ALGR: A multi-purpose agricultural landscape generator in R - Eyal Goldstein; Antonia Deutscher; Eamon O’Keeffe; Kerstin Wiegand (PLoS ONE, 2025)
Agricultural and ecological modelers commonly use maps as input for spatially explicit simulations. While real world maps are often used, they are limited by being static objects, therefore making it difficult to assess how patterns within the landscape contribute to ecological processes. Agricultural landscape generators (ALG) are a useful tool for simulating maps in a more flexible way. They can increase robustness of models that rely on landscape maps as input, they allow modelers to give spatial representation to non-spatial models, and they are a useful tool for recreating spatial patterns in agricultural-dominated landscapes. A limitation of previous ALGs is that they have rarely been designed for general use (non-open source software, not written in R, and designed for specific projects). Furthermore, they are typically either extremely general and thus oversimplified or have a high specificity for particular use cases. ALGR bridges this gap by providing a general-purpose, dynamic landscape generator that balances structural realism with adaptability. ALGR generates agricultural landscapes with a three-step approach: first, outlining potential space, second, field placement inside of that space, and third, enrichment of the landscape with information. This stepwise approach ensures that ALGR generates landscapes with realistic spatial patterns while remaining adaptable to diverse regions and applications. It is the first ALG that is specifically designed to allow a simple integration within the R programming environment and the r-spatial package environment. ALGR is designed as a general-purpose generator, which is simple to use and facilitates an easy integration in modelling workflow. We present several examples of workflows using ALGR , to demonstrate its usefulness. Our examples include: 1) simulating different land use shares, 2) parameter tuning of ALGR to recreate real world landscape, patterns 3) spatially distributing crop portfolios, and 4) using real-world maps as a basis for field placement.