Evolutionary forecast of vineyard yield in a mediterranean climate: A multi-temporal machine learning approach in Cádiz, Spain

Abstract Purpose Accurate and early prediction of crop yield is essential for agricultural management, economic planning, and market stability, particularly for high-value products such as wine. This study presents a multi-temporal modeling framework to predict grapevine yield (in kilograms) in the province of Cádiz, Spain, a region with a deep-rooted winemaking heritage. Methods Using a 12-year dataset that includes historical harvest records, meteorological variables, and time series of remote