Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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    Can greenhouse gases in breath be used to genetically improve feed efficiency of dairy cows?
    Difford, G.F. ; Løvendahl, P. ; Veerkamp, R.F. ; Bovenhuis, H. ; Visker, M.H.P.W. ; Lassen, J. ; Haas, Y. de - \ 2020
    Journal of Dairy Science 103 (2020)3. - ISSN 0022-0302 - p. 2442 - 2459.
    breath gas measurement - carbon dioxide - feed efficiency - methane - residual feed intake

    There is considerable interest in improving feed utilization of dairy cattle while limiting losses to the environment (i.e., greenhouse gases, GHG). To breed for feed-efficient or climate-friendly cattle, it is first necessary to obtain accurate estimates of genetic parameters and correlations of feed intake, greenhouse gases, and production traits. Reducing dry matter take (DMI) requirements while maintaining production has high economic value to farmers, but DMI is costly to record and thus limited to small research or nucleus herds. Conversely, enteric methane (CH4) currently has no economic value, is also costly to record, and is limited to small experimental trials. However, breath gas concentrations of methane (CH4c) and carbon dioxide (CO2c) are relatively cheap to measure at high throughput under commercial conditions by installing sniffers in automated milking stations. The objective of this study was to assess the genetic correlations between DMI, body weight (BW), fat- and protein-corrected milk yield (FPCM), and GHG-related traits: CH4c and CO2c from Denmark (DNK) and the Netherlands (NLD). A second objective was to assess the genetic potential for improving feed efficiency and the added benefits of using CH4c and CO2c as indicators. Feed intake data were available on 703 primiparous cows in DNK and 524 in NLD; CH4c and CO2c records were available on 434 primiparous cows in DNK and 656 in NLD. The GHG-related traits were heritable (e.g., CH4c h2: DNK = 0.26, NLD = 0.15) but were differentially genetically correlated with DMI and feed efficiency in both magnitude and sign, depending on the population and the definition of feed efficiency. Across feed efficiency traits and DMI, having bulls with 100 daughters with FPCM, BW, and GHG traits resulted in sufficiently high accuracy to almost negate the need for DMI records. Despite differences in genetic correlation structure, the relatively cheap GHG-related traits showed considerable potential for improving the accuracy of breeding values of highly valuable feed intake and feed efficiency traits.

    Enteric methane emission from Jersey cows during the spring transition from indoor feeding to grazing
    Szalanski, Marcin ; Kristensen, Troels ; Difford, Gareth ; Lassen, Jan ; Buitenhuis, Albert J. ; Pszczola, Marcin ; Løvendahl, Peter - \ 2019
    Journal of Dairy Science 102 (2019)7. - ISSN 0022-0302 - p. 6319 - 6329.
    dairy - genotype by environment interaction - grazing - Jersey - methane

    Organic dairy cows in Denmark are often kept indoors during the winter and outside at least part time in the summer. Consequently, their diet changes by the season. We hypothesized that grazing might affect enteric CH 4 emissions due to changes in the nutrition, maintenance, and activity of the cows, and they might differentially respond to these factors. This study assessed the repeatability of enteric CH 4 emission measurements for Jersey cattle in a commercial organic dairy herd in Denmark. It also evaluated the effects of a gradual transition from indoor winter feeding to outdoor spring grazing. Further, it assessed the individual-level correlations between measurements during the consecutive feeding periods (phenotype × environment, P × E) as neither pedigrees nor genotypes were available to estimate a genotype by environment effect. Ninety-six mixed-parity lactating Jersey cows were monitored for 30 d before grazing and for 24 d while grazing. The cows spent 8 to 11 h grazing each day and had free access to an in-barn automatic milking system (AMS). For each visit to the AMS, milk yield was recorded and logged along with date and time. Monitoring equipment installed in the AMS feed bins continuously measured enteric CH 4 and CO 2 concentrations (ppm) using a noninvasive “sniffer” method. Raw enteric CH 4 and CO 2 concentrations and their ratio (CH 4 :CO 2 ) were derived from average concentrations measured during milking and per day for each cow. We used mixed models equations to estimate variance components and adjust for the fixed and random effects influencing the analyzed gas concentrations. Univariate models were used to precorrect the gas measurements for diurnal variation and to estimate the direct effect of grazing on the analyzed concentrations. A bivariate model was used to assess the correlation between the 2 periods (in-barn vs. grazing) for each gas concentration. Grazing had a weak P × E interaction for daily average CH 4 and CO 2 gas concentrations. Bivariate repeatability estimates for average CH 4 and CO 2 concentrations and CH 4 :CO 2 were 0.77 to 0.78, 0.73 to 0.80, and 0.26, respectively. Repeatability for CH 4 :CO 2 was low (0.26) but indicated some between-animal variation. In conclusion, grazing does not create significant shifts compared with indoor feeding in how animals rank for average CH 4 and CO 2 concentrations and CH 4 :CO 2 . We found no evidence that separate evaluation is needed to quantify enteric CH 4 and CO 2 emissions from Jersey cows during in-barn and grazing periods.

    Ranking cows’ methane emissions under commercial conditions with sniffers versus respiration chambers
    Difford, G.F. ; Olijhoek, D.W. ; Hellwing, A.L.F. ; Lund, P. ; Bjerring, M.A. ; Haas, Y. de; Lassen, J. ; Løvendahl, P. - \ 2019
    Acta Agriculturae Scandinavica Section A-Animal Science 68 (2019)1. - ISSN 0906-4702 - p. 25 - 32.
    breath concentration - Methane - respiration chambers - sniffers

    This study assessed the ranking of dairy cows using individual-level correlations for methane (CH 4 ) emission on-farm using sniffers and in respiration chambers. In total 20 lactating dairy cows, ten Holstein and ten Jerseys were recorded using sniffers installed in milking robots for three weeks of lactation and subsequently in respiration chambers (RC) where they were each recorded on three occasions within the RC. Bivariate linear mixed models were used to determine the individual-level correlations (r I ) between sniffer and RC phenotypes as proxies for genetic correlations. Despite differences in feeding and management, the predicted CH 4 production from sniffers correlated highly with RC CH 4 production r I = 0.77 ± 0.18 and CH 4 breath concentration correlated nearly as well with RC CH 4 production r I = 0.75 ± 0.20. These correlations between sniffers on-farm and RC demonstrate the potential of sniffers measurements as large-scale indicator traits for CH 4 emissions in dairy cattle.

    6S rRNA sequence of rumen microbes in dairy cattle
    Difford, Gareth ; Plichta, Damian Rafal ; Løvendahl, Peter ; Lassen, Jan ; Noel, Samantha Joan ; Højberg, Ole ; Wright, André Denis G. ; Zhu, Zhigang ; Kristensen, Lise ; Nielsen, Henrik Bjørn ; Guldbrandtsen, Bernt ; Sahana, Goutam - \ 2018
    Aarhus University
    PRJEB28065 - ERP110230
    The 16S rRNA sequence data was generated in the project entitled "Reduction of methane emissions from dairy cows and concurrent improvement of feed efficiency obtained through host genetics and next generation sequencing of rumen microbiome" using Illumina sequencing technology.
    Review: Selecting for improved feed efficiency and reduced methane emissions in dairy cattle
    Løvendahl, P. ; Difford, G.F. ; Li, B. ; Chagunda, M.G.G. ; Huhtanen, P. ; Lidauer, M.H. ; Lassen, J. ; Lund, P. - \ 2018
    Animal 12 (2018)s2. - ISSN 1751-7311 - p. s336 - s349.
    digestibility - genetics - holobiont - microbiome - ranking

    It may be possible for dairy farms to improve profitability and reduce environmental impacts by selecting for higher feed efficiency and lower methane (CH4) emission traits. It remains to be clarified how CH4 emission and feed efficiency traits are related to each other, which will require direct and accurate measurements of both of these traits in large numbers of animals under the conditions in which they are expected to perform. The ranking of animals for feed efficiency and CH4 emission traits can differ depending upon the type and duration of measurement used, the trait definitions and calculations used, the period in lactation examined and the production system, as well as interactions among these factors. Because the correlation values obtained between feed efficiency and CH4 emission data are likely to be biased when either or both are expressed as ratios, therefore researchers would be well advised to maintain weighted components of the ratios in the selection index. Nutrition studies indicate that selecting low emitting animals may result in reduced efficiency of cell wall digestion, that is NDF, a key ruminant characteristic in human food production. Moreover, many interacting biological factors that are not measured directly, including digestion rate, passage rate, the rumen microbiome and rumen fermentation, may influence feed efficiency and CH4 emission. Elucidating these mechanisms may improve dairy farmers ability to select for feed efficiency and reduced CH4 emission.

    Host genetics and the rumen microbiome jointly associate with methane emissions in dairy cows
    Difford, Gareth Frank ; Plichta, Damian Rafal ; Løvendahl, Peter ; Lassen, Jan ; Noel, Samantha Joan ; Højberg, Ole ; Wright, André Denis G. ; Zhu, Zhigang ; Kristensen, Lise ; Nielsen, Henrik Bjørn ; Guldbrandtsen, Bernt ; Sahana, Goutam - \ 2018
    Plos Genetics 14 (2018)10. - ISSN 1553-7404

    Cattle and other ruminants produce large quantities of methane (~110 million metric tonnes per annum), which is a potent greenhouse gas affecting global climate change. Methane (CH4) is a natural by-product of gastro-enteric microbial fermentation of feedstuffs in the rumen and contributes to 6% of total CH4 emissions from anthropogenic-related sources. The extent to which the host genome and rumen microbiome influence CH4 emission is not yet well known. This study confirms individual variation in CH4 production was influenced by individual host (cow) genotype, as well as the host's rumen microbiome composition. Abundance of a small proportion of bacteria and archaea taxa were influenced to a limited extent by the host's genotype and certain taxa were associated with CH4 emissions. However, the cumulative effect of all bacteria and archaea on CH4 production was 13%, the host genetics (heritability) was 21% and the two are largely independent. This study demonstrates variation in CH4 emission is likely not modulated through cow genetic effects on the rumen microbiome. Therefore, the rumen microbiome and cow genome could be targeted independently, by breeding low methane-emitting cows and in parallel, by investigating possible strategies that target changes in the rumen microbiome to reduce CH4 emissions in the cattle industry.

    Is rumination time an indicator of methane production in dairy cows?
    Zetouni, L. ; Difford, G.F. ; Lassen, J. ; Byskov, M.V. ; Norberg, E. ; Løvendahl, P. - \ 2018
    Journal of Dairy Science 101 (2018)12. - ISSN 0022-0302 - p. 11074 - 11085.
    dairy cow - dry matter intake - methane - rumination time

    As long as large-scale recording of expensive-to-measure and labor-consuming traits, such as dry matter intake (DMI) and CH4 production (CH4P), continues to be challenging in practical conditions, alternative traits that are already routinely recorded in dairy herds should be investigated. An ideal indicator trait must, in addition to expressing genetic variation, have a strong correlation with the trait of interest. Our aim was to estimate individual level and phenotypic correlations between rumination time (RT), CH4P, and DMI to determine if RT could be used as an indicator trait for CH4P and DMI. Data from 343 Danish Holstein cows were collected at the Danish Cattle Research Centre for a period of approximately 3 yr. The data set consisted of 14,890 records for DMI, 15,835 for RT, and 6,693 for CH4P. Data were divided in primiparous cows only (PC) and all cows (MC), and then divided in lactation stage (early, mid, late, and whole lactation) to analyze the changes over lactation. Linear mixed models, including an animal effect but no pedigree, were used to estimate the correlations among traits. Phenotypic and individual level correlations between RT and both CH4P and DMI were close to zero, regardless of lactation stage and data set (PC or MC). However, CH4P and DMI were highly correlated, both across lactation stages and data sets. In conclusion, RT is unsuitable to be used as an indicator trait for either CH4P or DMI. Our study failed to validate RT as a useful indicator trait for both CH4P and DMI, but more studies with novel phenotypes can offer different approaches to select and incorporate important yet difficult to record traits into breeding goals and selection indexes.

    Do breath gas measurements hold the key to unlocking the genetics of feed efficiency in dairy cows?
    Difford, Gareth ; Haas, Y. de; Visker, M.H.P.W. ; Lassen, Jan ; Bovenhuis, H. ; Veerkamp, R.F. ; Lovendahl, P. - \ 2017
    In: Book of Abstracts of the 68th Annual Meeting of the European Federation of Animal Science. - Wageningen Academic Publishers (Book of abstracts 23) - ISBN 9789086863129 - p. 184 - 184.
    Predicting methane emissions of lactating Danish Holstein cows using Fourier transform mid-infrared spectroscopy of milk
    Shetty, N.P. ; Difford, G. ; Lassen, J. ; Løvendahl, P. ; Buitenhuis, A.J. - \ 2017
    Journal of Dairy Science 100 (2017)11. - ISSN 0022-0302 - p. 9052 - 9060.
    CH production - CH:CO ratio - Infrared spectroscopy - Prediction - Validation
    Enteric methane (CH4), a potent greenhouse gas, is among the main targets of mitigation practices for the dairy industry. A measurement technique that is rapid, inexpensive, easy to use, and applicable at the population level is desired to estimate CH4 emission from dairy cows. In the present study, feasibility of milk Fourier transform mid-infrared (FT-IR) spectral profiles as a predictor for CH4:CO2 ratio and CH4 production (L/d) is explained. The partial least squares regression method was used to develop the prediction models. The models were validated using different random test sets, which are independent from the training set by leaving out records of 20% cows for validation and keeping records of 80% of cows for training the model. The data set consisted of 3,623 records from 500 Danish Holstein cows from both experimental and commercial farms. For both CH4:CO2 ratio and CH4 production, low prediction accuracies were found when models were obtained using FT-IR spectra. Validated coefficient of determination (R2 Val) = 0.21 with validated model error root mean squared error of prediction (RMSEP) = 0.0114 L/d for CH4:CO2 ratio, and R2 Val = 0.13 with RMSEP = 111 L/d for CH4 production. The important spectral wavenumbers selected using the recursive partial least squares method represented major milk components fat, protein, and lactose regions of the spectra. When fat and protein predicted by FT-IR were used instead of full spectra, a low R2 Val of 0.07 was obtained for both CH4:CO2 ratio and CH4 production prediction. Other spectral wavenumbers related to lactose (carbohydrate) or additional wavenumbers related to fat or protein (amide II) are providing additional variation when using the full spectral profile. For CH4:CO2 ratio prediction, integration of FT-IR with other factors such as milk yield, herd, and lactation stage showed improvement in the prediction accuracy. However, overall prediction accuracy remained modest; R2 Val increased to 0.31 with RMSEP = 0.0105. For prediction of CH4 production, the added value of FT-IR along with the aforementioned traits was marginal. These results indicated that for CH4 production prediction, FT-IR profiles reflect primarily information related to milk yield, herd, and lactation stage rather than individual milk fatty acids related to CH4 emission. Thus, it is not feasible to predict CH4 emission based on FT-IR spectra alone.
    Interchangeability between methane measurements in dairy cows assessed by comparing precision and agreement of two non-invasive infrared methods
    Difford, G.F. ; Lassen, J. ; Løvendahl, P. - \ 2016
    Computers and Electronics in Agriculture 124 (2016). - ISSN 0168-1699 - p. 220 - 226.
    Equivalence - Instrument comparison - Interchangeability - Methane - Sniffer - Statistical agreement

    In this study we assess the interchangeability and statistical agreement of two prevalent instruments from the non-invasive "sniffer" method and compare their precision. Furthermore, we develop and validate an effective algorithm for aligning time series data from multiple instruments to remove the effects of variable and fixed time shifts from the instrument comparison. The CH4 and CO2 gas concentrations for both instruments were found to differ for population means (P < 0.05) and intra-cow variation (precision) (P < 0.05) and for inter-cow variation (P < 0.05). The CH4 and CO2 gas concentrations from both instruments can be used interchangeably to increase statistical power for example, in genetic evaluations, provided sources of disagreement are corrected through calibration and standardisation. Additionally, averaging readings of cows over a longer period of time (one week) is an effective noise reduction technique which provides phenotypes with considerable inter-cow variation.

    Comparing methods to analyse raw, large-scale methane data
    Szalanski, M. ; Difford, Gareth ; Lovendahl, P. ; Lassen, J. - \ 2016
    In: Book of Abstracts of the 67st Annual Meeting of the European Federation of Animal Science. - Wageningen Academic Publishers - ISBN 9789086862849 - p. 126 - 126.
    Genes and microbes, the next step in dairy cattle breeding
    Difford, Gareth ; Lassen, Jan ; Lovendahl, P. - \ 2016
    In: Book of Abstracts of the 67th Annual Meeting of the European Federation of Animal Science. - Wageningen : Wageningen Academic Publishers (Book of abstracts 22) - ISBN 9789086862849 - p. 285 - 285.
    Genomic prediction for dry matter intake of dairy cattle from an international data set consisting of research herds in Europe, North America and Australasia
    Haas, Y. de; Pryce, J.E. ; Calus, M.P.L. ; Wall, E. ; Berry, D.P. ; Lovendahl, P. ; Krattenmacher, N. ; Miglior, F. ; Weigel, K. ; Spurlock, D. ; MacDonald, K.A. ; Hulsegge, B. ; Veerkamp, R.F. - \ 2015
    Journal of Dairy Science 98 (2015)9. - ISSN 0022-0302 - p. 6522 - 6534.
    With the aim of increasing the accuracy of genomic estimated breeding values for dry matter intake (DMI) in Holstein-Friesian dairy cattle, data from 10 research herds in Europe, North America, and Australasia were combined. The DMI records were available on 10,701 parity 1 to 5 records from 6,953 cows, as well as on 1,784 growing heifers. Predicted DMI at 70 d in milk was used as the phenotype for the lactating animals, and the average DMI measured during a 60- to 70-d test period at approximately 200 d of age was used as the phenotype for the growing heifers. After editing, there were 583,375 genetic markers obtained from either actual high-density single nucleotide polymorphism (SNP) genotypes or imputed from 54,001 marker SNP genotypes. Genetic correlations between the populations were estimated using genomic REML. The accuracy of genomic prediction was evaluated for the following scenarios: (1) within-country only, by fixing the correlations among populations to zero, (2) using near-unity correlations among populations and assuming the same trait in each population, and (3) a sharing data scenario using estimated genetic correlations among populations. For these 3 scenarios, the data set was divided into 10 sub-populations stratified by progeny group of sires; 9 of these sub-populations were used (in turn) for the genomic prediction and the tenth was used for calculation of the accuracy (correlation adjusted for heritability). A fourth scenario to quantify the benefit for countries that do not record DMI was investigated (i.e., having an entire country as the validation population and excluding this country in the development of the genomic predictions). The optimal scenario, which was sharing data, resulted in a mean prediction accuracy of 0.44, ranging from 0.37 (Denmark) to 0.54 (the Netherlands). Assuming near-unity among-country genetic correlations, the mean accuracy of prediction dropped to 0.40, and the mean within-country accuracy was 0.30. If no records were available in a country, the accuracy based on the other populations ranged from 0.23 to 0.53 for the milking cows, but were only 0.03 and 0.19 for Australian and New Zealand heifers, respectively; the overall mean prediction accuracy was 0.37. Therefore, there is a benefit in collaboration, because phenotypic information for DMI from other countries can be used to augment the accuracy of genomic evaluations of individual countries.
    Genomic predictions for dry matter intake using the international reference population of gDMI
    Haas, Y. de; Pryce, J.E. ; Calus, M.P.L. ; Hulsegge, B. ; Spurlock, D.M. ; Berry, D.P. ; Wall, E. ; Lovendahl, P. ; Weigel, K. ; MacDonald, K. ; Miglior, F. ; Krattenmacher, N. ; Veerkamp, R.F. - \ 2014
    In: Proceedings of the The 2014 Interbull Meeting. - - p. 94 - 99.
    In this study, we have demonstrated that using dry matter intake (DMI) phenotypes from multiplecountries increases the accuracy of genomic breeding values for this important trait, provided a multi-trait approach is used. Data from Australia, Canada, Denmark, Germany, Ireland, the Netherlands,New Zealand, United Kingdom and two institutions in the United States were combined to estimatethe accuracy of genomic prediction for DMI multi-trait models. The average accuracies was 0.44, andranged from 0.37 (Denmark) to 0.54 (the Netherlands). Enlarging the combined dataset with uniquephenotypes does increase the accuracy of the genomic prediction for DMI. This stimulates furtherinternational collaboration.
    Progress with genetic selection for low methane traits in dairy cows
    Lassen, J. ; Garnsworthy, P.C. ; Chagunda, M.G. ; Negussie, E. ; Lovendahl, P. ; Haas, Y. de - \ 2014
    The road to genetic selection for methane emission from ruminants - a global approach
    Haas, Y. de; Lassen, J. ; Pickering, N.K. ; Oddy, V.H. ; Wall, E. ; Gengler, N. ; Dehareng, F. ; Lovendahl, P. ; Rowe, S. - \ 2014
    International genetic evaluations for feed intake in dairy cattle through the collation of data from multiple sources
    Berry, D.P. ; Coffey, M.P. ; Pryce, J.E. ; Haas, Y. de; Lovendahl, P. ; Krattenmacher, N. ; Crowley, J. ; Wang, Z. ; Spurlock, D.M. ; Weigel, K. ; MacDonald, K. ; Veerkamp, R.F. - \ 2014
    Journal of Dairy Science 97 (2014)6. - ISSN 0022-0302 - p. 3894 - 3905.
    body condition score - dry-matter intake - random regression-models - daily energy-balance - milk-yield - short-communication - live weight - genomic information - research herds - grass intake
    Feed represents a large proportion of the variable costs in dairy production systems. The omission of feed intake measures explicitly from national dairy cow breeding objectives is predominantly due to a lack of information from which to make selection decisions. However, individual cow feed intake data are available in different countries, mostly from research or nucleus herds. None of these data sets are sufficiently large enough on their own to generate accurate genetic evaluations. In the current study, we collate data from 10 populations in 9 countries and estimate genetic parameters for dry matter intake (DMI). A total of 224,174 test-day records from 10,068 parity 1 to 5 records of 6,957 cows were available, as well as records from 1,784 growing heifers. Random regression models were fit to the lactating cow test-day records and predicted feed intake at 70 d postcalving was extracted from these fitted profiles. The random regression model included a fixed polynomial regression for each lactation separately, as well as herd-year-season of calving and experimental treatment as fixed effects; random effects fit in the model included individual animal deviation from the fixed regression for each parity as well as mean herd-specific deviations from the fixed regression. Predicted DMI at 70 d postcalving was used as the phenotype for the subsequent genetic analyses undertaken using an animal repeatability model. Heritability estimates of predicted cow feed intake 70 d postcalving was 0.34 across the entire data set and varied, within population, from 0.08 to 0.52. Repeatability of feed intake across lactations was 0.66. Heritability of feed intake in the growing heifers was 0.20 to 0.34 in the 2 populations with heifer data. The genetic correlation between feed intake in lactating cows and growing heifers was 0.67. A combined pedigree and genomic relationship matrix was used to improve linkages between populations for the estimation of genetic correlations of DMI in lactating cows; genotype information was available on 5,429 of the animals. Populations were categorized as North America, grazing, other low input, and high input European Union. Albeit associated with large standard errors, genetic correlation estimates for DMI between populations varied from 0.14 to 0.84 but were stronger (0.76 to 0.84) between the populations representative of high-input production systems. Genetic correlations with the grazing populations were weak to moderate, varying from 0.14 to 0.57. Genetic evaluations for DMI can be undertaken using data collated from international populations; however, genotype-by-environment interactions with grazing production systems need to be considered.
    Selection on Feed intake or Feed Efficiency: A Position Paper from gDMI Breeding Goal Discussions
    Veerkamp, R.F. ; Pryce, J. ; Spurlock, D.M. ; Berry, D.P. ; Coffey, M. ; Lovendahl, P. ; Linde, R. van der; Bryant, J.M. ; Migliore, G. ; Wang, Z. ; Winters, M. ; Krattenmacher, N. ; Haas, Y. de - \ 2013
    In: Interbull Bulletin nr 47 Nantes : - p. 15 - 22.
    The widespread use of genomic information in dairy cattle breeding programs has opened up the possibility to select for novel traits, especially for traits that are traditionally difficult to record in a progeny testing scheme. Feed intake and efficiency is such a difficult to measure trait. In February 2013, the co-authors discussed how information on DMI should be incorporated in the breeding decisions. The aim of this paper is to present the overall discussion and main positions taken by the group on four topics related to feed efficiency: i) breeding goal definition; ii) biological variation in feed utilisation; iii) optimal recording of feed intake and predictor traits; and iv) unwanted correlated responses and validation
    International genetic evaluations for feed intake in dairy cattle
    Berry, D.P. ; Coffey, M.P. ; Pryce, J. ; Haas, Y. de; Lovendahl, P. ; Thaller, G. ; Crowley, J. ; Spurlock, D.M. ; Weigel, K. ; MacDonald, K. ; Veerkamp, R.F. - \ 2013
    In: 47th Proceedings of the 2013 Interbull meeting. - Nantes : Interbull - p. 52 - 57.
    Feed represents a large proportion of the variable costs in dairy production systems. The omission of feed intake measures explicitly from national dairy cow breeding objectives is predominantly due to a lack of information on which to make selection decisions. Individual cow feed intake data are available in different countries, mostly from research or nucleus herds. None of these datasets are sufficiently large enough on their own to generate accurate genetic evaluations. Here we collate data from ten populations in nine countries. A total of 224,174 test-day records from parity one to five animals, as well as 1,784 records from growing heifers were available. Random regression models fitted to lactating cow test-day records were used to predict feed intake at 70 days post calving. Heritability estimates of predicted cow feed intake 70-days post-calving was 0.34 across the entire dataset and varied, within population, from 0.08 to 0.52. Repeatability of feed intake across lactations was 0.66. Heritability of feed intake in growing heifers was 0.20 to 0.34. The genetic correlation between feed intake in lactating cows and heifers was 0.67. A combined pedigree and genomic relationship matrix was used to improve linkages between populations for the estimation of genetic correlations between countries categorized as North America, Grazing, Other low input, and High input EU. Genetic correlation estimates between populations varied from 0.14 to 0.84 but was stronger (0.76 to 0.84) between the populations representative of high input production systems.
    Electrical Conductivity of milk: ability to predict mastitis status
    Norberg, E. ; Hogeveen, H. ; Korsgaard, I.R. ; Friggens, N.C. ; Sloth, K.H.M.N. ; Lovendahl, P. - \ 2004
    Journal of Dairy Science 87 (2004). - ISSN 0022-0302 - p. 1099 - 1107.
    somatic-cell count - subclinical mastitis - genetic-parameters - infected cows - performance - efficacy
    Electrical conductivity (EC) of milk has been introduced as an indicator trait for mastitis over the last decade, and it may be considered as a potential trait in a breeding program where selection for improved udder health is included. In this study, various EC traits were investigated for their association with udder health. In total, 322 cows with 549 lactations were included in the study. Cows were classified as healthy or clinically or subclinically infected, and EC was measured repeatedly during milking on each quarter. Four EC traits were defined; the inter-quarter ratio (IQR) between the highest and lowest quarter EC values, the maximum EC level for a cow, IQR between the highest and lowest quarter EC variation, and the maximum EC variation for a cow. Values for the traits were calculated for every milking throughout the entire lactation. All EC traits increased significantly (P
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