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.

Soil heterogeneity and acidity are major constraints to Coffea arabica production in the Amazonian soils of Peru. This study developed a spatial predictive framework that integrates a weighted Soil Quality Index (SQIw) and geostatistical modelling (Regression–Kriging and Ordinary Kriging) to estimate lime requirements (LRs) and delineate management zones. A total of 69 coffee-cultivated soil samples were analysed, and spectral information (NDVI) was incorporated to estimate relative yield (RR). Multivariate analysis defined a Minimum Data Set (MDS) composed of exchangeable Na, available P, pH and silt percentage; the highest weights were assigned to P (Wi = 0.292) and pH (Wi = 0.276). SQIw exhibited wide variability (0.01–0.87; CV = 51.8%) and was grouped into five classes, with low (43.5%)- and very low (21.7%)-quality classes predominating. SQIw showed a strong relationship with RR (r = 0.64). Geostatistical models performed differently between localities: in Nuevo Huancabamba, Regression–Kriging improved prediction accuracy (SQIw: R² = 0.58; LR: R² = 0.396), whereas in San José de Sisa, Ordinary Kriging provided better fits only for LRs (R² = 0.32). Nuevo Huancabamba is dominated by moderate-to-high-quality soils (87.29%; SQIw > 0.6) and low lime requirements (74.94%; <0.84 t ha⁻¹), in contrast with San José de Sisa, where low-quality soils prevail (89.45%; SQIw < 0.4) alongside high LRs (75.26%; 2.54–7.13 t ha⁻¹). The resulting maps enable targeted interventions—precision liming and focused P fertilisation—to correct acidity and phosphorus deficiency, thereby improving input-use efficiency and enhancing the sustainability of Amazonian coffee systems.

Background: Soil fertility management in mango and banana production systems in northern Peru is limited by marked spatial heterogeneity, driven by chemical imbalances, particularly salinity, alkalinity, and sodicity. Aim: This study developed a spatial predictive framework to assess soil quality across the agricultural area of Morropón Province, Piura, by integrating a weighted soil‐quality index (SQIw), specifically designed to diagnose salinity‐, sodicity‐, and alkalinity‐related constraints, with hybrid machine learning–geostatistical modeling. Methods: Soil sampling was carried out in homogeneous units delimited according to slope, texture, and soil color. Within each unit, subsamples were composed of the 0–30 cm layer, resulting in 1304 observations representative of mango and banana fields. The SQIw was constructed using laboratory‐derived physicochemical soil properties combined with environmental covariates, including vegetation indices extracted from Sentinel‐2 images in Google Earth Engine. A minimum data set was defined through transformation, collinearity screening, and principal component analysis. The variables were standardized, weighted according to their contribution to the explained variance, normalized using scoring functions, and linearly integrated to calculate the index, which was classified into five quality levels. Spatial prediction was performed using ordinary kriging (OK), random forest (RF), and a hybrid RF with residual kriging (RF + RK). In random cross‐validation, OK showed the lowest performance ( R ² = 0.245; root mean square error [RMSE] = 0.155), whereas RF improved accuracy ( R ² = 0.315; RMSE = 0.147), and RF + RK ( R ² = 0.309; RMSE = 0.148) achieved comparable improvements. Results: A reduced RF + RK model based on the most relevant predictors achieved the best numerical performance ( R ² = 0.340; RMSE = 0.144), although it was not statistically different from the other RF‐based models. Conclusion: The resulting map supports site‐specific fertility management in mango and banana production systems in northern Peru.