Discriminating Heat Stress and Feed Scarcity in Bali Cattle Using Multivariate Trait Analysis

Discriminating Heat Stress and Feed Scarcity in Bali Cattle Using Multivariate Trait Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Discriminating Heat Stress and Feed Scarcity in Bali Cattle Using Multivariate Trait Analysis Ikhsan Suhendro, Ronny Rachman Noor, Jakaria Jakaria, Aeni Nurlatifah, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4904288/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Jun, 2025 Read the published version in Tropical Animal Health and Production → Version 1 posted 4 You are reading this latest preprint version 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. principal component discrimination heat stress heat tolerance Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION The animal production system has undergone a radical transformation in recent decades, with significant advancements in technologies and genetics. Innovations such as precision livestock farming (PLF), automated milking and smart feeding systems, drones and remote sensing, genetic advancements, and nutrigenomics have significantly improved cattle management, productivity, and efficiency. These advancements have led to higher livestock production yields of milk, meat, and eggs, which has helped meet the growing demand for animal-based products (Smith et al. 2013). However, environmental challenges, with heat stress and feed scarcity being the most significant factors, impose high adaptation costs and constrain performance, particularly in high-performing cattle that have undergone intensive genetic selection and are highly dependent on technological management. Feed scarcity and heat stress interact negatively, compromising Bali cattle performance. Feed scarcity limits energy intake, while heat stress increases metabolic demands for thermoregulation, exacerbating energy and metabolic imbalances (Baumgard & Rhoads, 2012). This combination can reduce body weight, feed efficiency, and increase disease risk (Sejian et al., 2018). The prevailing heat stress conditions were marked by high temperatures and humidity, representing a great challenge, especially for exotic breeds of cattle. These adverse led to alterations in the animals' behavioral, endocrine, and physiological systems (Sejian et al. 2018). This causes problems with management and nutrition, which often come with high costs. Therefore, it is necessary to select local breeds with thermotolerance ability. Bali cattle, native Indonesian cattle, are known for their tolerance to heat stress due to their ability to adapt to the hot and humid climate (Suhendro et al. 2022; Freitas et al. 2021), making them a valuable breed for tropical areas. Over 2.95 million Bali cattle, or almost 26% of all cattle in Indonesia, are in the Eastern Islands and South Sumatra, with most of them being in these regions. Over the past few decades, the number of cattle in Bali has gradually increased at an annual rate of roughly 3% per year (Talib et al. 2003). Due to their number and hardiness, Bali cattle are often used as the foundation for crossbreeding programs (Widyas et al. 2022). Cattle breeding and management are critical components in agricultural practices with their ability to tolerate the heat stress being one of the key factors in determining the efficacy of cattle farming (Terry et al. 2021). It is essential to concentrate on breeding and selecting cattle with capabilities related to heat tolerance amid the shifting climate trends and rising temperatures. To evaluate the heat tolerance ability of cattle, it is essential to have reliable measurement and calculation methods. Multivariate statistical analysis, which examines multiple variables simultaneously to identify patterns and relationships, offers a powerful approach to handling complex biological datasets. This study aimed to assess the effectiveness and reliability of multivariate techniques, particularly principal component analysis (PCA) and discriminant analysis (LDA and CDA), in identifying key physical and physiological traits that distinguish Bali cattle under different management systems. The improvement of livestock systems should focus on predictive models and involve the key traits for resilience and developing cost-effective assessment tools to support cattle production in tropical environments. MATERIALS AND METHODS Animal ethics and sample collection The animal protocol followed the Animal Ethics Committee of Udayana University, Denpasar, Indonesia (Code ID: B/184/un14.2.9/pt.01.04/2021). Eighty-six sick less male Bali cattle collected in different experiments were used. Fifty were obtained from the Denpasar Breeding Centre (BPTU HPT Denpasar) with good feed (TNWF: 21 bulls with neutral temperature and well fed; HSWF: 32 bulls heat stress and well fed, pasture). The other 30 came from Serading with heat stress conditions and feed scarcity (HSRF). TNWF represents a no-stressor condition, where cattle are provided with adequate feed and are not exposed to external stressors. HSWF represents a single-stressor condition, where cattle experience heat stress but still receive sufficient feed intake. HSRF represents a multiple-stressor condition, where cattle are exposed to both heat stress and feed scarcity, making it the most challenging environment. These classifications allow for a structured analysis of how Bali cattle respond physiologically and behaviorally under varying degrees of stress. The rearing system has been in place and patented for a long time, and this condition represents natural conditions in tropical regions of Indonesia, variations in heat stress, and lack of feed. Microclimate status and feeding management varied across different conditions (Table 1). In heat stress condition, cattle were not providing with shelter. They exhibit heat stress especially at the noon when air temperature and solar radiation in highest condition. In well feed condition, cattle were provided with a nutritionally adequate diet supporting optimal growth. Conversely, feeding in HSRF was restricted due to environmental and management constraints. Cattle in this condition were only provided with approximately 5% of their body weight in feed, which was chopped and offered inconsistently, varying with seasonal availability. Traits measurements Traits measurements were carried out in a squeeze chute with the cattle sorted and queued in an alley. Physical and physiological traits were measured by professional personnel trained for animal collection and measurements. The nine physical measurements (Table 2) were: body weight (BW), wither height (WH), body length (BL), chest circumference (CC), skin thickness (ST), body darkness (BD), hair length (HL), blood glucose level (BGL), and body condition score (BCS). The four physiological measurements (Table 2) were rectal temperature (TR), respiration rate (RR), heart rate (HR), and heat tolerance coefficient (HTC), these traits were collected both in the morning and afternoon and then denoted as a subscript in the variables. The dataset consisted of samples collected from naturally occurring populations with different management systems. However, the sample sizes and age distributions were not balanced across groups. Despite these variations, all sampled individuals were males, clinically healthy, and above two years old. Due to these inherent imbalances, statistical analyses were performed to check data balance and implement appropriate preprocessing steps for sorting and quality control. Data Sorting and Quality Control Normality was evaluated through skewness analysis with the skewness value outside the range of -1 to +1 were transformed using inverse transformation. Outliers were detected and managed using Winsorization or trimming for extreme values beyond ±3 Z-score. Traits with unequal variance (heteroscedasticity with Levene’s test p -value>0.05) were standardized using Robust Scaling transformation to ensure comparability across variables. Breusch-Pagan test was used to check the traits with heteroscedasticity in regression model, the selected traits were adjusted using Weighting Least Square (WLS). These preprocessing steps ensured that statistical models could be applied effectively without distortion due to sample size imbalance. All data preprocessing and transformations were performed in RStudio (version X.X) using the dplyr, MASS, lmtest, and car packages. Dimensionality Reduction and Group Differentiation IBM SPSS Statistics 25.0 (IBM Corp, Armonk, NY) and RStudio (RStudio, PBC, Boston, MA) were used to generate multivariate analysis. The procedures included Pearson correlation between physiological and physical traits, principal component analysis (PCA) to understand the best variables and scattered total data variation, and discriminant analysis (DA) to visualize the form of clusters and distances between the groups. The optimal number of principal components (PCs) was determined based on scree plot visualization and cross-validation using the caret package. Loadings from the PCA were then examined to identify the most contribution traits contributing to variation within the dataset. PCA was performed to reduce dimensionality and identify key traits that explain the most variation in the dataset. The selected traits were then used in Discriminant Analysis to maximize the separation between management systems (TNWF, HSWF, HSRF). This two-step approach ensures that DA is applied to the most informative variables while reducing noise and redundancy. Linear Discriminant Analysis (LDA) was performed with a five-fold approach using the trainControl() function from the caret package to ensure model robustness,. The model’s classification accuracy, Euclidean distances between groups, and canonical correlations were assessed to determine the degree of separation among groups. The canonical scores were used to hierarchical clustering and then visualized as a dendrogram. Model Evaluation The final model’s performance was assessed using multiple validation metrics, including cross-validation for PCA, effect sizes based on canonical correlations, and Euclidean distances, and classification accuracy derived from a confusion matrix. This workflow was designed to ensure the robustness and reliability of statistical analyses in differentiating heat tolerance traits in Bali cattle RESULTS Table 2 showed the physiological and physical traits of experimental animals of Bali cattle samples. Of the 27 initial variable measurements, 17 were retained in the calculation. The CDA, based on the selected variables, significantly discriminated against the three groups (Hotelling’s test p <0.001) by extracting three canonical functions whose DIMs were reported in Table 3. Dim1 and Dim2 scatter plots of PCA (Figure 1) explained the total variation of the data, it also showed a clear separation between the groups. Dim1 separated the heat stress and scarce feed of TNWF and HSRF, whereas the HSWF was separated from others by Dim2. There were also indicated a clear grouping of vector traits corresponding to specific management groups. The traits of BW and BL were oriented towards TNWF with positioned oppositely to HSRF. Meanwhile, traits of TR and RR exhibit stronger vector dominance towards HSWF. The significant contributions of each dimension are further detailed in Table 4. Principal Component Analysis (PCA) was performed with cross-validation to determine the optimal number of components while ensuring stability in dimensionality reduction. The first two principal components (PC1 and PC2) explained a total of 52.69% of the variance, which was insufficient to capture the full data structure. Including a third principal component (PC3) increased the total explained variance to 62.68%, providing a better representation. Cross-validation confirmed the robustness of this selection by minimizing reconstruction error. Due to the limitations of two-dimensional visualization, the 3D PCA plot is provided as supplementary material. The LD1 versus LD2 scatter plot (Figure 2) showed a clear separation between the three heat stress conditions. The distances between group centroids reflect phenotypic or genetic divergence, with HSRF and HSWF showing closer proximity compared to TNWF, which is more isolated. This suggests TNWF may have unique traits distinguishing it from the other two breeds. The axes' scales (LD1: ±2.5; LD2: ±5.0) highlight LD2's greater discriminatory power in this analysis. The Linear Discriminant Analysis (LDA) model demonstrated a high classification accuracy of 93.03%, indicating strong differentiation among the three groups (HSRF, HSWF, TNWF). The Kappa statistics (0.8927) further confirmed excellent agreement between predicted and actual classifications. Cross-validation using a 5-fold approach showed consistent accuracy across different data subsets, ensuring the model's robustness and reliability. These results highlight the effectiveness of discriminant analysis in distinguishing group differences based on the selected predictors. There was also hierarchical clustering of three different rearing groups (Figure 3) which were accordance with the origin of the treatment groups. The Euclidean distance revealed the degree of separation between the three groups in the discriminant function space. The largest distance was observed between TNWF and HSRF (5.95), indicating that TNWF exhibited the most distinct characteristics compared to the other groups. Similarly, the distance between TNWF and HSWF was 4.24, suggesting a notable differentiation. In contrast, HSRF and HSWF had the smallest Euclidean distance (3.40), implying a closer similarity between these two groups. These findings align with the results from canonical discriminant analysis (CDA) and linear discriminant analysis (LDA), confirming that TNWF is the most divergent group while HSRF and HSWF share more overlapping characteristics. The Euclidean distance values further support the robustness of the discriminant model in effectively separating the groups based on the selected traits. The correlation analysis (Figure 4) reveals a clear clustering of variables into three distinct groups, separating physical, physiological, and behavioral traits. This clustering pattern is consistent with the expectation that traits within the same category exhibit stronger internal correlations due to their shared biological or functional basis. However, an exception is observed for heart rate and chest circumference, which display an inverse correlation, suggesting potential compensatory physiological mechanisms or differences in metabolic regulation across individuals. This finding aligns with previous studies that highlights the interplay between cardiovascular response and body conformation in livestock. The hierarchical clustering method further supports these groupings, emphasizing the structured relationships among the measured traits. DISCUSSION Multivariate analyses have been widely applied in previous studies (Moindjie et al. 2023) to evaluate variation across breeds, experimental treatments, rearing management systems, and heat stress conditions (McManus et al. 2011; Acciaro et al. 2020). However, most studies have focused on isolated factors, such as thermoregulatory responses (McManus et al. 2011) or behavioural adaptations under specific stressors (Acciaro et al. 2020). In contrast, our study integrates physiological, physical, and behavioural parameters to differentiate three distinct management conditions (TNWF, HSWF, and HSRF) that represent no stressor, a single stressor, and multiple stressors, respectively. The present study extends this approach by utilizing multivariate analyses to simultaneously evaluate multi traits, aiming to determine which indicators are most effective in differentiating between rearing management conditions associated with varying levels of stress. There are some variables that tend to respond stronger on specific observational clusters. The identified of most contribution traits such as BW, WH, BL, TR, and RR can guide targeted interventions (Table 4). The comfort treatment (TNWF) tent to gain weight and physique (BW, WH, and BL) of the animals than either the single or multiple stressor treatments. Meanwhile, non-ideal BW in multiple HSRF stressors is interpreted as a trade-off to reduce the stress load by reducing its growth. BW (+13.02) as the highest variable contribution in Dim.1 was suggested to be the most affected variable due to well-feed management. Body weight (BW) serves as a crucial indicator for various aspects such as lifespan, fertility, survival capability, and overall health status (Tiezzi et al. 2013; Yin and König 2018). The weight of cattle is influenced by factors including their activity levels, diets, environment, and social interactions (Mwangi et al. 2019). When the two indicators of diet and environment are not achieved, cattle tend to have less than ideal growth. Our findings suggest that TNWF and HSWF, which provide relatively adequate feeding and environmental conditions, lead to improved or moderate growth performance and lower stress responses. Conversely, HSRF, characterized by minimal and inconsistent feeding strategies, is reflected in traits indicating higher stress and lower weight gain. The single stressor (HSWF) has a moderate body weight, but surprisingly their RR and TR physiology is the highest. The moderate body weight in HSWF compared to TNWF affirmed the reports that the drop in production is caused not just by a decrease in dry matter intake due to heat stress but may directly cause by heat stress itself (Collier et al. 2012). This higher of RR and TR compared to the HSRF might be related to the amount of heat produced from consumption activity. Nutrient consumption leads increased heat production during digestion, which exacerbates heat dissipation under heat stress conditions (Case et al. 2011). This raised metabolic activity, coupled with environmental heat, intensifies the body's thermoregulatory mechanisms, leading to greater heat dissipation issues (Pawar et al. 2016; Onagbesan et al. 2023). The previous studies also reported that elevated air temperature and energy consumption affected the physiology which could lead to the increase of the rectal temperature, respiration rate, and heart rate (Talmón et al. 2023; Tüfekci and Sejian 2023). The multiple stressor (HSRF) has the highest HR and lowest body weight. This was in line with a previous study which found multiple stressors significantly ( p <0.01) affected body weight, physiological response, and biochemical and endocrine responses (Sejian et al. 2018). In tropical environments, multiple stressors are frequently observed as an extreme consistent climate, feed availability, and poor management infrastructure (Aguilar-Jiménez et al. 2019; Saatchi et al. 2021). The elevation of HR in HSRF was considered as a mechanism to dissipate excess body heat, regulate body temperature, and cope with the combined stressors of heat and restricted feed. This pattern in heart rate PC values underscores the sensitivity of cattle to heat stress, especially when coupled with nutritional limitations, highlighting the importance of managing both environmental conditions and feed availability for optimal animal welfare and performance. The multiple stressors could have profound and compounding effects on their overall well-being and performance (Sejian et al. 2018). Multiple stressors impose burden on the animals, diverting energy towards cooling mechanisms and away from productive activities, forcing physical traits (BW, WH, HL) to diminish. These multiple stressors not only pose a threat to the health and welfare of the cattle but also carry economic implications for livestock producers. However, the chance for Bali cattle to consume more nutrients in the HSWF treatment was an endeavor of self-defense to minimize losses owing to heat stress. Admittedly, increased feed intake can generate more metabolic heat (Kadzere et al. 2002). However, our observations indicate that Bali cattle remain within normal physiological ranges for rectal temperature (TR), respiratory rate (RR), and heart rate (HR) (Reece et al. 2015), even under HSWF conditions. Specifically, the average TR for Bali cattle in this study were 38.87 ± 0.05 °C, and average RR was 32.93±0.91 breaths/min, values which are consistent with typical homeostatic limits for the breed. By contrast, low energy intake triggers greater oxidation of glucose and amino acids, potentially leading to hyperinsulinemia (Stewart et al. 2022), a risk factor that appears less pronounced in Bali cattle receiving sufficient nutrients under HSWF. Thus, while the HSWF treatment can result in somewhat higher heat production, Bali cattle’s robust thermoregulatory capacity allows them to effectively dissipate this additional heat, avoiding detrimental effects on metabolic health Bali cattle appear to maintain lower physiological responses compared to more heat-susceptible breeds. For instance, Curacu cattle can experience rectal temperatures above 39.5 °C under heat stress (Abduch et al. 2022), whereas Bali cattle remain around 38.87±0.05 °C (Table 2). Similarly, Angus under heat stress conditions may exhibit heart rates ranging from 50 to 102 bpm (Scharf et al. 2010), while Bali cattle show a notably lower average of 32.93±0.91 bpm (Table 2). This suggests that Bali cattle possess more efficient thermoregulatory mechanisms, reflected in their reduced heart rate and rectal temperature under challenging conditions. Additionally, respiratory rate serves as both a thermal conservation and heat dissipation mechanism. Respiratory rate play as both a thermal conservation and heat dissipation mechanism, poorly adapted phenotypes exhibit a greater reduction in respiratory rate at higher ambient temperatures (Habibu et al. 2019). Furthermore, TR and BW also seemed to play a crucial role in management systems and discriminating against groups of heat-susceptible and heat-tolerant cattle. The TRn can be considered in the analysis since it serves as a crucial indicator of the physiological well-being and health status of the cattle (Reece et al. 2015). This was also proven by the negative correlation of -0.6 between TRn and BW (Figure 4). Cattle under heat stress situations (HSWF and HSRF) have a higher TR, whilst those in TNWF have a higher BW. These variables might provide valuable insights into the overall performance and welfare of cattle under various rearing conditions, making it a pivotal aspect in understanding the effects of different management systems. Rectal temperature as a direct measure of animal's core body temperature serves as crucial biomarker of heat stress (Rejeb et al. 2016). The hypothalamus regulates thermoregulatory responses such as vasodilation, sweating, or panting to dissipate excess heat (Sarubbi et al. 2024). An elevated rectal temperature indicates that the body is struggling to maintain homeostasis and experiencing physiological strain due to excessive heat, making it a real-time indicator of thermal stress severity. Body weight was influenced by multiple stressors of feed scarcity and heat stress. Limited feed availability reduce nutrient intake (Hoffman, et al. 2007) and heat stress disrupts metabolic efficiency (Swanson et al. 2020). Body weight reflects the cumulative effects of various environmental challenges that impact an animal’s overall health and productivity Overall, while advances in animal production technology and genetics have resulted in better yields of animal-based products, it is critical to balance productivity with animal welfare. Prioritizing animal welfare and implementing sustainable animal production practices to ensure that animals are treated welfare and ethically whilst meeting the growing demand for animal-based products. Animals can typically cope with one or even two stressors without severe consequences; however, when multiple stressors accumulate simultaneously, they exert a cumulative effect that significantly compromises the animal’s physiological and behavioural resilience. Management systems of TNWF proves to be the most favourable, offering sufficient feed and minimal stressors. Followed by HSWF due to moderate phenotype responses despite the in single stressor, Bali cattle can still cope with adequate nutritional support, especially Bali cattle have tolerant heat stress. However, HSRF was not recommended to rise Bali cattle due to the cumulative effect of multiple stressors significantly compromises cattle welfare and productivity. Despite rigorous quality control measures, potential biases may still arise due to unequal variance in trait measurements and unbalanced sample sizes across the three management systems. These imbalances could influence classification accuracy and obscure true biological patterns. Future studies could mitigate these issues by increasing sample sizes, employing repeated-measures designs, or integrating advanced statistical approaches to improve robustness and reliability. CONCLUSION This study confirms the effectiveness of multivariate statistical analysis in distinguishing Bali cattle management systems under feed scarcity and heat stress, highlighting rectal temperature (TR) and body weight (BW) as the most discriminating variables. The integrated approach with combining physiological, physical, and behavioural traits have success to capture the multifaceted impacts of restricted feeding and heat stress. The results suggest that cattle under multiple stressors exhibit distinct response patterns, supporting a theoretical explanation that physiological and morphological indicators synergistically reflect adaptive mechanisms. These findings can guide future research to refine trait selection such genetic and multi-omics for deeper insight and develop targeted experimental management strategies. Declarations DATA AVAILABILITY The datasets created in this study are not publicly accessible but can be obtained from the corresponding author upon reasonable request. ACKNOWLEDGEMENTS The authors are grateful for the grant from PMDSU (127/IT3.L1/PN/2021) and BRIN (39/III.5/HK/2022), all the facilities provided by BPTU-HMT Denpasar, the head of BPT-HMT Serading, and the Breeding Village Center Sembalun, West Nusa Tenggara, during the research. FUNDING The authors declare that there was no funding from any agency for the publication of this article. AUTHOR CONTRIBUTIONS All authors conceived and designed the study. Data collection and analysis were conducted by IS, AN, and AF. IS and AF performed the research. IS and AF design and write-up of the manuscript. RRN and JJ contributed to conceived method and reviewed the paper. ETHICS DECLARATIONS ANIMAL WELFARE STATEMENT The manuscript does not contain clinical studies or patient data. 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Gerrits. 2023. “Effect of Animal Activity and Air Temperature on Heat Production, Heart Rate, and Oxygen Pulse in Lactating Holstein Cows.” Journal of Dairy Science 106 (2): 1475–87. https://doi.org/10.3168/jds.2022-22257. Terry, Stephanie A., John A. Basarab, Le Luo Guan, and Tim A. McAllister. 2021. “Strategies to Improve the Efficiency of Beef Cattle Production.” Canadian Journal of Animal Science 101 (1): 1–19. https://doi.org/10.1139/cjas-2020-0022. Tiezzi, F., C. Maltecca, A. Cecchinato, M. Penasa, and G. Bittante. 2013. “Thin and Fat Cows, and the Nonlinear Genetic Relationship between Body Condition Score and Fertility.” Journal of Dairy Science 96 (10): 6730–41. https://doi.org/10.3168/jds.2013-6863. Tüfekci, Hacer, and Veerasamy Sejian. 2023. “Stress Factors and Their Effects on Productivity in Sheep.” Animals 13 (17): 2769. https://doi.org/10.3390/ani13172769. Wheelock, J. B., R. P. Rhoads, M. J. VanBaale, S. R. Sanders, and L. H. Baumgard. 2010. “Effects of Heat Stress on Energetic Metabolism in Lactating Holstein Cows1.” Journal of Dairy Science 93 (2): 644–55. https://doi.org/10.3168/jds.2009-2295. Widyas, Nuzul, Tri Satya Mastuti Widi, Sigit Prastowo, Ika Sumantri, Ben J. Hayes, and Heather M. Burrow. 2022. “Promoting Sustainable Utilization and Genetic Improvement of Indonesian Local Beef Cattle Breeds: A Review.” Agriculture 12 (10): 1566. https://doi.org/10.3390/agriculture12101566. Yin, Tong, and Sven König. 2018. “Genetic Parameters for Body Weight from Birth to Calving and Associations between Weights with Test-Day, Health, and Female Fertility Traits.” Journal of Dairy Science 101 (3): 2158–70. https://doi.org/10.3168/jds.2017-13835. Tables Table 1 Microclimate Status and feeding management in each observation environment Microclimate HSRF HSWF TNWF Statistic Morn. Noon Morn. Noon Morn. Noon SE CV% Ta (°C) 28.37 34.10 27.02 30.89 23.97 26.70 0.62 14.05 RH (%) 58.33 40.33 65.22 54.55 89.67 77.50 2.17 22.4 THI 76.51 81.45 76.22 80.04 74.14 77.26 0.67 5.56 Shelter NA NA Available Water Drink Ad libitum Ad libitum Ad libitum Roughage (%BW) 5% 8% + roughage 10% Concentrate (%BB) - - 1-1,5% Table 2 Statistical descriptive of physical and Physiological of Bali cattle Traits Unit Mean SD CV SE Docility score morning (DSm) 1.76 0.81 0.46 0.09 Docility score afternoon (DSn) 1.85 0.76 0.41 0.08 Respiration rate morning (RRm) freq/min 28.17 5.45 0.19 0.60 Respiration rate afternoon (RRn) freq/min 32.93 8.24 0.25 0.91 Heart rate morning (HRm) pulse/min 63.60 16.96 0.27 1.86 Heart rate afternoon (HRn) pulse/min 71.30 21.05 0.30 2.31 Rectal temperature morning (TRm) C 38.49 0.53 0.01 0.06 Rectal temperature afternoon (TRn) C 38.87 0.41 0.01 0.05 Blood glucose level (Glukosa) mg/dL 54.39 11.40 0.21 1.32 Body condition score (BCS) fat = 4 2.96 0.92 0.31 0.10 Body darkness (BD) dark = 4 2.88 1.18 0.41 0.13 Body weight (BW) kg 180.71 58.78 0.33 6.49 Wither height (WH) cm 104.84 11.51 0.11 1.27 Body length (BL) cm 104.85 8.69 0.08 0.96 Chest circumference (CC) cm 142.44 21.65 0.15 2.39 Skin thickness (ST) mm 11.91 4.76 0.40 0.54 Scrotum circumference (SC) cm 23.29 6.42 0.28 0.84 Table 3 Eigenvalue of PCA and CDA dimension Dimension Multivariate Eigenvalue Variance percent % Cumulative % Dim.1 PCA 6.37 39.79 39.79 Dim.2 PCA 2.07 12.91 52.69 Dim.3 PCA 1.60 9.98 62.68 Dim.4 PCA 1.10 6.86 69.54 Dim.5 PCA 0.91 5.69 75.23 Dim.6 PCA 0.80 5.03 80.26 Dim.7 PCA 0.74 4.63 84.89 Dim.8 PCA 0.63 3.94 88.83 Dim.9 PCA 0.51 3.19 92.02 Dim.10 PCA 0.35 2.21 94.23 Dim.11 PCA 0.27 1.71 95.94 Dim.12 PCA 0.22 1.38 97.33 Dim.13 PCA 0.18 1.09 98.42 Dim.14 PCA 0.12 0.73 99.15 Dim.15 PCA 0.09 0.57 99.72 Dim.16 PCA 0.04 0.28 100.00 Dim.1 CDA 263.27 66.68 66.68 Dim.2 CDA 131.56 33.32 100.00 Table 4 Variable contribution of the total variation Traits Traits set Dim.1 Dim.2 Dim.3 DSm Behavior 0.36 1.11 19.43 DSn Behavior 0.00 2.16 31.26 RRm Physiological 2.02 18.35 4.17 RRn Physiological 1.05 18.05 7.73 TRm Physiological 2.46 20.95 0.02 TRn Physiological 6.15 16.57 0.12 HRm Physiological 8.14 1.16 2.46 HRn Physiological 7.01 0.91 4.44 Glucose Physiological 1.88 1.79 11.19 BCS Physical 10.43 2.38 0.43 BD Physical 8.50 2.56 0.19 BW Physical 13.02 0.14 2.32 WH Physical 11.75 2.04 3.13 BL Physical 11.77 0.20 4.61 CC Physical 7.21 0.38 8.42 ST Physical 8.25 11.26 0.07 Supplementary Files Supplementaryfile.docx Cite Share Download PDF Status: Published Journal Publication published 11 Jun, 2025 Read the published version in Tropical Animal Health and Production → Version 1 posted Reviewers agreed at journal 09 Apr, 2025 Reviewers invited by journal 08 Apr, 2025 Editor assigned by journal 08 Apr, 2025 First submitted to journal 25 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4904288","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":440358257,"identity":"89865e63-8e7c-4d98-98b1-718ba9237a0b","order_by":0,"name":"Ikhsan Suhendro","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-7339-5707","institution":"National Research and Innovation Agency","correspondingAuthor":true,"prefix":"","firstName":"Ikhsan","middleName":"","lastName":"Suhendro","suffix":""},{"id":440358258,"identity":"652c7c59-b5bf-45c5-8981-916df8fa6816","order_by":1,"name":"Ronny Rachman Noor","email":"","orcid":"","institution":"Bogor Agricultural University: Institut Pertanian Bogor","correspondingAuthor":false,"prefix":"","firstName":"Ronny","middleName":"Rachman","lastName":"Noor","suffix":""},{"id":440358259,"identity":"fe65df5f-c521-49a3-851f-e2c5bfeff7e5","order_by":2,"name":"Jakaria Jakaria","email":"","orcid":"","institution":"Bogor Agricultural University: Institut Pertanian Bogor","correspondingAuthor":false,"prefix":"","firstName":"Jakaria","middleName":"","lastName":"Jakaria","suffix":""},{"id":440358260,"identity":"b9abd2bb-cf2b-45aa-a88f-1bca5e7ca0f4","order_by":3,"name":"Aeni Nurlatifah","email":"","orcid":"","institution":"Gadjah Mada University: Universitas Gadjah Mada","correspondingAuthor":false,"prefix":"","firstName":"Aeni","middleName":"","lastName":"Nurlatifah","suffix":""},{"id":440358261,"identity":"10a34440-4f7d-423c-b7a0-b16629f94267","order_by":4,"name":"Ahmad Furqon","email":"","orcid":"","institution":"National Research and Innovation Agency","correspondingAuthor":false,"prefix":"","firstName":"Ahmad","middleName":"","lastName":"Furqon","suffix":""}],"badges":[],"createdAt":"2024-08-13 05:41:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4904288/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4904288/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11250-025-04508-2","type":"published","date":"2025-06-11T15:58:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80326300,"identity":"0c69b6a4-7bd8-4c3f-9732-568744077cb3","added_by":"auto","created_at":"2025-04-10 14:24:16","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":23077,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal Component Analysis (PCA) with 2D scatter plot showing PC1 vs. PC2, explaining 39.8% + 12.9% of the variance. 3D PCA plot (provided in supplementary materials) offers additional separation insights among breeds\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4904288/v1/685b5a7f734aaedd77c5d49f.jpg"},{"id":80326677,"identity":"8940c0fd-3c4d-47ca-ac3a-7bcef9cfa3ef","added_by":"auto","created_at":"2025-04-10 14:32:16","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":17489,"visible":true,"origin":"","legend":"\u003cp\u003eDiscriminant analysis of canonical variables in three groups of rearing systems. The Linear Discriminant Analysis (LDA) plot illustrates the separation of Bali cattle in different management (HSRF, HSWF, TNWF) based on discriminant functions LD1 and LD2. The distinct clustering of groups indicates clear breed differentiation.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4904288/v1/85b41167c0f7ef4df91752ec.jpg"},{"id":80325511,"identity":"8d250b00-e3a5-49d6-bf60-e094f518a73e","added_by":"auto","created_at":"2025-04-10 14:16:16","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":39319,"visible":true,"origin":"","legend":"\u003cp\u003eHierarchical clustering for the group treatments from the sample performances. Hierarchical clustering dendrogram (Ward's method) showing the relationships between TNWF, HSRF, and HSWF groups. Euclidean distances indicate the degree of separation, with TNWF exhibiting the greatest differentiation (TNWF-HSRF: 5.95, TNWF-HSWF: 4.24, HSRF-HSWF: 3.40). The clustering structure supports the distinct grouping observed in CDA and LDA analyses.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4904288/v1/181bc93823ae8fe8cc2b5aba.jpg"},{"id":80326301,"identity":"74729474-22e3-4414-882f-773288b72c02","added_by":"auto","created_at":"2025-04-10 14:24:16","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":70106,"visible":true,"origin":"","legend":"\u003cp\u003ePearson correlation plot of selected traits, illustrating clustering patterns based on hierarchical clustering (Ward.D2 method). Three distinct groups of variables are evident, corresponding to physical traits, physiological traits, and behavioral traits, with the exception of heart rate and chest circumference, which exhibit an inverse association. The color gradient represents correlation strength, ranging from negative (red) to positive (blue).\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4904288/v1/6fc2c47f07ffd295e6959aee.jpg"},{"id":84726529,"identity":"b28610d5-a9ce-4d3b-8ba6-f5f7a0cd5de9","added_by":"auto","created_at":"2025-06-16 16:06:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":810589,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4904288/v1/704e10c8-ca38-4a63-b644-a9ceeee27eb8.pdf"},{"id":80325518,"identity":"55a1a9c2-2881-437c-95b4-f5c6709ca521","added_by":"auto","created_at":"2025-04-10 14:16:17","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":14273,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-4904288/v1/acc809d781744e8892c6afe9.docx"}],"financialInterests":"","formattedTitle":"Discriminating Heat Stress and Feed Scarcity in Bali Cattle Using Multivariate Trait Analysis","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe animal production system has undergone a radical transformation in recent decades, with significant advancements in technologies and genetics.\u0026nbsp;Innovations such as precision livestock farming (PLF), automated milking and smart feeding systems, drones and remote sensing, genetic advancements, and nutrigenomics have significantly improved cattle management, productivity, and efficiency. These advancements have led to higher livestock production yields of milk, meat, and eggs, which has helped meet the growing demand for animal-based products (Smith et al. 2013).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, environmental challenges, with heat stress and feed scarcity being the most significant factors, impose high adaptation costs and constrain performance, particularly in high-performing cattle that have undergone intensive genetic selection and are highly dependent on technological management. Feed scarcity and heat stress interact negatively, compromising Bali cattle performance. Feed scarcity limits energy intake, while heat stress increases metabolic demands for thermoregulation, exacerbating energy and metabolic imbalances (Baumgard \u0026amp; Rhoads, 2012). This combination can reduce body weight, feed efficiency, and increase disease risk (Sejian et al., 2018). The prevailing heat stress conditions were marked by high temperatures and humidity, representing a great challenge, especially for exotic breeds of cattle. These adverse led to alterations in the animals' behavioral, endocrine, and physiological systems (Sejian et al. 2018). This causes problems with management and nutrition, which often come with high costs. Therefore, it is necessary to select local breeds with thermotolerance ability.\u003c/p\u003e\n\u003cp\u003eBali cattle, native Indonesian cattle, are known for their tolerance to heat stress due to their ability to adapt to the hot and humid climate (Suhendro et al. 2022; Freitas et al. 2021), making them a valuable breed for tropical areas. Over 2.95 million Bali cattle, or almost 26% of all cattle in Indonesia, are in the Eastern Islands and South Sumatra, with most of them being in these regions. Over the past few decades, the number of cattle in Bali has gradually increased at an annual rate of roughly 3% per year (Talib et al. 2003). Due to their number and hardiness, Bali cattle are often used as the foundation for crossbreeding programs (Widyas et al. 2022).\u003c/p\u003e\n\u003cp\u003eCattle breeding and management are critical components in agricultural practices with their ability to tolerate the heat stress being one of the key factors in determining the efficacy of cattle farming (Terry et al. 2021). It is essential to concentrate on breeding and selecting cattle with capabilities related to heat tolerance amid the shifting climate trends and rising temperatures. To evaluate the heat tolerance ability of cattle, it is essential to have reliable measurement and calculation methods. Multivariate statistical analysis, which examines multiple variables simultaneously to identify patterns and relationships, offers a powerful approach to handling complex biological datasets. This study aimed to assess the effectiveness and reliability of multivariate techniques, particularly principal component analysis (PCA) and discriminant analysis (LDA and CDA), in identifying key physical and physiological traits that distinguish Bali cattle under different management systems. The improvement of livestock systems should focus on predictive models and involve the key traits for resilience and developing cost-effective assessment tools to support cattle production in tropical environments.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003ch2\u003eAnimal ethics and sample collection\u003c/h2\u003e\n\u003cp\u003eThe animal protocol followed the Animal Ethics Committee of Udayana University, Denpasar, Indonesia (Code ID: B/184/un14.2.9/pt.01.04/2021). Eighty-six sick less male Bali cattle collected in different experiments were used. Fifty were obtained from the Denpasar Breeding Centre (BPTU HPT Denpasar) with good feed (TNWF: 21 bulls with neutral temperature and well fed; HSWF: 32 bulls heat stress and well fed, pasture). The other 30 came from Serading with heat stress conditions and feed scarcity (HSRF). TNWF represents a no-stressor condition, where cattle are provided with adequate feed and are not exposed to external stressors. HSWF represents a single-stressor condition, where cattle experience heat stress but still receive sufficient feed intake. HSRF represents a multiple-stressor condition, where cattle are exposed to both heat stress and feed scarcity, making it the most challenging environment. These classifications allow for a structured analysis of how Bali cattle respond physiologically and behaviorally under varying degrees of stress. The rearing system has been in place and patented for a long time, and this condition represents natural conditions in tropical regions of Indonesia, variations in heat stress, and lack of feed.\u003c/p\u003e\n\u003cp\u003eMicroclimate status and feeding management varied across different conditions (Table 1). In heat stress condition, cattle were not providing with shelter. They exhibit heat stress especially at the noon when air temperature and solar radiation in highest condition. In well feed condition, cattle were provided with a nutritionally adequate diet supporting optimal growth. Conversely, feeding in HSRF was restricted due to environmental and management constraints. Cattle in this condition were only provided with approximately 5% of their body weight in feed, which was chopped and offered inconsistently, varying with seasonal availability.\u003c/p\u003e\n\u003ch2\u003eTraits measurements\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eTraits measurements were carried out in a squeeze chute with the cattle sorted and queued in an alley. Physical and physiological traits were measured by professional personnel trained for animal collection and measurements. The nine physical measurements (Table 2) were: body weight (BW), wither height (WH), body length (BL), chest circumference (CC), skin thickness (ST), body darkness (BD), hair length (HL), blood glucose level (BGL), and body condition score (BCS). The four physiological measurements (Table 2) were rectal temperature (TR), respiration rate (RR), heart rate (HR), and heat tolerance coefficient (HTC), these traits were collected both in the morning and afternoon and then denoted as a subscript in the variables.\u003c/p\u003e\n\u003cp\u003eThe dataset consisted of samples collected from naturally occurring populations with different management systems. However, the sample sizes and age distributions were not balanced across groups. Despite these variations, all sampled individuals were males, clinically healthy, and above two years old. Due to these inherent imbalances, statistical analyses were performed to check data balance and implement appropriate preprocessing steps for sorting and quality control.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Sorting and Quality Control\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNormality was evaluated through skewness analysis with the skewness value outside the range of -1 to +1 were transformed using inverse transformation. Outliers were detected and managed using Winsorization or trimming for extreme values beyond ±3 Z-score. Traits with unequal variance (heteroscedasticity with Levene’s test \u003cem\u003ep\u003c/em\u003e-value\u0026gt;0.05) were standardized using Robust Scaling transformation to ensure comparability across variables. Breusch-Pagan test was used to check the traits with heteroscedasticity in regression model, the selected traits were adjusted using Weighting Least Square (WLS). These preprocessing steps ensured that statistical models could be applied effectively without distortion due to sample size imbalance. All data preprocessing and transformations were performed in RStudio (version X.X) using the dplyr, MASS, lmtest, and car packages.\u003c/p\u003e\n\u003ch2\u003eDimensionality Reduction and Group Differentiation\u003c/h2\u003e\n\u003cp\u003eIBM SPSS Statistics 25.0 (IBM Corp, Armonk, NY) and RStudio (RStudio, PBC, Boston, MA) were used to generate multivariate analysis. The procedures included Pearson correlation between physiological and physical traits, principal component analysis (PCA) to understand the best variables and scattered total data variation, and discriminant analysis (DA) to visualize the form of clusters and distances between the groups. The optimal number of principal components (PCs) was determined based on scree plot visualization and cross-validation using the caret package. Loadings from the PCA were then examined to identify the most contribution traits contributing to variation within the dataset.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePCA was performed to reduce dimensionality and identify key traits that explain the most variation in the dataset. The selected traits were then used in Discriminant Analysis to maximize the separation between management systems (TNWF, HSWF, HSRF). This two-step approach ensures that DA is applied to the most informative variables while reducing noise and redundancy. Linear Discriminant Analysis (LDA) was performed with a five-fold approach using the trainControl() function from the caret package to ensure model robustness,. The model’s classification accuracy, Euclidean distances between groups, and canonical correlations were assessed to determine the degree of separation among groups. The canonical scores were used to hierarchical clustering and then visualized as a dendrogram.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel Evaluation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe final model’s performance was assessed using multiple validation metrics, including cross-validation for PCA, effect sizes based on canonical correlations, and Euclidean distances, and classification accuracy derived from a confusion matrix. This workflow was designed to ensure the robustness and reliability of statistical analyses in differentiating heat tolerance traits in Bali cattle\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eTable 2 showed the physiological and physical traits of experimental animals of Bali cattle samples. Of the 27 initial variable measurements, 17 were retained in the calculation. The CDA, based on the selected variables, significantly discriminated against the three groups (Hotelling’s test \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001) by extracting three canonical functions whose DIMs were reported in Table 3.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDim1 and Dim2 scatter plots of PCA (Figure 1) explained the total variation of the data, it also showed a clear separation between the groups. Dim1 separated the heat stress and scarce feed of TNWF and HSRF, whereas the HSWF was separated from others by Dim2. There were also indicated a clear grouping of vector traits corresponding to specific management groups. The traits of BW and BL were oriented towards TNWF with positioned oppositely to HSRF. Meanwhile, traits of TR and RR exhibit stronger vector dominance towards HSWF. The significant contributions of each dimension are further detailed in Table 4.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrincipal Component Analysis (PCA) was performed with cross-validation to determine the optimal number of components while ensuring stability in dimensionality reduction. The first two principal components (PC1 and PC2) explained a total of 52.69% of the variance, which was insufficient to capture the full data structure. Including a third principal component (PC3) increased the total explained variance to 62.68%, providing a better representation. Cross-validation confirmed the robustness of this selection by minimizing reconstruction error. Due to the limitations of two-dimensional visualization, the 3D PCA plot is provided as supplementary material.\u003c/p\u003e\n\u003cp\u003eThe LD1 versus LD2 scatter plot (Figure 2) showed a clear separation between the three heat stress conditions. The distances between group centroids reflect phenotypic or genetic divergence, with HSRF and HSWF showing closer proximity compared to TNWF, which is more isolated. This suggests TNWF may have unique traits distinguishing it from the other two breeds. The axes' scales (LD1: ±2.5; LD2: ±5.0) highlight LD2's greater discriminatory power in this analysis. The Linear Discriminant Analysis (LDA) model demonstrated a high classification accuracy of 93.03%, indicating strong differentiation among the three groups (HSRF, HSWF, TNWF). The Kappa statistics (0.8927) further confirmed excellent agreement between predicted and actual classifications. Cross-validation using a 5-fold approach showed consistent accuracy across different data subsets, ensuring the model's robustness and reliability. These results highlight the effectiveness of discriminant analysis in distinguishing group differences based on the selected predictors.\u003c/p\u003e\n\u003cp\u003eThere was also hierarchical clustering of three different rearing groups (Figure 3) which were accordance with the origin of the treatment groups. The Euclidean distance revealed the degree of separation between the three groups in the discriminant function space. The largest distance was observed between TNWF and HSRF (5.95), indicating that TNWF exhibited the most distinct characteristics compared to the other groups. Similarly, the distance between TNWF and HSWF was 4.24, suggesting a notable differentiation. In contrast, HSRF and HSWF had the smallest Euclidean distance (3.40), implying a closer similarity between these two groups. These findings align with the results from canonical discriminant analysis (CDA) and linear discriminant analysis (LDA), confirming that TNWF is the most divergent group while HSRF and HSWF share more overlapping characteristics. The Euclidean distance values further support the robustness of the discriminant model in effectively separating the groups based on the selected traits.\u003c/p\u003e\n\u003cp\u003eThe correlation analysis (Figure 4) reveals a clear clustering of variables into three distinct groups, separating physical, physiological, and behavioral traits. This clustering pattern is consistent with the expectation that traits within the same category exhibit stronger internal correlations due to their shared biological or functional basis. However, an exception is observed for heart rate and chest circumference, which display an inverse correlation, suggesting potential compensatory physiological mechanisms or differences in metabolic regulation across individuals. This finding aligns with previous studies that highlights the interplay between cardiovascular response and body conformation in livestock. The hierarchical clustering method further supports these groupings, emphasizing the structured relationships among the measured traits.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eMultivariate analyses have been widely applied in previous studies (Moindjie et al. 2023) to evaluate variation across breeds, experimental treatments, rearing management systems, and heat stress conditions (McManus et al. 2011; Acciaro et al. 2020). However, most studies have focused on isolated factors, such as thermoregulatory responses (McManus et al. 2011) or behavioural adaptations under specific stressors (Acciaro et al. 2020). In contrast, our study integrates physiological, physical, and behavioural parameters to differentiate three distinct management conditions (TNWF, HSWF, and HSRF) that represent no stressor, a single stressor, and multiple stressors, respectively. The present study extends this approach by utilizing multivariate analyses to simultaneously evaluate multi traits, aiming to determine which indicators are most effective in differentiating between rearing management conditions associated with varying levels of stress.\u003c/p\u003e\n\u003cp\u003eThere are some variables that tend to respond stronger on specific observational clusters. The identified of most contribution traits such as BW, WH, BL, TR, and RR can guide targeted interventions (Table 4). The comfort treatment (TNWF) tent to gain weight and physique (BW, WH, and BL) of the animals than either the single or multiple stressor treatments. Meanwhile, non-ideal BW in multiple HSRF stressors is interpreted as a trade-off to reduce the stress load by reducing its growth. BW (+13.02) as the highest variable contribution in Dim.1 was suggested to be the most affected variable due to well-feed management. Body weight (BW) serves as a crucial indicator for various aspects such as lifespan, fertility, survival capability, and overall health status (Tiezzi et al. 2013; Yin and König 2018). The weight of cattle is influenced by factors including their activity levels, diets, environment, and social interactions (Mwangi et al. 2019). When the two indicators of diet and environment are not achieved, cattle tend to have less than ideal growth.\u003c/p\u003e\n\u003cp\u003eOur findings suggest that TNWF and HSWF, which provide relatively\u0026nbsp;adequate feeding and environmental conditions, lead to improved or moderate growth performance and lower stress responses. Conversely, HSRF, characterized by minimal and inconsistent feeding strategies, is reflected in traits indicating higher stress and lower weight gain.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe single stressor (HSWF) has a moderate body weight, but surprisingly their RR and TR physiology is the highest. The moderate body weight in HSWF compared to TNWF affirmed the reports that the drop in production is caused not just by a decrease in dry matter intake due to heat stress but may directly cause by heat stress itself (Collier et al. 2012).\u0026nbsp;This higher of RR and TR compared to the HSRF might be related to the amount of heat produced from consumption activity. Nutrient consumption leads increased heat production during digestion, which exacerbates heat dissipation under heat stress conditions\u0026nbsp;(Case et al. 2011). This raised metabolic activity, coupled with environmental heat, intensifies the body's thermoregulatory mechanisms, leading to greater heat dissipation issues\u0026nbsp;(Pawar et al. 2016; Onagbesan et al. 2023). The previous studies also reported that elevated air temperature and energy consumption affected the physiology which could lead to the increase of the rectal temperature, respiration rate, and heart rate\u0026nbsp;(Talmón et al. 2023; Tüfekci and Sejian 2023).\u003c/p\u003e\n\u003cp\u003eThe multiple stressor (HSRF) has the highest HR and lowest body weight. This was in line with a previous study which found multiple stressors significantly (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.01) affected body weight, physiological response, and biochemical and endocrine responses (Sejian et al. 2018). In tropical environments, multiple stressors are frequently observed as an extreme consistent climate, feed availability, and poor management infrastructure (Aguilar-Jiménez et al. 2019; Saatchi et al. 2021). The elevation of HR in HSRF was considered as a mechanism to dissipate excess body heat, regulate body temperature, and cope with the combined stressors of heat and restricted feed. This pattern in heart rate PC values underscores the sensitivity of cattle to heat stress, especially when coupled with nutritional limitations, highlighting the importance of managing both environmental conditions and feed availability for optimal animal welfare and performance. The multiple stressors could have profound and compounding effects on their overall well-being and performance (Sejian et al. 2018). Multiple stressors impose burden on the animals, diverting energy towards cooling mechanisms and away from productive activities, forcing physical traits (BW, WH, HL) to diminish. These multiple stressors not only pose a threat to the health and welfare of the cattle but also carry economic implications for livestock producers.\u003c/p\u003e\n\u003cp\u003eHowever, the chance for Bali cattle to consume more nutrients in the HSWF treatment was an endeavor of self-defense to minimize losses owing to heat stress. Admittedly, increased feed intake can generate more metabolic heat (Kadzere et al. 2002). However, our observations indicate that Bali cattle remain within normal physiological ranges for rectal temperature (TR), respiratory rate (RR), and heart rate (HR) (Reece et al. 2015), even under HSWF conditions. Specifically, the average TR for Bali cattle in this study were 38.87 ± 0.05 °C, and average RR was 32.93±0.91 breaths/min, values which are consistent with typical homeostatic limits for the breed. By contrast, low energy intake triggers greater oxidation of glucose and amino acids, potentially leading to hyperinsulinemia (Stewart et al. 2022), a risk factor that appears less pronounced in Bali cattle receiving sufficient nutrients under HSWF. Thus, while the HSWF treatment can result in somewhat higher heat production, Bali cattle’s robust thermoregulatory capacity allows them to effectively dissipate this additional heat, avoiding detrimental effects on metabolic health\u003c/p\u003e\n\u003cp\u003eBali cattle appear to maintain lower physiological responses compared to more heat-susceptible breeds. For instance, Curacu cattle can experience rectal temperatures above 39.5 °C under heat stress (Abduch et al. 2022), whereas Bali cattle remain around 38.87±0.05 °C (Table 2). Similarly, Angus under heat stress conditions may exhibit heart rates ranging from 50 to 102 bpm (Scharf et al. 2010), while Bali cattle show a notably lower average of 32.93±0.91 bpm (Table 2). This suggests that Bali cattle possess more efficient thermoregulatory mechanisms, reflected in their reduced heart rate and rectal temperature under challenging conditions. Additionally, respiratory rate serves as both a thermal conservation and heat dissipation mechanism. Respiratory rate play as both a thermal conservation and heat dissipation mechanism, poorly adapted phenotypes exhibit a greater reduction in respiratory rate at higher ambient temperatures (Habibu et al. 2019).\u003c/p\u003e\n\u003cp\u003eFurthermore, TR and BW also seemed to play a crucial role in management systems and discriminating against groups of heat-susceptible and heat-tolerant cattle. The TRn can be considered in the analysis since it serves as a crucial indicator of the physiological well-being and health status of the cattle (Reece et al. 2015). This was also proven by the negative correlation of -0.6 between TRn and BW (Figure 4). Cattle under heat stress situations (HSWF and HSRF) have a higher TR, whilst those in TNWF have a higher BW. These variables might provide valuable insights into the overall performance and welfare of cattle under various rearing conditions, making it a pivotal aspect in understanding the effects of different management systems.\u003c/p\u003e\n\u003cp\u003eRectal temperature as a direct measure of animal's core body temperature serves as crucial biomarker of heat stress (Rejeb et al. 2016). The hypothalamus regulates thermoregulatory responses such as vasodilation, sweating, or panting to dissipate excess heat (Sarubbi et al. 2024). An elevated rectal temperature indicates that the body is struggling to maintain homeostasis and experiencing physiological strain due to excessive heat, making it a real-time indicator of thermal stress severity. Body weight was influenced by multiple stressors of feed scarcity and heat stress. Limited feed availability reduce nutrient intake (Hoffman, et al. 2007) and heat stress disrupts metabolic efficiency (Swanson et al. 2020). Body weight reflects the cumulative effects of various environmental challenges that impact an animal’s overall health and productivity\u003c/p\u003e\n\u003cp\u003eOverall, while advances in animal production technology and genetics have resulted in better yields of animal-based products, it is critical to balance productivity with animal welfare. Prioritizing animal welfare and implementing sustainable animal production practices to ensure that animals are treated welfare and ethically whilst meeting the growing demand for animal-based products. Animals can typically cope with one or even two stressors without severe consequences; however, when multiple stressors accumulate simultaneously, they exert a cumulative effect that significantly compromises the animal’s physiological and behavioural resilience. Management systems of TNWF proves to be the most favourable, offering sufficient feed and minimal stressors. Followed by HSWF due to moderate phenotype responses despite the in single stressor, Bali cattle can still cope with adequate nutritional support, especially Bali cattle have tolerant heat stress. However, HSRF was not recommended to rise Bali cattle due to the cumulative effect of multiple stressors significantly compromises cattle welfare and productivity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDespite rigorous quality control measures, potential biases may still arise due to unequal variance in trait measurements and unbalanced sample sizes across the three management systems. These imbalances could influence classification accuracy and obscure true biological patterns. Future studies could mitigate these issues by increasing sample sizes, employing repeated-measures designs, or integrating advanced statistical approaches to improve robustness and reliability.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study confirms the effectiveness of multivariate statistical analysis in distinguishing Bali cattle management systems under feed scarcity and heat stress, highlighting rectal temperature (TR) and body weight (BW) as the most discriminating variables. The integrated approach with combining physiological, physical, and behavioural traits have success to capture the multifaceted impacts of restricted feeding and heat stress. The results suggest that cattle under multiple stressors exhibit distinct response patterns, supporting a theoretical explanation that physiological and morphological indicators synergistically reflect adaptive mechanisms. These findings can guide future research to refine trait selection such genetic and multi-omics for deeper insight and develop targeted experimental management strategies.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eDATA AVAILABILITY\u003c/p\u003e\n\u003cp\u003eThe datasets created in this study are not publicly accessible but can be obtained from the corresponding author upon reasonable request.\u003c/p\u003e\u003cp\u003eACKNOWLEDGEMENTS\u003c/p\u003e\n\u003cp\u003eThe authors are grateful for the grant from PMDSU (127/IT3.L1/PN/2021) and BRIN (39/III.5/HK/2022), all the facilities provided by BPTU-HMT Denpasar, the head of BPT-HMT Serading, and the Breeding Village Center Sembalun, West Nusa Tenggara, during the research.\u003c/p\u003e\n\u003cp\u003eFUNDING\u003c/p\u003e\n\u003cp\u003eThe authors declare that there was no funding from any agency for the publication of this article.\u003c/p\u003e\n\u003cp\u003eAUTHOR CONTRIBUTIONS\u003c/p\u003e\n\u003cp\u003eAll authors conceived and designed the study. Data collection and analysis were conducted by IS, AN, and AF. IS and AF performed the research. IS and AF design and write-up of the manuscript. RRN and JJ contributed to conceived method and reviewed the paper.\u003c/p\u003e\n\u003cp\u003eETHICS DECLARATIONS\u003c/p\u003e\n\u003cp\u003eANIMAL WELFARE STATEMENT\u003c/p\u003e\n\u003cp\u003eThe manuscript does not contain clinical studies or patient data.\u003c/p\u003e\n\u003cp\u003eCONFLICT OF INTEREST\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbduch, Natalya G., Bianca V. Pires, Luana L. Souza, Rogerio R. Vicentini, Lenira El Faro Zadra, Breno O. Fragomeni, Rafael M. O. Silva, Fernando Baldi, Claudia C. P. Paz, and Nedenia B. 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Spiers. 2010. \u0026ldquo;Evaluation of Physiological and Blood Serum Differences in Heat-Tolerant (Romosinuano) and Heat-Susceptible (Angus) Bos Taurus Cattle during Controlled Heat Challenge.\u0026rdquo; \u003cem\u003eJournal of Animal Science\u003c/em\u003e 88 (7): 2321\u0026ndash;36. https://doi.org/10.2527/jas.2009-2551.\u003c/li\u003e\n\u003cli\u003eSejian, V., R. Bhatta, J. B. Gaughan, F. R. Dunshea, and N. Lacetera. 2018. \u0026ldquo;Review: Adaptation of Animals to Heat Stress.\u0026rdquo; \u003cem\u003eAnimal\u003c/em\u003e 12 (January):s431\u0026ndash;44. https://doi.org/10.1017/S1751731118001945.\u003c/li\u003e\n\u003cli\u003eSmith, Jimmy, Keith Sones, Delia Grace, Susan MacMillan, Shirley Tarawali, and Mario Herrero. 2013. \u0026ldquo;Beyond Milk, Meat, and Eggs: Role of Livestock in Food and Nutrition Security.\u0026rdquo; \u003cem\u003eAnimal Frontiers\u003c/em\u003e 3 (1): 6\u0026ndash;13. https://doi.org/10.2527/af.2013-0002.\u003c/li\u003e\n\u003cli\u003eStewart, J. W., A. G. Arneson, M. K. H. Byrd, V. M. Negron-Perez, H. M. Newberne, R. R. White, S. W. El-Kadi, A. D. Ealy, R. P. Rhoads, and M. L. Rhoads. 2022. \u0026ldquo;Comparison of Production-Related Responses to Hyperinsulinemia and Hypoglycemia Induced by Clamp Procedures or Heat Stress of Lactating Dairy Cattle.\u0026rdquo; \u003cem\u003eJournal of Dairy Science\u003c/em\u003e 105 (10): 8439\u0026ndash;53. https://doi.org/10.3168/jds.2022-21922.\u003c/li\u003e\n\u003cli\u003eSuhendro, I., J. Jakaria, R. Priyanto, W. Manalu, and R. R. Noor. 2022. \u0026ldquo;The Association of Single Nucleotide Polymorphism -69T\u0026gt;G HSPA1A Gene with Bali Cattle Heat Tolerance.\u0026rdquo; \u003cem\u003eTropical Animal Science Journal\u003c/em\u003e 45 (4): 429\u0026ndash;35. https://doi.org/10.5398/tasj.2022.45.4.429.\u003c/li\u003e\n\u003cli\u003eSwanson, Rebecca M, Richard G Tait, Beth M Galles, Erin M Duffy, Ty B Schmidt, Jessica L Petersen, and Dustin T Yates. 2020. \u0026ldquo;Heat Stress-Induced Deficits in Growth, Metabolic Efficiency, and Cardiovascular Function Coincided with Chronic Systemic Inflammation and Hypercatecholaminemia in Ractopamine-Supplemented Feedlot Lambs.\u0026rdquo; \u003cem\u003eJournal of Animal Science\u003c/em\u003e 98 (6): skaa168. https://doi.org/10.1093/jas/skaa168.\u003c/li\u003e\n\u003cli\u003eTalib, C, K Entwistle, A Siregar, S Budiarti-Turner, and D Lindsay. 2003. \u0026ldquo;Survey of Population and Production Dynamics of Bali Cattle and Existing Breeding Programs in Indonesia.\u0026rdquo; \u003cem\u003eStrategies to Improve Bali Cattle in Eastern Indonesia\u003c/em\u003e, no. 110.\u003c/li\u003e\n\u003cli\u003eTalm\u0026oacute;n, Daniel, Mengting Zhou, Mariana Carriquiry, Andre J. A. Aarnink, and Walter J. J. Gerrits. 2023. \u0026ldquo;Effect of Animal Activity and Air Temperature on Heat Production, Heart Rate, and Oxygen Pulse in Lactating Holstein Cows.\u0026rdquo; \u003cem\u003eJournal of Dairy Science\u003c/em\u003e 106 (2): 1475\u0026ndash;87. https://doi.org/10.3168/jds.2022-22257.\u003c/li\u003e\n\u003cli\u003eTerry, Stephanie A., John A. Basarab, Le Luo Guan, and Tim A. McAllister. 2021. \u0026ldquo;Strategies to Improve the Efficiency of Beef Cattle Production.\u0026rdquo; \u003cem\u003eCanadian Journal of Animal Science\u003c/em\u003e 101 (1): 1\u0026ndash;19. https://doi.org/10.1139/cjas-2020-0022.\u003c/li\u003e\n\u003cli\u003eTiezzi, F., C. Maltecca, A. Cecchinato, M. Penasa, and G. Bittante. 2013. \u0026ldquo;Thin and Fat Cows, and the Nonlinear Genetic Relationship between Body Condition Score and Fertility.\u0026rdquo; \u003cem\u003eJournal of Dairy Science\u003c/em\u003e 96 (10): 6730\u0026ndash;41. https://doi.org/10.3168/jds.2013-6863.\u003c/li\u003e\n\u003cli\u003eT\u0026uuml;fekci, Hacer, and Veerasamy Sejian. 2023. \u0026ldquo;Stress Factors and Their Effects on Productivity in Sheep.\u0026rdquo; \u003cem\u003eAnimals\u003c/em\u003e 13 (17): 2769. https://doi.org/10.3390/ani13172769.\u003c/li\u003e\n\u003cli\u003eWheelock, J. B., R. P. Rhoads, M. J. VanBaale, S. R. Sanders, and L. H. Baumgard. 2010. \u0026ldquo;Effects of Heat Stress on Energetic Metabolism in Lactating Holstein Cows1.\u0026rdquo; \u003cem\u003eJournal of Dairy Science\u003c/em\u003e 93 (2): 644\u0026ndash;55. https://doi.org/10.3168/jds.2009-2295.\u003c/li\u003e\n\u003cli\u003eWidyas, Nuzul, Tri Satya Mastuti Widi, Sigit Prastowo, Ika Sumantri, Ben J. Hayes, and Heather M. Burrow. 2022. \u0026ldquo;Promoting Sustainable Utilization and Genetic Improvement of Indonesian Local Beef Cattle Breeds: A Review.\u0026rdquo; \u003cem\u003eAgriculture\u003c/em\u003e 12 (10): 1566. https://doi.org/10.3390/agriculture12101566.\u003c/li\u003e\n\u003cli\u003eYin, Tong, and Sven K\u0026ouml;nig. 2018. \u0026ldquo;Genetic Parameters for Body Weight from Birth to Calving and Associations between Weights with Test-Day, Health, and Female Fertility Traits.\u0026rdquo; \u003cem\u003eJournal of Dairy Science\u003c/em\u003e 101 (3): 2158\u0026ndash;70. https://doi.org/10.3168/jds.2017-13835.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 Microclimate Status and feeding management in each observation environment\u003c/p\u003e\n\u003ctable width=\"100%\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"25%\"\u003e\n\u003cp\u003eMicroclimate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"18%\"\u003e\n\u003cp\u003eHSRF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"20%\"\u003e\n\u003cp\u003eHSWF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"18%\"\u003e\n\u003cp\u003eTNWF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"16%\"\u003e\n\u003cp\u003eStatistic\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"9%\"\u003e\n\u003cp\u003eMorn.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"9%\"\u003e\n\u003cp\u003eNoon\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003eMorn.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"9%\"\u003e\n\u003cp\u003eNoon\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"9%\"\u003e\n\u003cp\u003eMorn.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"9%\"\u003e\n\u003cp\u003eNoon\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003eSE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"9%\"\u003e\n\u003cp\u003eCV%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003eTa (\u0026deg;C)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"9%\"\u003e\n\u003cp\u003e28.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"9%\"\u003e\n\u003cp\u003e34.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e27.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"9%\"\u003e\n\u003cp\u003e30.89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"9%\"\u003e\n\u003cp\u003e23.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"9%\"\u003e\n\u003cp\u003e26.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e0.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"9%\"\u003e\n\u003cp\u003e14.05\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003eRH (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"9%\"\u003e\n\u003cp\u003e58.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"9%\"\u003e\n\u003cp\u003e40.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e65.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"9%\"\u003e\n\u003cp\u003e54.55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"9%\"\u003e\n\u003cp\u003e89.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"9%\"\u003e\n\u003cp\u003e77.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e2.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"9%\"\u003e\n\u003cp\u003e22.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003eTHI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"9%\"\u003e\n\u003cp\u003e76.51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"9%\"\u003e\n\u003cp\u003e81.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e76.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"9%\"\u003e\n\u003cp\u003e80.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"9%\"\u003e\n\u003cp\u003e74.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"9%\"\u003e\n\u003cp\u003e77.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e0.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"9%\"\u003e\n\u003cp\u003e5.56\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003eShelter\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"18%\"\u003e\n\u003cp\u003eNA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"20%\"\u003e\n\u003cp\u003eNA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"18%\"\u003e\n\u003cp\u003eAvailable\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003eWater Drink\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"18%\"\u003e\n\u003cp\u003e\u003cem\u003eAd libitum\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"20%\"\u003e\n\u003cp\u003e\u003cem\u003eAd libitum\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"18%\"\u003e\n\u003cp\u003e\u003cem\u003eAd libitum\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003eRoughage (%BW)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"18%\"\u003e\n\u003cp\u003e5%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"20%\"\u003e\n\u003cp\u003e8% + \u003cem\u003eroughage\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"18%\"\u003e\n\u003cp\u003e10%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003eConcentrate (%BB)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"18%\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"20%\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"18%\"\u003e\n\u003cp\u003e1-1,5%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"16%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 2 Statistical descriptive of physical and Physiological of Bali cattle\u003c/p\u003e\n\u003ctable width=\"100%\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"51%\"\u003e\n\u003cp\u003eTraits\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003eUnit\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003eMean\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003eSD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003eCV\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003eSE\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51%\"\u003e\n\u003cp\u003eDocility score morning (DSm)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e1.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e0.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e0.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e0.09\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51%\"\u003e\n\u003cp\u003eDocility score afternoon (DSn)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e1.85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e0.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e0.41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e0.08\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51%\"\u003e\n\u003cp\u003eRespiration rate morning (RRm)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003efreq/min\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e28.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e5.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e0.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e0.60\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51%\"\u003e\n\u003cp\u003eRespiration rate afternoon (RRn)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003efreq/min\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e32.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e8.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e0.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e0.91\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51%\"\u003e\n\u003cp\u003eHeart rate morning (HRm)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003epulse/min\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e63.60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e16.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e0.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e1.86\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51%\"\u003e\n\u003cp\u003eHeart rate afternoon (HRn)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003epulse/min\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e71.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e21.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e0.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e2.31\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51%\"\u003e\n\u003cp\u003eRectal temperature morning (TRm)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003eC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e38.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e0.53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51%\"\u003e\n\u003cp\u003eRectal temperature afternoon (TRn)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003eC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e38.87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e0.41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51%\"\u003e\n\u003cp\u003eBlood glucose level (Glukosa)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003emg/dL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e54.39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e11.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e0.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e1.32\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51%\"\u003e\n\u003cp\u003eBody condition score (BCS)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003efat = 4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e2.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e0.92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e0.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e0.10\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51%\"\u003e\n\u003cp\u003eBody darkness (BD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003edark = 4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e2.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e1.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e0.41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e0.13\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51%\"\u003e\n\u003cp\u003eBody weight (BW)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003ekg\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e180.71\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e58.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e0.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e6.49\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51%\"\u003e\n\u003cp\u003eWither height (WH)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003ecm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e104.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e11.51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e0.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e1.27\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51%\"\u003e\n\u003cp\u003eBody length (BL)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003ecm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e104.85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e8.69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e0.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e0.96\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51%\"\u003e\n\u003cp\u003eChest circumference (CC)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003ecm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e142.44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e21.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e0.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e2.39\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51%\"\u003e\n\u003cp\u003eSkin thickness (ST)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003emm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e11.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e4.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e0.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e0.54\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"51%\"\u003e\n\u003cp\u003eScrotum circumference (SC)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003ecm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e23.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e6.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e0.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e0.84\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;Table 3 Eigenvalue of PCA and CDA dimension\u003c/p\u003e\n\u003ctable width=\"100%\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003eDimension\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003eMultivariate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003eEigenvalue\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003eVariance percent %\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003eCumulative %\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003eDim.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003ePCA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e6.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e39.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003e39.79\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003eDim.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003ePCA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e2.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e12.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003e52.69\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003eDim.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003ePCA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e1.60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e9.98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003e62.68\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003eDim.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003ePCA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e1.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e6.86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003e69.54\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003eDim.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003ePCA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e0.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e5.69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003e75.23\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003eDim.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003ePCA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e0.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e5.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003e80.26\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003eDim.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003ePCA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e0.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e4.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003e84.89\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003eDim.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003ePCA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e0.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e3.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003e88.83\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003eDim.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003ePCA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e0.51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e3.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003e92.02\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003eDim.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003ePCA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e0.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e2.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003e94.23\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003eDim.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003ePCA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e0.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e1.71\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003e95.94\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003eDim.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003ePCA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e0.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e1.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003e97.33\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003eDim.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003ePCA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e0.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e1.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003e98.42\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003eDim.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003ePCA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e0.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e0.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003e99.15\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003eDim.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003ePCA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e0.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e0.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003e99.72\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003eDim.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003ePCA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e0.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e0.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003e100.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003eDim.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003eCDA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e263.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e66.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003e66.68\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003eDim.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003eCDA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"18%\"\u003e\n\u003cp\u003e131.56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e33.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003e100.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 4 Variable contribution of the total variation\u003c/p\u003e\n\u003ctable width=\"100%\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"24%\"\u003e\n\u003cp\u003eTraits\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003eTraits set\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003eDim.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\n\u003cp\u003eDim.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\n\u003cp\u003eDim.3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"24%\"\u003e\n\u003cp\u003eDSm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003eBehavior\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003e\u0026nbsp;0.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\n\u003cp\u003e\u0026nbsp;1.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;19.43 \u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"24%\"\u003e\n\u003cp\u003eDSn\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003eBehavior\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003e\u0026nbsp;0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\n\u003cp\u003e\u0026nbsp;2.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;31.26 \u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"24%\"\u003e\n\u003cp\u003eRRm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003ePhysiological\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003e\u0026nbsp;2.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;18.35 \u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\n\u003cp\u003e\u0026nbsp;4.17\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"24%\"\u003e\n\u003cp\u003eRRn\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003ePhysiological\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003e\u0026nbsp;1.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;18.05 \u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\n\u003cp\u003e\u0026nbsp;7.73\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"24%\"\u003e\n\u003cp\u003eTRm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003ePhysiological\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003e\u0026nbsp;2.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;20.95 \u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\n\u003cp\u003e\u0026nbsp;0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"24%\"\u003e\n\u003cp\u003eTRn\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003ePhysiological\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003e\u0026nbsp;6.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;16.57 \u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\n\u003cp\u003e\u0026nbsp;0.12\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"24%\"\u003e\n\u003cp\u003eHRm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003ePhysiological\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003e\u0026nbsp;8.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\n\u003cp\u003e\u0026nbsp;1.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\n\u003cp\u003e\u0026nbsp;2.46\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"24%\"\u003e\n\u003cp\u003eHRn\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003ePhysiological\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003e\u0026nbsp;7.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\n\u003cp\u003e\u0026nbsp;0.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\n\u003cp\u003e\u0026nbsp;4.44\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"24%\"\u003e\n\u003cp\u003eGlucose\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003ePhysiological\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003e\u0026nbsp;1.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\n\u003cp\u003e\u0026nbsp;1.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\n\u003cp\u003e\u0026nbsp;11.19\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"24%\"\u003e\n\u003cp\u003eBCS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003ePhysical\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003e10.43 \u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\n\u003cp\u003e\u0026nbsp;2.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\n\u003cp\u003e\u0026nbsp;0.43\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"24%\"\u003e\n\u003cp\u003eBD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003ePhysical\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003e\u0026nbsp;8.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\n\u003cp\u003e\u0026nbsp;2.56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\n\u003cp\u003e\u0026nbsp;0.19\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"24%\"\u003e\n\u003cp\u003eBW\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003ePhysical\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd 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width=\"22%\"\u003e\n\u003cp\u003e\u0026nbsp;0.07\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"tropical-animal-health-and-production","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"trop","sideBox":"Learn more about [Tropical Animal Health and Production](https://www.springer.com/journal/11250)","snPcode":"11250","submissionUrl":"https://submission.nature.com/new-submission/11250/3","title":"Tropical Animal Health and Production","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"principal component, discrimination, heat stress, heat tolerance","lastPublishedDoi":"10.21203/rs.3.rs-4904288/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4904288/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAnimal 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.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAim\u003c/strong\u003e: 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.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: 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.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: 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.\u003c/p\u003e","manuscriptTitle":"Discriminating Heat Stress and Feed Scarcity in Bali Cattle Using Multivariate Trait Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-10 14:16:12","doi":"10.21203/rs.3.rs-4904288/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-04-09T08:21:35+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-09T02:40:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-09T00:13:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Tropical Animal Health and Production","date":"2025-03-25T12:17:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"tropical-animal-health-and-production","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"trop","sideBox":"Learn more about [Tropical Animal Health and Production](https://www.springer.com/journal/11250)","snPcode":"11250","submissionUrl":"https://submission.nature.com/new-submission/11250/3","title":"Tropical Animal Health and Production","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"1d67c17b-82b8-478f-8e7e-8f6332af2d3d","owner":[],"postedDate":"April 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-16T16:02:31+00:00","versionOfRecord":{"articleIdentity":"rs-4904288","link":"https://doi.org/10.1007/s11250-025-04508-2","journal":{"identity":"tropical-animal-health-and-production","isVorOnly":false,"title":"Tropical Animal Health and Production"},"publishedOn":"2025-06-11 15:58:00","publishedOnDateReadable":"June 11th, 2025"},"versionCreatedAt":"2025-04-10 14:16:12","video":"","vorDoi":"10.1007/s11250-025-04508-2","vorDoiUrl":"https://doi.org/10.1007/s11250-025-04508-2","workflowStages":[]},"version":"v1","identity":"rs-4904288","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4904288","identity":"rs-4904288","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}