Digital Agriculture and Soil Health: Evaluating Technology-Assisted Strategies for Sustainable Nutrient Management and Crop
Keywords:
Digital agriculture, soil health, precision agriculture, nutrient management, machine learning, remote sensing, soil sensors, crop productivity, sustainable agriculture, variable-rate fertilizationAbstract
Soil health is a critical foundation of sustainable agricultural production because soil physical, chemical, and biological properties directly influence nutrient availability, water retention, crop growth, and long-term productivity. Conventional nutrient management practices often rely on uniform fertilizer recommendations that may not adequately account for spatial and temporal variability in soil conditions. Digital agriculture provides an opportunity to address this limitation by integrating soil sensors, remote sensing, geographic information systems, weather information, farm-management records, and machine-learning-based decision-support systems. This study evaluates a technology-assisted framework for soil-health monitoring and site-specific nutrient management. A dataset representing 360 farm units was developed, with 180 farms using a digital nutrient-management system and 180 farms following conventional nutrient-management practices. Soil moisture, pH, electrical conductivity, soil organic carbon, available nitrogen, phosphorus and potassium, remote-sensing vegetation indices, weather variables, fertilizer application, and crop yield were integrated. Random Forest, XGBoost, and Support Vector Regression models were evaluated for prediction of crop yield and soil-health status. XGBoost achieved the strongest predictive performance, with R² of 0.88, RMSE of 0.43 t/ha, and MAE of 0.32 t/ha. Technology-assisted farms showed higher mean crop yield (4.51 t/ha versus 3.96 t/ha), lower fertilizer application (136.4 versus 157.8 kg/ha), and greater nutrient-use efficiency. Soil organic carbon increased from 0.61% to 0.66% in technology-assisted farms during the observation period, compared with 0.61% to 0.63% under conventional management. Regression analysis indicated that soil organic carbon, available nitrogen, soil moisture, NDVI, and technology-assisted management were significant predictors of crop productivity. The findings suggest that digital agriculture can support more precise nutrient decisions while simultaneously improving productivity and selected soil-health indicators.
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