Recommendation of Suitable Planting Locations for Hybrid Maize Varieties Based on Graph Collaborative Filtering

Abstract Enhancing the precision of crop variety adaptability evaluation is critical for maximizing yields and ensuring food security under climate change. Traditional methods relying on expert knowledge or process-based models face scalability challenges and difficulties in capturing complex variety–environment interactions. This study proposes a scalable graph-based collaborative filtering framework for assessing suitable planting locations for hybrid maize varieties, reformulating adaptability evaluation as a ranking task. The framework integrates two key innovations: a multi-layer perceptron (MLP) encoding module that leverages 15 variety trait features and 24 environmental variables to address the cold-start problem, enabling recommendations for previously unseen varieties; and a BPR+ loss function that distinguishes unsuitable environments from merely unplanted ones to accommodate the unique sparsity structure of agricultural trial data. Evaluated against six state-of-the-art graph-based recommendation models on a dataset of 189 locations and 1,102 hybrid maize varieties across China's major agro-ecological zones, our method consistently outperforms existing approaches, achieving approximately 5% improvement over the strongest baseline. The MLP module attains cold-start NDCG above 0.8, and BPR+ yields significant gains in Recall@1–4 and NDCG@1–5. A case study of the widely cultivated variety ZD958, validated against both national promotion data (2019–2024) and multi-year field-trial yields from 40 locations (2017–2024), confirms that the model's rank-ordered recommendations align closely with independent yield evidence: top-ranked locations achieve both the highest productivity and the greatest yield stability. This framework provides an AI-based decision support tool for variety placement optimization and climate-resilient agriculture.