The regulation of soil water retention by biochar amendment has been concerned especially in cropland ecosystem. However, the quantification of biochar's effects on soil hydrological properties and crop water use efficiency (WUE) is still limited, and the factors driving the biochar effect need to be investigated. Based on a database with 681 observations, meta‐analysis and structural equation model (SEM) were employed to reveal how biochar amendment affects water supply capacity and WUE. The results showed that biochar application increased available water content (AWC) and WUE by 26.8% and 4.7% on average, respectively. According to the SEM of AWC (R² = 0.70–0.96), the increase of soil organic carbon (+36.1%) by biochar application can not only directly improve AWC but also indirectly improve AWC by affecting permanent wilting point (−1.0%) and mean weight diameter (+11.1%). The SEM of WUE (R² = 0.74) indicated that soil moisture and porosity were increased by 10.8% and 7.0% under biochar amendment, which was the reason why biochar improved WUE. This study emphasized that biochar can improve soil hydrology and crop yield by increasing soil water supply conditions. And a rational rate of biochar is the precondition to obtaining the benefits of soil hydrology, otherwise, the excessive use of biochar may lead to the decline of WUE.

Process-based models are useful tools to integrate the effects of detailed agricultural practices, soil characteristics, mass balance, and climate change on soil N2O emissions from soil - plant ecosystems, whereas static, seasonal or annual models often exist to estimate cumulative N2O emissions under data-limited conditions. A study was carried out to compare the capability of four models to estimate seasonal cumulative N2O fluxes from 419 field measurements representing 65 studies across China's croplands. The models were 1) the DAYCENT model, 2) the DNDC model, 3) the linear regression model (YLRM) of Yue et al. (2018), and 4) IPCC Tier 1 emission factors. The DAYCENT and DNDC models estimated crop yields with R2 values of 0.60 and 0.66 respectively, but both models showed significant underestimation for all measurements. The estimated seasonal N2O emissions with R2 of 0.31, 0.30, 0.21 and 0.17 for DAYCENT, DNDC, YLRM, and IPCC, respectively. Based on RMSE, modelling efficiency and bias analysis, YLRM performed well on N2O emission prediction under no fertilization though bias still existed, while IPCC performed well for cotton and rapeseed and DNDC for soybean. The DAYCENT model accurately predicted the emissions with no bias across other crop and fertilization types whereas the DNDC model underestimated seasonal N2O emissions by 0.42 kg N2O-N ha-1 for all observed values. Model evaluation indicated that the DAYCENT and DNDC models simulated temporal patterns of daily N2O emissions effectively, but both models had difficulty in simulating the timing of the N2O fluxes following some events such as fertilization and water regime. According to this evaluation, algorithms for crop production and N2O emission should be improved to increase the accuracy in the prediction of unfertilized fields both for DAYCENT and DNDC. The effects of crop types and management modes such as fertilizations should also be further refined for YLRM.

Water quality regulation is an important ecosystem service function of soil. In this study, the mechanism by which soil regulates water quality was reviewed, and the effects of soil management on water quality were explored. A scientometrics analysis was also conducted to explore the research fields and hotspots of water quality regulation of soil in the past 5 years. This review found that the pollutants entering the soil can be mitigated by precipitation, adsorption and desorption, ion exchange, redox and metabolic decomposition. As an optimal substrate, soil in constructed wetlands has perfect performance in the adsorption and passivation of pollutants such as nitrogen, phosphorus and heavy metals in water, and degradation of pesticides and emerging contaminants. Mangrove wetlands play an important role in coastal zone protection and coastal water quality restoration. However, the excessive application of agricultural chemicals causes soil overload, which leads to the occurrence of agricultural non-point source pollution. Under the dual pressures of climate change and food insecurity in the future, developing environmentally friendly and economically feasible sustainable soil management measures is crucial for maintaining the water purification function of soil by relying on the accurate quantification of soil function based on big data and modelling. This article is part of the theme issue ‘The role of soils in delivering Nature's Contributions to People’.

The growing global population and increasing agricultural demands have made nitrogen fertilizers essential for modern agriculture. However, nearly 50% of applied nitrogen fertilizers are lost to the environment, causing pollution and greenhouse gas (GHG) emissions. Biochar-based fertilizers (BBFs), combining biochar with chemical fertilizers, enhance nutrient efficiency, boost crop yields, and reduce N₂O emissions. However, comprehensive field studies on BBF impacts remain limited. This study uses a global dataset of BBF field experiments to build predictive models with three machine learning algorithms for crop yields and N₂O emissions, and to assess BBFs’ potential to increase yields and mitigate emissions in China’s major crops. The artificial neural network (ANN) model outperformed random forest (RF) and support vector machine (SVM) in predicting N₂O emissions (R²: 0.99; EF: 0.99), while all models showed high accuracy for crop yields (R², EF: 0.98–0.99). Variable importance analysis revealed that BBF C/N and BBF N/Mineral N explained 4.25% and 3.95% of yield variation, and 3.19% and 0.55% of N₂O emission variation, respectively. BBFs could increase China’s major crop yields by 4.3–5.0% and reduce N₂O emissions by 3.7–6.3%, based on simulations. Challenges like high costs and limited adaptability persist, necessitating optimized production, standardized protocols, and expanded trials.