Application of machine learning approaches to predict ammonium nitrogen transport in different soil types and evaluate the contribution of control factors.

The loss of nitrogen in soil damages the environment. Clarifying the mechanism of ammonium nitrogen (NH4+-N) transport in soil and increasing the fixation of NH4+-N after N application are effective methods for improving N use efficiency. However, the main factors are not easily identified because of the complicated transport and retardation factors in different soils. This study employed machine learning (ML) to identify the main influencing factors that contribute to the retardation factor (Rf) of NH4+-N in soil. First, NH4+-N transport in the soil was investigated using column experiments and a transport model. The Rf (1.29 - 17.42) was calculated and used as a proxy for the efficacy of NH4+-N transport. Second, the physicochemical parameters of the soil were determined and screened using lasso and ridge regressions as inputs for the ML model. Third, six machine learning models were evaluated: Adaptive Boosting, Extreme Gradient Boosting (XGB), Random Forest, Gradient Boosting Regression, Multilayer Perceptron, and Support Vector Regression. The optimal ML model of the XGB model with a low mean absolute error (0.81), mean squared error (0.50), and high test r2 (0.97) was obtained by random sampling and five-fold cross-validation. Finally, SHapely Additive exPlanations, entropy-based feature importance, and permutation characteristic importance were used for global interpretation. The cation exchange capacity (CEC), total organic carbon (TOC), and Kaolin had the greatest effects on NH4+-N transport in the soil. The accumulated local effect offered a fundamental insight: When CEC > 6 cmol+ kg-1, and TOC > 40 g kg-1, the maximum resistance to NH4+-N transport within the soil was observed. This study provides a novel approach for predicting the impact of the soil environment on NH4+-N transport and guiding the establishment of an early-warning system of nutrient loss.

Arsenic stress on soil microbial nutrient metabolism interpreted by microbial utilization of dissolved organic carbon.

In a 120-day microcosm incubation experiment, we investigated the impact of arsenic contamination on soil microbial nutrient metabolism, focusing on carbon cycling processes. Our study encompassed soil basal respiration, key enzyme activities (particularly, ß-1,4-N-acetylglucosaminidase and phosphatases), microbial biomass, and community structure. Results revealed a substantial increase (1.21-2.81 times) in ß-1,4-N-acetylglucosaminidase activities under arsenic stress, accompanied by a significant decrease (9.86%-45.20%) in phosphatase activities (sum of acid and alkaline phosphatases). Enzymatic stoichiometry analysis demonstrated the mitigation of microbial C and P requirements in response to arsenic stress. The addition of C-sources alleviated microbial C requirements but exacerbated P requirements, with the interference amplitude increasing with the complexity of the C-source. Network analysis unveiled altered microbial nutrient requirements and an increased resistance process of microbes under arsenic stress. Microbial carbon use efficiency (CUE) and basal respiration significantly increased (1.17-1.59 and 1.18-3.56 times, respectively) under heavy arsenic stress (500 mg kg-1). Arsenic stress influenced the relative abundances of microbial taxa, with Gemmatimonadota increasing (5.5-50.5%) and Bacteroidota/ Nitrospirota decreasing (31.4-47.9% and 31.2-63.7%). Application of C-sources enhanced microbial resistance to arsenic, promoting cohesion among microorganisms. These findings deepen our understanding of microbial nutrient dynamics in arsenic-contaminated areas, which is crucial for developing enzyme-based toxicity assessment systems for soil arsenic contamination.