Abstract
The present major agricultural issues are to feed the world and reduce negative environmental impacts. To this end, organic farming appears as a promising solution. However organic farming has several drawbacks such as difficult weed management. Indeed weeds can reduce crop yields. Therefore there is a need for improved decision support tools for weed management in organic farming. An existing weed competition model actually predicts the effect of early multispecies weed density, both on organic wheat yield loss and on the weed density at flowering stage. However main existing models do not take into account the activity of end-users, e.g. farmers, during model design. Therefore we analysed weed information acquisition by farmers using the dynamic environment theory to design a decision support system that takes into account end-users. We interviewed eight French organic farmers. We analysed interview data using a coding scheme inspired by dynamic environment theory. Our results show that weed quantity was the information most frequently collected by organic farmers both for short- and long-term crop management. This information was compatible with early weed density, the main input of the previously developed models. Findings also show that procedures for gathering information and processing depended on farmer profiles. We also show that a conceptual model based on dynamic situations and a coding structure were appropriate for taking into account the information elaborated by end-users. Finally we propose further design of a decision support system for tactical organic weed management using a participatory approach.
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References
Aubry C, Papy F, Capillon A (1998) Modelling decision-making processes for annual crop management. Agric Syst 56:45–65
Bàrberi P (2002) Weed management in organic agriculture: are we addressing the right issues? Weed Res 42:177–193
Bond W, Grundy AC (2001) Non-chemical weed management in organic farming systems. Weed Res 41:383–405
Casagrande M, Makowski D, Jeuffroy M-H, Valantin-Morison M, David C (2010) The benefits of using quantile regression for analysing the effect of weeds on organic winter wheat. Weed Res 50:199–208
Chatelin M-H, Aubry C, Poussin J et al (2005) DéciBlé®, a software package for wheat crop management simulation. Agric Syst 83:77–99
Colbach N, Dessaint F, Forcella F (2000) Evaluating field-scale sampling methods for the estimation of mean plant densities of weeds. Weed Res 40:411–430
Cousens R, Doyle CJ, Wilson BJ, Cussans GW (1986) Modelling the economics of controlling Avena fatua in winter wheat. Pestic Sci 17:1–12
Cox PG (1996) Some issues in the design of agricultural decision support systems. Agric Syst 52:355–381
David C, Mundler P, Demarle O, Ingrand S (2010) Long-term strategies and flexibility of organic farmers in southeastern France. Int J Agr Sustain 8:305–318
Hoc J-M, Amalberti R (2005) Modeling naturalistic decision-making cognitive activities in dynamic situations: the role of a coding scheme. In: Montgomery H, Brehmer B, Lipshitz R (eds) How professionals make decisions. Lawrence Erlbaum Associates, Mahwah, pp 319–334
US North-Central Regional Research in Farm Information Systems (2000) Farm information systems: their development and use in decision-making. Iowa State University, Bulletin 601
Jakku E, Thorburn PJ (2010) A conceptual framework for guiding the participatory development of agricultural decision support systems. Agric Syst 103:675–682
Kristensen K, Rasmussen IA (2002) The use of a Bayesian network in the design of a decision support system for growing malting barley without use of pesticides. Comput Electron Agric 33:197–217
Macé K, Morlon P, Munier-Jolain NM, Quéré L (2007) Time scales as a factor in decision-making by French farmers on weed management in annual crops. Agric Syst 93:115–142
Magne M-A, Cerf M, Ingrand S (2010) A conceptual model of farmers’ informational activity: a tool for improved support of livestock farming management. Anim 4:842–852
McCown RL (2002) Changing systems for supporting farmers’ decisions: problems, paradigms, and prospects. Agric Syst 74:179–220
Menalled FD, Gross KL, Hammond M (2001) Weed aboveground and seedbank community responses to agricultural management systems. Ecol Appl 11:1586–1601
Munier-Jolain NM, Savois V, Kubiak P, et al. (2004) DECID'Herb: a web decision support system for weed control programs in cultivated fields. In: AFPP-Dix-neuvième conférence du COLUMA Journées Internationales sur la lutte contre les mauvaises herbes, Dijon
Neef A, Neubert D (2010) Stakeholder participation in agricultural research projects: a conceptual framework for reflection and decision-making. Agr Human Values 28:179–194
Neuhoff D, Schulz DG, Köpke U (2004) Application of a decision support system (DSS-WECOF) for weed management in organic winter wheat production. In: XII Colloque International sur la Biologie des Mauvaises Herbes, Dijon
Parker CG (2004) Decision support systems: barriers to uptake and use. Aspects Appl Biol 72:31–41
Parsons DJ, Benjamin LR, Clarke J et al (2009) Weed Manager—a model-based decision support system for weed management in arable crops. Comput Electron Agric 65:155–167
Primot S, Valantin-Morison M, Makowski D (2006) Predicting the risk of weed infestation in winter oilseed rape crops. Weed Res 46:22–33
Reganold JP, Glover JD, Andrews PK, Hinman HR (2001) Sustainability of three apple production systems. Nature 410:926–930
Rydahl P, Boejer, OQ (2007) A Danish Decision Support System for integrated management of weeds. In: 14th European Weed Research Society Symposium, Hamar, pp 135
Solano C, Leon H, Pérez E, Herrero M (2003) The role of personal information sources on the decision-making process of Costa Rican dairy farmers. Agric Syst 76:3–18
Sorensen CG, Pesonen L, Fountas S et al (2010) A user-centric approach for information modelling in arable farming. Comput Electron Agric 73:44–55
Tilman D, Cassman KG, Matson PA, Naylor R, Polasky S (2002) Agricultural sustainability and intensive production practices. Nature 418:671–677
Wilkerson GG, Wiles LJ, Bennett AC (2002) Weed management decision models: pitfalls, perceptions, and possibilities of the economic threshold approach. Weed Sci 50:411–424
Acknowledgements
The authors gratefully acknowledge the funding from the European Community financial participation under the Sixth Framework Programme for Research, Technological Development and Demonstration Activities, for the Integrated Project QUALITYLOWINPUTFOOD, FP6-FOOD-CT-2003-506358. We would like to thank M Cerf (UMR 1048 SAD-APT, INRA) for her useful reading recommendations about cognitive ergonomics. We would like to give special thanks to the organic farmers. We also thank C Holland and FK Murphy for their editorial work in English.
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Casagrande, M., Joly, N., Jeuffroy, MH. et al. Evidence for weed quantity as the major information gathered by organic farmers for weed management. Agron. Sustain. Dev. 32, 715–726 (2012). https://doi.org/10.1007/s13593-011-0073-6
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DOI: https://doi.org/10.1007/s13593-011-0073-6