A conceptual framework for simultaneous optimization of integrated climate scenario data and phenology models

TL;DR

This paper introduces a new framework that improves predictions of crop development under future climate conditions by accounting for uncertainties in both the model and climate data.

Contribution

A novel dynamic crop phenology model framework that simultaneously integrates uncertainties from the model, climate data, and their interaction.

Findings

Ignoring climate data uncertainties leads to contradictory predictions of future crop phenology.

Future climate projections indicate a 22-day earlier heading for winter wheat by 2070–2099 compared to 1981–2010.

The model identifies key environmental factors driving different phenological stages.

Abstract

Climate-resilient crop varieties are exceedingly important for global food security. High-accuracy predictions of crop phenology in future climate scenarios require a more precise description of putative crop-environment interactions. While the accuracy of these predictions is affected by three primary sources of uncertainty — the phenology model, the climate scenario data, and their interaction — commonly used approaches consider only the phenology model as a source of uncertainty, while uncertainties arising from climate scenario data and their interaction with the model are commonly neglected. To address this gap, we developed a novel dynamic crop phenology model framework that simultaneously integrates uncertainties arising from all three sources. This model framework was developed using a large dataset of winter wheat phenology ratings and environmental covariate observations, encompassing more than 2500 locations spanning more than 80 years. It incorporates and combines up to seven environmental covariates in an additive manner, resulting in a large set of possible models from which to select. For each model, the phenological development follows a defined response curve per environmental covariate. Using climate scenario projections in Switzerland, our proposed effective model selection process helps to find the best model that reduces uncertainties in predicting future crop phenology. In addition to predictions, the model can pinpoint the driving environmental covariates for different phenological phases. Four major phenological stages are predicted for an independent subset of the large dataset and future climate scenario projections. Predictions for future climate scenario projections indicate a 22-day earlier heading for 2070–2099 compared to the reference period (1981–2010). Ignoring uncertainties arising from climate data results in contradictory predictions. These findings underscore the importance of considering all three uncertainty sources — phenology model, climate scenario data, and their interaction — in accurately predicting phenology under future climates.

The online version contains supplementary material available at 10.1007/s13593-026-01091-0.

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