Assessment of Soil Quality in Peruvian Andean Smallholdings: A Comparative Study of PCA and Expert Opinion Approaches

Tomás Samaniego, Beatriz Sales and Richard Solórzano ()
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Tomás Samaniego: Estación Experimental Agraria Donoso, Dirección de Servicios Estratégicos Agrarios, Instituto Nacional de Innovación Agraria (INIA), Lima 15200, Peru
Beatriz Sales: Estación Experimental Agraria Donoso, Dirección de Servicios Estratégicos Agrarios, Instituto Nacional de Innovación Agraria (INIA), Lima 15200, Peru
Richard Solórzano: Centro Experimental La Molina, Dirección de Servicios Estratégicos Agrarios, Instituto Nacional de Innovación Agraria (INIA), Lima 15024, Peru

Sustainability, 2025, vol. 17, issue 17, 1-25

Abstract: Soil degradation poses a significant threat to the sustainability of agroecosystems, particularly in mountainous regions where environmental conditions are highly variable and management practices are often suboptimal. In this context, soil quality assessment emerges as a key tool for guiding sustainable land use and informing decision-making processes. This study aimed to develop and spatially evaluate a Soil Quality Index (SQI) tailored to the northeast sector of Jangas district, Ancash, Peru. A total of 24 soil indicators were initially considered and reduced using Spearman’s correlations to avoid multicollinearity. Depending on the weighting strategy applied, the final SQI configurations incorporated between 14 and 15 indicators. Two weighting strategies—Principal Component Analysis (PCA) and Expert Opinion (EO)—were combined with linear and non-linear (sigmoidal) scoring functions, resulting in four distinct SQI configurations. The spatial performance of each index was tested using Geographically Weighted Regression Kriging (GWRK), incorporating covariates like NDMI, elevation, slope, and aspect. The SQI constructed using PCA combined with non-linear scoring achieved the highest performance, effectively minimizing skewness and while achieving the highest predictive accuracy under GWRK. By contrast, although the EO-based index with linear scoring demonstrated similar statistical robustness, it failed to achieve comparable effectiveness in terms of spatial predictive accuracy. The SQIs generated offer a practical framework for local institutions to identify and prioritize areas requiring intervention. Through the interpretation of complex soil data into accessible, spatially explicit maps, these indices facilitate the targeted application of inputs—such as organic amendments in low-SQI zones—and support the implementation of improved management practices, including crop rotation and soil conservation, without necessitating advanced technical expertise.

Keywords: principal component analysis; agroecosystems; spatial modeling (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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