Examinando por Autor "Canaza Cayo, Ali William"

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A tool for predicting live weight in Huacaya alpacas from southern Peru
(ResearchersLinks Ltd, England, UK, 2026-07-23) Churata Huacani, Roxana; Canaza Cayo, Ali William; Amarilho Silveira, Fernando; Rodriguez Huanca, Francisco Halley; Marchezan Barchet, Fernanda; Fonseca de Freitas, Rilke Tadeu; Núñez Pérez, Herbert Jesús
The aim of this study was to identify the most suitable model for predicting live weight using both original body measurements (BM) and principal component scores (PC), and to assess the relationship between body measurements (BM) and live weight (LW). LW and BM of Huacaya alpacas (n = 117) were collected from the Quimsachata Center of the National Institute of Agricultural Innovation in Peru. The following BM, including LW, withers height (WH), croup height (CH), thoracic circumference (TC), abdominal circumference (AC), cannon-bone length (CL), neck-base circumference (NBC), cannon circumference (CC), tail insertion length (TIL), rump width (RW), tail insertion circumference (TIC), body width (BDW), rump length (RL), forelimb length (FL), body length (BDL), back length (BKL), were taken. Principal component analysis (PCA) was utilized to extract and clarify the correlation between LW and their BMs. Regression equations relating LW to BM and their PCs were computed. Additionally, the root mean squared error (RMSE), Bayesian information criterion (BIC), Akaike information criterion (AIC), and coefficients of multiple determination (R²) were used to assess the models. Four components were extracted from the PCA of BM and LW, which accounted for 67.3% of the total variance. The prediction model for LW, utilizing two PCs, showed the highest R² as well as the lowest BIC, AIC and RMSE values in contrast to models based on the original measurements. The findings suggest that this method is a viable alternative for predicting live weight and may be useful in breeding programs, as well as in the design of management and selection strategies for Huacaya alpacas from Peru.
Genetic structure of the population of Suri alpaca from Peru
(ResearchersLinks Ltd, 2023-11-01) Gallegos Acero, Roberto; Canaza Cayo, Ali William; Rodríguez Huanca, Francisco Halley; Mamani Cato, Rubén Herberht
The objective of the study was to evaluate the genetic structure of the Suri alpaca population, from the Quimsachata Research and Production Center of the Illpa-Puno Experimental Station of the National Institute of Agrarian Innovation, Peru. Data from 1350 Suri alpacas born from 1993 to 2015 (636 males and 714 females) were analyzed using the method of genealogical analysis method. ENDOG program v.4.8 was used for the calculation of the following parameters of the genetic structure such as: average inbreeding coefficient (F), average relatedness coefficient (AR), the effective numbers of founders (ƒe), effective number of ancestors (ƒa), generation interval (GI) and the pedigree completeness, the ENDOG program v.4.8 was used. The F and AR were 0.06% and 0.40%, respectively, the number of ancestors that gave rise to the reference population was 288, the ƒa for the reference population was 132. The ƒe was 338 animals. The average generational interval was 5.53 years, being higher in the gametic pathways: sire-daughter and sire-son. The pedigree completeness level by the maternal pathway was 72.07% and by the paternal pathway was 46.0%. In conclusion, the generational interval in Suri alpacas of the Germplasm Center was long. The F was of small magnitude, so mating practices were appropriate during the evaluation period.
Hierarchical Bayesian modeling for comparing nonlinear functions and estimating heritability of growth curve parameters in llamas
(Elsevier B.V. en representación de KeAi Communications Co. Ltd., 2026-09-07) Canaza Cayo, Ali William; Mamani Cato, Ruben Herberht; Rodríguez Huanca, Francisco Halley; Churata Huacani, Roxana; Cardenas Minaya, Oscar Efrain; Huacani Pacori, Ferdynand Marcos; Calsin Cari, Maribel; Bueno Filho, Júlio Sílvio de Sousa
Genetic modeling of growth curve parameters in llamas is fundamental for breeding programs; however, previous studies have relied predominantly on two-stage frequentist approaches or conventional MCMC algorithms (Gibbs/Metropolis-Hastings), which present limitations regarding computational efficiency and simultaneous incorporation of multiple information sources. The objective of this study was to compare nonlinear functions (Brody, Gompertz, and von Bertalanffy) and jointly estimate growth curve parameters (asymptotic weight, A; scaling parameter, B; and maturation rate, k), variance components, and heritabilities in young llamas through hierarchical Bayesian modeling implemented with the No-U-Turn Sampler (NUTS) algorithm via Stan/brms. We analyzed 11,409 monthly weight records from birth to 365 days of age from 1000 llamas (456 males and 544 females) of K'ara and Ch'accu breeds from the Quimsachata Experimental Station (Peru). A three-stage hierarchical Bayesian model was adopted: (i) normal likelihood for weights conditioned on individual curves; (ii) multivariate animal model for parameters A, B, and k, including systematic effects (sex and breed) and additive genetic effects; and (iii) normal prior distributions for standard deviations. Posterior sampling (4000 post-warmup iterations) showed adequate convergence according to the Gelman-Rubin, Geweke, and Effective sample size diagnostics. The von Bertalanffy model was selected as the most parsimonious (Watanabe-Akaike Information Criterion (WAIC) = 20104.8; Leave-One-Out Information Criterion (LOOIC) = 27588). Estimated heritabilities for A were high (0.79 - 0.87), while those associated with parameters B and k were close to zero (0.007 – 0.075). We conclude that the hierarchical Bayesian approach with NUTS/brms is computationally efficient and statistically robust for analyzing growth curves in llamas, evidencing high genetic potential for adult weight selection, in contrast to the low heritability of curve shape parameters. This study represents the first application of the NUTS algorithm in hierarchical growth models for South American camelids.
Modeling growth curve parameters in Peruvian llamas using a Bayesian approach
(Elsevier, 2025-03-20) Canaza Cayo, Ali William; Mamani Cato, Rubén Herberth; Churata Huacani, Roxana; Rodríguez Huanca, Francisco Halley; Calsin Cari, Maribel; Huacani Pacori, Ferdeynand Marcos; Cardenas Minaya, Oscar Efrain; de Sousa Bueno Filho, Júlio Sílvio
The objective of this study was to fit four nonlinear models (Brody, von Bertalanffy, Gompertz and Logistic) to realizations of llama weight, using frequentist and Bayesian approaches. Animals from both sexes and types (K'ara and Ch'accu) were observed. Data consisted of 43,332 monthly body weight records, taken from birth to 12 months of age from 3611 llamas, collected from 1998 to 2017 in the Quimsachata Experimental Station of the Instituto Nacional de Innovación Agraria (INIA) in Peru. Parameters for Non-linear models for growth curves were estimated by frequentist and Bayesian procedures. The MCMC method using the Metropolis-Hastings algorithm with noninformative prior distributions was applied in the Bayesian approach. All non-linear functions closely fitted actual body weight measurements, while the Brody function provided the best fit in both frequentist and Bayesian approaches in describing the growth data of llamas. The analysis revealed that female llamas reached higher asymptotic weights than males, and K'ara-type llamas exhibited higher asymptotic weights compared to Ch'accu-type animals. The asymptotic body weight, estimated for all data using the Brody model, was 42 kg at 12 months of age in llamas from Peru. The results of this research highlight the potential of applying nonlinear functions to model the weight-age relationship in llamas using a Bayesian approach. However, limitations include the use of historical data, which may not fully represent current growth patterns, and the reliance on non-informative priors, which could be improved with prior knowledge. Future studies should refine these aspects.