Applying at the economic optimal nitrogen rate (EONR) has the potential to increase nitrogen (N) fertilization efficiency and profits while reducing negative environmental impacts. On-farm precision experimentation (OFPE) provides the opportunity to collect large amounts of data to estimate the EONR. Machine learning (ML) methods such as generalized additive models (GAM) and random forest (RF) are promising methods for estimating yields and EONR. Twenty OFPE N trials in wheat and barley were conducted and analyzed with soil, terrain and remote-sensed variables to address the following objectives: (1) to quantify the spatial variability of winter crops yield and the yield response to N using OFPE, (2) to evaluate and compare the performance of GAM and RF models to predict yield and yield response to N and, (3) to quantify the impact of soil, crop and field characteristics on the EONR estimation. Machine learning techniques were able to model wheat and barley yield with an average error of 13.7% (624 kg ha⁻¹). However, similar yield prediction accuracy from RF and GAM resulted in widely different economic optimal nitrogen rates. Across sites, soil available phosphorus and soil organic matter were the most influential variables; however, the magnitude and direction of the effect varied between fields. These indicate that training a model using data coming from different fields may lead to unreliable site-specific EONR when it is applied to another field. Further evaluation of ML methods is needed to ensure a robust automation of N recommendation while producers transition into the digital ag era.

以经济最优施氮量 (EONR) 施用有可能提高氮 (N) 施肥效率和利润，同时减少对环境的负面影响。农场精确试验 (OFPE) 提供了收集大量数据以估算 EONR 的机会。广义加性模型 (GAM) 和随机森林 (RF) 等机器学习 (ML) 方法是用于估计产量和 EONR 的有前途的方法。对小麦和大麦进行了 20 项 OFPE N 试验，并使用土壤、地形和遥感变量进行了分析，以实现以下目标：(1) 使用 OFPE 量化冬季作物产量的空间变异性和对 N 的产量响应，(2 ) 评估和比较 GAM 和 RF 模型预测产量和对 N 的产量响应的性能，以及 (3) 量化土壤的影响，EONR 估计中的作物和田地特征。机器学习技术能够对小麦和大麦产量进行建模，平均误差为 13.7%（624 公斤/公顷^-1）。然而，RF 和 GAM 相似的产量预测精度导致经济最优施氮率大相径庭。在不同地点，土壤有效磷和土壤有机质是影响最大的变量；然而，影响的幅度和方向因领域而异。这些表明，使用来自不同领域的数据训练模型在应用于另一个领域时可能会导致不可靠的特定于站点的 EONR。需要对 ML 方法进行进一步评估，以确保在生产者过渡到数字农业时代时 N 推荐的稳健自动化。