WEBAFSA harnesses the power of Fourier transforms to reveal hidden similarities between agricultural ecosystems — empowering researchers and policymakers with actionable insights.
WEBAFSA implements the Agroecology Fourier-based Similarity Assessment (AFSA), an innovative methodology that applies principles of the Fourier transform to systematically evaluate similarities among agroecological sites across the globe.
Designed for researchers, practitioners, and policymakers, WEBAFSA transforms complex multivariate climate and soil data into intuitive, interactive similarity maps — no deep technical expertise required.
Four integrated computational stages transform raw agroecological data into actionable similarity insights.
High-resolution climate and soil data is retrieved, filtered, and validated from global databases
FFT-based temporal alignment corrects seasonal shifts between reference and target sites
Weighted, normalized distances are computed across all selected environmental variables
Results are rendered as an interactive, color-coded map overlaid on a global canvas
Every feature is designed to bridge the gap between complex agroecological data and actionable insights.
Explore results on a dynamic Leaflet-powered map with switchable OpenStreetMap, LULC, and AFSA layers
Choose between WorldClim (10 km) for global analysis or CHELSA BIOCLIM+ (1 km) for regional precision
Select and weight temperature, precipitation, solar radiation, wind speed, and soil properties
Multi-core computation engine efficiently handles continental-scale datasets
Cross-reference similarity results with ESA land use and land cover classification data
Download similarity maps as GeoTIFF files for use in QGIS, ArcGIS, and other platforms
AFSA was rigorously tested through a maize land suitability assessment across Tanzania, leveraging six agroecological variables from the CHELSA BIOCLIM+ dataset and nearly 1,000 georeferenced yield observations from the TAMASA project. Chi-square analysis confirmed a statistically significant association between the computed suitability map and actual maize yields.
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