Prospective Effects of Regrouping, Number of Animals in Each Group and Concentrate Specificity on Profitability of Lactating Dairy Cows


1 Department of Animal Science, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran

2 Department of Animal Science, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran Iran


Profitability of different grouping criteria was simulated in a dairy farm with 153 lactating cows that were divided into three groups of high (79), medium (40) and low (34) cows based on the milk records. Animals were reassigned to the new groups based on the following criteria using a decision support tool and the same three groups scenario: fat corrected milk 4%, days in milk (DIM), dairy merit (fat-corrected milk (FCM)/body weight (BW)0.75), and cluster (cow’s energy and protein requirements). Four total mixed ration (TMR) were formulated for feeding three simulated groups in which group 2 (medium producing animals) could consume either concentrate type I or concentrate type II, whereas groups 1 and 3 always had their specific concentrate mixture. The number of animals in the high, medium and low producing groups altered following the re-grouping and the highest number of cows fell in either the new medium (FCM and dairy merit) or low (DIM and cluster) producing groups. Cluster and dairy merit grouping criteria resulted the most income over feed costs (IOFC) and maximum profitability compared to the milk records and other simulated scenarios. In all of the grouping criteria, when the second group consumed concentrate type II, the amount of IOFC was higher than situations where this group utilized concentrate type I. Overall, profitability and economic efficiency of the herd increased when a more precise grouping method was used. Furthermore, cluster method provided a liberty for choosing the type of concentrate for medium producing animals with a negligible effect on the calculated IOFC from the simulated data.



Precision feeding according to an individual cow’s requirements guarantees her nutritional necessities, but this is not yet practical, especially in larger herds (Sniffen et al. 1993). Therefore, cows are often group-fed. Diet is usually formulated for high-producing cows to ensure that milk production is maintained, but it provides extra nutrients to the less productive animals, which makes it unsuitable (Weiss, 2014). Therefore, distributing lactating cows in smaller groups and feeding group-specific rations provides more precise nutrients to similar cows in the same group (Cabrera et al. 2012) and decreases the variability among the cows within the group. Hence, nutritional grouping can be beneficial through saving feed costs, improving productivity (Bach, 2014), improving herd health through promoting optimal body condition, and decreasing the excretion of nutrients such as ammonia to the environment (Cabrera and Kalantari, 2016). The criteria for grouping lactating dairy cows are fat correct milk (FCM), days in milk (DIM), dairy merit (FCM/BW0.75) and cluster (McGilliard et al. 1983). However, the cluster strategy uses cow’s energy and protein requirements which is, theoretically, more accurate than the other methods and can increase profitability by placing more similar cows in one group and precise group feeding (Cabrera and Kalantari, 2016; Kalantari et al. 2016). In order to estimate cow’s energy and protein requirements, the milk yield, composition, body weight, and condition score of individual animals have to be measured. However, other information such as DIM, lactation number and days pregnant are also required (NRC, 2001). This would lead to more farm work which may not be feasible in practice, especially when there is lack of information about the economic advantages of cluster technique over the other common methods used for nutritional grouping of lactating dairy cows. The purpose of this study was to compare the economic efficiency and profitability of different nutritional grouping scenarios (simulated by online decision support tool using the records of 153 lactating dairy cow) with the following assumptions: three groups of lactating dairy cows in each strategy could consume their own TMR and total herd milk output and composition did not change by re-grouping.



This study was conducted at the Dairy Cattle Research Center of Ferdowsi University of Mashhad, Mashhad, Iran. Conventionally, 153 lactating dairy cows (Table 1) had been divided into three groups of high, medium and low milk producing cows based on their daily milk yield (existing grouping) including 79, 40 and 34 cows, respectively. Milking was performed three times a day and milk production was measured for each individual cow by a milking machine (Metatrron 21, Westfalia-Surge, Inc. Germany). Sum of the three consecutive values were further subjected as a record. During each milking session, 100 mL of milk samples were analyzed using Milko-Scan 605 analyzer (Foss electric, hillerod, Denmark) and the weighted mean values of milk components were used for estimation of the nutrient requirements of each cow (McGilliard et al. 1983). Body weight was measured after morning milking for two consecutive days (McGilliard et al. 1983). Body condition score (0-5 point) was determined using the method proposed by Edmonson et al. (1989). NRC (2001) was used for calculating the energy and protein requirements of animals and diet formulation and all the other required inputs including DIM and lactation number (Table 2). 


Re-grouping criteria

Using an online decision support tool ( developed by Cabrera (2016), 153 cows were re-assigned to three new groups based on the following criteria: fat corrected milk 4% (FCM), days in milk (DIM), dairy merit (FCM/BW0.75), and cluster (McGilliard et al. 1983). Because of the limitations in farm facilities, the similar three-group scenario was used in all of the above criteria. Furthermore, the following assumptions were entered into the decision support tool:

Corn grain: 19 cents/kg

Soybean meal: 38.3 cents/kg

Raw milk price: 36 cents/kg



The existing feeding criteria for cows consisted of two types of concentrates; one for the high and medium groups (with different quantity) and another for the low producing group. In this strategy, average milk yield of each group was used to formulate diet as shown in Table 2. The logic behind the number of animals in each group was to maximize the possibility of grouping the high producing cows into the same group which receives the highest amount of concentrate. To compare the different grouping criteria, the rations were formulated based on the new groups of animals and 83rd percentile of group nutritional requirements as described by Stallings and McGilliard (1984). Four TMR were formulated for the three groups, in which group no. 2 (medium producing animals) could consume either concentrate type I (similar to the existing situation) or concentrate type II. Groups 1 and 3 always consumed their specific concentrate mixture. Earlier studies have indicated that group-specific TMR was preferred in order to reduce feed costs (Cabrera and Kalantari, 2016). However, similarity of TMRs between groups decreases changes in the rumen microbial population and adaptation period and subsequently minimizes the depression of milk production after alteration of the groups (Kalantari et al. 2016). For the above reasons, two types of concentrate were assumed to calculate income over feed costs (IOFC). Rations were formulated with the least possible costs. After getting new group members and formulating the ration, feed costs were calculated using individual ingredients cost and average IOFC was estimated for the group members present in the existing and new simulated groups.



The number of animals in each group based on different re-grouping criteria, average DIM, average milk yield, 83rd percentile values for net energy and metabolizable protein are presented in Table 2. As explained earlier, the existing groups had 79, 40 and 34 cows in high, medium and low producing classes, respectively.


Table 1 Distribution of lactating cows with a herd average milk yield= 36.3 kg and average days in milk= 165.94 (min=11 and max=680 d) used in the grouping study



Table 2 Specifications of nutritional groups simulated using different grouping methods


MY: average milk yield (kg); DIM: days in milk; DMI: dry matter intake (kg); NE: net energy requirement (Mcal/d); MP: metabolizable protein requirement (g/d); F/C: forage to concentrate ratio; FCM:fat corrected milk 4% and DIM: days in milk.


Table 3 Group and herd average income over feed cost (IOFC, $/cow/d) for different criteria of grouping, a and b determine when the second group consumed concentrate type I (formulated for high producing animals) and type II (formulated for low producing animals), respectively




Based on the findings of this study, it may be concluded that grouping dairy cows by considering a lowest mean (23.12 kg/d) and equalizing of the average milk intervals and putting animals in the groups in such a way as to have maximum high producers in one group (the method used in the present study) did not guarantee highest efficiency. Using real records of 153 lactating dairy cows and simulating new groups with different number of animals in each group, profitability and economic efficiency of the herd increased when a more accurate grouping method was used with major implications from the cluster technique. Furthermore, cluster method provided a liberty for choosing the type of concentrate only with a negligible effect on the IOFC.



The authors wish to acknowledge Mr. Morteza Atashafrooz, Ali Sharafkhani, Ali Khandan and Ms. Zohreh Zarnegar, Samira Asiyaban and Elham Salari. In addition, the authors are grateful to the Dairy Cattle Research Center of Ferdowsi University of Mashhad, Mashhad, Iran, for providing the required experimental facilities.

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Volume 9, Issue 2
June 2019
Pages 225-228
  • Receive Date: 10 January 2018
  • Revise Date: 21 July 2018
  • Accept Date: 31 July 2018
  • First Publish Date: 01 June 2019