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LiGAPS-Beef, a mechanistic model to explore potential and feed-limited beef production 1: model description and illustration

Published online by Cambridge University Press:  12 July 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

The expected increase in the global demand for livestock products calls for insight in the scope to increase actual production levels across the world. This insight can be obtained by using theoretical concepts of production ecology. These concepts distinguish three production levels for livestock: potential (i.e. theoretical maximum) production, which is defined by genotype and climate only; feed-limited production, which is limited by feed quantity and quality; and actual production. The difference between the potential or limited production and the actual production is the yield gap. The objective of this paper, the first in a series of three, is to present a mechanistic, dynamic model simulating potential and feed-limited production for beef cattle, which can be used to assess yield gaps. A novelty of this model, named LiGAPS-Beef (Livestock simulator for Generic analysis of Animal Production Systems – Beef cattle), is the identification of the defining factors (genotype and climate) and limiting factors (feed quality and available feed quantity) for cattle growth by integrating sub-models on thermoregulation, feed intake and digestion, and energy and protein utilisation. Growth of beef cattle is simulated at the animal and herd level. The model is designed to be applicable to different beef production systems across the world. Main model inputs are breed-specific parameters, daily weather data, information about housing, and data on feed quality and quantity. Main model outputs are live weight gain, feed intake and feed efficiency (FE) at the animal and herd level. Here, the model is presented, and its use is illustrated for Charolais and Brahman × Shorthorn cattle in France and Australia. Potential and feed-limited production were assessed successfully, and we show that FE of herds is highest for breeds most adapted to the local climate conditions. LiGAPS-Beef also identified the factors that define and limit growth and production of cattle. Hence, we argue the model has scope to be used as a tool for the assessment and analysis of yield gaps in beef production systems.

Type
Research Article
Copyright
© The Animal Consortium 2018 

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References

Alexandratos, N and Bruinsma, J 2012. World Agriculture towards 2030/2050: the 2012 revision. ESA Working paper No. 12-03, FAO, Rome, Italy.Google Scholar
Baldwin, RL, Smith, NE, Taylor, J and Sharp, M 1980. Manipulating metabolic parameters to improve growth-rate and milk secretion. Journal of Animal Science 51, 14161428.Google Scholar
Bouman, BAM, van Keulen, H, van Laar, HH and Rabbinge, R 1996. The ‘School of de Wit’ crop growth simulation models: a pedigree and historical overview. Agricultural Systems 52, 171198.Google Scholar
Burrow, HM 2012. Importance of adaptation and genotype × environment interactions in tropical beef breeding systems. Animal 6, 729740.Google Scholar
Chandler, P 1994. Is heat increment of feeds an asset or liability to milk production? Feedstuffs April 11, 1213.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, 259275.Google Scholar
Commonwealth Scientific and Industrial Research Organisation (CSIRO) 2007. Nutrient requirements of domesticated ruminants. CSIRO Publishing, Collingwood, Australia.Google Scholar
Cortez-Arriola, J, Groot, JCJ, Massiotti, RDA, Scholberg, JMS, Aguayo, DVM, Tittonell, P and Rossing, WAH 2014. Resource use efficiency and farm productivity gaps of smallholder dairy farming in North-west Michoacan, Mexico. Agricultural Systems 126, 1524.Google Scholar
De Vries, M, van Middelaar, CE and de Boer, IJM 2015. Comparing environmental impacts of beef production systems: a review of life cycle assessments. Livestock Science 178, 279288.Google Scholar
Emmans, GC 1994. Effective energy – a concept of energy-utilization applied across species. British Journal of Nutrition 71, 801821.Google Scholar
Faverdin, P, Baratte, C, Delagarde, R and Peyraud, JL 2011. GrazeIn: a model of herbage intake and milk production for grazing dairy cows. 1. Prediction of intake capacity, voluntary intake and milk production during lactation. Grass and Forage Science 66, 2944.Google Scholar
Fox, DG, Sniffen, CJ and O’Connor, JD 1988. Adjusting nutrient-requirements of beef-cattle for animal and environmental variations. Journal of Animal Science 66, 14751495.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
Hornick, JL, Van Eenaeme, C, Gerard, O, Dufrasne, I and Istasse, L 2000. Mechanisms of reduced and compensatory growth. Domestic Animal Endocrinology 19, 121132.Google Scholar
Institut National de la Recherche Agronomique (INRA) 2007. Alimentation des bovins, ovins et caprins. Besoins des animaux - valeurs des aliments. Tables INRA 2007. Quae éditions, Paris, France.Google Scholar
Jarrige, R, Demarquilly, C, Dulphy, JP, Hoden, A, Robelin, J, Beranger, C, Geay, Y, Journet, M, Malterre, C, Micol, D and Petit, M 1986. The INRA fill unit system for predicting the voluntary intake of forage-based diets in ruminants - a review. Journal of Animal Science 63, 17371758.Google Scholar
Jenkins, TG and Ferrell, CL 1992. Lactation characteristics of 9 breeds of cattle fed various quantities of dietary energy. Journal of Animal Science 70, 16521660.Google Scholar
Johnson, IR, Chapman, DF, Snow, VO, Eckard, RJ, Parsons, AJ, Lambert, MG and Cullen, BR 2008. DairyMod and EcoMod: biophysical pasture-simulation models for Australia and New Zealand. Australian Journal of Experimental Agriculture 48, 621631.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, 179204.Google Scholar
Mayberry, D, Ash, A, Prestwidge, D, Godde, CM, Henderson, B, Duncan, A, Blummel, M, Reddy, YR and Herrero, M 2017. Yield gap analyses to estimate attainable bovine milk yields and evaluate options to increase production in Ethiopia and India. Agricultural Systems 155, 4351.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, 8192.Google Scholar
National Research Council (NRC) 1981. Nutritional energetics of domestic animals & glossary of energy terms, 2nd revised edition. National Academic Press, Washington, DC, USA.Google Scholar
National Research Council (NRC) 2000. Nutrient requirements of beef cattle, 7th revised edition. National Academic Press, Washington, DC, USA.Google Scholar
Pfuhl, R, Bellmann, O, Kuhn, C, Teuscher, F, Ender, K and Wegner, J 2007. Beef versus dairy cattle: a comparison of feed conversion, carcass composition, and meat quality. Archiv Fur Tierzucht (Archives of Animal Breeding) 50, 5970.Google Scholar
RCoreTeam 2013. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.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
Turnpenny, JR, McArthur, AJ, Clark, JA and Wathes, CM 2000. Thermal balance of livestock 1. A parsimonious model. Agricultural and Forest Meteorology 101, 1527.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, Oosting, SJ, Van de Ven, GWJ, Veysset, P, de Boer, IJM and Van Ittersum, MK 2018c. Yield gap analysis of feed-crop livestock systems: the case of grass-based beef production in France. Agricultural Systems 159, 2131.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, 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 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, 417.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 Milgen, J, Valancogne, A, Dubois, S, Dourmad, JY, Seve, B and Noblet, J 2008. InraPorc: a model and decision support tool for the nutrition of growing pigs. Animal Feed Science and Technology 143, 387405.Google Scholar
Wellock, IJ, Emmans, GC and Kyriazakis, I 2004. Describing and predicting potential growth in the pig. Animal Science 78, 379388.Google Scholar
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