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Highlights
• The SALCA models transitioned from an Excel tool to a Python implementation.
• A modular structure separates parameters, tables, models, and calculation levels.
• The long-term and large collaborative effort required coordination and harmonization.
• The open source aims to facilitate reproducibility, reuse, and external collaboration.

Description
Agriculture is a complex sector which delivers numerous products and is a major driver of environmental impacts. It requires efficient and flexible tools that can estimate such impacts for different crop and animal production systems. The Swiss Agricultural Life Cycle Assessment (SALCA) models apply to Swiss and, more generally, Central European conditions and have been developed over more than two decades. By now, they cover several types of direct emissions, from carbon dioxide to heavy metals, as well as biodiversity and soil quality. While initially implemented in Excel, the increasing complexity of the models, connections between some of the models, large input data requirements, the need for continuous updates to integrate new scientific findings, the gradual expansion of the models to capture additional impact categories, and the interest from external researchers in using such models for their own studies in and outside of Switzerland motivated a transition to a Python implementation and eventually its open-source publication.
The Python implementation follows a modular structure to enable flexible application in different types of research projects, as well as easy adaptations and extensions of the models. The development of the SALCA models has been a long-term process and large collaborative effort among researchers across multiple generations and with diverse disciplinary backgrounds and different experience levels and styles in Python programming. This collaborative effort was needed to ensure a high quality of the models necessitating different specialist expertise. To facilitate reuse by and collaboration with external researchers while also making the software development process more sustainable and ensuring continuity despite potential changes within the research team, we defined guidelines and a short-term time plan for cleaning our codebase and outlined future plans to further improve documentation and refactor the code for increased reproducibility and efficiency, alongside further developments of individual modules.