Search Results (2)

Coffee production is a vital source of income for smallholder farmers in Mexico’s Chiapas, Oaxaca, Puebla, and Veracruz regions. However, climate change, fluctuating yields, and the lack of decision-support tools pose challenges to the implementation of sustainable agricultural practices. The SABERES project aims to address these challenges through a Seq2Seq-LSTM model for predicting coffee yields in the short term, using datasets from Mexican national institutions, including the Agricultural Census (SIAP) and environmental data from the National Water Commission (CONAGUA). The model has demonstrated high accuracy in replicating historical yields for Chiapas and can forecast yields for the next two years. As a first step, we assessed coffee yield prediction for Bali, Indonesia, by comparing the LSTM, ARIMA, and Seq2Seq-LSTM models using historical data. The results show that the Seq2Seq-LSTM model provided the most accurate predictions, outperforming LSTM and ARIMA. Optimal performance was achieved using the maximum data sequence. Building on these findings, we aimed to apply the best configuration to forecast coffee yields in Chiapas, Mexico. The Seq2Seq-LSTM model achieved an average difference of only 0.000247, indicating near-perfect accuracy. It, therefore, demonstrated high accuracy in replicating historical yields for Chiapas, providing confidence for the next two years’ predictions. These results highlight the potential of Seq2Seq-LSTM to improve yield forecasts, support decision making, and enhance resilience in coffee production under climate change. Full article

Given the present climate change context, accurate and timely coffee yield prediction is critical to all farmers who work in the coffee industry worldwide. The aim of this study is to develop and assess a coffee yield forecasting method at the regional scale in Dak Lak province in the central highlands of Vietnam using the Crop Growth Monitoring System Statistical Tool (CGMSstatTool—CST) software and vegetation biophysical variables (NDVI, LAI, and FAPAR) derived from satellite remote sensing (SPOT-VEGETATION and PROBA-V). There has been no research to date applying this approach to this specific crop, which is the main contribution of this study. The findings of this research reveal that the elaboration of multiple linear regression models based on a combination of information from satellite-derived vegetation biophysical variables (LAI, NDVI, and FAPAR) corresponding to the first six months of the years 2000–2019 resulted in coffee yield forecast models presenting satisfactory accuracy (Adj.R² = 64 to 69%, RMSEp = 0.155 to 0.158 ton/ha and MAPE = 3.9 to 4.7%). These results demonstrate that the CST may efficiently predict coffee yields on a regional scale by using only satellite-derived vegetation biophysical variables. This study findings are likely to aid local governments and decision makers in precisely forecasting coffee production early and promptly, as well as in recommending relevant local agricultural policies. Full article