Identification of critical timing when abnormal weather affects maize and soft wheat yields in Italy using functional quantile regression

In this study, we apply functional regression analysis to identify the specific within-season periods during which temperature and precipitation anomalies most affect cereal 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 during the phases from stem elongation to grain filling (June to August), with the peak placed during flowering (mid July). For soft wheat, the unusually high temperatures have a generally negative impact on yields, particularly during the stem elongation phase (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.