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Multi-criteria evaluation of dairy cattle feed resources and animal characteristics for nutritive and environmental impacts

Published online by Cambridge University Press:  24 August 2018

H. J. van Lingen
Affiliation:
Department of Animal Science, University of California, Davis, CA 95616, USA
J. G. Fadel
Affiliation:
Department of Animal Science, University of California, Davis, CA 95616, USA
A. Bannink
Affiliation:
Wageningen Livestock Research, Wageningen University & Research, PO Box 338, 6700 AH, Wageningen, the Netherlands
J. Dijkstra
Affiliation:
Animal Nutrition Group, Wageningen University & Research, PO Box 338, 6700 AH, Wageningen, The Netherlands
J. M. Tricarico
Affiliation:
DMI Innovation Center for US Dairy, Rosemont, IL 60018, USA
D. Pacheco
Affiliation:
AgResearch Limited, Grasslands Research Centre, Private Bag 11008, Palmerston North 4442, New Zealand
D. P. Casper
Affiliation:
Furst McNess Company, Freeport, IL61032, USA
E. Kebreab*
Affiliation:
Department of Animal Science, University of California, Davis, CA 95616, USA
*
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Abstract

On-farm nutrition and management interventions to reduce enteric CH4 (eCH4) emission, the most abundant greenhouse gas from cattle, may also affect volatile solids and N excretion. The objective was to jointly quantify eCH4 emissions, digestible volatile solids (dVS) excretion and N excretion from dairy cattle, based on dietary variables and animal characteristics, and to evaluate relationships between these emissions and excreta. Univariate and Bayesian multivariate mixed-effects models fitted to 520 individual North American dairy cow records indicated dry matter (DM) intake and dietary ADF and CP to be the main predictors for production of eCH4 emissions and dVS and N excreta (g/day). Yields (g/kg DM intake) of eCH4 emissions and dVS and N excreta were best predicted by dietary ADF, dietary CP, milk yield and milk fat content. Intensities (g/kg fat- and protein-corrected milk) of eCH4, dVS and N excreta were best predicted by dietary ADF, dietary CP, days in milk and BW. A K-fold cross-validation indicated that eCH4 and urinary N variables had larger root mean square prediction error (RMSPE; % of observed mean) than dVS, fecal N and total N production (on average 24.3% and 26.5% v. 16.7%, 15.5% and 16.2%, respectively), whereas intensity variables had larger RMSPE than production and yields (29.4%, 14.7% and 14.6%, respectively). Univariate and multivariate equations performed relatively similar (18.8% v. 19.3% RMSPE). Mutual correlations indicated a trade-off for eCH4v. dVS yield. The multivariate model indicated a trade-off between eCH4 and dVS v. total N production, yield and intensity induced by dietary CP content.

Type
Research Article
Copyright
© The Animal Consortium 2018 

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References

Appuhamy, JADRN, Moraes, LE, Wagner-Riddle, C, Casper, DP and Kebreab, E 2018. Predicting manure volatile solid output of lactating dairy cows. Journal of Dairy Science 101, 820829.CrossRefGoogle ScholarPubMed
Bates, D, Maechler, M, Bolker, B and Walker, S 2015. Fitting linear mixed-effects models using lme4. Journal of Statistical Software 67, 148.CrossRefGoogle Scholar
Belanche, A, Doreau, M, Edwards, JE, Moorby, JM, Pinloche, E and Newbold, CJ 2012. Shifts in the rumen microbiota due to the type of carbohydrate and level of protein ingested by dairy cattle are associated with changes in rumen fermentation. Journal of Nutrition 142, 16841692.CrossRefGoogle ScholarPubMed
Bernard, L, Leroux, C and Chilliard, Y 2008. Expression and nutritional regulation of lipogenic genes in the ruminant lactating mammary gland. Advances in Experimental Medicine and Biology 606, 67108.CrossRefGoogle ScholarPubMed
Bibby, J and Toutenburg, T 1977. Prediction and improved estimation in linear models. John Wiley & Sons, Chichester.Google Scholar
Broderick, GA 2003. Effects of varying dietary protein and energy levels on the production of lactating dairy cows. Journal of Dairy Science 86, 13701381.CrossRefGoogle ScholarPubMed
Centraal VeevoederBureau 2008. CVB Table booklet feeding of ruminants. CVB series no. 43. Centraal Veevoederbureau, Lelystad, the Netherlands.Google Scholar
Dijkstra, J, Oenema, O and Bannink, A 2011. Dietary strategies to reducing N excretion from cattle: implications for methane emissions. Current Opinion in Environmental Sustainability 3, 414422.CrossRefGoogle Scholar
Fredeen, A, Juurlink, S, Main, M, Astatkie, T and Martin, RC 2013. Implications of dairy systems on enteric methane and postulated effects on total greenhouse gas emission. Animal 7, 18751883.CrossRefGoogle ScholarPubMed
Hadfield, JD 2010. MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R package. Journal of Statistical Software 33, 122.CrossRefGoogle Scholar
Hellwing, ALF, Weisbjerg, MR and Møller, HB 2014. Enteric and manure-derived methane emissions and biogas yield of slurry from dairy cows fed grass silage or maize silage with and without supplementation of rapeseed. Livestock Production Science 165, 189199.CrossRefGoogle Scholar
Hindrichsen, IK, Wettstein, HR, Machmüller, A and Kreuzer, M 2006. Methane emission, nutrient degradation and nitrogen turnover in dairy cows and their slurry at different milk production scenarios with and without concentrate supplementation. Agriculture, Ecosystems and Environment 113, 150161.CrossRefGoogle Scholar
Hristov, AN, Ott, T, Tricarico, J, Rotz, A, Waghorn, G, Adesogan, A, Dijkstra, J, Montes, F, Oh, J, Kebreab, E, Oosting, SJ, Gerber, PJ, Henderson, B, Makkar, HPS and Firkins, JL 2013. Mitigation of methane and nitrous oxide emissions from animal operations: III. A review of animal management mitigation options. Journal of Animal Science 91, 50955113.CrossRefGoogle Scholar
Huhtanen, P, Ramin, M and Cabezas-Garcia, EH 2016. Effects of ruminal digesta retention time on methane emissions: a modelling approach. Animal Production Science 56, 501506.CrossRefGoogle Scholar
Huhtanen, P, Rinne, M and Nousiainen, J 2009. A meta-analysis of feed digestion in dairy cows. 2. The effects of feeding level and diet composition on digestibility. Journal of Dairy Science 92, 50315042.CrossRefGoogle ScholarPubMed
James, G, Witten, D, Hastie, T and Tibshirani, R 2014. An introduction to statistical learning with applications in R. Springer, New York, NY, USA.Google Scholar
Krämer, M, Weisbjerg, MR, Lund, P, Jensen, CS and Pedersen, MG 2012. Estimation of indigestible NDF in forages and concentrates from cell wall composition. Animal Feed Science and Technology 177, 4051.CrossRefGoogle Scholar
Moe, PW and Tyrrell, HF 1979. Methane production in dairy cows. Journal of Dairy Science 62, 15831586.CrossRefGoogle Scholar
Montes, F, Meinen, R, Dell, C, Rotz, A, Hristov, AN, Oh, J, Waghorn, G, Gerber, PJ, Henderson, B, Makkar, HP and Dijkstra, J 2013. Special topics – mitigation of methane and nitrous oxide emissions from animal operations: II. A review of manure management mitigation options. Journal of Animal Science 91, 50705094.CrossRefGoogle Scholar
National Research Council 2001. Nutrient requirements of dairy cattle, 7th edition. National Research Council, National Academy Press, Washington, DC, USA.Google Scholar
Roman-Garcia, Y, White, RR and Firkins, JL 2016. Meta-analysis of postruminal microbial nitrogen flows in dairy cattle. I. Derivation of equations. Journal of Dairy Science 99, 79187931.CrossRefGoogle Scholar
Spiegelhalter, DJ, Best, N, Carlin, BP and Van der Linde, A 2002. Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 64, 583639.CrossRefGoogle Scholar
Staerfl, SM, Amelchanka, SL, Kälber, T, Soliva, CR, Kreuzer, M and Zeitz, JO 2012. Effect of feeding dried high-sugar ryegrass (‘AberMagic’) on methane and urinary nitrogen emissions of primiparous cows. Livestock Science 150, 293301.CrossRefGoogle Scholar
Viechtbauer, W 2010. Conducting meta-analyses in R with the metafor package. Journal of Statistical Software 36, 148.CrossRefGoogle Scholar
Warner, D, Bannink, A, Hatew, B, Laar, H van and Dijkstra, J 2017. Effects of grass silage quality and level of feed intake on enteric methane production in lactating dairy cows. Journal of Animal Science 95, 36873699.Google ScholarPubMed
Wilkerson, VA, Casper, DP and Mertens, DR 1995. The prediction of methane production of Holstein cows by several equations. Journal of Dairy Science 78, 24022414.CrossRefGoogle ScholarPubMed
Wilkerson, VA, Mertens, DR and Casper, DP 1997. Prediction of excretion of manure and nitrogen by Holstein dairy cattle. Journal of Dairy Science 80, 31933204.CrossRefGoogle ScholarPubMed
Yan, T, Frost, JP, Agnew, RE, Binnie, RC and Mayne, CS 2006. Relationships among manure nitrogen output and dietary and animal factors in lactating dairy cows. Journal of Dairy Science 89, 39813991.CrossRefGoogle ScholarPubMed
Zwillinger, D and Kokoska, S 2000. CRC Standard probability and statistics tables and formulae. CRC Press. Boca Raton, FL, USA.Google Scholar

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