Improving feed efficiency in dairy cattle

Reducing the use of natural resources

Reduced carbon footprint associated with dairy production (Capper et al., 2009).

Dairy production requires an initial time and investment of resources for heifer breeding prior to achieving a saleable dairy product.

Even more so, during their typical dry period of 60 days, cows consume feed without contributing to income. In fact, Schutz (2002) estimates that about 23% to 28% of the total dry matter (DM) consumption of the herd is carried out by non-lactating animals.

Therefore, there are opportunities to save on feed costs during these non-productive periods. Employing better herd management practices while only keeping those animals which are most efficient.

Estimating feed efficiency in dairy cattle

[register]Multiple terms have been used to define feed efficiency in dairy cattle. The simplest of which is GFE, expressed as:

“The relationship between milk production and feed input.”

Total milk is normally standardized to milk components such as solid- or energy-corrected milk production.

Alternatively, the efficiency of specific dietary nutrients can also be assessed. For example protein efficacy is calculated as:

“Milk protein yield per amount of CP intake”.

GFE (expressed as milk production over feed input) has a strong positive genetic correlation with milk production (Oldenbroek, 1989; Prendiville et al., 2009; Spurlock et al., 2012), which is already a primary goal of genetic improvement.

• Therefore, as improvements in milk production continue to be made among dairy cattle populations, corresponding gains in GFE can also be achieved, without the additional burden of quantifying feed consumption of individual cows.

Revenue over feed cost (ROFC): Another term used to assess and define dairy feed efficiency examines efficiency directly from a profitability standpoint and is known as feed cost income, also called return on feed (ROF).

It is calculated as:

“The difference between the total revenue earned from the sale of milk during a selected time interval and the feed costs associated with its production.”

It is a useful tool to monitor the profitability of dairy producers

• However, due to the fact that this profit indicator depends on fluctuating milk prices and the cost of feed ingredients, it can be difficult to calculate. Especially for individual cows where individual feed intake information is required in order to obtain accurate data.

RFI differs from GFE in that it is designed to estimate the net feed efficiency or metabolic efficiency of the cow.

It is calculated as:

“The difference between the cow’s actual feed intake (or energy consumption) and its expected feed intake (or energy consumption)”,

Based on a mathematical model that takes into account the energy costs for milk maintenance and production during a certain period.

Although it is less common, intake can also be estimated from table values. For example, those of the National Research Council (NRC, 2001);

Considering that RFI represents the difference between actual consumption and the expected intake required to support maintenance and production; a low or negative RFI value is desirable. This can be a source of confusion which can limit its acceptance among dairy farmers as a target for genetic improvement.

Multiple studies have evaluated the heritability of RFI in both grazing and confined systems of dairy cows during a 305-day lactation period. Reported heritability estimates are generally low to moderate, with values ranging from 0.01 to 0.40 among lactating cows (de Haas et al., 2011;  Connor et al.,  2012a and 2013) and 0.22 to 0.40 among growing heifers (Pryce et al., 2012;   Lin et al., 2013).

Finally, low RFI in livestock is potentially associated with decreased manure production per animal, relative to cattle with high RF. This is partly due to to reduced DMI (Basarab et al., 2013;  Berry and Crowley, 2013).

• Therefore, release of methane and nitrous oxide derived from manure into the atmosphere can be reduced through improved feed efficiency in dairy herds.

As an alternative to the direct measurement of feed consumption or feed efficiency, indicators such as the use of traits which are genetically correlated with RFI (Berry, 2009) or other values that can be used to predict intake have been suggested (Berry and Crowley, 2013).

For example, a selection index based on the combination of four readily available features:

• It was found that performance, body weight, chest width and height account for almost 90% of the genetic variation in Holstein steer feed consumption (Berry y Crowley, 2013).

Genomic selection

If shown to have sufficient accuracy, genomic selection provides the most practical approach to improving feed efficiency because it eliminates the need to collect phenotypic data from each animal in the target population. Which is necessary when using traditional selection tools in order to make genetic progress.

Genomic selection consists of estimating the cumulative effects of a large number of genetic markers placed throughout the genome on the trait of interest (e.g., RFI). Using genotypes and phenotypes which are obtained from a reference population, and then developing a prediction equation which estimates the genetic values for the trait within the target population (Meuwissen et al.,  2001).

Rumen microbial populations

Considering that ruminants get most of their dietary energy from volatile fatty acids produced from microbial fermentation of carbohydrates in the rumen; microorganism populations found in the rumen are critical in the regulation of multiple processes that ultimately affect the feeding efficiency of dairy cows.

• Thus, microbial rumen profiles may contribute to variation in RFI and may be a potential method for identifying more feed-efficient animals (Guan et al. , 2009;  Hernández-Sanabria et al., 2010 and 2012;  Carberry et al., 2012).

Feeding behavior and physical activity

Richardson and Herd (2004) suggested that cattle feeding patterns are likely to contribute to ∼2% of the variation in RFI.

– In fact, multiple studies report positive phenotypic correlations between RFI measured during growth and:

feeding rate (amount of food consumed per unit of time)

the number of meals consumed per day

and time spent feeding each day (Basarab et al. , 2013)

Indicating that more efficient animals ate at a slower rate, ate fewer times, and spent less time eating each day compared to those which were less efficient.

Finally,an increased feed passage rate is associated with reduced feed digestibility. Which indicates that a faster  feed consumption rate could adversely affect feed digestibility.

• Thus, similar to findings in growing beef cattle, feeding behaviors could greatly influence the efficiency of milk feed conversion in lactating cows and contribute to variations in RFI.

Conclusions

Although feed costs contribute up to 60% of total production costs,it is possible to increase the profitability of milk production through genetic selection for greater feed efficiency, both for growing and lactating dairy cattle.

Feed efficiency estimates for dairy cattle have been calculated through various methods. However,a growing number of recent studies are evaluating the use of RFI and its relationship with other production parameters.

Source: E.E. Conor

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