Reliable and timely prediction of robusta coffee (Coffea canephora Pierre ex A. Froehner) yield is pivotal to the profitability of the coffee industry worldwide. In this study we assess the performance of a simple process-based model for simulating and predicting robusta coffee yield at the regional scale in Vietnam. The model includes the key processes of coffee growth and development and simulates its response to variation in climate and potential water requirements throughout the growing season. The model was built and evaluated for the major Vietnamese robusta coffee-producing provinces Dak Lak, Dak Nong, Gia Lai, Kon Tum, and Lam Dong, using official provincial coffee yield data and climate station data for the 2001–2014 period, and field data collected during a 10-year (2008–2017) survey. Overall, good agreements were found between the observed and predicted coffee yields. Root mean square error (RMSE) and mean absolute percentage error (MAPE) values ranged from 0.24 to 0.33 t ha⁻¹, and 9% to 14%, respectively. Willmott's index of agreement (WI) was greater than or equal to 0.710 in model evaluation steps for three out of five provinces. The relatively low values of WI were found for provinces with relatively low inter-annual yield variability (i.e. Dak Lak and Dak Nong). Moreover, the model was successfully tested using remote sensing satellite and model-based gridded climate data: MAPE values were ≤ 12% and RMSE were ≤ 0.29 t ha⁻¹. Such evaluation is important for long-term coffee productivity studies in these regions where long-term climate stations data are not readily available. The simple process-based model presented in this study could serve as a basis for developing an integrated seasonal climate-robusta coffee yield forecasting system, which would offer substantial benefits to coffee growers and industry through better supply chain management and preparedness for extreme climate events, and increased profitability.

可靠和及时地预测罗布斯塔咖啡（Coffea canephoraPierre ex A. Froehner的收益对全球咖啡行业的盈利能力至关重要。在这项研究中，我们评估了一个简单的基于过程的模型在越南区域规模上模拟和预测罗布斯塔咖啡产量的性能。该模型包括咖啡生长和发育的关键过程，并模拟其在整个生长季节对气候变化和潜在需水量的响应。使用官方的省级咖啡产量数据和气候站数据（2001-2014年），对越南主要罗布斯塔咖啡生产省份达勒克，达农，盖莱，孔通和林同建立并评估了该模型。十年（2008-2017）调查期间收集的数据。总体而言，在观察到的咖啡产量与预测的咖啡产量之间达成了良好的协议。^-1和9％至14％。在五分之三的省份的模型评估步骤中，威尔莫特的同意指数（WI）大于或等于0.710。WI的年值变异性相对较低的省份（即Dak Lak和Dak Nong）的WI值相对较低。此外，该模型已使用遥感卫星和基于模型的网格气候数据成功进行了测试：MAPE值≤12％，RMSE≤0.29 t ha ^-1。这种评估对于这些地区的长期咖啡生产率研究非常重要，因为这些地区长期气候站数据不易获得。这项研究中提出的基于过程的简单模型可以作为开发集成的季节性气候-罗布斯塔咖啡产量预测系统的基础，该系统将通过更好的供应链管理和应对极端气候事件的准备，为咖啡种植者和行业带来实质性收益，并提高了盈利能力。