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Application, adoption and opportunities for improving decision support systems in irrigated agriculture: A review

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  • Ara, Iffat
  • Turner, Lydia
  • Harrison, Matthew Tom
  • Monjardino, Marta
  • deVoil, Peter
  • Rodriguez, Daniel

Abstract

Decision support systems (DSS) have long been used in research, service provision and extension. Despite the diversity of technological applications in which past agricultural DSS canvass, there has been relatively little information on either the functional aspects of DSS designed for economic decisions in irrigated cropping, or the human and social factors influencing the adoption of knowledge from such DSS. The objectives of the study were to (1) review the functionality and target end-users of economic DSS for irrigated cropping systems, (2) document the extent to which these DSS account for and visualise uncertainty in DSS outputs, (3) examine tactical or strategic decisions able to be explored in DSS (with irrigation infrastructure being a key strategic decision), and (4) explore the human and social factors influencing adoption of DSS heuristics. This study showed that development of previous DSS has often occurred as a result of a technology push instead of end-user pull, which has meant that previous DSS have been generated in a top-down fashion rather than being demand-driven by end-user needs. We found that few DSS enable analysis of both tactical and strategic decisions, and that few DSS account for uncertainty in their outputs. We uncover a surprising lack of documented end-user feedback on economic DSS for irrigated cropping, such as end-user satisfaction with DSS functionality or future intentions to use the technology, as well as a lack of DSS application outside regions in which they were originally developed. Declining adoption of DSS does not necessarily imply declining adoption of DSS heuristics; in fact, declining DSS uptake may indicate that knowledge and heuristics extended by the DSS has been successful, obviating the need for use of the DSS per se. Future DSS could be improved through the use of demand-driven participatory approaches more aligned with user needs, with more training to build human capacity including understanding uncertainty and ability to contrast tactical and strategic decisions using multiple economic, environmental and social metrics.

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  • Ara, Iffat & Turner, Lydia & Harrison, Matthew Tom & Monjardino, Marta & deVoil, Peter & Rodriguez, Daniel, 2021. "Application, adoption and opportunities for improving decision support systems in irrigated agriculture: A review," Agricultural Water Management, Elsevier, vol. 257(C).
    • Handle: RePEc:eee:agiwat:v:257:y:2021:i:c:s0378377421004388
    DOI: 10.1016/j.agwat.2021.107161
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    6. Monjardino, Marta & Harrison, Matthew T. & DeVoil, Peter & Rodriguez, Daniel & Sadras, Victor O., 2022. "Agronomic and on-farm infrastructure adaptations to manage economic risk in Australian irrigated broadacre systems: A case study," Agricultural Water Management, Elsevier, vol. 269(C).
    7. Huo, Dongyang & Malik, Asad Waqar & Ravana, Sri Devi & Rahman, Anis Ur & Ahmedy, Ismail, 2024. "Mapping smart farming: Addressing agricultural challenges in data-driven era," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    8. Vasileios P. Georgopoulos & Dimitris C. Gkikas & John A. Theodorou, 2023. "Factors Influencing the Adoption of Artificial Intelligence Technologies in Agriculture, Livestock Farming and Aquaculture: A Systematic Literature Review Using PRISMA 2020," Sustainability, MDPI, vol. 15(23), pages 1-19, November.
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