Improving crop yield and stability is crucial for sustainable food production, which is predominantly influenced by climate. Nutrient management mitigates the negative impacts of climate change on yield stability, but little is known about the explanatory capability of climate variables (especially canopy, soil, and nighttime temperatures) and soil nutrient interactions for yield anomalies. This study evaluated the long-term (1992–2020) responses of wheat and maize yields and yield anomalies to various climatic variables under distinct combinations of nitrogen (N), phosphorus (P), and potassium (K) nutrient supplies in the North China Plain. Results showed that NPK treatment improved the stability of relative yield anomalies (RYA) for wheat and maize by up to 65 % compared to the unfertilized control, while negatively affecting the stability of absolute yield anomalies (AYA). Nutrient supply affected the yield stability of maize more than that of wheat. Ground and soil temperatures contributed the most to the yield and yield anomalies, while air temperature was less associated. Models relying solely on climate data explained 34 % and 28 % of the wheat RYA and AYA, respectively, and 44 % and 49 % of the maize RYA and AYA, respectively. Incorporating nutrient-climate interactions improved the model explanatory power to 67 % for wheat RYA and to 62 % for maize RYA. Additionally, annual random effects were less critical in explaining maize yield and yield anomalies but significant for wheat RYA. The nutrient-climate interactions greatly improved the explanatory capability of models to crop yield anomalies, thereby supporting strategies for sustainable food production amidst changing climate.

中文翻译：

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土壤-气候相互作用增强了对作物长期产量稳定性的理解


提高作物产量和稳定性对于可持续粮食生产至关重要，而可持续粮食生产主要受气候影响。养分管理减轻了气候变化对产量稳定性的负面影响，但人们对气候变量（尤其是冠层、土壤和夜间温度）和土壤养分相互作用对产量异常的解释能力知之甚少。本研究评估了华北平原氮 （N）、磷 （P） 和钾 （K） 养分供应的不同组合下小麦和玉米产量以及产量异常对各种气候变量的长期 （1992-2020） 响应。结果表明，与未施肥的对照相比，NPK 处理将小麦和玉米的相对产量异常 （RYA） 的稳定性提高了 65%，同时对绝对产量异常 （AYA） 的稳定性产生了负面影响。养分供应对玉米产量稳定性的影响大于小麦。地面和土壤温度对产量和产量异常的贡献最大，而空气温度的相关性较小。仅依靠气候数据的模型分别解释了 34% 和 28% 的小麦 RYA 和 AYA，以及 44% 和 49% 的玉米 RYA 和 AYA。结合养分-气候相互作用，将小麦 RYA 的模型解释能力提高到 67%，玉米 RYA 的模型解释能力提高到 62%。此外，年度随机效应在解释玉米产量和产量异常方面不太重要，但对小麦 RYA 很重要。养分与气候的相互作用大大提高了模型对作物产量异常的解释能力，从而支持在气候变化中实现可持续粮食生产的战略。