Allometric Coefficients for Body Measurements and Morphometric Indices in Young Huacaya Alpacas from the Peruvian Highlands Ali William Canaza-Cayo, Roxana Churata-Huacani, Francisco Halley Rodriguez-Huanca, Diana Carla Fernandes Oliveira, Carola Trinidad Melo-Rojas, et al. Life, 2025 (1) Background: Alpacas play a crucial role in the livelihood and cultural heritage of Andean communities, yet limited scientific information exists regarding their morphometric growth patterns under high-altitude conditions. Understanding how environmental and biological factors influence their body development is essential for optimizing management and genetic improvement programs. (2) Methods: This study aimed to characterize the morphometric profile and allometric growth patterns of young Huacaya alpacas, evaluating the influence of sex, birth month, and fiber color on 18 linear body measurements and 6 morphometric indices from 146 animals. (3) Results: General linear models revealed that birth month had a significant effect (p < 0.05) on the compactness index, body side index, and body index while sex, fiber color, and their interaction did not significantly affect most indices. Allometric analysis showed that head traits exhibited low allometric coefficients (0.08–0.23), whereas torso-related measures such as dorsal length and abdominal perimeter showed higher coefficients (0.33 and 0.36, respectively). The compactness index showed marked sexual dimorphism in the allometric coefficient (0.83 in females, 0.95 in males). Thoracic perimeter exhibited a strong relationship with body weight and low variability, highlighting this measure as a key predictor of body size. (4) Morphometric and allometric analyses provide the first growth coefficients for young Huacaya alpacas at high altitude, offering a scientific basis for phenotypic selection of animals with superior meat potential and adaptability, thereby directly improving breeding efficiency and management in Andean production systems.
Assessing the adaptability and stability of maize hybrids using a Bayesian factor analytic model Carlos Pereira da Silva, Alessandra Querino da Silva, Joel Jorge Nuvunga, Fabrício Goecking Avelar, Renisio Braulio, et al. Crop Science, 2025 Maize (Zea mays L.) is an important crop globally, and obtaining more productive and resistant commercial cultivars is of paramount importance. In this context, adequate analysis of data from multi‐environment trials is essential for the accurate modeling of genotype × environment interaction (GEI), thus providing crucial support for decision‐making in plant breeding programs. This study uses a Bayesian analytical factorial model (Bayesian factor analytic [BFA]) to analyze the adaptability and stability of grain yield in a collection of 100 maize hybrids evaluated in 14 representative environments of the Southeast region of Brazil. The aim was to highlight and discuss aspects related to the application of the BFA, addressing the advantages and challenges involved. The goal was to explore the interpretations and limitations of the analysis, in order to assist breeders and researchers in the proper use of the employed method. The results allowed us to identify distinct subgroups of genotypes and environments with similar effects, as well as to identify stable genotypes in relation to GEI and to suggest genotype recommendations for specific environments. To achieve this goal, the flexibility of the BFA model was exploited to incorporate inferences to the various parameters, especially bilinear parameters that describe G + GEI in the biplot.
Machine learning based on extended generalized linear model applied in mixture experiments Gilberto Rodrigues Liska, Marcelo Ângelo Cirillo, Fortunato Silva de Menezes, Julio Silvio de Sousa Bueno Filho Communications in Statistics Simulation and Computation, 2022 When performing mixture experiments, we observe that maximum likelihood methods present problems related to the collinearity, small sample size, and over/under dispersion. In order to overcome these problems, this investigation proposes a model built in accordance with a machine learning approach. This approach will be called Boosted Simplex Regression, which has been evaluated both in terms of accuracy and precision for the odds ratio. The advantages of this new approach are illustrated in a mixture experiment, which has made us conclude that the model Boosted Simplex Regression has unveiled not only better fit quality but also more precise odds ratio confidence intervals.
