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LiGAPS-Beef, a mechanistic model to explore potential and feed-limited beef production 3: model evaluation

Published online by Cambridge University Press:  16 October 2018

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
*
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Abstract

LiGAPS-Beef (Livestock simulator for Generic analysis of Animal Production Systems – Beef cattle) is a generic, mechanistic model designed to quantify potential and feed-limited growth, which provides insight in the biophysical scope to increase beef production (i.e. yield gap). Furthermore, it enables identification of the bio-physical factors that define and limit growth, which provides insight in management strategies to mitigate yield gaps. The aim of this paper, third in a series of three, is to evaluate the performance of LiGAPS-Beef with independent experimental data. After model calibration, independent data were used from six experiments in Australia, one in Uruguay and one in the Netherlands. Experiments represented three cattle breeds, and a wide range of climates, feeding strategies and cattle growth rates. The mean difference between simulated and measured average daily gains (ADGs) was 137 g/day across all experiments, which equals 20.1% of the measured ADGs. The root mean square error was 170 g/day, which equals 25.0% of the measured ADGs. LiGAPS-Beef successfully simulated the factors that defined and limited growth during the experiments on a daily basis (genotype, heat stress, digestion capacity, energy deficiency and protein deficiency). The simulated factors complied well to the reported occurrence of heat stress, energy deficiency and protein deficiency at specific periods during the experiments. We conclude that the level of accuracy of LiGAPS-Beef is acceptable, and provides a good basis for acquiring insight in the potential and feed-limited production of cattle in different beef production systems across the world. Furthermore, its capacity to identify factors that define or limit growth and production provides scope to use the model for yield gap analysis.

Type
Research Article
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, 109130.Google Scholar
Bennett, ND, Croke, BFW, Guariso, G, Guillaume, JHA, Hamilton, SH, Jakeman, AJ, Marsili-Libelli, S, Newham, LTH, Norton, JP, Perrin, C, Pierce, SA, Robson, B, Seppelt, R, Voinov, AA, Fath, BD and Andreassian, V 2013. Characterising performance of environmental models. Environmental Modelling & Software 40, 120.Google Scholar
Beretta, V, Simeone, A, Elizalde, JC and Baldi, F 2006. Performance of growing cattle grazing moderate quality legume-grass temperate pastures when offered varying forage allowance with or without grain supplementation. Australian Journal of Experimental Agriculture 46, 793797.Google Scholar
Bibby, J and Toutenburg, H 1977. Prediction and improved estimation in linear models. John Wiley & Sons, London, UK.Google Scholar
Dixon, RM and Coates, DB 2008. Diet quality and liveweight gain of steers grazing Leucaena-grass pasture estimated with faecal near infrared reflectance spectroscopy (F. NIRS). Australian Journal of Experimental Agriculture 48, 835842.Google Scholar
Evans, TR and Hacker, JB 1992. An evaluation of the production potential of 6 tropical grasses under grazing. 2. Assessment of quality using variable stocking rates. Australian Journal of Experimental Agriculture 32, 2937.Google Scholar
Freer, M, Moore, AD and Donnelly, JR 1997. GRAZPLAN: decision support systems for Australian grazing enterprises. 2. The animal biology model for feed intake, production and reproduction and the GrazFeed DSS. Agricultural Systems 54, 77126.Google Scholar
Frisch, JE and Hunter, RA 1990a. Interaction of compudose 200 and resistance to parasites on growth of steers of 2 cattle breeds. Journal of Agricultural Science 115, 259264.Google Scholar
Frisch, JE and Hunter, RA 1990b. Influence of the growth promotant Synovex-H on growth, resistance to parasites and reproduction of cattle heifers of 3 breeds. Journal of Agricultural Science 114, 107113.Google Scholar
Godfray, HCJ, Beddington, JR, Crute, IR, Haddad, L, Lawrence, D, Muir, JF, Pretty, J, Robinson, S, Thomas, SM and Toulmin, C 2010. Food security: the challenge of feeding 9 billion people. Science 327, 812818.Google Scholar
Hacker, JB and Evans, TR 1992. An evaluation of the production potential of 6 tropical grasses under grazing. 1. Yield and yield components, growth-rates and phenology. Australian Journal of Experimental Agriculture 32, 1927.Google Scholar
Herrero, M and Thornton, PK 2013. Livestock and global change: emerging issues for sustainable food systems. Proceedings of the National Academy of Sciences of the United States of America 110, 2087820881.Google Scholar
Hill, JO, Coates, DB, Whitbread, AM, Clem, RL, Robertson, MJ and Pengelly, BC 2009. Seasonal changes in pasture quality and diet selection and their relationship with liveweight gain of steers grazing tropical grass and grass-legume pastures in northern Australia. Animal Production Science 49, 983993.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, 235265.Google Scholar
Jouven, M, Agabriel, J and Baumont, R 2008. A model predicting the seasonal dynamics of intake and production for suckler cows and their calves fed indoors or at pasture. Animal Feed Science and Technology 143, 256279.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, 267288.Google Scholar
Petty, SR and Poppi, DP 2008. Effect of muddy conditions in the field on the liveweight gain of cattle consuming Leucaena leucocephala Digitaria eriantha pastures in north-west Australia. Australian Journal of Experimental Agriculture 48, 818820.Google Scholar
Petty, SR and Poppi, DP 2012. The liveweight gain response of heifers to supplements of molasses or maize while grazing irrigated Leucaena leucocephala/Digitaria eriantha pastures in north-west Australia. Animal Production Science 52, 619623.Google Scholar
Petty, SR, Poppi, DP and Triglone, T 1998. Effect of maize supplementation, seasonal temperature and humidity on the liveweight gain of steers grazing irrigated Leucaena leucocephala Digitaria eriantha pastures in north-west Australia. Journal of Agricultural Science 130, 95105.Google Scholar
Rufino, MC, Herrero, M, Van Wijk, MT, Hemerik, L, De Ridder, N and Giller, KE 2009. Lifetime productivity of dairy cows in smallholder farming systems of the Central highlands of Kenya. Animal 3, 10441056.Google Scholar
Tuyen, DV, Tolosa, XM, Poppi, DP and McLennan, SR 2015. Effect of varying the proportion of molasses in the diet on intake, digestion and microbial protein production by steers. Animal Production Science 55, 1726.Google Scholar
Undi, M, Wilson, C, Ominski, KH and Wittenberg, KM 2008. Comparison of techniques for estimation of forage dry matter intake by grazing beef cattle. Canadian Journal of Animal Science 88, 693701.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, 507525.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, 100109.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 2. Sensitivity analysis and evaluation of sub-models. Animal, first published online 12 July 2018. https://doi.org/10.1017/S1751731118001738 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 1. Model description and illustration. Animal, first published online 12 July 2018. https://doi.org/10.1017/S1751731118001726.Google Scholar
Van Ittersum, MK and Rabbinge, R 1997. Concepts in production ecology for analysis and quantification of agricultural input-output combinations. Field Crops Research 52, 197208.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, 417.Google Scholar
Wallis de Vries, MF 1996. Nutritional limitations of free-ranging cattle: the importance of habitat quality. Journal of Applied Ecology 33, 688702.Google Scholar
Zemmelink, G 1980. Effect of selective consumption on voluntary intake and digestibility of tropical forages. PhD thesis, Wageningen University, Wageningen, The Netherlands.Google Scholar
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