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Strategic test-day recording regimes to estimate lactation yield in tropical dairy animals.

McGill DM, Thomson PC, Mulder HA, Lievaart JJ - Genet. Sel. Evol. (2014)

Bottom Line: Comparisons between the different test-day sampling regimes showed that, with the same number of records per lactation (for example, quarterly and four test-days), strategically timed test-days can result in more accurate estimates of lactation yield than test-days at equal intervals.An important outcome of these results is that combining Wood's model for lactation yield estimation and as few as four, five or six strategically placed test-day records can produce estimates of lactation yield that are comparable with estimates based on monthly test-day records using the test-interval method.Furthermore, calculations show that although using fewer test-days results in a decrease in the accuracy of selection, it does provide an opportunity to progeny-test more sires.

View Article: PubMed Central - PubMed

Affiliation: Graham Centre for Agricultural Innovation, Locked Bag 588, Wagga Wagga 2678, NSW, Australia. dmcgill@csu.edu.au.

ABSTRACT

Background: In developing dairy sectors, genetic improvement programs have limited resources and recording of herds is minimal. This study evaluated different methods to estimate lactation yield and sampling schedules with fewer test-day records per lactation to determine recording regimes that (1) estimate lactation yield with a minimal impact on the accuracy of selection and (2) optimise the available resources.

Methods: Using Sahiwal cattle as a tropical dairy breed example, weekly milk records from 464 cows were used in a simulation study to generate different shaped lactation curves. The daily milk yields from these simulated lactation curves were subset to equally spaced (weekly, monthly and quarterly) and unequally spaced (with four, five or six records per lactation) test-day intervals. Lactation yield estimates were calculated from these subsets using two methods: the test-interval method and Wood's (Nature 216:164-165, 1967) lactation curve model. Using the resulting lactation yields, breeding values were predicted and comparisons were made between the sampling regimes and estimation methods.

Results: The results show that, based on the mean square error of prediction, use of Wood's lactation curve model to estimate total yield was more accurate than use of the test-interval method. However, the differences in the ranking of animals were small, i.e. a 1 to 5% difference in accuracy. Comparisons between the different test-day sampling regimes showed that, with the same number of records per lactation (for example, quarterly and four test-days), strategically timed test-days can result in more accurate estimates of lactation yield than test-days at equal intervals.

Conclusions: An important outcome of these results is that combining Wood's model for lactation yield estimation and as few as four, five or six strategically placed test-day records can produce estimates of lactation yield that are comparable with estimates based on monthly test-day records using the test-interval method. Furthermore, calculations show that although using fewer test-days results in a decrease in the accuracy of selection, it does provide an opportunity to progeny-test more sires. Thus, using strategically timed test-days and Wood's model to estimate lactation yield, can lead to a more efficient use of the allocated resources.

Show MeSH
The ‘average’ shape of simulated lactation curves using Wood’s model based on Pakistani Sahiwal data. One population of animals was simulated with an average peak and persistent tail (APHP; dashed line ---), which represented 4-year old animals. The other population had a higher peak and less persistent tail (HPLP; solid line ―) to simulate 9-year old animals.
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Fig1: The ‘average’ shape of simulated lactation curves using Wood’s model based on Pakistani Sahiwal data. One population of animals was simulated with an average peak and persistent tail (APHP; dashed line ---), which represented 4-year old animals. The other population had a higher peak and less persistent tail (HPLP; solid line ―) to simulate 9-year old animals.

Mentions: The outcomes of this simulation were values for ki, bi and ci for a population of Sahiwal cattle (where n = 464) that calved in January 2006 at 4 years of age. The resulting lactation curves had a general shape that had an average peak of production and high persistency (APHP; a slowly declining curve). A second set of data was simulated in which only the fixed effect of age was changed to 9 years to generate lactation curves that on average had a higher peak and a less persistent tail (HPLP). The fixed effects of 4 and 9 years of age were selected for the simulation since they yielded lactation curves that were quite different in shape, yet still typical of Sahiwal cows. Plots of the average simulated lactation curves that highlight differences between these fixed effects are in Figure 1.Figure 1


Strategic test-day recording regimes to estimate lactation yield in tropical dairy animals.

McGill DM, Thomson PC, Mulder HA, Lievaart JJ - Genet. Sel. Evol. (2014)

The ‘average’ shape of simulated lactation curves using Wood’s model based on Pakistani Sahiwal data. One population of animals was simulated with an average peak and persistent tail (APHP; dashed line ---), which represented 4-year old animals. The other population had a higher peak and less persistent tail (HPLP; solid line ―) to simulate 9-year old animals.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4248470&req=5

Fig1: The ‘average’ shape of simulated lactation curves using Wood’s model based on Pakistani Sahiwal data. One population of animals was simulated with an average peak and persistent tail (APHP; dashed line ---), which represented 4-year old animals. The other population had a higher peak and less persistent tail (HPLP; solid line ―) to simulate 9-year old animals.
Mentions: The outcomes of this simulation were values for ki, bi and ci for a population of Sahiwal cattle (where n = 464) that calved in January 2006 at 4 years of age. The resulting lactation curves had a general shape that had an average peak of production and high persistency (APHP; a slowly declining curve). A second set of data was simulated in which only the fixed effect of age was changed to 9 years to generate lactation curves that on average had a higher peak and a less persistent tail (HPLP). The fixed effects of 4 and 9 years of age were selected for the simulation since they yielded lactation curves that were quite different in shape, yet still typical of Sahiwal cows. Plots of the average simulated lactation curves that highlight differences between these fixed effects are in Figure 1.Figure 1

Bottom Line: Comparisons between the different test-day sampling regimes showed that, with the same number of records per lactation (for example, quarterly and four test-days), strategically timed test-days can result in more accurate estimates of lactation yield than test-days at equal intervals.An important outcome of these results is that combining Wood's model for lactation yield estimation and as few as four, five or six strategically placed test-day records can produce estimates of lactation yield that are comparable with estimates based on monthly test-day records using the test-interval method.Furthermore, calculations show that although using fewer test-days results in a decrease in the accuracy of selection, it does provide an opportunity to progeny-test more sires.

View Article: PubMed Central - PubMed

Affiliation: Graham Centre for Agricultural Innovation, Locked Bag 588, Wagga Wagga 2678, NSW, Australia. dmcgill@csu.edu.au.

ABSTRACT

Background: In developing dairy sectors, genetic improvement programs have limited resources and recording of herds is minimal. This study evaluated different methods to estimate lactation yield and sampling schedules with fewer test-day records per lactation to determine recording regimes that (1) estimate lactation yield with a minimal impact on the accuracy of selection and (2) optimise the available resources.

Methods: Using Sahiwal cattle as a tropical dairy breed example, weekly milk records from 464 cows were used in a simulation study to generate different shaped lactation curves. The daily milk yields from these simulated lactation curves were subset to equally spaced (weekly, monthly and quarterly) and unequally spaced (with four, five or six records per lactation) test-day intervals. Lactation yield estimates were calculated from these subsets using two methods: the test-interval method and Wood's (Nature 216:164-165, 1967) lactation curve model. Using the resulting lactation yields, breeding values were predicted and comparisons were made between the sampling regimes and estimation methods.

Results: The results show that, based on the mean square error of prediction, use of Wood's lactation curve model to estimate total yield was more accurate than use of the test-interval method. However, the differences in the ranking of animals were small, i.e. a 1 to 5% difference in accuracy. Comparisons between the different test-day sampling regimes showed that, with the same number of records per lactation (for example, quarterly and four test-days), strategically timed test-days can result in more accurate estimates of lactation yield than test-days at equal intervals.

Conclusions: An important outcome of these results is that combining Wood's model for lactation yield estimation and as few as four, five or six strategically placed test-day records can produce estimates of lactation yield that are comparable with estimates based on monthly test-day records using the test-interval method. Furthermore, calculations show that although using fewer test-days results in a decrease in the accuracy of selection, it does provide an opportunity to progeny-test more sires. Thus, using strategically timed test-days and Wood's model to estimate lactation yield, can lead to a more efficient use of the allocated resources.

Show MeSH