Lactation Curve and Factors Affecting Persistency in Ongole Cattle

The shape of the lactation curve and persistency are crucial traits in dairy cattle. The lactation curve is a graphical representation of milk yield and time after calving (seahin et al., 2015). It generally shows an ascending phase from calving to peak yield and a descending phase from the peak to drying off. Lactation persistency is an important trait of lactation curve and it usually refers to the ability of a cow to sustain milk production at a high level after peak yield (Pedrosa et al., 2021). There are various definitions of lactation persistency in the literature, as are the measures of it.
       
Traditionally, the selection of dairy cattle has been primarily done based on the lactation milk yield which indicates gross income from milk per lactation. The persistency which denotes efficiency of milk production and net return from a cow has often been ignored in selection programmes due to the challenges in its measurement (Ferris et al., 1985). A higher persistency may lead to reduction in costs associated with feeding and health care due to lower peak yield and consequent freedom from negative energy balance and stress associated metabolic and reproductive disorders during peak production (Koloi et al., 2018; Torshizi and Mashhadi, 2018). For lactations of 305 days, selection based on persistency effects only the feed and health care costs, but for other lactation lengths, it had a greater impact on milk returns per lactation than on the costs involved in milk production. Therefore, inclusion of the persistency as a tool of selection in cattle breeding programmes is the need of hour.
       
Most of the studies reported on this topic (Shija et al., 2022; Hermiz and Hadad, 2020; Güler and Yanar, 2009) were conducted on high yielding cattle and there is a dearth of information regarding lactation persistency of medium and low yielding breeds. Thus, the objective of the present study was to know about the lactation curve and factors affecting persistency in the medium yielding cattle breed i.e. Ongole which may provide valuable information about selection of animals and herd management.

The present study was carried out with daily milk production records and history sheets of the Ongole cattle available in the Livestock Research Station, Mahanandi of Sri Venkateswara Veterinary University, Andhra Pradesh. A total of 92,100 morning and evening milk yields of 137 lactations recorded between 2018-2023 were considered for the present study.
       
The cows were allowed to stay in calving shed along with their calves for a week after calving. After that, the cows were transferred to milch animal shed for extracting milk for sale and the calves were transferred to calf shed. Calves were allowed to suckle before and after milking as weaning is not in practice in the farm.
       
The milk yields of morning and evening sessions of milking were aggregated to obtain the daily yields. Various milk production traits such as peak yield (PY), day of peak yield (DPY), lactation length (LL), standard lactation milk yield (SLMY) and total lactation milk yield (TLMY) were computed using the daily milk yields. PY is the highest daily milk yield and DPY is the day of the highest yield in a lactation. LL is the total days in milking in a lactation. SLMY was obtained by adding daily milk yields up to 305 days of lactation. TLMY is the total milk yield from an animal in a lactation.
       
Weekly milk yields were obtained by dividing the lactation length into weeks and summing the daily yields. The average weekly yield (AWY) for a week of lactation was calculated as the average of total milk yields during that week across all lactations. The formulae given by Turner (1926), Ludwick and Petersen (1943) and Prasad et al., (1999) were used for calculation of the lactation persistency index as presented in Table 1.


       
Parity, age at first calving (AFC), season of calving (Rainy, Autumn, Winter or Summer), sex and birth weight of calf were obtained from the history sheets available in the farm.
       
To visualize the lactation curve, Average Weekly Yields (AWYs) were plotted against different weeks of lactation using MS Excel 2016. One-way ANOVA, independent samples t-test and correlation were used to know the effect of PY, DPY, LL, SLMY, TLMY, parity, AFC, season of calving, sex and birth weight of calf on the persistency and multiple linear regression was used to evolve the prediction equations for the persistency indices through SPSS Software.

Lactation curve
 
The lactation curve provides information about the pattern of milk production over the period of lactation. Complete knowledge about the lactation curve helps in conducting feeding trials with lactating cattle, estimating total lactational yield from incomplete records and forecasting performance of a herd. The lactation curve obtained using the AWYs starting from 2^nd week of lactations was depicted in Fig 1. The AWYs starting from 2^nd week of lactations were given in Table 2, which elucidated the weekly milk production trends, providing specific data points for each week of lactation.




       
The lactation curve obtained in the present study was of typical concave downward shape and it has an ascending part till peak yield followed by a descending part (Fig 1). Shija et al., (2022), Khalifa et al., (2018), seahin et al., (2015); Andersen et al., (2011) and Ali et al., (1996) reported similar shaped lactation curves in Ayrshire cows, Holstein cows, Anatolian buffaloes, Norwegian Red and Holstein Friesian cows, respectively. The peak of average weekly milk yield (21.570±0.568 kg) occurred in eighth week of lactation in the present study (Table 2) which was almost similar to the values reported by Khan (1997); Raja et al., (2018) and Bhutkar  et al. (2014) in Sahiwal, Frieswal and Deoni cows, respectively. The consistent findings across different studies and cattle breeds underscore the universal nature of the lactation curve. The identification of the time of peak yield and understanding the dynamics of milk production throughout the lactation period are crucial for optimizing milk production in dairy cattle through effective management.
 
Lactation Persistency
 
The lactation persistency is an important economic trait of dairy cattle because of its impact on fertility, health and feed costs (Dekkers et al., 1998). It is generally defined as the ability of cow to maintain the same level of production during lactation. Some studies (Cole and Null, 2009) reported that the cows with high persistency produced less milk than expected at the beginning and more than expected at the end of lactation. The mean values of P1, P2 and P3 in the present study were 75.799±0.979, 88.038±0.929 and 57.897±0.775 per cent respectively (Table 3). The result regarding P1 was in agreement with those of George et al., (2021) in Tharparkar cows. However, Kaushal et al., (2016), Yilmaz and Koc (2013) and Kumar and Singh (2006) reported higher values in Sahiwal, Red Holstein and Karan Fries cows, respectively than the present study which might be due to breed variations. Chaudhari et al., (2022), Zurwan et al., (2017) and Pareek and Narang (2015) reported similar values of P2 in Jaffarabadi buffaloes, Sahiwal cattle and Murrah buffaloes, respectively. The value of P3 was similar to that of George et al., (2021) in Tharparkar cattle. These findings suggest that the persistency indices observed in Ongole cattle are consistent with those reported in other cattle and buffalo breeds, indicating the uniformity of the persistency trait across different genetic backgrounds.


 
Factors affecting lactation persistency
 
The analysis of various factors influencing lactation persistency was conducted and the results were presented in Table 3. Additionally, the correlation coefficients between factors and persistency indices were provided in Table 4.


 
Peak yield (PY)
 
It refers to the highest recorded daily milk production during a lactation period. Most of cows attain the peak yield by 45 to 90 days in milking and then slowly lose production over time (Litherland, 2021). In the present study, lactations grouped based on Peak Yield did not exhibit significant differences in terms of persistency (Table 3). Moreover, a non-significant negative correlation was found between PY and the persistency index P1, while a non-significant positive correlation was observed between PY and the persistency indices P2 and P3 (Table 4). Previous research has yielded varied findings regarding the relationship between Peak Yield and lactation persistency. Some studies reported significant negative correlations (Yamazaki et al., 2011; Sorensen et al., 2008; Fadlelmoula et al., 2007; Hickson et al., 2006; Kumar and Singh, 2006), while others found significant positive correlations (Albarrán-Portillo and Pollott, 2011) or non-significant associations (Garudkar et al., 2018). The discrepancies were probably due to variations in breeds, management practices and environmental factors. Further investigation is warranted to unravel the intricacies of this relationship in dairy cattle.
 
Day of peak yield (DPY)
 
The present study revealed that the values of P1 and P2 differ significantly with the time of peak yield and there was a significant positive correlation (P<0.01) between the time of peak yield and persistency, indicating that lactations with delayed peak yields are more persistent. This finding aligns with the results reported by Torshizi and Mashhadi (2018), seahin et al., (2015), Albarrán-Portillo and Pollott (2011) and Yamazaki et al., (2011).
 
Lactation length (LL)
 
In the present study, it was found that lactations of different lengths differed significantly (P<0.01) in terms of persistency (P1 and P2), with a significant (P<0.01) positive correlation observed between Lactation Length and persistency (P1 and P2). This suggests that the longer the LL, the higher the persistency. These findings are supported by Garudkar et al., (2018). However, seahin et al., (2015) reported no significant association between LL and persistency, which could be attributed to variations in the methodology of calculating the persistency index.
 
Standard LMY and Total LMY
 
It was revealed that the lactations grouped based on LMYs differ significantly in terms of persistency and there was a significant (P<0.01) positive association between LMY and persistency. It was in agreement with the findings of Garudkar et al., (2018), Torshizi and Mashhadi (2018), seahin et al., (2015), Albarrán-Portillo and Pollott (2011) and Tekerli et al., (2000). However, no association between LMY and persistency was reported by Yamazaki et al., (2011).
 
Parity
 
The ANOVA results indicated that the persistency indices had not differed significantly with parity. However, correlation analysis revealed a significant negative association between parity and persistency index P1, while the association with P2 and P3 was non-significant. This discrepancy between ANOVA and correlation analysis results may stem from the grouping of samples for ANOVA. Previous studies including Hermiz and Hadad (2020), Zurwan et al., (2017), Pareek and Narang (2015), Albarrán-Portillo and Pollott (2011) and Fadlelmoula et al., (2007) reported a significant effect of parity on persistency. However, Garudkar et al., (2018), Güler and Yanar (2009) and Das et al., (2007) found no effect of parity on persistency, which could be attributed to variations in the persistency measures used.
 
Age at first calving (AFC)
 
The study revealed that there was a significant positive association between persistency indices (P1 and P2) and AFC. These findings contradict the reports of Garudkar et al., (2018), who found no effect of AFC on persistency. The variation between results of ANOVA and correlation regarding the association of P2 with AFC was probably due to the effect of grouping in ANOVA. The association between P3 and AFC was non- significantly negative which was in agreement with Atashi et al., (2021), who observed that an increased AFC was associated with decreased persistency.
 
Season of calving
 
The values of persistency index P1 and P3 were not influenced by the season. However, the values of persistency index P2 exhibited significant variation (P< 0.01) among seasons of calving, with the highest value recorded in Autumn (92.926±1.624%) and the lowest in Summer (81.619±2.311%). Contrary to this, Elmaghraby (2009) noticed higher lactation persistency in summer and spring seasons. The results regarding P1 and P3 were similar to those of Hermiz and Hadad (2020), Garudkar et al., (2018) and Fadlelmoula et al., (2007) while the result pertaining to P2 was in agreement with those of Hermiz and Hadad (2020), Zurwan et al., (2017), Albarrán-Portillo and Pollott (2011) and Güler and Yanar (2009).
 
Sex and birth weight of calf
 
Both the Sex and birth weight of calf had no effect on persistency and there is no significant correlation between them and persistency. Hermiz and Hadad (2020) reported similar results. Contrary to the results of the present study, Chegini et al., (2015) reported that lactations with female calves were of higher persistency.
 
Prediction of persistency
 
Based on the significant correlations found in case of DPY, LL, SLMY, TLMY, parity and AFC, prediction equations were evolved for the persistency indices using multiple linear regression. The regression equation, regression coefficients and coefficients of determination were depicted in Table 5. Additionally, the relationships between actual values of dependent variables (P1, P2 and P3) and their predicted values were depicted in Fig 2, 3 and 4.








       
The P1 could be predicted from the regression equation [P1% = 13.661 + 0.036 (DPY) + 0.148 (LL) + 0.064 (SLMY) -0.053 (TLMY) -0.041 (parity) + 1.133 (AFC)] with an accuracy of 62.20 percent and the regression equations P2% = 54.230 + 0.043 (DPY) + 0.011 (LL) + 0.000 (SLMY) + 0.009 (TLMY) + 0.800 (parity) + 3.164 (AFC) and P3% = 56.409 -0.023 (DPY) -0.060 (LL) -0.006 (SLMY) + 0.022 (TLMY) + 0.437 (parity) + 1.655 (AFC) could be used for prediction of P2 and P3 with 28.20 and 22.30 percent of accuracy respectively (Table 5). Though all the three regression models of P1, P2 and P3 were statistically significant, the P1 can be considered as better measure for predicting persistency as its regression model had higher precision and its calculation is easy.

In conclusion, the study had several key findings regarding lactation curve and persistency in Ongole cattle. Firstly, the study concluded that the lactation curve is of a typical concave downward shape, with the peak occurring around the eighth week of lactation. Secondly, the study underscored the importance of selecting an appropriate measure for calculating lactation persistency, as different metrics may yield varying results. Thirdly, the study showed that lactations characterized by higher Lactation Milk Yield demonstrate greater persistency. Lastly, the persistency index formula proposed by Turner (1926) emerged as a preferable measure compared to that of Ludwick and Petersen (1943) and Prasad et al., (1999) for assessing persistency in Ongole cattle.

The present study was supported by the Sri Venkateswara Veterinary University, Tirupati, Andhra Pradesh.
 
Disclaimers
 
The views and conclusions expressed in this article are solely those of the authors and do not necessarily represent the views of their affiliated institutions. The authors are responsible for the accuracy and completeness of the information provided, but do not accept any liability for any direct or indirect losses resulting from the use of this content.
 
Informed consent
 
No approval of Committee of Experimental Animal care was required to accomplish the goals of this study.

The authors declare that there is no conflict of interest regarding the publication of this article. No funding or sponsorship influenced the design of the study, data collection, analysis, decision to publish, or preparation of the manuscript.