Attribution and Resilience Assessment in Crop Rotation Patterns under Extreme Climate Events

Extreme climate events — such as heavy precipitation, heatwaves, and droughts — are increasing in frequency under global climate change and increasingly threaten sustainable agricultural systems. Crop rotation is a key management practice for sustaining soil health and enhancing ecosystem resilience. However, the extent to which extreme climate events drive observable changes in rotation patterns remains insufficiently quantified. This study presents a modeling framework to analyze crop rotation patterns, attribute changes to climatic factors, and identify resilient rotation strategies under future climate conditions.

We examine three core questions: (1) Do extreme climate events significantly alter crop rotation dynamics? (2) Do regions with higher event frequency show systematically different rotation structures? (3) Do rotation sequences that change after extreme events perform differently under climate stress?

Using 1 km resolution meteorological data across Germany, we identify extreme rainfall, heatwave, and drought events. We analyze crop sequences from over 900,000 fields (2012–2024) using Markov chain models to characterize rotation patterns, transition probabilities, and stability indicators. Statistical comparisons are conducted of rotation patterns before and after extreme events, as well as between regions with different event frequencies. To evaluate resilience, a process-based crop model is used to simulate selected crop rotation patterns under various climate conditions, assessing indicators of resistance, recovery, and yield stability.

By integrating climate data, stochastic crop sequence modeling, and process-based crop model simulation, this study establishes a framework for attributing rotation pattern changes to climate factors and evaluating agricultural system resilience. Our findings contribute to understanding climate adaptation in cropping systems and support the development of targeted strategies for sustainable agriculture under global change.