TY  - JOUR
T1  - Application of Models to Predict Methane Emissions by Dairy Cattle
AU - Seo, Seongwon 
JO  - Journal of Animal and Veterinary Advances
VL  - 11
IS  - 17
SP  - 3198
EP  - 3201
PY  - 2012
DA  - 2001/08/19
SN  - 1680-5593
DO  - javaa.2012.3198.3201
UR  - https://makhillpublications.co/view-article.php?doi=javaa.2012.3198.3201
KW  - modeling
KW  -cattle
KW  -Methane
KW  -breed
KW  -feed stuff
KW  -climate
AB  - As environmental concerns grow globally, many countries are 
  elaborating upon a plan to reduce greenhouse gas emissions which can result 
  in global climate change. Cattle production is one of the recognized sectors 
  in agriculture that produce a large amount of methane from enteric fermentation, 
  one of the major greenhouse gases being targeted for reduction. Enteric methane 
  production by cattle varies between 2-12% of gross energy intake and a recent 
  statistics showed that it contributes >20% of the total methane emissions 
  in the US dairy cattle is known to produce more enteric methane than beef cattle 
  due to a relatively large amount of forage in the diet and a high level of intake. 
  Therefore, reducing methane emissions by dairy cattle has become one of the 
  most important areas of research in the modern agriculture and accurate quantification 
  of methane emissions by dairy cattle is critical. Direct measurement of methane 
  emissions by dairy herds requires a large amount of time, labor and money and 
  it cannot be practically used to estimate methane emissions from each farm. 
  Application of modeling to predict methane emissions thus could be an alternative 
  and better way of quantifying methane emissions from dairy herds. A common modeling 
  approach is to develop a methane emission model empirically which is heavily 
  dependent on statistical analysis on available data. An Empirical Model is very 
  useful and its predictability may be satisfactory as long as it is built from 
  sufficient and appropriate accumulated data. Interpolation beyond the range 
  of data should be avoided. Many published models can be classified as Empirical 
  Models. A Mechanistic Model, on the contrary, emphasizes more on the underlying 
  mechanism. Experimental data are only used for parameterization of the variables 
  and evaluation of the model. In many cases a Mechanistic Model requires more 
  variabes to be estimated than an Empirical Model which may limit its versatile 
  use. One important feature of a Mechanistic Model is that unlike an Empirical 
  Model it can be easily modified and applied to different conditions (climate, 
  feedstuff, breed and management) without changing the structure of the model. 
  A relatively small number of Mechanistic Models have been published. Each type 
  of models has its pros and cons and one should thus be cautious when choosing 
  a model for a specific condition. According to the model comparisons in literature, 
  the overall predictability of the published models is still low and needs to 
  be improved with further research. More accurate predictions of methane emission 
  by dairy cattle require the development of a more mechanistic model that accounts 
  for more of the biologically important variables that affects methane emissions 
  and this model should be able to integrate all of the farm-specific components. 
  It can be concluded that modeling is very useful to predict the methane emissions 
  by dairy cattle and it is also helpful to find the most appropriate mitigation 
  strategy for a specific condition.
ER  -