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A conceptual framework for guiding the participatory development of agricultural decision support systems


  • Jakku, E.
  • Thorburn, P.J.


Scientists develop decision support systems (DSSs) to make agricultural science more accessible for farmers and extension officers. Despite the growing use of participatory approaches in agricultural DSS development, reflection on this endeavour has largely focused on the 'doing' of participation or the 'problem of implementation' when DSSs have not been adopted by stakeholders. There has been little reference to relevant theoretical approaches to the social processes involved in 'participation' or 'implementation'. However, if DSS use is to reach its full potential, a more conceptually informed understanding of how stakeholders collaborate in the participatory development of DSSs is required. To contribute to this conceptualisation, we developed a framework based on three concepts drawn from the field of science and technology studies: technological frames, interpretative flexibility and boundary objects. The framework highlights the importance and value of social learning for participatory DSS development, which relies upon exploring the participating parties' different perspectives on the agricultural system represented in the DSS. Our framework provides a broad definition of success for participatory DSS development, placing greater weight on learning during the participatory process compared with subsequent use of the DSS by farmers and/or advisors. Two case studies of stakeholder collaboration to develop an irrigation scheduling DSS for sugarcane production were used to explore the relevance of the framework. The concepts in the framework were clearly displayed during the case studies. At the conclusion of the studies there were contrasting outcomes for the DSS. One group of farmers was keen to apply it in their ongoing irrigation management, while another saw little relative advantage in use of the DSS. In both instances co-learning occurred amongst case study participants, so the participatory process was clearly a success.

Suggested Citation

  • Jakku, E. & Thorburn, P.J., 2010. "A conceptual framework for guiding the participatory development of agricultural decision support systems," Agricultural Systems, Elsevier, vol. 103(9), pages 675-682, November.
  • Handle: RePEc:eee:agisys:v:103:y:2010:i:9:p:675-682

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    Cited by:

    1. Hermans, Frans & Stuiver, Marian & Beers, P.J. & Kok, Kasper, 2013. "The distribution of roles and functions for upscaling and outscaling innovations in agricultural innovation systems," Agricultural Systems, Elsevier, vol. 115(C), pages 117-128.
    2. Garb, Yaakov & Friedlander, Lonia, 2014. "From transfer to translation: Using systemic understandings of technology to understand drip irrigation uptake," Agricultural Systems, Elsevier, vol. 128(C), pages 13-24.
    3. Moraine, Marc & Grimaldi, Juliette & Murgue, Clément & Duru, Michel & Therond, Olivier, 2016. "Co-design and assessment of cropping systems for developing crop-livestock integration at the territory level," Agricultural Systems, Elsevier, vol. 147(C), pages 87-97.
    4. Vänninen, Irene & Pereira-Querol, Marco & Engeström, Yrjö, 2015. "Generating transformative agency among horticultural producers: An activity-theoretical approach to transforming Integrated Pest Management," Agricultural Systems, Elsevier, vol. 139(C), pages 38-49.
    5. Eastwood, C.R. & Chapman, D.F. & Paine, M.S., 2012. "Networks of practice for co-construction of agricultural decision support systems: Case studies of precision dairy farms in Australia," Agricultural Systems, Elsevier, vol. 108(C), pages 10-18.
    6. Paul E. McNamara & Joyous S. Tata, 2015. "Principles of Designing and Implementing Agricultural Extension Programs for Reducing Post-harvest Loss," Agriculture, MDPI, Open Access Journal, vol. 5(4), pages 1-12, October.
    7. Martin, G., 2015. "A conceptual framework to support adaptation of farming systems – Development and application with Forage Rummy," Agricultural Systems, Elsevier, vol. 132(C), pages 52-61.
    8. repec:eee:agisys:v:159:y:2018:i:c:p:9-20 is not listed on IDEAS
    9. Duru, M., 2013. "Combining agroecology and management science to design field tools under high agrosystem structural or process uncertainty: Lessons from two case studies of grassland management," Agricultural Systems, Elsevier, vol. 114(C), pages 84-94.
    10. Soraya Tanure & Carlos Nabinger & João Luiz Becker, 2015. "Bioeconomic Model of Decision Support System for Farm Management: Proposal of a Mathematical Model," Systems Research and Behavioral Science, Wiley Blackwell, vol. 32(6), pages 658-671, November.
    11. Tanure, Soraya & Nabinger, Carlos & Becker, João Luiz, 2013. "Bioeconomic model of decision support system for farm management. Part I: Systemic conceptual modeling," Agricultural Systems, Elsevier, vol. 115(C), pages 104-116.
    12. Kragt, Marit Ellen & Llewellyn, Rick S., 2013. "Using choice experiments to improve the design of weed decision support tools," Working Papers 147031, University of Western Australia, School of Agricultural and Resource Economics.
    13. Klerkx, Laurens & van Bommel, Severine & Bos, Bram & Holster, Henri & Zwartkruis, Joyce V. & Aarts, Noelle, 2012. "Design process outputs as boundary objects in agricultural innovation projects: Functions and limitations," Agricultural Systems, Elsevier, vol. 113(C), pages 39-49.


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