Nixon Haro

Coastal sediments integrate contaminant inputs over time and are widely used to assess anthropogenic pressure, yet baseline data remain scarce along the Peruvian coast. Metal concentrations and their associated ecological risk were assessed in nearshore marine sediments along 16 km of coast at Ilo (Moquegua, Peru), across five stations subject to contrasting anthropogenic pressures and three sampling campaigns (n = 15). Concentrations were determined by energy-dispersive X-ray fluorescence on the p = 0.022), the two elements without enrichment, and did not alter the spatial pattern of contamination. Cu in the area facing the smelter is therefore the main contamination concern along this coast.

This study evaluated the outcomes associated with the Avanzar Rural Project among young entrepreneurs from five regions of Peru. The research was conducted in 13 provinces across Amazonas, Áncash, Cajamarca, Lima, and San Martín, involving 146 participants from 60 producer organizations. Data were collected between September and October 2025 through a validated survey and analyzed using descriptive statistics, binary logistic regression, and Multiple Correspondence Analysis (MCA). The study follows an ex post design without a baseline or control group; therefore, the results reflect participants’ reported changes and statistical associations rather than causal effects. The findings indicate widespread reported improvements in productive performance, including income, asset acquisition, and production efficiency, alongside strengthened adaptive capacities and technical skills. Regression results show that access to productive assets and training in production technologies are key factors associated with income growth and increased sales. In contrast, climate adaptation capacities and sustainable management training are linked to improved resilience. In addition, limited access to digital training and infrastructure constrains market engagement. The MCA reveals significant territorial differences in economic performance, institutional development, environmental management, and market integration. In conclusion, the results suggest the importance of integrated and territorially differentiated support strategies that combine productive investment, capacity building, and market-oriented interventions to strengthen youth-led agricultural systems.

Globally, coffee-based agroforestry systems are recognized for their capacity to integrate agricultural production with biodiversity conservation, particularly in tropical landscapes under intense anthropogenic pressure. However, significant knowledge gaps remain regarding floristic composition, arboreal structure, and the ecological importance of woody species in Andean agroforestry systems of the Peruvian Amazon, especially along altitudinal gradients. The objective of this study was to characterize the diversity, floristic composition, arboreal structure, and ecological value of woody species in coffee-based agroforestry systems in the Department of Amazonas, Peru. Forest inventories were conducted in twelve one-hectare plots, recording dasometric variables, estimating diversity indices, analyzing floristic affinity, and calculating the Importance Value Index of species. A total of 57 tree species belonging to 41 genera and 25 families were recorded, with moderate diversity levels and a marked dominance of species from the Fabaceae family. The structure showed a predominance of young individuals, concentrated in low and intermediate diameter and height classes, and a moderate shade cover suitable for coffee cultivation. The species with the highest ecological and productive value were Pinus tecunumanii, Colubrina glandulosa, Clitoria juninensis, Inga edulis, and Inga mendozana, which perform key functions related to shade provision and soil fertility. These results are transferable to other coffee agroforestry systems in tropical montane regions and provide relevant evidence for sustainable forest management, biodiversity conservation, and productive optimization, issues of international interest in the agricultural and agroforestry sectors.

In this research, species distribution prediction models (i.e., MaxEnt) were applied to analyze the suitability of the ecological environment among the clades of the genus Gynoxys in Peru. Bioclimatic, edaphic, and topographic variables were integrated to predict the areas with the most significant potential for optimal development of this genus. These data were combined to generate potential distribution maps, taking into account the most relevant variables for each clade. The validation of the MaxEnt model showed an outstanding performance, reaching AUC indices above 0.9, reflecting the high accuracy of the predictions. The results reveal that the key variables influencing the selection of the clade occurrence areas are: mintempwarmest (47.70% contribution) in the Discoide clade, topowet (33.20%) in the Gynoxys clade, and monthcountbytemp10 (33.30%) in the Praegynoxys clade. The potential distribution areas of these clades were 132,594 km² for Discoide, 168,574 km² for Gynoxys, and 37,392 km² for Praegynoxys. The areas with the highest probability of presence of the genus were found in the Andean regions of northern and central Peru. However, a significant proportion of these areas were threatened by habitat fragmentation and land degradation. In terms of conservation, it was found that 32.05, 35.46, and 61.02% of the potential distribution areas of the discoid, Gynoxys, and Praegynoxys clades, respectively, are conserved, which could be a relevant factor for the preservation of this genus. These findings underscore the relevance of safeguarding key areas for conserving Gynoxys and montane ecosystems in Peru, emphasizing the need for protection strategies that guarantee the long-term sustainability of these species and their associated habitats.

One of the world’s major agricultural crops is rice (Oryza sativa), a staple food for more than half of the global population. In this research, synthetic aperture radar (SAR) and optical images are used to analyze the monthly dynamics of this crop in the lower Utcubamba river basin, Peru. In addition, this study addresses the need to obtain accurate and timely information on the areas under cultivation in order to calculate their agricultural production. To achieve this, SAR sensor and Sentinel-2 optical remote sensing images were integrated using computer technology, and the monthly dynamics of the rice crops were analyzed through mapping and geometric calculation of the surveyed areas. An algorithm was developed on the Google Earth Engine (GEE) virtual platform for the classification of the Sentinel-1 and Sentinel-2 images and a combination of both, the result of which was improved in ArcGIS Pro software version 3.0.1 using a spatial filter to reduce the “salt and pepper” effect. A total of 168 SAR images and 96 optical images were obtained, corrected, and classified using machine learning algorithms, achieving a monthly average accuracy of 96.4% and 0.951 with respect to the overall accuracy (OA) and Kappa Index (KI), respectively, in the year 2019. For the year 2020, the monthly averages were 94.4% for the OA and 0.922 for the KI. Thus, optical and SAR data offer excellent integration to address the information gaps between them, are of great importance to obtaining more robust products, and can be applied to improving agricultural production planning and management.