@article{MAKHILLJAVA201211173708,
    title = {Prediction of Ruminal Methane Production from Cattle},
    journal = {Journal of Animal and Veterinary Advances},
    volume = {11},
    number = {17},
    pages = {3228-3233},
    year = {2012},
    issn = {1680-5593},
    doi = {javaa.2012.3228.3233},
    url = {https://makhillpublications.co/view-article.php?issn=1680-5593&doi=javaa.2012.3228.3233},
    author = {Seongwon,Sang-Moon,Jin-Suk,Sang-Cheol and},
    keywords = {Methane,modeling,cattle,animal,green house,bulls},
    abstract = {Methane, one of the major greenhouse gases is produced primarily 
  from cattle among livestock. Many researches have been conducted to reduce methane 
  production and also to develop methods and/or equations to predict methane production 
  in cattle. The objectives of this study were thus to construct a database containing 
  experimental observations of methane production from cattle and to develop equations 
  that predict methane production by cattle accurately. The database developed 
  in this study contains experimental observations from the research articles 
  published in the Journal of Dairy Science, Journal of Animal Science, Animal 
  Feed Science and Technology, Canadian Journal of Animal Science, International 
  Congress Series and Journal of Nutrition from 1964 till 2009. A total of 350 
  treatment means from 75 studies were obtained from the scientific journal articles 
  that were found by searching for with methane and cattle as keywords. There 
  were different methods measuring methane production; a chamber system, indirect 
  respiratory hood, Sulfur hexafluoride (SF<SUB>6</SUB>) and stoichiometric calculation. 
  Only measured data were used in the subsequent analysis. Consequently the actual 
  database used for the analysis is composed of a total of 256 treatment means 
  from 57 studies. The types of animal in the database were 110 lactating dairy 
  cows, 12 non-lactating dairy cows, 47 heifers, 65 steers, 10 calves, 10 bulls 
  and 2 mixed. The mean (&plusmn;SD) methane (g day<SUP>-1</SUP>) methane (Mcal 
  day<SUP>-1</SUP>) and methane (GE%) of the data were 204.50 (&plusmn;104.22), 
  2.76 (&plusmn;1.38) and 5.56 (&plusmn;1.87), respectively. Among the variables 
  tested, DMI (kg) or NDF intake (NDFI, kg) was the most significant single variable 
  that correlates with methane production. Using a random coefficient model with 
  study as a random effect, researchers obtained -24.27 (&plusmn;17.76) + 13.93 
  (&plusmn;1.68) DMI (kg) + 0.57 (&plusmn;0.20) FpDM + 8.43 (&plusmn;4.16) NDFI 
  (kg) (n = 145, -2 Res log likelihood = 1434.9) for predicting methane production 
  (g). Using a simple linear regression, the best equation was CH<SUB>4</SUB> 
  (g) = &#150;18.53 (&plusmn;14.90) + 11.89 (&plusmn;1.50) DMI (kg) + 0.49 (&plusmn;0.18) 
  FpDM + 14.19 (&plusmn;3.77) NDFI (kg) (R<SUP>2</SUP> = 0.84, root mean square 
  error = 42.25). Although, DMI and NDFI are inherently correlated, a single variable 
  was not sufficient to explain the variations in methane production of cattle. 
  When both NDFI and DMI were present in the model statement type of animal or 
  method of methane measurement was no longer significant. The results from this 
  study suggest that methane production from cattle can be predicted accurately 
  with DMI and NDFI. More research however is needed to improve accuracy of the 
  model predictions.}
    }