Soil degradation poses a threat to agricultural productivity and environmental sustainability. Intensive use, mechanical disturbances, and inappropriate practices deteriorate soil structure, reducing its water retention capacity, resistance to erosion, and support for plant growth. A key indicator of this physical degradation is structural stability, assessed using the dispersion ratio (DR), which reflects the soil’s resistance to disintegration. The objective of this study was to evaluate the effects of the spatial distribution of soil structural stability, as well as the influence of management practices, in three land use systems (conventional, organic, and fruit) in La Molina, Lima, Peru. In an area of 120,000 m ² , 100 georeferenced points (0–20 cm deep) were sampled, analyzing particle size distribution, bulk density (BD), and DR. Spatial variability was characterized using geostatistical interpolation (kriging), generating gradient maps. Differences between systems were evaluated using the Games–Howell test, which is suitable for nonhomogeneous variances. The results showed sandy loam texture in all fields and significant differences in DR, with marked spatial dependence. Spatial trend analysis revealed a deterministic component, justifying the use of universal kriging (UK). The spherical (Sph) semivariogram model was the best fit. Cross‐validation (ME = −0.067; RMSE _ep = 7.13; MSDR = 1.3) indicated accurate and unbiased predictions. Soils under fruit crop fields exhibited greater structural stability, whereas soils under organic and conventional management systems showed medium to low aggregate stability. In addition, a negative correlation was observed between BD and DR, suggesting that BD is not a good indicator of aggregate formation. Spatial positioning and agricultural management significantly influence soil structural stability, with less intensive practices favoring its conservation.

Soil organic carbon (SOC) is a crucial component related to various processes that ensure soil health and function. Its modeling is vital for assessing and monitoring soil degradation caused by the potential impact of agricultural activities.This study aimed to model SOC in the Northern highlands of Peru, characterized by a high amount of SOC, which is being affected by crop expansion. Crop rotation (CR) history was linked to ground-truthed soil data via a multi-year crop classification model trained on data from 534 fields across 2022–2024. Each cropland field was represented as a polygon delineating its boundaries and indicating its dominant crop cover. Time series of multispectral Sentinel-2 Level-2A Top of Canopy imagery were used to derive phenological features—such as the timing of maximum canopy cover and the length of the growing period—based on Normalized Difference Vegetation Index (NDVI) time series. A Random Forest classifier was used as the baseline model. The cropland classification model demonstrated a strong overall performance, with F ₁ scores ranging from 0.81 to 0.98 across the different classes. The model performed well for lupin and pasture but scored lower for beans and potatoes. Predictions of cropland classes from 2019 to 2022 were created, resulting in frequency layers that represent crop rotations. Four feature configurations were evaluated: (i) including all features as a benchmark, (ii) excluding climatology, (iii) excluding crop rotation history, and (iv) excluding soil properties. Configurations including all features and excluding crop rotation history showed the highest performance ( R ² = 0.63), while those excluding climatology or soil properties performed worse ( R 2 ≈ 0.52 -- 0.53 ). Although soil features were the most important, fallow frequency emerged as the most critical predictor of SOC in crop rotations. When soil data were excluded, fallow frequency, combined with climatic features, explained over half of the SOC variability. The findings emphasize the importance of incorporating remote sensing-derived CR into SOC mapping efforts.