Sustainability of bread wheat in France: A GAMLSS based risk and return period assessment.

Soft wheat is one of the most important crops in the French agricultural production, making France the 6th largest producer in the world and the leading producer in Europe. However, since the 1990s, yields have stagnated. Several studies have identified adverse climatic events as a major factor in this stagnation, including drought, heat stress, early and late wet conditions. Since 2000, these phenomena have led to four years of critically low yields: 2003, 2007, 2016, and 2024. Given the increasing likelihood of agroclimatic risks due to climate change, it is crucial to investigate the frequency and future occurrence of these risks—whether isolated or combined—for bread wheat production in France. This is particularly important in the context of global food security.

Characterising the exceedance probability of adverse climatic events represents a major challenge, especially in a context marked by a disruption in climate stationarity. Furthermore, given the crucial role of phenology in risk characterisation, it is essential to consider phenological shifts influenced by rising temperatures when analyzing these risks, rather than relying on fixed periods. To address these challenges, we propose developing risk indicators tailored to the sensitive phenological phases of wheat (ecoclimatic indicators). These indicators will be employed in generalised additive models (GAMLSS) to identify their exceedance probability across various French climatic zones (provisionally identified by k-means clustering method). The analysis will encompass three distinct emissions scenarios  based on climate projections from 17 coupled GCM-RCM models.

These individual risk trajectories will help determine the predominant risks within each climatic region at various time horizons. Since yield losses often result from the combination of adverse events, a probabilistic analysis of combined risks will also be conducted. This analysis will leverage copula functions to model dependencies between climatic variables. The results of this study will provide critical insights into future high-risk production zones and enable the establishment of timelines to prioritize adaptation actions based on the dominant risks identified for each region. These findings will be essential for strengthening the agricultural sector's resilience to the growing impacts of climate change. They will also provide a strategic foundation to guide breeders in identifying priority improvement pathways, while adhering to temporal constraints.