PyCIAT: A Configurable Python Framework for Scalable, Multi-Model Assessment of Climate Change Impacts and Adaptations in Agriculture

Synthesizing climate change impacts on agriculture requires integrating diverse climate projections, spatial data, multiple process-based crop models, and various management scenarios. Existing approaches often rely on cumbersome, specific scripting, hindering scalability, reproducibility, and the robust exploration of uncertainty through multi-model ensembles. We present PyCIAT (Python Climate Impact and Adaptation Toolkit for Agriculture), an open-source, configuration-driven framework designed to orchestrate complex agricultural impact assessments. PyCIAT utilizes a modular Python architecture, driven by a central YAML configuration file, to manage workflows encompassing climate data processing (GCMs/RCMs), soil data integration, simulation setup across multiple locations, parallelized execution of crop simulation models via standardized interfaces (placeholders for DSSAT, APSIM, STICS provided), and automated post-processing for impact and adaptation analysis. Key features include explicit handling of multi-model ensembles, HPC-readiness via support for cluster job arrays, standardized output variable mapping, and optional integration points for advanced modules (e.g., detailed water dynamics, biotic stress) and machine learning surrogates potentially leveraging advances in AI for agriculture [1]. This framework significantly reduces boilerplate code, enhances reproducibility, and facilitates large-scale, systematic exploration of climate impacts [2] and adaptation strategy effectiveness across diverse agricultural systems. PyCIAT provides a scalable and extensible platform for advancing agricultural modeling under climate change.