Evaluation of the performance of existing mathematical models predicting enteric methane emissions from ruminants: Animal categories and dietary mitigation strategies

Mohammed Benaouda, Cécile Martin, Xinran Li, Ermias Kebreab, Alexander N. Hristov, Zhongtang Yu, David R. Yáñez-Ruiz, Christopher K. Reynolds, L.A. Crompton, Jan Dijkstra, André Bannink, Angela Schwarm, Michael Kreuzer, Mark McGee, P. Lund, Anne L.F. Hellwing, Martin R. Weisbjerg, Peter J. Moate, A.R. Bayat, Kevin J. Shingfield & 2 others Nico Peiren, M. Eugène*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)

Abstract

The objective of this study was to evaluate the performance of existing models predicting enteric methane (CH4) emissions, using a large database (3183 individual data from 103 in vivo studies on dairy and beef cattle, sheep and goats fed diets from different countries). The impacts of dietary strategies to reduce CH4 emissions, and of diet quality (described by organic matter digestibility (dOM) and neutral-detergent fiber digestibility (dNDF)) on model performance were assessed by animal category. The models were first assessed based on the root mean square prediction error (RMSPE) to standard deviation of observed values ratio (RSR) to account for differences in data between models and then on the RMSPE. For dairy cattle, the CH4 (g/d) predicting model based on feeding level (dry matter intake (DMI)/body weight (BW)), energy digestibility (dGE) and ether extract (EE) had the smallest RSR (0.66) for all diets, as well as for the high-EE diets (RSR = 0.73). For mitigation strategies based on lowering NDF or improving dOM, the same model (RSR = 0.48 to 0.60) and the model using DMI and neutral- and acid-detergent fiber intakes (RSR = 0.53) had the smallest RSR, respectively. For diets with high starch (STA), the model based on nitrogen, ADF and STA intake presented the smallest RSR (0.84). For beef cattle, all evaluated models performed moderately compared with the models of dairy cattle. The smallest RSR (0.83) was obtained using variables of energy intake, BW, forage content and dietary fat, and also for the high-EE and the low-NDF diets (RSR = 0.84 to 0.86). The IPCC Tier 2 models performed better when dietary STA, dOM or dNDF were high. For sheep and goats, the smallest RSR was observed from a model for sheep based on dGE intake (RSR = 0.61). Both IPCC models had low predictive ability when dietary EE, NDF, dOM and dNDF varied (RSR = 0.57 to 1.31 in dairy, and 0.65 to 1.24 in beef cattle). The performance of models depends mostly on explanatory variables and not on the type of data (individual vs. treatment means) used in their development or evaluation. Some empirical models give satisfactory prediction error compared with the error associated with measurement methods. For better prediction, models should include feed intake, digestibility and additional information on dietary concentrations of EE and structural and nonstructural carbohydrates to account for different dietary mitigating strategies.

Original languageEnglish
Article number114207
JournalAnimal Feed Science and Technology
Volume255
DOIs
Publication statusPublished - 1 Aug 2019

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methane
ruminants
mathematical models
animals
digestibility
ethers
neutral detergent fiber
beef cattle
extracts
dairy cattle
diet
prediction
sheep
dry matter intake
goats
starch
dietary carbohydrate
body weight
nutritional adequacy
feeding level

Keywords

  • Dietary strategy
  • Methane emission
  • Model evaluation
  • Ruminant

Cite this

Benaouda, Mohammed ; Martin, Cécile ; Li, Xinran ; Kebreab, Ermias ; Hristov, Alexander N. ; Yu, Zhongtang ; Yáñez-Ruiz, David R. ; Reynolds, Christopher K. ; Crompton, L.A. ; Dijkstra, Jan ; Bannink, André ; Schwarm, Angela ; Kreuzer, Michael ; McGee, Mark ; Lund, P. ; Hellwing, Anne L.F. ; Weisbjerg, Martin R. ; Moate, Peter J. ; Bayat, A.R. ; Shingfield, Kevin J. ; Peiren, Nico ; Eugène, M. / Evaluation of the performance of existing mathematical models predicting enteric methane emissions from ruminants: Animal categories and dietary mitigation strategies. In: Animal Feed Science and Technology. 2019 ; Vol. 255.
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abstract = "The objective of this study was to evaluate the performance of existing models predicting enteric methane (CH4) emissions, using a large database (3183 individual data from 103 in vivo studies on dairy and beef cattle, sheep and goats fed diets from different countries). The impacts of dietary strategies to reduce CH4 emissions, and of diet quality (described by organic matter digestibility (dOM) and neutral-detergent fiber digestibility (dNDF)) on model performance were assessed by animal category. The models were first assessed based on the root mean square prediction error (RMSPE) to standard deviation of observed values ratio (RSR) to account for differences in data between models and then on the RMSPE. For dairy cattle, the CH4 (g/d) predicting model based on feeding level (dry matter intake (DMI)/body weight (BW)), energy digestibility (dGE) and ether extract (EE) had the smallest RSR (0.66) for all diets, as well as for the high-EE diets (RSR = 0.73). For mitigation strategies based on lowering NDF or improving dOM, the same model (RSR = 0.48 to 0.60) and the model using DMI and neutral- and acid-detergent fiber intakes (RSR = 0.53) had the smallest RSR, respectively. For diets with high starch (STA), the model based on nitrogen, ADF and STA intake presented the smallest RSR (0.84). For beef cattle, all evaluated models performed moderately compared with the models of dairy cattle. The smallest RSR (0.83) was obtained using variables of energy intake, BW, forage content and dietary fat, and also for the high-EE and the low-NDF diets (RSR = 0.84 to 0.86). The IPCC Tier 2 models performed better when dietary STA, dOM or dNDF were high. For sheep and goats, the smallest RSR was observed from a model for sheep based on dGE intake (RSR = 0.61). Both IPCC models had low predictive ability when dietary EE, NDF, dOM and dNDF varied (RSR = 0.57 to 1.31 in dairy, and 0.65 to 1.24 in beef cattle). The performance of models depends mostly on explanatory variables and not on the type of data (individual vs. treatment means) used in their development or evaluation. Some empirical models give satisfactory prediction error compared with the error associated with measurement methods. For better prediction, models should include feed intake, digestibility and additional information on dietary concentrations of EE and structural and nonstructural carbohydrates to account for different dietary mitigating strategies.",
keywords = "Dietary strategy, Methane emission, Model evaluation, Ruminant",
author = "Mohammed Benaouda and C{\'e}cile Martin and Xinran Li and Ermias Kebreab and Hristov, {Alexander N.} and Zhongtang Yu and Y{\'a}{\~n}ez-Ruiz, {David R.} and Reynolds, {Christopher K.} and L.A. Crompton and Jan Dijkstra and Andr{\'e} Bannink and Angela Schwarm and Michael Kreuzer and Mark McGee and P. Lund and Hellwing, {Anne L.F.} and Weisbjerg, {Martin R.} and Moate, {Peter J.} and A.R. Bayat and Shingfield, {Kevin J.} and Nico Peiren and M. Eug{\`e}ne",
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month = "8",
day = "1",
doi = "10.1016/j.anifeedsci.2019.114207",
language = "English",
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Benaouda, M, Martin, C, Li, X, Kebreab, E, Hristov, AN, Yu, Z, Yáñez-Ruiz, DR, Reynolds, CK, Crompton, LA, Dijkstra, J, Bannink, A, Schwarm, A, Kreuzer, M, McGee, M, Lund, P, Hellwing, ALF, Weisbjerg, MR, Moate, PJ, Bayat, AR, Shingfield, KJ, Peiren, N & Eugène, M 2019, 'Evaluation of the performance of existing mathematical models predicting enteric methane emissions from ruminants: Animal categories and dietary mitigation strategies', Animal Feed Science and Technology, vol. 255, 114207. https://doi.org/10.1016/j.anifeedsci.2019.114207

Evaluation of the performance of existing mathematical models predicting enteric methane emissions from ruminants: Animal categories and dietary mitigation strategies. / Benaouda, Mohammed; Martin, Cécile; Li, Xinran; Kebreab, Ermias; Hristov, Alexander N.; Yu, Zhongtang; Yáñez-Ruiz, David R.; Reynolds, Christopher K.; Crompton, L.A.; Dijkstra, Jan; Bannink, André; Schwarm, Angela; Kreuzer, Michael; McGee, Mark; Lund, P.; Hellwing, Anne L.F.; Weisbjerg, Martin R.; Moate, Peter J.; Bayat, A.R.; Shingfield, Kevin J.; Peiren, Nico; Eugène, M.

In: Animal Feed Science and Technology, Vol. 255, 114207, 01.08.2019.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Evaluation of the performance of existing mathematical models predicting enteric methane emissions from ruminants: Animal categories and dietary mitigation strategies

AU - Benaouda, Mohammed

AU - Martin, Cécile

AU - Li, Xinran

AU - Kebreab, Ermias

AU - Hristov, Alexander N.

AU - Yu, Zhongtang

AU - Yáñez-Ruiz, David R.

AU - Reynolds, Christopher K.

AU - Crompton, L.A.

AU - Dijkstra, Jan

AU - Bannink, André

AU - Schwarm, Angela

AU - Kreuzer, Michael

AU - McGee, Mark

AU - Lund, P.

AU - Hellwing, Anne L.F.

AU - Weisbjerg, Martin R.

AU - Moate, Peter J.

AU - Bayat, A.R.

AU - Shingfield, Kevin J.

AU - Peiren, Nico

AU - Eugène, M.

PY - 2019/8/1

Y1 - 2019/8/1

N2 - The objective of this study was to evaluate the performance of existing models predicting enteric methane (CH4) emissions, using a large database (3183 individual data from 103 in vivo studies on dairy and beef cattle, sheep and goats fed diets from different countries). The impacts of dietary strategies to reduce CH4 emissions, and of diet quality (described by organic matter digestibility (dOM) and neutral-detergent fiber digestibility (dNDF)) on model performance were assessed by animal category. The models were first assessed based on the root mean square prediction error (RMSPE) to standard deviation of observed values ratio (RSR) to account for differences in data between models and then on the RMSPE. For dairy cattle, the CH4 (g/d) predicting model based on feeding level (dry matter intake (DMI)/body weight (BW)), energy digestibility (dGE) and ether extract (EE) had the smallest RSR (0.66) for all diets, as well as for the high-EE diets (RSR = 0.73). For mitigation strategies based on lowering NDF or improving dOM, the same model (RSR = 0.48 to 0.60) and the model using DMI and neutral- and acid-detergent fiber intakes (RSR = 0.53) had the smallest RSR, respectively. For diets with high starch (STA), the model based on nitrogen, ADF and STA intake presented the smallest RSR (0.84). For beef cattle, all evaluated models performed moderately compared with the models of dairy cattle. The smallest RSR (0.83) was obtained using variables of energy intake, BW, forage content and dietary fat, and also for the high-EE and the low-NDF diets (RSR = 0.84 to 0.86). The IPCC Tier 2 models performed better when dietary STA, dOM or dNDF were high. For sheep and goats, the smallest RSR was observed from a model for sheep based on dGE intake (RSR = 0.61). Both IPCC models had low predictive ability when dietary EE, NDF, dOM and dNDF varied (RSR = 0.57 to 1.31 in dairy, and 0.65 to 1.24 in beef cattle). The performance of models depends mostly on explanatory variables and not on the type of data (individual vs. treatment means) used in their development or evaluation. Some empirical models give satisfactory prediction error compared with the error associated with measurement methods. For better prediction, models should include feed intake, digestibility and additional information on dietary concentrations of EE and structural and nonstructural carbohydrates to account for different dietary mitigating strategies.

AB - The objective of this study was to evaluate the performance of existing models predicting enteric methane (CH4) emissions, using a large database (3183 individual data from 103 in vivo studies on dairy and beef cattle, sheep and goats fed diets from different countries). The impacts of dietary strategies to reduce CH4 emissions, and of diet quality (described by organic matter digestibility (dOM) and neutral-detergent fiber digestibility (dNDF)) on model performance were assessed by animal category. The models were first assessed based on the root mean square prediction error (RMSPE) to standard deviation of observed values ratio (RSR) to account for differences in data between models and then on the RMSPE. For dairy cattle, the CH4 (g/d) predicting model based on feeding level (dry matter intake (DMI)/body weight (BW)), energy digestibility (dGE) and ether extract (EE) had the smallest RSR (0.66) for all diets, as well as for the high-EE diets (RSR = 0.73). For mitigation strategies based on lowering NDF or improving dOM, the same model (RSR = 0.48 to 0.60) and the model using DMI and neutral- and acid-detergent fiber intakes (RSR = 0.53) had the smallest RSR, respectively. For diets with high starch (STA), the model based on nitrogen, ADF and STA intake presented the smallest RSR (0.84). For beef cattle, all evaluated models performed moderately compared with the models of dairy cattle. The smallest RSR (0.83) was obtained using variables of energy intake, BW, forage content and dietary fat, and also for the high-EE and the low-NDF diets (RSR = 0.84 to 0.86). The IPCC Tier 2 models performed better when dietary STA, dOM or dNDF were high. For sheep and goats, the smallest RSR was observed from a model for sheep based on dGE intake (RSR = 0.61). Both IPCC models had low predictive ability when dietary EE, NDF, dOM and dNDF varied (RSR = 0.57 to 1.31 in dairy, and 0.65 to 1.24 in beef cattle). The performance of models depends mostly on explanatory variables and not on the type of data (individual vs. treatment means) used in their development or evaluation. Some empirical models give satisfactory prediction error compared with the error associated with measurement methods. For better prediction, models should include feed intake, digestibility and additional information on dietary concentrations of EE and structural and nonstructural carbohydrates to account for different dietary mitigating strategies.

KW - Dietary strategy

KW - Methane emission

KW - Model evaluation

KW - Ruminant

U2 - 10.1016/j.anifeedsci.2019.114207

DO - 10.1016/j.anifeedsci.2019.114207

M3 - Article

VL - 255

JO - Animal Feed Science and Technology

JF - Animal Feed Science and Technology

SN - 0377-8401

M1 - 114207

ER -