E. Wall

Mid-infrared prediction of bovine milk fatty acids across multiple breeds, production systems, and countries

Increasing consumer concern exists over the relationship between food composition and human health. Because of the known effects of fatty acids on human health, the development of a quick, inexpensive, and accurate method to directly quantify the fatty acid (FA) composition in milk would be valuable for milk processors to develop a payment system for milk pertinent to their customer requirements and for farmers to adapt their feeding systems and breeding strategies accordingly. The aim of this study was (1) to confirm the ability of mid-infrared spectrometry (MIR) to quantify individual FA content in milk by using an innovative procedure of sampling (i.e., samples were collected from cows belonging to different breeds, different countries, and in different production systems); (2) to compare 6 mathematical methods to develop robust calibration equations for predicting the contents of individual FA in milk; and (3) to test interest in using the FA equations developed in milk as basis to predict FA content in fat without corrections for the slope and the bias of the developed equations. In total, 517 samples selected based on their spectral variability in 3 countries (Belgium, Ireland, and United Kingdom) from various breeds, cows, and production systems were analyzed by gas chromatography (GC). The samples presenting the largest spectral variability were used to calibrate the prediction of FA by MIR. The remaining samples were used to externally validate the 28 FA equations developed. The 6 methods were (1) partial least squares regression (PLS); (2) PLS+repeatability file (REP); (3) first derivative of spectral data+PLS; (4) first derivative+REP+PLS; (5) second derivative of spectral data+PLS; and (6) second derivative+REP+PLS. Methods were compared on the basis of the cross-validation coefficient of determination (R2cv), the ratio of standard deviation of GC values to the standard error of cross-validation (RPD), and the validation coefficient of determination (R2v). The third and fourth methods had, on average, the highest R2cv, RPD, and R2v. The final equations were built using all GC and the best accuracy was observed for the infrared predictions of C4:0, C6:0, C8:0, C10:0, C12:0, C14:0, C16:0, C18:0, C18:1 trans, C18:1 cis-9, C18:1 cis, and for some groups of FA studied in milk (saturated, monounsaturated, unsaturated, short-chain, medium-chain, and long-chain FA). These equations showed R2cv greater than 0.95. With R2cv equal to 0.85, the MIR prediction of polyunsaturated FA could be used to screen the cow population. As previously published, infrared predictions of FA in fat are less accurate than those developed from FA content in milk (g/dL of milk) and no better results were obtained by using milk FA predictions if no corrections for bias and slope based on reference milk samples with known contents of FA were used. These results indicate the usefulness of equations with R2cv greater than 95% in milk payment systems and the usefulness of equations with R2cv greater than 75% for animal breeding purposes.

Developing breeding schemes to assist mitigation of greenhouse gas emissions.

Genetic improvement of livestock is a particularly effective technology, producing permanent and cumulative changes in performance. This paper highlights some of the options for including mitigation in livestock breeding schemes, focusing on ruminant species, and details three routes through which genetic improvement can help to reduce emissions per kg product via: (i) improving productivity and efficiency, (ii) reducing wastage in the farming system and (iii) directly selecting on emissions, if or when these are measurable. Selecting on traits that improve the efficiency of the system (e.g. residual feed intake, longevity) will have a favourable effect on the overall emissions from the system. Specific examples of how genetic selection will have a favourable effect on emissions for UK dairy systems are described. The development of breeding schemes that incorporate environmental concerns is both desirable and possible. An example of how economic valuation of public good outcomes can be incorporated into UK dairy selection indices is given. This paper focuses on genetic selection tools using, on the whole, currently available traits and tools. However, new direct and indirect measurement techniques for emissions will improve the potential to reduce emissions by genetic selection. The complexities of global forces on defining selection objectives are also highlighted.

The effect of improving cow productivity, fertility, and longevity on the global warming potential of dairy systems

This study compared the environmental impact of a range of dairy production systems in terms of their global warming potential (GWP, expressed as carbon dioxide equivalents, CO(2)-eq.) and associated land use, and explored the efficacy of reducing said impact. Models were developed using the unique data generated from a long-term genetic line × feeding system experiment. Holstein-Friesian cows were selected to represent the UK average for milk fat plus protein production (control line) or were selected for increased milk fat plus protein production (select line). In addition, cows received a low forage diet (50% forage) with no grazing or were on a high forage (75% forage) diet with summer grazing. A Markov chain approach was used to describe the herd structure and help estimate the GWP per year and land required per cow for the 4 alternative systems and the herd average using a partial life cycle assessment. The CO(2)-eq. emissions were expressed per kilogram of energy-corrected milk (ECM) and per hectare of land use, as well as land required per kilogram of ECM. The effects of a phenotypic and genetic standard deviation unit improvement on herd feed utilization efficiency, ECM yield, calving interval length, and incidence of involuntary culling were assessed. The low forage (nongrazing) feeding system with select cows produced the lowest CO(2)-eq. emissions of 1.1 kg/kg of ECM and land use of 0.65 m(2)/kg of ECM but the highest CO(2)-eq. emissions of 16.1t/ha of the production systems studied. Within the herd, an improvement of 1 standard deviation in feed utilization efficiency was the only trait of those studied that would significantly reduce the reliance of the farming system on bought-in synthetic fertilizer and concentrate feed, as well as reduce the average CO(2)-eq. emissions and land use of the herd (both by about 6.5%, of which about 4% would be achievable through selective breeding). Within production systems, reductions in CO(2)-eq. emissions per kilogram of ECM and CO(2)-eq. emissions per hectare were also achievable by an improvement in feed utilization. This study allowed development of models that harness the biological trait variation in the animal to improve the environmental impact of the farming system. Genetic selection for efficient feed use for milk production according to feeding system can bring about reductions in system nutrient requirements, CO(2)-eq. emissions, and land use per unit product.

The use of mid-infrared spectrometry to predict body energy status of Holstein cows

Energy balance, especially in early lactation, is known to be associated with subsequent health and fertility in dairy cows. However, its inclusion in routine management decisions or breeding programs is hindered by the lack of quick, easy, and inexpensive measures of energy balance. The objective of this study was to evaluate the potential of mid-infrared (MIR) analysis of milk, routinely available from all milk samples taken as part of large-scale milk recording and milk payment operations, to predict body energy status and related traits in lactating dairy cows. The body energy status traits investigated included energy balance and body energy content. The related traits of body condition score and energy intake were also considered. Measurements on these traits along with milk MIR spectral data were available on 17 different test days from 268 cows (418 lactations) and were used to develop the prediction equations using partial least squares regression. Predictions were externally validated on different independent subsets of the data and the results averaged. The average accuracy of predicting body energy status from MIR spectral data was as high as 75% when energy balance was measured across lactation. These predictions of body energy status were considerably more accurate than predictions obtained from the sometimes proposed fat-to-protein ratio in milk. It is not known whether the prediction generated from MIR data are a better reflection of the true (unknown) energy status than the actual energy status measures used in this study. However, results indicate that the approach described may be a viable method of predicting individual cow energy status for a large scale of application.

Improved accuracy of genomic prediction for dry matter intake of dairy cattle from combined European and Australian data sets

With the aim of increasing the accuracy of genomic estimated breeding values for dry matter intake (DMI) in dairy cattle, data from Australia (AU), the United Kingdom (UK), and the Netherlands (NL) were combined using both single-trait and multi-trait models. In total, DMI records were available on 1,801 animals, including 843 AU growing heifers with records on DMI measured over 60 to 70 d at approximately 200 d of age, and 359 UK and 599 NL lactating heifers with records on DMI during the first 100 d in milk. The genotypes used in this study were obtained from the Illumina Bovine 50K chip (Illumina Inc., San Diego, CA). The AU, UK, and NL genomic data were matched using the single nucleotide polymorphism (SNP) name. Quality controls were applied by carefully comparing the genotypes of 40 bulls that were available in each data set. This resulted in 30,949 SNP being used in the analyses. Genomic predictions were estimated with genomic REML, using ASReml software. The accuracy of genomic prediction was evaluated in 11 validation sets; that is, at least 3 validation sets per country were defined. The reference set (in which animals had both DMI phenotypes and genotypes) was either AU or Europe (UK and NL) or a multi-country reference set consisting of all data except the validation set. When DMI for each country was treated as the same trait, use of a multi-country reference set increased the accuracy of genomic prediction for DMI in UK, but not in AU and NL. Extending the model to a bivariate (AU-EU) or trivariate (AU-UK-NL) model increased the accuracy of genomic prediction for DMI in all countries. The highest accuracies were estimated for all countries when data were analyzed with a trivariate model, with increases of up to 5.5% compared with univariate models within countries.

Genotype by environment interaction for first-lactation female fertility traits in UK dairy cattle.

The objective of this study was to examine whether there was genotype by environment interaction (G x E) for female fertility traits for various environmental descriptors used in the United Kingdom. Records on days to first service (DFS), nonreturn rate at 56 d (NR56), calving interval (CI), and number of inseminations (NINS) on approximately 200,000 first-lactation Holstein cows in 3,192 herds and from 1,147 sires were analyzed using both random regression reaction norm models and multiple-trait models. The environmental descriptors were herd averages of DFS, CI, and NR56, and herd production and intensity indices, the latter based on 305-d milk, fat, and protein yields, age at first calving, temperature, and rainfall. No GxE was found for NR56 and NINS. There was an indication of G x E for DFS and CI with respect to the herd average of that trait, but only from the reaction norm model. Genotype by environment interaction existed for DFS and CI with respect to both production and intensity indexes-genetic correlations between top and bottom quartiles ranged from 0.73 to 0.84, with similar results from both models, indicating reranking of bulls. Part of this G x E might be explained by low production and intensity herds also having more concentrated spring calving.

Risk factors for culling in Holstein-Friesian dairy cows

Risk factors associated with voluntary and involuntary culling within a Holstein-Friesian dairy cow research herd were identified. Data were studied from 3498 completed lactations from the Langhill Holstein-Friesian dairy herd between January 1990 and June 2008. During this period the cows were based on two different farms in Scotland. The culling rate of the milking herd was approximately 25 per cent per annum. Approximately 68 per cent of cows culled were classified as involuntary. The association between different risk factors and the incidence of culling was investigated using a general linear mixed model. Of the 838 cows culled, 59 per cent were culled before the fourth lactation. Culling was associated with cows that had an assisted calving (P<0.01), aborted (P<0.01) and/or suffered from mastitis (P<0.05). Cows that were culled were also more likely to be older cows (P<0.01), have a low number of milking days (P<0.001) and/or a greater number of days from calving to conception (P<0.01). Culling was also associated with conception failure (r=0.752, P<0.001). Further work might help reduce the number of animals culled involuntarily, by identifying key factors associated with the incidence of an assisted calving, abortion and mastitis, and improving milking and fertility performance using detailed data from the Langhill herd.

Effect of breeding for milk yield, diet and management on enteric methane emissions from dairy cows

Enteric methane production from livestock is an important source of anthropogenic greenhouse gas emissions. The aim of the present study was to (1) assess the effect of long-term breeding for kilograms of milk fat plus protein production and (2) investigate the influence of parity, genetic line and diet on predicted enteric methane emissions of Holstein Friesian dairy cows. Analyses were based on 17 years of experimental data for lactating and dry cows, housed and at pasture. Restricted maximum likelihood (REML) was used to assess the effects of parity, genetic line and diet on the predicted enteric methane output of lactating and dry cows. A non-linear equation based on metabolisable energy intake (MEI) was used to predict daily enteric methane output. The present study found that selection for kilograms of milk fat plus protein production, zero-grazing low-forage diets and maintaining persistently high-yielding older cows can reduce a cow’s enteric methane emissions per kilogram milk by up to 12%, on average. Comparing the first 5 years to the most recent 5 years of the study period showed that large savings of 19% and 23% in enteric methane per kilogram milk were made in cows selected for milk fat plus protein or selected to remain close to the average genetic merit for milk fat plus protein production for all animals evaluated in the UK, respectively. Additionally, management to minimise the length of the drying-off period can help reduce enteric methane emissions during a cow’s lactation period.

Dairy cattle in a temperate climate: the effects of weather on milk yield and composition depend on management

A better understanding of how livestock respond to weather is essential to enable farming to adapt to a changing climate. Climate change is mainly expected to impact dairy cattle through heat stress and an increase in the frequency of extreme weather events. We investigated the effects of weather on milk yield and composition (fat and protein content) in an experimental dairy herd in Scotland over 21 years. Holstein Friesian cows were either housed indoors in winter and grazed over the summer or were continuously housed. Milk yield was measured daily, resulting in 762 786 test day records from 1369 individuals, and fat and protein percentage were sampled once a week, giving 89 331 records from 1220 cows/trait. The relative influence of 11 weather elements, measured from local outdoor weather stations, and two indices of temperature and humidity (THI), indicators of heat stress, were compared using separate maximum likelihood models for each element or index. Models containing a direct measure of temperature (dry bulb, wet bulb, grass or soil temperature) or a THI provided the best fits to milk yield and fat data; wind speed and the number of hours of sunshine were most important in explaining protein content. Weather elements summarised across a weeks timescale from the test day usually explained milk yield and fat content better than shorter-scale (3 day, test day, test day -1) metrics. Then, examining a subset of key weather variables using restricted maximum likelihood, we found that THI, wind speed and the number of hours of sunshine influenced milk yield and composition. The shape and magnitude of these effects depended on whether animals were inside or outside on the test day. The milk yield of cows outdoors was lower at the extremes of THI than at average values, and the highest yields were obtained when THI, recorded at 0900 h, was 55 units. Cows indoors decreased milk yield as THI increased. Fat content was lower at higher THIs than at intermediate THIs in both environments. Protein content decreased as THI increased in animals kept indoors and outdoors, and the rate of decrease was greater when animals were outside than when they were inside. Moderate wind speeds appeared to alleviate heat stress. These results show that milk yield and composition are impacted at the upper extreme of THI under conditions currently experienced in Scotland, where animals have so far experienced little pressure to adapt to heat stress.

Understanding the genetics of survival in dairy cows

Premature mortality and culling causes great wastage in the dairy industry, as a large number of heifers born never become productive or are culled before their full lactation potential is reached. The objectives of this study were to characterize survival and estimate genetic parameters for alternative longevity traits that considered (1) the survival of replacement heifers and (2) functional longevity of milking cows in the UK Holstein Friesian population, using combined information from the British Cattle Movement Service and milk recording organizations. Mortality of heifers was highest in the first month of life and was proportionately highest in calves born during winter months. Heifer mortality tended to decrease with age until about 16 mo onward; it then gradually increased, expected to be associated with culls due to reproductive failure or problems during pregnancy and calving. In milking cows, days of productive life (DPL) was analyzed as an alternative to the current trait lifespan score. Cows that died in 2009 on average lived for 6.8 yr with an average production of 4.3 yr. Heritability estimates were low for both heifer and cow survival and were ~0.01 and ~0.06, respectively. The positive genetic correlation between heifer survival with lifespan score (0.31) indicates that bulls that sire daughters with longer productive lives are also likely to have calves that survive and become replacement heifers. However, the magnitude of the genetic correlation suggests that survival in the rearing period and the milking herd are different traits. Genetic correlations were favorable between DPL with somatic cell count and fertility traits indicating that animals with a longer productive life tend to have lower somatic cell count, a shorter calving interval, fewer days to first service, and require fewer inseminations. However, an antagonistic relationship existed between DPL with milk and fat yield traits.