Variation in residual feed intake in Holstein-Friesian dairy heifers in southern Australia

Williams, Y. J.; Pryce, J.E.; Grainger, C.; Wales, W. J.; Linden, N.; Porker, M.; Hayes, Ben J.

doi:10.3168/jds.2010-4015

Cited by 91 publications

(120 citation statements)

References 44 publications

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“…Williams et al (2011) found that Holstein-Friesian dairy heifers with the 10% lowest RFI ate 1.7 kg less DM each day than the 10% highest RFI ones to achieve the same rate of growth. Similarly, our results showed that the more efficient dairy cattle (the low RFI group) ate 1.59 kg less DM each day and produced nearly equal 4% FCM.…”

Section: Discussionmentioning

confidence: 99%

Biological mechanisms related to differences in residual feed intake in dairy cows

Zhao

et al. 2016

Animal

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Residual feed intake (RFI), defined as the difference between an animal's actual feed intake and expected feed intake over a specific period, is an inheritable character of feed conversion efficiency in dairy cows. Research has shown that a lower RFI could improve the profitability of milk production. This study explored variation in RFI by comparing the differences in body size, milk performance, feeding behavior, and serum metabolites in 29 Holstein cows in mid lactation. The cows were selected from a total of 84 animals based on their RFI following feedlot tests. Selected cows were ranked into high RFI (RFI > 1 SD above the mean, n = 14) and low RFI (RFI < 1 SD below the mean, n = 15). The low RFI cows (more efficient) consumed 1.59 kg/day less dry matter than the high RFI group ( P < 0.01), while they produced nearly equal 4% fat-corrected milk. The milk : feed ratio was higher for the low RFI group than for the high RFI group ( P < 0.05). The levels of milk protein ( P < 0.01), total solids ( P < 0.05), and nonfat solids ( P < 0.05) were also higher for the low RFI group, whereas milk urea nitrogen was lower ( P < 0.01). The daily feeding duration was shorter for the low RFI group than for the high RFI group ( P < 0.01). No significant differences were found in levels of glucose, β-hydroxybutyrate, prolactin, insulin, IGF-1, growth hormone or ghrelin, but the level of neuropeptide Y was higher ( P < 0.01) and levels of leptin and non-esterified fatty acid ( P < 0.05) were lower for the low RFI group than for the high RFI group. There were substantial differences between cows with different RFI, which might affect the efficiency of milk protein metabolism and fat mobilization.

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Section: Discussionmentioning

confidence: 99%

Biological mechanisms related to differences in residual feed intake in dairy cows

Zhao

et al. 2016

Animal

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“…Residual feed intake (RFI), sometimes referred to as net feed intake, is the difference between actual and predicted DMI (Koch et al, 1963;Williams et al, 2011;Berry and Crowley, 2013). RFI is calculated as the residual from a regression model of DMI on various energy sinks, such as milk production and live weight (for maintenance requirements).…”

Section: Definitions Of Feed Efficiencymentioning

confidence: 99%

“…A recent Australian and New Zealand project measured RFI in around 2000 growing Holstein heifers and genotyped them with 625 000 SNP (Pryce et al, 2012a;Waghorn et al, 2012;Williams et al, 2011). A prediction equation for growing heifer RFI was derived in this data set.…”

Section: Genomic Selection For Feed Efficiency In Growing Heifersmentioning

confidence: 99%

Genomic selection for feed efficiency in dairy cattle

Pryce

Wales

Haas

et al. 2014

Animal

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Feed is a major component of variable costs associated with dairy systems and is therefore an important consideration for breeding objectives. As a result, measures of feed efficiency are becoming popular traits for genetic analyses. Already, several countries account for feed efficiency in their breeding objectives by approximating the amount of energy required for milk production, maintenance, etc. However, variation in actual feed intake is currently not captured in dairy selection objectives, although this could be possible by evaluating traits such as residual feed intake (RFI), defined as the difference between actual and predicted feed (or energy) intake. As feed intake is expensive to accurately measure on large numbers of cows, phenotypes derived from it are obvious candidates for genomic selection provided that: (1) the trait is heritable; (2) the reliability of genomic predictions are acceptable to those using the breeding values; and (3) if breeding values are estimated for heifers, rather than cows then the heifer and cow traits need to be correlated. The accuracy of genomic prediction of dry matter intake (DMI) and RFI has been estimated to be around 0.4 in beef and dairy cattle studies. There are opportunities to increase the accuracy of prediction, for example, pooling data from three research herds (in Australia and Europe) has been shown to increase the accuracy of genomic prediction of DMI from 0.33 within country to 0.35 using a three-country reference population. Before including RFI as a selection objective, genetic correlations with other traits need to be estimated. Weak unfavourable genetic correlations between RFI and fertility have been published. This could be because RFI is mathematically similar to the calculation of energy balance and failure to account for mobilisation of body reserves correctly may result in selection for a trait that is similar to selecting for reduced (or negative) energy balance. So, if RFI is to become a selection objective, then including it in an overall multi-trait selection index where the breeding objective is net profit is sensible, as this would allow genetic correlations with other traits to be properly accounted for. If genetic parameters are accurately estimated then RFI is a logical breeding objective. If there is uncertainty in these, then DMI may be preferable.

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“…Negative values are indicative of cows that eat less than predicted for their level of milk production. Developing a genomic prediction tool for this trait has been the focus of a large collaboration between researchers in Australia and New Zealand, initially involving measurement of feed-intake phenotypes of~1000 Holstein-Friesian heifers of~6 months of age in each country (Williams et al 2011b). The accuracy of trait prediction using this population was~0.4 , which is equivalent to a reliability of 0.16.…”

Section: Genomic Selectionmentioning

confidence: 99%

“…An example of the implementation of genomic selection within the Australian Holstein-Friesian population is selection for improved feed conversion efficiency (Williams et al 2011b). The trait used by Williams et al (2011b) was residual feed intake (RFI), which is the difference between actual and predicted feed intake. Negative values are indicative of cows that eat less than predicted for their level of milk production.…”

Section: Genomic Selectionmentioning

confidence: 99%

Reducing the carbon footprint of Australian milk production by mitigation of enteric methane emissions

Moate¹,

Deighton²,

Williams³

et al. 2016

Anim. Prod. Sci.

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Abstract. This review examines research aimed at reducing enteric methane emissions from the Australian dairy industry. Calorimeter measurements of 220 forage-fed cows indicate an average methane yield of 21.1 g methane (CH 4 )/kg dry matter intake. Adoption of this empirical methane yield, rather than the equation currently used in the Australian greenhouse gas inventory, would reduce the methane emissions attributed to the Australian dairy industry by~10%. Research also indicates that dietary lipid supplements and feeding high amounts of wheat substantially reduce methane emissions. It is estimated that, in 1980, the Australian dairy industry produced~185 000 t of enteric methane and total enteric methane intensity was 33.6 g CH 4 /kg milk. In 2010, the estimated production of enteric methane was 182 000 t, but total enteric methane intensity had declined~40% to 19.9 g CH 4 /kg milk. This remarkable decline in methane intensity and the resultant improvement in the carbon footprint of Australian milk production was mainly achieved by increased per-cow milk yield, brought about by the on-farm adoption of research findings related to the feeding and breeding of dairy cows. Options currently available to further reduce the carbon footprint of Australian milk production include the feeding of lipid-rich supplements such as cottonseed, brewers grains, cold-pressed canola, hominy meal and grape marc, as well as feeding of higher rates of wheat. Future technologies for further reducing methane emissions include genetic selection of cows for improved feed conversion to milk or low methane intensity, vaccines to reduce ruminal methanogens and chemical inhibitors of methanogenesis.

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Variation in residual feed intake in Holstein-Friesian dairy heifers in southern Australia

Cited by 91 publications

References 44 publications

Biological mechanisms related to differences in residual feed intake in dairy cows

Biological mechanisms related to differences in residual feed intake in dairy cows

Genomic selection for feed efficiency in dairy cattle

Reducing the carbon footprint of Australian milk production by mitigation of enteric methane emissions

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