Time required to determine performance variables and production efficiency of lactating dairy cows

J. Dairy Sci. 97:1–14 http://dx.doi.org/10.3168/jds.2013-7265 © American Dairy science Association®, 2014.

Time required to determine performance variables and production efficiency of lactating dairy cows A. Asher,*† A. Shabtay,* A. Haim,† Y. Aharoni,* J. Miron,‡ G. Adin,§ A. Tamir,# A. Arieli,ǁ I. Halachmi,¶ U. Moallem,‡ A. Orlov,* and A. Brosh*1

*Newe-Ya’ar research Center, Beef Cattle section, Department of ruminant science, Institute of Animal science, Agricultural research Organization, PO Box 1021, ramat Yishay 30-09500, Israel †Israeli Center for Interdisciplinary research in Chronobiology, University of haifa, haifa 31905, Israel ‡Department of ruminant science, Institute of Animal science, Agricultural research Organization, PO Box 6, Bet-Dagan 50250, Israel §Department of Cattle husbandry, extension service, Ministry of Agriculture, PO Box 28, Bet-Dagan 50250, Israel #Faculty of Medicine, technion–Israel Institute of technology, PO Box 9649, haifa 31096, Israel ǁDepartment of Animal Science, Faculty of Agriculture, the Hebrew University of Jerusalem, PO Box 12, Rehovot 76100, Israel ¶Institute of Agricultural engineering. Agricultural research Organization, PO Box 6, Bet-Dagan 50250, Israel

ABSTRACT

kappa index P-value was used. Throughout WOL 16 to 30, the effects of increasing test period duration on between-animal coefficient of variation differed with respect to the various performance variables and RE/ DMI: it tended to change with respect to BW, did not change with respect to DMI, and decreased with respect to RE and RE/DMI. In conclusion, compared with a 15-wk study, a 2-wk study can classify RFI and RE/DMI to 3 efficiency levels, with an individual correlation coefficient of 0.6. When the study was carried out over 3 wk or more, the lowest significant index of the classification was P < 0.004, the lowest individual correlation coefficient was 0.65, and its lowest significance was P < 0.01. The current study indicated that the insignificant effect of the BW of dairy lactating cows on their DMI should be validated in more studies. Key words: dairy lactating cow, feed efficiency, optimal test duration

Thirty-five lactating dairy cows throughout weeks of lactation (WOL) 16 to 30 were used to determine optimal time needed for reliable measurement of performance variables, and to classify the cows into high-, medium-, and low-efficiency groups. Individual performance variables [body weight (BW), dry matter intake (DMI), and milk production] were measured daily with a computerized monitoring system. Body condition was visually scored weekly and used to calculate retained or depleted body energy as a result of fat content change (REF). Milk composition was analyzed weekly. Body weight, DMI, and total recovered energy (RE), which represents energy in milk production plus REF, were summarized weekly. Efficiency was calculated as RE/ DMI and as residual feed intake (RFI; i.e., the difference between actual and expected DMI), which was calculated from multiple linear regression of DMI dependence on BW0.75 and RE. Unexpectedly, it was found that BW did not affect DMI and RE/DMI. Changes and relative changes in phenotypic coefficient of variation and correlations among data from shortened tests ranging from 1 wk (WOL 16) to a sequence of 15-wk tests were used to determine optimal test period durations for 5 traits: BW, DMI, RE, RE/DMI, and RFI. Traits were fitted into a mixed model with repeated measures. For each week, the traits were summarized as a sequence of cumulative data, starting from WOL 16 and cumulated over periods that increased in 1-wk steps up to WOL 16 to 29. Weekly cumulations were compared with those for entire test period (WOL 16 to 30). Consistency of each cow’s efficiency classification as high, medium, or low was tested by the total-agreement procedure; the

INTRODUCTION

Feed accounts for 50 to 60% of dairy cow production costs (Connor et al., 2012) and, according to the Israeli Dairy Council (2012), it incurs 58.4% of the total production cost, including expenses during heifer growth from 1 wk of age up to first calving and cow nonlactating periods. Connor et al. (2012) discussed in detail the methods and conditions for monitoring dairy cow efficiency. The conventional indirect way to enhance dairy cow production efficiency is by culling out the less productive animals, thereby increasing the average production rate, which decreases maintenance requirements in relation to the entire energy requirements. However, increasing cow production rates and feed intake may lead to decreased feed digestibility (NRC, 2001) and increased fecal excretions, which, in turn, increase environmental contamination per unit of DMI. Further-

Received July 17, 2013. Accepted March 8, 2014. 1 Corresponding author: [email protected]

1

2

Asher et al.

more, direct selection for increased production led to increased BW (Hansen, 2000), which, by its nature, is expected to increase the energy cost of maintenance without directly contributing to production throughout heifer growth and between lactations. Numerous studies have documented the fact that considerable between-animal variation exists for feed efficiency; this phenomenon is independent of differences in productivity [Arthur et al. (2004) in beef cattle and Connor et al. (2013) in dairy cattle]. Thus, direct selection for increased feed efficiency should be the main selection target; it would improve farm profits, increase farm production per DMI, and decrease the costs of waste contamination and treatment. Identifying and ranking cows according to their individual feed efficiency is an essential procedure for increasing the herd collective efficiency at the farm level. In the modern dairy herd, individual performance variables, such as milk production, milk composition, BW, and BCS, are measured routinely. However, feed efficiency cannot be calculated, as individual DMI is not monitored, because high operating costs render its measurement very expensive. The feed conversion ratio (DMI/milk production) is the simple classical characteristic used to determine feed efficiency. However, it would be more accurate to replace the term “milk production” with “recovered energy” (RE), which encompasses the energy content of the milk produced daily (REL) and the retained or depleted body energy as a result of changes in fat content (REF). To quantify efficiency, it would be more logical to replace the conversion ratio DMI/RE with its reciprocal, RE/DMI, whose direction of change matches the direction of the efficiency changes (i.e., increased value of RE/DMI reflects increased efficiency). An alternative index—residual feed intake (RFI)—recently has been proposed as a measure of feed efficiency in cattle; it was defined as the difference between an animal’s actual DMI and its expected DMI according to its maintenance and production requirements (Archer et al., 1997, 1999; Arthur et al., 2001). In practice, calculation of RFI for growing animals is based on 3 variables: DMI, ADG, and BW, of which BW and ADG represent energy sinks for maintenance and growth, respectively. Therefore, ADG as used to describe growing animals could be replaced, in the case of lactating cows, with milk production and milk energy (Bauman et al., 1985; Connor et al., 2012) or, more accurately, with RE. It is not valid to measure the ADG of ruminants over a short period, because of considerable daily variations in their digestive weight. However, measuring milk production, BCS, and, consequently, RE of lactating cows over a short period is easier and more reliable than measuring ADG. Therefore, the time required for ranking high-yielding Journal of Dairy Science Vol. 97 No. 7, 2014

lactating dairy cows according to their efficiency would be expected to be significantly shorter than the time required for ranking growing calves. It is critical to define the optimal measurement duration for accurate evaluation of DMI, RE, and BW—the key factors that determine individual efficiency—to reduce feed and management costs. In practice, selection is carried out on a group basis; for example, by (a) culling out the least efficient individuals, (b) using the most efficient cows for superovulation and implantation, or (c) selecting as replacement cows the potentially more efficient heifers (i.e., those born to the more efficient parents). In a modern sophisticated cowshed of lactating dairy cows, most of the performance variables are measured automatically on a daily basis. When the current study was carried out and written we did not find published studies on lactating dairy cows that aimed to determine, at weekly intervals, the optimal duration of such automatically regulated measurements to enable ranking cows according to their efficiency. We found only a citation by Connor et al. (2012) of an unpublished study, which has recently been published (Connor et al., 2013) in which measurement of RFI of dairy cattle from DIM 21 to 70 provided 81% of the information gained from a test period of 90 DIM. Connor et al. (2012) recommended a minimum test length of 70 d, followed by an adjustment period of 21 d, for estimating RFI in early-lactating Holstein cows. However, according to Asher (2010), cow efficiency as determined early in the lactation period does not reflect the situation in cows in the middle of their lactation period. According to Asher (2010), during lactation, cow bodily energy reserves, as expressed in BCS measurements, significantly decreased up to week of lactation (WOL) 7, remained constant from WOL 7 through 30, and then increased significantly. In addition, DMI of these cows significantly increased up to WOL 7, and then gradually increased until WOL 16 to 18. In ruminants, increases in DMI on the same diet are accompanied by increases in animal gut fill. This probably causes errors in monitoring energy balance, as part of the increase in cow BW is the result of additional feed and water in the digestive tract. The first objective of this study was to investigate individual variations of lactating dairy cows in the performance variables (BW, DMI, and RE) that are used to calculate cow efficiency and to evaluate the optimal duration of the measurement procedures. The second objective was to determine the optimal measurement time required for reliable ranking of cows into 3 efficiency subgroups: high (HI), medium (MED), and low (LO), using RE/DMI and RFI as efficiency traits. We assumed that an individual cow could be transferred only between 2 adjacent subgroups (i.e., between MED

TIME REQUIRED TO DETERMINE DAIRY COW EFFICIENCY

and HI, or MED and LO). Our hypothesis was that within the designated test period, where the expected changes in BCS were minor, the length of time needed to reliably rank lactating high-yielding dairy cows according to efficiency levels HI, MED, and LO would be significantly shorter than that used for growing animals. MATERIALS AND METHODS Cows and Management

All procedures involving animals in the present study were approved by the Israeli Committee for Animal Care and Experimentation (Bet-Dagan, Israel; Volcani file number 11807 IL). The study was carried out from February 1 to June 15, 2007, on 35 Israeli Holstein cows that exhibited no health problems during the study period. They were selected for data analysis from a larger number of cows that previously had been tested from WOL 1 through 35 (Asher, 2010). The larger group of cows had an average BW of 675 kg in the first WOL, the BW decreased to its lowest value of 618 kg in WOL 10, and increased up to 638 kg in WOL 35. The ADG between WOL 10 and 35 was 174 g/d. The DMI, REL, and REF of the larger group were respectively 16.5 kg of DM/d, 21.9 Mcal/d, and −6.8 Mcal/d in the first WOL; reached a peak of 27.9 kg of DM/d, 35.7 Mcal/d, and 1.05 Mcal/d in WOL 18, 7, and 11, respectively; and were respectively 23.8 kg of DM/d, 25.6 Mcal/d, and 0.048 Mcal/d in WOL 35. The average REF between WOL 11 and 35 was 0.364 Mcal/d. The WOL of the selected cows ranged from 16 to 30. The average milk production of cows selected for this study was 46.6 kg/d from 16 to 30 WOL. Milk production was 51.7 kg/d during WOL 16, and decreased in a linear manner (−0.819 kg/wk) to an average of 40.4 kg/d during WOL 30. As discussed in the introduction, changes in BCS, digestive tract gut fill, and visceral tissue weight can cause significant errors in calculating cow energy balance and estimating the efficiency of cows. According to Asher (2010), the peak of lactation occurred in WOL 7, and from WOL 9 onward, REF was positive and small. The average of the REL/RE ratio in the test period was 0.988 ± 0.004. According to Asher (2010) and for the cows tested in the present study, the peak of DMI occurred in WOL 18. The cows used in this study were selected for the following reasons: (1) the minor value of REF throughout this period; (2) the minor changes in gut fill to be expected during this period, because the DMI was relatively stable; and (3) the high proportion of healthy cows. This choice was compatible with the findings of Connor et al. (2012) that during WOL 16 through 30, changes in the cow net energy balance were relatively small.

3

In the test period, the age of cows at the beginning of lactation ranged from 3 to 8 yr, with an average age of 5 yr and 8 mo. Parity numbers ranged from 2 to 7. The parity distribution was sorted into groups, as suggested by Connor et al. (2013): 6 cows were in second parity and 29 cows were in third-and-higher parity, with an average parity of 3.8. The performance variables and efficiency traits of the second-parity cows compared with the third-and-greater parity cows were respectively as follows: BW of 586 vs. 687 kg (P = 0.00030), DMI of 24.13 vs. 26.87 kg/d (P = 0.026), REL of 28.86 vs. 30.84 Mcal/d (P = 0.0099), REF of 0.26 vs. 0.42 Mcal/d (P = 0.55), RE of 29.13 vs. 31.25 Mcal/d (P = 0.020), RE/DMI of 1.21 vs. 1.17 Mcal/kg of DM (P = 0.27), and RFI of −1.02 vs. 0.2 kg of DM/d (P = 0.24). The current study focused on phenotypic characters and not on genetic characters, and on the variation among variables of the performance characters and efficiency traits throughout the test WOL and not on the values per se. The efficiency traits, which were the main focus of the current study, were not affected by parity. Thus, we did not found any reason to consider the effect of parity in the statistics. Of the 35 tested cows, 19 were pregnant when they were tested; 5 had conceived before WOL 16 and the other 14 had conceived by WOL 30. In the pregnant cows, conception occurred between WOL 13 and 29, with an average of 21.2 ± 1.17 WOL (i.e., an average of 5.2 wk of pregnancy at the beginning of the test). Conception dates were deduced from the last insemination date. The effect of pregnant versus not pregnant on performance and efficiency of the cows was tested by t-test analysis, by using the average cow data from WOL 16 to 30. It was found that pregnancy did not affect cow performance and efficiency. The respective pregnant versus not pregnant variables were as follows: DMI of 26.7 vs. 26.1 kg/d (P = 0.48), BW of 638 vs. 637 kg (P = 0.98), REL of 30.73 vs. 30.22 Mcal/d (P = 0.54), REF of 0.260 vs. 0.545 Mcal/d (P = 0.27), RE of 30.99 vs. 30.76 Mcal/d (P = 0.80), RE/DMI of 1.167 vs. 1.182 Mcal/kg of DM (P = 0.63), and RFI of 0.314 vs. −0.373 kg of DM/d (P = 0.32). Thus, gestational stage was not considered in the current study to compute feed efficiency traits. Cows were housed at the Agricultural Research Organization dairy farm, with 1 shaded open barn with a maximum roof height of 7 m that provided an area of 20 m2/cow under the roof, plus an open yard that provided 8 m2/cow. During the study, the cows had free access to water. The cowshed had 42 individual feed troughs, and the cows were introduced to the testing cowshed at least 2 wk before they were monitored for data analysis. Journal of Dairy Science Vol. 97 No. 7, 2014

4

Asher et al.

Because calving dates differed among cows, the recorded dates of cows in WOL16 ranged from February 1 through March 2, 2007. Diets and Sampling Procedures

One TMR diet (Table 1) was used in the study; it was offered once daily at 1000 h for ad libitum intake, allowing for about 5 to 10% orts. Cows were milked 3 times daily, at 0600, 1400, and 2200 h. Cows were fed individually via a computerized monitoring system designed to electronically identify each individual cow and to automatically control and record its daily feed intake. This feeding system was described by Miron et al. (2003). Voluntary daily DMI of each individual cow was determined; DM content (AOAC International, 2001) was based on daily samples of TMR and individual feed refusals.

The milk yield (kg/d) of each cow was recorded daily by automatic meters (Afimilk; SAE Afikim, Kibbutz Afikim, Israel). Milk samples from each cow were collected weekly at 3 successive milking events throughout the study. Each set of milk samples for each cow was stored at 4°C with a preservative tablet (2-bromo2-nitropropane-1,3-diol; bronopol) supplied by the Israeli Cattle Breeders’ Association (Caesarea, Israel), pending infrared analysis for fat, protein, and lactose contents at the Israeli Cattle Breeders’ Association Laboratory, by means of a MilkoScan 4000 instrument (Foss Electric A/S, Hillerød, Denmark). Daily REL (i.e., NEL) was calculated in energy units according to NRC (2001): REL (Mcal/d) = milk yield (kg/d) × {[0.0929 × milk fat (%)] + [0.0547 × milk protein (%)] + [0.0395 × milk lactose (%)]}.

Cow Performance and Efficiency Measurements

Cow performance data (BW, DMI, and milk production) were monitored daily, and weekly changes in BCS were used to calculate REF. Performance data (BW, DMI, REL, REF, and RE) were summarized weekly. Energy balance variables are presented as megacalories per day per cow unless stated otherwise. Table 1. Ingredients, chemical composition, in vitro digestibility, and calculated energy concentrations of the TMR diet Item Ingredient (% of DM)   Wheat silage   Corn silage   Oat hay   Barley grain, rolled   Corn grain, ground   Wheat bran   Corn gluten feed   Dried distillers grains  Cottonseed   Soybean meal   Sunflower meal   Rapeseed meal   Calcium bicarbonate   Protected fat  Ca-LCFA1  Limestone   Vitamins and minerals  Urea Chemical composition and calculated energy concentrations   DM (%)   OM (% of DM)   CP (% of DM)   Ether extract (% of DM)   NDF (% of DM)  NEL2 (Mcal/kg of DM) 1

Calcium salts of long-chain FA. Diet NEL, based on NRC (1989) for 3 maintenance levels.

2

Journal of Dairy Science Vol. 97 No. 7, 2014

Amount 18.20 5.10 10.10 3.40 24.50 3.60 5.70 2.10 5.50 4.40 7.90 2.70 0.70 0.90 2.70 2.10 0.11 0.30 68.10 92.70 17.00 4.25 40.90 1.76

Throughout the study, 1 trained person estimated BCS once weekly according to a 1 to 5 scale (Edmonson et al., 1989). To minimize the effect of random error in BCS determination, weekly observed BCS data were smoothed, using a moving average of 3 wk of observations. Cow BW were recorded 3 times/d (i.e., after each milking) with an automatic walk-in scale (Afimilk; SAE Afikim). The daily BW was calculated as the average of the 3 weights measured each day. The BW were first calculated as the average daily BW of 7 d, where Sunday was the intermediate day. At the second stage of calculation, weekly BW data were smoothed by using a moving average of 3 wk of measurements. Weekly BW gains (or losses) were calculated from the difference between the smoothed BW. Calculated REF was based on Holstein BW, BCS, and daily changes in BCS (∆BCS), according to NRC (2001, pages 24 and 25) and Fox et al. (1999); ∆BCS values were calculated from the weekly estimates of BCS. REF was calculated as follows: REF = 84.6 × ∆BCS × (583.8)−1 × BW × 5.47, where BW is measured in units of 583.8 kg and BCS ranges between 3 and 2; 1 unit of ∆BCS represents changes of 84.6 kg in BW and 5.47 Mcal/kg in energy content of BW. For each cow in each WOL, data were corrected according to the BW divided by 583.8 and multiplied by the measured BW. Efficiency was assessed in terms of the RE/DMI ratio and the RFI. The expected DMI for each cow was calculated from the slopes and intercepts of a multiple linear regression of DMI dependence on BW0.75 and on

TIME REQUIRED TO DETERMINE DAIRY COW EFFICIENCY

RE (presented as Mcal/d). The RE was used instead of ADG, which is used for growing calves. Statistical Analysis

Data were analyzed with SPSS software (version 17; SPSS Inc., Chicago, IL). The performance variables used for the efficiency calculations were BW, RE, and DMI, and efficiency was calculated as RE/DMI. Analysis of variance was applied, with a repeated-measurement procedure, to the differences of these variables among cows in each WOL and of their cumulative values over increasing sequences of WOL, starting from WOL 16 and continuing through WOL 16 to 29. Each cumulated value of each variable was compared with the average of the same variable as measured throughout the test period (WOL 16 to 30). The test was carried out to estimate the changes and variations among cows in each WOL and in the sequence of WOL. The last test was done to determine how many weeks of measurements were required to establish a stable value of each variable. The cumulative data for each WOL and averages for the same WOL are equal because both are based on the same subperiod of 1 wk. The coefficient of variation of each variable in each WOL was calculated and subjected to the same repeated test procedure. Linear regression procedures were used for examining the changes in the course of each WOL, for each variable and for its coefficient of variation. The number of weeks needed to determine RE/DMI and RFI were calculated by testing the consistency of each cow’s ranking as HI, MED, or LO efficiency. Cow RFI and their cumulated values were calculated for each test WOL and for WOL sequences from WOL 16 through 16 to 29; the calculated values were compared with those for the entire measurement period (i.e., the cumulative values for WOL 16 through 30). Group HI comprised cows whose efficiency values were 0.5 × standard deviation above the average, group LO comprised those whose efficiency values were 0.5 × standard deviation lower than the average, and group MO comprised all other cows. The average RFI was zero, by definition. The consistency of each cow’s efficiency ranking was evaluated according to the frequency of identical rankings in levels HI, MED, and LO (total agreement); this was statistically tested according to a chance-corrected measure of agreement, the kappa index (Fleiss, 1981). The effects of number of weeks needed to assess efficiency, calculated as RE/DMI and as RFI separately, were also presented by calculating the Pearson correlation coefficients between the cumulative data from the various WOL sequences (WOL 16 and each of WOL

5

16 through 29) on the one hand, and those for the entire test period (i.e., WOL 16 through 30) on the other hand. In addition, the course of changes of the correlation coefficients as data were cumulated from increasing numbers of WOL was tested by regression analysis of both RE/DMI and RFI. As discussed in the introduction, consistency over time of cow efficiency rankings as HI, MED, or LO is taken to be a good criterion for reliable efficiency selection; in particular, transitions of individuals between groups HI and LO is not desirable. The percentage of cow transitions from group HI to LO and vice versa was calculated as a percentage of the total number of cows in these 2 groups; it was calculated for the grouped data of RE/DMI and of RFI, by comparing the results obtained by ranking cows according to the data from WOL 16 and the cumulated data from increasing WOL sequences through WOL 16 to 29, on the one hand, with those obtained by ranking the cows according to the cumulated data from the entire test period (i.e., WOL 16 to 30). RESULTS Correlations Among Performance Components Plus Efficiency Traits of the Cows: Data Analysis of Averages of WOL 16 to 30

The correlation coefficient values and their significance among performance and efficiency traits are presented in Table 2. The data represents average data (n = 35 cows) of the entire tested period (WOL 16 to 30). Body weight was significantly correlated only with ADG. Dry matter intake was significantly correlated with most of the variables except BW and ADG and tended to be correlated with REF. The REL was significantly correlated with DMI, RE, and RE/DMI. The REF tended to be correlated with DMI and significantly correlated with ADG and RE. The RE tended to be correlated with ADG and significantly correlated with DMI, REL, and RE/DMI. The RE/DMI ratio was significantly correlated with DMI, REL, RE, and RFI. The RFI was significantly correlated with DMI and to RE/DMI. Multiple Regression Analysis of the Dependencies of DMI of the Cows on BW, REL, and REF, and of DMI on BW and RE: Data Analysis of Average WOL 16 to 30

According to the multiple regression analysis, DMI depended on BW0.75, REL, and REF (R2 = 0.37; P = 0.0023); the P-values of DMI dependencies on BW0.75, REL, and REF were 0.36, 0.0020, and 0.12, respectively. Dry matter intake also depended on BW0.75 and RE (R2 Journal of Dairy Science Vol. 97 No. 7, 2014

6

Asher et al.

Table 2. Correlations (r values, above the diagonal) and significance (P-values, below the diagonal) among variables that represent performance and efficiency traits1 Variable

BW

DMI

ADG

REL2

REF3

RE4

RE/DMI

RFI5

BW (kg) DMI (kg/d) ADG (kg/d) REL (Mcal/d) REF (Mcal/d) RE (Mcal/d) RE/DMI (Mcal/kg) RFI (kg/d)

— 0.14 0.044 0.30 0.37 0.25 0.66 0.74

0.26 — 0.41 9.7E−4 0.070 2.2E−4 0.003 6.6E−11

0.34 0.14 — 0.20 0.042 0.074 0.29 0.67

0.16 0.53 0.22 — 0.56 1.8E−19 0.012 0.40

0.18 0.31 0.35 0.10 — 0.023 0.65 0.18

0.20 0.58 0.31 0.96 0.38 — 0.014 0.24

−0.071 −0.50 0.18 0.42 0.079 0.41 — 4.0E−7

0.058 0.85 −0.075 0.15 0.23 0.20 −0.74 —

1

Data represent average measurements (n = 35 cows) of the entire tested period (week of lactation 16 to 30). REL = energy in milk production. 3 REF = retained or depleted body energy as a result of fat content change. 4 RE = total recovered energy, calculated as REL + REF. 5 RFI = residual feed intake. 2

= 0.36; P = 0.00074); the P-values of DMI dependencies on BW0.75 and RE were 0.32 and 0.0005, respectively. Cow Performance and RE/DMI in WOL

The main performance variables and efficiency as RE/DMI throughout WOL 16 to 30 and the linear trend of changes throughout the week are presented in Table 3. The averages of these variables, measured and cumulated throughout WOL 16 to 30 are presented in Table 4.

Body weights in all tested weeks did not differ significantly from the average over the entire test period, either for individual weeks (Table 3) or for cumulated weekly data (Table 4). Body weight gains were slightly increased (P < 0.05) throughout the test period, whether calculated for each WOL (385 g/wk) or cumulated over sequences of WOL (192 g/wk). The among-cow percentage coefficient of variation of the BW measurements increased (P < 0.001) throughout the test period, but its rate of increase was minor [0.1% per week (i.e., 1.5% over the entire test)].

Table 3. Single-week values of cow performance variables: BW (kg), DMI (kg/d), recovered energy (RE; Mcal/d), and efficiency (RE/DMI; Mcal/kg), measured in each week of lactation (WOL) BW Item WOL  16  17  18  19  20  21  22  23  24  25  26  27  28  29  30 Mean P-value3 Slope4 R2 4 P-slope4

kg 634 632 633 635 638 641 639 640 641 640 638 636 639 638 639 637.5 0.088 0.385 0.33 <0.05

DMI

CV1 (%) P-value2 8.35 8.33 8.16 7.87 8.33 9.09 8.91 9.13 9.15 9.06 9.12 9.33 9.23 9.30 9.60 8.86

0.18 0.12 0.20 0.60 0.53 0.11 0.34 0.17 0.10 0.14 0.48 0.62 0.18 0.54 0.18 —

0.10 0.77 0.001

1

kg/d 27.86 27.62 27.49 27.04 27.50 27.93 27.00 26.90 26.47 25.60 25.24 25.09 24.84 24.76 24.71 26.40 0.0001 −0.258 0.88 0.001

CV1 (%) P-value2 8.96 10.33 13.98 11.86 11.83 11.35 11.81 12.51 13.34 12.18 13.02 12.84 11.63 13.41 13.74 12.19 0.18 0.37 0.05

0.0001 0.001 0.046 0.14 0.002 0.0001 0.097 0.095 0.84 0.008 0.0001 0.001 0.0001 0.001 0.002 —

RE

RE/DMI

Mcal/d CV1 (%) P-value2

Mcal/kg CV1 (%) P-value2

33.65 33.75 33.25 33.21 32.00 32.40 31.37 30.70 30.61 29.88 29.01 29.02 28.99 28.45 27.02 30.89 0.0001 −0.468 0.97 0.001

11.42 11.56 14.87 13.41 12.21 11.15 11.65 13.91 12.90 12.49 10.61 11.97 14.14 14.63 14.19 12.74 0.10 0.10 NS

0.0001 0.0001 0.0002 0.0003 0.015 0.0021 0.081 0.61 0.66 0.43 0.019 0.037 0.023 0.0026 0.0001 —

1.211 1.227 1.225 1.239 1.175 1.174 1.173 1.152 1.167 1.175 1.160 1.167 1.172 1.154 1.100 1.178 0.012 −0.007 0.68 0.001

10.67 11.05 16.45 14.35 14.14 15.73 14.56 15.13 13.08 11.98 11.18 13.55 13.41 11.38 11.28 13.20

0.002 0.001 0.022 0.005 0.38 0.41 0.35 0.98 0.56 0.22 0.75 0.61 0.50 0.99 0.024 —

−0.11 0.066 NS

Represents the percentage variation among cows, calculated as SD/mean, for each WOL. Represents the significance of the difference of the value for each WOL from the average over the entire test period. 3 Represents the significant difference among WOL of the tested variable. 4 Slope, R2, and P-slope represent the linear regression slope, the regression-explained variance by WOL, and its significance, respectively. 2

Journal of Dairy Science Vol. 97 No. 7, 2014

7

TIME REQUIRED TO DETERMINE DAIRY COW EFFICIENCY

Table 4. Averages of cow-performance variables: BW (kg), DMI (kg/d), recovered energy (RE, Mcal/d), and efficiency (RE/DMI, Mcal/kg), measured and cumulated throughout week of lactation (WOL) 16 to 30 BW Item WOL  16  16–17  16–18  16–19  16–20  16–21  16–22  16–23  16–24  16–25  16–26  16–27  16–28  16–29  16–30 P-value3 Slope 4 R2 4 P-slope4

kg 634 633 633 634 636 637 636 637 637 637 636 635 636 636 637 0.083 0.192 0.33 <0.05

DMI

CV1 (%) P-value2 8.35 8.27 8.16 8.03 8.26 8.62 8.52 8.64 8.62 8.60 8.64 8.73 8.67 8.65 8.81 0.04 0.70 <0.001

0.18 0.11 0.13 0.30 0.77 0.65 0.96 0.81 0.61 0.87 0.63 0.13 0.89 0.46

kg/d 27.9 27.7 27.7 27.5 27.5 27.6 27.5 27.4 27.3 27.1 27.0 26.8 26.7 26.5 26.4 <0.0002 −0.100 0.93 <0.001

RE

CV1 (%) P-value2 8.96 8.85 9.60 9.80 9.64 9.30 9.21 9.29 9.34 9.32 9.36 9.30 9.15 8.99 8.83

0.0001 0.0001 0.0001 0.002 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.001 0.002

−0.02 0.080 NS

Mcal/d

RE/DMI

CV1 (%) P-value2

33.65 11.42 33.70 11.01 33.45 11.82 33.43 10.10 32.83 9.22 33.02 8.54 32.51 9.29 32.18 9.64 32.13 9.67 31.76 9.55 31.33 8.21 31.33 8.56 31.32 9.90 31.05 9.65 30.34 9.89 <0.0001   −0.234 −0.13 0.97 0.30 <0.001 <0.05

0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.001 0.002 0.001 0.003

Mcal/kg CV1 (%) P-value2 1.211 1.218 1.213 1.221 1.200 1.206 1.189 1.180 1.181 1.176 1.168 1.174 1.179 1.175 1.153 <0.001 −0.004 0.85 <0.001

10.67 9.53 10.14 9.53 9.51 11.02 10.50 10.88 9.41 10.24 9.29 9.16 9.37 9.23 9.59

0.002 0.0001 0.0001 0.0001 0.002 0.001 0.005 0.076 0.018 0.037 0.12 0.057 0.008 0.013

−0.06 0.20 NS

1

Represents the between-cow percentage variation in each of the cumulative values over WOL sequences, as a percentage of SD/mean. Represents the significance of the difference between the cumulated sum over each WOL sequence and the average over the entire test period. 3 Represents significant differences among cumulative values for all WOL sequences. 4 Slope, R2, and P-slope represent the linear regression slope, regression-explained variance by sum of WOL, and its significance of changes in each variable and in its percentage CV over the whole test period, respectively. 2

Dry matter intakes in most of the test weeks were significantly different from the average over the entire period, whether they were calculated for each WOL (Table 3) or cumulated over sequences of WOL (Table 4). Throughout the test period, DMI decreased (P < 0.001) by 258 g/wk when calculated for each WOL and by 100 g/wk when cumulative values were used. When the between-cows percentage coefficient of variation of the DMI was calculated for each WOL, it increased (P < 0.05) throughout the test period at 0.18% per week, but did not change when it was calculated for values cumulated over WOL sequences. The RE in most of the test weeks differed significantly from the average over the entire test period, whether calculated for each WOL (Table 3) or cumulated over WOL sequences (Table 4). Throughout the 15-wk test period, RE decreased (P < 0.001) by −0.468 Mcal/wk when RE was computed for each WOL and by −0.234 Mcal/wk when based on cumulative data. The between-cow percentage coefficient of variation of the RE measurements did not change throughout the test period when it was calculated for each WOL, but decreased (P < 0.05) when calculated for cumulative data. Throughout the test period, RE/DMI for single weeks differed significantly (range from P = 0.002 to P = 0.022) from the average for the entire period up

to WOL 19 (Table 3), and cumulated data for most of the WOL sequences differed significantly (range from P = 0.0001 to P = 0.12) from the average over the entire test period. The RE/DMI ratio decreased (P < 0.001) throughout the test period by 0.007 Mcal/kg per week when calculated for each WOL and by 0.004 Mcal/kg per week when calculated for cumulated data. The between-cow percentage coefficient of variation of the RE/DMI values did not change throughout the test period, whether calculated for each WOL or for cumulated data. Optimal Time Needed for Reliable Efficiency Ranking of Cows

Statistical analysis of consistency of cow efficiency rankings as HI, MED, or LO is presented in Table 5; efficiency was calculated as both RE/DMI and RFI. Ranking of cows was tested for data from WOL 16 and for data cumulated over increasing WOL sequences from WOL 16 to 29; data cumulated through each WOL were compared with those for the same group of cows cumulated over the entire test period. Values of RE/DMI differed only between cows whose rankings were calculated for WOL 16 and those ranked according to the entire test period (P = 0.18; total agreement = 43%; kappa = 16%). Values of RFI did Journal of Dairy Science Vol. 97 No. 7, 2014

8

Asher et al.

not differ between cows whose rankings increasing WOL sequences from WOL 16 through 29 and those ranked according to the entire test period (Table 5). Correlations Between Efficiency Traits Calculated for Shorter Periods and for the Entire Test Period

Correlations between cumulative results from shorter periods (WOL 16, and WOL 16 to each of WOL 17 to 29) on the one hand, and from the entire test period on the other hand, were all significant in the case of RE/ DMI (starting from r = 0.64 and P < 0.05 for WOL 16; Table 5). However, at least 2 wk of measurements (WOL 16 and 17) were required to achieve significant correlation in the case of RFI (r = 0.61; P < 0.05; Table 5). For both efficiency traits, the significance of total agreement, kappa, and correlation coefficient values increased as data from more weeks were used in calculating efficiency (Table 5; P < 0.001); for RE/DMI and RFI, these correlations included correlation coefficients of 0.88, 0.90, and 0.93 for RE/DMI and correlation coefficients of 0.89, 0.88, and 0.88 for RFI, for total agreement, kappa, and correlation coefficient values, respectively. Transitions of Individual Cows Among Efficiency Rankings

For RE/DMI, the maximum percentage of cows transferring between efficiency groups HI and LO, as indicated by comparison between accumulated data for each sequence of WOL and those for the entire test period, was 5.3% (Table 5). This occurred for 4 cumulations of data from WOL sequences. This transition rate represents a shift of 1 of the 19 selected cows between groups LO and HI, and each of the 4 transitions involved a different individual cow. The last transition between groups HI and LO appeared in the cumulative data for WOL 16 to 27. In parallel, the maximum transition rate between groups HI and LO with respect to RFI was 22.7% (Table 5), which occurred only in WOL 16. Other transition rates were 18.2% in the cumulative data for WOL 16 and 17, 9.1% in accumulated data for WOL 16 to 18, and 4.5% in accumulated data for WOL 16 to 19 and 16 to 22, a shift of 1 cow of the 24 selected cows between groups LO and HI. Correlation Between RE/DMI and RFI Throughout WOL

Correlations between RE/DMI and RFI were significant (P < 0.001) for all the cumulative test results Journal of Dairy Science Vol. 97 No. 7, 2014

from WOL 16 (r = 0.71) and WOL sequences from 16 through to 27 (r = 0.89; Table 5). Significance of the RE/DMI versus RFI correlation increased (P < 0.05; r = 0.61) with increasing lengths of WOL sequences, at a rate of 0.81% per week. RE/DMI and RFI Dependency on BW Over the Test Period and Among Cows

The average BW increased from 634 ± 8.9 kg at WOL 16 to 639 ± 10.4 kg at WOL 30 (Table 3). The individual BW, averaged over each test period, ranged from 522 to 735 kg; the efficiency parameters RE/ DMI and RFI did not depend on BW (R2 = 0.036 and 0.0033, respectively). DISCUSSION

The present study addressed lactating, high-yielding dairy cows, and focused on 2 main aspects of their efficiency: (1) The time variation of the performance variables BW, RE, and DMI, and of the basic efficiency trait RE/DMI; and (2) the length of time needed to reliably rank the cows into 3 groups according to efficiency, calculated as RE/DMI and RFI. The data used for the analysis were received as part of a longer study where performance and efficiencies were tested throughout WOL 1 to 35. Dependencies Among Performance Variables

The performance dependency analyses that are presented in this section were analyzed for average data from the entire test period (WOL 16 to 30); therefore, the analysis represents variation among cows and not variations that occurred throughout the lactation. The REF was estimated and not measured directly. To minimize the error in efficiency calculations, the present study data analysis was planned for a stage of lactation where REF was small; thus, the errors in the entire energy balance calculation, which can be caused by the REF calculation, were minimal. This was compatible with the finding that DMI tended to depend on REF as a single independent variable and was not significantly affected by REF when DMI dependency on BW0.75, REL, and REF were tested by multiple-regression analysis. Therefore, REF was not used as an independent variable in the calculation of expected DMI for the RFI calculation. The significance of DMI dependency on RE (P = 2.2E−4; r = 0.58), was a little greater than the significance of DMI dependency on REL (P = 9.7E−4; r = 0.53). Consequently, calculation of the expected DMI for RFI calculation was based on BW0.75 and RE.

42.9 51.4 62.9 57.1 62.9 60.0 62.9 57.1 71.4 65.7 68.6 71.4 71.4 77.1 1.90 0.878 <0.001

15.5 27.0 42.3 35.6 42.8 38.9 44.5 36.0 56.3 47.6 53.8 58.0 56.3 65.6 2.88 0.898 <0.001

%

4

0.18 0.022 0.0001 0.0020 0.0001 0.0010 0.0001 0.0020 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001

P-value

Kappa

0.64 0.64 0.72 0.65 0.74 0.77 0.83 0.75 0.82 0.86 0.88 0.83 0.88 0.90 0.020 0.93 <0.001

r <0.05 <0.05 <0.01 <0.01 <0.01 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001

P-value

Correlation3

0.0 5.3 5.3 5.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5.3 0.0 0.0

HI&LO

5

Transit (%)

62.9 57.1 68.6 62.9 57.1 65.7 71.4 77.1 68.6 85.7 85.7 80.0 85.7 88.6 2.35 0.89 <0.001

T-agreement (%) 44.1 35.6 52.9 44.4 34.9 48.3 57.3 65.5 52.6 78.6 78.5 70.3 78.6 82.9 3.55 0.88 <0.001

%

4

0.0002 0.0027 0.0000 0.0002 0.0033 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001

P-value

Kappa

RFI

0.49 0.61 0.71 0.67 0.74 0.80 0.86 0.86 0.89 0.94 0.94 0.93 0.95 0.96 0.033 0.88 <0.001

r NS <0.05 <0.01 <0.01 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001

P-value

Correlation3

22.7 18.2 9.1 4.5 0.0 0.0 4.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0

HI&LO5

Transit (%)

0.71 0.72 0.77 0.87 0.86 0.82 0.82 0.79 0.86 0.82 0.88 0.89 0.83 0.82 0.008 0.61 <0.05

Correlation of RE/DMI and RFI2

1 Efficiency variables were calculated as recovered energy (RE)/DMI and as residual feed intake (RFI); data cumulation started in week of lactation (WOL) 16 and continued through WOL sequences up to WOL 16 to 29. For each variable, total agreement (T-agreement) and kappa were compared for cow efficiency-ranking transitions (Transit) between HI and LO over increasing WOL sequences from WOL 16 to 30. 2 The correlation coefficient between cow-efficiency traits (RE/DMI and RFI) for cumulated data for each WOL sequence. 3 The correlation coefficient (r) and its P-value, respectively, represent, for each variable, the correlation and its significance between cow efficiency for data cumulated over each WOL sequence on the one hand, and the value calculated for the entire test period on the other hand. 4 P-value represents the significance of the kappa value. 5 The transition HI&LO represents the number of cows moved from ranking LO to HI and vice versa in each data cumulation over a WOL sequence (row), and is presented as a percentage of the number of cows in rankings HI and LO. 6 Slopew, rw, and Pw represent the linear regression slope, correlation, and its significance between cumulated data from each WOL sequence and each statistical datum, respectively.

WOL  16  16–17  16–18  16–19  16–20  16–21  16–22  16–23  16–24  16–25  16–26  16–27  16–28  16–29 Slopew6 rw6 Pw6

Item

T-agreement (%)

RE/DMI

Table 5. Tests of consistency of cow-efficiency rankings as high (HI), medium (MED), and low (LO)1

TIME REQUIRED TO DETERMINE DAIRY COW EFFICIENCY

9

Journal of Dairy Science Vol. 97 No. 7, 2014

10

Asher et al.

The average (n = 35 cows) of the REL/RE ratio was 0.9881 ± 0.004 over the test period and the weekly average of the REL/RE ratio over WOL 16 to 30 (n = 15 wk) was 0.9986 ± 0.0034. Therefore, it is reasonable to conclude that in similar measurement conditions, where REF is so minor, using REL instead of RE for the regression equation for calculating the expected DMI cannot affect significantly the RFI values and individual ranking of cows according to efficiency. In the tested multiple-regression model of DMI dependency on RE and BW (or BW0.75), whether it was tested among cows (n = 35) or throughout WOL 16 to 30 (n = 15), the effect of BW on DMI was not significant (P = 0.32 and P = 0.60, respectively). In addition, BW (or BW0.75) as a single independent variable did not significantly affect DMI (P = 0.14 and P = 0.94, respectively). Connor et al. (2013) found a very significant effect of BW0.75 on the predicted ME intake (MEI), and Van Arendonk et al. (1991) found a correlation coefficient of 0.65 between BW and energy intake. Analysis of the dependencies of DMI on BW and on RE (multiple regression) carried out on data from WOL 1 to 35 (our unpublished data) showed significant effect (P = 7.0E−10; R2 = 0.73), but the partial effect of BW on the regression was not significant (P = 0.157). In that study, the DMI dependency on BW as a single independent variable was (P = 1.4E−6; R2 = 0.51), but this dependency reflected the effect of BW and RE throughout WOL 1 to 35. The dependency of DMI on RE throughout WOL 1 to 35 as a single independent variable was highly significant (P = 3.0E−14; R2 = 0.83). The finding that, in reality, BW did not affect DMI was not expected, and it will be discussed later on. Correlation Between Efficiency Indices and Performance Variables

As expected from the method of calculation and as was found previously (Herd and Arthur, 2009), RFI was very significantly correlated with DMI and not correlated with BW and production variables. As expected, the RE/DMI ratio was highly negatively correlated with DMI and significantly positively correlated with the main performance variables (i.e., REL and RE). Dry matter intake and RE were not affected by BW; this can explain why the efficiency RE/DMI value was not significantly affected by BW. Objective 1: Time Variation of Performance Variables

Production rate and, consequently, BW, RE, and DMI changed throughout the lactation period (NRC, 2001); therefore, RE/DMI was expected to change, too. In Journal of Dairy Science Vol. 97 No. 7, 2014

growing animals, BW and, consequently, maintenance costs increase during the growing period. Thus, RE/ DMI decreases throughout the growing period because maintenance energy input increases as a proportion of MEI. Using RFI to characterize animal efficiency and compare efficiency among individuals is perceived to help to overcome the problem of increasing maintenance cost; in lactating dairy cows, it is reasonable to assume that RFI is a reliable index of individual efficiency for selection purposes. However, RE/MEI or RE/DMI are appropriate measures for a specific diet, representing better the simple economic efficiency value. Contrary to expectations of relative stability of performance variables, in the present study, all tested performance variables and RE/DMI changed significantly throughout the 15 test weeks (WOL 16 to 30). The average weekly rates of change, expressed as percentages of the change in each variable over the entire period, were very minor for BW (0.03%), and greater for DMI (−0.37%), RE (−0.73%), and RE/DMI (−0.34%). As explained in the introduction, shortening the time needed to reliably determine the values of performance variables is critical to reducing the time required to evaluate individual efficiency. The bottleneck in the process of shortening this period is presented by the natural variation of each variable, but this bottleneck can be cleared by increasing the frequency of monitoring. In the present study, BW was determined 3 times/d; intake and milk production were initially summarized daily, and all the above performance variables were summarized and statistically analyzed weekly. Changes in the energy retained in the body mass, which were calculated from the changes in BCS, were determined weekly. Determination of BCS is partly subjective, but in the present study, the possible contribution to an error in the entire RE calculation was minor because the average REF/RE ratio over WOL 16 to 30 was 0.00863 ± 0.00239. Because wide daily variation exists in ruminant BW, the variation in calculations of ADG can be regarded as the main factor that limits shortening the duration of individual efficiency measurements of growing animals. Indeed, increasing the frequency of BW measurements can shorten the time needed for selection of individual growing calves according to their RFI (Kearney et al., 2004). As BW is used as an independent variable for calculating expected intake, the increase in its monitoring frequency can decrease the time needed to determine RFI. In the present study, variation among cows in each of the performance variables and in RE/DMI is presented as coefficient of variation percentage. Changes in variation were investigated twice, starting with the effect of WOL alone (Table 3), which we did not expect to be significant. Then, we tested the effect of increasing

11

TIME REQUIRED TO DETERMINE DAIRY COW EFFICIENCY

Table 6. Direction of changes during the lactation period [weeks of lactation (WOL)] in performance variables and efficiency Greatest group1 Variable

Start

3

Slope

3

r

Smallest group1 3

P-value

Start

3

Slope

3

r

3

P-value

Slope2 of CV (%)

3

Single WOL   BW (kg)   DMI (kg/d)  RE4 (Mcal/d)   RE/DMI (Mcal/kg) Cumulative WOL3   BW (kg)   DMI (kg/d)   RE (Mcal/d)   RE/DMI (Mcal/kg)

700 28.2 37.5 1.307

1.4620 0.0033 −0.6910 −0.0181

0.810 0.022 −0.970 −0.900

<0.01 NS <0.001 <0.001

574 27.0 28.9 1.105

0.0880 −0.4216 −0.2030 0.0060

0.150 −0.970 −0.660 0.610

NS <0.001 <0.05 <0.05

0.10 0.18 0.10 −0.11

700 30.7 37.6 1.351

0.7309 −0.1902 −0.3058 −0.0066

0.850 −0.994 −0.959 −0.865

<0.001 <0.001 <0.001 <0.001

572 25.2 28.8 1.064

0.0444 −0.0302 −0.0836 −0.0006

0.150 −0.557 −0.573 −0.173

NS <0.01 <0.01 NS

0.04 −0.02 −0.13 −0.06

1

For each cow, for each value of RE/DMI and each of the variables, the average value over the first 3 wk was calculated. The cows were then ranked according to these average values, and the results obtained for the 10 highest ranked and for the 10 lowest ranked cows were examined. The rate of change of each tested variable was compared with the linear regression slope of weekly change variation over the period WOL 16 to 30. 2 Linear regression slopes of CV (%) along week. 3 Data for each WOL (based on Table 3) and for cumulative WOL (based on Table 4) of regression slope, correlation coefficient values, and P-values of each variable per week by group. 4 RE = recovered energy.

measurement time (i.e., cumulating data over lengthening sequences of WOL; Table 4); we expected this to lead to reduced variation among cows, as represented by coefficient of variation percentage, simply because the errors caused by short-term measurement of the performance variable were expected to decrease as the measurements of each cow’s performance were based on greater numbers of samples and longer measuring periods. However, contrary to expectation, the coefficient of variation percentage of weekly measurements of BW and DMI increased significantly with increasing measurement time, and the coefficient of variation percentage of RE did not change (Table 3). These unexpected results can be attributed to the trend of the changes: for example, lighter cows showed less daily BW gain (i.e., kg/d) than heavier ones. Similarly, cows with smaller DMI showed greater daily decrease in intake (i.e., kg/d per cow) than those with greater intake. Consequently, between-cow variation in BW and DMI would be expected to increase throughout the test period. To further evaluate this interpretation, we conducted the following calculation test: for each cow, for each value of RE/DMI and each of the above variables, we calculated the average value over the first 3 wk. We then ranked the cows according to these average values, and examined the results obtained for the 10 highest ranked and for the 10 lowest ranked cows. We compared the rate of change of each tested variable with the linear regression slope of weekly change variation, over the period WOL 16 to 30 (Table 6). We did this comparison first for each week’s data, as presented in Table 3, and then for the cumulated weekly data, as presented in Table 4.

The analyses (Table 6) clearly show that the increases in between-cow variations in BW and DMI throughout the WOL were not random; BW gains of the heaviest cows were greater than those of the lightest cows; this is supported also by the significant and positive correlation between the ADG and BW (Table 2). Throughout the WOL, DMI of the 10 cows with the greatest intakes were stable, whereas those of the 10 cows with the smallest intakes decreased significantly (Table 6). A similar nonrandom but opposite tendency was found for RE and RE/DMI throughout the lactation (Table 6) and a significant positive correlation was found between RE and RE/DMI (Table 2). Analysis of the coefficient of variation percentage of the cumulative values of variables (BW, DM, RE, and RE/DMI) over WOL sequences showed largely the same results, with only 1 exception: the coefficient of variation percentage of DMI decreased throughout lactation (Table 6). Objective 2: Time Needed for Reliable Efficiency Ranking

As discussed under objective 1, performance variables and efficiency (calculated as RE/DMI) changed significantly during the course of the test period. Although we limited the tests to a relatively stable period within lactation (WOL 16 to 30), we did not investigate the effect of WOL on RFI because the expected intake was calculated by a multiple-regression procedure in which, by definition, the average RFI is zero. Despite the lack of stability in performance and RE/DMI, a consistent hierarchy might, nevertheless, exist among cow efficiencies throughout the WOL of the tests. Ranking of inJournal of Dairy Science Vol. 97 No. 7, 2014

12

Asher et al.

dividual animals basically involves comparison among animals, and not absolute values. Thus, testing the consistency of cow rankings among 3 efficiency levels (HI, MED, and LO) can be regarded as a valuable basis for testing the time needed for efficiency-based selection. Indeed, this testing was the main objective of the present study. All the sets of cumulative measurements, starting from those for 1 wk (WOL 16) and followed by those for increasing WOL sequences, from WOL 16 to 17, up to WOL 16 through 29, were compared with those for the entire 105-d test period. This test period was substantially longer than those recommended for use with growing cattle (Wang et al., 2006) and the time duration reference used for dairy lactating cows (Connor et al., 2013). Researchers and producers who collect similar data at similar frequencies could use the present results to guide their own decisions regarding time needed for efficiency selection of lactating dairy cows. When cow efficiencies were compared with those of the entire study period to classify the cows as belonging to HI, MED, or LO efficiency groups of RFI and of RE/ DMI traits, the significance of the kappa index for a 2-wk study was P < 0.03 and the correlation coefficient was 0.6 (P < 0.05). When 3 wk or more were spent to classify the cow efficiency (RFI and RE/DMI), the significance of the lowest kappa index was P < 0.004, the lowest correlation coefficient was 0.65 and its lowest significance was P < 0.01 (Table 5). In practice, producers who want to improve their herd efficiency cull out the least efficient cows. To illustrate potential errors in such selection we present a calculation—not statistically tested—of likely percentages of wrong decisions resulting from culling out cows from the HI- and the LO-efficiency groups (Table 5, transition percentage). When efficiency is represented by RE/DMI, shortening the cow-selection test period to 1 wk would entail a maximum wrong-decision rate of only 5.3% (i.e., 1 cow in 19); when it is represented by RFI, a test period of 4 wk is needed to achieve a maximum error rate of 4.5% (i.e., 1 cow in 22). For testing the time needed to determine the RFI, Connor et al. (2013) used different statistical methods and tested the cows throughout the first 90 d of the lactation period. They showed that a test period of 53 DIM explained 81% of the variation in RFI provided by a test period of 90 DIM. Using the same criteria, the correlation coefficients presented in Table 5 were powered to coefficient of determination values for analyzing the explained variance in the current study. The analysis showed that 10 wk (70 d) are needed to explain 74% of the variance in RE/DMI, and 88% of the variance in RFI, provided by a test period of 15 wk (105 d). In the current study, we analyzed differently (different Journal of Dairy Science Vol. 97 No. 7, 2014

calculations and statistics) the time needed for characterizing cow efficiency. We concentrated on testing the time needed for classifying the cows into 3 efficiency groups. In addition, we tested the cows during a period of lactation when the possible errors in energy-balance variables, which can be attributed to changes in body fat content and rumen digesta fill volume, are minimal. The current study recommends a significantly shorter time to classify the cows according to their efficiency than Connor et al. (2013) did. The above-mentioned differences in the way of testing and the decision to test the cows in different periods of lactation can explain the difference in the time requirement recommendation between the 2 studies. From the aspect of selecting cows according to their efficiency, we could not see any special advantage of testing dairy cow efficiency at an early stage of lactation, when changes in the energy balance and gut fill are considerable (NRC, 2001). Hence, we recommend here to test cow efficiency in periods of lactation when intake stops increasing and to classify cows into 3 groups according to their efficiency. The period needed for efficiency-ranking lactating dairy cows is shorter than that recommended in previous studies; therefore, we suggest that the most efficient cows, which will be selected for intensive reproduction (e.g., by embryo transfer), should be tested in the course of more than 1 lactation. Total-agreement and kappa calculations and the tested correlations of RE/DMI and RFI increased linearly as the number of WOL used to provide cumulative results increased relative to the entire test period (Table 5). However, careful examination of Table 5 also highlights events for which values of kappa statistics and correlations of cumulations over greater numbers of WOL were smaller than those of the former WOL. This phenomenon was conspicuous also for the data in the transition column (Table 5); for example, the cumulated data for WOL 16 to 27 show that 1 cow was moved between groups HI and LO. To the best of our understanding, these events show that the problems in determining the efficiency of lactating dairy cows do not derive from random errors in the monitored data, as probably happens with growing animals, whose BW changes during the day. We suggest that temporary but real changes in cow performance cause the above temporary changes in cow efficiency scores. Possible Explanation and Application for the Insignificant Effect of Cow BW on DMI

Individual-cow average BW during the entire test period ranged from 522 to 735 kg, with an average of 637 ± 9.4 kg. Following the method of calculating RFI, it was expected and was found that RFI for the

TIME REQUIRED TO DETERMINE DAIRY COW EFFICIENCY

entire period had not been affected by BW. However, unexpectedly, efficiencies calculated as RE/DMI for the same period were not affected by BW either. The above result is compatible with and explained by the finding of the current study that DMI was not affected by BW. Koch et al. (1963) recognized that differences in both constant BW and BW gain affected the feed requirements of growing cattle. It was reasonable to expect that, for a large variation of BW in the current study, BW would be positively correlated with DMI and negatively correlated with the RE/DMI ratio. The biological explanation for the current finding was not investigated. It is possible that increased BW would increase the rumen fill volume, which could increase digesta mean retention time in the rumen and, hence, diet digestibility. The finding of the current study should be validated in more studies, including investigation of the possible mechanism. As explained in the introduction, RE/DMI directly represents the ratio between production rate and its cost. The current study was carried out on a defined breed of dairy cattle. No statistical reason exists for relating RFI to BW when DMI is not affected by BW. In this case, it was logical to relate dairy lactating cow efficiency to the RE/DMI or RE/ MEI ratios, but as mentioned previously, the present study finding should be validated by more studies before being applied. Because of the difficulty of measuring intake for evaluating the efficiency of cows, efficiency selection of cows is mainly based on indirect selection for increased production. When BW does not change, this procedure generally increases efficiency because of the dilution effect of maintenance (Bauman et al., 1985); in fact, according to Hansen (2000), selection for higher production directly increases dairy cow BW. Using RFI instead of high production as a criterion for selecting efficient animals would not be expected to overcome the above problem of selection for higher BW, because RFI, as it is calculated, is insensitive to BW. Efficiencies of using ME for NEL and NEM are similar (NRC, 2001); therefore, the selection for greater production is not expected to significantly and directly decrease cow efficiency over the whole lactation period; this is supported by the finding in the present study that the RE/ DMI ratio was not affected by BW. Yerex et al. (1988) found that for a similar production rate, high-yielding Holstein cows with smaller BW were more efficient than larger ones. This means that selection specifically for efficiency would enable achievement of greater efficiency without increasing BW. Furthermore, heavier cows were found to have significantly more health problems than lighter ones (Mahoney et al., 1986). However, increasing BW is supposed to increase DMI also throughout the periods when the cows are not lactating

13

(i.e., when growing as heifers and between lactating periods). Consequently, the entire effect of heavy BW over the entire life of the cows should be calculated. CONCLUSIONS

Even during a relatively stable stage of lactation (WOL 16 to 30), most performance variables tend to change, as with BW, or change significantly, as with DMI, RE, and RE/DMI. Effects of increased duration of measurement periods on the between-cow variation of measured results were found to be slight for BW, negligible for DMI, and negative for RE and RE/DMI. When efficiency was evaluated as RE/DMI or RFI, and the test duration required to classify cows according to their efficiency was compared with a 15-wk study, it was found that a 2-wk study can enable classification of the cows into 3 efficiency levels (P < 0.03), with a correlation coefficient of 0.6 (P < 0.05). When a period of 3 wk or more is spent to classify efficiency, the lowest significance index of the classification was P < 0.004, with a correlation coefficient of 0.65 and its lowest significance was P < 0.01. The finding of the insignificant effect of the variation among BW on DMI should be validated in more studies. ACKNOWLEDGMENTS

Contribution from the Agricultural Research Organization (Bet Dagan, Israel; No. 635/13) is acknowledged. This study was supported by the United States-Israel Binational Agricultural Research and Development Fund (BARD; Grant IS-39988-07) and Chief Scientist of the Ministry of Agriculture and Rural Development (Bet-Dagan, Israel; Grant 362-0125-07). We express our thanks to the Volcani Center (Bet-Dagan, Israel) dairy farm team and its manager Shamay Yaakovy. REFERENCES AOAC International. 2001. Official Methods of Analysis. AOAC International, Gaithersburg, MD. Archer, J. A., P. F. Arthur, R. M. Herd, P. F. Parnell, and W. S. Pitchford. 1997. Optimum postweaning test for measurement of growth rate, feed intake and feed efficiency in British breed cattle. J. Anim. Sci. 75:2024–2032. Archer, J. A., E. C. Richardson, R. M. Herd, and P. F. Arthur. 1999. Potential for selection to improve efficiency of feed use in beef cattle: A review. Aust. J. Agric. Res. 50:147–162. Arthur, P. F., J. A. Archer, and R. M. Herd. 2004. Feed intake and efficiency in beef cattle: Overview of recent Australian research and challenges for the future. Aust. J. Exp. Agric. 44:361–369. Arthur, P. F., G. Renand, and D. Krauss. 2001. Genetic and phenotypic relationships among different measures of growth and feed efficiency in young Charolais bulls. Livest. Prod. Sci. 68:131–139. Asher, A. 2010. The influence of period in lactation on the energy expenditure and the efficiency characters in Holstein cows. MS Thesis. Robert H. Smith Faculty of Agricultural, Food and EnJournal of Dairy Science Vol. 97 No. 7, 2014

14

Asher et al.

vironmental Quality Sciences, Hebrew University of Jerusalem, Rehovot, Israel. Bauman, D. E., S. N. McCutcheon, W. D. Steinhour, P. J. Eppard, and S. J. Sechen. 1985. Sources of variation and prospects for improvement of productive efficiency in the dairy cow: A review. J. Anim. Sci. 60:583–592. Connor, E. E., J. L. Hutchison, and H. D. Norman. 2012. Estimating feed efficiency of lactating dairy cattle using residual feed intake. Pages 159–173 in Feed Efficiency in the Beef Industry. 1st ed. R. A. Hill, ed. John Wiley & Sons, New York, NY. Connor, E. E., J. L. Hutchison, H. D. Norman, K. M. Olson, C. P. Van Tassell, J. M. Leith, and R. L. Baldwin VI. 2013. Use of residual feed intake in Holsteins during early lactation shows potential to improve feed efficiency through genetic selection. J. Anim. Sci. 91:3978–3988. Edmonson, A. J., I. J. Lean, L. D. Weaver, T. Farver, and G. Webster. 1989. A body condition scoring chart for Holstein dairy cows. J. Dairy Sci. 72:68–78. Fleiss, J. L. 1981. The measurement of interrater agreement. Pages 212–221 in Statistical Methods for Rates and Proportions. John Wiley & Sons, New York, NY. Fox, D. G., M. E. Van Amburgh, and T. P. Tylutki. 1999. Predicting requirements for growth, maturity, and body reserves in dairy cattle. J. Dairy Sci. 82:1968–1977. Hansen, L. B. 2000. Consequences of selection for milk yield from a geneticist’s viewpoint. J. Dairy Sci. 83:1145–1150. Herd, R. M., and P. F. Arthur. 2009. Physiological basis for residual feed intake. J. Anim. Sci. 87:E64–E71.

Journal of Dairy Science Vol. 97 No. 7, 2014

Israeli Dairy Council. 2012. The profitability of Israeli dairy cattle branch in 2011. Israeli Dairy Council, Tel Aviv, Israel. Kearney, G. A., B. W. Knee, J. F. Graham, and S. A. Knott. 2004. The length of test required to measure liveweight change when testing for feed efficiency in cattle. Aust. J. Agric. Res. 44:411–414. Koch, R. M., L. A. Swiger, D. Chambers, and K. E. Gregory. 1963. Efficiency of feed use in beef cattle. J. Anim. Sci. 22:486–494. Mahoney, C. B., L. B. Hansen, C. W. Young, G. D. Marx, and J. K. Reneau. 1986. Health care of Holsteins selected for large or small body size. J. Dairy Sci. 69:3131–3139. Miron, J., E. Yosef, E. Maltz, and I. Halachmi. 2003. Soybean hulls as a replacement of forage neutral detergent fiber in total mixed rations of lactating cows. Anim. Feed Sci. Technol. 106:21–28. NRC. 1989. Nutrient Requirements of Dairy Cattle. 6th ed. Natl. Acad. Press, Washington, DC. NRC. 2001. Nutrient Requirements of Dairy Cattle. Natl. Acad. Press, Washington, DC. Van Arendonk, J. A. M., G. J. Nieuwhof, H. Vos, and S. Korver. 1991. Genetic aspects of feed intake and efficiency in lactating dairy heifers. Livest. Prod. Sci. 29:263–275. Wang, Z., J. D. Nkrumah, C. Li, J. A. Basarab, L. A. Goonewardene, E. K. Okine, D. H. Crews, and S. S. Moore. 2006. Test duration for growth, feed intake, and feed efficiency in beef cattle using the GrowSafe system. J. Anim. Sci. 84:2289–2298. Yerex, R. P., C. W. Young, J. D. Donker, and G. D. Marx. 1988. Effects of selection for body size on feed efficiency and size of Holsteins. J. Dairy Sci. 71:1355–1360.