Related Experiment Video
Updated: May 9, 2026

Protocol for Assessing the Relative Effects of Environment and Genetics on Antler and Body Growth for a Long-lived Cervid
Published on: August 8, 2017
Regularized canonical correlation models improve prediction of weather impacts on semen quality in Murciano-Granadina
María Pía Peláez Caro1,2, Ander Arando Arbulu1, José Manuel León Jurado3
1Department of Genetics, Faculty of Veterinary Medicine, University of Córdoba, Córdoba, Spain.
Abstract:
This study examined the influence of environmental variables on semen quality in Murciano-Granadina bucks over a 10-year period (2010-2019), analyzing 115 males and 6868 ejaculates. Regularized Canonical Correlation Analysis (rCCA) was applied to overcome multicollinearity and improve prediction of relationships between climatic factors and semen traits. Results showed that bucks displayed resilience to high temperatures, with positive associations between temperature and sperm motility, viability, and morphology. In contrast, cold stress, particularly when combined with strong wind gusts and low barometric pressure, negatively impacted ejaculate volume, motility, and membrane integrity. Rainfall also influenced sperm concentration and acrosome integrity. The first two canonical functions explained over 84% of shared variance, highlighting thermo-biological and atmospheric gradients as key determinants of reproductive performance. These findings demonstrate the utility of rCCA as a predictive tool and underscore the importance of integrating climatic monitoring into artificial insemination programs to enhance semen management, breeding efficiency, and the sustainability of goat production.
Related Concept Videos
Correlation and Regression
Correlation of Experimental Data
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity, and...
Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
The...
Multiple Regression
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Correlations
Calibration Curves: Correlation Coefficient

