Timely and reliable coffee yield forecasts using agroclimatic information are pivotal to the success of agricultural climate risk management throughout the coffee value chain. The capability of statistical models to forecast coffee yields at different lead times during the growing season at the farm scale was assessed. Using data collected during a 10-year period (2008-2017) from 558 farmers across the four major coffee-producing provinces in Vietnam (Dak Lak, Dak Nong, Gia Lai, and Lam Dong), the models were built through a robust statistical modelling approach involving Bayesian and machine learning methods. Overall, coffee yields were estimated with reasonable accuracies across the four study provinces based on agroclimate variables, satellite-derived actual evapotranspiration, and crop and farm management information. Median values of prediction mean absolute percentage error (MAPE) ranged generally from 8% to 13%, and median root mean square errors (RMSE) between 295 kg ha⁻¹ and 429 kg ha⁻¹. For forecasts at four to one month before harvest, errors did not vary markedly when comparing the median MAPE and RMSE values. For farms in Dak Lak, Dak Nong, and Lam Dong, the median forecasting MAPE and RMSE varied between 13% and 16% and between 420 kg ha⁻¹ and 456 kg ha⁻¹, respectively. Using readily and freely available data, the modelling approach explored in this study appears flexible for an application to a larger number of coffee farms across the Vietnamese coffee-producing regions. Moreover, the study can serve as basis for developing a coffee yield predicting forecasting system that will offer substantial benefits to the entire coffee industry through better supply chain management in coffee-producing countries worldwide.

利用农业气候信息进行及时，可靠的咖啡产量预测对于整个咖啡价值链中农业气候风险管理的成功至关重要。评估了统计模型在农场规模的生长季节中在不同交货时间预测咖啡产量的能力。使用从越南四个主要咖啡生产省份（Dak Lak，Dak Nong，Gia Lai和Lam Dong）的558名农民在10年期间（2008-2017）收集的数据，这些模型是通过强大的统计数据建立的建模方法，涉及贝叶斯方法和机器学习方法。总体而言，根据农业气候变量，卫星得出的实际蒸散量以及作物和农场管理信息，在四个研究省份中以合理的准确度估算了咖啡产量。^-1和429 kg ha ^-1。对于收获前四到一个月的预报，比较MAPE和RMSE的中位数时，误差没有显着变化。对于Dak Lak，Dak Nong和Lam Dong的农场，预测的MAPE和RMSE的中位数在13％至16％之间以及420 kg ha ^-1至456 kg ha ^{-1之间变化}， 分别。使用容易获得和免费获得的数据，本研究中探索的建模方法对于在越南咖啡生产地区的大量咖啡农场中的应用而言似乎很灵活。此外，该研究可以作为开发咖啡产量预测预测系统的基础，该系统将通过在世界范围内的咖啡生产国提供更好的供应链管理，为整个咖啡行业带来可观的收益。