Accurate modeling of site-specific crop yield response is key to providing farmers with accurate site-specific economically optimal input rates (EOIRs) recommendations. Many studies have demonstrated that machine learning models can accurately predict yield. These models have also been used to analyze the effect of fertilizer application rates on yield and derive EOIRs. But models with accurate yield prediction can still provide highly inaccurate input application recommendations. This study quantified the uncertainty generated when using machine learning methods to model the effect of fertilizer application on site-specific crop yield response. The study uses real on-farm precision experimental data to evaluate the influence of the choice of machine learning algorithms and covariate selection on yield and EOIR prediction. The crop is winter wheat, and the inputs considered are a slow-release basal fertilizer NPK 25–6–4 and a top-dressed fertilizer NPK 17–0–17. Random forest, XGBoost, support vector regression, and artificial neural network algorithms were trained with 255 sets of covariates derived from combining eight different soil properties. Results indicate that both the predicted EOIRs and associated gained profits are highly sensitive to the choice of machine learning algorithm and covariate selection. The coefficients of variation of EOIRs derived from all possible combinations of covariate selection ranged from 13.3 to 31.5% for basal fertilization and from 14.2 to 30.5% for top-dressing. These findings indicate that while machine learning can be useful for predicting site-specific crop yield levels, it must be used with caution in making fertilizer application rate recommendations.

对特定地点作物产量响应的准确建模是为农民提供准确的特定地点经济最佳投入率 (EOIR) 建议的关键。许多研究表明机器学习模型可以准确预测产量。这些模型还用于分析化肥施用量对产量的影响并得出 EOIR。但具有准确产量预测的模型仍然可以提供高度不准确的输入应用建议。这项研究量化了使用机器学习方法模拟施肥对特定地点作物产量反应的影响时产生的不确定性。该研究使用真实的农场精度实验数据来评估机器学习算法的选择和协变量选择对​​产量和 EOIR 预测的影响。作物为冬小麦，考虑的投入为缓释基肥 NPK 25-6-4 和追肥 NPK 17-0-17。随机森林、XGBoost、支持向量回归和人工神经网络算法使用 255 组协变量进行训练，这些协变量是通过组合八种不同的土壤特性而得出的。结果表明，预测的 EOIR 和相关的利润对机器学习算法和协变量的选择高度敏感。从所有可能的协变量选择组合得出的 EOIR 变异系数，基肥的范围为 13.3% 至 31.5%，追肥的范围为 14.2% 至 30.5%。这些发现表明，虽然机器学习可用于预测特定地点的作物产量水平，但在提出施肥量建议时必须谨慎使用。