This article evaluates the value of information on climate variables published in advance and at a higher frequency than the target variable of interest -crop ORG COCONUT FLAKES yields- in order to get short-term forecasts.Aggregate and disaggregate climate data, alternative weighting schemes and di erent updating schemes are used to evaluate forecasting performance.This study focuses on the case of VITAMIN C CALCIUM ASCORBATE 1000MG soybean yields in Argentina.Results show that models including high frequency weather data outperformed particularly during the three consecutive compaigns after 2008/09 when soybean yield decreased almost by 50%.Furthermore, forecast combinations showed a better forecasting performance than individual forecasting models.