Designing Rice Cropping Schedules Using a Heading Date Prediction Model: An Integrated Approach for Climate Adaptation, Workload Leveling, and Spatial Optimization

In large-scale rice farming systems, the design of efficient cropping schedules is essential for improving labor management and operational efficiency. However, climate change, including rising temperatures and increased frequency of extreme weather events, has altered crop growth dynamics, making it difficult to achieve optimal management using conventional experience-based scheduling. In addition, the need to distribute operations across numerous fields and optimize labor allocation has increased the complexity of schedule design. In this study, we propose a decision-support method for designing rice cropping schedules using a heading date prediction model and climatological temperature data. The method adjusts transplanting dates based on predicted heading and maturity dates and determines operation periods through both forward and backward scheduling. A case study conducted on a large-scale farming system in Chiba Prefecture demonstrated that the proposed method effectively dispersed the distribution of heading and maturity dates, leading to improved temporal distribution of operations. The standard deviation of heading dates decreased from 11.7 to 8.7 days, indicating a reduction in peak labor demand. The novelty of this study lies in extending a heading date prediction model from growth prediction to practical applications in cropping schedule design and visualization. This approach enables a transition from experience-based planning to data-driven decision-making and contributes to labor distribution in large-scale farming under climate change conditions.