A novel integrated computational approach for agroecological similarity

Assessing agroecological similarity is crucial for shaping sustainable agricultural practices and resource allocation, especially in regions undergoing rapid environmental changes. Current evaluation methods face challenges such as managing large datasets, adjusting for temporal variations across locations, and the need for accessible, comprehensive analytical tools. Addressing these challenges, this paper presents the Agroecology Fourier-based Similarity Assessment (AFSA), an innovative computational approach that applies principles of the Fourier transform to systematically evaluate similarities among agroecological sites. To enhance usability, AFSA is complemented by webafsa , a user-friendly web application designed for researchers and policymakers, emphasizing ease of use and broad applicability. The implementation of AFSA and webafsa aims to improve land suitability assessments, enhance decision-making for resource allocation, and support better adaptation strategies for sustainable agriculture. By offering both a sophisticated computational methodology and an accessible decision-support tool, this study paves the way for more informed and environmentally considerate agricultural practices. • This study introduces the Agroecology Fourier-based Similarity Assessment (AFSA), a novel computational method leveraging Fourier transform for evaluating agroecological similarity. • AFSA is integrated into a web application, webafsa , featuring an intuitive interface that simplifies access for both researchers and practitioners, broadening applicability. • AFSA and webafsa are validated via Tanzania maize suitability case study. AFSA’s suitability map aligns with observed yield data, confirmed by Chi-square test.