Soil spectroscopy is known as a rapid and cost-effective method for predicting soil properties from spectral data. The objective of this work was to build a statistical model to predict soil carbon content from spectral data by partial least squares regression using a limited number of soil samples. Soil samples were collected from two soil orders (Andisol and Ultisol), where the dominant land cover is native Nothofagus forest. Total carbon was analyzed in the laboratory and samples were scanned using a spectroradiometer. We found evidence that the reflectance was influenced by soil carbon content, which is consistent with the literature. However, the reflectance was not useful for building an appropriate regression model. Thus, we report here intriguing results obtained in the calibration process that can be confusing and misinterpreted. For instance, using the Savitzky–Golay filter for pre-processing spectral data, we obtained R² = 0.82 and root-mean-squared error (RMSE) = 0.61% in model calibration. However, despite these values being comparable with those of other similar studies, in the cross-validation procedure, the data showed an unusual behavior that leads to the conclusion that the model overfits the data. This indicates that the model should not be used on unobserved data.

Soil organic carbon (SOC) is an important indicator of soil quality; an elevated percentage of SOC indicates very high-quality soil, physically as well as chemically. As such, the principal objective of the present study was to determine the concentration of SOC at different depths, as well as its accumulation through the entire soil profile. The Carrizal-Chone system (SCCH) area was stratified by agricultural use. Sixty-three soil samples were taken from different depths of up to a maximum of 150 cm. The physical and chemical properties of the soil were determined. SOC was determined by the Walkley and Black method. The following results are highlighted: (1) 21 different varieties of soil management were identified; (2) the largest area was livestock grazing land, which had the greatest concentration of SOC; (3) the type of soil with the greatest SOC sequestration capacity was silty clay loam; (4) the area cultivated with corn presented the highest accumulation of total carbon; and (5) the highest concentration of SOC was found in the top 40 cm, with a tendency to decrease with depth. It is concluded that soil management influences the concentration and accumulation of SOC in the topsoil layers and the entire soil profile.

Desde hace unas décadas se han implementado técnicas espaciales en el estudio de los recursos naturales, la más importante es la clasificación temática de imágenes satelitales para determinar el tipo de cobertura. Las técnicas convencionales de clasificación no garantizan una cartografía temática exacta, se basan en el análisis a nivel de pixel, los que pueden contener información mezclada en el caso de los pixeles borde, esto conlleva a errores cualitativos (límites) y cuantitativos (áreas y perímetros) en los mapas temáticos. Se aplicó la metodología de clasificación orientada a objeto a una imagen SPOT de la cuenca Santiváñez ubicada en Cochabamba-Bolivia. Se realizaron 72 segmentaciones con el módulo Featureextraction de Envi Ex, variando los parámetros scale y merge cada 10 unidades. La evaluación de las segmentaciones se realizó mediante una función objetivo propuesta por (Espindola et al., 2006), basada en la varianza y el índice de de Moran I. Se determinó que los parámetros óptimos para la segmentación fueron; para scale->20 y para merge->50. Se obtuvieron segmentos de entrenamiento para 6 clases, la clasificación se realizó en base a los atributos espectrales y texturales. La evaluación se realizó con zonas de muestreo tomadas sobre la imagen, respaldadas por puntos de control tomados en campo y Google Earth. Con el resultado de la matriz de confusión se calcularon dos indicadores de calidad; la precisión global que llegó a 94.61% y el coeficiente Kappa resultó 0.92.

Forests are key ecosystems for climate change mitigation, playing a pivotal role in C and N land sequestering and storage. However, the sustainable management of forests is challenging for foresters who need continuous and reliable information on the status of soil conditions. Yet, the monitoring of soils in temperate evergreen forests, via satellite data, is jeopardized by the year round prevailing heavily dense canopy. In this study, the Sentinel-2 spectral imagery derived normalized difference vegetation index (NDVI), proved to be a reliable tool to determine the C/N ratio in two managed pine-dominated forests, in southern Poland. Results showed a strong negative correlation between NDVI values and the on-site C/N ratios measured at the upper soil horizons in 100 and 99 randomly distributed sampling points across the Kup (r${}^2$ = −0.8019) and Koniecpol (r${}^2$ = −0.7281) forests. This indicates the feasibility of using the NDVI to predict the microbial driven soil C/N ratio in evergreen forests, and to foresee alterations in the vegetation patterns elicited by microbial hindering soil abiotic or biotic factors. Spatial/temporal variations in C/N ratio also provide information on C and N soil dynamics and land ecosystem function in a changing climate.

This study analyzes the socioeconomic dynamics and land-use practices of small-scale Shuar farmers in Taisha, Tuutinentsa, and Macuma, in the Ecuadorian Amazon. Based on surveys of 43 cacao producing households, we analyzed perceptions of well-being, land allocation, and livelihood diversification. Households typically include five to six members and rely on diversified portfolios combining cacao (Theobroma cacao L.), peanuts (Arachis hypogaea L.), plantain (Musa × paradisiaca L.), livestock, forest, and off-farm activities. Gendered labor divisions are present: men are more involved in marketed production and commercialization, while women play central roles in chakra-based agroforestry management, food provisioning, and household economies that reduce cash expenditures and strengthen food sovereignty. Average farm sizes range from 31 to 40 hectares, with forest and pasture, with smaller areas allocated to perennial and short-cycle crops. These patterns reflect multifunctional strategies balancing income generation, risk management, cultural continuity, and environmental stewardship rather than single-crop optimization. Agriculture and livestock constitute the main income sources in Taisha and Tuutinentsa, whereas Macuma exhibits greater economic diversification due to proximity to urban markets. High transportation costs and limited infrastructure constrain profitability. Reported cacao yields are lower than provincial and national averages; however, chakra-based systems prioritize multiple outputs beyond yield, including food security, ecosystem services, and cultural values. Opportunity cost analysis suggests higher short-term returns for peanuts, while cacao remains central due to its larger cultivated area. These findings highlight the complexity of Indigenous agriculture and the need for culturally grounded development policies aligned with socio-ecological realities.

La expansión del cultivo de cacao en la parroquia Eloy Alfaro del cantón Chone y su impacto en la cobertura vegetal natural ha sido objeto de estudio. Mediante el uso de imágenes satelitales Sentinel-2 y Landsat 7, se aplicaron técnicas de clasificación supervisada y no supervisada para la identificación de áreas cultivadas y su evolución temporal. La clasificación supervisada permitió una caracterización más precisa de los cultivos. Además, el análisis de índices de vegetación mostró que el NDVI es un indicador robusto para evaluar la salud del cultivo de cacao, presentando una alta correlación con otros índices espectrales. No obstante, la expansión del cultivo ha resultado en una reducción de la vegetación natural. Los hallazgos resaltan la necesidad de implementar prácticas agrícolas sostenibles, como sistemas agroforestales y manejo eficiente del suelo, para equilibrar la producción de cacao con la conservación ambiental.