Adjustment of Animal Growth Rate Responses in Repeat Measurement Grazing Trials
Identifiers and Pagination:Year: 2011
First Page: 46
Last Page: 55
Publisher Id: TOASJ-5-46
Article History:Received Date: 15/6/2011
Revision Received Date: 15/09/2011
Acceptance Date: 28/09/2011
Electronic publication date: 12/12/2011
Collection year: 2011
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
In animal-response grazing trials there are sources of uncertainty in one-period one-off measurements, which as partial factorisation over time, become traceable and quantifiable sources of variation in repeat measurement trials. This is illustrated for a trial comparing sheep and goat live-weight gains under two stocking rates on mixed species pastures established by three contrasting sowing methods. It used variable plot size to give uniform animal numbers and tracked changes in individual animal performance and pasture growth in different periods. It was repeated on the same plots and animals over 17 grazing cycles. The variation explainable was greater with growth rates expressed as percent live-weight increase per day, than as weight or metabolic weight increase per day. The base data sets were adjusted for specific weighing-day effects of estimated gut-fill using moving averages, and for calibration for individual animal effects by genotype/environment analysis. Collectively these significantly increased the proportion explainable by 3.1-3.8% in variance analyses using qualitative treatment variables, and 2.7-3.7% in response function analyses relative to measured climate, pasture, plot and collective animal covariates. Simulation studies, to allow for variability in the independent variables as well as the dependent variables, indicated that the proportion explainable could increase by a further 0-1.2% and 1.1-1.9% respectively for the variance or response function approaches.