Genomic selection for feed efficiency in dairy cattle

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.     

Invited review: overview of new traits and phenotyping strategies in dairy cattle with a focus on functional traits

For several decades, breeding goals in dairy cattle focussed on increased milk production. However, many functional traits have negative genetic correlations with milk yield, and reductions in genetic merit for health and fitness have been observed. Herd management has been challenged to compensate for these effects and to balance fertility, udder health and metabolic diseases against increased production to maximize profit without compromising welfare. Functional traits, such as direct information on cow health, have also become more important because of growing concern about animal well-being and consumer demands for healthy and natural products. There are major concerns about the impact of drugs used in veterinary medicine on the spread of antibiotic-resistant strains of bacteria that can negatively impact human health. Sustainability and efficiency are also increasingly important because of the growing competition for high-quality, plant-based sources of energy and protein. Disruptions to global environments because of climate change may encourage yet more emphasis on these traits. To be successful, it is vital that there be a balance between the effort required for data recording and subsequent benefits. The motivation of farmers and other stakeholders involved in documentation and recording is essential to ensure good data quality. To keep labour costs reasonable, existing data sources should be used as much as possible. Examples include the use of milk composition data to provide additional information about the metabolic status or energy balance of the animals. Recent advances in the use of mid-infrared spectroscopy to measure milk have shown considerable promise, and may provide cost-effective alternative phenotypes for difficult or expensive-to-measure traits, such as feed efficiency. There are other valuable data sources in countries that have compulsory documentation of veterinary treatments and drug use. Additional sources of data outside of the farm include, for example, slaughter houses (meat composition and quality) and veterinary labs (specific pathogens, viral loads). At the farm level, many data are available from automated and semi-automated milking and management systems. Electronic devices measuring physiological status or activity parameters can be used to predict events such as oestrus, and also behavioural traits. Challenges concerning the predictive biology of indicator traits or standardization need to be solved. To develop effective selection programmes for new traits, the development of large databases is necessary so that high-reliability breeding values can be estimated. For expensive-to-record traits, extensive phenotyping in combination with genotyping of females is a possibility.     

Influence of estrus on dry matter intake,water intake and BW of dairy cows

The objectives of this study were to analyze whether dry matter intake (DMI), water intake (WI) and BW were influenced by estrus. A second objective was to determine whether correlations exist among these traits in non-estrous days. Data collection included 34 Holstein-Friesian cows from the research farm ‘Haus Riswick’ of the Agricultural Chamber North Rhine-Westphalia, Germany. On an individual basis, daily DMI and daily WI were measured automatically by a scale in the feeding trough and a WI monitoring system, respectively. BW was determined by a walk-through scale fitted with two gates – one in front and one behind the scale floor. Data were analyzed around cow’s estrus with day 0 (the day of artificial insemination leading to conception). Means during the reference period, defined as days −3 to −1 and 1 to 3, were compared with the means during estrus (day 0). DMI, WI and BW were affected by estrus. Of all cows, 85.3% and 66.7% had reduced DMI and WI, respectively, on day 0 compared with the reference period. Lower BW was detected in 69.2% of all cows relative to the reference period. During the reference period, average DMI, WI and BW were 23.0, 86.6 and 654.8 kg. A minimum DMI of 20.4 kg and a minimum BW of 644.2 kg were detected on the day of estrus, whereas the minimum WI occurred on the day before estrus. After estrus, DMI, WI and BW returned to baseline values. Intake of concentrated feed did not seem to be influenced by estrus. Positive correlations existed between daily DMI and daily WI (r=0.63) as well as between cows’ daily BW and daily WI (r=0.23). The results warrant further investigations to determine whether monitoring of DMI, WI and BW may assist in predicting estrus.     

Prediction of enteric methane production,yield, and intensity in dairy cattle using an intercontinental database

Enteric methane (CH₄) production from cattle contributes to global greenhouse gas emissions. Measurement of enteric CH₄ is complex, expensive, and impractical at large scales; therefore, models are commonly used to predict CH₄ production. However, building robust prediction models requires extensive data from animals under different management systems worldwide. The objectives of this study were to (1) collate a global database of enteric CH₄ production from individual lactating dairy cattle; (2) determine the availability of key variables for predicting enteric CH₄ production (g/day per cow), yield [g/kg dry matter intake (DMI)], and intensity (g/kg energy corrected milk) and their respective relationships; (3) develop intercontinental and regional models and cross‐validate their performance; and (4) assess the trade‐off between availability of on‐farm inputs and CH₄ prediction accuracy. The intercontinental database covered Europe (EU), the United States (US), and Australia (AU). A sequential approach was taken by incrementally adding key variables to develop models with increasing complexity. Methane emissions were predicted by fitting linear mixed models. Within model categories, an intercontinental model with the most available independent variables performed best with root mean square prediction error (RMSPE) as a percentage of mean observed value of 16.6%, 14.7%, and 19.8% for intercontinental, EU, and United States regions, respectively. Less complex models requiring only DMI had predictive ability comparable to complex models. Enteric CH₄ production, yield, and intensity prediction models developed on an intercontinental basis had similar performance across regions, however, intercepts and slopes were different with implications for prediction. Revised CH₄ emission conversion factors for specific regions are required to improve CH₄ production estimates in national inventories. In conclusion, information on DMI is required for good prediction, and other factors such as dietary neutral detergent fiber (NDF) concentration, improve the prediction. For enteric CH₄ yield and intensity prediction, information on milk yield and composition is required for better estimation.     

Combined analysis of data from two granddaughter designs: A simple strategy for QTL confirmation and increasing experimental power in dairy cattle

A joint analysis of five paternal half-sib Holstein families that were part of two different granddaughter designs (ADR- or Inra-design) was carried out for five milk production traits and somatic cell score in order to conduct a QTL confirmation study and to increase the experimental power. Data were exchanged in a coded and standardised form. The combined data set (JOINT-design) consisted of on average 231 sires per grandsire. Genetic maps were calculated for 133 markers distributed over nine chromosomes. QTL analyses were performed separately for each design and each trait. The results revealed QTL for milk production on chromosome 14, for milk yield on chromosome 5, and for fat content on chromosome 19 in both the ADR- and the Inra-design (confirmed within this study). Some QTL could only be mapped in either the ADR- or in the Inra-design (not confirmed within this study). Additional QTL previously undetected in the single designs were mapped in the JOINT-design for fat yield (chromosome 19 and 26), protein yield (chromosome 26), protein content (chromosome 5), and somatic cell score (chromosome 2 and 19) with genomewide significance. This study demonstrated the potential benefits of a combined analysis of data from different granddaughter designs.     

Effect of zinc supplementation from two sources on growth,nutrient utilization and immune response in male crossbred cattle (Bos indicus×Bos taurus) bulls

To investigate effects of Zn supplementation on performance, mineral balance and immune response, 15 male crossbred cattle (Bos indicus×Bos taurus) bulls of about 14 ± 0.4 months of age and weighing 226.0 ± 9.1 kg were divided in to three groups of five. Bulls in the control group were fed wheat straw and a concentrate mixture (basal diet with 32.5 mg Zn/kg dry matter (DM)), and in ZnSO₄ and ZnProp groups 35 mg Zn/kg DM was added through Zn sulphate and Zn propionate, respectively. All bulls were fed their respective treatment diets for 180 days. Daily feed intake was recorded and bulls were weighed at every 15 days to determine change in body weight (BW). After 120 days of feeding, bulls were vaccinated with Brucella abortus strain 19, and cell mediated and humoral immune responses were assessed between 120 and 148 days of experimental feeding. After 150 days of feeding, a metabolism study of 6 days duration was completed to determine nutrient digestibility and mineral balances (i.e., Ca, P, Zn, Cu, Fe and Mn). Intake of total DM, digestibility of DM, crude protein, ether extract, neutral detergent fibre and acid detergent fibre, N balance, average daily gain, feed: gain did not differ between the groups. Intake, excretion and balance of Ca, P, Zn, Cu, Fe and Mn also did not differ between the groups. However, retention of Zn was higher (P<0.001) in the ZnProp group. Bulls supplemented with Zn propionate had higher cell mediated (P<0.01) and humoral (P<0.05) immune response, while there was no alteration in immune response due to Zn sulphate supplementation. Results indicate that a diet containing about 32.5 mg Zn/kg DM was adequate to support normal growth and digestibility, but a better immune response occurred when Zn propionate was added to the diet at 35 mg/kg DM versus Zn sulphate.