Substantial Differences in Crop Yield Sensitivities Between Models Call for Functionality‐Based Model Evaluation

Abstract

Crop models are often used to project future crop yield under climate and global change and typically show a broad range of outcomes. To understand differences in modeled responses, we analyzed modeled crop yield response types using impact response surfaces along four drivers of crop yield: carbon dioxide (C), temperature (T), water (W), and nitrogen (N). Crop yield response types help to understand differences in simulated responses per driver and their combinations rather than aggregated changes in yields as the result of simultaneous changes in various drivers. We find that models' sensitivities to the individual drivers are substantially different and often more different across models than across regions. There is some agreement across models with respect to the spatial patterns of response types but strong differences in the distribution of response types across models and their configurations suggests that models need to undergo further scrutiny. We suggest establishing standards in model evaluation based on emergent functionality not only against historical yield observations but also against dedicated experiments across different drivers to analyze emergent functional patterns of crop models.

How Interpretable Machine Learning Can Benefit Process Understanding in the Geosciences

“…However, the univariate approach may overlook compensating errors that arise from interactions among multiple variables within a system, potentially masking problems in model structure or parameterization (Touzé-Peiffer et al, 2020). C. Müller et al (2024) emphasize the need to include analyses of functional properties in process-based model evaluation, which may reveal more about model plausibility and skill than merely comparing variables, since different model responses to drivers may offset each other in the historical evaluation period, but not in future scenarios. To this end, the use of IML to evaluate these multifaceted relationships holds promise to provide geoscientists with a tool that complements and enhances traditional evaluation techniques and moves toward pattern-and process-oriented model evaluation (Reichstein et al, 2019).…”