial stress but also be interpreted as positive priming on the decomposition of the labile SOC pool following the addition of readily degradable carbon substrates to the soil⁵⁶. In this present investigation, higher qCO₂ 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. The management of weeds with herbicides was found to have increased qCO₂, indicating stress or disorder, probably due to the detrimental effects of applied herbicides on the soil microbial population. Since the application of chemical compounds such as herbicides among others in the soil requires an adaptation of soil microbial biomass, which uses their reserves to degrade these compounds, C from microbial biomass ultimately becomes lost, thus increasing qCO₂.

Soil enzyme activity and microbial population

The functional groups of culturable microbes are often interlinked with carbon cycling activities and are related to rhizosphere soil enzymes⁵⁷, demonstrating that alterations in SOC fractions through adoption of ZT with the retention of crop residues can become the chief driver of soil microorganism constituents⁵⁸. Since rhizosphere enzymes are secreted by specific groups of microbes, the diversity of crops under ZT + crop residues play a key role in the enhancement of soil enzymes activity, which agrees with the results of this study in which the ZT(C) + SrR-ZT(M) + CR-ZT(Sr) + MS (zero tillage with diverse crop residues retained in the soil surface) resulted in an increased activity of rhizosphere enzymes. Residue retention in conservation agriculture, through high residue quality and C: N ratios from diverse crops, significantly influence soil enzyme activity⁵⁹. This C: N ratios in residues may lead microbes to prioritize nutrient acquisition by targeting nutrient-rich soil organic matter, which may in turn alter the balance between carbon and nutrient acquisition enzymes⁵⁹. This modulation can be ascribed to changes in microbial community structure and activity, nutrient availability, and the decomposition processes of organic matter.

These crop species diversification (cotton-maize-Sesbania rostrata) in rotation significantly modified the activity of all the enzymes, probably due to variety of crops within the system, having a greater distinct litterfall and rhizosphere exudates; thus, the ZT(C) + SrR-ZT(M) + CR-ZT(Sr) + MS, single hand-weeded control and IWM under these diverse crops species have shown an extensive influence on rhizosphere functional diversity. The significant overall increase in the enzyme activity with crop growth advancement could possibly be due to secretion of beneficial nutrients, the decomposition of organic substrates and herbicides degradation.

The application of herbicides at pre-emergence reduced soil biological indicators drastically, at 30 DAS of maize which could probably be due to the herbicide’s direct application into the soil rhizosphere. Similarly, the inhibitory effects of pre-emergence herbicides viz., alachlor and atrazine on rhizosphere microbial counts was reported by Konstantinovic et al.⁶⁰. The results of this present experiment also indicated that the application of various herbicides evinced high potency on inhibiting the growth of fungi (Supplementary Table 3b). The inhibition of mycelial growth of rhizosphere and rhizoplane fungi by herbicides (Glyphosate, Paraquat, Atrazine and Linuron) applied was consistent with previous studies⁶¹. Higher biological activities associated with ZT(C) + SrR-ZT(M) + CR-ZT(Sr) + MS particularly after pre-emergence application of the herbicides, at 30 DAS of maize could be ascribed to partial inhibition of pre-emergence herbicides reaching the soil. This inhibition is likely a result of the presence of crop residues (cotton crop residues retained for maize) on the soil surface. These findings are supported by Varsha et al.⁶² in which soil enzyme activity such as soil urease was noticed with a decreasing trend following herbicide application, and the activity-regained normalcy with the increase in time. This might be due to the herbicide effect on enzyme activity, stabilized after time or the herbicide themselves adsorbed irreversibly on soil colloids with an increase in time, resulting in decreased inhibition. The overall decrease in enzyme activity after herbicides application at 30 DAS of the crop in W₁ and W₂ might be due to the change in species composition of soil microorganisms and variation in the availability of organic substrate. Thus, it may be deduced that from this study that herbicides applied in W₁ and W₂ sub-treatments resulted in a significant decline in rhizosphere enzyme activities.

Fungal diversity

A Vast fungal diversity has been interlinked with plant systems, viz., epiphytic, endophytic and rhizosphere fungi. All these fungi in association with the plant systems play an essential role in plant growth, crop yield and soil health improvement⁶³. Thus, adopting zero tillage (ZT) and conservation tillage (ZT + crop residue retention) with IWM harboured beneficial fungal species; Talaromyces flavus; rhizosphere soil micro-inhabitant, identified under CT(C)-ZT(M)-ZT(Sr) in combination with IWM (T₂W₃) and ZT(C) + SrR-ZT(M) + CR-ZT(Sr) + MS on interaction with IWM (T₃W₃). This soil inhabitant fungi has been newly reported as soil stabilizer and plant growth promoting fungi (PGPF) with high potential to inhibit other pathogenic fungal species (bio-control agent) while benefiting the plant and the soil⁶⁴. However, CT(C)-CT(M)-Fallow(NSr), CT(C)-ZT(M)-ZT(Sr) and ZT(C) + SrR-ZT(M) + CR-ZT(Sr) + MS treatments in combination with herbicide rotation and chemical weed control were found to have contained general pathogenic fungal species (Aspergillus niger, Penicillin limosum, Aspergillus terreus, Apiospora serenensis, Zasmidium cellare, Ochraceocephala foeniculi).

It is evident that the implementation of single hand-weeded control, chemical weed control and herbicide rotation (in alternative year) with any tillage practice (s), changed the fungal composition into pathogens and these results agree with the findings of Bhardwaj et al.⁶³. who studied the impact of herbicides on irrigated tropical rice field, in which predominance of pathogenic fungi (Humicola, Nigrospora,, Paramyrothecium, Mariannaea, Ceratobasidium, Funneliformis, Aspergillus, Pseudorhypophila, and Lecythophora) were identified with a single hand-weeded control at critical period of weed competition, and herbicide-treated plots, indicating adverse effects of herbicides and high weed density population on microbial dynamics. These results obtained on fungal diversity signifies the adoption of ZT(C) + SrR-ZT(M) + CR-ZT(Sr) + MS and ZT in combination with IWM in CA practices relative to CT in combination with herbicides and single hand-weeded control sub-treatments.

Crop productivity

Maize grain yield and system productivity (Cotton equivalent yield)

The importance of crop-weed interactions in determining the competition faced by crop plants for light, moisture and space is well-established⁶⁵. Confined root growth leads to decreased nutrient uptake and poor crop growth⁶⁵. In the present investigation, the maize grain was greater in the ZT(C) + SrR-ZT(M) + CR-ZT(Sr) + MS than in the other tillage systems. This superior performance can be interconnected with the preservation of crop residues on the soil surface in combination with rainfall received in monsoon and provision of supplemental irrigation (winter) during the developmental stage of cotton and maize, respectively, which likely contributed to the enhanced retention and availability of soil moisture. These led towards the development of robust, deep-rooted systems in crops facilitated by the practice of zero tillage. This aspect is especially crucial during the post tasseling 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.⁶⁶. 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 yield⁶⁷. A number of previous studies conducted on short-term conservation tillage have not fully paid attention as to how yield can be improved in conservation agriculture⁶⁷.

No-till enhances root biomass and shoot biomass, regulates the shoot-to-root ratio and increases yield in comparison with plough-till and rotary-till⁶⁸. Crop residue retention can also enhance crop biomass and yield due to enhanced soil buffering capacity⁶⁹. The post-emergence tank-mix combination of atrazine and tembotrione herbicides was applied at recommended rates in chemical weed control and 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 single hand-weeded control, which was ultimately reflected in yield. This could be due to the high weed density at the critical crop growth stage, which out-competed with the crop for available moisture, nutrients, light and rooting space. The results similar to this present study were reported by Ganapathi et al.⁷⁰ who recorded higher kernel yield with IWM than with the use of only recommended herbicides and single hand-weeded control due to less weed infestation. In the present investigation, there was an increase in corn yield and system CEY when employing zero tillage with crop residue retention (ZT(C) + SrR-ZT(M) + CR-ZT(Sr) + MS) with IWM and chemical weed control/ herbicide rotation. This improvement could be attributed to the synergistic effects of efficient weed management achieved using chemical and cultural mechanical control tactics, as well as moisture and nutrient preservation facilitated by no-till practices, which retain crop residues.

The system CEY increased by nearly 14% with ZT + residue retention treatment and by 116% with weed management interventions. It was also observed that system CEY was higher with integrated weed management (IWM), followed by the herbicide application. This could be due to the herbicides ability for weed suppression, which reduces weed competition, thus giving access to the plant for adequate resources (moisture, air, light and nutrients) required for the growth and development. So, considering system yield, the ZT(C) + SrR-ZT(M) + CR-ZT(Sr) + MS gave better system yield. This might be due to IWM in cotton-maize-Sesbania rostrata sequential system for provision of scientifically substantiated benefits, including higher yields. This could also be due to low weed density at the initial crop developmental phase. The application of herbicides in time and subsequent control of later germinated weeds by the supplemented inter-cultural operation followed by hand weeding caused a reduction in weed competition, leading to increased photosynthetic activity⁷¹, and this agrees with the results of the present investigation.

Correlation among various soil biological attributes and crop yield

The correlation of these attributes (enzyme activities, microbial population and activity) was non-significant with the yield (kernel and System CEY). This could be due to crop yield determinant factors such as moisture, sunlight etc. These results concur with Yankit et al.⁷². who observed no significant correlation between soil biological parameters with tomato yields in the mid-hill zone of Himachal Pradesh in India, attributed to variability in tomato yield over the 2-year experiment, which led to non-significant difference among the natural, organic and conventional farming systems. The correlation between qCO₂ with other biological attributes (enzyme activity, microbial population and activity) shows that both qCO₂ and other soil biological parameters have an inverse relationship. The qCO2 decreased with an increase in other soil biological attributes, increased with a decrease in other biological parameters examined. The increase or higher values of qCO2 may signify difficulties in using organic substrates during microbial activity probably due to high necessity of maintenance energy or lower metabolic efficiency, which reflects high maintenance carbon demand ecologically⁷³.

Principal component analysis and variable selection

The role of SOC is known to alter and bolster many soil functions such as enzyme activities, microbial population and activity (microbial quotient) etc. Therefore, all these soil functions examined thereof, were selected in PC 1 at both sampling stages (30 and 60 DAS) of maize. Fitly, SOC has been associated and correlated positively with all examined soil functions, which could be ascribed to improved soil health owing to adoption of conservation agriculture practice. However, metabolic quotient indicated a negative correlation, thus, suggesting the negative impact of the herbicides and intensive tillage practices on soil health. The observed selected variables (enzyme and microbial parameters) in PC 1 (Table 9a and b), demonstrates soil health brought by effective tillage practice and weed management, i.e., ZT(C) + SrR-ZT(M) + CR-ZT(Sr) + MS and single hand-weeded control combinations, respectively, well known to have the potential to unlock sustainability, economical success of marginal soils and stimulate microbial population being measured as SMBC, which in turn enhances enzyme activities⁷⁴. soil health and quality can better be gauged through assessment of β-galactosidase activity, microbial quotient, dehydrogenase activity, soil urease activity, acid phosphatase activity, alkaline phosphatase activity, fluorescein di-acetate activity, Azotobacter population, Azospirillum population, rhizosphere fungal population, rhizoplane fungal population, metabolic quotient, soil organic carbon and soil pH in conservation agricultural system under cotton-maize-Sesbania rostrata system in the semi-arid regions of Southern India.

Conclusion

On the basis of cumulative effect of different tillage practices and weed management options in conservation agriculture (CA) on soil biological attributes (enzyme activities, microbial population and activity), fungal diversity, and crop productivity, the following conclusions can be drawn; conservation tillage i.e., ZT(C) + SrR-ZT(M) + CR-ZT(Sr) + MS) in combination with single hand-weeded control, followed by integrated weed management (IWM), significantly enhanced soil biological attributes, except metabolic quotient (qCO₂) which decreased by 15.91–22.73% and 56.76–59.46% at 30 and 60 DAS, respectively under ZT(C) + SrR-ZT(M) + CR-ZT(Sr) + MS) in combination with single hand-weeded control and IWM over the CT(C)-CT(M)-Fallow (NSr) and chemical weed control combinations. Conventionally tilled (CT) in combination with chemical/herbicides-treated plots resulted in a drastic reduction of soil microbiological parameters after early post, pre- and post-emergence application of herbicides at 30 DAS of maize. The qCO₂ correlated negatively with other soil biological attributes examined as evidenced by Pearson correlation matrix in Table 8. Overall, soil biological attributes did not correlate with yield (Kernel and system CEY), suggesting that yield depend on abiotic and biotic factors. The system CEY was 51.60% higher under the ZT(C) + SrR-ZT(M) + CR-ZT(Sr) + MS practices in combination with IWM and Talaromyces flavus was a beneficial soil micro-inhabitant that was harboured under such treatment combination. Even though the soil biological parameters responded positively with the adoption of ZT(C) + SrR-ZT (M) + CR-ZT(Sr) + MS in combination with single hand-weeded control, crop productivity in terms of system CEY (2157 kg ha^− 1) was very poor, being 51.56% lower than in the ZT (M) + CR-ZT(Sr) + MS combined with IWM. A thorough understanding of tillage along-with weed management and herbicide behaviour as well as functioning in the cotton-maize-Sebania rostrata ecosystem, particularly in the Inceptisol of the semi-arid zones is required to successfully implement nature-based solution (NBS) for sustainable farming system. We suggest further research to look into nature-based herbicides that can potentially manage the weeds while encouraging the soil biological activities and increasing crop productivity in the cotton-maize-Sesbania rostrata systems in conservation agriculture.

Acknowledgements

The authors are extremely thankful to All India Coordinated Research Project (AICRP) on weed management for the financial sponsorship received for the implementation and execution of this on-going conservation agriculture experiment, carried-out at college farm of the Professor Jayashankar Telangana State Agricultural University (PJTSAU), Rajendranagar, Telangana, India.

Author contributions

The contributions of all authors must be described in the following manner: The authors confirm contribution to the manuscript as follows: study conception and design: K.N., R. P.T., P. B. and J.G.; data analysis and interpretation of results: K.N, N.K.S ., M.B.N.Y and M.A., draft manuscript preparation: K.N. and L.P.C. All authors reviewed the results and approved the final version of the manuscript.

Data availability

“The datasets generated and/or analysed during the current study are available in the [National Centre for Biotechnology Information (NCBI) repository from the GenBank], [ accession numbers to datasets are as follows; SUB14162715 1-PJTSAU-KNIGHT-23 PP177339, SUB14162715 2-PJTSAU-KNIGHT-23 PP177340, SUB14162715 3-PJTSAU-KNIGHT-23 PP177341, SUB14162715 4-PJTSAU-KNIGHT-23 PP177342, SUB14162715 5-PJTSAU-KNIGHT-23 PP177343, SUB14162715 6-PJTSAU-KNIGHT-23 PP177344, SUB14162715 7-PJTSAU-KNIGHT-23 PP177345SUB14162715 8-PJTSAU-KNIGHT-23 PP177346].”

Declarations

Competing interests

The authors declare no competing interests.

Conflict of interest

The authors declared that no conflicts of interests exist.

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References

1. Nthebere, K et al. Biological indicators of soil fertility under intensive cropping system. Multi-Disciplinary Res. Bull.; 2022; 1, 3 pp. 1-2. ISSN 2583–5122

2. Naseri, B. The potential of agroecological properties in fulfilling the promise of organic farming: a case study of bean root rots and yields in Iran. Adv. Resting-state Funct. MRI; 2023; 203, 236.

3. Gollner, G; Starz, W; Friedel, JK. Crop performance, biological N fixation and pre-crop effect of pea ideotypes in an organic farming system. Nutr. Cycl. Agrosyst.; 2019; 115, pp. 391-405.

4. Naseri, B; Nazer Kakhki, SH. Predicting common bean (Phaseolus vulgaris) productivity according to rhizoctonia root and stem rot and weed development at field plot scale. Front. Plant Sci.; 2022; 13, 1038538. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36531360][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9749423]

5. Bardgett, RD; Van der Putten, WH. Below ground biodiversity and ecosystem functioning. Nature; 2014; 515, pp. 505-511.2014Natur.515.505B1:CAS:528:DC%2BC2cXitFamsrnM [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25428498]

6. Naseri, B. Legume root rot control through soil management for sustainable agriculture. In Sustainable Management of Soil and Environment (eds Meena, R., Kumar, S., Bohra, J. & Jat, M) (Springer, Singapore). https://doi.org/10.1007/978-981-13-8832-3_7 (2019).

7. Naseri, B; Younesi, H. Beneficial microbes in biocontrol of root rots in bean crops: A meta-analysis (1990–2020). Physiol. Mol. Plant Pathol.; 2021; 116, 101712.

8. Castle, SC et al. Nutrient limitation of soil microbial activity during the earliest stages of ecosystem development. Oecologia; 2017; 185, pp. 513-524.2017Oecol.185.513C [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28983721]

9. Amorim, HC et al. Soil quality indices as affected by long-term burning, irrigation, tillage, and fertility management. Soil Sci. Soc. Am. J.; 2021; 85, 2 pp. 379-395.2021SSASJ.85.379A1:CAS:528:DC%2BB3MXlsV2lsLY%3D

10. Six, J et al. Stabilization mechanisms of soil organic matter: implications for C-saturation of soils. Plant. Soil.; 2002; 241, pp. 155-176.1:CAS:528:DC%2BD38XltV2jsbo%3D

11. Cardoso, EJBN et al. Soil health: looking for suitable indicators. What should be considered to assess the effects of use and management on soil health?. Scientia Agricola; 2016; 70, pp. 274-289.

12. Bissett, A et al. Microbial community responses to anthropogenically induced environmental change: towards a systems approach. Ecol. Lett.; 2013; 16, pp. 128-139. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/23679012]

13. Kumar, N. et al. Tillage and weed management practice influences on weed dynamics and yield of Greengram in maize-wheat-greengram cropping system. Indian J. Weed Sci.55(4), 388–395 (2023).

14. Hazra, KK et al. Diversification of maize-wheat cropping system with legumes and integrated nutrient management increase soil aggregation and carbon sequestration. Geoderma; 2019; 353, pp. 308-319.2019Geode.353.308H1:CAS:528:DC%2BC1MXhvFCnsbnM

15. Dass, A et al. Weed management in rice using crop competition-a review. Crop Prot.; 2017; 95, pp. 45-52.

16. Rao, D et al. Changes in soil microbial activity, bacterial community composition and function in a long-term continuous soybean cropping system after corn insertion and fertilization. Front. Microbiol.; 2021; 12, 638326. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33897643][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8059791]

17. Bolinder, MA et al. An approach for estimating net primary productivity and annual carbon inputs to soil for common agricultural crops in Canada. Agriculture Ecosystems Environment; 2007; 118, 1–4 pp. 29-42.

18. Soil Survey Staff. Soil Taxonomy: A Basic System of Soil Classification for Making and Interpreting Soil Surveys. USDA, SCS Agric. Handb. No. 436 (U.S. Government Printing Office, 1975).

19. Bouyoucos, GJ. The hydrometer is a new method for the mechanical analysis of soils. Soil Sci.; 1927; 23, pp. 343-3543.1927SoilS.23.343B1:CAS:528:DyaB1cXisFWh

20. Jackson, M. L. Soil chemical analysis. An advanced course, Second Edition, University of Wisconsin, Madison, USA. (1973).

21. Blake, G. R. & Hartge, K. H. Bulk density. Methods of soil analysis: Part 1 Physical and mineralogical methods.; 5: 363–375. (1986).

22. Walkley, A; Black, CA. Estimation of organic carbon by chromic acid Titration method. Soil Sci.; 1934; 37, pp. 29-38.1934SoilS.37..29W1:CAS:528:DyaA2cXitlGmug%3D%3D

23. Subbiah, BV; Asija, GL. A rapid procedure for the determination of available nitrogen in soils. Curr. Sci.; 1956; 25, pp. 259-260.1:CAS:528:DyaG1cXpvFOgtQ%3D%3D

24. Olsen, S. R. Estimation of Available Phosphorus in Soils by Extraction with Sodium BicarbonateNo. 939 (US Department of Agriculture, 1954).

25. Wu, X et al. Effects of soil moisture and temperature on CO₂ and CH₄ soil–atmosphere exchange of various land use/cover types in a semi-arid grassland in inner mongolia, China. Soil. Biololy Biochem.; 2010; 42, pp. 773-787.1:CAS:528:DC%2BC3cXjt1emu7w%3D

26. Da-Silva, E. E., Azevedo, P. H. S. & De-Polli, H. Determinação Da respiração basal (RBS) e quociente metabólico do solo (qCO₂). Comunicado Técnico EMBRAPA99, 1–4 (2007).

27. Witt, C et al. A rapid chloroform-fumigation extraction method for measuring soil microbial biomass carbon and nitrogen in flooded rice soils. Biol. Fertil. Soils; 2000; 30, pp. 510-519.1:CAS:528:DC%2BD3cXhtlCrt7k%3D

28. Brookes, PC et al. Chloroform fumigation and the release of soil nitrogen: a rapid direct extraction method to measure microbial biomass nitrogen in soil. Soil Biol. Biochem.; 1985; 17, 6 pp. 837-842.1:CAS:528:DyaL28XhvFSgug%3D%3D

29. Anderson, TH; Domsch, KH. Application of eco-physiological quotients (qCO₂ and qD) on microbial biomasses from soils of different cropping histories. Soil Biol. Biochem.; 1990; 22, 2 pp. 251-255.

30. Singh, K et al. Effect of different leaf litters on carbon, nitrogen and microbial activities of sodic soils. Land Degrad. Dev.; 2016; 27, pp. 1215-1226.

31. Casida, L; Klein, DA; Santoro, T. Soil dehydrogenase activity. Soil Sci.; 1964; 98, 6 pp. 371-376.1964SoilS.98.371C1:CAS:528:DyaF2MXjvFyktQ%3D%3D

32. Green, VS; Stott, DE; Diack, M. Assay for fluorescein diacetate hydrolytic activity: optimization for soil samples. Soil Biol. Biochem.; 2006; 38, pp. 693-701.1:CAS:528:DC%2BD28XivVaqsbg%3D

33. Tabatabai, MA; Bremner, JM. Assay of urease activity in soils. Soil Biol. Biochem.; 1972; 4, 4 pp. 479-487.1:CAS:528:DyaE3sXivVertg%3D%3D

34. Eivazi, F; Tabatabai, MA. Glucosidases and galactosidases in soils. Soil Biol. Biochem.; 1988; 20, pp. 601-606.1:CAS:528:DyaL1MXjvVWnug%3D%3D

35. Tabatabai, MA; Bremner, JM. Use of p-nitrophenyl phosphate for assay of soil phosphatase activity. Soil Biol. Biochem.; 1969; 1, 4 pp. 301-307.1:CAS:528:DyaE3cXhtFShu7Y%3D

36. Albino, U. B. & Andrade, G. Evaluation of the functional group of microorganisms as bioindicators on the rhizosphere microcosm. In Handbook of Microbial Biofertilizers 1st edn, Vol. 532 (CRC, 2007).

37. Schmidt, E. & Caldwell, A. C. A practical manual of soil microbiology laboratory methods. Food and agriculture organization of the united nations. Soils Bulletin144862, 72–75 (1967).

38. Dobereiner, J; Marriel, IE; Nery, M. Ecological distribution of Spirillum lipoferum Beijerinck. Can. J. Microbiol.; 1976; 22, pp. 1464-1473.1:CAS:528:DyaE28XlslCitbc%3D [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/10062]

39. Alexander, M. Most-probable‐number method for microbial populations. Methods Soil. Analysis: Part. 2 Chem. Microbiol. Prop.; 1965; 9, pp. 1467-1472.

40. Turnbull, C et al. Primordial enemies: fungal pathogens in thrips societies. PLoS One; 2012; 7, 11 e49737.2012PLoSO..749737T1:CAS:528:DC%2BC38XhvVags7%2FM [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/23185420][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3504084]

41. William, J; Bruno, ND; Socci,; Aaron, L; Halpern,. Weighted neighbor joining: A Likelihood-Based approach to Distance-Based phylogeny reconstruction. Mol. Biol. Evol.; 2000; DDSKVG13, 1 pp. 189-197. 2E. O

42. Wiley, D. R., Brooks, D., Siegel-Causey, V. A. & Funk The Compleat Cladist: A Primer of Phylogenetic Procedures. (1991).

43. Baldoni, DB et al. Brown rotting fungus closely related to Pseudomerulius Curtisii (Boletales) recorded for the first time in South America. Mycosphere; 2012; 3, 5 pp. 533-541.

44. Tamura, K., Nei, M. & Kumar, S. Prospects for inferring very large phylogenies by using the neighbor-joining method. Proceedings of the National Academy of Sciences. U. S. A.; 101: 11030–11035. (2004).

45. Saitou, N; Nei, M. The neighbor-joining method: A new method for reconstructing phylogenetic trees. Mol. Biol. Evol.; 1987; 4, pp. 406-425.1:STN:280:DyaL1c7ovFSjsA%3D%3D [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/3447015]

46. Staden, R; Judje, DP; Bonfield, JK. Krawetz, SA; Womble, DD. Managing sequencing projects in the GAP4 environment. Introduction To Bioinformatics. A Theoretical and Practical Approach; 2003; Totawa, Human Press Inc:

47. Panse, V. G. & Sukhatme, P. V. Statistical methods for agricultural works, ICAR, New Delhi. (1978).

48. Mohanty, M. SQI CAL Software. Division of Soil Physics (Indian Institute of Soil Science. Bhopal, 2020).

49. Nthebere, K; Prakash, TR; Kumar, NV; Yadav, MBN. Capability of conservation agriculture for preservation of organic carbon and succeeding effect on soil properties and productivity-a review. Arch. Agron. Soil. Sci.; 2024; 70, 1 pp. 1-28.1:CAS:528:DC%2BB2cXisV2gurfM

50. Nuralykyzy, B et al. Influence of land use types on soil carbon fractions in the Qaidam basin of the Qinghai-Tibet plateau. Catena; 2023; 231, 107273.1:CAS:528:DC%2BB3sXht1yqtLrF

51. Chaudhari, DD et al. Tillage and weed management influence on physico-chemical and biological characteristics of soil under cotton-green gram cropping system. Indian J. Weed Sci.; 2020; 52, 1 pp. 37-42.

52. Lin, N et al. Black biodegradable mulching increases grain yield and net return while decreasing carbon footprint in rain-fed conditions of the loess plateau. Field Crops Res.; 2024; 318, 109590.

53. Janssens, IA et al. Reduction of forest soil respiration in response to nitrogen deposition. Nat. Geosci.; 2010; 3, 5 pp. 315-322.2010NatGe..3.315J1:CAS:528:DC%2BC3cXlsFSksLg%3D

54. Engell, I et al. The effects of conservation tillage on chemical and microbial soil parameters at four sites across Europe. Plants; 2022; 11, 13 1747.1:CAS:528:DC%2BB38XhvVGqtbrJ [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/35807698][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9268763]

55. Zuber, SM; Villamil, MB. Meta-analysis approach to assess effect of tillage on microbial biomass and enzyme activities. Soil Biol. Biochem.; 2016; 97, pp. 176-187.1:CAS:528:DC%2BC28Xms1KltLc%3D

56. Zhou, H et al. Changes in microbial biomass and the metabolic quotient with Biochar addition to agricultural soils: A Meta-analysis. Agriculture Ecosystems Environment; 2017; 239, pp. 80-89.1:CAS:528:DC%2BC2sXhs1GktLk%3D

57. Cardoso, EJBN et al. Soil health: looking for suitable indicators. What should be considered to assess the effects of use and management on soil health?. Scientia Agricola; 2013; 70, pp. 274-289.

58. Liang, S et al. Functional distribution of bacterial community under different land use patterns based on faprotax function prediction. Pol. J. Environ. Stud.; 2020; 29, pp. 1245-1261.1:CAS:528:DC%2BB3MXnt1OitA%3D%3D

59. Agumas, B; Agegnehu, G; Feyisa, T. Interactive effect of residue quality and agroecologies modulate soil C-and N‐Cycling enzyme activities, microbial gene abundance, and metabolic quotient. Appl. Environ. Soil. Sci.; 2024; 2024, 1 8498801.

60. Konstantinovic, B. et al. Herbicide efficiency and their impact on microbiological activity in soil. In Research progress in plant protection and plant nutrition, AAM, Beijing, China Agriculture Press.; 228–232. (1999).

61. Mohiuddin, M; Mohammed, MK. Fungicide (carbendazim) and herbicide (2-4-D and Atrazine) influence on soil microorganisms and soil enzymes of rhizospheric soil of groundnut crop. Int. J. Recent. Sci. Res.; 2014; 5, 3 pp. 585-589.

62. Varsha, N; Prakash, R; Madhavi, T; Devi, KS. Urease and dehydrogenase enzyme activity influenced by diuron. Int. J. Chem. Stud.; 2018; 6, 5 pp. 1656-1659.1:CAS:528:DC%2BC1MXjvVekt70%3D

63. Bhardwaj, L et al. Influence of herbicide on rhizospheric microbial communities and soil properties in irrigated tropical rice field. Ecol. Ind.; 2024; 158, 111534.1:CAS:528:DC%2BB2cXhsVOrsbg%3D

64. Naraghi, L; Heydari, A; Rezaee, S; Razavi, M. Biocontrol agent Talaromyces flavus stimulates the growth of cotton and potato. J. Plant Growth Regul.; 2021; 31, pp. 471-477.

65. Kumar, A; Panda, A; Srivastava, LK; Mishra, VN. Effect of conservation tillage on biological activity in soil and crop productivity under rainfed vertisols of central India. Int. J. Chem. Stud.; 2017; 5, 6 pp. 1939-1946.

66. You, D et al. Short-term effects of tillage and residue on spring maize yield through regulating root-shoot ratio in Northeast China. Sci. Rep.; 2016; 7, 1 13314.2017NatSR..713314Y3436026

67. Radicetti, E et al. 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.; 2016; 155, pp. 329-338.

68. Jin, YH; Zhou, DW; Jiang, SC. Comparison of soil water content and corn yield in furrow and conventional ridge sown systems in a semi-arid region of China. Agric. Water Manage.; 2010; 97, 2 pp. 326-332.

69. Getahun, GT; Munkholm, LJ; Schjønning, P. The influence of clay-to-carbon ratio on soil physical properties in a humid sandy loam soil with contrasting tillage and residue management. Geoderma; 2016; 264, pp. 94-102.2016Geode.264..94G1:CAS:528:DC%2BC2MXhslCnsrfO

70. Ganapathi, S. et al. 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. Agri. Sci.56(2), 1–10 (2022).

71. Sapre, N; Kewat, ML; Sharma, AR. Effect of tillage and weed management on weed dynamics and yield of rice in rice-wheat-greengram cropping system in vertisols of central India. Indian J. Weed Sci.; 2022; 54, pp. 233-239.

72. Yankit, P et al. Insights on soil biological properties and crop yields under natural farming in Western himalaya. Indian J. Agricultural Sci.; 2024; 94, 3 – 1 pp. 89-94.1:CAS:528:DC%2BB2cXivVWjtb%2FO

73. Anderson, TH; Domsch, KH. Soil microbial biomass: the Eco-Physiological approach. Soil Biol. Biochem.; 2010; 42, pp. 2039-2043.1:CAS:528:DC%2BC3cXhtlCjsLnN

74. Adetunji, AT; Lewu, FB; Mulidzi, R; Ncube, B. The biological activities of β-glucosidase, phosphatase and urease as soil quality indicators: A review. J. Soil. Sci. Plant. Nutr.; 2017; 17, pp. 794-807.1:CAS:528:DC%2BC1MXht1KmtL7M

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Conservation agriculture experiment was undertaken to investigate synergistic effects of tillage and weed management on soil microbiome, and fungal diversity at vegetative (30 DAS) and tasselling (60 DAS) of maize and monitor yield. Main-treatments included T₁: Conventional tillage (CT) with Cotton- CT with maize- fallow, T₂: CT with Cotton- Zero tillage (ZT) with Maize- ZT with Sesbania rostrata (Sr) and T₃: ZT with Cotton + Sr residues- ZT with Maize + Cotton residues- ZT with Sr + Maize stubbles. Weed management (Sub-plots) were W₁: Chemical weed control, W₂: Herbicide rotation, W₃: Integrated weed management (IWM) and W₄: Single hand-weeded. Rhizo-sphere and plane samples were collected at 30 and 60 DAS for enzymatic, microbial analysis. The results demonstrated 25.90-44.72% and 20.31–50.72% decline on microbial and enzyme activities in T₁ + W₁, and in T₂ + W_2, respectively compared to T₃ and W₄ combinations at 30 DAS, due to herbicidal impact, which increased by 24.67–68.41% and 20.71–62.90% at tasseling. Metabolic quotient (qCO₂) decreased with T₃ and W₄ combinations. Kernel and system yield were 39.42% and 51.60% higher under T₃ + W₁ and T₃ + IWM combinations, respectively. Talaromyces flavus was identified under T₃ + IWM. The qCO₂ was exhibited with significant negative correlation with all biological attributes, while yield did not correlate. This suggest qCO₂ as potential indicator to assess agro-ecosystem. The PCA selected variables (enzymes, organic carbon, and microbial parameters) are highly supported by zero-till + residues, and can indicate improved soil health and sustained productivity.

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Title

Cumulative impact of herbicides and tillage in conservation agriculture on soil microbiome, fungal diversity and crop productivity

Author

Nthebere, Knight¹; Tata, Ram Prakash¹; Bhimireddy, Padmaja¹; Chandran, Latha P.²; Gudapati, Jayasree¹; Yadav, Manikyala Bhargava Narasimha³; Sinha, Nishant Kumar⁴; Admala, Meena¹

¹ Jayashankar Telangana State Agricultural University, Hyderabad, India (GRID:grid.444440.4) (ISNI:0000 0004 4685 9566)
² ICAR-IIRR-Indian Institute of Rice Research, Hyderabad, India (GRID:grid.464820.c)
³ University of Agricultural Sciences, Dharwad, India (GRID:grid.465109.f) (ISNI:0000 0004 1761 5159)
⁴ ICAR-IISS; Indian Institute of Soil Science, Bhopal, India (GRID:grid.464869.1) (ISNI:0000 0000 9288 3664)

Pages

24992

Publication year

2025

Publication date

2025

Publisher

Nature Publishing Group

e-ISSN

20452322

Source type

Scholarly Journal

Language of publication

English

DOI

https://doi.org/10.1038/s41598-025-09616-5

ProQuest document ID

3228992111

Cumulative impact of herbicides and tillage in conservation agriculture on soil microbiome, fungal diversity and crop productivity

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