Early crop yield forecasts provide valuable information for growers and industry to base decisions on. This work considers early forecasting of macadamia nut yield at the individual orchard block level with input variables derived from spatio-temporal datasets including remote sensing, weather and elevation. Yield data from 2012–2019, for 101 blocks belonging to 10 orchards, was obtained. We forecast yield on each test year from 2014–2019 using models trained on data from years prior to the test year. Forecasts are generated in January, for the coming harvest in March–September. A linear model using ridge regularized regression produced consistently good predictions compared with other machine learning algorithms including lasso, support vector regression and random forest. Adding meteorological variables offered little improvement over using only remote sensing variables. The 2019 forecast root mean square error at the block level was 0.8 t/ha, and mean absolute percentage error was 20.9%. When block level predictions were aggregated across the multiple orchards per region, production prediction errors were between 0–15% from 2016–2019. The ridge regression model can be easily implemented in GIS platforms to deliver block-level yield forecast maps to end users.

早期的作物单产预测为种植者和产业提供有价值的信息，以作为决策依据。这项工作考虑使用从时空数据集（包括遥感，天气和海拔）得出的输入变量，对单个果园块一级的澳洲坚果产量进行早期预测。获得了2012-2019年10个果园的101个块的产量数据。我们使用对测试年份之前的数据进行训练的模型来预测2014-2019年每个测试年份的收益。在一月产生了预报，即将在三月至九月收获。与其他机器学习算法（包括套索，支持向量回归和随机森林）相比，使用岭正则化回归的线性模型产生了一致的良好预测。与仅使用遥感变量相比，添加气象变量几乎没有改善。对区块级别的2019年预测均方根误差为0.8 t / ha，平均绝对百分比误差为20.9％。当将每个区域的多个果园的区块水平预测汇总时，2016-2019年的生产预测误差在0-15％之间。岭回归模型可以在GIS平台中轻松实现，以将块级收益预测图交付给最终用户。