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Full Research Article

Evaluating the Efficacy of Body Measurements in Predicting 305-Day Milk Production in Sahiwal Cattle

S
Sumit Gangwar1
S
Shive Kumar1,*
shivekumar197@rediffmail.com
R
R.K. Sharma1
J
Jyoti Palod1
A
Anita Arya2
M
Maansi3
G
Gautami Sarma1
1Department of Livestock Production Management, College of Veterinary and Animal Sciences, G.B. Pant University of Agriculture and Technology, Pantnagar-263 145, Uttarakhand, India.
2Department of Livestock Product Technology, College of Veterinary and Animal Sciences, G.B. Pant University of Agriculture and Technology, Pantnagar-263 145, Uttarakhand, India.
3Department of Veterinary Public Health and Epidemiology, College of Veterinary and Animal Sciences, G.B. Pant University of Agriculture and Technology, Pantnagar-263 145, Uttarakhand, India.

ABSTRACT

Background: Body measurements in cattle, are indicators of growth, health and productivity. These measurements help in selecting high-performing animals, prediction of milk yield and optimizing dairy farm management.

Methods: A total of 89 Sahiwal cattle were selected from Instructional Dairy Farm, GBPUA and T, Pantnagar. The data of body measurements like body length (BL), heart girth (HG) and height at wither (HW) at birth, 3, 6, 9, 12, 18, 24 months were recorded. Production and reproduction data like age at first service (AFS), age at first calving (AFC) and 305-day milk yield (305 DMY) were collected. The linear regression analysis for prediction of 305 DMY from body measurements at different ages was carried out using SPSS software version 21.

Result: The R² values for predicting first lactation 305-DMY at 6 months, HG alone (12.2 per cent) and combinations with BL and HW achieved R² values of up to 13.7 per cent, with the best prediction from HG combined with BL and HW. At 9 months, the R² values ranged up to 14.6 per cent, again favoring HG with BL and HW. At 3, 12, 18 and 24 months and at age at first calving, R² values were non-significant, highlighting the best predictive accuracy of HG and its combinations at earlier ages.

INTRODUCTION

Phenotypic traits like body size and body measurements in cattle are vital indicators of health, productivity and overall efficiency in milk production. Traits such as body length, height at withers, heart girth, paunch girth and body weight are closely associated with both productive and reproductive performance, particularly in cows and buffaloes (Stephens et al., 2007; Kern et al., 2014; Mashalji et al., 2016). High-producing Holstein crossbred cattle were found to be taller and larger, with greater heart girth, wither height, body and rump length (Lin et al. 1987; Hamadani et al., 2020). Agasti et al. (1984) reported significant positive correlations between 300-day lactation yield and live weight, rump width and paunch girth. Regression models using these traits allow accurate milk yield prediction, enhancing breeding, resource allocation and overall herd management (De Hass et al., 2007).
       
In India, Sahiwal cattle are renowned for their high milk yield, adaptability to local climates and strong disease resistance. However, smallholder farmers often lack detailed production or reproduction records for better selection of animals. Therefore, there is a need for reliable regression equations based on linear body measurements to enable more accurate prediction of milk yield and support better selection decisions in Sahiwal cattle. Body measurement traits such as body length and heart girth can be used to predict body weight and milk yield through regression equations (Pasaribu et al., 2015). Since many cows are culled annually, selecting animals with superior physical conformation is essential to improve productivity and economic efficiency (Kumar et al., 2023; Ganapathi et al., 2025). The objective of this study is to develop regression equations using linear body measurements to predict first lactation 305-day milk yield (305 DMY) in Sahiwal cattle, aiming to identify and select the best-performing individuals of this premier indigenous milch breed.

MATERIALS AND METHODS

A total of 89 purebred Sahiwal cattle were chosen for the study from the Instructional Dairy Farm located at Nagla, G.B. Pant University of Agriculture & Technology, Pantnagar, Uttarakhand, India. The animals were maintained under the all India coordinated research project (AICRP) Sahiwal cattle.
 
Body measurements and milk yield data
 
The data on various body measurements, including body length (BL), heart girth (HG) and height at withers (HW) were recorded at multiple time points: 0 month, 3, 6, 9, 12, 18 and 24 months. For reproduction parameters the age at first service (AFS) and age at first calving (AFC) data were noted. The 305-day milk yield (305 DMY) data for each cow were also collected, providing a comprehensive overview of milk production.
 
Description of parameters recorded
 
The parameters studied in this research included body length (BL), heart girth (HG), height at withers (HW) and 305-day milk yield (305 DMY). Body length was measured as the distance from the point of shoulder to the pin bone. Heart girth was recorded as the circumference of the chest just behind the shoulder and elbow joint. Height at withers was measured as the vertical distance from the ground to the highest point of the withers. The total milk produced during a standardized 305-day lactation period was calculated using formula, allowing for accurate comparison of milk productivity among animals.
 
Statistical analysis
 
Simple and multiple linear regression analyses were carried out with phenotypic body measurements considered as continuous variables and age classified as categorical variables to predict the 305-day milk yield (305 DMY). Regression equations were obtained using the stepwise method through SPSS software version 21. The linear regression model used to estimate the 305 DMY was:
 
Simple linear regression
 
Y = a + bX
 
Multiple linear regressions
 
Y = a + b1X1 + b2X2 + b3X3 + ... + btXt
 
Where,
Y = Dependent variable (305DMY).
X = Explanatory or the independent variable (BL, HG and HW).
b = Slope or the beta coefficient of the line.
a = Intercept (the value of y, when x=0).
       
The R² value is often used to represent the proportion of variance in the measured data that is explained by the model. It ranges from 0 to 1, with higher values (P≥0.5) signifying reduced error variance (Moriasi et al., 2007).

RESULTS AND DISCUSSION

Prediction of first lactation 305-day milk yield (305 DMY) of Sahiwal cattle from body measurements
 
Prediction using one independent variable
 
The linear regression equations of first lactation 305-day milk yield for different body measurement traits taking single independent variables have been presented in Table 1. The R² values for predicting 305 DMY from birth weight and body measurements i.e., body length (BL), heart girth (HG) and height at withers (HW) at 3, 12, 18, 24 months and AFC were non-significant, indicating these measurements are not useful for prediction of 305 DMY at this stage. The R² value for predicting 305 DMY was better from HG alone at different ages compared to other individual measurement.

Table 1: Simple linear regression of first lactation 305-day milk yield (305 DMY) on independent variables.


       
The present study revealed that heart girth (HG) had a significant and positive association with milk yield in dairy cows, which is in agreement with previous research highlighting the importance of HG in predicting production performance. However other body measurements did not show significant prediction of first lactation 305 DMY, which was supported by previous findings of Mishra et al., (2017). The current findings align with Alsheikh (2013), who reported that morphometric traits in Shami goats, including HG, could aid in predicting daily milk yield (DMY). Similar findings were noted by Dahiya and Rathi (2002) in indigenous cattle, Ahmad et al., (2013) in Nili-Ravi buffaloes and by Ahlem et al., (2022) in goats.
       
Body measurements such as stature and heart girth (HG) are well-established indicators of body weight (Heinrichs et al., 1992; Mantysaari, 1996), which is closely linked to metabolic capacity, feed efficiency and energy balance (Søndergaard et al., 2002). Larger-framed animals with greater HG possess enhanced thoracic capacity, supporting better-developed heart and lungs, improved circulation and respiratory efficiency, all of which contribute to higher milk production (Bais, 2023). HG also serves as an indicator for biological capacities related to lactation (Durón-Benítez and Huang, 2016) and reflects changes in body condition during lactation, underscoring its value as a selection criterion for dairy productivity improvement (Dijkstra et al., 2005).
 
Prediction using two independent variables
 
The linear regression equations of first lactation 305-day milk yield for different body measurement traits taking two independent variables have been presented in Table 2. At 6 and 9 months and also at AFS, the R² values for predicting 305 DMY from combinations of HG with BL and HW showed higher prediction efficiency. For ages 3, 12, 18, 24 months and AFC the R² values for various body measurement combinations were non-significant, suggesting these measurements are not useful for predicting 305 DMY at these ages. 

Table 2: Multiple linear regression of first lactation 305-day milk yield (305 DMY) on two independent variables.


       
The regression analysis using two independent variables showed that HG in combination with BL and HW provided better prediction at 6 and 9 months of first lactation 305 DMY. In line with the present findings Alsheikh (2013) reported that HG combined with withers height and hip height improved accuracy, reaching an R² of 80% in joint models of does and kids. Similar findings were reported by Ahmad et al., (2013) and Dhillod et al., (2017) in buffalo, while in goats were noted by Kouri et al., (2019) and Makamu et al., (2023). Heart girth reflects thoracic capacity, impacting intake, digestion, respiratory efficiency, enhancing metabolism, oxygenation and overall productivity (de Melo et al., 2018). Moreover, the present findings showed a negative relationship of 305 DMY with HW and BL. In line with the present findings Mota et al., (2014) reported that negative canonical correlations of BL and HW with lactation length (LL) suggest that smaller buffalo may have longer lactation periods due to lower maintenance energy requirements. de Melo et al. (2020) also reported body morphometric traits like BW had higher cross-loadings for milk and reproductive traits. Conversely, Sieber et al., 1988; Ahlem et al., (2022) found that wither height had a positive correlation (P<0.05) with yield traits during the first parity which supported the present findings.
 
Prediction using three independent variables
 
The multiple linear regression equations of first lactation 305-day milk yield for different body measurement traits taking three independent variables have been presented in Table 3. The results revealed that HG alone and particularly in combination with BL and HW, provides the most consistent predictive value for 305 DMY at earlier ages.

Table 3: Multiple linear regression of first lactation 305-day milk yield (305 DMY) on three independent variables.


       
In agreement to the present findings Dhillod et al., (2017) stated that HG and BL were reliable predictors of milk yield and recommended using these parameters as selection criteria in breeding programs to improve milk productivity. Similar findings were noted by Lin et al., (1987), who reported that selecting animals for higher milk production resulted in proportional increases in BL, HG and HW, indicating their strong association with productive potential. Singh and Prasad (1983) also demonstrated significant correlations between these measurements and milk yield in both cows and buffaloes, reinforcing their predictive value across breeds and species. Bhakat et al., (2010) further observed strong correlations between HG and abdominal girth, suggesting that these traits are interconnected indicators of overall body capacity. Supporting this, Kumar et al., (1995) reported that multiple linear regression using BL, HG and HW explained 12.8% of the variation in 305 DMY, highlighting their utility in early selection programs for dairy cattle. However, Mishra et al., (2017) observed non-significant effect of body measure-ments on first lactation milk yield in Jersey and Holstein Friesian crossbred cattle.
       
The present findings revealed that HG along with BL and HW, provides the most consistent predictive value for 305 DMY at earlier ages, making it a practical trait for field-level evaluation. This association is likely because HG serves as a reliable indicator of body size, which directly correlates with rumen capacity and the efficiency of energy intake and utilization in dairy cows (Sloniewski et al., 2005). A larger HG generally signifies greater thoracic and abdominal volume, enabling higher feed intake and enhanced digestive efficiency (Kennedy et al., 1999). This in turn leads to improved nutrient absorption and utilization, supporting increased metabolic activity and energy turnover (Oldham, 1996). Such physiological advantages facilitate greater energy availability for milk synthesis, thereby explaining the higher yields observed in cows with larger heart girth.
       
In the present study, BL, when combined with HG and HW, showed improved prediction of 305 DMY compared to using BL alone. This was supported by previous findings of Zujović et al. (2011), Ahmad et al., (2013), Dhillod et al., (2017) and Makamu et al., (2023). Lin et al., (1987) reported that selection for higher milk production increased BL, HG and HW simultaneously which is in agreement with the present findings. According to Soeharsono et al., (2020), BL shows only a weak correlation (r = 0.15) with daily milk production, explaining just 2.31% of the variability (R²). This suggests that while BL contributes to milk yield prediction, it is not a strong independent predictor. Hamid et al., (2003) reported that BL, PG and HG together explained 12.9% of the variation in first lactation milk yield and when BL was excluded, the remaining traits explained 12.3%, indicating that BL added only marginal predictive value. However, Petrovska and Jonkus (2014) emphasized that BL influences small intestine size, which is vital for digestion and nutrient absorption. Longer BL is associated with greater absorptive capacity, which may enhance nutrient utilization and support higher daily milk production. These findings underline that while BL can be a valuable component when used alongside other body measure-ments like HG and HW, its influence on milk yield is more pronounced in combination rather than isolation.

CONCLUSION

The study revealed that heart girth (HG) measured at early growth stages, particularly between 6-9 months of age, serves as a better indicator for identifying future high-yielding dairy cows. Combining HG with other traits like body length (BL) and height at withers (HW) during this period can further enhance selection accuracy. By focusing on morphometric traits during these stages, farmers can enhance the accuracy of identifying heifers with higher genetic potential for milk production.

ACKNOWLEDGEMENT

The authors acknowledge All India Coordinated Research Project (AICRP) on Sahiwal and College of Veterinary and Animal Sciences, G.B. Pant University of Agriculture and Technology for providing facilities to conduct the research work.
 
Disclaimer
 
The statements, interpretations and conclusions in this article are solely attributable to the authors. The authors have exercised due diligence to ensure the accuracy and completeness of the content; however, they assume no responsibility for any direct or indirect consequences arising from its use.
 
Informed consent
 
This study was carried out with the approval of the Institutional Animal Ethics Committee of G.B. Pant University of Agriculture and Technology.

CONFLICT OF INTEREST

On behalf of all authors, the corresponding author declares that there are no potential conflicts of interest associated with this study.

REFERENCES


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    Full Research Article

    Evaluating the Efficacy of Body Measurements in Predicting 305-Day Milk Production in Sahiwal Cattle

    S
    Sumit Gangwar1
    S
    Shive Kumar1,*
    shivekumar197@rediffmail.com
    R
    R.K. Sharma1
    J
    Jyoti Palod1
    A
    Anita Arya2
    M
    Maansi3
    G
    Gautami Sarma1
    1Department of Livestock Production Management, College of Veterinary and Animal Sciences, G.B. Pant University of Agriculture and Technology, Pantnagar-263 145, Uttarakhand, India.
    2Department of Livestock Product Technology, College of Veterinary and Animal Sciences, G.B. Pant University of Agriculture and Technology, Pantnagar-263 145, Uttarakhand, India.
    3Department of Veterinary Public Health and Epidemiology, College of Veterinary and Animal Sciences, G.B. Pant University of Agriculture and Technology, Pantnagar-263 145, Uttarakhand, India.

    ABSTRACT

    Background: Body measurements in cattle, are indicators of growth, health and productivity. These measurements help in selecting high-performing animals, prediction of milk yield and optimizing dairy farm management.

    Methods: A total of 89 Sahiwal cattle were selected from Instructional Dairy Farm, GBPUA and T, Pantnagar. The data of body measurements like body length (BL), heart girth (HG) and height at wither (HW) at birth, 3, 6, 9, 12, 18, 24 months were recorded. Production and reproduction data like age at first service (AFS), age at first calving (AFC) and 305-day milk yield (305 DMY) were collected. The linear regression analysis for prediction of 305 DMY from body measurements at different ages was carried out using SPSS software version 21.

    Result: The R² values for predicting first lactation 305-DMY at 6 months, HG alone (12.2 per cent) and combinations with BL and HW achieved R² values of up to 13.7 per cent, with the best prediction from HG combined with BL and HW. At 9 months, the R² values ranged up to 14.6 per cent, again favoring HG with BL and HW. At 3, 12, 18 and 24 months and at age at first calving, R² values were non-significant, highlighting the best predictive accuracy of HG and its combinations at earlier ages.

    INTRODUCTION

    Phenotypic traits like body size and body measurements in cattle are vital indicators of health, productivity and overall efficiency in milk production. Traits such as body length, height at withers, heart girth, paunch girth and body weight are closely associated with both productive and reproductive performance, particularly in cows and buffaloes (Stephens et al., 2007; Kern et al., 2014; Mashalji et al., 2016). High-producing Holstein crossbred cattle were found to be taller and larger, with greater heart girth, wither height, body and rump length (Lin et al. 1987; Hamadani et al., 2020). Agasti et al. (1984) reported significant positive correlations between 300-day lactation yield and live weight, rump width and paunch girth. Regression models using these traits allow accurate milk yield prediction, enhancing breeding, resource allocation and overall herd management (De Hass et al., 2007).
           
    In India, Sahiwal cattle are renowned for their high milk yield, adaptability to local climates and strong disease resistance. However, smallholder farmers often lack detailed production or reproduction records for better selection of animals. Therefore, there is a need for reliable regression equations based on linear body measurements to enable more accurate prediction of milk yield and support better selection decisions in Sahiwal cattle. Body measurement traits such as body length and heart girth can be used to predict body weight and milk yield through regression equations (Pasaribu et al., 2015). Since many cows are culled annually, selecting animals with superior physical conformation is essential to improve productivity and economic efficiency (Kumar et al., 2023; Ganapathi et al., 2025). The objective of this study is to develop regression equations using linear body measurements to predict first lactation 305-day milk yield (305 DMY) in Sahiwal cattle, aiming to identify and select the best-performing individuals of this premier indigenous milch breed.

    MATERIALS AND METHODS

    A total of 89 purebred Sahiwal cattle were chosen for the study from the Instructional Dairy Farm located at Nagla, G.B. Pant University of Agriculture & Technology, Pantnagar, Uttarakhand, India. The animals were maintained under the all India coordinated research project (AICRP) Sahiwal cattle.
     
    Body measurements and milk yield data
     
    The data on various body measurements, including body length (BL), heart girth (HG) and height at withers (HW) were recorded at multiple time points: 0 month, 3, 6, 9, 12, 18 and 24 months. For reproduction parameters the age at first service (AFS) and age at first calving (AFC) data were noted. The 305-day milk yield (305 DMY) data for each cow were also collected, providing a comprehensive overview of milk production.
     
    Description of parameters recorded
     
    The parameters studied in this research included body length (BL), heart girth (HG), height at withers (HW) and 305-day milk yield (305 DMY). Body length was measured as the distance from the point of shoulder to the pin bone. Heart girth was recorded as the circumference of the chest just behind the shoulder and elbow joint. Height at withers was measured as the vertical distance from the ground to the highest point of the withers. The total milk produced during a standardized 305-day lactation period was calculated using formula, allowing for accurate comparison of milk productivity among animals.
     
    Statistical analysis
     
    Simple and multiple linear regression analyses were carried out with phenotypic body measurements considered as continuous variables and age classified as categorical variables to predict the 305-day milk yield (305 DMY). Regression equations were obtained using the stepwise method through SPSS software version 21. The linear regression model used to estimate the 305 DMY was:
     
    Simple linear regression
     
    Y = a + bX
     
    Multiple linear regressions
     
    Y = a + b1X1 + b2X2 + b3X3 + ... + btXt
     
    Where,
    Y = Dependent variable (305DMY).
    X = Explanatory or the independent variable (BL, HG and HW).
    b = Slope or the beta coefficient of the line.
    a = Intercept (the value of y, when x=0).
           
    The R² value is often used to represent the proportion of variance in the measured data that is explained by the model. It ranges from 0 to 1, with higher values (P≥0.5) signifying reduced error variance (Moriasi et al., 2007).

    RESULTS AND DISCUSSION

    Prediction of first lactation 305-day milk yield (305 DMY) of Sahiwal cattle from body measurements
     
    Prediction using one independent variable
     
    The linear regression equations of first lactation 305-day milk yield for different body measurement traits taking single independent variables have been presented in Table 1. The R² values for predicting 305 DMY from birth weight and body measurements i.e., body length (BL), heart girth (HG) and height at withers (HW) at 3, 12, 18, 24 months and AFC were non-significant, indicating these measurements are not useful for prediction of 305 DMY at this stage. The R² value for predicting 305 DMY was better from HG alone at different ages compared to other individual measurement.

    Table 1: Simple linear regression of first lactation 305-day milk yield (305 DMY) on independent variables.


           
    The present study revealed that heart girth (HG) had a significant and positive association with milk yield in dairy cows, which is in agreement with previous research highlighting the importance of HG in predicting production performance. However other body measurements did not show significant prediction of first lactation 305 DMY, which was supported by previous findings of Mishra et al., (2017). The current findings align with Alsheikh (2013), who reported that morphometric traits in Shami goats, including HG, could aid in predicting daily milk yield (DMY). Similar findings were noted by Dahiya and Rathi (2002) in indigenous cattle, Ahmad et al., (2013) in Nili-Ravi buffaloes and by Ahlem et al., (2022) in goats.
           
    Body measurements such as stature and heart girth (HG) are well-established indicators of body weight (Heinrichs et al., 1992; Mantysaari, 1996), which is closely linked to metabolic capacity, feed efficiency and energy balance (Søndergaard et al., 2002). Larger-framed animals with greater HG possess enhanced thoracic capacity, supporting better-developed heart and lungs, improved circulation and respiratory efficiency, all of which contribute to higher milk production (Bais, 2023). HG also serves as an indicator for biological capacities related to lactation (Durón-Benítez and Huang, 2016) and reflects changes in body condition during lactation, underscoring its value as a selection criterion for dairy productivity improvement (Dijkstra et al., 2005).
     
    Prediction using two independent variables
     
    The linear regression equations of first lactation 305-day milk yield for different body measurement traits taking two independent variables have been presented in Table 2. At 6 and 9 months and also at AFS, the R² values for predicting 305 DMY from combinations of HG with BL and HW showed higher prediction efficiency. For ages 3, 12, 18, 24 months and AFC the R² values for various body measurement combinations were non-significant, suggesting these measurements are not useful for predicting 305 DMY at these ages. 

    Table 2: Multiple linear regression of first lactation 305-day milk yield (305 DMY) on two independent variables.


           
    The regression analysis using two independent variables showed that HG in combination with BL and HW provided better prediction at 6 and 9 months of first lactation 305 DMY. In line with the present findings Alsheikh (2013) reported that HG combined with withers height and hip height improved accuracy, reaching an R² of 80% in joint models of does and kids. Similar findings were reported by Ahmad et al., (2013) and Dhillod et al., (2017) in buffalo, while in goats were noted by Kouri et al., (2019) and Makamu et al., (2023). Heart girth reflects thoracic capacity, impacting intake, digestion, respiratory efficiency, enhancing metabolism, oxygenation and overall productivity (de Melo et al., 2018). Moreover, the present findings showed a negative relationship of 305 DMY with HW and BL. In line with the present findings Mota et al., (2014) reported that negative canonical correlations of BL and HW with lactation length (LL) suggest that smaller buffalo may have longer lactation periods due to lower maintenance energy requirements. de Melo et al. (2020) also reported body morphometric traits like BW had higher cross-loadings for milk and reproductive traits. Conversely, Sieber et al., 1988; Ahlem et al., (2022) found that wither height had a positive correlation (P<0.05) with yield traits during the first parity which supported the present findings.
     
    Prediction using three independent variables
     
    The multiple linear regression equations of first lactation 305-day milk yield for different body measurement traits taking three independent variables have been presented in Table 3. The results revealed that HG alone and particularly in combination with BL and HW, provides the most consistent predictive value for 305 DMY at earlier ages.

    Table 3: Multiple linear regression of first lactation 305-day milk yield (305 DMY) on three independent variables.


           
    In agreement to the present findings Dhillod et al., (2017) stated that HG and BL were reliable predictors of milk yield and recommended using these parameters as selection criteria in breeding programs to improve milk productivity. Similar findings were noted by Lin et al., (1987), who reported that selecting animals for higher milk production resulted in proportional increases in BL, HG and HW, indicating their strong association with productive potential. Singh and Prasad (1983) also demonstrated significant correlations between these measurements and milk yield in both cows and buffaloes, reinforcing their predictive value across breeds and species. Bhakat et al., (2010) further observed strong correlations between HG and abdominal girth, suggesting that these traits are interconnected indicators of overall body capacity. Supporting this, Kumar et al., (1995) reported that multiple linear regression using BL, HG and HW explained 12.8% of the variation in 305 DMY, highlighting their utility in early selection programs for dairy cattle. However, Mishra et al., (2017) observed non-significant effect of body measure-ments on first lactation milk yield in Jersey and Holstein Friesian crossbred cattle.
           
    The present findings revealed that HG along with BL and HW, provides the most consistent predictive value for 305 DMY at earlier ages, making it a practical trait for field-level evaluation. This association is likely because HG serves as a reliable indicator of body size, which directly correlates with rumen capacity and the efficiency of energy intake and utilization in dairy cows (Sloniewski et al., 2005). A larger HG generally signifies greater thoracic and abdominal volume, enabling higher feed intake and enhanced digestive efficiency (Kennedy et al., 1999). This in turn leads to improved nutrient absorption and utilization, supporting increased metabolic activity and energy turnover (Oldham, 1996). Such physiological advantages facilitate greater energy availability for milk synthesis, thereby explaining the higher yields observed in cows with larger heart girth.
           
    In the present study, BL, when combined with HG and HW, showed improved prediction of 305 DMY compared to using BL alone. This was supported by previous findings of Zujović et al. (2011), Ahmad et al., (2013), Dhillod et al., (2017) and Makamu et al., (2023). Lin et al., (1987) reported that selection for higher milk production increased BL, HG and HW simultaneously which is in agreement with the present findings. According to Soeharsono et al., (2020), BL shows only a weak correlation (r = 0.15) with daily milk production, explaining just 2.31% of the variability (R²). This suggests that while BL contributes to milk yield prediction, it is not a strong independent predictor. Hamid et al., (2003) reported that BL, PG and HG together explained 12.9% of the variation in first lactation milk yield and when BL was excluded, the remaining traits explained 12.3%, indicating that BL added only marginal predictive value. However, Petrovska and Jonkus (2014) emphasized that BL influences small intestine size, which is vital for digestion and nutrient absorption. Longer BL is associated with greater absorptive capacity, which may enhance nutrient utilization and support higher daily milk production. These findings underline that while BL can be a valuable component when used alongside other body measure-ments like HG and HW, its influence on milk yield is more pronounced in combination rather than isolation.

    CONCLUSION

    The study revealed that heart girth (HG) measured at early growth stages, particularly between 6-9 months of age, serves as a better indicator for identifying future high-yielding dairy cows. Combining HG with other traits like body length (BL) and height at withers (HW) during this period can further enhance selection accuracy. By focusing on morphometric traits during these stages, farmers can enhance the accuracy of identifying heifers with higher genetic potential for milk production.

    ACKNOWLEDGEMENT

    The authors acknowledge All India Coordinated Research Project (AICRP) on Sahiwal and College of Veterinary and Animal Sciences, G.B. Pant University of Agriculture and Technology for providing facilities to conduct the research work.
     
    Disclaimer
     
    The statements, interpretations and conclusions in this article are solely attributable to the authors. The authors have exercised due diligence to ensure the accuracy and completeness of the content; however, they assume no responsibility for any direct or indirect consequences arising from its use.
     
    Informed consent
     
    This study was carried out with the approval of the Institutional Animal Ethics Committee of G.B. Pant University of Agriculture and Technology.

    CONFLICT OF INTEREST

    On behalf of all authors, the corresponding author declares that there are no potential conflicts of interest associated with this study.

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