Spatial modelling of soil quality index using regression–kriging and delineation of nutrient management zones in high-Andean quinoa fields, southern Peru

• Autores: Cuellar Condori, Nestor Edwin, Mejia Maita, Sharon Yahaira, Quiñones Trejo, Robert Adrián, Mercado Chinchay, Ruth Lizbeth, Silva Ali, Cristhian, Chávez Zea, Karla Licelly, Ccosi, Elvis, Cahuide, Madeleiny, Quispe Matos, Kenyi Rolando
• Formato: artículo
• Fecha de Publicación: 2025
• Institución: Instituto Nacional de Innovación Agraria
• Repositorio: INIA-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.inia.gob.pe:20.500.12955/3084
• Enlace del recurso: http://hdl.handle.net/20.500.12955/3084; https://doi.org/10.3390/agronomy16070680
• Nivel de acceso: acceso abierto
• Materia: Spatial soil mapping; High-andean soils; Variable-rate fertilisation; Gypsum requirement; Mapeo espacial de suelos; Suelos altoandinos; Fertilización de tasa variable; Requerimiento de yeso; https://purl.org/pe-repo/ocde/ford#4.01.04; Quinoa; Nitrógeno; Nitrogen; Fósforo; Phosphorus; Potasio; Potassium; Materia orgánica del suelo; Soil organic matter; Aplicación de abonos; Fertilizer application

The pronounced heterogeneity of high-Andean soils constitutes a critical constraint to the sustainable productivity of quinoa in southern Peru, where current yields (1.6 t ha⁻¹) remain well below potential (>5 t ha⁻¹). This study aimed to develop a spatially predictive model of a weighted soil quality index (SQIw), the edaphic supply of nitrogen (N), phosphorus (P) and potassium (K), and the agricultural gypsum requirement by integrating edaphoclimatic covariates through regression–kriging. A total of 198 quinoa-cultivated soil samples were analysed; a minimum data set (MDS) was defined using correlation and principal component analyses, and regression–kriging was applied to map SQIw and the variables of interest. The MDS comprised electrical conductivity (EC), organic matter (OM), available P, exchangeable Na, sand, clay, and effective cation exchange capacity (ECEC); exchangeable Na (Wi = 0.160) and available P (Wi = 0.158) received the largest weights in the SQIw. SQIw values ranged from 0.22 to 0.84 and supported a five-class soil quality taxonomy; spatial modelling revealed a dominance of moderate-quality soils across the territory (85.21% of the agricultural area, 13,461.19 ha). The model achieved R² = 0.56, RMSE = 0.05, and MAE = 0.04 for SQIw. Most of the area (12,175.65 ha; 77%) exhibited an intermediate gypsum requirement (9.73–14.33 t ha⁻¹). Nitrogen and phosphorus showed the greatest territorial limitations, whereas potassium was largely non-limiting (84.82–570.17 kg ha⁻¹). These results indicate that sodicity and N–P deficiencies are the primary functional constraints; the generated maps enable prioritisation of gypsum amendments and targeted variable-rate fertilisation strategies to optimise the sustainability of quinoa production in the Altiplano.