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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 CH4. 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.
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 CH4. 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.
The spatial prediction of soil CO2 flux is of great significance for assessing regional climate change and high-quality agricultural development. Using a single satellite to predict soil CO2 flux is limited by climatic conditions and land cover, resulting in low prediction accuracy. To this end, this study proposed a strategy of multi-source spectral satellite coordination and selected seven optical satellite remote sensing data sources (i.e., GF1-WFV, GF6-WFV, GF4-PMI, CB04-MUX, HJ2A-CCD, Sentinel 2-L2A, and Landsat 8-OLI) to extract auxiliary variables (i.e., vegetation indices and soil texture features). We developed a tree-structured Parzen estimator (TPE)-optimized extreme gradient boosting (XGBoost) model for the prediction and spatial mapping of soil CO2 flux. SHapley additive explanation (SHAP) was used to analyze the driving effects of auxiliary variables on soil CO2 flux. A scatter matrix correlation analysis showed that the distributions of auxiliary variables and soil CO2 flux were skewed, and the linear correlations between them (r < 0.2) were generally weak. Compared with single-satellite variables, the TPE-XGBoost model based on multiple-satellite variables significantly improved the prediction accuracy (RMSE = 3.23 kg C ha−1 d−1, R2 = 0.73), showing a stronger fitting ability for the spatial variability of soil CO2 flux. The spatial mapping results of soil CO2 flux based on the TPE-XGBoost model revealed that the high-flux areas were mainly concentrated in eastern and northern farmlands. The SHAP analysis revealed that PC2 and the TCARI of Sentinel 2-L2A and the TVI of HJ2A-CCD had significant positive driving effects on the prediction accuracy of soil CO2 flux. The above results indicate that the integration of multiple-satellite data can enhance the reliability and accuracy of spatial predictions of soil CO2 flux, thereby supporting regional agricultural sustainable development and climate change response strategies.
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