Analysis of Crop Planting Strategies Based on Linear Programming

Abstract

As China's primary grain-producing region, the North China Plain faces critical challenges from population growth, resource constraints, and food security, making optimal crop configuration vital for national food sovereignty. This study developed a novel neural network-based optimization model to address agricultural land allocation, which employed systematic calibration of agronomic and economic parameters to determine profit-maximizing crop configurations. Under normal climatic conditions, the model's predictions for wheat and maize planting areas showed strong alignment with observed data. This validates the model's effectiveness in resolving the complex interdependencies among yield, revenue, and resource utilization. The model serves as a powerful tool for quantitative agricultural decision support. Key innovations include: (1) Applying neural network optimization to agricultural land allocation; (2) Successfully quantifying the coupled effects of multiple factors (yield-revenue-resource) ; and (3) Providing a theoretical and practical foundation for sustainable agriculture in resource-constrained regions. Enhancing resilience to extreme weather and long-term projection capabilities are identified for future improvement.