Addressing the intertwined challenges of food security and climate vulnerability requires robust and regionally tailored strategies for staple crops such as common beans. Although adjusting sowing dates is a key adaptive practice, spatio-temporal climate variability complicates the identification of optimal planting windows. This study integrates crop modeling with Functional Data Analysis (FDA) to quantify sowing-date-dependent yield losses for rainfed common beans across Central-Southern Brazil. The CSM-CROPGRO-Dry Bean model, driven by long-term climate data (1980–2016), soil properties, and management practices, was used to simulate yields for the BRS Estilo cultivar. FDA was subsequently applied to cluster yield-loss curves across municipalities and growing seasons, generating representative regional risk profiles. The results reveal clear spatial patterns. During the wet season, earlier sowing minimizes losses in Goiás, Minas Gerais, and western Paraná, whereas later sowing is beneficial in São Paulo, Santa Catarina, and eastern Paraná. In the dry season, earlier sowing consistently reduces losses across most regions. These patterns are primarily driven by water deficits and suboptimal temperatures during critical phenological phases. The resulting spatio-temporal sowing calendar provides an evidence-based decision-support tool to help farmers mitigate climatic risks. Moreover, it offers a scientific foundation for policymakers to refine sustainable management practices, improve crop insurance design, and enhance agricultural resilience and productivity under increasing climate uncertainty.
Justino et al. (Wed,) studied this question.