Examinando por Autor "Pizarro, Samuel"

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Agro-Environmental Vulnerability and Ecosystem Sustainability in Peruvian Family Farming: Integrating Survey Data, Spatial Modeling and Remote Sensing
(MDPI (Multidisciplinary Digital Publishing Institute), 2026-01-30) Pizarro, Samuel; Ccopi Trucios, Dennis; Otoya Barrenechea, José; Romero Vasquez, Juan; Tolentino Soriano, María; Cotrina Sanchez, Alexander; Barboza, Elgar
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 1552 districts fall within moderate to very high vulnerability, with highest concentration in the Amazon region (Loreto, Ucayali, Madre de Dios), Andean-Amazonian transitions, and highland districts (Huancavelica, Apurímac, Ayacucho, Puno) where biophysical constraints, ecosystem pressure, and socioeconomic isolation converge. Dimensional spatial complementarity EnvExp peaking on coast, EcoCond in Amazon, SocioCap in Andes demonstrates effective vulnerability reduction requires dimension-specific interventions. Despite divergent weighting schemes, spatial patterns remained consistent, validating identified hotspots. IVAPT provides a reproducible framework supporting evidence-based territorial planning and targeted investments in water infrastructure, ecosystem restoration, and climate adaptation.
Monitoring ryegrass-clover grasslands with multi-spectral UAV imagery will improve the sustainability of small-and medium-sized livestock farmers in the northern Peruvian highlands
(Informa UK Limited, trading as Taylor & Francis Group, 2026-02-04) Vallejos Fernández, Luis; Alvarez García, Wuesley Yusmein; Abanto urbina, Maycol; Gutiérrez Arce, Felipe; Tapia Acosta, Eduardo; Pizarro, Samuel; Ciprian, Cesar; Naupari, Javier
The underutilization of remote sensing technology has compromised sustainable forage resource management, impeding the progress of livestock farmers in the northern Peruvian highlands. To accurately predict forage biomass in six high-altitude (2600-2800 m) ryegrass (Lolium multiflorum Lam) -clover (Trifolium repens) paddocks, we applied machine learning models implemented in Google Earth Engine using spectral indices derived from UAV-based multispectral imagery captured by a Micasense RedEdge MX camera mounted on a DJI Matrice 600. A total of 75 forage samples were collected from precisely geo-referenced plots to train and validate machine learning models based on 13 spectral indices. The Random Forest (RF) model, comprising 500 trees for green forage and dry matter, demonstrated high accuracy and efficiency. UAV-based biomass prediction using GEE and ML techniques was validated, achieving R² values of 0.671 and 0.747 and low errors. By integrating UAVs, sensors, and cloud-based ML, we can decision-support potential in the inter-Andean valley. This innovative approach reduces costs, ensures high-resolution snapshot biomass assessment, and empowers producers to make data-driven decisions.
Phenotypic variability of tarwi (Lupinus mutabilis S.) in Peruvian germplasm collections
(Genetic Resources Journal, 2026-01-28) Ortega Quispe, Kevin Abner; Peña Elme, Eunice Dorcas; Girón Aguilar, Rita Carolina; Amaro Camarena, Nery Amelia; Rios Chavarría, Claudia; Lopez Pariona, Bertha; Cerrón Mercado, Francis Gladys; Camargo Hinostroza, Steve; Pizarro, Samuel
The growing global loss of genetic diversity, phenotypic characterization becomes essential for identifying resilient varieties capable of diversifying and strengthening the agricultural production of underutilized crops such as tarwi (Lupinus mutabilis S.). This study aimed to characterize the phenotypic variability of 41 tarwi accessions conserved in the germplasm bank of the National Institute of Agricultural Innovation (INIA) of Peru. The accessions were evaluated over two consecutive agricultural seasons at the Santa Ana Agrarian Experimental Station under local conditions. Thirty morphological descriptors (17 qualitative and 13 quantitative) were used following IBPGR guidelines. Data were analyzed using descriptive statistics, principal component analysis, hierarchical clustering and correlation analysis for quantitative descriptors, as well as frequency tables and the Shannon-Weaver diversity index for qualitative descriptors. The results revealed high phenotypic variability, particularly in traits related to yield, plant architecture and floral attributes. The accessions were grouped into three morpho-agronomic types: (1) highly productive accessions, (2) accessions with vigorous vegetative development, and (3) short-cycle plants with moderate yields. Yield per plant was significantly associated with the total pod number, total seed mass in hundred seeds and seed thickness. The study revealed considerable phenotypic diversity, characterized by significant correlations among key agronomic traits, the delineation of three distinct phenotypic clusters, and the identification of valuable qualitative attributes, which reinforces their potential for conservation and breeding programmes. However, expanded germplasm evaluation and multi-environment trials are required to validate genotype stability and refine selection criteria. However, additional accessions and further analyses are needed to validate the observed patterns.