Examinando por Autor "Temoche Socola, Víctor Alexander"

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Goat producers’ perception of climate change and adaptation strategies in the tropical dry forest of Northern Peru
(Mary Ann Liebert, Inc., 2024-12-20) Temoche Socola, Víctor Alexander; Barrantes, Christian; Godoy, David
Climate change affects countries worldwide, challenging economies and livelihoods. It negatively impacts food production due to temperature variability, irregular precipitation, frost, and drought, increasing pressure on agrosilvopastoral resources and reducing agricultural and livestock productivity. In Peru, the tropical dry forest, an ecosystem highly vulnerable to climate change, supports traditional goat farming, a primary livelihood for many, with a population of approximately 256,860 goats in Piura. This ecosystem is particularly sensitive to temperature and precipitation changes, which directly affect forage availability and livestock productivity. This study aimed to determine goat producers' perceptions and adaptation strategies to climate change in Marcavelica, Lancones, and La Brea. Data from 130 goat producers were analyzed using descriptive and multivariate statistics (principal component analysis, multiple correspondence analysis, and cluster analysis). Results showed that goat farming occurs predominantly in extensive systems (84.62%). Most producers (56.9%) acknowledged climate change, perceiving changes in temperature (69.9%), precipitation patterns (100%), soil productivity (79.2%), and water availability (50%). Four producer clusters were identified based on adaptive capacity: excellent (6.16%), good (23.08%), regular (75.38%), and poor (24.62%). Producers with higher education, associativity, and training demonstrated better knowledge and adaptive capacity. Climate change is evident in the dry forest ecosystem, negatively affecting goat farming. These findings underscore the importance of education, technical support, and associativity to enhance producers' resilience and sustain livestock production under climate variability.
Using biometric analysis to estimate body weight in Creole goats
(Eldaghayes Publisher, 2025-09-30) Trillo Zárate, Fritz Carlos; Paredes Chocce, Miguel Enrique; Salinas Marcos, Jorge; Temoche Socola, Víctor Alexander; Tafur Gutiérrez, Lucinda; Sessarego Dávila, Emmanuel Alexander; Acosta Granados, Irene Carol; Palomino Guerrera, Walter; Cruz Luis, Juancarlos Alejandro; Ruiz Chamorro, Jose Antonio
Background: Creole goat husbandry for milk and meat improves food security in rural areas in Perú. Body weight (BW) is a key trait for selecting breeding stock, and it is estimated to be using algorithms. Likewise, BW is common in livestock farming. Aim: This study aimed to compare BW prediction models using a data mining algorithm in Creole goats, considering their biometric measurements. Methods: Data from 1,075 females aged between 1 and 4 years were used. Measurements of chest width, thoracic perimeter, wither height, sacrum height, rump width and length, body length, cannon bone perimeter, age, and region of the herd were recorded. The regression trees (classification and regression tree), support vector regression (SVR), and random forest regression (RFR) algorithms were used. Results: The SVR was better at predicting BWs in Creole goat herds. Similarly, the results were stable during training (R² = 0.765) and testing (R² = 0.707). However, it should be noted that RFR performed better with training data (R² = 0.942). Conclusion: The proposed predictive models have demonstrated significant potential for accurately predicting BW based on biometric data. Finally, it contributes to better selection, feeding, and sanitary management of Creole goats.