Hostname: page-component-586b7cd67f-rcrh6 Total loading time: 0 Render date: 2024-11-28T01:00:17.216Z Has data issue: false hasContentIssue false
  • English
  • Français

Development of a dynamic energy-partitioning model for enteric methane emissions and milk production in goats using energy balance data from indirect calorimetry studies

Published online by Cambridge University Press:  24 June 2020

C. Fernández Open the ORCID record for C. Fernández [Opens in a new window] ,
I. Hernando ,
E. Moreno-Latorre  and
J. J. Loor Open the ORCID record for J. J. Loor [Opens in a new window]
C. Fernández*
Affiliation:
Departamento de Ciencia Animal, Edificio 7G, Camino de Vera s/n, Instituto de Ciencia y Tecnología Animal, Universitat Politecnica de Valencia, 46022Valencia, Spain
I. Hernando
Affiliation:
Campus Edetania, Facultad de Magisterio y Ciencias de la Educación, Sagrado Corazón, 5, Universidad Católica de Valencia, 46110 Godella, Valencia, Spain
E. Moreno-Latorre
Affiliation:
Campus Edetania, Facultad de Magisterio y Ciencias de la Educación, Sagrado Corazón, 5, Universidad Católica de Valencia, 46110 Godella, Valencia, Spain
J. J. Loor
Affiliation:
Department of Animal Sciences, Division of Nutritional Sciences, University of Illinois, 1207 West Gregory Drive, Urbana, IL61801, USA
*
†E-mail: [email protected]
Get access

Abstract

The main objective of this study was to develop a dynamic energy balance model for dairy goats to describe and quantify energy partitioning between energy used for work (milk) and that lost to the environment. Increasing worldwide concerns regarding livestock contribution to global warming underscore the importance of improving energy efficiency utilization in dairy goats by reducing energy losses in feces, urine and methane (CH4). A dynamic model of CH4 emissions from experimental energy balance data in goats is proposed and parameterized (n = 48 individual animal observations). The model includes DM intake, NDF and lipid content of the diet as explanatory variables for CH4 emissions. An additional data set (n = 122 individual animals) from eight energy balance experiments was used to evaluate the model. The model adequately (root MS prediction error, RMSPE) represented energy in milk (E-milk; RMSPE = 5.6%), heat production (HP; RMSPE = 4.3%) and CH4 emissions (E-CH4; RMSPE = 11.9%). Residual analysis indicated that most of the prediction errors were due to unexplained variations with small mean and slope bias. Some mean bias was detected for HP (1.12%) and E-CH4 (1.27%) but was around zero for E-milk (0.14%). The slope bias was zero for HP (0.01%) and close to zero for E-milk (0.10%) and E-CH4 (0.22%). Random bias was >98% for E-CH4, HP and E-milk, indicating non-systematic errors and that mechanisms in the model are properly represented. As predicted energy increased, the model tended to underpredict E-CH4 and E-milk. The model is a first step toward a mechanistic description of nutrient use by goats and is useful as a research tool for investigating energy partitioning during lactation. The model described in this study could be used as a tool for making enteric CH4 emission inventories for goats.

Keywords

energy transferenvironmentmixed dietslactationgoats
Type
Research Article
Information
animal , Volume 14 , Issue S2: 9th Workshop on Modelling Nutrient Digestion and Utilization in Farm Animals (MODNUT) , August 2020 , pp. s382 - s395
Copyright
© The Author(s), 2020. Published by Cambridge University Press on behalf of The Animal Consortium

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Agricultural and Food Research Council (AFRC) 1997. The nutrition of goats. Nutrition Abstract and Reviews (Series B) 67, 776–861.Google Scholar
Aguilera, JF, Prieto, C and Fonollá, J 1990. Protein and energy metabolism of lactating Granadina goats. British Journal of Nutrition 63, 165–175.CrossRefGoogle ScholarPubMed
Association of Official Analytical Chemists (AOAC) 2000. Official Methods of Analysis of the Association of Official Analytical Chemists, 18th edition. Association of Official Analytical Chemists, Arlington, VA.Google Scholar
Association of Official Analytical Chemists (AOAC) 2012. Official methods of analysis, 19th edition. AOAC International, Gaithersburg, MD, USA.Google Scholar
Bannink, A, France, J, López, S, Gerrits, WJJ, Kebreab, E, Tamminga, S and Dijkstra, I 2008. Modelling the implications of feeding strategy on rumen fermentation and functioning of the rumen wall. Animal Feed Science and Technology 143, 3–26.CrossRefGoogle Scholar
Bava, L, Rapetti, L, Crovetto, GM, Tamburini, A, Sandrucci, A, Galassi, G and Succi, G 2001. Effect of a non-forage diet on milk production, energy and nitrogen metabolism in dairy goats throughout lactation. Journal of Dairy Science 84, 2450–2459.CrossRefGoogle Scholar
Beauchemin, K, McAllister, T and McGinn, S 2009. Dietary mitigation of enteric CH4 from cattle. CAB Reviews: Perspectives in Agriculture, Veterinary Science, Nutrition and Natural Resources 4, 035.CrossRefGoogle Scholar
Bibby, J and Toutenburg, H 1977. Prediction and improved estimation in linear models. John Wiley& Son, London, UK.Google Scholar
Blaxter, KL and Clapperton, JL 1965. Prediction of the amount of CH4 produced by ruminants. British Journal of Nutrition 19, 511–522.CrossRefGoogle Scholar
Brouwer, E 1965. Report of sub-committee on constants and factors. In Proceeding of the 3th EAAP Symposium on Energy Metabolism (ed. Blaxter, KL), pp. 441–443. Academic Press, London, UK.Google Scholar
Criscioni, P, Marti, JV, Pérez-Baena, I, Palomares, JL, Larsen, T and Fernández, C 2016. Replacement of alfalfa (Medicago sativa) hay with maralfalfa (Pennisetum sp.) hay in diets of lactating dairy goats. Animal Feed Science and Technology 219, 1–12.CrossRefGoogle Scholar
Ellis, JL, Kebreab, E, Odongo, NE, McBride, BW, Okine, EK and France, J 2007. Predictions of methane productions from dairy and beef cattle. Journal of Dairy Science 90, 3456–3466.CrossRefGoogle Scholar
Food and Agriculture Organization (FAO) 2010. Greenhouse gas emissions from dairy sector. Food and Agriculture organization of the United Nations, Rome, Italy.Google Scholar
Statistical data base Food and Agriculture Organization (FAOSTAT) 2018. FAO Statistical data base Food and Agriculture Organization of the United Nations, Rome, Italy. Retrieved on 25 June 2018 from http://faostat.fao.org/Google Scholar
Fernández, C, López, MC and Lachica, M 2015. Low cost open-circuit hood system for measuring gas exchange in small ruminants: from manual to automatic recording. Journal of Agricultural Science. 153, 1302–1309.CrossRefGoogle Scholar
Fernández, C, Marti, JV, Pérez-Baena, I, Palomares, JL, Ibáñez, C and Segarra, JV 2018. Effect of lemon leaves on energy and CN balances, methane emission and milk performance in Murciano-Granadina dairy goats. Journal of Animal Science 96, 1508–1518.CrossRefGoogle Scholar
Fernández, C 2018. Dynamic model development of enteric methane emission from goats based on energy balance measured in indirect open circuit respiration calorimeter. Global Ecology and Conservation 15, e00439.CrossRefGoogle Scholar
Fernández, C, Pérez-Baena, I, Marti, JV, Palomares, JL, Jorro-Ripoll, J and Segarra, JV 2019. Use of orange leaves as a replacement for alfalfa in energy and nitrogen partitioning, methane emissions and milk performance of Murciano-Granadinas goats. Animal Feed Science and Technology 247, 103–111.CrossRefGoogle Scholar
Fernández, C, Gomis-Tena, J, Hernández, A and Saiz, J 2019. An open-circuit indirect calorimetry head hood system for measuring methane emission and energy metabolism in small ruminants. Animals 9, 380; doi: 10.3390/ani9060380.CrossRefGoogle ScholarPubMed
Grainger, C and Beauchemin, KA 2011. Can enteric methane emissions from ruminants be lowered without lowering their production? Animal Feed Science and Technology 166–167, 308–320.CrossRefGoogle Scholar
Howarth, RW 2015. Methane emissions and climatic warming risk from hydraulic fracturing and shale gas development; implication for policy. Energy and Emission Control Technologies 2015, 45–53.CrossRefGoogle Scholar
Hristov, AN, Kebreab, E, Niu, M, Oh, J, Bannink, A, Bayat, AR, Boland, TM, Brito, AF, Casper, DP, Crompton, LA, Dijkstra, J, Eugène, M, Garnsworthy, PC, Haque, N, Hellwing, ALF, Huhtanen, P, Kreuzer, M, Kuhla, B, Lund, P, Madsen, J, Martin, C, Moate, PJ, Muetzel, S, Muñoz, C, Peiren, N, Powell, JM, Reynolds, CK, Schwarm, A, Shingfield, KJ, Storlien, TM, Weisbjerg, MR, Yáñez-Ruiz, DR and Yu, Z 2017. Symposium review: uncertainties in enteric methane inventories, measurement techniques, and prediction models. Journal of Dairy Science 101, 6655–6674.CrossRefGoogle Scholar
Ibáñez, C, López, MC, Criscioni, P and Fernández, C 2014. Effect of replacing dietary corn with beet pulp on energy partitioning, substrate oxidation and methane production in lactating dairy goats. Animal Production Science 55, 56–63.CrossRefGoogle Scholar
Institute Nationale Recherche Agronomique (INRA) 2017. Feeding system for ruminants. Wageningen Academic Publishers, Wageningen, the Netherlands.Google Scholar
International Panel on Climate Change (IPCC) 2007. Fourth IPCC assessment report. Cambridge University Press, Cambridge, UK.Google Scholar
Jørgensen, SE 2015. New method to calculate the work energy of information and organisms. Ecology Modelling 295, 18–20.CrossRefGoogle Scholar
Kebreab, E, Johnson, KA, Archibeque, SL, Pape, D and Wirth, T 2008. Model for estimating enteric CH4 emission from United States Dairy and feedlot cattle. Journal of Animal Science 86, 2738–2748.CrossRefGoogle Scholar
Kleiber, M 1972. The fire of life: an introduction to animal energetics. John Wiley and Sons Inc., New York, NY, USA.Google Scholar
Knapp, JR, Laur, GL, Vadas, PA, Weis, WP and Tricarico, JM 2014. Invited review: enteric methane in dairy cattle production: quantifying the opportunities and impact of reducing emissions. Journal of Dairy Science 97, 3231–3261.CrossRefGoogle ScholarPubMed
Lin, LIK 1989. A concordance correlation coefficient to evaluate reproducibility. Biometrics 45, 255–268.CrossRefGoogle ScholarPubMed
López, MC, Estellés, F, Moya, VJ and Fernández, C 2014. Use of dry citrus pulp or soybean hulls as a replacement for corn and grain in energy and nitrogen partitioning, methane emissions and milk performance in lactating Murciano-Granadina goats. Journal of Dairy Science 97, 7821–7832.CrossRefGoogle ScholarPubMed
López, MC and Fernández, C 2013. Energy partitioning and substrate oxidation by Murciano-Granadina goats during mid lactation fed soy hulls and corn gluten feed blend as a replacement for corn grain. Journal of Dairy Science 96, 4542–4552.CrossRefGoogle ScholarPubMed
Martí, JV, Pérez-Baena, I and Fernández, C 2012. Replacement of barley grain with lemon pulp on energy partitioning in lactating goats. Unpublished.Google Scholar
Merino, P, Ramirez-Fanlo, E, Arriaga, H, del Hierro, O, Artetxe, A and Viguria, M 2011. Regional inventory of methane and nitrous oxide emission from ruminant livestock in the Basque Country. Animal Feed Science and Technology 166–167, 628–640.CrossRefGoogle Scholar
Mills, JAN, Kebreab, E, Yates, CW, Crompton, LA, Cammell, SB, Dhanoa, MS, Agnew, RE and France, J 2003. Alternative approaches to predicting methane emissions from dairy cows. Journal of Animal Science 81, 3143–3150.CrossRefGoogle ScholarPubMed
Moorby, JM, Fleming, HR, Theobald, MJ and Fraser, MD 2015. Can live weight be used as a proxy for enteric methane emissions from pasture fed sheep? Scientific Report 5, 17915.CrossRefGoogle ScholarPubMed
Niu, M, Kebreab, E, Hristov, AN, Oh, J, Arndt, C, Bannik, A, Bayat, AR, Brito, AF, Boland, T, Casper, D, Crompton, LA, Dijkstra, J, Eugène, MA, Garnsworthy, PC, Haque, MN, Hellwing, ALF, Huhtanen, P, Kreuzer, M, Kuhla, B, Lund, P, Madsen, J, Martin, C, McClelland, SC, McGee, M, Moate, PJ, Muetzel, S, Muñoz, C, O'Kiely, P, Peiren, N, Reynolds, CK, Schwarm, A, Shingfield, KJ, Storlien, TM, Weisbjerg, MR, Yáñez-Ruiz, DR and Yu, Z 2018. Prediction of enteric methane production, yield and intensity in dairy cattle using an intercontinental database. Global Change Biology 24, 3368–3389.CrossRefGoogle ScholarPubMed
Patra, AK and Lalhriatpuii, M 2016. Development of statistical models for prediction of enteric methane emission from goats using nutrients composition and intake variables. Agriculture Ecosystem and Environment 215, 89–99.CrossRefGoogle Scholar
Pérez-Baena, I, Martí, JV and Fernández, C 2012. Effect of replace barley grain with beet pulp in lactating goats diet; energy balance and milk performance. Unpublished.Google Scholar
R Core Team 2016. R: A language and environment for statistical computing. Version 1.1.447. R Foundation for Statistical Computing, Vienna, Austria. Retrieved from https://www.R-project.org/Google Scholar
Ramin, M and Huhtanen, P 2013. Development of equations for predicting methane emissions from ruminants. Journal of Dairy Science 96, 2476–2493.CrossRefGoogle ScholarPubMed
Tovar-Luna, I, Puchala, R, Sahlu, T, Freetly, HC and Goetsch, AL 2010. Effects of stage of lactation and dietary concentrate level on energy utilization by Alpine dairy goats. Journal of Dairy Science 93, 4818–4828.CrossRefGoogle ScholarPubMed
United Nations Framework Convention on Climate Change 2015. UN Climate Change Newsroom. Historic Paris agreement on climate change. 195 nations set path to keep temperature rise well below 2 degrees Celsius. Retrieved on 1 July 2018 from http://newsroom.unfccc.int/unfccc-newsroom/finale-cop21/Google Scholar
Yan, T, Porter, MG and Mayne, CS 2009. Prediction of methane emissions from beef cattle using data measured in indirect open circuit respiration calorimeters. Animal 3, 1455–1462.CrossRefGoogle Scholar
Supplementary material: PDF

Fernández et al. supplementary material

Fernández et al. supplementary material 1

Download Fernández et al. supplementary material(PDF)
PDF 54.5 KB
Supplementary material: PDF

Fernández et al. supplementary material

Fernández et al. supplementary material 2

Download Fernández et al. supplementary material(PDF)
PDF 81.9 KB