The usefulness of the soil quality index (SQI) as a tool to evaluate management options has mostly been studied within the boundaries of a crop or experimental field, calling for the need to enhance its utility in regional-scale soil health assessment. Thus, four quantitative approaches for computing the SQI were evaluated with samples collected from 0 to 15 and 15 to 30 cm depths at 156 points from the Trans-Gangetic Plains of North India. Principal component analysis (PCA) and soil function (SF)-based approaches were used to select the minimum dataset from 18 soil parameters and assign weights to key indicators. In both approaches, two different data transformation methods were followed: 1) routine method with maximum or minimum values of indicator parameters and 2) percentile method with the 90th or 10th percentile value as the denominator or numerator for “more is better” and “less is better” scoring functions, respectively. The PCA output with factor loadings from the varimax rotation showed six principal components accounting for 75% of the total variance, with PC1 explaining the highest variance (26.8%) followed by PC2 (16%). The SF-based approach was better than PCA in terms of a higher correlation of SQI with rice and wheat yields. The percentile method showed a higher correlation in both PCA and SF methods. The SQI computed from 0 to 30 cm soil data did not show any superiority over that from 0 to 15 cm soil. Thus, the soil function–based approach with the percentile method of data transformation proved better to compute the SQI and establish a relationship with production function.

Greenhouse gas emissions from agricultural production systems are a major area of concern in mitigating climate change. Therefore, a study was conducted to investigate the effects of crop residue, nutrient management, and soil moisture on methane (CH₄) emissions from maize, rice, soybean, and wheat production systems. In this study, incubation experiments were conducted with four residue types (maize, rice, soybean, wheat), seven nutrient management treatments {N0P0K0 (no nutrients), N0PK, N100PK, N150PK, N100PK + manure@ 5 Mg ha⁻¹, N100PK + biochar@ 5 Mg ha⁻¹, N150PK+ biochar@ 5 Mg ha⁻¹}, and two soil moisture levels (80% FC, and 60% FC). The results of this study indicated that interactive effects of residue type, nutrient management, and soil moisture significantly affected methane (CH₄) fluxes. After 87 days of incubation, the treatment receiving rice residue with N100PK at 60% FC had the highest cumulative CH₄ mitigation of −19.4 µg C kg⁻¹ soil, and the highest emission of CH₄ was observed in wheat residue application with N0PK at 80% FC (+12.93 µg C kg⁻¹ soil). Nutrient management had mixed effects on CH₄ emissions across residue and soil moisture levels in the following order: N150PK > N0PK > N150PK + biochar > N0P0K0 > N100PK + manure > N100PK + biochar > N100PK. Decreasing soil moisture from 80% FC to 60% FC reduced methane emissions across all residue types and nutrient treatments. Wheat and maize residues exhibited the highest carbon mineralization rates, followed by rice and soybean residues. Nutrient inputs generally decreased residue carbon mineralization. The regression analysis indicated that soil moisture and residue C mineralization were the two dominant predictor variables that estimated 31% of soil methane fluxes in Vertisols. The results of this study show the complexity of methane dynamics and emphasize the importance of integrated crop, nutrient, and soil moisture (irrigation) management strategies that need to be developed to minimize methane emissions from agricultural production systems to mitigate climate change.

Developing successful mitigation strategies for greenhouse gases (GHGs) from crop residue returned to the soil can be difficult due to an incomplete understanding of factors controlling their magnitude and direction. Therefore, this study investigates the effects of varying levels of wheat residue (WR) and nutrient management on GHGs emissions (CO₂, N₂O, and CH₄) across three soil types: Alfisol, Vertisol, and Inceptisol. A combination of laboratory-based measurements and a variety of data analysis techniques was used to assess the GHG responses under four levels of WR inputs (0, 5, 10, and 15 Mg/ha; WR0, WR5, WR10, and WR15) and three levels of nutrient (NP0: no nutrient, NP1: nutrients (N and P) were added to balance the residue C/nutrient stoichiometry of C/N/P= 100: 8.3: 2.0 to achieve 30% stabilization of added residue C input at 5 Mg/ha (R5), and NP2: 3 × NP1). The results of this study clearly showed that averaged across residue and nutrient input, Inceptisol showed negative N₂O flux, suggesting consumption which was supported by its high legacy phosphorus (19.7 mg kg⁻¹), elevated pH (8.49), and lower clay content (13%), which reduced microbial activity, as indicated by lower microbial biomass carbon (MBC) and alkaline phosphatase (Alk-P) levels. N₂O emissions were more responsive to nutrient inputs, particularly in Vertisol under high WR (15 Mg/ha) input, while CH₄ fluxes were significantly reduced under high residue inputs, especially in Vertisol and Inceptisol. Alfisol exhibited the highest total carbon mineralization and GWP, with cumulative GWP being 1.2 times higher than Vertisol and 1.4 times higher than Inceptisol across residue and nutrient input. The partial least square (PLS) regression revealed that anthropogenic factors significantly influenced CO₂ and N₂O fluxes more than CH_4. The anthropogenic drivers contributed 62% and 44% of the variance explained for N₂O and CH₄ responses. Our study proves that different biogeochemical mechanisms operate simultaneously depending on the stoichiometry of residue C and nutrients influencing soil GHG responses. Our findings provide insight into the relative contribution of anthropogenic and natural drivers to agricultural GHG emissions, which are relevant for developing process-based models and addressing the broader challenge of climate change mitigation through crop residue management.

Crop residues as key organic carbon inputs have the potential for soil carbon sequestration. However, previous studies have shown an inconsistent effect of residue return on the direction and magnitude of soil nitrous oxide (N₂O) emission. We used a laboratory-based soil incubation study to test the response of N₂O emission to crop residue type, soil moisture, and how nutrient management modulates these responses. In this study, we incorporated crop residues with different qualities (wheat, rice, soybean, and maize) at two soil moisture contents {80% field capacity (FC) and 60% FC} and under seven nutrient levels: N0P0K0 (no nutrients), N0PK, N100PK, N150PK, N100PK + manure@ 5 Mg ha⁻¹, N100PK + biochar@ 5 Mg ha⁻¹, and N150PK + biochar@ 5 Mg ha⁻¹. The results demonstrated significant (p < 0.01) differences in the magnitude of N₂O emissions among treatments. However, only the interaction effect of residue × nutrient and nutrient × moisture was significant (p < 0.05). N100PK and N150PK at 80% FC mitigated N₂O emission by approximately 20% in wheat residue-amended soil (cf. control soil without residue). In contrast, maize residue amendment (cf. control soil) increased N₂O emission by 130% under N0P0K0 and 80% FC. Residue effects were negatively correlated with the C:N ratio, and a strong positive correlation (p < 0.01) was obtained between N₂O emission and CO₂ respiration, labile carbon, mineral N, and residue total nitrogen (TN). When no nutrients were added, N₂O emission was higher in residue returned soil. However, cumulative fluxes of N₂O decreased by 6–17% when maize and wheat residues (cf. control soil) were applied with nutrients. Negative fluxes of N₂O indicating consumption were observed in every treatment after 57 days of incubation and were most pronounced in control soil without residue and nutrients. Decreasing the soil moisture from 80% FC to 60% FC, the N₂O consumption rate increased by 6.6 times across residue types and nutrient management. The regression analysis and structural equation modeling (SEM) results showed that residue TN, soil CO₂ emission, NO₃-N, and labile SOC were the key predictor variables and could explain 82% variability in the soil N₂O emission in the Vertisols of Central India. The results suggested that nutrient addition (NPK) could alter the magnitude and direction of soil N₂O flux by residue type and soil moisture by influencing the underlying soil microbial processes of the C and N cycle in the Vertisol of subtropical India.