Linked Life Data

19854989

Source:http://linkedlifedata.com/resource/pubmed/id/19854989

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PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2010-2-26
pubmed:abstractText
Accurate voluntary feed intake (VFI) prediction is critical to the productivity and profitability of ruminant livestock production systems. Simple empirical models have been used to predict VFI for decades, but they are inflexible, restrictive, and poorly accommodate many feeding conditions, such as those of developing countries. We have developed a mechanistic model to predict VFI over a range of forage diets (low- and high-quality grasses and legumes) by wild and domestic ruminants of varying physiological states (growth, lactation, gestation, nonproductive). Based on chemical reactor theory, the model represents the reticulorumen, large intestine, and blood plasma as continuous stirred-tank reactors and the small intestine as a plug flow reactor. Predicted VFI is that which 1) fulfills an empirical relationship between chemostatic and distention feedback observed in the literature, and 2) leads to steady-state conditions. Agreement between observed and actual VFI was great (generally R(2) >0.9, root mean square prediction error <1.4 kg/d, CV <25%). Root mean square prediction error for our model was only 67% that of the Beef NRC (2000) model, the leading empirical prediction system for cattle. Together, these results demonstrate that our model can predict ruminant VFI more broadly and accurately than prior methods and, by consequence, serve as a crucial tool to ruminant livestock production systems.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Mar
pubmed:issn
1525-3163
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
88
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1108-24
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
A mechanistic model for predicting intake of forage diets by ruminants.
pubmed:affiliation
Division of Animal Sciences, University of Missouri, Columbia 65211, USA.
pubmed:publicationType
Journal Article