Advertisement
Animal Nutrition and Farm Systems Short Communication Short Communication|Articles in Press

Evaluation of Intergovernmental Panel on Climate Change (IPCC) equations to predict enteric methane emission from lactating cows fed Mediterranean diets

Open AccessPublished:February 09, 2023DOI:https://doi.org/10.3168/jdsc.2022-0240
Evaluation of Intergovernmental Panel on Climate Change (IPCC) equations to predict enteric methane emission from lactating cows fed Mediterranean diets
Previous ArticleA review of once-a-day milking in dairy cow grazing systems
Next ArticleIn vitro evaluation of the effect of transport medium, temperature, and time on the recovery of Mannheimia haemolytica and Pasteurella multocida

      Highlights

      • •
        The IPCC 2019 Tier 2 equations predict methane emission adequately.
      • •
        The knowledge of DE improves the accuracy of CH4 prediction.
      • •
        Hay-based diets reduce digestibility and consequently a measured DE should be applied.
      • •
        Local values of the CH4 conversion factor (Ym) and DE are proposed for the Mediterranean area.

      Abstract

      The study aimed to evaluate Intergovernmental Panel on Climate Change (IPCC) Tier 2 (2006 and 2019) to predict enteric CH4 emissions from lactating cows fed Mediterranean diets. The effects of the CH4 conversion factor (Ym; CH4 energy loss as a percentage of gross energy intake) and digestible energy (DE) of the diet were evaluated as model predictors. A data set was created using individual observations derived from 3 in vivo studies on lactating dairy cows housed in respiration chambers and fed diets typical of the Mediterranean region based on silages and hays. Five models using different Ym and DE were evaluated following a Tier 2 approach: (1) average values of Ym (6.5%) and DE (70%) from IPCC (2006); (2) average value of Ym (5.7%) and DE (70.0%) from IPCC (2019; 1YM); (3) Ym = 5.7% and DE measured in vivo (1YMIV); (4) Ym = 5.7 or 6.0%, depending on dietary NDF, and DE = 70% (2YM); and (5) Ym = 5.7 or 6.0%, depending on dietary NDF, and DE measured in vivo (2YMIV). Finally, a Tier 2 model for Mediterranean diets (MED) was derived from the Italian data set (Ym = 5.58%; DE = 69.9% for silage-based diets and 64.8% for hay-based diets) and validated on an independent data set of cows fed Mediterranean diets. The most accurate models tested were 2YMIV, 2YM, and 1YMIV with predictions of 384, 377, and 377 (g of CH4/d), respectively, versus the in vivo value of 381. The most precise model was 1YM (slope bias = 1.88%; r = 0.63). Overall, 1YM showed the highest concordance correlation coefficient value (0.579), followed by 1YMIV (0.569). Cross-validation on an independent data set of cows fed Mediterranean diets (corn silage and alfalfa hay) resulted in concordance correlation coefficient of 0.492 and 0.485 for 1YM and MED, respectively. The prediction of MED (397) was more accurate than 1YM (405) in comparison with the corresponding in vivo value of 396 g of CH4/d. The results of this study showed that the average values proposed by IPCC (2019) can adequately predict CH4 emissions from cows fed typical Mediterranean diets. However, the use of specific factors for the Mediterranean area, such as DE, improved the accuracy of the models.

      Graphical Abstract

      Figure thumbnail fx1
      Graphical AbstractSummary: Intergovernmental Panel on Climate Change (IPCC) equations were evaluated to study the precision and accuracy in predicting methane (CH4) emission by lactating dairy cows. The predicted values were compared with in vivo values obtained from experimental studies conducted using cows fed diets based on hay or silage forages in the Mediterranean region. Hay-based diets differed significantly from silage-based diets having lower energy digestibility (DE). The IPCC 2019 equations predicted CH4 emission well and hence may be used for the computation of greenhouse gas inventories. However, specific equations are proposed to predict CH4 emission by dairy cows in the Mediterranean region.
      Livestock and manure management contribute to 5.8% of anthropogenic-caused greenhouse gas (GHG) emissions, and considering CH4 emissions only, enteric fermentation and manure management represent 32% of anthropic CH4 emissions (
      • World Resources Institute
      World Greenhouse Gas Emissions: 2019.
      https://www.wri.org/data/world-greenhouse-gas-emissions-2019
      Date: 2019
      Date accessed: November 3, 2021
      ). Therefore, it is important to predict the amount of enteric CH4 emitted in a specific livestock production system (
      • Niu M.
      • Kebreab E.
      • Hristov A.N.
      • Oh J.
      • Arndt C.
      • Bannink A.
      • Bayat A.R.
      • Brito A.F.
      • Boland T.
      • Casper D.
      • Crompton L.A.
      • Dijkstra J.
      • Eugène M.A.
      • Garnsworthy P.C.
      • Haque M.N.
      • Hellwing A.L.F.
      • Huhtanen P.
      • Kreuzer M.
      • Kuhla B.
      • Lund P.
      • Madsen J.
      • Martin C.
      • McClelland S.C.
      • McGee M.
      • Moate P.J.
      • Muetzel S.
      • Muñoz C.
      • O'Kiely P.
      • Peiren N.
      • Reynolds C.K.
      • Schwarm A.
      • Shingfield K.J.
      • Storlien T.M.
      • Weisbjerg M.R.
      • Yáñez-Ruiz D.R.
      • Yu Z.
      Prediction of enteric methane production, yield, and intensity in dairy cattle using an intercontinental database.
      Glob. Chang. Biol. 2018; 24 (29450980): 3368-3389
      https://doi.org/10.1111/gcb.14094
      ). The Intergovernmental Panel on Climate Change (IPCC) guidelines for GHG inventories were developed to provide internationally agreed methodologies for estimating GHG emissions (
      • IPCC
      Chapter 1: Introduction to the 2006 guidelines.
      in: IPCC Guidelines for National Greenhouse Gas Inventories Volume 1: General guidance and reporting. Intergovernmental Panel on Climate Change, 2006
      ). The latest version of
      • IPCC
      2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Overview Chapter.
      Intergovernmental Panel on Climate Change, 2019
      was published to refine the previous version (
      • IPCC
      Chapter 10: Emissions from livestock and manure management.
      in: IPCC Guidelines for National Greenhouse Gas Inventories Volume 4: Agriculture, forestry and other land use. Intergovernmental Panel on Climate Change, 2006
      ), providing supplementary methodologies and updated default values. Regarding enteric CH4 emission from ruminants, the conversion factor of gross energy intake into enteric CH4 energy (Ym) can be chosen according to the level of productivity and diet characteristics (
      • IPCC
      2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Overview Chapter.
      Intergovernmental Panel on Climate Change, 2019
      ). The criteria to choose the value of Ym are milk production, dietary NDF concentration, and digestible energy (DE, as % of GE intake). For example, considering the whole data set,
      • IPCC
      2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Chapter 10: Emissions from livestock and manure management.
      in: IPCC Guidelines for National Greenhouse Gas Inventories Volume 4: Agriculture, forestry and other land use. Intergovernmental Panel on Climate Change, 2019
      suggests a value of Ym equal to 5.7% for high-yielding cows (>8,500 kg of milk/head/yr−1), associated with DE ≥70% and NDF ≤35% of diet DM. However, for this production level, the proposed values of NDF and Ym may not represent the diets used in regions with Mediterranean climate characteristics. The Ym values in
      • IPCC
      2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Chapter 10: Emissions from livestock and manure management.
      in: IPCC Guidelines for National Greenhouse Gas Inventories Volume 4: Agriculture, forestry and other land use. Intergovernmental Panel on Climate Change, 2019
      were based on the data set of
      • Niu M.
      • Kebreab E.
      • Hristov A.N.
      • Oh J.
      • Arndt C.
      • Bannink A.
      • Bayat A.R.
      • Brito A.F.
      • Boland T.
      • Casper D.
      • Crompton L.A.
      • Dijkstra J.
      • Eugène M.A.
      • Garnsworthy P.C.
      • Haque M.N.
      • Hellwing A.L.F.
      • Huhtanen P.
      • Kreuzer M.
      • Kuhla B.
      • Lund P.
      • Madsen J.
      • Martin C.
      • McClelland S.C.
      • McGee M.
      • Moate P.J.
      • Muetzel S.
      • Muñoz C.
      • O'Kiely P.
      • Peiren N.
      • Reynolds C.K.
      • Schwarm A.
      • Shingfield K.J.
      • Storlien T.M.
      • Weisbjerg M.R.
      • Yáñez-Ruiz D.R.
      • Yu Z.
      Prediction of enteric methane production, yield, and intensity in dairy cattle using an intercontinental database.
      Glob. Chang. Biol. 2018; 24 (29450980): 3368-3389
      https://doi.org/10.1111/gcb.14094
      , which included 154 studies, 82 of them conducted in European research institutes, but none conducted in southern European countries. Diets from northern Europe (based on ryegrass and corn silages,
      • March M.D.
      • Haskell M.J.
      • Chagunda M.G.G.
      • Langford F.M.
      • Roberts D.J.
      Current trends in British dairy management regimens.
      J. Dairy Sci. 2014; 97 (25306285): 7985-7994
      https://doi.org/10.3168/jds.2014-8265
      ) are different from the diets of the Mediterranean region, since the latter are widely based on corn silage and alfalfa/grass hays (
      • Gislon G.
      • Bava L.
      • Colombini S.
      • Zucali M.
      • Crovetto G.M.
      • Sandrucci A.
      Looking for high-production and sustainable diets for lactating cows: A survey in Italy.
      J. Dairy Sci. 2020; 103 (32113778): 4863-4873
      https://doi.org/10.3168/jds.2019-17177
      ). For this reason,
      • IPCC
      2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Chapter 10: Emissions from livestock and manure management.
      in: IPCC Guidelines for National Greenhouse Gas Inventories Volume 4: Agriculture, forestry and other land use. Intergovernmental Panel on Climate Change, 2019
      encourages the development of country-specific Ym factors for a more accurate estimation of enteric CH4. The present study aimed to (1) compare the CH4 emissions estimated with
      • IPCC
      2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Chapter 10: Emissions from livestock and manure management.
      in: IPCC Guidelines for National Greenhouse Gas Inventories Volume 4: Agriculture, forestry and other land use. Intergovernmental Panel on Climate Change, 2019
      and
      • IPCC
      Chapter 10: Emissions from livestock and manure management.
      in: IPCC Guidelines for National Greenhouse Gas Inventories Volume 4: Agriculture, forestry and other land use. Intergovernmental Panel on Climate Change, 2006
      equations with the values obtained in vivo by cows fed typical Mediterranean diets; (2) develop possible improvements for the IPCC parameters (Ym and DE) for a more accurate prediction of CH4 emission from lactating cows fed Mediterranean diets.
      A data set of 66 individual observations was created to evaluate different IPCC equations to predict CH4 emissions from Italian lactating Holstein dairy cows. The data set included individual observations from 3 in vivo studies (
      • Gislon G.
      • Colombini S.
      • Borreani G.
      • Crovetto G.M.
      • Sandrucci A.
      • Galassi G.
      • Tabacco E.
      • Rapetti L.
      Milk production, methane emissions, nitrogen, and energy balance of cows fed diets based on different forage systems.
      J. Dairy Sci. 2020; 103 (32622607): 8048-8061
      https://doi.org/10.3168/jds.2019-18134
      ;
      • Pirondini M.
      • Colombini S.
      • Mele M.
      • Malagutti L.
      • Rapetti L.
      • Galassi G.
      • Crovetto G.M.
      Effect of dietary starch concentration and fish oil supplementation on milk yield and composition, diet digestibility, and methane emissions in lactating dairy cows.
      J. Dairy Sci. 2015; 98 (25465540): 357-372
      https://doi.org/10.3168/jds.2014-8092
      ; and
      • Colombini S.
      • Zucali M.
      • Rapetti L.
      • Crovetto G.M.
      • Sandrucci A.
      • Bava L.
      Substitution of corn silage with sorghum silages in lactating cow diets: In vivo methane emission and global warming potential of milk production.
      Agric. Syst. 2015; 136: 106-113
      https://doi.org/10.1016/j.agsy.2015.02.006
      ), with 32, 16, and 18 observations, respectively. The experiments were carried out at the University of Milan Research Center, in the Po plain (Milan, Italy; 45°30′N, 9°1′E) where the climate of the region is mainly humid subtropical according to the Geiger–Köppen climate classification. The diets fed in each experiment were representative of typical diets fed in the region and were based on the following forages: alfalfa and grass silages, alfalfa and grass hays, wheat silage, corn silage, sorghum forage silage, and sorghum grain whole plant silage. Methane emissions were measured through individual open-circuit respiration chambers, and the DE was determined in vivo by total feces collection. Further details are reported in the above-cited studies, but briefly the air flow through the chambers was measured using diaphragm flow-meters at ambient temperature, pressure, and relative humidity. The air flux was then referred to standard conditions (0°C, 1 atm, relative humidity = 0) by a calculation based on temperature, pressure, and relative humidity measured in the outgoing air entering the flow-meter.
      Before each respiration chamber cycle, calibrations were made with a certified reference gas with methane concentrations close to the maximum values detectable by the analyzer (2,000 ppm of CH4) and pure N2 to calibrate the minimum value. The calculation of CH4 energy (kJ) was determined by multiplying CH4 production in liters at standard conditions by 39.5388. The production of CH4 in grams was obtained multiplying the CH4 production (in liters at standard conditions) by 0.71682. Five predicting equations were applied based on different Ym and DE values. The first equation (IPCC06) was based on IPCC 2006 (
      • IPCC
      Chapter 10: Emissions from livestock and manure management.
      in: IPCC Guidelines for National Greenhouse Gas Inventories Volume 4: Agriculture, forestry and other land use. Intergovernmental Panel on Climate Change, 2006
      ) Tier 2 (IPCC Table 10.12) with Ym = 6.5% and DE = 70%. Two other equations were based on IPCC 2019 (
      • IPCC
      2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Chapter 10: Emissions from livestock and manure management.
      in: IPCC Guidelines for National Greenhouse Gas Inventories Volume 4: Agriculture, forestry and other land use. Intergovernmental Panel on Climate Change, 2019
      ) Tier 2 as follows: (1) the average Ym (5.7%) and DE fixed at 70% were used to calculate the ratios of net energy available in a diet for maintenance and growth to DE consumed (equations 10.14 and 10.15, respectively) and to calculate the gross energy (GE) requirements (equation 10.16; 1YM) or (2) the average Ym (5.7%) and the replacement of fixed DE with the average in vivo digestibility from the experiments (1YMIV). The final 2 equations were based on IPCC 2019 (
      • IPCC
      2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Chapter 10: Emissions from livestock and manure management.
      in: IPCC Guidelines for National Greenhouse Gas Inventories Volume 4: Agriculture, forestry and other land use. Intergovernmental Panel on Climate Change, 2019
      ; Table 10.12) as follows: for diets with NDF ≤35%, the value 5.7% of Ym was used, whereas for diets with NDF >35%, the value 6.0% of Ym was applied; for DE a fixed digestibility at 70 (2YM) or the average in vivo digestibility from the experiments was used (2YMIV). The emission factor was calculated from IPCC 2019 (equation 10.21) on the basis of the estimate of GE intake since according to
      • IPCC
      2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Chapter 10: Emissions from livestock and manure management.
      in: IPCC Guidelines for National Greenhouse Gas Inventories Volume 4: Agriculture, forestry and other land use. Intergovernmental Panel on Climate Change, 2019
      “the Tier 2 estimate of gross energy is the preferred method” for the calculation of the emission factor.
      An IPCC Tier 2 model for cows fed typical Mediterranean diets (MED) was also proposed using the average value of Ym (5.58) and the average DE values of 69.9 or 64.8% for silage- or hay-based diets respectively obtained from the in vivo experiments, as described in a following section of the paper. The MED was validated on an independent data set based on individual cow observations from the study of
      • Enriquez-Hidalgo D.
      • Teixeira D.L.
      • Pinheiro Machado Filho L.C.
      • Hennessy D.
      • Toro-Mujica P.
      • Williams S.R.O.
      • Pereira F.C.
      Incorporating a fresh mixed annual ryegrass and berseem clover forage into the winter diet of dairy cows resulted in reduced milk yield, but reduced nitrogen excretion and reduced methane yield.
      Front. Vet. Sci. 2020; 7 (33330696)576944
      https://doi.org/10.3389/fvets.2020.576944
      conducted in a central region of Chile characterized by a Mediterranean climate with hot summers. The Chilean study evaluated 2 diets, one similar to diets typically fed in Italy with a forage basis including corn silage and alfalfa hay, and the other not typically Mediterranean, based on corn silage and a mixture of fresh annual ryegrass and berseem clover (MIX). The CH4 emission was measured with the SF6 technique. For this data set, the 1YM and MED equations were applied. It was not possible to apply the other equations because DE was not measured and the 2 diets had NDF <35% DM.
      The predictive equations for CH4 emission were evaluated according to
      • Tedeschi L.O.
      Assessment of the adequacy of mathematical models.
      Agric. Syst. 2006; 89: 225-247
      https://doi.org/10.1016/j.agsy.2005.11.004
      . The root mean square prediction error (RMSPE) was decomposed into error due to mean bias (MB), error due to slope bias (SB), and error due to random bias (RB;
      • Bibby J.
      • Toutenburg H.
      Prediction and improved estimation in linear models.
      John Wiley & Sons, 1977
      ).
      The concordance correlation coefficient (CCC) was calculated according to
      • Lin L.I.
      A concordance correlation coefficient to evaluate reproducibility.
      Biometrics. 1989; 45 (2720055): 255-268
      https://doi.org/10.2307/2532051
      as follows:
      CCC = r × Cb,


      where r is the Pearson correlation coefficient and Cb is a bias correction factor (a measure of accuracy), calculated as follows:
      Cb = 2/(V + 1/V + µ2),


      where V is a measure of scale shift (i.e., the change in standard deviation between predicted and observed values), and μ is a measure of location shift. V and μ were calculated as below:
      V = SDO/SDP,


      µ = (MO − MP)/(SDO × SDP)1/2,


      where MO and MP are the means of observed and predicted values, respectively, and SDO and SDP are the standard deviations of observed and predicted values, respectively.
      Data were analyzed using the Proc Mixed procedure of SAS 9.4 (SAS Institute Inc.) with the model Yjkl = μ + Dj + Sk + COWl(Sk) + εjkl, where Yjkl is the dependent variable (DE and Ym); μ is the overall mean; Dj is the diet effect (j = 1, 5); Sk is the random study effect (k = 1, 3); COWl is the random animal effect nested within study (l = 1, 18), and εjkl is the residual error.
      The results of IPCC predictions are in Table 1. The RMSPE (%) was similar among models (on average 10.5); all values were <13%, and according to
      • Kaewpila C.
      • Sommart K.
      Development of methane conversion factor models for Zebu beef cattle fed low-quality crop residues and by-products in tropical regions.
      Ecol. Evol. 2016; 6 (28725409): 7422-7432
      https://doi.org/10.1002/ece3.2500
      , a model may be considered inadequate when RMSPE is higher than 16.0%. The RMSPE reported by
      • Niu M.
      • Kebreab E.
      • Hristov A.N.
      • Oh J.
      • Arndt C.
      • Bannink A.
      • Bayat A.R.
      • Brito A.F.
      • Boland T.
      • Casper D.
      • Crompton L.A.
      • Dijkstra J.
      • Eugène M.A.
      • Garnsworthy P.C.
      • Haque M.N.
      • Hellwing A.L.F.
      • Huhtanen P.
      • Kreuzer M.
      • Kuhla B.
      • Lund P.
      • Madsen J.
      • Martin C.
      • McClelland S.C.
      • McGee M.
      • Moate P.J.
      • Muetzel S.
      • Muñoz C.
      • O'Kiely P.
      • Peiren N.
      • Reynolds C.K.
      • Schwarm A.
      • Shingfield K.J.
      • Storlien T.M.
      • Weisbjerg M.R.
      • Yáñez-Ruiz D.R.
      • Yu Z.
      Prediction of enteric methane production, yield, and intensity in dairy cattle using an intercontinental database.
      Glob. Chang. Biol. 2018; 24 (29450980): 3368-3389
      https://doi.org/10.1111/gcb.14094
      ,
      • Niu P.
      • Schwarm A.
      • Bonesmo H.
      • Kidane A.
      • Aspeholen Åby B.
      • Storlien T.M.
      • Kreuzer M.
      • Alvarez C.
      • Sommerseth J.K.
      • Prestløkken E.
      A basic model to predict enteric methane emission from dairy cows and its application to update operational models for the national inventory in Norway.
      Animals (Basel). 2021; 11 (34202055)1891
      https://doi.org/10.3390/ani11071891
      ) were comparable to those obtained in the present study.
      Table 1Results of RMSPE and CCC analysis for CH4 production for the individual lactating cow database predicted using IPCC06 (Ym = 6.5%, DE = 70%), or using
      • IPCC
      2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Chapter 10: Emissions from livestock and manure management.
      in: IPCC Guidelines for National Greenhouse Gas Inventories Volume 4: Agriculture, forestry and other land use. Intergovernmental Panel on Climate Change, 2019
      as follows: 1YM (Ym = 5.7%, DE = 70%), 1YMIV (Ym = 5.7%, DE measured in vivo), 2YM (Ym = 5.7 or 6.0% depending on diet NDF, DE = 70%), or 2YMIV (Ym = 5.7 or 6.0% depending on diet NDF, DE measured in vivo)
      DE = energy digestibility; 69.8% for silage-based diets, 64.8% for hay-based diets. Ym = conversion factor of gross energy intake into enteric CH4 energy. RMSPE = root mean square prediction error expressed as a percentage of the observed mean. MB = error due to bias, as a percent of total RMSPE. SB = error due to slope, as a percent of total RMSPE. RB = error due to random bias as a percent of total RMSPE. CCC = concordance correlation coefficient, where CCC = r × Cb. r = Pearson correlation coefficient. Cb = bias correction factor. V = scale shift. μ = location shift. IPCC06 = first equation based on Intergovernmental Panel on Climate Change 2006 (IPCC, 2006b).
      ModelDiet NDF (%)DE (%)Ym (%)CH4 (g/d)RMSPE (%)MB (%)SB (%)RB (%)CCCrCbVμ
      In vivo———381—————————
      IPCC06—706.542812.659.72.9638.00.4000.6300.6361.18−1.05
      1YM—705.73709.409.691.8889.90.5790.6300.9191.350.296
      1YMIV—69.85.737710.10.96819.281.50.5690.5720.9951.030.091
      —64.85.737710.10.96819.281.50.5690.5720.9951.030.091
      2YM<35705.73779.270.9282.4698.20.5360.5720.9371.420.095
      >35706.03779.270.9282.4698.20.5360.5720.9371.420.095
      2YMIV<3569.85.738411.20.61428.272.80.4880.4900.9970.968−0.079
      >3564.86.038411.20.61428.272.80.4880.4900.9970.968−0.079
      1 DE = energy digestibility; 69.8% for silage-based diets, 64.8% for hay-based diets. Ym = conversion factor of gross energy intake into enteric CH4 energy. RMSPE = root mean square prediction error expressed as a percentage of the observed mean. MB = error due to bias, as a percent of total RMSPE. SB = error due to slope, as a percent of total RMSPE. RB = error due to random bias as a percent of total RMSPE. CCC = concordance correlation coefficient, where CCC = r × Cb. r = Pearson correlation coefficient. Cb = bias correction factor. V = scale shift. μ = location shift. IPCC06 = first equation based on Intergovernmental Panel on Climate Change 2006 (
      • IPCC
      Chapter 10: Emissions from livestock and manure management.
      in: IPCC Guidelines for National Greenhouse Gas Inventories Volume 4: Agriculture, forestry and other land use. Intergovernmental Panel on Climate Change, 2006
      ).
      The IPCC06 was the less accurate model (MB = 59.7%; Cb = 0.636) with an overestimate of CH4 emission (428 vs. 381 g/d, respectively, for IPCC06 and in vivo). Overestimation of IPCC06 was also found by
      • Appuhamy J.A.D.R.N.
      • France J.
      • Kebreab E.
      Models for predicting enteric methane emissions from dairy cows in North America, Europe, and Australia and New Zealand.
      Glob. Chang. Biol. 2016; 22 (27148862): 3039-3056
      https://doi.org/10.1111/gcb.13339
      for North American diets. Similarly,
      • Jiménez L.E.
      • Hernández A.X.
      • Benaouda M.
      • Avalos J.O.
      • Corona L.
      • Castillo-Gallegos E.
      • Castelan-Ortega O.A.
      • Gonzalez-Ronquillo M.
      Concentrate supplementation on milk yield, methane and CO2 production in crossbred dairy cows grazing in tropical climate regions.
      J. Anim. Behav. Biometeorol. 2021; 92118
      showed that the calculation of CH4 using specific emission factors for tropical climate regions is better than the IPCC 2006 default emission factors, which overestimated CH4 production. The Ym value used in the IPCC06 equation (6.5%) explains the overestimation. To the best of our knowledge, only one study (
      • Benaouda M.
      • González-Ronquillo M.
      • Appuhamy J.A.D.R.N.
      • Kebreab E.
      • Molina L.T.
      • Herrera-Camacho J.
      • Ku-Vera J.C.
      • Ángeles-Hernández J.C.
      • Castelán-Ortega O.A.
      Development of mathematical models to predict enteric methane emission by cattle in Latin America.
      Livest. Sci. 2020; 241104177
      https://doi.org/10.1016/j.livsci.2020.104177
      ) applied the IPCC refinement (
      • IPCC
      2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Chapter 10: Emissions from livestock and manure management.
      in: IPCC Guidelines for National Greenhouse Gas Inventories Volume 4: Agriculture, forestry and other land use. Intergovernmental Panel on Climate Change, 2019
      ) to estimate cattle enteric CH4 in Latin America, and the results showed that the new factors of
      • IPCC
      2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Chapter 10: Emissions from livestock and manure management.
      in: IPCC Guidelines for National Greenhouse Gas Inventories Volume 4: Agriculture, forestry and other land use. Intergovernmental Panel on Climate Change, 2019
      made a substantial improvement in the prediction compared with the previous IPCC Tier 2 (
      • IPCC
      Chapter 10: Emissions from livestock and manure management.
      in: IPCC Guidelines for National Greenhouse Gas Inventories Volume 4: Agriculture, forestry and other land use. Intergovernmental Panel on Climate Change, 2006
      ). Given the importance of Ym, for large regions such as Europe, using specific values that better represent the characteristics of local production systems is advisable. For example, a recent study showed that the predicted Ym ranged from 6.22 to 6.72% for Norway (
      • Niu P.
      • Schwarm A.
      • Bonesmo H.
      • Kidane A.
      • Aspeholen Åby B.
      • Storlien T.M.
      • Kreuzer M.
      • Alvarez C.
      • Sommerseth J.K.
      • Prestløkken E.
      A basic model to predict enteric methane emission from dairy cows and its application to update operational models for the national inventory in Norway.
      Animals (Basel). 2021; 11 (34202055)1891
      https://doi.org/10.3390/ani11071891
      ), whereas in the Netherlands, a Tier 3 approach used a predicted Ym ranging from 5.88% to 6.07% (
      • Bannink A.
      • van Schijndel M.W.
      • Dijkstra J.
      A model of enteric fermentation in dairy cows to estimate methane emission for the Dutch National Inventory Report using the IPCC Tier 3 approach.
      Anim. Feed Sci. Technol. 2011; 166–167: 603-618
      https://doi.org/10.1016/j.anifeedsci.2011.04.043
      ).
      The 1YM model was the second least accurate model (MB = 9.69; Cb = 0.919) with underestimated emissions (370 vs. 381 g/d). The underestimation was mainly due to the hay-based diets: for these diets, the use of a fixed DE (70%) rather than the lower in vivo value (64.8%) reduced the predicted GE requirement (Equation 10.16) and hence the related CH4 emission.
      The models 2YMIV, 2YM, and 1YMIV all have a MB below 1% and a Cb > 0.93. Using the in vivo DE improved the accuracy of the models, as confirmed also considering the V values. The models that best predicted the in vivo variability (V close to 1) were the ones using DE measured in vivo (V = 1.03 and 0.968 for 1YMIV and 2YMIV, respectively). The importance of DE as a key factor to estimate CH4 emissions has long been known, although it is not an easily measurable parameter. Other studies suggested the use of OM digestibility to predict CH4 emissions (
      • Ramin M.
      • Huhtanen P.
      Development of an in vitro method for determination of methane production kinetics using a fully automated in vitro gas system—A modelling approach.
      Anim. Feed Sci. Technol. 2012; 174: 190-200
      https://doi.org/10.1016/j.anifeedsci.2012.03.008
      ;
      • Bell M.
      • Eckard R.
      • Moate P.J.
      • Yan T.
      Modelling the effect of diet composition on enteric methane emissions across sheep, beef cattle and dairy cows.
      Animals (Basel). 2016; 6 (27618107): 54
      https://doi.org/10.3390/ani6090054
      ). However, models that include OM digestibility had lower precision and accuracy than those based on DE (
      • Benaouda M.
      • Martin C.
      • Li X.
      • Kebreab E.
      • Hristov A.N.
      • Yu Z.
      • Yáñez-Ruiz D.R.
      • Reynolds C.K.
      • Crompton L.A.
      • Dijkstra J.
      • Bannink A.
      • Schwarm A.
      • Kreuzer M.
      • McGee M.
      • Lund P.
      • Hellwing A.L.F.
      • Weisbjerg M.R.
      • Moate P.J.
      • Bayat A.R.
      • Shingfield K.J.
      • Peiren N.
      • Eugène M.
      Evaluation of the performance of existing mathematical models predicting enteric methane emissions from ruminants: Animal categories and dietary mitigation strategies.
      Anim. Feed Sci. Technol. 2019; 255114207
      https://doi.org/10.1016/j.anifeedsci.2019.114207
      ).
      In contrast, using in vivo DE values decreased the model precision (SB = 19.2 and 28.2% and r = 0.572 and 0.490 for 1YMIV and 2YMIV, respectively). The most precise model was 1YM (SB = 1.88%; r = 0.63) followed by IPCC06 (SB = 2.96; r = 0.63) and 2YM (SB = 2.46; r = 0.572). Given the differences in precision and accuracy, the CCC parameter has been suggested to simultaneously account for accuracy and precision (
      • Tedeschi L.O.
      Assessment of the adequacy of mathematical models.
      Agric. Syst. 2006; 89: 225-247
      https://doi.org/10.1016/j.agsy.2005.11.004
      ) and the CCC evaluation showed the highest (best) value for the model 1YM (0.579) followed, with a similar value, by 1YMIV (0.569).
      The in vivo values of DE and Ym for cows fed Mediterranean diets are shown in Table 2. The mean DE varied across study and diet (P < 0.001), with the hay diet studied by
      • Gislon G.
      • Colombini S.
      • Borreani G.
      • Crovetto G.M.
      • Sandrucci A.
      • Galassi G.
      • Tabacco E.
      • Rapetti L.
      Milk production, methane emissions, nitrogen, and energy balance of cows fed diets based on different forage systems.
      J. Dairy Sci. 2020; 103 (32622607): 8048-8061
      https://doi.org/10.3168/jds.2019-18134
      having significantly lower DE values than all other studies and diets except the corn silage diet within the study of
      • Colombini S.
      • Zucali M.
      • Rapetti L.
      • Crovetto G.M.
      • Sandrucci A.
      • Bava L.
      Substitution of corn silage with sorghum silages in lactating cow diets: In vivo methane emission and global warming potential of milk production.
      Agric. Syst. 2015; 136: 106-113
      https://doi.org/10.1016/j.agsy.2015.02.006
      . Several studies (i.e.,
      • Broderick G.A.
      Performance of lactating dairy cows fed either alfalfa silage or alfalfa hay as the sole forage.
      J. Dairy Sci. 1995; 78 (7745152): 320-329
      https://doi.org/10.3168/jds.S0022-0302(95)76640-1
      ;
      • Gislon G.
      • Colombini S.
      • Borreani G.
      • Crovetto G.M.
      • Sandrucci A.
      • Galassi G.
      • Tabacco E.
      • Rapetti L.
      Milk production, methane emissions, nitrogen, and energy balance of cows fed diets based on different forage systems.
      J. Dairy Sci. 2020; 103 (32622607): 8048-8061
      https://doi.org/10.3168/jds.2019-18134
      ) reported a higher DMI for cows fed hay-based diets than silage diets, and increased intake may increase passage rate, thus decreasing the DE (
      • Gislon G.
      • Colombini S.
      • Borreani G.
      • Crovetto G.M.
      • Sandrucci A.
      • Galassi G.
      • Tabacco E.
      • Rapetti L.
      Milk production, methane emissions, nitrogen, and energy balance of cows fed diets based on different forage systems.
      J. Dairy Sci. 2020; 103 (32622607): 8048-8061
      https://doi.org/10.3168/jds.2019-18134
      ). Moreover, the hay-based diet was characterized by a low NDF digestibility, further decreasing energy digestibility. Overall, there is scarce information about the DE measured in vivo of cows fed hay diets. For example,
      • Klevenhusen F.
      • Kreuzer M.
      • Soliva C.R.
      Enteric and manure-derived methane and nitrogen emissions as well as metabolic energy losses in cows fed balanced diets based on maize, barley or grass hay.
      Animal. 2011; 5 (22445412): 450-461
      https://doi.org/10.1017/S1751731110001795
      reported a DE of 65.3% in lactating dairy cows fed a hay-based diet, a value comparable to the Italian hay diet data set.
      Table 2Digestible energy (DE; %) and Ym (%), and dietary NDF and starch (% DM) determined in cows fed Mediterranean diets with a different forage basis
      CS = diet based on corn silage; AGS = diet based on alfalfa and grass silage; WS = diet based on wheat silage; Hay = diet based on hay; SS = diet based on sorghum silage; Ym = conversion factor of gross energy intake into enteric CH4 energy.
      ItemStudySEMP-value
      • Gislon G.
      • Colombini S.
      • Borreani G.
      • Crovetto G.M.
      • Sandrucci A.
      • Galassi G.
      • Tabacco E.
      • Rapetti L.
      Milk production, methane emissions, nitrogen, and energy balance of cows fed diets based on different forage systems.
      J. Dairy Sci. 2020; 103 (32622607): 8048-8061
      https://doi.org/10.3168/jds.2019-18134
      • Pirondini M.
      • Colombini S.
      • Mele M.
      • Malagutti L.
      • Rapetti L.
      • Galassi G.
      • Crovetto G.M.
      Effect of dietary starch concentration and fish oil supplementation on milk yield and composition, diet digestibility, and methane emissions in lactating dairy cows.
      J. Dairy Sci. 2015; 98 (25465540): 357-372
      https://doi.org/10.3168/jds.2014-8092
      • Colombini S.
      • Zucali M.
      • Rapetti L.
      • Crovetto G.M.
      • Sandrucci A.
      • Bava L.
      Substitution of corn silage with sorghum silages in lactating cow diets: In vivo methane emission and global warming potential of milk production.
      Agric. Syst. 2015; 136: 106-113
      https://doi.org/10.1016/j.agsy.2015.02.006
      CSAGSWSHayCSCSSS
      DE73.6
      Least squares mean values within a row with different superscripts differ significantly at P < 0.05.
      		72.6
      Least squares mean values within a row with different superscripts differ significantly at P < 0.05.
      		70.3
      Least squares mean values within a row with different superscripts differ significantly at P < 0.05.
      		64.8
      Least squares mean values within a row with different superscripts differ significantly at P < 0.05.
      		68.6
      Least squares mean values within a row with different superscripts differ significantly at P < 0.05.
      		65.2
      Least squares mean values within a row with different superscripts differ significantly at P < 0.05.
      		68.9
      Least squares mean values within a row with different superscripts differ significantly at P < 0.05.
      		1.30<0.001
      Ym5.67
      Least squares mean values within a row with different superscripts differ significantly at P < 0.05.
      		5.92
      Least squares mean values within a row with different superscripts differ significantly at P < 0.05.
      		5.82
      Least squares mean values within a row with different superscripts differ significantly at P < 0.05.
      		5.68
      Least squares mean values within a row with different superscripts differ significantly at P < 0.05.
      		5.41
      Least squares mean values within a row with different superscripts differ significantly at P < 0.05.
      		5.05
      Least squares mean values within a row with different superscripts differ significantly at P < 0.05.
      		5.52
      Least squares mean values within a row with different superscripts differ significantly at P < 0.05.
      		0.2080.007
      NDF32.827.137.736.633.036.636.4
      Starch25.926.525.9
      a–e Least squares mean values within a row with different superscripts differ significantly at P < 0.05.
      1 CS = diet based on corn silage; AGS = diet based on alfalfa and grass silage; WS = diet based on wheat silage; Hay = diet based on hay; SS = diet based on sorghum silage; Ym = conversion factor of gross energy intake into enteric CH4 energy.
      The mean Ym also varied across study and diet (P = 0.007), although to a lesser extent and with no individual forage standing out as being different to the others. The IPPC refinement Tier 2 (
      • IPCC
      2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Chapter 10: Emissions from livestock and manure management.
      in: IPCC Guidelines for National Greenhouse Gas Inventories Volume 4: Agriculture, forestry and other land use. Intergovernmental Panel on Climate Change, 2019
      ) for the European data set (milk yield >8,500 kg) assumes a Ym value of 6.0%. This value is higher than the average value (5.58%) observed in the data set of the present study. The most studied forage type for Europe is grass silage, followed by corn silage and fresh forage; diets based on grass silage are expected to have higher Ym values than diets based on corn silage due to the higher starch content of the latter, which increases the dietary starch concentration (
      • Hassanat F.
      • Gervais R.
      • Julien C.
      • Massé D.I.
      • Lettat A.
      • Chouinard P.Y.
      • Petit H.V.
      • Benchaar C.
      Replacing alfalfa silage with corn silage in dairy cow diets: Effects on enteric methane production, ruminal fermentation, digestion, N balance, and milk production.
      J. Dairy Sci. 2013; 96 (23684039): 4553-4567
      https://doi.org/10.3168/jds.2012-6480
      ;
      • Benchaar C.
      • Hassanat F.
      • Gervais R.
      • Chouinard P.Y.
      • Petit H.V.
      • Massé D.I.
      Methane production, digestion, ruminal fermentation, nitrogen balance, and milk production of cows fed corn silage- or barley silage-based diets.
      J. Dairy Sci. 2014; 97 (24359826): 961-974
      https://doi.org/10.3168/jds.2013-7122
      ). Based on these results, Mediterranean coefficients of Ym and DE appropriate for the MED equation were proposed as follows: Ym = 5.58% (average of Ym values for all diets, since no effect of diet was observed) and DE = 69.9 and 64.8% for silage- and hay-based diets, respectively.
      The cross-validation results on the independent data set (
      • Enriquez-Hidalgo D.
      • Teixeira D.L.
      • Pinheiro Machado Filho L.C.
      • Hennessy D.
      • Toro-Mujica P.
      • Williams S.R.O.
      • Pereira F.C.
      Incorporating a fresh mixed annual ryegrass and berseem clover forage into the winter diet of dairy cows resulted in reduced milk yield, but reduced nitrogen excretion and reduced methane yield.
      Front. Vet. Sci. 2020; 7 (33330696)576944
      https://doi.org/10.3389/fvets.2020.576944
      ) are in Table 3. To highlight potential differences between equations, both a typical Mediterranean-type corn silage and hay-based diet, and a corn silage and a mixture of fresh annual ryegrass and berseem clover diet (MIX) were cross-validated. The average RMSPE (28.9%) was higher than that of the Italian data set (10.5%). Part of the reason for this discrepancy could be the method used to measure CH4 emission (respiration chambers for MED model, SF6 in the data set for cross-validation). Moreover, the SF6 technique collects the animal breath CH4 emissions, but not the rectum CH4 emissions, and this can partially explain the differences observed between the in vivo and the model estimations.
      • Enriquez-Hidalgo D.
      • Teixeira D.L.
      • Pinheiro Machado Filho L.C.
      • Hennessy D.
      • Toro-Mujica P.
      • Williams S.R.O.
      • Pereira F.C.
      Incorporating a fresh mixed annual ryegrass and berseem clover forage into the winter diet of dairy cows resulted in reduced milk yield, but reduced nitrogen excretion and reduced methane yield.
      Front. Vet. Sci. 2020; 7 (33330696)576944
      https://doi.org/10.3389/fvets.2020.576944
      modified the SF6 technique according to
      • Deighton M.H.
      • Williams S.R.O.
      • Hannah M.C.
      • Eckard R.J.
      • Boland T.M.
      • Wales W.J.
      • Moate P.J.
      A modified sulphur hexafluoride tracer technique enables accurate determination of enteric methane emissions from ruminants.
      Anim. Feed Sci. Technol. 2014; 197: 47-63
      https://doi.org/10.1016/j.anifeedsci.2014.08.003
      , to reduce the variability of CH4 yield estimation between cows, obtaining an accuracy similar to respiration chambers, although respiration chambers remain the gold standard method for measuring CH4 emission (
      • Garnsworthy P.C.
      • Difford G.F.
      • Bell M.J.
      • Bayat A.R.
      • Huhtanen P.
      • Kuhla B.
      • Lassen J.
      • Peiren N.
      • Pszczola M.
      • Sorg D.
      • Visker M.H.P.W.
      • Yan T.
      Comparison of methods to measure methane for use in genetic evaluation of dairy cattle.
      Animals (Basel). 2019; 9 (31640130): 837
      https://doi.org/10.3390/ani9100837
      ).
      Table 3Results of RMSPE and CCC analysis on CH4 production for an independent individual lactating cow database derived from
      • Enriquez-Hidalgo D.
      • Teixeira D.L.
      • Pinheiro Machado Filho L.C.
      • Hennessy D.
      • Toro-Mujica P.
      • Williams S.R.O.
      • Pereira F.C.
      Incorporating a fresh mixed annual ryegrass and berseem clover forage into the winter diet of dairy cows resulted in reduced milk yield, but reduced nitrogen excretion and reduced methane yield.
      Front. Vet. Sci. 2020; 7 (33330696)576944
      https://doi.org/10.3389/fvets.2020.576944
      using
      • IPCC
      2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Chapter 10: Emissions from livestock and manure management.
      in: IPCC Guidelines for National Greenhouse Gas Inventories Volume 4: Agriculture, forestry and other land use. Intergovernmental Panel on Climate Change, 2019
      1YM (Ym = 5.7%, DE = 70%) or the Tier 2 coefficients derived from the present study (MED, Ym = 5.58%; DE = 69.9%)
      DE = energy digestibility; 69.8% for silage-based diets, 64.8% for hay-based diets. Ym = conversion factor of gross energy intake into enteric CH4 energy. RMSPE = root mean square prediction error expressed as a percentage of the observed mean. DE = digestible energy. MB = error due to bias, as a percent of total RMSPE. SB = error due to regression, as a percent of total RMSPE. RB = error due to random bias, as a percent of total RMSPE. CCC = concordance correlation coefficient, where CCC = r × Cb. r = Pearson correlation coefficient. Cb = bias correction factor. V = scale shift. μ = location shift.
      ModelCH4 (g/d)RMSPE (%)MB (%)SB (%)RB (%)CCCrCbVμ
      Corn silage and alfalfa hay diet
       In vivo396
       1YM40530.80.54752.347.10.4920.8580.5733.16−0.103
       MED39731.50.00953.246.70.4850.8560.5653.22−0.013
      Corn silage and fresh annual ryegrass/berseem herbage (MIX)
       In vivo334
       1YM36626.79.701.0390.30.1690.2770.6102.64−0.511
       MED35826.75.780.90493.30.1720.2770.6212.69−0.392
      1 DE = energy digestibility; 69.8% for silage-based diets, 64.8% for hay-based diets. Ym = conversion factor of gross energy intake into enteric CH4 energy. RMSPE = root mean square prediction error expressed as a percentage of the observed mean. DE = digestible energy. MB = error due to bias, as a percent of total RMSPE. SB = error due to regression, as a percent of total RMSPE. RB = error due to random bias, as a percent of total RMSPE. CCC = concordance correlation coefficient, where CCC = r × Cb. r = Pearson correlation coefficient. Cb = bias correction factor. V = scale shift. μ = location shift.
      The 1YM and MED had a higher (best) accuracy (evaluated in terms of MB) and CCC for corn silage and hay diet, rather than for the MIX diet. The predicted CH4 was higher than the in vivo value but differed between diets with a greater overestimation (evaluated in terms of µ) for the MIX diet (µ = −0.452, on average of 1YM and MED) than the corn silage + hay diet (−0.058, on average of 1YM and MED). The presence in the MIX diet of berseem clover, a forage containing natural substances able to reduce methanogenesis (
      • Enriquez-Hidalgo D.
      • Teixeira D.L.
      • Pinheiro Machado Filho L.C.
      • Hennessy D.
      • Toro-Mujica P.
      • Williams S.R.O.
      • Pereira F.C.
      Incorporating a fresh mixed annual ryegrass and berseem clover forage into the winter diet of dairy cows resulted in reduced milk yield, but reduced nitrogen excretion and reduced methane yield.
      Front. Vet. Sci. 2020; 7 (33330696)576944
      https://doi.org/10.3389/fvets.2020.576944
      ), partially explains the IPCC method's overestimation of CH4 emissions for this diet compared with direct in vivo measurement. In contrast, the predicted CH4 emission on the corn silage diet using our proposed Mediterranean coefficients was very similar to that measured in vivo (396 and 397 g/d, respectively, for in vivo and MED).
      The RB value was higher for the MIX diet (91.8%, on average) than for the corn silage and hay diet (46.9%, on average); however, the r values were very low for the prediction of the MIX diet (on average, 0.277) compared with the corn silage and hay diet (on average, 0.857). The high RB for the MIX diet is explained by the lower values of predicted standard deviation compared with the in vivo and the low r value.
      In conclusion, the study showed that IPCC 2019 predicts CH4 emission accurately and then it can be used as a tool for the prediction of CH4 emissions for inventories; however, for Mediterranean diets specific values of Ym and DE may be preferable, especially for hay-based diets.

      Notes

      The study was part of the project Life+Forage4Climate–Forage systems for less GHG emissions and more soil carbon sink in continental and Mediterranean agricultural areas (LIFE15 CCM/IT/000039), funded by the EU Commission (Brussels, Belgium). The authors thank the Department of Agricultural and Environmental Science, University of Milan, for funding the paper publication, Paolo Roveda (Università degli Studi di Milano, Milano, Italy) for the technical assistance with respiration chambers, and Vincenzo D'Ardes (Università degli Studi di Milano, Milano, Italy) for the calorimetric analyses.
      D. Enriquez-Hidalgo was sponsored by Rothamsted Research's Institute Strategic Programme supported by the Biotechnology and Biological Sciences Research Council (BBS/E/C/000I0320; Rothamsted Research, North Wyke, Net Zero and Resilient Farming, Okehampton, Devon, United Kingdom).
      The authors have not stated any conflicts of interest.

      References

        • Appuhamy J.A.D.R.N.
        • France J.
        • Kebreab E.
        Models for predicting enteric methane emissions from dairy cows in North America, Europe, and Australia and New Zealand.
        Glob. Chang. Biol. 2016; 22 (27148862): 3039-3056
        https://doi.org/10.1111/gcb.13339
        View in Article
        • Bannink A.
        • van Schijndel M.W.
        • Dijkstra J.
        A model of enteric fermentation in dairy cows to estimate methane emission for the Dutch National Inventory Report using the IPCC Tier 3 approach.
        Anim. Feed Sci. Technol. 2011; 166–167: 603-618
        https://doi.org/10.1016/j.anifeedsci.2011.04.043
        View in Article
        • Bell M.
        • Eckard R.
        • Moate P.J.
        • Yan T.
        Modelling the effect of diet composition on enteric methane emissions across sheep, beef cattle and dairy cows.
        Animals (Basel). 2016; 6 (27618107): 54
        https://doi.org/10.3390/ani6090054
        View in Article
        • Benaouda M.
        • González-Ronquillo M.
        • Appuhamy J.A.D.R.N.
        • Kebreab E.
        • Molina L.T.
        • Herrera-Camacho J.
        • Ku-Vera J.C.
        • Ángeles-Hernández J.C.
        • Castelán-Ortega O.A.
        Development of mathematical models to predict enteric methane emission by cattle in Latin America.
        Livest. Sci. 2020; 241104177
        https://doi.org/10.1016/j.livsci.2020.104177
        View in Article
        • Benaouda M.
        • Martin C.
        • Li X.
        • Kebreab E.
        • Hristov A.N.
        • Yu Z.
        • Yáñez-Ruiz D.R.
        • Reynolds C.K.
        • Crompton L.A.
        • Dijkstra J.
        • Bannink A.
        • Schwarm A.
        • Kreuzer M.
        • McGee M.
        • Lund P.
        • Hellwing A.L.F.
        • Weisbjerg M.R.
        • Moate P.J.
        • Bayat A.R.
        • Shingfield K.J.
        • Peiren N.
        • Eugène M.
        Evaluation of the performance of existing mathematical models predicting enteric methane emissions from ruminants: Animal categories and dietary mitigation strategies.
        Anim. Feed Sci. Technol. 2019; 255114207
        https://doi.org/10.1016/j.anifeedsci.2019.114207
        View in Article
        • Benchaar C.
        • Hassanat F.
        • Gervais R.
        • Chouinard P.Y.
        • Petit H.V.
        • Massé D.I.
        Methane production, digestion, ruminal fermentation, nitrogen balance, and milk production of cows fed corn silage- or barley silage-based diets.
        J. Dairy Sci. 2014; 97 (24359826): 961-974
        https://doi.org/10.3168/jds.2013-7122
        View in Article
        • Bibby J.
        • Toutenburg H.
        Prediction and improved estimation in linear models.
        John Wiley & Sons, 1977
        View in Article
        • Broderick G.A.
        Performance of lactating dairy cows fed either alfalfa silage or alfalfa hay as the sole forage.
        J. Dairy Sci. 1995; 78 (7745152): 320-329
        https://doi.org/10.3168/jds.S0022-0302(95)76640-1
        View in Article
        • Colombini S.
        • Zucali M.
        • Rapetti L.
        • Crovetto G.M.
        • Sandrucci A.
        • Bava L.
        Substitution of corn silage with sorghum silages in lactating cow diets: In vivo methane emission and global warming potential of milk production.
        Agric. Syst. 2015; 136: 106-113
        https://doi.org/10.1016/j.agsy.2015.02.006
        View in Article
        • Deighton M.H.
        • Williams S.R.O.
        • Hannah M.C.
        • Eckard R.J.
        • Boland T.M.
        • Wales W.J.
        • Moate P.J.
        A modified sulphur hexafluoride tracer technique enables accurate determination of enteric methane emissions from ruminants.
        Anim. Feed Sci. Technol. 2014; 197: 47-63
        https://doi.org/10.1016/j.anifeedsci.2014.08.003
        View in Article
        • Enriquez-Hidalgo D.
        • Teixeira D.L.
        • Pinheiro Machado Filho L.C.
        • Hennessy D.
        • Toro-Mujica P.
        • Williams S.R.O.
        • Pereira F.C.
        Incorporating a fresh mixed annual ryegrass and berseem clover forage into the winter diet of dairy cows resulted in reduced milk yield, but reduced nitrogen excretion and reduced methane yield.
        Front. Vet. Sci. 2020; 7 (33330696)576944
        https://doi.org/10.3389/fvets.2020.576944
        View in Article
        • Garnsworthy P.C.
        • Difford G.F.
        • Bell M.J.
        • Bayat A.R.
        • Huhtanen P.
        • Kuhla B.
        • Lassen J.
        • Peiren N.
        • Pszczola M.
        • Sorg D.
        • Visker M.H.P.W.
        • Yan T.
        Comparison of methods to measure methane for use in genetic evaluation of dairy cattle.
        Animals (Basel). 2019; 9 (31640130): 837
        https://doi.org/10.3390/ani9100837
        View in Article
        • Gislon G.
        • Bava L.
        • Colombini S.
        • Zucali M.
        • Crovetto G.M.
        • Sandrucci A.
        Looking for high-production and sustainable diets for lactating cows: A survey in Italy.
        J. Dairy Sci. 2020; 103 (32113778): 4863-4873
        https://doi.org/10.3168/jds.2019-17177
        View in Article
        • Gislon G.
        • Colombini S.
        • Borreani G.
        • Crovetto G.M.
        • Sandrucci A.
        • Galassi G.
        • Tabacco E.
        • Rapetti L.
        Milk production, methane emissions, nitrogen, and energy balance of cows fed diets based on different forage systems.
        J. Dairy Sci. 2020; 103 (32622607): 8048-8061
        https://doi.org/10.3168/jds.2019-18134
        View in Article
        • Hassanat F.
        • Gervais R.
        • Julien C.
        • Massé D.I.
        • Lettat A.
        • Chouinard P.Y.
        • Petit H.V.
        • Benchaar C.
        Replacing alfalfa silage with corn silage in dairy cow diets: Effects on enteric methane production, ruminal fermentation, digestion, N balance, and milk production.
        J. Dairy Sci. 2013; 96 (23684039): 4553-4567
        https://doi.org/10.3168/jds.2012-6480
        View in Article
        • IPCC
        Chapter 1: Introduction to the 2006 guidelines.
        in: IPCC Guidelines for National Greenhouse Gas Inventories Volume 1: General guidance and reporting. Intergovernmental Panel on Climate Change, 2006
        View in Article
        • IPCC
        Chapter 10: Emissions from livestock and manure management.
        in: IPCC Guidelines for National Greenhouse Gas Inventories Volume 4: Agriculture, forestry and other land use. Intergovernmental Panel on Climate Change, 2006
        View in Article
        • IPCC
        2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Chapter 10: Emissions from livestock and manure management.
        in: IPCC Guidelines for National Greenhouse Gas Inventories Volume 4: Agriculture, forestry and other land use. Intergovernmental Panel on Climate Change, 2019
        View in Article
        • IPCC
        2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Overview Chapter.
        Intergovernmental Panel on Climate Change, 2019
        View in Article
        • Jiménez L.E.
        • Hernández A.X.
        • Benaouda M.
        • Avalos J.O.
        • Corona L.
        • Castillo-Gallegos E.
        • Castelan-Ortega O.A.
        • Gonzalez-Ronquillo M.
        Concentrate supplementation on milk yield, methane and CO2 production in crossbred dairy cows grazing in tropical climate regions.
        J. Anim. Behav. Biometeorol. 2021; 92118
        View in Article
        • Kaewpila C.
        • Sommart K.
        Development of methane conversion factor models for Zebu beef cattle fed low-quality crop residues and by-products in tropical regions.
        Ecol. Evol. 2016; 6 (28725409): 7422-7432
        https://doi.org/10.1002/ece3.2500
        View in Article
        • Klevenhusen F.
        • Kreuzer M.
        • Soliva C.R.
        Enteric and manure-derived methane and nitrogen emissions as well as metabolic energy losses in cows fed balanced diets based on maize, barley or grass hay.
        Animal. 2011; 5 (22445412): 450-461
        https://doi.org/10.1017/S1751731110001795
        View in Article
        • Lin L.I.
        A concordance correlation coefficient to evaluate reproducibility.
        Biometrics. 1989; 45 (2720055): 255-268
        https://doi.org/10.2307/2532051
        View in Article
        • March M.D.
        • Haskell M.J.
        • Chagunda M.G.G.
        • Langford F.M.
        • Roberts D.J.
        Current trends in British dairy management regimens.
        J. Dairy Sci. 2014; 97 (25306285): 7985-7994
        https://doi.org/10.3168/jds.2014-8265
        View in Article
        • Niu M.
        • Kebreab E.
        • Hristov A.N.
        • Oh J.
        • Arndt C.
        • Bannink A.
        • Bayat A.R.
        • Brito A.F.
        • Boland T.
        • Casper D.
        • Crompton L.A.
        • Dijkstra J.
        • Eugène M.A.
        • Garnsworthy P.C.
        • Haque M.N.
        • Hellwing A.L.F.
        • Huhtanen P.
        • Kreuzer M.
        • Kuhla B.
        • Lund P.
        • Madsen J.
        • Martin C.
        • McClelland S.C.
        • McGee M.
        • Moate P.J.
        • Muetzel S.
        • Muñoz C.
        • O'Kiely P.
        • Peiren N.
        • Reynolds C.K.
        • Schwarm A.
        • Shingfield K.J.
        • Storlien T.M.
        • Weisbjerg M.R.
        • Yáñez-Ruiz D.R.
        • Yu Z.
        Prediction of enteric methane production, yield, and intensity in dairy cattle using an intercontinental database.
        Glob. Chang. Biol. 2018; 24 (29450980): 3368-3389
        https://doi.org/10.1111/gcb.14094
        View in Article
        • Niu P.
        • Schwarm A.
        • Bonesmo H.
        • Kidane A.
        • Aspeholen Åby B.
        • Storlien T.M.
        • Kreuzer M.
        • Alvarez C.
        • Sommerseth J.K.
        • Prestløkken E.
        A basic model to predict enteric methane emission from dairy cows and its application to update operational models for the national inventory in Norway.
        Animals (Basel). 2021; 11 (34202055)1891
        https://doi.org/10.3390/ani11071891
        View in Article
        • Pirondini M.
        • Colombini S.
        • Mele M.
        • Malagutti L.
        • Rapetti L.
        • Galassi G.
        • Crovetto G.M.
        Effect of dietary starch concentration and fish oil supplementation on milk yield and composition, diet digestibility, and methane emissions in lactating dairy cows.
        J. Dairy Sci. 2015; 98 (25465540): 357-372
        https://doi.org/10.3168/jds.2014-8092
        View in Article
        • Ramin M.
        • Huhtanen P.
        Development of an in vitro method for determination of methane production kinetics using a fully automated in vitro gas system—A modelling approach.
        Anim. Feed Sci. Technol. 2012; 174: 190-200
        https://doi.org/10.1016/j.anifeedsci.2012.03.008
        View in Article
        • Tedeschi L.O.
        Assessment of the adequacy of mathematical models.
        Agric. Syst. 2006; 89: 225-247
        https://doi.org/10.1016/j.agsy.2005.11.004
        View in Article
        • World Resources Institute
        World Greenhouse Gas Emissions: 2019.
        https://www.wri.org/data/world-greenhouse-gas-emissions-2019
        Date: 2019
        Date accessed: November 3, 2021
        View in Article

      Article info

      Publication history

      Published online: February 09, 2023
      Accepted: November 17, 2022
      Received: April 12, 2022

      Publication stage

      In Press Corrected Proof

      Identification

      DOI: https://doi.org/10.3168/jdsc.2022-0240

      Copyright

      © 2023

      User license

      Creative Commons Attribution (CC BY 4.0) |
      How you can reuse Information Icon
      Creative Commons Attribution (CC BY 4.0)

      Permitted

      • Read, print & download
      • Redistribute or republish the final article
      • Text & data mine
      • Translate the article
      • Reuse portions or extracts from the article in other works
      • Sell or re-use for commercial purposes

      Elsevier's open access license policy

      ScienceDirect

      Access this article on ScienceDirect

      The content on this site is intended for healthcare professionals.


      We use cookies to help provide and enhance our service and tailor content. To update your cookie settings, please visit the Cookie Preference Center for this site.
      Copyright © 2023 Elsevier Inc. except certain content provided by third parties.


      RELX