Role of Environment Variables in Spatial Distribution of Soil C, N, P Ecological Stoichiometry in the Typical Black Soil Region of Northeast China

Soil Factors Key to 3,4-Dimethylpyrazole Phosphate (DMPP) Efficacy: EC and SOC Dominate over Biotic Influences

Nitrification inhibitors like 3,4-dimethylpyrazole phosphate (DMPP) are crucial in agriculture to reduce nitrogen losses. However, the efficacy of DMPP varies in different soils. This microcosm incubation study with six soils was conducted to elucidate how soil abiotic factors (physicochemical properties) and biotic factors (nitrogen-cycling microbial abundance and diversity) influence the performance of DMPP. The DMPP efficacy was evaluated through the ammonium-N retention rate (NH4+_RA), inhibition rate of net nitrification rate (NNR_IR), and reduction rate of N2O emissions (N2O_ERR). The results showed that DMPP had significantly different effects on mineral nitrogen conversion and N2O emissions from different soils. NH4+_RA, NNR_IR, and N2O_ERR ranged from -71.15% to 65.37%, 18.77% to 70.23%, and 7.93% to 82.51%, respectively. Correlation analyses and random forest revealed abiotic factors, particularly soil EC and SOC, as the primary determinants of DMPP efficiency compared to microbial diversity. This study sheds new light on the complex interactions between DMPP efficacy and soil environments. The identification of soil EC and SOC as the dominant factors influencing DMPP efficacy provides valuable insights for optimizing its application strategies in agricultural systems. Future research could explore the mechanisms underlying these interactions and develop tailored DMPP formulations that are responsive to specific soil conditions.

Digital Mapping of Soil Organic Carbon with Machine Learning in Dryland of Northeast and North Plain China

Due to the importance of soil organic carbon (SOC) in supporting ecosystem services, accurate SOC assessment is vital for scientific research and decision making. However, most previous studies focused on single soil depth, leading to a poor understanding of SOC in multiple depths. To better understand the spatial distribution pattern of SOC in Northeast and North China Plain, we compared three machine learning algorithms (i.e., Cubist, Extreme Gradient Boosting (XGBoost) and Random Forest (RF)) within the digital soil mapping framework. A total of 386 sampling sites (1584 samples) following specific criteria covering all dryland districts and counties and soil types in four depths (i.e., 0–10, 10–20, 20–30 and 30–40 cm) were collected in 2017. After feature selection from 249 environmental covariates by the Genetic Algorithm, 29 variables were used to fit models. The results showed SOC increased from southern to northern regions in the spatial scale and decreased with soil depths. From the result of independent verification (validation dataset: 80 sampling sites), RF (R2: 0.58, 0.71, 0.73, 0.74 and RMSE: 3.49, 3.49, 2.95, 2.80 g kg−1 in four depths) performed better than Cubist (R2: 0.46, 0.63, 0.67, 0.71 and RMSE: 3.83, 3.60, 3.03, 2.72 g kg−1) and XGBoost (R2: 0.53, 0.67, 0.70, 0.71 and RMSE: 3.60, 3.60, 3.00, 2.83 g kg−1) in terms of prediction accuracy and robustness. Soil, parent material and organism were the most important covariates in SOC prediction. This study provides the up-to-date spatial distribution of dryland SOC in Northeast and North China Plain, which is of great value for evaluating dynamics of soil quality after long-term cultivation.