Prediction and evaluation of enteric methane ... - CSIRO Publishing

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Animal Production Science, 2016, 56, 557–564 http://dx.doi.org/10.1071/AN15496

Prediction and evaluation of enteric methane emissions from lactating dairy cows using different levels of covariate information B. Santiago-Juarez A,B,C, L. E. Moraes B, J. A. D. R. N. Appuhamy B, W. F. Pellikaan A, D. P. Casper D, J. Tricarico E and E. Kebreab B,F A

Animal Nutrition Group, Wageningen University, 6700 AH, Wageningen, The Netherlands. Department of Animal Science, One Shields Avenue, University of California, Davis, CA 95616, USA. C Department of Agriculture and Agrofood, University of Toulouse – École d’Ingénieurs de Purpan, 31076, France. D Department of Dairy Science, South Dakota State University, Brookings, SD 57007, USA. E Innovation Center for US Dairy, Rosemont, IL 60018, USA. F Corresponding author. Email: [email protected] B

Abstract. The dairy sector contributes to global warming through enteric methane (CH4) emissions. Methane is also a loss of energy to the ruminant. Several studies have developed CH4 prediction models to assess mitigation strategies to reduce emissions. However, the majority of these models have low predictive ability or require numerous inputs that are often not readily available in commercial dairy operations. In this context, the objective of the present paper was to develop CH4 prediction models by using varying levels of information available at the farm level. The seven complexity levels used the following information: (1) dietary nutrient composition, (2) milk yield and composition, (3) Levels 1 and 2, (4) Level 3 plus dry matter intake (DMI), (5) Level 4 plus bodyweight, (6) Level 2 plus DMI, and (7) DMI only. Models were fitted to 489 individual enteric-CH4 measurements from 30 indirect calorimetry studies and evaluated with an independent database comprising 215 treatment means from 62 studies collected from the literature. Within each complexity level, all possible mixed-effect models were fitted and those with the lowest values of Akaike or Bayesian information criteria were selected using lme4 package in R. Models were evaluated using mean square prediction error (MSPE) based statistic, root MSPE (RMSPE) to observation standard deviation ratio, concordance correlation coefficient and Nash–Sutcliffe efficiency methods. All fitted models performed well with an acceptable error estimates (RMSPE as a percentage of observed mean (RMSPE%) = 16–24%), with more than two-thirds of total error originating from random bias. Overall, models with DMI were more accurate (RMSPE% = 16–20%) than those without (RMSPE% = 20–24%). Although the best prediction model (RMSPE% = 16%) was developed using Level 5 information, a model using Level 2 information is recommended for on-farm methane estimates if DMI is not measured. The proposed models offer easy and practical tools to dairy producers for predicting CH4 emissions and evaluating CH4 mitigation strategies. Additional keywords: commercial dairy, greenhouse gas emissions, model evaluation. Received 28 August 2015, accepted 28 October 2015, published online 9 February 2016

Introduction Greenhouse gases (GHG), such as carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O), contribute to global warming and climate change (Lashof and Ahuja 1990). The livestock sector plays an important role in global climate change, contributing ~14.5% to the total anthropogenic GHG emissions (Gerber et al. 2013). Due to rising population and income, demand for animal-source protein is expected to nearly double by 2050, compared with 2011 (Gerber et al. 2013). The largest source of GHG emissions from agriculture is CH4 from enteric fermentation of ruminants, which is a by-product of microbial fermentation of mostly fibre in feed (Kebreab et al. 2008). Besides the negative effect of CH4 on the environment, it represents an energy loss to the ruminant (1–11% of gross energy intake) and, consequently, a loss of efficiency for Journal compilation CSIRO 2016

animal production systems (Kristensen et al. 2011; Moraes et al. 2014). Measurement of CH4 emissions from cattle requires expensive specialised equipment; therefore, mathematical models have been developed to estimate emissions at cow, farm, national and global levels (Kebreab et al. 2006). Although numerous prediction models have been developed in the past few decades, their predictive ability remains low or requires covariates (or input variables) that may not be readily available for stakeholders such as dairy producers. Ellis et al. (2010) evaluated CH4 prediction equations frequently used in whole-farm models and suggested that, in general, predictive ability was poor. Moreover, Moraes et al. (2014) developed CH4 prediction models with reduced prediction errors when compared with the IPCC (2006) and FAO (2010) models, but they required covariates that are frequently www.publish.csiro.au/journals/an

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not available to producers. Similarly, mechanistic models of rumen digestion such as those described by Bannink et al. (2011) often adequately represent the generation of CH4 in the rumen and hindgut but require a large number of model inputs, often not available in commercial farms. Therefore, a balance between model complexity and predictive capability must be achieved for wider use among stakeholders in dairy production system. Our hypothesis is that model accuracy increases with model complexity, but the majority of emission can be explained using readily available information in a commercial dairy farm. In this context, the objectives of the project were to (1) develop models to predict enteric CH4 emissions from dairy cattle using covariates available to dairy producers and (2) investigate a potential reduction in the prediction error with the use of additional explanatory variables. Materials and methods The database The database used for model development was composed of individual indirect respiration calorimetry records from the former USDA Energy Metabolism Unit at Beltsville, Maryland (n = 489; Wilkerson et al. 1995, 1997) collected from 1970 to 1995. A complete list of references of individual studies is available in Moraes et al. (2014) and summary statistics of the database are in Table 1. A comprehensive description of the experimental procedures has been reported by Flatt et al. (1958) and Moe et al. (1972). Records represent total energy-balance trials from lactating Holstein and Jersey cows. So as to develop prediction equations suitable for predicting emissions from modern commercial dairy production systems, a subset of the database (n = 489) was taken in which the dry matter intake (DMI) was greater than in the sample second quartile (16.4 kg DM/day). Model specification The data have a hierarchical structure, such that there were multiple observations on the same animal although animals were not fully nested within studies because they were used in multiple studies. The models were specified as Table 1. Summary statistics of dietary nutrient composition and animal characteristics of the model-development database (n = 489) ADF, acid detergent fibre; BW, bodyweight; CH4, methane; CP, crude protein; DMI, dry matter intake; EE, ether extract; ME, metabolisable energy; NDF, neutral detergent fibre Item

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20.1 10.9 34.6 16.9 3.2 618 30.1 3.2 3.7 20.3 1.0

16.5 7.3 16.5 10.8 1.2 341 10.4 2.3 1.5 5.4 0.3

29.4 13.1 69.9 22.1 7.0 854 56.6 4.3 6.5 30.7 1.4

2.8 0.8 6.3 2.0 1.1 69.9 8.8 0.3 0.6 3.9 0.2

yijk ¼ xTijk b þ ai þ g j þ "ijk

ð1Þ

where yijk is kth record on ith animal in the jth study, xijk is the corresponding vector of covariates, b is the vector of fixed regression coefficients, ai is the random effect associated with the ith animal, g j is the random effect associated with the jth study and eijk is the error. The superscript T denotes the vector transpose. Random effects and errors were assumed to be mutually independent and normally distributed. All models were fitted using the lme4 package in the R software (Bates et al. 2014). Model-complexity levels Models for predicting CH4 emissions from lactating dairy cows were developed using various levels of covariate information. Gain in prediction accuracy with the use of additional variables available to the user was assessed. Specifically, the following complexity levels were defined: (1) dietary composition level for which emissions were predicted using diet nutrient composition, i.e. neutral detergent fibre (NDF), ether extract (EE), crude protein (CP) and metabolisable energy (ME) contents as potential covariates, (2) the US national dairy herd information association (DHIA) level, which uses milk yield and composition (available to producers from DHIA records) to predict emissions, (3) a combination of Levels 1 and 2 in which CH4 emissions were predicted using dietary nutrients, milk yield and composition, (4) same as Level 3 with addition of DMI as a covariate, (5) same as Level 4 with the addition of bodyweight (BW) information, (6) same as Level 2 with the addition of DMI as covariates, and (7) prediction equation developed using only DMI information. At each modelcomplexity level, all possible models were fitted with the covariates available at that particular level and the models that minimised the Akaike and Bayesian model-selection criteria (AIC and BIC, respectively) were selected. Both AIC- and BIC-minimised models were selected due to the different underlying philosophies of these two model-selection criteria (Burnham and Anderson 2004). Therefore, if AIC and BIC values did not agree, models within the same complexity level that had lower AIC or BIC values were reported in the present study. Model evaluation A literature search was conducted in ScienceDirect (http://www. sciencedirect.com/, verified June 2015) online database for research articles published in English from January 2000 to April 2015. The titles and abstracts of retrieved articles were screened for in vivo studies measuring enteric CH4 emissions from lactating dairy cows. After excluding duplicates, searches from both databases resulted in 62 articles carrying 215 treatment means of enteric CH4 emission measurements along with corresponding DMI, dietary ingredient and nutrient composition, milk yield and composition, days in milk and BW data. Missing dietary nutrient-composition values were estimated from NRC (2001) tables. The evaluation database contained 59, 109, and 47 CH4 measurements conducted in North America, Europe, and Australia and New Zealand, respectively, using chamber (64% of data) or the SF6 method. The database contained a wide range of ingredient and nutrient compositions and a summary of the data is given in Table 2.

Modeling methane emission with limited input variables


Model performances were assessed both graphically and by using goodness-of-fit statistics. The graphical assessment was carried out by plotting the data against the predicted values (observed vs predicted) or by plotting residual error against the predicted values centred on its mean (error vs predicted). Regression lines representing relationships between the x and y variables were computed for each plot. Deviations of the regression lines from the unity line of the observed versus predicted plot or from zero-intercept–zero-slope line in the error versus predicted plot indicate the presence of systematic error, which was decomposed to mean and slope biases. Four goodness-of-fit statistics, mean square prediction error (MSPE), root MSPE (RMSPE), RMSPE as a percentage of observed mean (RMSPE%), ratio of RMSPE to observation standard deviation (RSR), concordance correlation coefficient (CCC), and Nash–Sutcliffe efficiency (NSE; a normalised statistic that Table 2. Summary statistics of dietary nutrient composition and animal characteristics of the model-evaluation database ADF, acid detergent fibre; BW, bodyweight; CH4, methane; CP, crude protein; DMI, dry matter intake; EE, ether extract; ME, metabolisable energy; NDF, neutral detergent fibre Item

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NDF (% DM) ME (MJ/kg DM) CP (% DM) EE (% DM) DMI (kg/day) BW (kg) Milk yield (kg/day) Milk fat (%) Milk CP (%) CH4 (MJ/day) CH4/DMI (MJ/kg DM)

38.4 10.6 17.6 4.1 18.7 607.5 25.7 4.1 3.3 21.4 1.1

26.5 8.2 8.2 1.1 9.7 420 8.3 2.7 2.4 8.3 0.6

56.4 12.6 25.5 8.4 28.6 762 45.2 5.5 4.1 36.1 2.0

6.2 0.7 2.9 1.6 3.2 56.5 6.9 0.6 0.3 4.7 0.2

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determines the relative magnitude of the residual variance compared with the measured data variance) were used to evaluate the models (Moriasi et al. 2007). The MSPE was decomposed into error due to overall bias of prediction, error due to deviation of the regression slope from unity, and error due to the disturbance (random variation; Bibby and Toutenburg 1977). Results and discussion Methane prediction models Not all covariate information required by extant models is available to commercial farms. Therefore, seven methane prediction models were developed by using one or more covariates that commercial producers might have access to. The models range from simple to complex that require detailed information. The models were then evaluated if inclusion of additional covariate was worth the effort in terms of model prediction accuracy. Some inputs are more easily accessible; for example, dietary nutrient composition is often available to producers because most dairy farms rely on diet-formulation software, which requires information on chemical analyses of feedstuffs. Likewise, most dairies in the USA are enrolled in the DHIA program and have information about their herd’s milk yield and composition. Therefore, developing CH4 prediction equations using these variables as model inputs would not require producers to measure additional variables to gain information on emissions. Various covariates were available for selection at each complexity level, as described in the previous section, and within each complexity level, models with the lowest AIC and BIC (from all possible models within complexity) are summarised in Table 3. Methane production has been closely linked to DMI (Ellis et al. 2007). However, dietary characteristics (i.e. NDF, EE, ME), animal information and milk yield and composition are

Table 3. Fitted equations in each complexity level with corresponding Akaike information criteria (AIC) and Bayesian information criteria (BIC) BW, bodyweight (kg); CH4, methane emissions (MJ/day); Diet EE, ether extract content (% DM); Diet ME, metabolisable energy content (MJ/kg DM); Diet NDF, neutral detergent fibre (% DM); DMI, dry matter intake (kg/day); MF, milk fat (%); MP, milk protein (%); MY, milk yield (kg/day). The ‘a’ or ‘b’ refer to the models that were selected with either AIC (a) or BIC (b). Level refers to complexity level in which various input variables or covariates were available for selection. The seven complexity levels used the following information: (1) dietary nutrient composition, (2) milk yield and composition, (3) Levels 1 and 2, (4) Level 3 plus dry matter intake (DMI), (5) Level 4 plus bodyweight, (6) Level 2 plus DMI, and (7) DMI only Level

Prediction equation

1a 1b 2 3a

CH4 = 27.992 + 0.054 · Diet NDF – 0.909 · Diet ME – 0.295 · Diet EE CH4 = 29.847 – 0.979 · Diet ME CH4 = 3.911 + 0.128 · MY + 1.274 · MP + 2.166 · MF CH4 = 8.967 + 0.141 · MY + 1.626 · MP + 1.919 · MF + 0.054 · Diet NDF – 0.707 · Diet ME CH4 = 11.496 + 0.134 · MY + 1.514 · MP + 1.952 · MF – 0.726 · Diet ME CH4 = –2.483 + 2.132 · MF + 0.069 · Diet NDF – 0.376 · Diet ME – 0.185 · Diet EE + 0.842 · DMI CH4 = –2.639 + 2.149 · MF + 0.068 · Diet NDF – 0.406 · Diet ME + 0.840 · DMI CH4 = –6.123 + 2.342 · MF – 0.573 · MP + 0.072 · Diet NDF – 0.351 · Diet ME – 0.232 · Diet EE + 0.784 · DMI + 0.009 · BW CH4 = –7.381 + 2.249 · MF + 0.071 · Diet NDF – 0.407 · Diet ME + 0.787 · DMI + 0.009 · BW CH4 = –5.124 + 2.300 · MF + 0.840 · DMI CH4 = 4.544 + 0.773 · DMI

3b 4a 4b 5a 5b 6 7

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1060.8 1054.4 996.3 990.9

954.6 760.3

988.2 798.0

760.5 741.7

794.1 787.9

743.1

780.8

772.1 912.8

797.3 933.7

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additional variables that predict CH4 production in cattle (e.g. Moraes et al. 2014). There were broadly three categories in the model performance. The best results were observed with Complexity levels 4, 5 and 6, which used DMI information along with diet composition, milk composition and BW (with AIC and BIC values ranging between 741 and 798). The second category of model performance was from Complexity levels 2,

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Modeling methane emission with limited input variables


coefficient of only 0.18 (Table 4). Most of the predictions were close to the mean, mainly because of lack of feed-intake information in the model covariates. The plot of residuals showed that, in Level 1 models, the discrepancy in predictions ranged from –10 to +10 MJ/day, which is ~50% of the observed mean (Fig. 2). Similarly, models in Levels 2 and 3 also showed greater residuals (Models 2, 3a and 3b; Fig. 2) due to lack of intake information. The plot of residuals also showed that the errors were concentrated along the mean with a wide range (Fig. 2). Models developed on the basis of some intake parameter, either DMI or milk yield (which is highly correlated with DMI), had a better predictive power. Most of the predictions by Levels 4, 5 and 6 models lie along the line of unity close to observed values (Model 4a,b, 5a,b, 6; Fig. 1). Model 4b, in particular, had a correlation coefficient of 0.72, whereas Models 4a, 5 and 6 had 0.71. Consistently, residual plots for these models were spread close to the zero line, with homogenous dispersion (Fig. 2). The model using only DMI information (Model 7; Table 3) was intermediate in its ability to predict CH4 production, with a tendency for under-prediction (Fig. 1). This is possibly because other variables are required to explain CH4 production, particularly fibre and lipid contents of the diet. The increase of CH4 emission with increasing dietary fibre is well established as fibrous diets promote higher acetate, resulting in more hydrogen and, thus, more CH4 production (Moe and Tyrrell 1979). Dry matter intake was the major driver of CH4 emission prediction among the models developed. However, it may be difficult and costly to measure DMI for individual cows at a commercial dairy facility. Instead, the model that uses milk yield and components might be more preferable in the absence of feed intake information. In agreement with Moraes et al. (2014), milk fat content was selected as a covariate to predict enteric CH4 emission. A reduction of 141 points in AIC was obtained in the equation that included MF and DMI (Model 6), compared with only DMI (Model 7, Table 3), indicating a better

fit and prediction power. Earlier studies have also correlated CH4 production from dairy cows with parameters that contain milk composition measurements (Holter and Young 1992). Milk fat and composition are influenced by diet composition and rumen fermentation characteristics (Bauman and Griinari 2003). Short chain fatty acids are the major lipogenic precursors in mammary gland during lactation (Vernon et al. 2001); therefore, a positive relationship between enteric CH4 and milk fatty acids was established by Chilliard et al. (2009). Furthermore, van Lingen et al. (2014) reported that milk fatty acid profile has a reasonable potential for predicting CH4 yield per unit of milk. Although a relatively small gain in prediction accuracy was achieved when BW was included in the model development (4b vs 5b; Table 3), the inclusion of BW has been reported to be an important variable in predicting CH4 emissions (Yan et al. 2006; Moraes et al. 2014). The difference in CH4 emission between animals with a different BW has been related to the amount of DMI consumed (e.g. Moate et al. 2011). Moraes et al. (2014) suggested that the positive relationship of BW with CH4 emission was due to increased gut capacity and ruminal kinetics. Two equations were fitted at Level 4 (4a, b; Table 3) for which emissions where predicted using diet composition, milk composition and DMI. The only difference between these two models was the inclusion of dietary EE in Model 4a. In contrast to Moraes et al. (2014), who observed a better prediction with EE in the model, our analysis did not show any benefit, probably because the data had a small standard deviation in diet EE (Table 1). It is generally accepted that inclusion of lipids in the diet has a depressing effect on CH4 emissions (e.g. Grainger and Beauchemin 2011; Broucek 2014). Eugène et al. (2008) reported a decrease of 9% of enteric CH4 production in dairy cows for every percentage unit increase in lipid content of the diet. The impact of lipids may have been indirectly captured through the ME parameter (Models 3b, 4a) because lipids increase the ME content, which had a negative relationship with CH4 emissions.

Table 4. Mean square prediction error decomposition and further model-evaluation statistics The mean square prediction error (MSPE) was estimated with the independent evaluation dataset (MJ/day)2. ECT, ER and ED are the decomposition of the mean square prediction error (MSPE) and expressed as % of MSPE. ECT is the error of central tendency (mean bias), ER is the error due to regression (slope bias) and ED is the error due to disturbance. RMSPE% is the root mean square prediction error as a % of mean observed CH4. RSR is the RMSPE divided by the standard deviation of observed values. CCC is the concordance correlation coefficient and is the product of a bias correction factor (Cb) and the Pearson correlation coefficient (R). NSE is the Nash–Sutcliffe efficiency. Asterisk indicates that the slope bias is statically significantly different from zero (P < 0.05) Model 1a 1b 2 3a 3b 4a 4b 5a 5b 6 7

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26.1 24.9 20.3 19.7 19.6 13.4 12.8 13.5 12.7 13.4 18.9

18.4 15.3 6.8 5.6 7.3 17.1 12.7 17.8 12.1 15.8 31.1

0.3 0.2 2.7* 1.5 3.4* 2.9* 4.1* 2.5* 3.5* 3.9* 1.6*

81.3 84.6 90.5 92.9 89.2 80.0 83.2 79.7 84.5 80.3 67.3

1.09 1.06 0.96 0.94 0.94 0.78 0.76 0.78 0.76 0.78 0.93

0.07 0.05 0.18 0.21 0.21 0.58 0.58 0.58 0.59 0.57 0.44

23.9 23.3 21.0 20.7 20.7 17.1 16.7 17.1 16.6 17.0 20.3

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0.18 0.20 0.40 0.41 0.45 0.72 0.72 0.71 0.71 0.71 0.65

–0.19 –0.14 0.07 0.10 0.10 0.39 0.42 0.39 0.42 0.39 0.14

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Model evaluation Ellis et al. (2010) suggested to include multiple statistics such as CCC and MSPE in evaluating prediction equations for a comprehensive assessment of models. In the present study, the dimensionless statistics CCC and NSE were used, along with RMSPE, to gain more insight into model performance (e.g. Tedeschi 2006; Moriasi et al. 2007; Lin 1989). Overall, CCC and NSE were more sensitive to changes in systematic bias

than was RMSPE. Additionally, NSE is used to benchmark model performance by comparing model performance with variability of the observed dataset (Moriasi et al. 2007). Models with RMSPE% lower than the CV of the data are generally considered to have good performance (Moriasi et al. 2007). For example, the CV of the present data used for evaluation was 21.8%, so models with RMSPE% of 67%) was due to random sources (Table 4). In agreement with model development process, models that included DMI as a covariate predicted CH4 emissions more accurately, as indicated by smaller RMSPE% (16.6–20.3% vs 21.0–23.9%) and RSR (0.76–0.93 vs 0.94–1.09), a larger CCC (0.44–0.59 vs 0.07–0.21) and NSE (0.14–0.39 vs –0.19–0.10). All models had a small systematic slope bias, ranging from 0.2% to 4.1% of MSPE. Mean biases were observed for most models, with the lowest being for Model 3a and the greatest for Model 7 that had used only DMI (Table 4). Model 7 had the greatest RMSPE% (20.3%) and RSR (0.93) among those using DMI in the model. Adding milk fat content (Model 6) reduced the mean bias by about half (15.8% vs 31.1%) and RMSPE% by 3 percentage points (Table 4). Model 5b that used most information available in the dataset (except EE) performed best with the lowest RMSPE% (16.6%) and RSR (0.76), and with the highest CCC (0.59) and NSE (0.42) statistics. However, a model including all those factors except BW (Model 4b) performed just as well as indicated by similar RMSPE% (16.7%), RSR (0.76), CCC (0.58) and NSE (0.42) values. Addition of dietary EE content or milk protein concentration in the presence of the other factors (Models 4a and 5a) did not improve the error or dimensionless indices (Table 4). Models using only dietary nutrient composition (Models 1a, b) showed poor performance, indicated particularly by the negative NSE values (Table 4). These models had considerable mean bias (15.3–18.4% of MSPE). A model including only milk yield and composition performed better than those based on only diet composition (Model 2), as indicated by smaller mean and slope biases, which together represented ~9% of total bias. Adding dietary ME content besides milk yield and composition (Model 3a) improved model performance (CCC = 0.21 vs 0.18 and NSE = 0.10 vs 0.07) owing to reduced mean (5.6 vs 6.8) and slope (1.5 vs 2.7) bias. However, adding dietary NDF content (Model 3b) did not improve error indices (Table 4). Better performances of models with DMI are consistent with DMI being the primary driver for CH4 production in the rumen and hindgut. The large mean bias associated with Model 7 that relied only on DMI could partly be a result of regional differences in CH4 intensities of the evaluating database. About 73% of the CH4 measurements used for model evaluation were from cows in Europe, Australia, and New Zealand. Cows in these regions had generally greater mean CH4 intensities than did cows in North America (21.5 vs 19.2 g/kg of DMI in the present data, respectively). The data used for model development were entirely from North America; therefore, a mean bias of underprediction may be expected. Differences in the CH4 intensity were related primarily to diet composition, particularly forage content, which was lower in cows in North America than in those in other regions (52 vs 71% of DM, respectively). Therefore, NDF and ME contents, and milk fat concentration directly related to dietary forage content may explain the discrepancy in CH4 intensity. Consistently, inclusion of those factors in a model with DMI (Models 4b and 5b vs 7) reduced the underprediction mean bias by more than half and improved RMSPE%based prediction accuracy by 18%.


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The better performance of models with milk yield in the absence of DMI indicates that milk yield was able to capture considerable amount of variability in CH4 emissions due to its intrinsic positive association with DMI. Inclusion of factors such as dietary ME content (Model 3b) improved prediction accuracy. Such models may allow evaluation of management practices on mitigating GHG emissions from dairy cows with uncertainty of ~21%. Conclusions Models that include DMI performed best when challenged with independent literature data. However, DMI information may not be readily available in commercial dairy farms; therefore, models that use information on milk yield and composition may be used instead, albeit with greater uncertainty. Increased level of complexity of the models did not always reduce prediction error. Adding dietary nutrient composition such as dietary NDF (Model 3a) to milk yield and composition only based model (Model 2) marginally reduced uncertainty of models (RMSPE% 20.7 vs 21, respectively). In general, best CH4 emission predictions were made with models that include intake measurement, and animal and dietary variables as covariates (RMSPE% = 16.6%); however, without animal variable (BW) and only using milk fat content, dietary NDF, ME and DMI had almost as good predictive ability (RMSPE% = 16.7%). Models proposed in the present study that use information available from DHIA records may be used as relatively easy and practical tools for predicting CH4 emissions in commercial dairy facilities compared with those models requiring inputs not routinely measured in dairy operations. Acknowledgements The project was supported by the Innovation Center for US Dairy (Chicago, IL, USA) and the Sesnon Endowed Chairs Program (UC Davis, CA, USA).

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