Ma, X., Christensen, O. F., Gao, H., Huang, R., Nielsen, B.
, Madsen, P., Jensen, J., Ostersen, T., Li, P.
, Shirali, M. & Su, G. (2021).
Prediction of breeding values for group-recorded traits including genomic information and an individually recorded correlated trait.
Heredity,
126(1), 206-217.
https://doi.org/10.1038/s41437-020-0339-3
Kargo, M., Clasen, J. B., Nielsen, H. M., Byskov, K.
& Norberg, E. (2021).
Heterosis and breed effects for milk production and udder health traits in crosses between Danish Holstein, Danish Red, and Danish Jersey.
Journal of Dairy Science,
104(1), 678-682.
https://doi.org/10.3168/jds.2019-17866
Hutchings, N., Lærke, P. E., Munkholm, L. J., Elsgaard, L., Kristensen, T., Rasmussen, J., Lund, P., Børsting, C. F., Løvendahl, P., Mikkelsen, M. H., Albrektsen, R., Gyldenkærne, S., Møller, H. B., Hansen, M. J., Feilberg, A. & Adamsen, A. P. S., (2020).
Opdatering af effekter og potentialer af klimavirkemidler til anvendelse i landbrug, Nr. 2019-0035910, 23 s., mar. 02, 2020.
Zaalberg, R. M., Bovenhuis, H.
, Poulsen, N. A., Larsen, L. B., Sehested, J. & Buitenhuis, A. J. (2020).
Genetic analysis on minerals predicted with Fourier transform infrared milk spectra for two Danish dairy cattle breeds. Abstract fra 71st Annual Meeting of European Federation of Animal Science .
https://www.wageningenacademic.com/doi/book/10.3920/978-90-8686-900-8
Sarup, P.
, Guo, X., Nielsen, N., Jensen, J. D., Mulder, F., Jihad, O., Edriss, V., Jahoor, A.
& Jensen, J. (2020).
Using NMR metabolomics in breeding for malt quality in spring barley. Abstract fra International Symposium of the Society for Plant Breeding (GPZ), Tulln, Østrig.
https://gpz2020.boku.ac.at/wp-content/uploads/2020/03/06_Sarup.pdfMesbah-Uddin, M., Guldbrandtsen, B., Capitan, A.
, Lund, M. S., Boichard, D.
& Sahana, G. (2020).
Strategy to imputation of large genomic deletions and utilizing them in mapping and genomic prediction in cattle. Abstract fra 6th International Conference of Quantitative Genetics, Brisbane, Australien.
https://icqg6.org/wp-content/uploads/2020/11/ICQG6_2020_Abstract_Book.pdf
Christensen, O. F., Nielsen, B.
, Su, G., Xiang, T., Madsen, P., Ostersen, T., Velander, I. & Strathe, A. B. (2020).
Correction to: A bivariate genomic model with additive, dominance and inbreeding depression effects for sire line and three-way crossbred pigs.
Genetics, selection, evolution : GSE,
52(1), [23].
https://doi.org/10.1186/s12711-020-00541-x
Zhang, Q., Difford, G., Sahana, G., Løvendahl, P., Lassen, J., Lund, M. S., Guldbrandtsen, B. & Janss, L. (2020).
Bayesian modeling reveals host genetics associated with rumen microbiota jointly influence methane emission in dairy cows.
The ISME Journal,
14(8), 2019-2033.
https://doi.org/10.1038/s41396-020-0663-x
Zhang, Q., Cai, Z., Lhomme, M.
, Sahana, G., Lesnik, P., Guerin, M., Fredholm, M. & Karlskov-Mortensen, P. (2020).
Inclusion of endophenotypes in a standard GWAS facilitate a detailed mechanistic understanding of genetic elements that control blood lipid levels.
Scientific Reports,
10(1), [18434].
https://doi.org/10.1038/s41598-020-75612-6
Zaalberg, R. M., Buitenhuis, A. J., Sundekilde, U. K., Poulsen, N. A. & Bovenhuis, H. (2020).
Genetic analysis of orotic acid predicted with Fourier transform infrared milk spectra.
Journal of Dairy Science,
103(4), 3334-3348.
https://doi.org/10.3168/jds.2018-16057
Wu, X., Mesbah-Uddin, M., Guldbrandtsen, B., Lund, M. S. & Sahana, G. (2020).
Novel haplotypes responsible for prenatal death in Nordic Red and Danish Jersey cattle.
Journal of Dairy Science,
103(5), 4570-4578.
https://doi.org/10.3168/jds.2019-17831
Wang, X., Su, G., Hao, D., Lund, M. S. & Kadarmideen, H. N. (2020).
Comparisons of improved genomic predictions generated by different imputation methods for genotyping by sequencing data in livestock populations.
Journal of Animal Science and Biotechnology,
11(1), [3].
https://doi.org/10.1186/s40104-019-0407-9
Wang, L., Janss, L. L., Madsen, P., Henshall, J., Huang, C-H., Marois, D.
, Alemu, S., Sørensen, A. C. & Jensen, J. (2020).
Effect of genomic selection and genotyping strategy on estimation of variance components in animal models using different relationship matrices.
Genetics, selection, evolution : GSE,
52(1), [31].
https://doi.org/10.1186/s12711-020-00550-w
Tsai, H-Y., Janss, L. L., Andersen, J. R., Orabi, J., Jensen, J. D., Jahoor, A.
& Jensen, J. (2020).
Genomic prediction and GWAS of yield, quality and disease-related traits in spring barley and winter wheat.
Scientific Reports,
10(1), [3347].
https://doi.org/10.1038/s41598-020-60203-2
Tsai, H-Y., Cericola, F., Edriss, V., Andersen, J. R., Orabi, J.
, Jensen, J. D., Jahoor, A.
, Janss, L. & Jensen, J. (2020).
Use of multiple traits genomic prediction, genotype by environment interactions and spatial effect to improve prediction accuracy in yield data.
PLOS ONE,
15(5), [e0232665].
https://doi.org/10.1371/journal.pone.0232665
Tessema, B. B., Liu, H., Sørensen, A. C., Andersen, J. R.
& Jensen, J. (2020).
Strategies Using Genomic Selection to Increase Genetic Gain in Breeding Programs for Wheat.
Frontiers in Genetics,
11, [578123].
https://doi.org/10.3389/fgene.2020.578123
Tausen, M., Clausen, M. M., Moeskjær, S., Shihavuddin, ASM., Dahl, A. B.
, Janss, L. & Andersen, S. U. (2020).
Greenotyper: Image-based plant phenotyping using distributed computing and deep learning.
Frontiers in Plant Science,
11, [1181].
https://doi.org/10.3389/fpls.2020.01181
Su, G., Sørensen, A. C., Chu, T. T., Meier, K., Nielsen, T.
& Lund, M. S. (2020).
Impact of phenotypic information and composition of reference population on genomic prediction in fish under the presence of genotype by environment interaction.
Aquaculture,
526, [735358].
https://doi.org/10.1016/j.aquaculture.2020.735358
Slagboom, M., Hjortø, L., Sørensen, A. C., Mulder, H. A.
, Thomasen, J. R. & Kargo, M. (2020).
Possibilities for a specific breeding program for organic dairy production.
Journal of Dairy Science,
103(7), 6332-6345.
https://doi.org/10.3168/jds.2019-16900
Poulsen, B. G., Ask, B.
, Nielsen, H. M., Ostersen, T.
& Christensen, O. F. (2020).
Prediction of genetic merit for growth rate in pigs using animal models with indirect genetic effects and genomic information.
Genetics, selection, evolution : GSE,
52, [58].
https://doi.org/10.1186/s12711-020-00578-y
Munksgaard, L., Weisbjerg, M. R., Henriksen, J. C. S. & Løvendahl, P. (2020).
Changes to steps, lying, and eating behavior during lactation in Jersey and Holstein cows and the relationship to feed intake, yield, and weight.
Journal of Dairy Science,
103(5), 4643-4653.
https://doi.org/10.3168/jds.2019-17565
Morgante, F., Huang, W.
, Sørensen, P., Maltecca, C. & Mackay, T. F. C. (2020).
Leveraging Multiple Layers of Data To Predict Drosophila Complex Traits.
G3 (Bethesda, Md.),
10(12), 4599-4613.
https://doi.org/10.1534/g3.120.401847Mao, X., Sahana, G., Johansson, A. M.
, Liu, A., Ismael, A., Løvendahl, P., De Koning, D-J.
& Guldbrandtsen, B. (2020).
Genome-wide association mapping for dominance effects in female fertility using real and simulated data from Danish Holstein cattle.
Scientific Reports,
10, [2953].
https://doi.org/10.1038/s41598-020-59788-5
Manzanilla-Pech, C. I. V., Gordo, D., Difford, G. F., Løvendahl, P. & Lassen, J. (2020).
Multitrait genomic prediction of methane emissions in Danish Holstein cattle.
Journal of Dairy Science,
103(10), 9195-9206.
https://doi.org/10.3168/jds.2019-17857
Mahmood, K., Orabi, J.
, Kristensen, P. S., Sarup, P., Jørgensen, L. N. & Jahoor, A. (2020).
De novo transcriptome assembly, functional annotation, and expression profiling of rye (Secale cereale L.) hybrids inoculated with ergot (Claviceps purpurea).
Scientific Reports,
10(1), [13475].
https://doi.org/10.1038/s41598-020-70406-2
Madsen, M. D., Villumsen, T. M., Hansen, B. K.
, Møller, S. H., Jensen, J. & Shirali, M. (2020).
Combined analysis of group recorded feed intake and individually recorded body weight and litter size in mink.
Animal : an international journal of animal bioscience,
14(9), 1793-1801.
https://doi.org/10.1017/S1751731120000762
Macedo, F. L.
, Christensen, O. F., Astruc, J-M., Aguilar, I., Masuda, Y. & Legarra, A. (2020).
Bias and accuracy of dairy sheep evaluations using BLUP and SSGBLUP with metafounders and unknown parent groups.
Genetics, selection, evolution : GSE,
52(1), [47].
https://doi.org/10.1186/s12711-020-00567-1Liu, T., Luo, C., Ma, J., Wang, Y., Shu, D.
, Su, G. & Qu, H. (2020).
High-Throughput Sequencing With the Preselection of Markers Is a Good Alternative to SNP Chips for Genomic Prediction in Broilers.
Frontiers in Genetics,
11, [108].
https://doi.org/10.3389/fgene.2020.00108Liu, A., Lund, M. S., Boichard, D., Mao, X.
, Karaman, E., Fritz, S., Aamand, G. P., Wang, Y.
& Su, G. (2020).
Imputation for sequencing variants preselected to a customized low-density chip.
Scientific Reports,
10, [9524].
https://doi.org/10.1038/s41598-020-66523-7
Liu, A., Lund, M. S., Boichard, D.
, Karaman, E., Guldbrandtsen, B., Fritz, S., Aamand, G. P., Nielsen, U. S.
, Sahana, G., Wang, Y.
& Su, G. (2020).
Weighted single-step genomic best linear unbiased prediction integrating variants selected from sequencing data by association and bioinformatics analyses.
Genetics Selection Evolution,
52(1), [48].
https://doi.org/10.1186/s12711-020-00568-0
Li, J.
, Gao, H., Madsen, P., Li, R., Liu, W., Bao, P., Xue, G., Gao, Y., Di, X.
& Su, G. (2020).
Impact of the Order of Legendre Polynomials in Random Regression Model on Genetic Evaluation for Milk Yield in Dairy Cattle Population.
Frontiers in Genetics,
11.
https://doi.org/10.3389/fgene.2020.586155Larsen, F., Guldbrandtsen, B., Christensen, D., Pitcovski, J.
, Kjærup, R. B. & Dalgaard, T. S. (2020).
Pustulan activates chicken bone marrow-derived dendritic cells in vitro and promotes ex vivo CD4+ T cell recall response to infectious bronchitis virus.
Vaccines,
8(2), [226].
https://doi.org/10.3390/vaccines8020226
Islam, M. S., Jensen, J., Løvendahl, P., Karlskov-Mortensen, P.
& Shirali, M. (2020).
Bayesian estimation of genetic variance and response to selection on linear or ratio traits of feed efficiency in dairy cattle.
Journal of Dairy Science,
103(10), 9150-9166.
https://doi.org/10.3168/jds.2019-17137
Guo, X., Sarup, P. M., Jensen, J. D., Jihad, O., Kristensen, N. H.
, Mulder, F. A. A., Jahoor, A.
& Jensen, J. (2020).
Genetic Variance of Metabolomic Features and Their Relationship With Malting Quality Traits in Spring Barley.
Frontiers in Plant Science,
11, [575467].
https://doi.org/10.3389/fpls.2020.575467
Guo, X., Svane, S. F., Füchtbauer, W. S., Andersen, J. R.
, Jensen, J. & Thorup-Kristensen, K. (2020).
Genomic prediction of yield and root development in wheat under changing water availability.
Plant Methods,
16, [90].
https://doi.org/10.1186/s13007-020-00634-0
Gebreyesus, G., Sahana, G., Christian Sørensen, A., Lund, M. S. & Su, G. (2020).
Novel approach to incorporate information about recessive lethal genes increases the accuracy of genomic prediction for mortality traits.
Heredity,
125, 155-166.
https://doi.org/10.1038/s41437-020-0329-5
Gebreyesus, G., Difford, G. F., Buitenhuis, B., Lassen, J., Noel, S. J., Højberg, O., Plichta, D. R.
, Zhu, Z., Poulsen, N. A., Sundekilde, U. K., Løvendahl, P. & Sahana, G. (2020).
Predictive ability of host genetics and rumen microbiome for subclinical ketosis.
Journal of Dairy Science,
103(5), 4557-4569.
https://doi.org/10.3168/jds.2019-17824
Gautason, E., Schonherz, A. A., Sahana, G. & Guldbrandtsen, B. (2020).
Relationship of Icelandic cattle with Northern and Western European cattle breeds, admixture and population structure.
Acta Agriculturae Scandinavica, Section A - Animal Science,
69(1-2), 25-38.
https://doi.org/10.1080/09064702.2019.1699951
Gan, Q. F., Li, Y. R., Liu, Q. H.
, Lund, M., Su, G. S. & Liang, X. W. (2020).
Genome-wide association studies for the concentrations of insulin, triiodothyronine, and thyroxine in Chinese Holstein cattle.
Tropical Animal Health and Production,
52(4), 1655-1660.
https://doi.org/10.1007/s11250-019-02170-zEydivandi, S., Sahana, G., Momen, M., Moradi, M. H.
& Schönherz, A. A. (2020).
Genetic diversity in Iranian indigenous sheep vis-à-vis selected exogenous sheep breeds and wild mouflon.
Animal Genetics,
51(5), 772-787.
https://doi.org/10.1111/age.12985
Esfandyari, H., Fè, D., Tessema, B. B., Janss, L. L. & Jensen, J. (2020).
Effects of different strategies for exploiting genomic selection in perennial ryegrass breeding programs.
G3: Genes, Genomes, Genetics,
10(10), 3783-3795.
https://doi.org/10.1534/g3.120.401382
Difford, G. F., Løvendahl, P., Veerkamp, R. F., Bovenhuis, H., Visker, M. H. P. W., Lassen, J. & de Haas, Y. (2020).
Can greenhouse gases in breath be used to genetically improve feed efficiency of dairy cows? Journal of Dairy Science,
103(3), 2442-2459.
https://doi.org/10.3168/jds.2019-16966
Dettmann, F., Warner, D.
, Buitenhuis, B., Kargo, M., Kjeldsen, A. M. H., Nielsen, N. H., Lefebvre, D. M. & Santschi, D. E. (2020).
Fatty Acid Profiles from Routine Milk Recording as a Decision Tool for Body Weight Change of Dairy Cows after Calving.
Animals,
10(11), [1958].
https://doi.org/10.3390/ani10111958Clasen, J. B., Fikse, W. F.
, Kargo, M., Rydhmer, L., Strandberg, E.
& Østergaard, S. (2020).
Economic consequences of dairy crossbreeding in conventional and organic herds in Sweden.
Journal of Dairy Science,
103(1), 514-528.
https://doi.org/10.3168/jds.2019-16958Chu, T. T., Madsen, P., Norberg, E., Wang, L., Marois, D., Henshall, J.
& Jensen, J. (2020).
Genetic analysis on body weight at different ages in broiler chicken raised in commercial environment.
Journal of Animal Breeding and Genetics (Online),
137(2), 245-259.
https://doi.org/10.1111/jbg.12448
Chu, T. T., Sørensen, A. C., Lund, M. S., Meier, K., Nielsen, T. & Su, G. (2020).
Phenotypically Selective Genotyping Realizes More Genetic Gains in a Rainbow Trout Breeding Program in the Presence of Genotype-by-Environment Interactions.
Frontiers in Genetics,
11, [866].
https://doi.org/10.3389/fgene.2020.00866
Cao, L., Liu, H., Mulder, H. A., Henryon, M.
, Thomasen, J. R., Kargo, M. & Sørensen, A. C. (2020).
Genomic Breeding Programs Realize Larger Benefits by Cooperation in the Presence of Genotype × Environment Interaction Than Conventional Breeding Programs.
Frontiers in Genetics,
11, [251].
https://doi.org/10.3389/fgene.2020.00251
Cai, Z., Dusza, M.
, Guldbrandtsen, B., Lund, M. S. & Sahana, G. (2020).
Distinguishing pleiotropy from linked QTL between milk production traits and mastitis resistance in Nordic Holstein cattle.
Genetics, selection, evolution : GSE,
52(1), [19].
https://doi.org/10.1186/s12711-020-00538-6
Cai, Z., Sarup, P., Ostersen, T., Nielsen, B., Fredholm, M., Karlskov-Mortensen, P.
, Sørensen, P., Jensen, J., Guldbrandtsen, B., Lund, M. S., Christensen, O. F. & Sahana, G. (2020).
Genomic diversity revealed by whole-genome sequencing in three Danish commercial pig breeds.
Journal of Animal Science,
98(7), [skaa229].
https://doi.org/10.1093/jas/skaa229
Bordbar, F.
, Jensen, J., Du, M., Abied, A., Guo, W., Xu, L., Gao, H., Zhang, L. & Li, J. (2020).
Identification and validation of a novel candidate gene regulating net meat weight in Simmental beef cattle based on imputed next-generation sequencing.
Cell Proliferation,
53(9), [e12870].
https://doi.org/10.1111/cpr.12870Ask, B.
, Christensen, O. F., Heidaritabar, M., Madsen, P. & Nielsen, H. M. (2020).
The predictive ability of indirect genetic models is reduced when culled animals are omitted from the data.
Genetics, selection, evolution : GSE,
52(1), [8].
https://doi.org/10.1186/s12711-020-0527-xNielsen, L. V., Nielsen, B.
, Christensen, O. F., Turner, S. P.
, Nielsen, H. M. & Ask, B. (2019).
Selection of pigs for social genetic effects improves growth in crossbreeds. Paper præsenteret ved Abstract from 70th Annual Meeting of the European Federation of Animal Science EAAP 2019, Ghent, Belgium, Ghent, Belgien.
Vargas-Beloo-Pérez, E., Dhakal, R.
, Kargo, M., Buitenhuis, A. J. & Poulsen, N. A. (2019).
Effect of feeding managements on the milk concentrations of short- and medium-chain fatty acids. Abstract fra 70th Annual Meeting of the European Federation of Animal Science (EAAP), Ghent, Belgien.
Slagboom, M., Hjortø, L., Sørensen, A. C., Mulder, H. A.
, Thomasen, J. R. & Kargo, M. (2019).
Possibilities for a specific breeding program for organic dairy production. 215-215. Abstract fra 70th Annual Meeting of the European Federation of Animal Science EAAP 2019, Ghent, Belgium, Ghent, Belgien.
Schmidtmann, C.
, Kargo, M., Ettema, J. F., Hinrichs, D. & Thaller, G. (2019).
Derivation of economic values for German dairy breeds. 609-609. Abstract fra 70th Annual Meeting of the European Federation of Animal Science EAAP 2019, Ghent, Belgium, Ghent, Belgien.
Rome, H. J. S., Chu, T. T., Hawken, R., Henshall, J.
& Jensen, J. (2019).
WGBLUP model improves accuracy of breeding values prediction in a commercial line of broilers. Abstract fra Abstract from 70th Annual Meeting of the European Federation of Animal Science EAAP 2019, Ghent, Belgium, Ghent, Belgien.
Poulsen, N. A., Buitenhuis, A. J., Sehested, J., Kargo, M., Wiking, L. & Larsen, L. B. (2019).
Sustainable milk production and the impact on milk quality and functionality. 16. Abstract fra Dairy Research Conference 2019, Billund, Danmark.
Olijhoek, D., Løvendahl, P., Hellwing, A. L. F., Weisbjerg, M. R. & Lund, P. (2019).
Feed sorting of low and high concentrate diets by Holstein and Jersey dairy cows. 199. Abstract fra British Society of Animal Science conference 2019, Edinburgh, Storbritannien.
Nielsen, B., Ask, B.
, Gao, H. & Su, G. (2019).
Prediction of individual breeding values from group recordings. Abstract fra Abstract from 70th Annual Meeting of the European Federation of Animal Science EAAP 2019, Ghent, Belgium, Ghent, Belgien.
Kargo, M., Hein, L.
, Poulsen, N. A. & Buitenhuis, A. J. (2019).
Keeping up with a healthy milk fatty acid profile requires selection. 604-604. Abstract fra 70th Annual Meeting of the European Federation of Animal Science EAAP 2019, Ghent, Belgium, Ghent, Belgien.
Kargo, M., Clasen, J.
, Ettema, J. F., Davis, R. B.
& Østergaard, S. (2019).
SimHerd Crossbred for estimating the economic effects of crossbreeding. 285-285. Abstract fra 70th Annual Meeting of the European Federation of Animal Science EAAP 2019, Ghent, Belgium, Ghent, Belgien.
Holmquist, D. D.
, Buitenhuis, A. J., Nielsen, N. H.
, Poulsen, N. A., Munk, A.
& Kargo, M. (2019).
Where will new techologies take milk recording? Large scale screening of the Danish dairy cattle population for their milk fatty acid profile. Abstract fra ICAR 2019, Prag, Tjekkiet.
https://www.icar.org/Documents/Prague-2019/Abstracts/S06(T)-OP-1.pdfGao, H., Nielsen, B.
, Su, G., Madsen, P., Jensen, J., Christensen, O. F., Ostersen, T.
& Shirali, M. (2019).
Use of repeated group measurements with drop out animals for genetic analysis. Abstract fra Abstract from 70th Annual Meeting of the European Federation of Animal Science EAAP 2019, Ghent, Belgium, Ghent, Belgien.
Clasen, J.
, Kargo, M., Østergaard, S., Fikse, W. F., Rydhmer, L. & Strandberg, E. (2019).
Consequences of crossbreeding, genotyping, and use of sexed semen and beef semen on genetic merit and profitability in Swedish dairy herds. 292-293. Abstract fra ADSA Annual Meeting 2019, Cincinnati, USA.
https://www.adsa.org/Portals/0/SiteContent/Docs/Meetings/2019ADSA/2019ADSA_Abstract_Book.pdf?v20190715Clasen, J.
, Kargo, M., Østergaard, S., Fikse, W. F., Rydhmer, L. & Strandberg, E. (2019).
Effect of crossbreeding and using new techniques on genetic merit and economy in Swedish dairy herds. 285-285. Abstract fra 70th Annual Meeting of the European Federation of Animal Science EAAP 2019, Ghent, Belgium, Ghent, Belgien.
Chu, T. T., Norberg, E., Henshall, J.
& Jensen, J. (2019).
Benefits of using genomic information for broiler breeding program in presence of GxE interactions. Abstract fra Abstract from 70th Annual Meeting of the European Federation of Animal Science EAAP 2019, Ghent, Belgium, Ghent, Belgien.
Chu, T. T., Rome, H. J. S., Norberg, E., Marois, D., Henshall, J.
& Jensen, J. (2019).
GxE interactions of body weight for broilers raised in bio-secure and commercial environments. Abstract fra Abstract from 70th Annual Meeting of the European Federation of Animal Science EAAP 2019, Ghent, Belgium, Ghent, Belgien.
Chu, T. T., Bastiaansen, J. WM.
, Berg, P. & Komen, H. (2019).
Use of genomic information to improve accuracy of prediction from group records. Abstract fra Abstract from 70th Annual Meeting of the European Federation of Animal Science EAAP 2019, Ghent, Belgium, Ghent, Belgien.
Lee, A., Krawczel, P.
, Ternman, E. M., Schneider, L.
, Løvendahl, P. & Munksgaard, L. (2019).
Evaluating the effects of mean occupation rate and milk production on two automatic milking systems. I R. C. Newberry & B. O. Braastad (red.),
Proceedings of the 53rd Congress of the ISAE: Animal Lives Worth Living (s. 302-302). Wageningen Academic Publishers.
https://www.applied-ethology.org/ISAE_Meetings.htmlLee, A. R., Krawczel, P.
, Ternman, E. M., Schneider, L. G.
, Løvendahl, P. & Munksgaard, L. (2019).
Evaluating the effects of mean stocking density on automatic milking system use and total milk production in Holstein cows. I
Proceedings of the 2nd International Precision Dairy Farming Conference (s. 71-72)
Madsen, M. D., Shirali, M., Jensen, J., Hansen, B. K.
, Møller, S. H. & Villumsen, T. M. (2019).
Selektion for fodereffektivitet uden negative konsekvenser for kropsvægt og kuldstørrelse. I
Temadag om aktuel minkforskning: bilag (s. 28-31). Aarhus Universitet.
https://anis.au.dk/fileadmin/ingen_mappe_valgt/Minktemadag_rapport_final_september2019.pdf
Kargo, M. (2019).
Avl. I T. Kristensen, J. O. Lehmann & L. Mogensen (red.),
Fremtidens helhedsorienterede og balancerede kvægproduktion - visioner og udfordringer for malkekvægsbedriften frem mod år 2040 (Bind 146, s. 52-64). DCA RAPPORT NR. 146 - FREMTIDENS HELHEDSORIENTEREDE OG BALANCEREDE KVÆGPRODUKTION
Dettmann, F., Warner, D.
, Buitenhuis, A. J., Kargo, M., Hostrup Kjeldsen, A. M., Nielsen, N. H., Lefebvre, D. M. & Santschi, D. E. (2019).
The use of fatty acid profiles from milk recording samples to predict body weight change of dairy cows in early lactation in commercial farms. I J. Kucera, P. Bucek, D. Lipovsky, X. Bourrigan & M. Burke (red.),
New traits and adding value to the recording and ID services in the animal production: Proceedings of the ICAR Conference held in Prague, CZ, 17-21 June 2019 (s. 143-154). ICAR. ICAR Technical Series Nr. 24
https://www.icar.org/wp-content/uploads/2019/12/ICAR-Technical-Series-24-Prague-2019-Proceedings.pdfWarner, D., Dettmann, F.
, Buitenhuis, A. J., Kargo, M., Hostrup Kjeldsen, A. M., Nielsen, N. H., Lefebvre, D. M. & Santschi, D. E. (2019).
Development of a prediction equation for body weight change in early-lactating dairy cows by Fourier-transform infrared predicted fatty acid profiles in milk.
Journal of Dairy Science,
102(Supplement 1), 109-110. [95].
https://www.adsa.org/Portals/0/SiteContent/Docs/Meetings/2019ADSA/2019ADSA_Abstract_Book.pdf?v20190715Lee, A., Krawczel, P.
, Ternman, E. M., Schneider, L.
, Løvendahl, P. & Munksgaard, L. (2019).
Effects of mean occupation rate on hourly automatic milking system use and total milk production in Holstein cows.
Journal of Dairy Science,
102(Suppl. 1), 250-250. [262].
https://www.adsa.org/Portals/0/SiteContent/Docs/Meetings/2019ADSA/2019ADSA_Abstract_Book.pdf?v20190715Chu, T. T., Bastiaansen, J. W. M.
, Berg, P., Romé, H., Marois, D., Henshall, J.
& Jensen, J. (2019).
Correction to: Use of genomic information to exploit genotype-by-environment interactions for body weight of broiler chicken in bio-secure and production environments.
Genetics Selection Evolution,
51(1), [68].
https://doi.org/10.1186/s12711-019-0513-3
