We present a fully automated model for in-season crop yield prediction, designed to work where there is a dearth of sub-national "ground truth" information. Our approach relies primarily on satellite data and is characterized by careful feature engineering combined with a simple regression model. As such, it can work almost anywhere in the world. Applying it to 10 different crop-country pairs (5 cereals -- corn, wheat, sorghum, barley and millet, in 2 countries -- Ethiopia and Kenya), we achieve RMSEs of 5\%-10\% for predictions 9 months into the year, and 7\%-14\% for predictions 3 months into the year. The model outputs daily forecasts for the final yield of the current year. It is trained using approximately 4 million data points for each crop-country pair. These consist of: historical country-level annual yields, crop calendars, crop cover, NDVI, temperature, rainfall, and evapotransporation.

翻译：我们提出了一个完全自动化的季节作物产量预测模型,目的是在缺乏国家以下各级“地面真相”信息的地方工作。我们的方法主要依靠卫星数据,其特点是仔细地进行特征工程,同时采用简单的回归模型。因此,它几乎可以在世界上任何地方工作。在埃塞俄比亚和肯尼亚这两个国家,将它应用于10对不同的作物国家(5种谷物 -- -- 玉米、小麦、高梁、大麦和小米,在2个国家 -- -- 埃塞俄比亚和肯尼亚),我们实现了5 ⁇ -10 ⁇ 的RMSE,用于预测到今年的9个月,在预测到今年的3个月中达到7 ⁇ -14 ⁇ 的RME,用于预测到今年的3个月。本年度最后产量的每日产出模型预测结果为每对作物国家的每对夫妇约400万个数据点,其中包括:国家一级的历史年产量、作物日历、作物覆盖、NDVI、温度、降雨和蒸发。