GxE

Quantifying Environmental Drivers of Weaning Weight in American Angus Cattle

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Abstract

In traditional genetic evaluations, mixed models partially account for non-genetic effects by fitting contemporary groups and other systematic effects in the models. However, contemporary grouping cannot fully capture the continuous and heterogeneous nature of production environments. This limitation reduces our ability to account for the influence of environmental factors on animals' performance, hindering efforts to improve the environment through management practices, develop methods for incorporating genotype-by-environment interaction into breeding programs, and predict performance in unobserved environments. This study aimed to quantify the effects of various environmental factors on beef cattle performance on farms across the United States, with a specific focus on weaning weight in Angus cattle. We analyzed 1,581,883 weaning weight records collected between 2014 and 2024 on 1,257 Angus farms. Environmental variables included nine static factors, comprising topographic variables (elevation, slope, aspect, and hillshade) and soil properties (organic matter, bulk density, sand and clay contents, and soil pH), as well as eight dynamic factors representing weekly weather conditions (precipitation, shortwave radiation, snow water equivalent, minimum and maximum air temperature, water vapor pressure, day length, and temperature-humidity index). In the first step, linear mixed models were used to estimate contemporary group effects (random) and farm effects (fixed). To represent recent performance, farm effects were then restricted to the 2021-2023 period. This subset included 441 farms that maintained consecutive records across all three years. In the second step, these farm estimates were modeled as a function of the static and dynamic variables using regularized polynomial regression (ridge, LASSO, and elastic net). Model hyperparameters were tuned via grid search, and model performance was compared using nested cross-validation (5-fold outer, 3-fold inner). Regularized models outperformed ordinary linear regression, yielding more stable estimates in the presence of correlated and high-dimensional predictor sets. Among the regularization methods, ridge regression achieved the lowest prediction error (RMSE = 22.95 kg) and explained 14.5% of the variance among farms. This study quantifies environmental effects and evaluates their relationship with farm-level performance in beef cattle production systems. The findings contribute to the development of an envirotyping framework and provide a foundation for future work aimed at identifying drivers of phenotypic plasticity and genotype-by-environment interaction in Angus cattle across diverse production environments.

Keywords: 2026

How to Cite:

Heo, S., Sigdel, A., Garcia, A., Huang, J., Brito, L., Gondro, C., Lourenco, D., Retallick, K., Rowan, T., Souza, S. & Rosa, G., (2026) “Quantifying Environmental Drivers of Weaning Weight in American Angus Cattle”, World Congress on Genetics Applied to Livestock Production Digital Archive 2026(1): 2286622. doi: https://doi.org/10.31274/wcgalp.24071

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