Precision Density Management as a Pathway to Sustainable Rice Intensification: A Climate–Soil Synergy Perspective from Northeast China

Optimizing planting density is a critical, cost-effective strategy for sustainable agricultural intensification, yet moving beyond static recommendations to environment-specific precision management remains a key challenge. This study establishes a three-step framework (comprising zoning, response extraction, and machine learning modeling) to determine optimum planting density (OPD) for rice (Oryza sativa L.). Utilizing a data-driven synthesis of 960 field observations from the Northeast Black Soil Region (NBSR) of China, we identified distinct spatial variability in OPD (16.6 to 37.4 × 104 hills ha−1). Northern regions computationally prioritized higher densities, aligning with agronomic strategies to offset thermal constraints, while southern regions favored lower densities to reduce canopy competition. Soil properties, particularly Soil Organic Carbon (SOC), pH, Cation Exchange Capacity (CEC), and Total Nitrogen (TN), were identified as the dominant predictive indicators, collectively surpassing climatic factors in their predictive importance. This highlights the foundational role of soil buffering capacity in estimating crop tolerance to density management. Based on model-derived estimates, optimized density management indicated potential yield improvements of 3.8% to 9.7% (up to 872.32 kg ha−1) compared to conventional practices. By replacing uniform practices with dynamic, environment-driven strategies, this work contributes to Sustainable Development Goals (SDGs) 2 (Zero Hunger), 12 (Responsible Consumption and Production), and 13 (Climate Action), offering a scalable solution for diverse rice production systems under climate change.