Scholarlink

Abstract <p>Animal production systems are challenged by environmental stressors with heat stress and feed scarcity being the most significant factors affecting production, reproduction, and health status. These concurrent challenges create compounding effects where cattle already struggling with thermoregulation further exacerbates with nutrient deficits.</p> Aim <p>This study aims to evaluate and validate the effectiveness of multivariate statistical analysis in accurately discriminating between the effects of feed scarcity and heat stress using physiological and physical traits of Bali cattle as diagnostic markers.</p> Methods <p>Physiological and physical traits of 83 heads of Bali cattle raised with different management systems of heat stress restricted feed (HSRF), heat stress well-feed (HSWF), and temperature normal well-feed (TNWF). Samples were sorted and quality control to ensure data reliability. Principal component analysis (PCA) was used to identify the most influential traits, while Linear Discriminant Analysis (LDA) and Canonical Discriminant Analysis (CDA) were applied to classify cattle based on management conditions. Clustering analysis further validated the grouping pattern of traits associated with each system.</p> Results <p>Multivariate analysis effectively distinguished Bali cattle based on management conditions. Principal Component Analysis (PCA) identified rectal temperature (TR) and body weight (BW) as the most influential traits differentiating cattle under varying stressors. Clustering analysis showed a strong grouping pattern corresponding to management systems, confirming that TNWF provided optimal conditions, while HSWF was manageable due to cattle’s ability to tolerate a single stressor. However, HSRF negatively impacted cattle performance, as multiple stressors led to physiological strain. Linear Discriminant Analysis (LDA) and Canonical Discriminant Analysis (CDA) successfully classified cattle within their respective management groups, demonstrating the robustness of multivariate approaches in evaluating adaptation and performance under different environmental conditions. These findings confirm the effectiveness of multivariate analysis in distinguishing cattle under different management systems. The identified key traits reinforce the utility of this approach in improving management strategies to optimize cattle performance and resilience under heat stress.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Discriminating heat stress and feed scarcity in Bali cattle using multivariate trait analysis

  • 期刊论文

  • 发表日期：

    2025年6月11日

查看全文
  • Ikhsan Suhendro,
  • Ronny Rachman Noor,
  • Jakaria Jakaria,
  • Aeni Nurlatifah,
  • Ahmad Furqon

摘要

Abstract

Animal production systems are challenged by environmental stressors with heat stress and feed scarcity being the most significant factors affecting production, reproduction, and health status. These concurrent challenges create compounding effects where cattle already struggling with thermoregulation further exacerbates with nutrient deficits.

Aim

This study aims to evaluate and validate the effectiveness of multivariate statistical analysis in accurately discriminating between the effects of feed scarcity and heat stress using physiological and physical traits of Bali cattle as diagnostic markers.

Methods

Physiological and physical traits of 83 heads of Bali cattle raised with different management systems of heat stress restricted feed (HSRF), heat stress well-feed (HSWF), and temperature normal well-feed (TNWF). Samples were sorted and quality control to ensure data reliability. Principal component analysis (PCA) was used to identify the most influential traits, while Linear Discriminant Analysis (LDA) and Canonical Discriminant Analysis (CDA) were applied to classify cattle based on management conditions. Clustering analysis further validated the grouping pattern of traits associated with each system.

Results

Multivariate analysis effectively distinguished Bali cattle based on management conditions. Principal Component Analysis (PCA) identified rectal temperature (TR) and body weight (BW) as the most influential traits differentiating cattle under varying stressors. Clustering analysis showed a strong grouping pattern corresponding to management systems, confirming that TNWF provided optimal conditions, while HSWF was manageable due to cattle’s ability to tolerate a single stressor. However, HSRF negatively impacted cattle performance, as multiple stressors led to physiological strain. Linear Discriminant Analysis (LDA) and Canonical Discriminant Analysis (CDA) successfully classified cattle within their respective management groups, demonstrating the robustness of multivariate approaches in evaluating adaptation and performance under different environmental conditions. These findings confirm the effectiveness of multivariate analysis in distinguishing cattle under different management systems. The identified key traits reinforce the utility of this approach in improving management strategies to optimize cattle performance and resilience under heat stress.