Dry forests are ecosystems of great importance worldwide, but in recent decades they have been affected by climate change and changes in land use. In this study, we evaluated land use and land cover changes (LULC) in dry forests in Peru between 2017 and 2021 using Sentinel-2 images, and cloud processing with Machine Learning (ML) models. The results reported a mapping with accuracies above 85% with an increase in bare soil, urban areas and open dry forest, and reduction in the area of crops and dense dry forest. Protected natural areas lost 2.47% of their conserved surface area and the areas with the greatest degree of land use impact are located in the center and north of the study area. The study provides information that can help in the management of dry forests in northern Peru.

Los estudios hidrológicos y geomorfológicos se han incrementado en los últimos años indicando su importancia para entender el funcionamiento de los sistemas fluviales. En esta investigación se evaluó la calidad hidrogeomorfológica del río Utcubamba, ubicado en la cuenca del Amazonas, mediante la aplicación del Índice Hidrogeomorfológico (IHG) dividiéndolo en ocho sectores fluviales. Los resultados mostraron que el río Utcubamba está afectado por notables impactos antropogénicos que alteran las condiciones hidrogeomorfológicas, con un creciente deterioro de las mismas desde su nacimiento a su desembocadura. La calidad de las riberas es la más afectada en toda la cuenca, principalmente en el curso bajo debido a las explotaciones agro pecuarias de carácter extensivo dominantes en este tramo. Por otra parte, el IHG presentó una adaptabilidad total a las condicione...

CONTEXT: Cacao cultivation is vital for rural economies in Peru, but its expansion often overlaps with sensitive ecosystems, raising concerns for biodiversity conservation. Despite international commitments to deforestation-free supply chains, integrated analyses combining agroecological suitability with land-use constraints remain scarce in Peru. OBJECTIVES: This study aims to identify suitable areas for cacao cultivation under multiple exclusion scenarios, evaluate conflicts with biodiversity and conservation areas, and quantify degraded lands that could provide opportunities for agroforestry-based restoration. METHODS: Cacao suitability was modelled with an ensemble of nine machine-learning algorithms using bioclimatic, edaphic, and topographic predictors. Outputs were filtered to exclude biophysical barriers and overlaid with national-scale layers of species richness, protected areas...

The Junín Lake basin, a critical high-altitude ecosystem in the central Peruvian Andes, faces severe contamination from potentially toxic elements (PTEs) driven by mining activities, agriculture, and urbanization. This study evaluates the spatial distribution, ecological risk, and human health implications of 14 heavy metals, metalloids, and trace elements in surface soils surrounding the lake. Using 211 soil samples, we integrated remote sensing, land cover classification, and Random Forest machine learning models with spectral, edaphic, topographic, and proximity-based environmental covariates to predict contamination patterns and assess risk. Results reveal extreme contamination, with arsenic (As), lead (Pb), cadmium (Cd), and zinc (Zn) concentrations exceeding ecological thresholds by over 100-fold in agricultural zones. Ecological risk assessments using contamination degree (mCD), ...

Subsistence family farming in Peru is increasingly constrained by ecosystem degradation, climate variability, and limited access to productive services, particularly where environmental exposure is high. This study develops an Agro-productive and Territorial Vulnerability Index (IVAPT) to evaluate environmental, ecosystem, and socioeconomic vulnerability of subsistence agriculture at the district level nationwide. The index integrates district-level agricultural survey data (ENA-2024) with multi-temporal MODIS NDVI series (2000–2024) and comprehensive climatic, topographic, land-cover, and accessibility indicators, processed through multivariate statistics. Three objective weighting schemes (ENTROPY, CRITIC, PCA) construct thematic sub-indices of Environmental Exposure (EnvExp), Ecosystem Condition (EcoCond), and Socioeconomic Capacity (SocioCap). Results show more than half of Peru's ...

Background Assessing the severity of forest fires allows us to identify changes that compromise the natural regeneration capacity of vegetation. In this study, we evaluated the severity and recovery of vegetation after a fire using Sentinel-2 satellite images for the Cajamarca department in northeastern Peru. Hot spots were downloaded from the Fire Information for Resource Management System (FIRMS). This allowed us to identify eight groups with an area >100 hectares, heat intensity >100 Fire Radiative Power (FRP), and the presence of trees. By applying the Normalized Burn Ratio (NBR) and the Normalized Difference Vegetation Index (NDVI), the levels of extreme, high, medium, and low severity were determined, as well as the recovery of vegetation before and after the fire events. Results and Conclusions The results indicated that 71.02% of the evaluated territory had low severity, 21.95% h...

Pitahaya (Hylocereus spp.) is an economically significant cactus fruit in Peru, renowned for its rich nutritional profile and antioxidant properties while exhibiting wide biological diversity. This study aimed to morphologically characterize seven pitahaya accessions using qualitative and quantitative descriptors related to the cladodes, flowers, and fruits. Univariate and multivariate (FAMD, PCA, MCA, and clustering) analyses were employed to identify and classify the accessions based on their morphological traits. The analyses revealed three distinct groups: one consisting solely of AC.07; another with AC.02, AC.04, and AC.06; and a third including AC.01, AC.03, and AC.05. The first group exhibited superior characteristics, particularly in fruit traits such as the stigma lobe count (23.3), number of bracts (26.5 mm), and length of apical bracts (15.75 mm). The second group recorded the...

Grassland ecosystems cover about 25% of the Earth’s surface, providing essential ecosystem services that benefit nature, people, and food security. This study evaluated agronomic and nutritional parameters of ryegrass (Lolium multiflorum Lam.) based on fertilization levels and cutting frequency in the Amazonas department. The INIA 910—Kumymarca variety was used with nitrogen fertilization rates (0, 60, 120, 180 kg/ha) and cutting intervals of 30 and 45 days for agronomic traits and 30, 45, and 60 days for nutritional traits. A randomized complete block design with eight treatments and three replications was applied. Repeated measures analysis and Tukey’s mean comparison tests (p < 0.005) were performed, along with Pearson correlation and response surface analysis using the central composite design in R. The results showed that applying 180 kg/ha of nitrogen with a 45-day cutting in...

The study used species distribution models (MaxEnt) to analyze the ecological suitability of the genus Gynoxys in Peru. Bioclimatic, edaphic and topographic variables were integrated to predict areas with high development potential. Potential distribution maps were generated, and model validation showed outstanding performance (AUC > 0.9). The results indicated that variables such as minimum temperature in the warmest season and soil moisture are determinants in the distribution of the Discoide, Gynoxys and Praegynoxys clades. It is concluded that the conservation of the genus requires protection strategies to ensure the sustainability of these montane ecosystems.

Coffee cultivation is central to rural livelihoods and Andean–Amazonian landscapes in Peru; however, it faces increasing pressure from climate change and land-use restrictions. This study aimed to assess the current and future ecological suitability of Coffea arabica at the national scale. A Maximum Entropy (MaxEnt) modeling framework was applied, integrating high-resolution bioclimatic, topographic, and edaphic variables. Model performance was robust (mean AUC = 0.858), and variable importance was evaluated using jackknife tests and contribution metrics. Elevation, precipitation of the driest quarter (bio17), soil nitrogen content, and bulk density were identified as the main determinants of habitat suitability. Under current climatic conditions, highly suitable areas cover 42,322.95 km2 (3.3% of Peru), mainly along the eastern Andean slopes. Spatial exclusion scenarios revealed a pro...