Livestock Production Science 65 2000 219–227 www.elsevier.com locate livprodsci
Estimating daily yields of cows from different milking schemes
a , a
a b
c
L.R. Schaeffer , J. Jamrozik , R. Van Dorp , D.F. Kelton , D.W. Lazenby
a
Centre for Genetic Improvement of Livestock , Department of Animal and Poultry Science, University of Guelph, Guelph, ON,
Canada N 1G 2 W1
b
Population Medicine , Ontario Veterinary College, University of Guelph, Guelph, Ontario, Canada
c
Ontario Dairy Herd Improvement Corporation , Guelph, Ontario, Canada
Received 6 August 1999; received in revised form 16 December 1999; accepted 19 December 1999
Abstract
A multiple regression model was used to derive equations for predicting 24 h milk, fat, and protein yields of dairy cows on either two-times or three-times-a-day milking under different testing schemes. New prediction equations were developed for
72 subclasses of days in milk, parity, and season of calving for each of 18 possible three-times-a-day testing schemes and for each of four possible two-times-a-day testing schemes. The prediction equations were compared to current official factors
and found to be slightly better than the official factors. For two-times-a-day testing schemes the accuracies of 24 h fat yield predictions from one milk weight and one fat and protein determination were 0.88 for an evening milking and 0.89 for a
morning milking. For three-times-a-day milkings the accuracies of 24 h fat and protein yields from two milk weights with fat and protein contents on each were 0.91–0.94 depending on which two of the three milkings were observed. If only one of
three milkings were recorded, then accuracies of 24 h predicted fat and protein yields dropped to 0.80–0.82. More data from herds milking three-times-a-day are needed on all breeds.
2000 Elsevier Science B.V. All rights reserved.
Keywords : Dairy cattle; Daily yields; Recording schemes; Prediction
1. Introduction evolution of computing hardware and software,
many producers can obtain many of the benefits of The cost in money, time, and inconvenience of
milk recording from automated milking systems. recording individual cow milk yields needs to be as
Therefore, traditional milk recording organizations low as possible to keep dairy producers enrolled on
need to evolve and participate in hardware and milk recording programs. For many years, Canadian
software developments in order that the yield data milk recording programs had very stringent rules and
for cows can be collected and utilized in national regulations, and had a limited number of milk
genetic evaluation systems. The role of supervising recording plans available to producers. With the
data collection has changed to a role of collecting and identifying the type of data that has been
collected, and letting the producer decide how often
Coresponding author. Tel.: 11-519-824-120; fax: 11-519-
to provide the data. At the same time, breed associa-
767-0573. E-mail address
: lrsherlock.aps.uoguelph.ca L.R. Schaeffer.
tions want to maintain awards programs for top
0301-6226 00 – see front matter
2000 Elsevier Science B.V. All rights reserved. P I I : S 0 3 0 1 - 6 2 2 6 0 0 0 0 1 5 3 - 6
220 L
.R. Schaeffer et al. Livestock Production Science 65 2000 219 –227
producing cows, so they continue to demand a high milk weight is recorded, but fat and protein content
level of supervised testing. are not necessarily determined for that sample. Thus,
A growing segment of the Canadian dairy industry a method to predict 24 h yields needs to incorporate
includes herds that milk cows three-times per day phenotypic correlations between milk, fat, and pro-
3 3 versus the traditional two-times per day 2 3 . tein yields.
Many 3 3 herds have not enrolled in milk recording An important feature in 3 3 herds is the interval
because of a lack of adjustment factors to estimate between milkings. In practice, time between startup
total 24 h yields from just one or two of the three of the milking machines is known, and this interval
milkings. With the increased flexibility in milk is assumed to be the same for all cows in a herd.
recording schemes, there are many possible scenarios However, in a milking parlour situation, the intervals
with 3 3 herds on a given test day. These combina- for individual cows could vary markedly. A cow
tions are shown in Table 1 along with the combina- may enter the parlour first out of 20 cows at one
tions for 2 3 herds in terms of availability of milk, milking and last out of 20 cows in the next milking.
fat, and protein yields. The word sample denotes one If the herd is large, then the cow may not enter the
of the three milkings in a 3 3 herd or one of the two holding pen in the same order every milking. Thus,
milkings in a 2 3 herd. At the time of sampling, a there could be significant deviations from the mean
interval between machine startups for many indi- viduals.
Stage of lactation of the cow has been shown to be
Table 1
important in predicting 24 h yields Schaeffer and
Possible data situations for cows on 3 3 and 2 3 milkings
Rennie, 1976; Lee and Wardrop, 1984. Also, parity
Number of Which
Number of Which
a
and season of calving may have effects on yield
milk weights milkings
fat and protein milkings
predictions. With these considerations in mind, the
determinations
objectives of this study were to develop a model for
3 3 Milkings
estimating 24 h milk and component yields, to
3 111
3 111
estimate the parameters to adjust sample yields, and
2 110
101
to verify the factors in terms of accuracy of predic-
011
tions for both 2 3 and 3 3 testing schemes.
1 100
010 001
2. Material and methods
