Advertisement

Tropical Animal Health and Production

, Volume 45, Issue 5, pp 1119–1129 | Cite as

Supporting strategic thinking of smallholder dairy farmers using a whole farm simulation tool

  • Pierre-Yves Le Gal
  • Jennifer Bernard
  • Charles-Henri Moulin
Regular Articles

Abstract

This article investigates how a one-to-one support process based on the use of a whole dairy farm simulation tool helps both farmers to reflect on their production strategies and researchers to better understand the farmers’ contexts of action and decision. The support process consists of a minimum of four discussion sessions with the farmer: designing the Initial Scenario and formulating a diagnosis, building and simulating the Project Scenario corresponding to the objective targeted by the farmer, building and comparing alternative scenarios proposed both by the farmer and the researcher, and evaluating the process with the farmer. The approach was tested with six smallholder farmers in Brazil. It is illustrated with the example of one farmer who aimed to develop his milk production by more than doubling his herd size on the same cultivated area. Two other examples illustrate the diversity of issues addressed with this approach. The first estimates the sensitivity of economic results to price variations of milk and concentrates. The second compares two scenarios in terms of forage supply autonomy. The discussion assesses the outcomes of the approach for farmers in terms of response to their specific issues and of knowledge acquired. The research outputs are discussed in terms of the value and limits of using simulation tools within both participatory action research and advisory processes.

Keywords

Scenario analysis Price sensitivity Participatory approach Learning process Brazil 

Notes

Acknowledgments

The authors would like to thank the livestock farmers from Unaí-MG who participated in the study and our colleagues at Embrapa Cerrados for their welcome and support. We are grateful to Grace Delobel for translating this paper from French to English. This work was partly funded by the Agence Nationale de la Recherche under the Systera Program: ANR-08-STRA-10 (ecological, technical and social innovation processes in Conservation Agriculture).

References

  1. Andrieu, N. and Nogueira, D.M., 2010. Modeling biomass flows at the farm level: a discussion support tool for farmers, Agronomy for Sustainable Development, 30, 505–513.CrossRefGoogle Scholar
  2. Basanta, M.V., Dourado-Neto, D., Reichardt, K., Bacchi, O.O.S., Oliveira, J.C.M., Trivelin, P.C.O., Timm, L.C., Tominaga, T.T., Correchel, V., Cassaro, F.A.M., Pires, L.F. and de Macedo, J.R., 2003. Management effects on nitrogen recovery in a sugarcane crop grown in Brazil, Geoderma, 116, 235–248.CrossRefGoogle Scholar
  3. Bernard, J., Le Gal, P.Y., Triomphe, B., Hostiou, N. and Moulin, C.H., 2011. Involvement of small-scale dairy farms in an industrial supply chain: when production standards meet farm diversity, Animal, 5, 961–971.PubMedCrossRefGoogle Scholar
  4. Bood, R. and Postma, T., 1997. Strategic Learning with Scenarios. European Management Journal, 15, 633–647.CrossRefGoogle Scholar
  5. Cabrera, V.E., Breuer, N.E., Hildebrand, P.E. and Letson, D., 2005. The dynamic North Florida dairy farm model: A user-friendly computerized tool for increasing profits while minimizing N leaching under varying climatic conditions, Computers and Electronics in Agriculture, 49, 286–308.CrossRefGoogle Scholar
  6. Dedieu, B., Aubin, J., Duteurtre, G., Alexandre, G., Vayssières, J., Bommel, P. and Faye, B., 2011. Conception et évaluation de systèmes d’élevage durables en régions chaudes, INRA Productions Animales, 24, 113–128.Google Scholar
  7. Dobos, R.C., Ashwood, A.M., Moore, C. and Youman, M., 2004. A decision tool to help in feed planning on dairy farms, Environmental Modelling & Software, 19, 967–974.CrossRefGoogle Scholar
  8. Duru, M., Bergez, J.E., Delaby, L., Justes, E., Theau, J.P. and Viegas, J., 2007. A spreadsheet model for developing field indicators and grazing management tools to meet environmental and production targets for dairy farms, Journal of Environmental Management, 82, 207–220.PubMedCrossRefGoogle Scholar
  9. Foltz, J.D., 2004. Entry, exit and farm size: assessing an experiment in dairy price policy, American Journal of Agricultural Economics, 86, 594–604.CrossRefGoogle Scholar
  10. Howden, S.M., Soussana, J.F., Tubiello, F.N., Chhetri, N., Dunlop, M. and Meinke, H., 2007. Adapting agriculture to climate change, Proceedings of the National Academy of Sciences, 104, 19691–19696.CrossRefGoogle Scholar
  11. Korndörfer, G.H. and Pereira de Melo, S., 2009. Effects of phosphorus sources (liquid or solid) on agricultural and industrial sugarcane yield, Ciencas e Agrotecnologia, 33, 92–97.CrossRefGoogle Scholar
  12. Le Gal, P.Y., Kuper, M., Moulin, C.H., Sraïri, M.T. and Rhouma, A., 2009. Linking water saving and productivity to agro-food supply chains: a synthesis from two North African cases, Irrigation and Drainage, 58, S320–S333.CrossRefGoogle Scholar
  13. Le Gal, P.Y., Dugué, P., Faure, G. and Novak, S., 2011. How does research address the design of innovative agricultural production systems at the farm level? A review, Agricultural Systems, 104, 714–728.CrossRefGoogle Scholar
  14. Martha Júnior, G.B., Barioni, L.G., Vilela, L. and Barcellos, A.O., 2003. Área do Piquete e Taxa de Lotação no Pastejo Rotacionado, Embrapa, Comunicado Téchnico 101.Google Scholar
  15. Martin, G., Felten, B. and Duru, M., 2011. Forage rummy: a game to support the participatory design of adapted livestock systems, Environmental Modelling & Software, 26, 1442–1453.CrossRefGoogle Scholar
  16. Matthews, K.B., Rivington, M., Blackstock, K.L., McCrum, G., Buchan, K. and Miller, D.G., 2011. Raising the bar?—The challenges of evaluating the outcomes of environmental modeling and software, Environmental Modelling & Software, 26, 247–257.CrossRefGoogle Scholar
  17. Moreau, J.C., Delaby, L., Duru, M. and Guérin, G., 2009. Démarches et outils de conseil autour du système fourrager: évolutions et concepts, Fourrages, 200, 565–586.Google Scholar
  18. Nelson, R.A., Holzworth, D.P., Hammer, G.L. and Hayman, P.T., 2002. Infusing the seasonal climate forecasting into crop management practice in north East Australia using discussion support software, Agricultural Systems, 74, 393–414.CrossRefGoogle Scholar
  19. NRC, 2001. Nutrient requirements of dairy cattle, 7th ed. National Academy Press, Washington, DC.Google Scholar
  20. Pedreira, C.G.S., Rosseto, F.A.A., da Silva, S.C., Nussio, L.G., Moreno, L.S.B., Lima, M.L.P. and Leme, P.R. 2005. Forage yield and grazing efficiency on rotationnaly stocked pasture of ‘Tanzania-1’ Guinea Grass and ‘Guaçu’ Elephant Grass, Scienta Agricola, 62, 433–439.Google Scholar
  21. Sraïri, M.T., Eljaouhari, M., Saydi, A., Kuper, M. and Le Gal, P.-Y., 2011. Supporting small-scale dairy farmers in increasing milk production: evidence from Morocco, Tropical Animal Health Production, 43, 41–49.CrossRefGoogle Scholar
  22. Vayssières, J., Bocquier, F. and Lecomte, P., 2009. GAMEDE: A global activity model for evaluating the sustainability of dairy enterprises. Part II—interactive simulation of various management strategies with diverse stakeholders, Agricultural Systems, 101, 139–151.CrossRefGoogle Scholar
  23. Voinov, A. and Bousquet, B., 2010. Modelling with stakeholders, Environmental Modelling & Software, 25, 1268–1281.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  • Pierre-Yves Le Gal
    • 1
  • Jennifer Bernard
    • 1
  • Charles-Henri Moulin
    • 2
    • 3
  1. 1.Centre de Coopération Internationale en Recherche Agronomique pour le DéveloppementUMR InnovationMontpellierFrance
  2. 2.Montpellier SupAgroUMR Systèmes d’élevage Méditerranéens et TropicauxMontpellierFrance
  3. 3.Institut National de la Recherche AgronomiqueUMR Systèmes d’élevage Méditerranéens et TropicauxMontpellierFrance

Personalised recommendations