Precision Agronomy Strategies for Improving Nitrogen Use Efficiency and Soil Quality in Maize

Abstract

Maize production faces mounting pressure to achieve sustainable intensification amid rising global food demand, inefficient nitrogen (N) use, and environmental degradation. This review synthesizes recent advances in precision agronomy strategies aimed at improving nitrogen use efficiency (NUE) and soil quality in maize systems. Key approaches include site-specific nitrogen management through remote sensing (UAV–satellite data fusion, red-edge vegetation indices), machine learning predictive modeling, variable rate application (VRA), sensor-based fertigation (Holland-Schepers algorithm), controlled-release fertilizers, organic-inorganic blends, variable depth tillage, variable rate seeding, and hybrid switching. These technologies enable synchronization of N supply with crop demand, reducing losses via leaching, volatilization, and denitrification while maintaining or increasing grain yield. Precision practices also enhance soil health indicators, including soil organic carbon, microbial enzyme activity (urease, dehydrogenase, phosphatase), and structural stability. Integration with conservation agriculture and cover cropping further supports long-term soil resilience. Despite demonstrated economic and environmental benefits (N savings of 20–50 kg/ha, improved partial factor productivity, and alignment with SDGs), adoption remains limited by high costs, technical complexity, rural connectivity gaps, and data security concerns. The paper highlights the transformative potential of AI, robotics, and data fusion while emphasizing the need for accessible decision-support tools and robust cybersecurity frameworks to accelerate farmer adoption.