Given the nonlinearity of the interaction between weather and soil variables, a novel deep neural network regressor (DNNR) was carefully designed with considerations to the depth, number of neurons of the hidden layers, and the hyperparameters with their optimizations. Additionally, a new metric, the average of absolute root squared error (ARSE) was proposed to address the shortcomings of root mean square error (RMSE) and mean absolute error (MAE) while combining their strengths. Using the ARSE metric, the random forest regressor (RFR) and the extreme gradient boosting regressor (XGBR), were compared with DNNR. The RFR and XGBR achieved yield errors of 0.0000294 t/ha, and 0.000792 t/ha, respectively, compared to the DNNR(s) which achieved 0.0146 t/ha and 0.0209 t/ha, respectively. All errors were impressively small. However, with changes to the explanatory variables to ensure generalizability to unforeseen data, DNNR(s) performed best. The unforeseen data, different from unseen data, is coined to represent sudden and unexplainable change to weather and soil variables due to climate change. Further analysis reveals that a strong interaction does exist between weather and soil variables. Using precipitation and silt, which are strong-negatively and strong-positively correlated with yield, respectively, yield was observed to increase when precipitation was reduced and silt increased, and vice-versa.

翻译：鉴于天气与土壤变量之间交互作用的非线性特性，本文精心设计了一种新型深度神经网络回归器（DNNR），充分考虑其深度、隐藏层神经元数量以及超参数及其优化。此外，提出了新指标——绝对均方根误差（ARSE），以弥补均方根误差（RMSE）和平均绝对误差（MAE）的不足，同时结合两者的优势。采用ARSE指标，将随机森林回归器（RFR）和极限梯度提升回归器（XGBR）与DNNR进行对比。RFR和XGBR的产量误差分别为0.0000294吨/公顷和0.000792吨/公顷，而DNNR(s)的误差分别为0.0146吨/公顷和0.0209吨/公顷，所有误差均极低。然而，当调整解释变量以确保对不可预见数据的泛化能力时，DNNR(s)表现最优。本文引入“不可预见数据”这一概念（区别于未见过数据），用以表征因气候变化导致的天气与土壤变量的突发性、不可解释性变化。进一步分析表明，天气与土壤变量之间存在强交互作用。利用与产量分别呈强负相关和强正相关的降水量与粉砂含量，观测到当降水量减少且粉砂含量增加时，产量随之上升，反之亦然。