In this study, we apply functional regression analysis to identify the specific within-season periods during which temperature and precipitation anomalies most affect crop yields. Using provincial data for Italy from 1952 to 2023, we analyze two major cereals, maize and soft wheat, and quantify how abnormal weather conditions influence yields across the growing cycle. Unlike traditional statistical yield models, which assume additive temperature effects over the season, our approach is capable of capturing the timing and functional shape of weather impacts. In particular, the results show that above-average temperatures reduce maize yields primarily between June and August, while exerting a mild positive effect in April and October. For soft wheat, unusually high temperatures negatively affect yields from late March to early April. Precipitation also exerts season-dependent effects, improving wheat yields early in the season but reducing them later on. These findings highlight the importance of accounting for intra-seasonal weather patterns to provide insights for climate change adaptation strategies, including the timely adjustment of key crop management inputs.

翻译：本研究采用函数回归分析方法，识别温度与降水异常在作物生长季内影响产量的关键时期。基于意大利1952年至2023年的省级数据，我们分析了玉米和软质小麦两种主要谷物，并量化了异常天气条件在整个生长周期中对产量的影响。与假定温度效应在季节内具有可加性的传统统计产量模型不同，我们的方法能够捕捉天气影响的时间特征与函数形态。具体而言，结果显示高于平均水平的温度主要在6月至8月期间降低玉米产量，而在4月和10月则产生轻微的正面效应。对于软质小麦，异常高温在3月下旬至4月上旬对产量产生负面影响。降水同样表现出季节依赖性效应：生长季早期降水有助于提高小麦产量，但后期降水则会降低产量。这些发现强调了考虑季节内天气模式的重要性，为气候变化适应策略（包括关键作物管理措施的适时调整）提供了科学依据。