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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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References

Bellocchi, G, Rivington, M, Donatelli, M and Matthews, K 2010. Validation of biophysical models: issues and methodologies. A review. Agronomy for Sustainable Development 30, 109–130.Google Scholar
Bibby, J and Toutenburg, H 1977. Prediction and improved estimation in linear models. John Wiley & Sons, London, UK.Google Scholar
Blaxter, KL and Wainman, FW 1964. The effect of increased air movement on heat production and emission of steers. Journal of Agricultural Science 62, 207–214.Google Scholar
Chilibroste, P, Aguilar, C and Garcia, F 1997. Nutritional evaluation of diets. Simulation model of digestion and passage of nutrients through the rumen-reticulum. Animal Feed Science and Technology 68, 259–275.Google Scholar
Commonwealth Scientific and Industrial Research Organisation (CSIRO) 2007. Nutrient requirements of domesticated ruminants. CSIRO Publishing, Collingwood, Australia.Google Scholar
Hamby, DM 1994. A review of techniques for parameter sensitivity analysis of environmental-models. Environmental Monitoring and Assessment 32, 135–154.Google Scholar
Holmes, CW and McLean, NA 1975. Effects of air temperature and air movement on heat produced by young Friesian and Jersey calves, with some measurements of effects of artificial rain. New Zealand Journal of Agricultural Research 18, 277–284.Google Scholar
Jones, JW, Hoogenboom, G, Porter, CH, Boote, KJ, Batchelor, WD, Hunt, LA, Wilkens, PW, Singh, U, Gijsman, AJ and Ritchie, JT 2003. The DSSAT cropping system model. European Journal of Agronomy 18, 235–265.Google Scholar
Keating, BA, Carberry, PS, Hammer, GL, Probert, ME, Robertson, MJ, Holzworth, D, Huth, NI, Hargreaves, JNG, Meinke, H, Hochman, Z, McLean, G, Verburg, K, Snow, V, Dimes, JP, Silburn, M, Wang, E, Brown, S, Bristow, KL, Asseng, S, Chapman, S, McCown, RL, Freebairn, DM and Smith, CJ 2003. An overview of APSIM, a model designed for farming systems simulation. European Journal of Agronomy 18, 267–288.Google Scholar
Kolver, E 2000. Nutrition guidelines for the high producing dairy cow. Proceedings of the Ruakura farmers conference 52, 17–28.Google Scholar
Lobell, DB, Cassman, KG and Field, CB 2009. Crop yield gaps: their importance, magnitudes, and causes. Annual Review of Environment and Resources 34, 179–204.Google Scholar
Lucas, HL, Smart, WWG, Cipolloni, MA and Gross, HD 1961. Relations between digestibility and composition of feeds and foods, S-45 Report. North Carolina State College, Raleigh, NC, USA.Google Scholar
Mader, TL, Davis, MS and Brown-Brandl, T 2006. Environmental factors influencing heat stress in feedlot cattle. Journal of Animal Science 84, 712–719.Google Scholar
Ministry of Agriculture, Fisheries & Food (MAFF) 1986. Feed composition – UK tables of feed composition and nutritive value for ruminants. Chalcombe Publications, Marlow, UK.Google Scholar
McGovern, RE and Bruce, JM 2000. A model of the thermal balance for cattle in hot conditions. Journal of Agricultural Engineering Research 77, 81–92.Google Scholar
National Research Council (NRC) 2000. Nutrient requirements of beef cattle, 7th revised edition. National Academy Press, Washington, DC, USA.Google Scholar
Parkhurst, AM 2010. Model for understanding thermal hysteresis during heat stress: a matter of direction. International Journal of Biometeorology 54, 637–645.Google Scholar
Pianosi, F, Beven, K, Freer, J, Hall, JW, Rougier, J, Stephenson, DB and Wagener, T 2016. Sensitivity analysis of environmental models: a systematic review with practical workflow. Environmental Modelling & Software 79, 214–232.Google Scholar
Prisley, SP and Mortimer, MJ 2004. A synthesis of literature on evaluation of models for policy applications, with implications for forest carbon accounting. Forest Ecology and Management 198, 89–103.Google Scholar
Saltelli, A and Annoni, P 2010. How to avoid a perfunctory sensitivity analysis. Environmental Modelling & Software 25, 1508–1517.Google Scholar
Thompson, VA, Barioni, LG, Oltjen, JW, Rumsey, T, Fadel, JG and Sainz, RD 2011. Development of a heat balance model for cattle under hot conditions. In Modelling nutrient digestion and utilization in farm animals (ed. D Sauvant, J Van Milgen, P Faverdin and N Friggens), pp. 243–251. Wageningen Academic Publishers, Wageningen, The Netherlands.Google Scholar
Thornley, JHM and France, J 2007. Mathematical models in agriculture: quantitative methods for the plant, animal and ecological sciences, 2nd edition. CABI, Wallingford, UK.Google Scholar
Turnpenny, JR, McArthur, AJ, Clark, JA and Wathes, CM 2000a. Thermal balance of livestock 1. A parsimonious model. Agricultural and Forest Meteorology 101, 15–27.Google Scholar
Turnpenny, JR, Wathes, CM, Clark, JA and McArthur, AJ 2000b. Thermal balance of livestock 2. Applications of a parsimonious model. Agricultural and Forest Meteorology 101, 29–52.Google Scholar
Van de Ven, GWJ, de Ridder, N, van Keulen, H and van Ittersum, MK 2003. Concepts in production ecology for analysis and design of animal and plant-animal production systems. Agricultural Systems 76, 507–525.Google Scholar
Van der Linden, A, Oosting, SJ, Van de Ven, GWJ, De Boer, IJM and Van Ittersum, MK 2015. A framework for quantitative analysis of livestock systems using theoretical concepts of production ecology. Agricultural Systems 139, 100–109.Google Scholar
Van der Linden, A, Van de Ven, GWJ, Oosting, SJ, Van Ittersum, MK and De Boer, IJM 2018a. LiGAPS-Beef, a mechanistic model to explore potential and feed-limited beef production 1. Model description and illustration. Animal, doi.org/10.1017/S1751731118001726.Google Scholar
Van der Linden, A, Van de Ven, GWJ, Oosting, SJ, Van Ittersum, MK and De Boer, IJM 2018b. LiGAPS-Beef, a mechanistic model to explore potential and feed limited beef production 3. Model evaluation. Animal (forthcoming).Google Scholar
Van Ittersum, MK, Cassman, KG, Grassini, P, Wolf, J, Tittonell, P and Hochman, Z 2013. Yield gap analysis with local to global relevance – a review. Field Crops Research 143, 4–17.Google Scholar
Van Soest, PJ 1994. Nutritional ecology of the ruminant, 2nd edition. Cornell University Press, Ithaca, NY, USA.Google Scholar
Zuidema, PA, Leffelaar, PA, Gerritsma, W, Mommer, L and Anten, NPR 2005. A physiological production model for cocoa (Theobroma cacao): model presentation, validation and application. Agricultural Systems 84, 195–225.Google Scholar