Development of predictive algorithms accounting for uncertainty in processes underpinning the maize (Zea Mays L.) yield response to nitrogen (N) are needed in order to provide new N fertilization guidelines. The aims of this study were to unravel the relative importance of crop management, soil, and weather factors on both the estimate and the size of uncertainty (as a risk magnitude assessment) of the main components of the maize yield response to N: i) yield without N fertilizer (B0); ii) yield at economic optimum N rate (YEONR); iii) EONR; and iv) the N fertilizer efficiency (NFE) at the EONR. Combining Bayesian statistics to fit the N response curves and a machine learning algorithm (extreme gradient boosting) to assess features importance on the predictability of the process, we analyzed data of 730 response curves from 481 site-years (4297 observations) in maize N rate fertilization studies conducted between 1999 and 2020 in the United States and Canada. The EONR was the most difficult attribute to predict, with an average uncertainty of 50 kg N ha⁻¹, increasing towards low (<100 kg N ha⁻¹) and high (>200 kg N ha⁻¹) EONR expected values. Crop management factors such as previous crop and irrigation contributed substantially (∼50%) to the estimation of B0, but minorly to other components of the maize yield response to N. Weather contributed about two-thirds of explained variance of the estimated values of YEONR, EONR, and NFE. Additionally, weather factors governed the uncertainty (72% to 81%) of all components of the N response process. Soil factors provided a consistent but limited (10% to 23%) contribution to explain both expected N response as well as its associated uncertainties. Efforts to improve N decision support tools should consider the uncertainty of models as a type of risk, potential in-season weather scenarios, and develop probabilistic frameworks for improving this data-driven decision-making process of N fertilization in maize crop.

开发预测算法，以解决支持玉米的过程中的不确定性（Zea MaysL.) 需要对氮 (N) 的产量响应，以提供新的施氮指南。本研究的目的是阐明作物管理、土壤和天气因素对玉米产量对 N 响应的主要组成部分的估计和不确定性大小（作为风险程度评估）的相对重要性：i)不施氮肥的产量（B0）；ii) 经济最佳氮肥率 (YEONR) 下的产量；iii) EONR；iv) EONR 的氮肥效率 (NFE)。结合贝叶斯统计来拟合 N 个响应曲线和机器学习算法（极端梯度提升）来评估特征对过程可预测性的重要性，我们分析了 1999 年至 2020 年在美国和加拿大进行的玉米施氮率研究中 481 个地点年（4297 次观察）的 730 条响应曲线数据。EONR 是最难预测的属性，平均不确定性为 50 kg N ha^-1，向低 (<100 kg N ha ^-1 ) 和高 (>200 kg N ha ^{-1 ) 增加}) EONR 预期值。作物管理因素，如先前的作物和灌溉，对 B0 的估计贡献很大（~50%），但对玉米产量对 N 响应的其他组成部分贡献较小。天气对 YEONR 估计值的解释方差贡献了约三分之二、EONR 和 NFE。此外，天气因素控制着 N 响应过程所有组成部分的不确定性（72% 到 81%）。土壤因素提供了一致但有限（10% 到 23%）的贡献来解释预期的 N 响应及其相关的不确定性。改进 N 决策支持工具的努力应将模型的不确定性视为一种风险、潜在的季节性天气情景，并开发概率框架以改进这种数据驱动的玉米作物 N 施肥决策过程。