Optimizing Pickling Cucumber ( Cucumis sativus L.) Multi‐Environment Trial Networks Using Envirotyping, Production Importance and Future Climate Projections

ABSTRACT Multi‐environment trials (METs) are essential in breeding programmes to identify high‐performing and stable genotypes across different environments. However, trial sites are often chosen based on convenience rather than representativeness, which leads to reduced relevance to actual grower environments and inefficient resource use. This study developed a data‐driven framework to optimize MET site selection for US pickling cucumber ( Cucumis sativus L.) breeding under both current and future climate scenarios. In this context, we integrated multi‐location and multi‐year historical yield data with weather and soil‐derived environmental covariates (ECs). Feature selection identified 14 ECs associated with yield that captured the main spatial and temporal environmental gradients associated with cucumber yield. These ECs were then used to cluster the 11 major cucumber‐producing counties, which represent approximately 95% of US pickling cucumber harvested area, into three distinct environmental groups. Next, the heritability‐optimized strategy reduced the historical trial network from seven to three locations, while maintaining high Cullis heritability . Finally, to evaluate potential shifts in production‐environment structure under future growing conditions (2050–2065), we compared present‐day environmental clusters with projections for two future planting windows (March–June and April–July). Future climate projections indicated that Michigan, Wisconsin and Ohio counties are likely to remain environmentally similar, whereas California's San Joaquin County may become a distinct future environment. Overall, this framework improves MET design by combining environmental representativeness, genetic precision, production importance and future climate risk, providing a practical strategy for reducing redundant testing while maintaining coverage of current and future target production environments.