DataSheet1_Statistical Analysis of the Weather Impact on Robusta Coffee Yield in Vietnam.PDF

Weather and climate strongly impact coffee; however, few studies have measured this impact on robusta coffee yield. This is because the yield record is not long enough, and/or the data are only available at a local farm level. A data-driven approach is developed here to 1) identify how sensitive Vietnamese robusta coffee is to weather on district and provincial levels, 2) during which key moments weather is most influential for yield, and 3) how long before harvest, yield could potentially be forecasted. Robusta coffee yield time series were available from 2000 to 2018 for the Central Highlands, where 40% of global robusta coffee is produced. Multiple linear regression has been used to assess the effect of weather on coffee yield, with regularization techniques such as PCA and leave-one-out to avoid over-fitting the regression models. The data suggest that robusta coffee in Vietnam is most sensitive to two key moments: a prolonged rainy season of the previous year favoring vegetative growth, thereby increasing the potential yield (i.e., number of fruiting nodes), while low rainfall during bean formation decreases yield. Depending on location, these moments could be used to forecast the yield anomaly with 3–6 months’ anticipation. The sensitivity of yield anomalies to weather varied substantially between provinces and even districts. In Dak Lak and some Lam Dong districts, weather explained up to 36% of the robusta coffee yield anomalies variation, while low sensitivities were identified in Dak Nong and Gia Lai districts. Our statistical model can be used as a seasonal forecasting tool for the management of coffee production. It can also be applied to climate change studies, i.e., using this statistical model in climate simulations to see the tendency of coffee in the following decades.

天气与气候对咖啡种植影响显著，但目前鲜有研究量化其对罗布斯塔咖啡（robusta coffee）产量的具体影响。究其原因，一是产量记录时长不足，二是相关数据仅能获取自地方农场层面。本文提出一种数据驱动方法，旨在实现三大目标：1）明确越南罗布斯塔咖啡在地区及省级尺度上对天气的敏感性；2）识别天气对产量影响最为关键的时段；3）确定收获前可开展产量预测的提前时长。全球40%的罗布斯塔咖啡产自越南中央高地（Central Highlands），该区域拥有2000年至2018年的罗布斯塔咖啡产量时序数据。本研究采用多元线性回归模型评估天气对咖啡产量的影响，并结合主成分分析（PCA）与留一法（leave-one-out）等正则化技术以避免回归模型过拟合。数据显示，越南罗布斯塔咖啡对两类关键时段的天气变化最为敏感：其一为前一年的长雨季，该时段利于植株营养生长，进而提升潜在产量（即结果节数量）；其二为豆粒形成期的低降水天气，会导致产量下降。基于种植区位的差异，可利用上述关键时段提前3至6个月预测产量异常值。产量异常值对天气的敏感性在不同省份，乃至同一省份的不同地区之间均存在显著差异。在多乐省（Dak Lak）及林同省（Lam Dong）部分地区，天气因素可解释高达36%的罗布斯塔咖啡产量异常波动；而在多农省（Dak Nong）与嘉莱省（Gia Lai）的部分地区，敏感性则相对较低。本研究构建的统计模型可作为咖啡生产管理的季节性预测工具，同时也可应用于气候变化研究：即在气候模拟中嵌入该模型，以预测未来数十年咖啡种植的变化趋势。