The impact of abnormal temperatures on crop yields in Italy: a functional quantile regression approach

TL;DR

This paper uses functional regression to analyze how abnormal temperatures and precipitation during specific periods within the growing season affect maize and soft wheat yields in Italy from 1952 to 2023, revealing season-dependent impacts.

Contribution

It introduces a functional quantile regression approach to identify critical intra-seasonal weather periods affecting crop yields, capturing timing and shape of weather impacts unlike traditional models.

Findings

High temperatures in June-August reduce maize yields

Unusual high temperatures in late March to early April decrease soft wheat yields

Precipitation effects vary seasonally, improving early wheat yields but reducing later yields

Abstract

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.