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|Title:||Optimising natural 13C marker based pasture intake estimates for cattle using a genetic algorithm approach||Contributor(s):||Cottle, David (author)||Publication Date:||2017||DOI:||10.1016/j.livsci.2017.01.004||Handle Link:||https://hdl.handle.net/1959.11/19956||Abstract:||The sensitivity of pasture intake estimates obtained from using 13C as a marker to differences in assumed diet composition and 13C diet-faecal discrimination was studied. Angus stud heifers grazed a silver grass, perennial ryegrass, bent grass and yorkshire fog pasture. The individual heifers were fed controlled and monitored daily amounts of maize and faecal samples were taken and analysed to estimate dry matter intake (DMI) and DMI/liveweight (LW). Daily methane production was also measured. Monte Carlo simulations using a uniform distribution of diet composition and an extreme value distribution for the 13C diet-faecal discrimination found that the DMI/LW ratio was twice as sensitive to assumed diet composition (and hence pasture 13C) than to the diet-faeces discrimination factor. DMI estimates would be useful for ranking animals on DMI intake alone as the rank correlations for DMI estimated using different input assumptions were high. A genetic algorithm approach was helpful as a means of determining the optimum diet selection or plant proportions to use for each animal and the diet-faecal discrimination to use when uncertainty exists as to their true values, which may often be the case. Some animals had non-credible DMI/LW values when using standard calculation methods. There are no definitive goals or constraints to use but careful choice of the range of individual DMI/LW values set as a hard constraint enabled credible DMI/LW values for all animals to be obtained when using a genetic algorithm approach.||Publication Type:||Journal Article||Source of Publication:||Livestock Science, v.197, p. 53-60||Publisher:||Elsevier BV||Place of Publication:||The Netherlands||ISSN:||1871-1413||Field of Research (FoR) 2008:||070204 Animal Nutrition||Field of Research (FoR) 2020:||300303 Animal nutrition||Socio-Economic Objective (SEO) 2008:||830301 Beef Cattle||Socio-Economic Objective (SEO) 2020:||100401 Beef cattle||Peer Reviewed:||Yes||HERDC Category Description:||C1 Refereed Article in a Scholarly Journal||Statistics to Oct 2018:||Visitors: 23|
|Appears in Collections:||Journal Article|
School of Environmental and Rural Science
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