Biochar can improve soil health and crop productivity. We studied the response of soil properties and wheat growth to four rates of wood biochar (0, 11.2, 22.4, and 44.8 Mg ha⁻¹) and two fertilizer rates [no fertilizer and fertilizer (90 kg N ha⁻¹, 45 kg P ha⁻¹, and 20 kg S ha⁻¹)]. Biochar application increased soil organic matter (SOM), soil pH, phosphorus (P), potassium (K), sulfur (S) contents, and the shoot and root biomass of wheat. However, these responses were observed at biochar rates below 22.4 Mg ha⁻¹, particularly in treatments without fertilizer. In fertilizer-applied treatments, soil nitrate levels decreased with an increase in biochar rates, mainly due to better crop growth and high nitrate uptake. However, without N addition, the high C:N ratio (500:1) possibly increased nutrient tie-up, reduced plant biomass, and SOM buildup at the highest biochar rate. Based on these results, we recommend biochar rates of about 22.4 Mg ha⁻¹ and below for Walla Walla silt loams.

Healthy soils provide the foundation for sustainable agriculture. However, soil health degradation has been a significant challenge for agricultural sustainability and environmental quality in water-limited environments, such as arid and semi-arid regions. Soils in these regions is often characterized by low soil organic matter (SOM), poor fertility, and low overall productivity, thus limiting the ability to build SOM. Soil health assessment frameworks developed for more productive, humid, temperate environments typically emphasize building SOM as a key to soil health and have identified the best management practices that are often difficult to implement in regions with water limitations. This study reviewed existing soil health assessment frameworks to assess their potential relevance for water-limited environments and highlights the need to develop a framework that links soil health with key ecosystem functions in dry climates. It also discusses management strategies for improving soil health, including tillage and residue management, organic amendments, and cropping system diversification and intensification. The assessment of indicators sensitive to water management practices could provide valuable information in designing soil health assessment frameworks for arid and semi-arid regions. The responses of soil health indicators are generally greater when multiple complementary soil health management practices are integrated, leading to the resilience and sustainability of agriculture in water-limited environments.

Soil moisture prediction remains a major challenge in arid and semi‐arid agroecosystems, where highly variable rainfall, intermittent irrigation, and heterogeneous soil properties create strong nonlinearities in soil‐water dynamics. Reliable forecasts and interpretable models are needed for optimizing irrigation scheduling and improving water‐use efficiency under such conditions. The purpose was development of a novel, dual‐resolution framework that integrates machine learning (ML) and deep learning (DL) approaches with explainable artificial intelligence to evaluate soil moisture dynamics under different cover crop treatments. A 2‐year field experiment was conducted in Clovis, NM, where soil moisture, temperature, and meteorological variables were continuously monitored across five treatments: fallow, pea, oat, pea–oat mixture, and a six‐species mixture. ML models (random forest, light gradient boosting machine [LGBM], and eXtreme gradient boosting) and DL models, such as long short‐term memory and Transformer models, were trained on both daily and 5‐min datasets. SHapley Additive exPlanations (SHAP) indicates that features from the three most recent days had the strongest influence on daily predictions, informing a 72‐h lookback for short‐term modeling. LGBM achieved the highest accuracy, while Transformer reduced temporal lag in high‐frequency predictions. SHAP analysis further identified treatment‐specific sensitivities and scale‐dependent relationships between environmental variables and soil moisture content, with historical soil moisture and total water input dominating daily predictions, and solar radiation, temperature, and recent water events prevailing at finer scales. This framework improves both predictive accuracy and interpretability of soil water dynamics, supporting data‐driven irrigation and crop management in arid and semi‐arid regions.