Hostname: page-component-cd9895bd7-gvvz8 Total loading time: 0 Render date: 2024-12-27T04:06:44.226Z Has data issue: false hasContentIssue false

Predicting enteric methane production from cattle in the tropics

Published online by Cambridge University Press:  11 August 2020

R. S. Ribeiro
Affiliation:
Bio-Engineering Department, Federal University of São João Del Rei, 36307-352São João Del Rei, Minas Gerais, Brazil
J. P. P. Rodrigues
Affiliation:
Federal University of Southern and Southeastern Pará (UNIFESSPA), 68557-335Xinguara, Pará, Brazil
R. M. Maurício
Affiliation:
Bio-Engineering Department, Federal University of São João Del Rei, 36307-352São João Del Rei, Minas Gerais, Brazil
A. L. C. C. Borges
Affiliation:
Federal University of Minas Gerais State (UFMG), 31270-901Belo Horizonte, Minas Gerais, Brazil
R. Reis e Silva
Affiliation:
Federal University of Minas Gerais State (UFMG), 31270-901Belo Horizonte, Minas Gerais, Brazil
T. T. Berchielli
Affiliation:
São Paulo State University (UNESP), 14884-900Jaboticabal, São Paulo, Brazil
S. C. Valadares Filho
Affiliation:
Federal University of Viçosa (UFV), 36570-900Viçosa, Minas Gerais, Brazil
F. S. Machado
Affiliation:
Brazilian Agricultural Research Corporation (EMBRAPA Dairy Cattle), 36038-330Juiz de Fora, Minas Gerais, Brazil
M. M. Campos
Affiliation:
Brazilian Agricultural Research Corporation (EMBRAPA Dairy Cattle), 36038-330Juiz de Fora, Minas Gerais, Brazil
A. L. Ferreira
Affiliation:
Brazilian Agricultural Research Corporation (EMBRAPA Dairy Cattle), 36038-330Juiz de Fora, Minas Gerais, Brazil
R. Guimarães Júnior
Affiliation:
Brazilian Agricultural Research Corporation (EMBRAPA Cerrados), 73310-970Brasília, Distrito Federal, Brazil
J. A. G. Azevêdo
Affiliation:
State University of Santa Cruz, 45662-900Ilhéus, Bahia, Brazil
R. D. Santos
Affiliation:
Brazilian Agricultural Research Corporation (EMBRAPA Semiárido), 56302-970Petrolina, Pernambuco, Brazil
T. R. Tomich
Affiliation:
Brazilian Agricultural Research Corporation (EMBRAPA Dairy Cattle), 36038-330Juiz de Fora, Minas Gerais, Brazil
L. G. R. Pereira*
Affiliation:
Brazilian Agricultural Research Corporation (EMBRAPA Dairy Cattle), 36038-330Juiz de Fora, Minas Gerais, Brazil
*
Get access

Abstract

Accurate estimates of methane (CH4) production by cattle in different contexts are essential to developing mitigation strategies in different regions. We aimed to: (i) compile a database of CH4 emissions from Brazilian cattle studies, (ii) evaluate prediction precision and accuracy of extant proposed equations for cattle and (iii) develop specialized equations for predicting CH4 emissions from cattle in tropical conditions. Data of nutrient intake, diet composition and CH4 emissions were compiled from in vivo studies using open-circuit respiratory chambers, SF6 technique or the GreenFeed® system. A final dataset containing intake, diet composition, digestibility and CH4 emissions (677 individual animal observations, 40 treatment means) obtained from 38 studies conducted in Brazil was used. The dataset was divided into three groups: all animals (GEN), lactating dairy cows (LAC) and growing cattle and non-lactating dairy cows (GCNL). A total of 54 prediction equations available in the literature were evaluated. A total of 96 multiple linear models were developed for predicting CH4 production (MJ/day). The predictor variables were DM intake (DMI), gross energy (GE) intake, BW, DMI as proportion of BW, NDF concentration, ether extract (EE) concentration, dietary proportion of concentrate and GE digestibility. Model selection criteria were significance (P < 0.05) and variance inflation factor lower than three for all predictors. Each model performance was evaluated by leave-one-out cross-validation. The Intergovernmental Panel on Climate Change (2006) Tier 2 method performed better for GEN and GCNL than LAC and overpredicted CH4 production for all datasets. Increasing complexity of the newly developed models resulted in greater performance. The GCNL had a greater number of equations with expanded possibilities to correct for diet characteristics such as EE and NDF concentrations and dietary proportion of concentrate. For the LAC dataset, equations based on intake and animal characteristics were developed. The equations developed in the present study can be useful for accurate and precise estimation of CH4 emissions from cattle in tropical conditions. These equations could improve accuracy of greenhouse gas inventories for tropical countries. The results provide a better understanding of the dietary and animal characteristics that influence the production of enteric CH4 in tropical production systems.

Type
Research Article
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.)

Footnotes

*

These two authors contributed equally to this work.

a

Present address: EMBRAPA Dairy Cattle, Rua Eugênio do Nascimento, 610, Dom Bosco, 36038-330 Juiz de Fora, Minas Gerais, Brazil.

References

Alexandratos, N and Bruinsma, J 2012. World agriculture towards 2030/2050: the 2012 revision. ESA Working paper No. 12-03. FAO, Rome, Italy.Google Scholar
Appuhamy, JADRN, France, J and Kebreab, E 2016. Models for predicting enteric methane emissions from dairy cows in North America, Europe, and Australia and New Zealand. Global Change Biology 22, 30393056.CrossRefGoogle ScholarPubMed
Bannink, A, France, J, Lopez, S, Gerrits, WJJ, Kebreab, E, Tamminga, S and Dijkstra, J 2008. Modelling the implications of feeding strategy on rumen fermentation and functioning of the rumen wall. Animal Feed Science and Technology 143, 326.CrossRefGoogle Scholar
Bateki, CA and Dickhoefer, U 2019. Predicting dry matter intake using conceptual models for cattle kept under tropical and subtropical conditions. Journal of Animal Science 97, 37273740.CrossRefGoogle Scholar
Bates, D, Mächler, M, Bolker, B and Walker, S 2015. Fitting linear mixed-effects models using lme4. Journal of Statistical Software 67, 151.CrossRefGoogle Scholar
Berndt, A and Tomkins, NW 2013. Measurement and mitigation of methane emissions from beef cattle in tropical grazing systems: a perspective from Australia and Brazil. Animal 7, 363372.CrossRefGoogle Scholar
Cabezas-Garcia, EH, Krizsan, SJ, Shingfield, KJ and Huhtanen, P 2017. Between-cow variation in digestion and rumen fermentation variables associated with methane production. Journal of Dairy Science 100, 44094424.CrossRefGoogle ScholarPubMed
Charmley, E, Williams, SRO, Moate, PJ, Hegarty, RS, Herd, RM, Oddy, VH, Reyenga, P, Staunton, KM, Anderson, A and Hannah, MC 2016. A universal equation to predict methane production of forage-fed cattle in Australia. Animal Production Science 56, 169180.CrossRefGoogle Scholar
Cunha, CS, Lopes, NL, Veloso, CM, Jacovine, LAG, Tomich, TR, Pereira, LGR and Marcondes, MI 2016. Greenhouse gases inventory and carbon balance of two dairy systems obtained from two methane-estimation methods. Science of the Total Environment 571, 744754.CrossRefGoogle ScholarPubMed
Detmann, E, Valadares Filho, SC, Pina, DS, Henriques, LT, Paulino, MF, Magalhães, KA, Silva, PA and Chizzotti, ML 2008. Prediction of the energy value of cattle diets based on the chemical composition of the feeds under tropical conditions. Animal Feed Science and Technology 143, 127147.CrossRefGoogle Scholar
Detmann, E, Gionbelli, MP and Huhtanen, P 2014. A meta-analytical evaluation of the regulation of voluntary intake in cattle fed tropical forage-based diets1. Journal of Animal Science 92, 46324641.CrossRefGoogle Scholar
Ellis, JL, Bannink, A, France, J, Kebreab, E and Dijkstra, J 2010. Evaluation of enteric methane prediction equations for dairy cows used in whole farm models. Global Change Biology 16, 32463256.CrossRefGoogle Scholar
Gomes, DI, Detmann, E, Valadares Filho, SC, Fukushima, RS, de Souza, MA, Valente, TNP, Paulino, MF and de Queiroz, AC 2011. Evaluation of lignin contents in tropical forages using different analytical methods and their correlations with degradation of insoluble fiber. Animal Feed Science and Technology 168, 206222.CrossRefGoogle Scholar
Herrero, M, Henderson, B, Havlík, P, Thornton, PK, Conant, RT, Smith, P, Wirsenius, S, Hristov, AN, Gerber, P, Gill, M, Butterbach-Bahl, K, Valin, H, Garnett, T and Stehfest, E 2016. Greenhouse gas mitigation potentials in the livestock sector. Nature Climate Change 6, 452461.CrossRefGoogle Scholar
Hristov, AN, Oh, J, Lee, C, Meinen, R, Montes, F, Ott, T, Firkins, J, Rotz, A, Dell, C, Adesogan, A, Yang, W, Tricarico, J, Kebreab, E, Waghorn, G, Dijkstra, J and Oosting, S 2013. Mitigation of greenhouse gas emissions in livestock production – A review of technical options for non-CO2 emissions. In FAO animal production and health (ed. Gerber, PJ, B, Henderson and Makkar, HPS). Paper No. 177. FAO, Rome, Italy.Google Scholar
Intergovernmental Panel on Climate Change (IPCC) 2006. 2006 IPCC guidelines for national greenhouse gas inventories, volume 4. Hayama, Kanagawa, Japan. Retrieved on 6 July 2020 from https://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/4_Volume4/V4_10_Ch10_Livestock.pdfGoogle Scholar
Intergovernmental Panel on Climate Change (IPCC) 2019. 2019 Refinement to the 2006 IPCC guidelines for National Greenhouse Gas Inventories, volume 4. IPCC, Hayama, Kanagawa, Japan. Retrieved on 6 July 2020 from https://www.ipcc-nggip.iges.or.jp/public/2019rf/pdf/4_Volume4/19R_V4_Ch10_Livestock.pdfGoogle Scholar
Jentsch, W, Chudy, A and Beyer, M 2003. Rostock feed evaluation system: reference numbers of feed value and requirement on the base of net energy. Plexus-Verlag, Frankfurt, Germany.Google Scholar
Kaewpila, C and Sommart, K 2016. Development of methane conversion factor models for Zebu beef cattle fed low-quality crop residues and by-products in tropical regions. Ecology and Evolution 6, 74227432.CrossRefGoogle ScholarPubMed
Knapp, JR, Laur, GL, Vadas, PA, Weiss, 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, 32313261.CrossRefGoogle ScholarPubMed
Kobayashi, K and Salam, MU 2000. Comparing simulated and measured values using mean squared deviation and its components. Agronomy Journal 92, 345352.CrossRefGoogle Scholar
Krizsan, SJ, Ahvenjärvi, S and Huhtanen, P 2010. A meta-analysis of passage rate estimated by rumen evacuation with cattle and evaluation of passage rate prediction models. Journal of Dairy Science 93, 58905901.CrossRefGoogle ScholarPubMed
Lage, JF, San Vito, E, Reis, RA, Dallantonia, EE, Simonetti, LR, Carvalho, IPC, Berndt, A, Chizzotti, ML, Friguetto, RTS and Berchielli, TT 2016. Methane emissions and growth performance of young Nellore bulls fed crude glycerine- v. fibre-based energy ingredients in low or high concentrate diets. The Journal of Agricultural Science 154, 12801290.CrossRefGoogle Scholar
Legesse, G, Small, JA, Scott, SL, Crow, GH, Block, HC, Alemu, AW, Robins, CD and Kebreab, E 2011. Predictions of enteric methane emissions for various summer pasture and winter-feeding strategies for cow calf production. Animal Feed Science and Technology 166–167, 678687.CrossRefGoogle Scholar
Lin, LI 1989. A concordance correlation coefficient to evaluate reproducibility. Biometrics 45, 255268.CrossRefGoogle ScholarPubMed
Machado, FS, Tomich, TR, Ferreira, AL, Cavalcanti, LFL, Campos, MM, Paiva, CAV, Ribas, MN and Pereira, LGR 2016. Technical note: a facility for respiration measurements in cattle. Journal of Dairy Science 99, 48994906.CrossRefGoogle ScholarPubMed
Menezes, ACB, Valadares Filho, SC, Costa e Silva, LF, Pacheco, MVC, Pereira, JMV, Rotta, PP, Zanetti, D, Detmann, E, Silva, FAS, Godoi, LA and Rennó, LN 2016. Does a reduction in dietary crude protein content affect performance, nutrient requirements, nitrogen losses, and methane emissions in finishing Nellore bulls? Agriculture, Ecosystems & Environment 223, 239249.CrossRefGoogle Scholar
Moraes, LE, Strathe, AB, Fadel, JG, Casper, DP and Kebreab, E 2014. Prediction of enteric methane emissions from cattle. Global Change Biology 20, 21402148.CrossRefGoogle ScholarPubMed
Moriasi, DN, Arnold, JG, Van Liew, MW, Bingner, EL, Harmel, RD and Veith, TL 2007. Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations. Transactions of the ASABE 50, 885900.CrossRefGoogle Scholar
Mottet, A, de Haan, C, Falcucci, A, Tempio, G, Opio, C and Gerber, P 2017. Livestock: On our plates or eating at our table? A new analysis of the feed/food debate. Global Food Security 14, 18.CrossRefGoogle Scholar
Mills, JA, Dijkstra, J, Bannink, A, Cammell, SB, Kebreab, E and France, J 2001. A mechanistic model of whole-tract digestion and methanogenesis in the lactating dairy cow: model development, evaluation, and application. Journal of Animal Science 79, 1584.CrossRefGoogle ScholarPubMed
Niu, M, Kebreab, E, Hristov, AN, Oh, J, Arndt, C, Bannink, 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, 33683389.CrossRefGoogle ScholarPubMed
Nousiainen, J, Ahvenjärvi, S, Rinne, M, Hellämäki, M and Huhtanen, P 2004. Prediction of indigestible cell wall fraction of grass silage by near infrared reflectance spectroscopy. Animal Feed Science and Technology 115, 295311.CrossRefGoogle Scholar
Patra, AK 2014. A meta-analysis of the effect of dietary fat on enteric methane production, digestibility and rumen fermentation in sheep, and a comparison of these responses between cattle and sheep. Livestock Science 162, 97103.CrossRefGoogle Scholar
Patra, AK 2017. Prediction of enteric methane emission from cattle using linear and non-linear statistical models in tropical production systems. Mitigation and Adaptation Strategies for Global Change 22, 629650.CrossRefGoogle Scholar
Paulino, PVR and Duarte, MS 2014 Brazilian beef production. In Beef cattle production and trade (eds. Kahn, L and Cottle, D) CSIRO Publishing, Collingwood, Australia.Google Scholar
Posada-Ochoa, SL, Noguera, RR, Rodríguez, NM, Costa, AL and Reis, R 2017. Indirect calorimetry to estimate energy requirements for growing and finishing Nellore bulls. Journal of Integrative Agriculture 16, 151161.CrossRefGoogle Scholar
Ramin, M and Huhtanen, P 2013. Development of equations for predicting methane emissions from ruminants. Journal of Dairy Science 96, 24762493.CrossRefGoogle ScholarPubMed
Sejian, V, Lal, R, Lakritz, J and Ezeji, T 2011. Measurement and prediction of enteric methane emission. International Journal of Biometeorology 55, 116.CrossRefGoogle ScholarPubMed
Souza, MC, Oliveira, AS, Araújo, CV, Brito, AF, Teixeira, RMA, Moares, EHBK and Moura, DC 2014. Short communication: Prediction of intake in dairy cows under tropical conditions. Journal of Dairy Science 97, 38453854.CrossRefGoogle ScholarPubMed
St-Pierre, NR 2001. Invited review: integrating quantitative findings from multiple studies using mixed model methodology. Journal of Dairy Science 84, 741755.CrossRefGoogle ScholarPubMed
Stergiadis, S, Zou, C, Chen, X, Allen, M, Wills, D and Yan, T 2016. Equations to predict methane emissions from cows fed at maintenance energy level in pasture-based systems. Agriculture, Ecosystems & Environment 220, 820.CrossRefGoogle Scholar
Storlien, TM, Volden, H, Almøy, T, Beauchemin, KA, McAllister, TA and Harstad, OM 2014. Prediction of enteric methane production from dairy cows. Acta Agriculturae Scandinavica, Section A — Animal Science 64, 98109.CrossRefGoogle Scholar
Tedeschi, LO 2006. Assessment of the adequacy of mathematical models. Agricultural Systems 89, 225247.CrossRefGoogle Scholar
Thornton, PK and Gerber, PJ 2010. Climate change and the growth of the livestock sector in developing countries. Mitigation and Adaptation Strategies for Global Change 15, 169184.CrossRefGoogle Scholar
Valadares Filho, SC, Costa e Silva, LF, Gionbelli, MP, Rotta, PP, Marcondes, MI, Chizzotti, ML and Prados, LF 2016. Nutrient requirements of Zebu and Crossbred cattle - BR-CORTE. Editora Federal de Viçosa, Viçosa, BR.CrossRefGoogle Scholar
Van Lingen, HJ, Niu, M, Kebreab, E, Valadares Filho, SC, Rooke, JA, Duthie, C-A, Schwarm, A, Kreuzer, M, Hynd, PI, Caetano, M, Eugène, M, Martin, C, McGee, M, O’Kiely, P, Hünerberg, M, McAllister, TA, Berchielli, TT, Messana, JD, Peiren, N, ChavesCharmley, AE VCharmley, E, Cole, NA, Hales, KE, Lee, S-S, Berndt, A, Reynolds, CK, Crompton, LA, Bayat, A-R, Yáñez-Ruiz, DR, Yu, Z, Bannink, A, Dijkstra, J, Casper, DP and Hristov, AN 2019. Prediction of enteric methane production, yield and intensity of beef cattle using an intercontinental database. Agriculture, Ecosystems & Environment 283, 106575.CrossRefGoogle Scholar
Supplementary material: File

Ribeiro et al. supplementary material

Ribeiro et al. supplementary material

Download Ribeiro et al. supplementary material(File)
File 7.9 MB