Nordic dairy cow model Karoline in predicting methane emissions: 2. Model evaluation

Ramin, Mohammad; Huhtanen, Pekka

doi:10.1016/j.livsci.2015.05.008

Cited by 15 publications

(11 citation statements)

References 75 publications

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“…In a recent study in the United Kingdom, Gardiner et al (2015) analyzed the accuracy of respiration chambers and concluded that considerable errors in CH 4 measurement can occur unless appropriate validation is performed on a regular basis. Similarly, Ramin and Huhtanen (2015) found significant between-laboratory differences in the residuals of both CH 4 emissions and OM digestion in the mechanistic model Karoline predictions. Karoline is a dynamic and mechanistic model describing digestion and metabolism in dairy cows.…”

Section: Study Effectmentioning

confidence: 73%

“…The slope bias can be due to increased efficiency of microbial protein synthesis with increased DMI (Broderick et al, 2010), which was not taken into account in predicted values. Simulations with the Karoline model have in-dicated that increased efficiency of microbial protein synthesis (g of microbial N per kg of OM truly digested in the rumen) markedly contributes to decreased CH 4 yield (g/kg of DMI) with increased DMI (Ramin and Huhtanen, 2015). Including the changes in microbial protein synthesis in the prediction model would probably reduce the slope bias between observed and predicted CH 4 emissions.…”

Section: Dmimentioning

confidence: 99%

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Evaluation of a gas in vitro system for predicting methane production in vivo

Danielsson

Ramin

Bertilsson

et al. 2017

Journal of Dairy Science

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Methane production from ruminant livestock varies with the diet as a result of factors such as dry matter intake, diet composition, and digestibility. To estimate the effect of dietary composition and feed additives, CH production can be measured in vitro as a first step because large numbers of samples can be incubated and analyzed at the same time. This study evaluated a recently developed in vitro method for prediction of in vivo CH production by examining the relationship between predicted and observed CH production values. A total of 49 different diets (observations), used in previous 13 in vivo studies, were selected to include diets varying in nutrient composition. Methane production was measured in all in vivo studies by respiration chambers or the GreenFeed system (C-Lock Inc., Rapid City, SD). Overall, the in vitro system predicted CH production well (R = 0.96), but the values obtained were slightly underestimated compared with observed in vivo values (mean 399 L/d compared with 418 L/d: root mean square prediction error = 51.6 L/d or 12.3% of observed mean). Further analysis of the effect on residuals showed no significant relationship between CH production and most factors known to affect CH production such as dry matter intake, digestibility, and dietary concentrations of fat and starch. However, some factors included in the model were not well predicted by the system, with residuals negatively related to neutral detergent fiber concentration and positively related to concentrate proportion. The in vitro system can thus be useful for screening diets and evaluation of feed additives as a first step that can be best interpreted when feeding cows at maintenance level.

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Section: Study Effectmentioning

confidence: 73%

Section: Dmimentioning

confidence: 99%

Evaluation of a gas in vitro system for predicting methane production in vivo

Danielsson

Ramin

Bertilsson

et al. 2017

Journal of Dairy Science

Self Cite

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“…Whereas high-starch concentrate supplements favour amylolytic fermentation with formation of propionate, which utilises two moles of hydrogen for every mole formed in the rumen (McDonald et al, 2010). Further, concentrate-based rations are more digestible, have a greater rumen flow rate and reduced potential methane production, notwithstanding the higher protein content of many supplementary feeds which have been shown to reduce methane formation in the rumen (Ramin and Huhtanen, 2015). Second, there is often an imbalance between readily available energy and rapidly degraded N in the rumen on pasture-based diets, reflecting a relatively high total N intake.…”

Section: Reasons For Human-edible Feed Usementioning

confidence: 99%

Review: Use of human-edible animal feeds by ruminant livestock

Wilkinson

Lee

2018

Animal

123

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The drive to increase the output of animal product in some sectors of ruminant livestock production has led to greater use of feeds such as cereal grains and soyabean meal that are potentially human-edible. This trend has caused concern since, by so doing, ruminants compete not only with monogastric livestock but also with the human population for a limited global area of cultivatable land on which to produce grain crops. Reasons for using potentially human-edible feeds in ruminant diets include increased total daily energy intake, greater supply of essential amino acids and improved ruminal balance between fermentable energy and degradable protein. Soyabean meal, produced on land that has been in arable cultivation for many years can fulfil a useful role as a supplier of undegraded dietary protein in diets for high-yielding dairy cows. However, in the context of sustaining the production of high-quality foods from livestock to meet the demands of a growing human population, the use of potentially human-edible feed resources by livestock should be restricted to livestock with the highest daily nutrient requirements; that is, potentially human-edible feed inputs should be constrained to meeting requirements for energy and protein and to rectifying imbalances in nutrient supply from pastures and forage crops such as high concentrations of nitrogen (N). There is therefore a role for human-edible feeds in milk production because forage-only systems are associated with relatively low output per head and also low N use efficiency compared with systems with greater reliance on human-edible feeds. Profitability on farm is driven by control of input costs as well as product value and examples are given of low-cost bovine milk and meat production with little or no reliance on potentially human-edible feeds. In beef production, the forage-only systems currently under detailed real-time life-cycle analysis at the North Wyke Farm Platform, can sustain high levels of animal growth at low feed cost. The potential of all-forage diets should be demonstrated for a wide range of ruminant milk and meat production systems. The challenge for the future development of ruminant systems is to ensure that potentially human-edible feeds, or preferably human-inedible by-products if available locally, are used to complement pastures and forage crops strategically rather than replace them.

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“…The data set used consisted of observations of CH 4 production from 267 treatments reported in 55 respiration chambers studies. A subset of 31 papers published between 1964 and 2013 with a total of 184 observations (each one a treatment mean) has been described in more detail by Ramin and Huhtanen (2015). The additional data added were identified from a survey of literature performed using the Web of Science database in early 2016.…”

Section: Development Of Data Setmentioning

confidence: 99%

“…The Molly cow model is a mechanistic, dynamic model describing digestion and metabolism of cattle with the ability to predict animal-related factors that affect the environment, including CH 4 production (Baldwin et al, 1987;Hanigan et al, 2013). The Nordic cow model Karoline is a dynamic, mechanistic model describing digestion and metabolism in dairy cows (Danfaer et al, 2006), and its revised form (Huhtanen et al, 2015) was confirmed by Ramin and Huhtanen (2015) to be a useful tool in predicting CH 4 production in cattle.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Molly and Karoline models to predict methane production in growing and dairy cattle

Kass

Ramin

Hanigan

et al. 2022

Journal of Dairy Science

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Numerous empirical and mechanistic models predicting methane (CH 4 ) production are available. The aim of this work was to evaluate the Molly cow model and the Nordic cow model Karoline in predicting CH 4 production in cattle using a data set consisting of 267 treatment means from 55 respiration chamber studies. The dietary and animal characteristics used for the model evaluation represent the range of diets fed to dairy and growing cattle. Feedlot diets and diets containing additives mitigating CH 4 production were not included in the data set. The relationships between observed and predicted CH 4 (pCH 4 ) were assessed by regression analysis using fixed and mixed model analysis. Residual analysis was conducted to evaluate which dietary factors were related to prediction errors. The fixed model analysis showed that the Molly predictions were related to the observed data (± standard error) as CH 4 (g/d) = 0.94 (±0.022) × pCH 4 (g/d) + 31 (±6.9) [root mean squared prediction error (RMSPE) = 45.0 g/d (14.9% of observed mean), concordance correlation coefficient (CCC) = 0.925]. The corresponding equation for the Karoline model was CH 4 (g/d) = CH 4 (g/d) = 0.98 (±0.019) × pCH 4 (g/d) + 7.0 (±6.0) [RMSPE = 35.0 g/d (11.6%), CCC = 0.953]. Proportions of mean squared prediction error attributable to mean and linear bias and random error were 10.6, 2.2, and 87.2% for the Molly model, and 1.3, 0.3, and 98.6% for the Karoline model, respectively. Mean and linear bias were significant for the Molly model but not for the Karoline model. With the mixed model regression analysis RM-SPE adjusted for random study effects were 10.9 and 7.9% for the Molly model and the Karoline model, re-spectively. The residuals of CH 4 predictions were more strongly related to factors associated with CH 4 production (feeding level, digestibility, fat concentrations) with the Molly model compared with the Karoline model. Especially large mean (underprediction) and linear bias (overprediction of low digestibility diets relative to high digestibility diets) contributed to the prediction error of CH 4 yield with the Molly model. It was concluded that both models could be used for prediction of CH 4 production in cattle, but Karoline was more accurate and precise based on smaller RMSPE, mean bias, and slope bias, and greater CCC. The importance of accurate input data of key variables affecting diet digestibility is emphasized.

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Nordic dairy cow model Karoline in predicting methane emissions: 2. Model evaluation

Cited by 15 publications

References 75 publications

Evaluation of a gas in vitro system for predicting methane production in vivo

Evaluation of a gas in vitro system for predicting methane production in vivo

Review: Use of human-edible animal feeds by ruminant livestock

Comparison of Molly and Karoline models to predict methane production in growing and dairy cattle

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