Hostname: page-component-76fb5796d-22dnz Total loading time: 0 Render date: 2024-04-27T04:47:42.641Z Has data issue: false hasContentIssue false

PASTRAB: a model for simulating intake regulation and growth of rabbits raised on pastures

Published online by Cambridge University Press:  04 December 2017

L. Joly
Affiliation:
AGIR, Université de Toulouse, INPT, INP-PURPAN, INRA, 31320 Auzeville, France
J.-P. Goby
Affiliation:
Université de Perpignan, IUT, F-66962 Perpignan, France
A. Duprat
Affiliation:
GenPhySE, Université de Toulouse, INPT, INP-ENVT, INRA, 31320 Auzeville, France
H. Legendre
Affiliation:
GenPhySE, Université de Toulouse, INPT, INP-ENVT, INRA, 31320 Auzeville, France
D. Savietto
Affiliation:
GenPhySE, Université de Toulouse, INPT, INP-ENVT, INRA, 31320 Auzeville, France
T. Gidenne
Affiliation:
GenPhySE, Université de Toulouse, INPT, INP-ENVT, INRA, 31320 Auzeville, France
G. Martin*
Affiliation:
AGIR, Université de Toulouse, INPT, INP-PURPAN, INRA, 31320 Auzeville, France
Get access

Abstract

Given the very recent investment in research on organic rabbit production, many knowledge gaps remain. Simulation models based on data from experiments and farms may help generate general principles for organic rabbit production. Our goals were to (i) develop a model to simulate intake regulation and growth of rabbits raised on pastures, (ii) validate this model under a diversity of conditions and (iii) conduct a simulation experiment to predict the potential to decrease the supply of complete feed by increasing the grazing area per rabbit. The model developed (PASTRAB) simulates organic rabbit fattening on pastures in four main submodels that represent dynamics of (i) herbage standing biomass, fill and feed values; (ii) intake of herbage, complementary feed (i.e. complete pellets, cereal–legume grain mixtures) and hay as regulated by herbage allowance, fill and feed values of feedstuffs and rabbit physiological parameters; (iii) conversion of rabbit intake into live weight gain; and (iv) rabbit mortality. The model also calculates gross margin per rabbit sold. Model accuracy was assessed by considering the fit between observed and predicted herbage intake, which was low, with a relative root mean square error (rRMSE) of 51% and 66% on grass-based and legume-based pastures, respectively. However, the standard deviations of observed herbage intake were similar to the root mean square error of predicted herbage intake, indicating that it would have been difficult to improve model calibration. The fit between observed and predicted rabbit live weight was acceptable, with an rRMSE of 11% and 10% for grass-based and legume-based pastures, respectively. Simulated scenarios showed that a decrease in complementary feed combined with an increase in the grazing area per rabbit had little impact on average daily growth and gross margin per rabbit but increased herbage use efficiency. With 90 g of complementary feed per day and grazing of 0.4 m²/rabbit per day, herbage use efficiency was 22%, with average daily growth of 21.6 g/day and gross margin of 18.80 €/rabbit. With no complementary feed and grazing of 1.2 m²/rabbit per day, average daily growth decreased (19.2 g/day), but herbage use efficiency reached 100% and gross margin reached 19.20 €/rabbit. We used PASTRAB in participatory workshops with farmers so that the latter could explore adaptations to their current practices. Overall, farmers considered the model predictions realistic, and some of them decided to adapt some of their management practices immediately after the workshops.

Type
Research Article
Copyright
© The Animal Consortium 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

*

The simulation model is available upon request to the corresponding author.

References

Ansquer, P, Al Haj Khaled, R, Cruz, P, Theau, JP, Therond, O and Duru, M 2009. Characterizing and predicting plant phenology in species-rich grasslands. Grass & Forage Science 64, 5770.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Cacho, OJ, Finlayson, JD and Bywater, AC 1995. A simulation model of grazing sheep. II – whole farm model. Agricultural Systems 48, 2750.CrossRefGoogle Scholar
Dillon, PG, Roche, JR, Shalloo, L and Horan, B 2005. Optimizing financial returns from grazing in temperate pastures. In Proceedings of the XX International Grassland Congress, Cork, Ireland, pp. 131–147.CrossRefGoogle Scholar
Duprat, A, Goby, JP, Roinsard, A, Van Der Horst, F, Le Stum, J, Legendre, H, Descombes, M, Theau, JP, Martin, G and Gidenne, T 2016. Pasture finishing of organic rabbit: grass intake and growth – first results. In Proceedings of the 11th World Rabbit Congress, Qingdao, China, pp. 931–934.Google Scholar
Duru, M, Adam, M, Cruz, P, Martin, G, Ansquer, P, Ducourtieux, C, Jouany, C, Theau, JP and Viegas, J 2009. Modelling above-ground herbage mass for a wide range of grassland community types. Ecological Modelling 220, 209225.CrossRefGoogle Scholar
Duru, M, Therond, O, Martin, G, Martin-Clouaire, R, Magne, M-A, Justes, E, Journet, E-P, Aubertot, J-N, Savary, S, Bergez, J-E and Sarthou, JP 2016. How to implement biodiversity-based agriculture to enhance ecosystem services: a review. Agronomy for Sustainable Development 35, 12591281.CrossRefGoogle Scholar
Gidenne, T and Lebas, F 2006. Feeding behaviour in rabbits. In Feeding in domestic vertebrates. From structure to behaviour (ed. V Bels), pp. 179209. CABI Publishing, Wallingford, UK.CrossRefGoogle Scholar
Inosys 2016. Réseaux d’élevage. Retrieved on 8 August 2017 from http://idele.fr/reseaux-et-partenariats/inosys-reseaux-delevage.html.Google Scholar
INRA 2007. Alimentation des bovins, ovins et caprins. Besoins des animaux – Valeur des aliments. Tables INRA 2007. Quae Editions, Paris.Google Scholar
Jouven, M and Baumont, R 2008. Simulating grassland utilization in beef suckler systems to investigate the trade-offs between production and floristic diversity. Agricultural Systems 96, 260272.CrossRefGoogle Scholar
Kalaugher, E, Bornman, JF, Clark, A and Beukes, P 2013. An integrated biophysical and socio-economic framework for analysis of climate change adaptation strategies: the case of a New Zealand dairy farming system. Environmental Modelling & Software 39, 176187.CrossRefGoogle Scholar
Lebas, F, Lebreton, L and Martin, T 2002. Lapins Bio sur prairie: des résultats chiffrés. Cuniculture 29, 7480.Google Scholar
Martin, G 2015. A conceptual framework to support adaptation of farming systems – development and application with Forage Rummy. Agricultural Systems 132, 5261.CrossRefGoogle Scholar
Martin, G, Duprat, A, Goby, J-P, Theau, J-P, Roinsard, A, Descombes, M, Legendre, H and Gidenne, T 2016. Herbage intake regulation and growth of rabbits raised on grasslands: back to basics and looking forward. Animal 10, 16091618.CrossRefGoogle ScholarPubMed
Martin, G, Martin-Clouaire, R and Duru, M 2013. Farming system design to feed the changing world. A review. Agronomy for Sustainable Development 33, 131149.CrossRefGoogle Scholar
Mayes, RW, Lamb, CS and Colgrove, PM 1986. The use of dosed and herbage n-alkanes as markers for the determination of herbage intake. Journal of Agricultural Science Cambridge 107, 161170.CrossRefGoogle Scholar
Niggli, U., Plagge, J, Reese, S, Fertl, T, Schmid, O, Brändli, U, Bärtschi, D, Pöpsel, G, Hermanowski, R, Hohenester, H and Grabmann, G 2015. Towards modern sustainable agriculture with organic farming as the leading model. A discussion document on Organic 3.0. Retrieved on 8 August 2017 from http://www.bioaktuell.ch/fileadmin/documents/ba/Bildung/Organic-Three-Zero-2015-12-07.pdf Google Scholar
Pérez-Prieto, LA, Peyraud, JL and Delagarde, R 2011. Pasture intake, grazing behaviour and performance of dairy cows grazing low-mass pastures at three daily allowances in winter. Livestock Science 137, 151160.CrossRefGoogle Scholar
Roinsard, A, Fortun Lamothe, L, Gidenne, T, Cabaret, J and Van der Horst, F 2016. Lapin Bio: développer une production cunicole durable en agriculture biologique. Innovations Agronomiques 49, 231245.Google Scholar
Romera, AJ, Morris, ST, Hodgson, J, Stirling, WD and Woodward, SJR 2004. A model for simulating rule-based management of cow-calf systems. Computers and Electronics in Agriculture 42, 6786.CrossRefGoogle Scholar
van Ittersum, MK, Leffelaar, PA, Van keulen, H, Kropff, MJ, Bastiaans, L and Goudriaan, J 2003. On approaches and applications of the Wageningen crop models. European Journal of Agronomy 18, 201234.CrossRefGoogle Scholar
Wallach, D, Makowski, D and Jones, J (eds) 2006. Working with dynamic crop models: evaluation, analysis, parameterization, and applications. Elsevier, Amsterdam, The Netherlands. 462 pp.Google Scholar
Whitbread, AM, Robertson, MJ, Carberry, PS and Dimes, JP 2010. How farming systems simulation can aid the development of more sustainable smallholder farming systems in southern Africa. European Journal of Agronomy 32, 5158.CrossRefGoogle Scholar