Abstract Maize (Zea Mays L.) yield responsiveness to nitrogen (N) fertilization depends on the yield under non-limiting N supply as well as on the inherent productivity under zero N fertilizer (Y0). Understanding the driving factors and developing predictive algorithms for Y0 will enhance the optimization of N fertilization in maize. Using a random forest algorithm, we analyzed data from 679 maize N fertilization studies (1031 Y0 observations) conducted between 1999–2019 in the United States and Canada. Predictability of Y0 was assessed while identifying determinant factors such as soil, crop management, and weather. The inclusion of weather variables as predictors improved the model efficiency (ME) from 51 up to 64 %, and reduced the root mean square error (RMSE) from 2.5 to 2.0 Mg ha−1, 34 to 27 % in relative terms (RRMSE). The most relevant predictors of Y0 were previous crop, irrigation, and soil organic matter (SOM), while the most influential weather data was linked to the radiation per unit of thermal time (Q quotient) around flowering and spring precipitations. The crop rotation effect resulted in Alfalfa (Medicago sativa L.) as the previous crop with the highest Y0 level (IQR = 11.5–15.0 Mg ha−1) as compared to annual legumes (IQR = 5.6–10.0 Mg ha−1) and other previous crops (IQR = 3.6–7.8 Mg ha−1). The Q quotient around flowering positively affected Y0, while spring precipitations and extreme temperature events during grain filling showed a negative association to Y0. Overall, these results reinforce the concept that yields are controlled not only by soil N supply but also by factors modifying plant demand and ability to capture N. Lastly, we foresee a promising future for the use of machine learning to address both prediction and interpretation of maize yield to obtain more reliable N guidelines.

中文翻译：

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评估不施氮肥玉米产量的不确定性

摘要 玉米 (Zea Mays L.) 产量对氮 (N) 施肥的响应取决于非限制氮供应下的产量以及零氮肥 (Y0) 下的固有生产力。了解驱动因素并开发 Y0 的预测算法将加强玉米施氮的优化。我们使用随机森林算法分析了 1999-2019 年间在美国和加拿大进行的 679 项玉米施氮研究（1031 Y0 观察）的数据。在确定土壤、作物管理和天气等决定性因素的同时，评估了 Y0 的可预测性。包含天气变量作为预测因子将模型效率 (ME) 从 51 % 提高到 64 %，并将均方根误差 (RMSE) 从 2.5 降低到 2.0 Mg ha−1，相对而言 (RRMSE) 降低了 34 % 到 27 % . 与 Y0 最相关的预测因子是先前的作物、灌溉和土壤有机质 (SOM)，而最具影响力的天气数据与开花和春季降水前后单位热时间 (Q 商) 的辐射有关。轮作效应导致紫花苜蓿（Medicago sativa L.）与一年生豆科植物（IQR = 5.6-10.0 Mg ha-1）相比具有最高的 Y0 水平（IQR = 11.5-15.0 Mg ha-1）和其他以前的作物（IQR = 3.6–7.8 Mg ha-1）。开花前后的 Q 商对 Y0 产生积极影响，而春季降水和灌浆期间的极端温度事件与 Y0 呈负相关。总的来说，这些结果强化了这样一个概念，即产量不仅受土壤氮供应的控制，而且还受改变植物需求和捕获氮能力的因素的控制。最后，