Submitted to: Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/20/2022
Publication Date: 8/25/2022
Citation: Smith, H.W., Ashworth, A.J., Owens, P.R. 2022. GIS-based evaluation of crop and soil suitability for optimized production on U.S. Tribal Lands. Agriculture. 12(9). Article 1307. https://doi.org/10.3390/agriculture12091307.
Interpretive Summary: Matching crops with appropriate soils is an important first step for improved food security, but little work has been done to effectively match soils with crops on U.S. Tribal lands. The objective of this study was therefore to develop first ever high-resolution crop suitability maps for their ability to optimize soil resource management of Quapaw Tribal Lands. We built on previously developed soil properties maps for 56,500 acres of Quapaw Tribal Lands to calculate and compare two widley used crop suitability indices (Dideriksen and Storie models). Our results were evaluated and validated using in-field yield measurements frrm over 130,000 points within the study area. Results from this study showed that very little (<13%) of the most suitable areas in Quapaw Tribal Lands are currently used for crop production. These results suggest changes in land-use patterns could improve productive capacity since some prime agricultural soils are currently underutilized. Of the two suitability models used, we found the Storie index was more appropriate than the Dideriksen for modeling crop suitability in this area, though future research could improve estimates through the development of novel suitability indices for optimizing production, closing yield gaps, and improving sustainable land use.
Technical Abstract: Optimizing soil-crop-landscape occurrence is essential for sustainable intensification and food security, but little work has been done to evaluate these parameters on Tribal lands. The objective of this study was to develop first ever high-resolution crop suitability maps and compare two established crop suitability models for their ability to optimize soil resource management of Quapaw Tribal Lands. Here, we build on previously developed continuous soil properties maps for 22,880-ha of Quapaw Tribal Lands using a digital elevation model and a fuzzy-logic based data mining approach to calculate Dideriksen and Storie crop suitability indices. Model results were evaluated and validated against relative yield (n=> 130,000) within the study area. Results from suitability indexing showed that very little (<13%) of highly suitable areas in Quapaw Tribal Lands, are currently used for crop production. These results suggest yield gaps in this area may stem from underutilization of prime soils and that changes in land-use patterns could improve productive capacity. Observed yield was positively correlated with both the Dideriksen (Spearman rho = 0.04, p = 0.03) and Storie suitability index (Spearman rho = 0.08, p < 0.01), though the Storie index showed a stronger association. Based on correlation analysis and variability in modeled values, the Storie index was more appropriate than the Dideriksen for modeling crop suitability, though future research could improve estimates through the development of novel suitability indices for closing yield gaps and improved sustainable intensification.