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

Abstract

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.

Dynamic genome-based metabolic modeling of the predominant cellulolytic rumen bacterium Fibrobacter succinogenes S85

“…The mathematical modeling of the rumen ecosystem is a useful endeavor to provide tools for improving rumen function. Current kinetic rumen models do not consider genomic information ( 31 , 65 , 73 , 74 ). GEMs are a promising tool to fill this lacking gap and allow a better understanding of the rumen systemic functionality ( 75 ) and the individual bacteria metabolism ( 76 ).…”

Dynamic genome-based metabolic modeling of the predominant cellulolytic rumen bacteriumFibrobacter succinogenesS85

“…The mathematical modelling of the rumen ecosystem is a useful endeavor to provide tools for improving rumen function. Current kinetic rumen models do not consider genomic information (31, 59, 69, 70). GEMs are a promising tool to fill this lacking gap and allowing a better understanding of the rumen systemic functionality (71) and the individual bacteria metabolism (72).…”

Methane Emission: Strategies to Reduce Global Warming in Relation to Animal Husbandry Units with Emphasis on Ruminants

“…Additionally, new technologies are being created to measure the amount of CH 4 produced during feeding at robotic milking stations or pasture feeders, or by using rumen sensors [44] to monitor CH 4 concentrations hourly [45]. Also, researchers have used mathematical and experimental models to predict CH 4 production from dairy cows in some studies, and the correlation between digestibility and CH 4 production in their findings has led to a novel strategy for solving this issue [46]. However, the comparison of CH 4 production data obtained from in vitro and in vivo methods has not had a high correlation.…”

“…Over the past 50 years, several studies have been conducted on the effects of some of these compounds [46,99]. For instance, among the most well-known and active compounds that have demonstrated CH 4 -reducing properties in very low concentrations are bromoform and chloroform (cows produce 40% less CH 4 when fed 1.5 mL per day, and the decreases lasts until the 42nd day) [125].…”