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.

    Machine learning ensemble algorithms in predictive analytics of dairycattle methane emission using imputed versus non-imputed datasets
    Negussie, E. ; Gonzalez-Recio, O. ; Haas, Y. de; Gengler, Nicolas ; Soyeurt, Hélène ; Peiren, Nico ; Pszczola, Marcin ; Garnsworthy, Phil C. ; Battagin, M. ; Bayat, Ali R. ; Lassen, Jan ; Yan, Tianhai ; Boland, Martijn ; Kuhla, Björn ; Strabel, Tomasz ; Schwarm, Angela ; Vanlierde, A. ; Biscarini, Filippo - \ 2019
    - p. 40 - 40.
    Comparison of methods to measure methane for use in genetic evaluation of dairy cattle
    Garnsworthy, Philip C. ; Difford, Gareth F. ; Bell, Matthew J. ; Bayat, Ali R. ; Huhtanen, Pekka ; Kuhla, Björn ; Lassen, Jan ; Peiren, Nico ; Pszczola, Marcin ; Sorg, Diana ; Visker, Marleen H.P.W. ; Yan, Tianhai - \ 2019
    Animals 9 (2019)10. - ISSN 2076-2615
    Dairy cows - Environment - Genetic evaluation - Greenhouse gases - Methane

    Partners in Expert Working Group WG2 of the COST Action METHAGENE have used several methods for measuring methane output by individual dairy cattle under various environmental conditions. Methods included respiration chambers, the sulphur hexafluoride (SF6) tracer technique, breath sampling during milking or feeding, the GreenFeed system, and the laser methane detector. The aim of the current study was to review and compare the suitability of methods for large-scale measurements of methane output by individual animals, which may be combined with other databases for genetic evaluations. Accuracy, precision and correlation between methods were assessed. Accuracy and precision are important, but data from different sources can be weighted or adjusted when combined if they are suitably correlated with the ‘true’ value. All methods showed high correlations with respiration chambers. Comparisons among alternative methods generally had lower correlations than comparisons with respiration chambers, despite higher numbers of animals and in most cases simultaneous repeated measures per cow per method. Lower correlations could be due to increased variability and imprecision of alternative methods, or maybe different aspects of methane emission are captured using different methods. Results confirm that there is sufficient correlation between methods for measurements from all methods to be combined for international genetic studies and provide a much-needed framework for comparing genetic correlations between methods should these become available.

    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.
    Wie eine niederländische Universität an der zweiten Grünen Revolution arbeitet
    Ende, Ernst van den; Marcelis, Leo ; Kema, Gert ; Zedde, Rick van de; Goot, Atze Jan van der - \ 2018

    Wageningen ist ein unwahrscheinlicher Ort für eine Revolution. Die Kleinstadt liegt etwas abseits an einem Seitenarm des Rheins, etwa 50 Kilometer östlich von Utrecht. Es gibt eine kleine backsteinerne Kirche, ein paar Festungsruinen aus dem 16. Jahrhundert und 37000 Einwohner. Nicht einmal einen eigenen Bahnhof hat die Stadt. Aber laut dem QS World University Ranking die beste Landwirtschaftsuniversität der Welt: die Wageningen University & Research. 11000 Studierende und 5000 Angestellte arbeiten dort, um sicherzustellen, dass die Menschen auch künftig genug zu essen haben.

    Es wird daran geforscht, wie sich Algen als Treibstoff und Lebensmittel nutzen lassen; wie man chemische Pestizide durch Insekten ersetzt; wie Hightech-Gewächshäuser entstehen können, die ohne Erde auskommen. Aber kaum ein Thema gilt derzeit als so vielversprechend wie die Robotik.

    Als Rick van der Zedde, 38, nach seinem Vortrag bei der jährlichen Messe für Agrar-Investoren die Bühne verlässt, dauert es fast eine halbe Stunde, bis er sich von all den Leuten losreißen kann, die zu ihm kommen. Vertreter großer Industrieroboterhersteller stecken ihm ihre Visitenkarten zu, Wissenschaftler wollen über Projekte reden - und über Robotik. Dabei geht es weniger um Automatisierung und mehr um Informationen, die über Kameras und Sensoren gesammelt werden können. 60 Forscher arbeiten in Wageningen inzwischen in dem Bereich.

    Wer über die Zukunft der Landwirtschaft redet, kommt am Fleisch nicht vorbei: an der fast grotesken Ineffizienz der Fleischproduktion, die unglaubliche Flächen in Anspruch nimmt. Fast 30 Prozent der eisfreien Erdoberfläche dienen der Viehwirtschaft, ein gutes Drittel aller Nutzpflanzen wird an Tiere verfüttert, 15500 Liter Wasser werden benötigt, um ein Kilo Fleisch herzustellen. Trotz dieses Aufwands trägt Fleisch nur mit 18 Prozent zur weltweiten Kalorienzufuhr des Menschen bei. Das Problem: Wir mögen es zu gern.

    "Sehr viele Menschen schätzen die Textur von Fleisch, diese bestimmte Art der Faserigkeit, wie weich es ist und saftig", sagt Atze Jan van der Goot. "Wenn wir ihnen etwas bieten können, das sich genauso anfühlt, dann werden sie eher bereit sein, darauf zu verzichten." Van der Goot ist Lebensmittelingenieur und arbeitet seit 16 Jahren an einer Maschine, die etwas schafft, was lange nahezu unmöglich schien: die Konsistenz eines Steaks zu imitieren. Acht verschiedene Versionen des Apparates hat er in den vergangenen Jahren gebaut, alle sehen aus wie die Hightech-Versionen eines Mixers. In einem Tiefkühler bewahrt er das künstliche Fleisch auf, hergestellt aus Sojaproteinen. Es sind große, rechteckige Lappen, die eine etwas gräuliche Färbung haben.

    What efficiency and resilience gains have we actually achieved in the past century or decade, and at what cost
    Haas, Y. de; Lassen, Jan - \ 2018
    1382-6077
    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.

    Combining heterogeneous across-country data for prediction of enteric methane from proxies in dairy cattle
    Negussie, E. ; Gonzalez Recio, O. ; Haas, Y. de; Gengler, N. ; Soyeurt, H. ; Peiren, N. ; Pszczola, M. ; Garnsworthy, P.C. ; Battagin, M. ; Bayat, A.R. ; Lassen, Jan ; Yan, T. ; Boland, T. ; Kuhla, B. ; Strabel, T. ; Schwarm, A. ; Vanlierde, A. ; Biscarini, F. - \ 2018
    In: Proceedings of the World Congress on Genetics Applied to Livestock Production. - - 8 p.
    Large-scale measurement of enteric methane (CH4) from individual animals is a requisite for estimation of genetic parameters and prediction of breeding values. Direct measurement of individual CH4 emissions is logistically demanding and expensive, and correlated traits (proxies) or models can be used instead as a means to predict emissions. However, most predictive models tend to be specific and are valid mainly within the circumstances under which they were developed. Robust prediction models that work across countries and production environments may be built by combining heterogeneous data from several sources. However, combining heterogeneous individual animal observations on CH4 proxies from several sources is challenging and reports are scant in literature. The main objective of this study was to combine heterogeneous individual animal observations on CH4 proxies to develop robust enteric CH4 prediction models. Data on dairy cattle CH4 emissions and related proxies from 16 herds were made available by 13 research centers across 9 European countries within the Methagene EU COST Action FA1302 consortium on “Large-scale methane measurements on individual ruminants for genetic evaluations”. After a through edition and harmonization, the final dataset comprised 48,804 observations from 2,391 cows. Random Forest (RF) models were used to predict CH4 emissions and to estimate the relative importance of proxies for across-country predictions. Principal component analysis (PCA) was used to detect potential data stratifications. Milk yield, milk fat, DIM, BW, herd and country of origin appeared to be the most relevant proxies in the prediction model. An overall prediction accuracy of 0.81 was estimated from the combined heterogeneous data. This study is a first attempt to develop methods and approaches to combine heterogeneous individual animal data on proxies for CH4 to build robust models for prediction of CH4 emissions across diverse production systems and environments. The methodology outlined here can be extended to combining heterogeneous data, pedigree information and genome-wide dense marker information for estimation of genetic parameters and prediction of breeding values for traits related to dairy system CH4 emissions. Keywords: enteric methane, heterogeneous data, prediction accuracy, methane proxies, random forest, dairy cattle.
    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.

    Where have we come with breeding for methane emissions : update from international collaborations
    Haas, Y. de; Wall, E. ; Garnsworthy, Phil C. ; Kuhla, Björn ; Negussie, E. ; Lassen, Jan - \ 2018
    In: Proceedings of the World Congress on Genetics Applied to Livestock Production. - - 8 p.
    Where have we come with breeding for methane emissions – update from international collaborations Climate change is a growing international concern and it is well established that release of greenhouse gases (GHG) is a contributing factor. So far, within animal production, there is little or no concerted effort on long-term breeding strategies to mitigate GHG from ruminants. In recent years, several consortia have been formed to collect and combine data for genetic evaluation. Discussion areas of these consortia focus on (1) What are genetic parameters for methane (CH4) emissions, (2) What proxies can be used to assess CH4 emission, and (3) What are the prospects of breeding for lower emitting animals? The estimated genetic parameters show that enteric CH4 is a heritable trait, and that it is highly genetically correlated with DMI. So far, the most useful proxies relate to feed intake, milk mid-infrared spectral data, and fatty acid concentrations in milk. To be able to move forward with a genetic evaluation and ranking of animals for CH4 emission, international collaboration is essential to make progress in this area. Collaboration is not only in terms of sharing ideas, experiences and phenotypes, but also in terms of coming to a consensus regarding what phenotype to collect and to select for. Keywords: greenhouse gas emission, enteric methane, genetic control
    Large-scale methane measurements on individual ruminants for genetic evaluation
    Haas, Y. de; Lassen, Jan - \ 2018
    Genetic control of methane emission, feed efficiency and metagenomics in dairy cattle
    Difford, Gareth Frank - \ 2018
    Wageningen University. Promotor(en): H. Bovenhuis, co-promotor(en): J. Lassen; Y. de Haas. - Wageningen : Wageningen University - ISBN 9789463433280 - 213

    The dairy industry faces the challenges of increasing production, remaining economically viable whilst simultaneously minimising impacts on the environment. The cost of feed is the highest variable cost of milk production, thus, improved feed efficiency is a strong wish. However, CH4 is a potent greenhouse gas with an energy value estimated as 2 -12% of the gross feed energy intake and thus represents a loss. There is, therefore, a need to identify the phenotypic and genetic relationships between efficiency of feed utilisation and CH4 production to ensure optimal breeding methods of increasing profitability and limiting environmental impact of dairy production.

    Feed is degraded and CH4 is produced by rumen microbes and not by the cow. The mechanisms which influence the composition of the rumen microbial community and how they, in turn, influence the feed efficiency and CH4 production of the host, are not well understood.

    Among the possible strategies, selective breeding has the benefit over others by being cumulative and persistent over generations. Genetic improvement through selection requires that phenotypes are recorded on large numbers of animals. Moreover, phenotypes must show variation, a portion of which must be genetic, and must have economic or societal value. Understanding the genetic co-variation behind and between these measures is crucial to simultaneous breeding for a more profitable and climate friendly dairy industry. However, the measurement of CH4 emissions, feed efficiency and the rumen microbiome under commercial conditions on a large scale is not a trivial task. The aim of this PhD project was to develop and integrate phenotyping measures for CH4 emission, feed efficiency and the rumen microbiome and to investigate their genetic potential for selective breeding.

    Firstly, in Chapter 2, improvements where made to the sniffer method of CH4 breath concentration recording in dairy cattle during automatic milking. An algorithm was developed to efficiently detect and correct for variable and random drift in time series between instruments and to detect when the cow’s head is out of the feed bin. Using linear mixed model methodology, repeated measures per cow were used to improve precision and control sources of inaccuracy such as sensor drift, background gas concentrations and diurnal variation, that were subsequently removed. Resultantly, highly repeatable phenotypes where obtained which demonstrated adequate agreement for the interchangeable use of two instruments. In Chapter 3, the ranking of cows under commercial conditions using the sniffer method was compared with the “gold standard” respiration chambers. Individual level correlations estimated as proxies for genetic correlations revealed a high correlation between sniffer-predicted CH4 production and CH4 production in the RC. These findings offer a proof of concept that sniffer CH4 phenotypes recorded over a week of lactation show substantial promise as large scale indicator traits for CH4 production using RC.

    In Chapter 4, genetic parameters were estimated between feed intake, milk production and CH4 breath concentration from sniffers over the course of the first lactation in Holstein cows in Denmark and The Netherlands. Through combining data between countries, genetic residual feed intake and breath gas concentrations were found to be significantly heritable, demonstrating that genetic improvement of feed efficiency and CH4 breath gas concentration is feasible in dairy cattle. The estimated genetic correlations from the largest dataset indicated that improved feed efficiency will also result in decreased gas emissions. Furthermore, including the breath gas concentrations in a multitrait genetic evaluation increased the accuracy of bull breeding values for gRFI, demonstrating an indirect economic value of CH4 and CO2 breath concentration phenotypes.

    In Chapter 5, we estimated the relative abundance of rumen bacteria and archaea and found a portion of these to be heritable in dairy cattle. The results demonstrate that host additive genetics has an influence on the abundance of some rumen bacteria and archaea. We detected significant associations between certain bacterial genera and differences in CH4 production of the host cow, further contributing to knowledge of the underlying biological mechanisms driving CH4 production of the host. We further extended quantitative genetic methods to estimate rumen microbial kinships between cows in place of additive genetic relationships. This enabled the quantification of variation in host CH4 production explained by the rumen microbial composition, expressed in the new term ‘microbiability’, as the relative proportion of host variation explained by associated microbes. Crucially the microbiability and the heritability of dairy cattle CH4 production were largely independent. Thus, selective breeding for reduced CH4 production can be extended by methods perturbing the rumen microbiota towards reduced CH4 production.

    In Chapter 6 (the general discussion), the value of method comparisons for phenotype development by comparatively quantifying sources of error between cheaper alternative methods and intensive gold standard methods was discussed. The primary constraint to breeding for improved feed efficiency and CH4 production remains the recording of feed intake on a large scale under commercial conditions and recording of “true” CH4 production. It was proposed that the accuracy of bull breeding values for both feed efficiency and CH4 production can be increased through the use of sniffer phenotypes in robot milking herds, using individual level correlations but a genetic correlation between sniffer phenotypes and RC CH4 production are still needed. The records required for estimating genetic correlations with meaningful standard errors can only be achieved through substantial financial investments, development of cheaper alternative methods of phenotype recording or international collaborations.

    Further to the general discussion, a portion of host phenotypic variation in CH4 production was found to be associated with the rumen bacterial and archaeal composition. However, research is needed to determine if microbial associations are causative and methods to direct desired changes in the rumen microbial composition are still needed to unlock the potential of this under-exploited resource. The methods developed for quantifying the microbial contribution to host phenotypic variation will be of value to inform research into complex microbial-associated phenotypes, such as diseases and digestion in dairy cattle, other livestock species and humans. This thesis therefore contributes to the understanding of the genetic variation in feed efficiency, methane emissions and rumen metagenome of dairy cows.

    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.

    Comparison of a laser methane detector with the GreenFeed and two breath analysers for on-farm measurements of methane emissions from dairy cows
    Sorg, Diana ; Difford, Gareth F. ; Mühlbach, Sarah ; Kuhla, Björn ; Swalve, Hermann H. ; Lassen, Jan ; Strabel, Tomasz ; Pszczola, Marcin - \ 2018
    Computers and Electronics in Agriculture 153 (2018). - ISSN 0168-1699 - p. 285 - 294.
    GreenFeed - Laser methane detector - Methane emission - Sniffer

    To measure methane (CH4) emissions from cattle on-farm, a number of methods have been developed. Combining measurements made with different methods in one data set could lead to an increased power of further analyses. Before combining the measurements, their agreement must be evaluated. We analysed data obtained with a handheld laser methane detector (LMD) and the GreenFeed system (GF), as well as data obtained with LMD and Fourier Transformed Infrared (FTIR) and Non-dispersive Infrared (NDIR) breath analysers (sniffers) installed in the feed bin of automatic milking systems. These devices record short-term breath CH4 concentrations from cows and make it possible to estimate daily CH4 production in g/d which is used for national CH4 emission inventories and genetic studies. The CH4 is released by cows during eructation and breathing events, resulting in peaks of CH4 concentrations during a measurement which represent the respiratory cycle. For LMD, the average CH4 concentration of all peaks during the measurement (P_MEAN in ppm × meter) was compared with the average daily CH4 production (g/d) measured by GF on 11 cows. The comparison showed a low concordance correlation coefficient (CCC; 0.02) and coefficient of individual agreement (CIA; 0.06) between the methods. The repeated measures correlation (rp) of LMD and GF, which can be seen as a proxy for the genetic correlation, was, however, relatively strong (0.66). Next, based on GF, a prediction equation for estimating CH4 in g/d (LMD_cal) using LMD measurements was developed. LMD_cal showed an improved agreement with GF (CCC = 0.22, CIA = 0.99, rp = 0.74). This prediction equation was used to compare repeated LMD measurements (LMD_val in g/d) with CH4 (g/d) measured with FTIR (n = 34 cows; Data Set A) or NDIR (n = 39 cows; Data Set B) sniffer. A low CCC (A: 0.28; B: 0.17), high CIA (A: 0.91; B: 0.87) and strong rp (A: 0.57; B: 0.60) indicated that there was some agreement and a minimal re-ranking of the cows between sniffer and LMD. Possible sources of disagreement were cow activity (LMD: standing idle; sniffer: eating and being milked) and the larger influence of wind speed on LMD measurement. The LMD measurement was less repeatable (0.14–0.27) than the other techniques studied (0.47–0.77). Nevertheless, GF, LMD and the sniffers ranked the cows similarly. The LMD, due to its portability and flexibility, could be used to study CH4 emissions on herd or group level, as a validation tool, or to strengthen estimates of genetic relationships between small-scale research populations.

    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.
    Large-scale methane measurements on individual ruminants for genetic evaluation
    Haas, Y. de; Lassen, Jan - \ 2017
    4 years of METHAGENE
    Haas, Y. de; Lassen, Jan - \ 2017
    4 years of METHAGENE
    Haas, Y. de; Lassen, Jan - \ 2017
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