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Gastrointestinal parasitism is a health issue in livestock, particularly in non-intensive farming systems. This research evaluated the prevalence and risk factors associated with gastrointestinal helminths in goats from three ecosystems in Peru: the Andean shrubland (Ancash), dry forest (Lambayeque), and coastal valley (Lima). The study used a cross-sectional design, with random sampling of goats from extensive production systems in each ecosystem. A total of 819 fecal samples were collected and analyzed using qualitative and quantitative parasitological methods. Additionally, coproculture was performed to identify infective larvae of nematodes. The FAMACHA© index was used to assess anemia levels, while body condition scores were recorded to evaluate the nutritional status of the animals. The highest prevalence was recorded in the Andean shrubland (74.2%), followed by the dry forest (63...

Goats are an important component of smallholder family farms along the coast and highlands of Peru. The weight of an animal is an important indicator of the production and economy of farmers in rural areas. Therefore, this study aimed to develop predictive models for Body Weight (BW) using Morphometric Measurements (MM) of Creole goats (Capra hircus) in Perú. BW and five MM were collected from 356 goats from the coast and highlands of Peru. Variables were analyzed using correlation and stepwise regression analysis to select the best model based on the coefficient of determination (r²), adjusted r², Residual Standard Error (RSE), and Akaike Information Criterion (AIC) using the RStudio statistical software. The highest correlation was found between BW and TG (0.76), followed by RW (0.67), and RH (0.65). The combinations of MM selected as predictors of BW by stepwise regression were TG,...

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 du...