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LiGAPS-Beef, a mechanistic model to explore potential and feed-limited beef production 2: sensitivity analysis and evaluation of sub-models

Published online by Cambridge University Press:  12 July 2018

A. van der Linden Open the ORCID record for A. van der Linden [Opens in a new window] ,
G. W. J. van de Ven ,
S. J. Oosting Open the ORCID record for S. J. Oosting [Opens in a new window] ,
M. K. van Ittersum  and
I. J. M. de Boer
A. van der Linden*
Affiliation:
Animal Production Systems Group, Wageningen University & Research, P.O. Box 338, 6700 AH Wageningen, The Netherlands Plant Production Systems Group, Wageningen University & Research, P.O. Box 430, 6700 AK Wageningen, The Netherlands
G. W. J. van de Ven
Affiliation:
Plant Production Systems Group, Wageningen University & Research, P.O. Box 430, 6700 AK Wageningen, The Netherlands
S. J. Oosting
Affiliation:
Animal Production Systems Group, Wageningen University & Research, P.O. Box 338, 6700 AH Wageningen, The Netherlands
M. K. van Ittersum
Affiliation:
Plant Production Systems Group, Wageningen University & Research, P.O. Box 430, 6700 AK Wageningen, The Netherlands
I. J. M. de Boer
Affiliation:
Animal Production Systems Group, Wageningen University & Research, P.O. Box 338, 6700 AH Wageningen, The Netherlands
*
†E-mail: aart.vanderlinden@wur.nl
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Abstract

The model LiGAPS-Beef (Livestock simulator for Generic analysis of Animal Production Systems – Beef cattle) has been developed to assess potential and feed-limited growth and production of beef cattle in different areas of the world and to identify the processes responsible for the yield gap. Sensitivity analysis and evaluation of model results with experimental data are important steps after model development. The first aim of this paper, therefore, is to identify which parameters affect the output of LiGAPS-Beef most by conducting sensitivity analyses. The second aim is to evaluate the accuracy of the thermoregulation sub-model and the feed intake and digestion sub-model with experimental data. Sensitivity analysis was conducted using a one-at-a-time approach. The upper critical temperature (UCT) simulated with the thermoregulation sub-model was most affected by the body core temperature and parameters affecting latent heat release from the skin. The lower critical temperature (LCT) and UCT were considerably affected by weather variables, especially ambient temperature and wind speed. Sensitivity analysis for the feed intake and digestion sub-model showed that the digested protein per kg feed intake was affected to a larger extent than the metabolisable energy (ME) content. Sensitivity analysis for LiGAPS-Beef was conducted for ¾ Brahman×¼ Shorthorn cattle in Australia and Hereford cattle in Uruguay. Body core temperature, conversion of digestible energy to ME, net energy requirements for maintenance, and several parameters associated with heat release affected feed efficiency at the herd level most. Sensitivity analyses have contributed, therefore, to insight which parameters are to be investigated in more detail when applying LiGAPS-Beef. Model evaluation was conducted by comparing model simulations with independent data from experiments. Measured heat production in experiments corresponded fairly well to the heat production simulated with the thermoregulation sub-model. Measured ME contents from two data sets corresponded well to the ME contents simulated with the feed intake and digestion sub-model. The relative mean absolute errors were 9.3% and 6.4% of the measured ME contents for the two data sets. In conclusion, model evaluation indicates the thermoregulation sub-model can deal with a wide range of weather conditions, and the feed intake and digestion sub-model with a variety of feeds, which corresponds to the aim of LiGAPS-Beef to simulate cattle in different beef production systems across the world.

Keywords

beef cattlemechanistic modellingproduction ecologysensitivity analysisyield gap
Type
Research Article
Information
animal , Volume 13 , Issue 4 , April 2019 , pp. 856 - 867
Copyright
© The Animal Consortium 2018 

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