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ARS Home » Southeast Area » Fayetteville, Arkansas » Poultry Production and Product Safety Research » Research » Publications at this Location » Publication #431486
Research Project: Developing Best Management Practices for Poultry Litter to Improve Agronomic Value and Reduce Air, Soil and Water Pollution

Location: Poultry Production and Product Safety Research

Title: Crop suitability mapping of major crops on Arizona tribal lands using multiple geospatial frameworks

Author
item VALENCIA-ORTIZ, MILTON - University Of Arkansas
item SMITH, HARRISON - University Of Arkansas
item Ashworth, Amanda
item WINZLER, EDWIN - University Of Texas At Arlington
item Blackstock, Joshua
item MUENICH, REBECCA - University Of Arkansas
item Owens, Phillip

Submitted to: ASABE Annual International Meeting
Publication Type: Abstract Only
Publication Acceptance Date: 1/15/2026
Publication Date: 2/2/2026
Citation: Valencia-Ortiz, M., Smith, H., Ashworth, A.J., Winzler, E., Blackstock, J.M., Muenich, R., Owens, P.R. 2026. Crop suitability mapping of major crops on Arizona tribal lands using multiple geospatial frameworks. ASABE Annual International Meeting. July 12-15, 2026, Indianapolis, Indiana.

Interpretive Summary:

Technical Abstract: Crop suitability analysis is vital for optimizing yields while minimizing environmental degradation and preserving natural resources. This study aimed to identify suitable areas for alfalfa and cotton across two tribes in Arizona, USA, by comparing four distinct crop suitability modeling frameworks: Boolean Logic (BL), Storie Index (SI), Fuzzy - Analytic Hierarchy Process (F-AHP), and Maximum Entropy (ME). Suitability was assessed using a comprehensive suite of topographic, weather, and edaphic variables including slope, elevation, average annual rainfall, average annual temperature, soil pH, calcium carbonate, texture, soil depth, soil salinity, soil organic carbon, soil fertility, soil acidity, soil erosion, microrelief, available water content, and drainage. For the ME model, these variables were complemented by additional remote sensing indices (NDVI) and terrain derivatives (aspect, valley depth, topographic wetness index, etc.). Suitability was assessed using rule-based classification, multiplicative scoring, weighted fuzzy integration, and predictive modeling within BL, SI, F-AHP, and ME, respectively. Finally, multivariate “Moran’s I” was used to calculate the relationship across suitability maps. The findings from this study quantified and geolocated the most suitable areas for alfalfa and cotton production on tribal lands. Furthermore, the comparative analysis provides a robust framework for empirically-based crop, nutrient, and soil water planning, while optimizing yields for promoting soil health to regenerate land productivity.