A standardized land capability classification system for land evaluation using mobile phone technology

Authors: QUANDT, AMY - New Mexico State University; Herrick, Jeffrey - Jeff; PEACOCK, G. - Non ARS Employee; Salley, Shawn; BUNI, ADANE - Non ARS Employee; MKALAWA, C - Non ARS Employee; NEFF, JASON - University Of Colorado

Submitted to: Journal of Soil and Water Conservation
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/1/2020
Publication Date: 6/11/2020
Citation: Quandt, A., Herrick, J.E., Peacock, G., Salley, S.W., Buni, A., Mkalawa, C., Neff, J. 2020. A standardized land capability classification system for land evaluation using mobile phone technology. Journal of Soil and Water Conservation. https://doi.org/10.2489/jswc.2020.00023.
DOI: https://doi.org/10.2489/jswc.2020.00023

Interpretive Summary: The Land Capability Classification System (LCC) has been used globally for land evaluation. The LCC classifies the land into eight classes; however, its use is currently limited by two factors: (1) the lack of digital platforms for data input, storage, and management, and (2) an insufficient technical capacity in many regions necessary to generate the required inputs. This paper describes the development of a system to facilitate rapid, flexible, and transparent determinations of LCC by non-soil scientists using a newly developed function of the Land-Potential Knowledge System (LandPKS) mobile app. The LandPKS app automates LCC and supports land use planning and management. It can also can serve as a foundation for crop-specific land suitability evaluations

Technical Abstract: One of the major causes of poverty globally is land degradation and poor natural resource conservation, leading to reduced agricultural productivity. This degradation is often caused by a mismatch between land use and land potential, specifically using marginal lands for agriculture. For over 50 years the Land Capability Classification (LCC) system has been used globally for land evaluation to support soil and natural resource conservation. The LCC system classifies the land into eight classes; however, its use is currently limited by two factors: the lack of digital platforms for data input, storage, and management, and an insufficient technical capacity in many regions necessary to generate the required inputs. This paper describes the development of a system to facilitate rapid, flexible, and transparent determinations of LCC by non-soil scientists using a newly developed function of the Land-Potential Knowledge System (LandPKS) mobile app. Inputs include soil texture and rock fragment volume by depth, slope, and site observations of soil limiting factors. A standardized system for evaluating inputs and calculated indicators was developed based on US and international implementations of LCC. The system was evaluated using USDA Natural Resources Conservation Service soil survey data in eight US counties. Results show that the standardized system predictions were within one class for 73.8% of the 1,312 soils tested, despite a high level of variability in how LCC was determined within the US database. The LandPKS LCC system was further tested in Tanzania and Ethiopia to examine site-specific applications, usability, and usefulness of the system for national land use planning efforts. It was concluded that the LandPKS app automates a globally applied system (LCC) for supporting natural resource conservation and sustainable land management and can serve as a foundation for crop-specific land suitability evaluations. More generally, improved land evaluation efforts can contribute to better soil and natural resource conservation, more sustainable agricultural systems, and increased food security.