IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v257y2021ics0378377421004388.html
   My bibliography  Save this article

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

Author

Listed:
  • 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.

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377421004388
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.agwat.2021.107161?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Schlindwein, Sandro L. & Eulenstein, Frank & Lana, Marcos & Sieber, Stefan & Boulanger, Jean-Philippe & Guevara, Edgardo & Meira, Santiago & Gentile, Elvira & Bonatti, Michelle, 2015. "What Can Be Learned about the Adaptation Process of Farming Systems to Climate Dynamics Using Crop Models?," Sustainable Agriculture Research, Canadian Center of Science and Education, vol. 4(4).
    2. J. Brian Hardaker & James W. Richardson & Gudbrand Lien & Keith D. Schumann, 2004. "Stochastic efficiency analysis with risk aversion bounds: a simplified approach," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 48(2), pages 253-270, June.
    3. Karami, Ezatollah, 2006. "Appropriateness of farmers' adoption of irrigation methods: The application of the AHP model," Agricultural Systems, Elsevier, vol. 87(1), pages 101-119, January.
    4. Darouich, Hanaa & Gonçalves, José M. & Muga, André & Pereira, Luis S., 2012. "Water saving vs. farm economics in cotton surface irrigation: An application of multicriteria analysis," Agricultural Water Management, Elsevier, vol. 115(C), pages 223-231.
    5. Monjardino, M. & McBeath, T. & Ouzman, J. & Llewellyn, R. & Jones, B., 2015. "Farmer risk-aversion limits closure of yield and profit gaps: A study of nitrogen management in the southern Australian wheatbelt," Agricultural Systems, Elsevier, vol. 137(C), pages 108-118.
    6. Kandulu, John M. & Bryan, Brett A. & King, Darran & Connor, Jeffery D., 2012. "Mitigating economic risk from climate variability in rain-fed agriculture through enterprise mix diversification," Ecological Economics, Elsevier, vol. 79(C), pages 105-112.
    7. Adamson, David & Loch, Adam, 2014. "Possible negative feedbacks from ‘gold-plating’ irrigation infrastructure," Agricultural Water Management, Elsevier, vol. 145(C), pages 134-144.
    8. 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.
    9. Kuehne, Geoff & Llewellyn, Rick & Pannell, David J. & Wilkinson, Roger & Dolling, Perry & Ouzman, Jackie & Ewing, Mike, 2017. "Predicting farmer uptake of new agricultural practices: A tool for research, extension and policy," Agricultural Systems, Elsevier, vol. 156(C), pages 115-125.
    10. Harrison, Matthew T. & Cullen, Brendan R. & Rawnsley, Richard P., 2016. "Modelling the sensitivity of agricultural systems to climate change and extreme climatic events," Agricultural Systems, Elsevier, vol. 148(C), pages 135-148.
    11. Christie, K.M. & Smith, A.P. & Rawnsley, R.P. & Harrison, M.T. & Eckard, R.J., 2020. "Simulated seasonal responses of grazed dairy pastures to nitrogen fertilizer in SE Australia: N loss and recovery," Agricultural Systems, Elsevier, vol. 182(C).
    12. Rose, David C. & Sutherland, William J. & Parker, Caroline & Lobley, Matt & Winter, Michael & Morris, Carol & Twining, Susan & Ffoulkes, Charles & Amano, Tatsuya & Dicks, Lynn V., 2016. "Decision support tools for agriculture: Towards effective design and delivery," Agricultural Systems, Elsevier, vol. 149(C), pages 165-174.
    13. Phelan, David C. & Harrison, Matthew T. & Kemmerer, Ernst P. & Parsons, David, 2015. "Management opportunities for boosting productivity of cool-temperate dairy farms under climate change," Agricultural Systems, Elsevier, vol. 138(C), pages 46-54.
    14. Colomb, Robert M., 1987. "Knowledge-Based Decision Support Systems: A Background to Expert Systems," Review of Marketing and Agricultural Economics, Australian Agricultural and Resource Economics Society, vol. 55(02), pages 1-5, August.
    15. Richards, Q.D. & Bange, M.P. & Johnston, S.B., 2008. "HydroLOGIC: An irrigation management system for Australian cotton," Agricultural Systems, Elsevier, vol. 98(1), pages 40-49, July.
    16. 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.
    17. Alcock, Douglas J. & Harrison, Matthew T. & Rawnsley, Richard P. & Eckard, Richard J., 2015. "Can animal genetics and flock management be used to reduce greenhouse gas emissions but also maintain productivity of wool-producing enterprises?," Agricultural Systems, Elsevier, vol. 132(C), pages 25-34.
    18. Cox, P. G., 1996. "Some issues in the design of agricultural decision support systems," Agricultural Systems, Elsevier, vol. 52(2-3), pages 355-381.
    19. Paul Watkiss & Alistair Hunt & William Blyth & Jillian Dyszynski, 2015. "The use of new economic decision support tools for adaptation assessment: A review of methods and applications, towards guidance on applicability," Climatic Change, Springer, vol. 132(3), pages 401-416, October.
    20. Mira da Silva, L. & Park, J. R. & Keatinge, J. D. H. & Pinto, P. A., 2001. "I. A decision support system to improve planning and management in large irrigation schemes," Agricultural Water Management, Elsevier, vol. 51(3), pages 187-201, November.
    21. Harrison, Matthew T. & Jackson, Tom & Cullen, Brendan R. & Rawnsley, Richard P. & Ho, Christie & Cummins, Leo & Eckard, Richard J., 2014. "Increasing ewe genetic fecundity improves whole-farm production and reduces greenhouse gas emissions intensities," Agricultural Systems, Elsevier, vol. 131(C), pages 23-33.
    22. Bjornlund, Henning & Nicol, Lorraine & Klein, K.K., 2007. "Challenges in implementing economic instruments to manage irrigation water on farms in southern Alberta," Agricultural Water Management, Elsevier, vol. 92(3), pages 131-141, September.
    23. McCown, R. L., 2002. "Changing systems for supporting farmers' decisions: problems, paradigms, and prospects," Agricultural Systems, Elsevier, vol. 74(1), pages 179-220, October.
    24. Christie, Karen M. & Smith, Andrew P. & Rawnsley, Richard P. & Harrison, Matthew T. & Eckard, Richard J., 2018. "Simulated seasonal responses of grazed dairy pastures to nitrogen fertilizer in SE Australia: Pasture production," Agricultural Systems, Elsevier, vol. 166(C), pages 36-47.
    25. Mira da Silva, L. & Park, J. R. & Keatinge, J. D. H. & Pinto, P. A., 2001. "II. The use of the DSSIPM in the Alentejo region of southern Portugal," Agricultural Water Management, Elsevier, vol. 51(3), pages 203-215, November.
    26. Stefan Hajkowicz & Kerry Collins, 2007. "A Review of Multiple Criteria Analysis for Water Resource Planning and Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(9), pages 1553-1566, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Arnis Dzalbs & Madara Bimbere & Jelena Pubule & Dagnija Blumberga, 2023. "Environmental Impact Decision Support Tools for Horticulture Farming: Evaluation of GHG Calculators," Agriculture, MDPI, vol. 13(12), pages 1-18, November.
    2. Atkočiūnienė Vilma & Papšienė Palmira, 2023. "Opportunities for Digitisation of Agricultural and Rural Development Solutions," Management Theory and Studies for Rural Business and Infrastructure Development, Sciendo, vol. 45(1), pages 1-8, March.
    3. Rapeepan Pitakaso & Kanchana Sethanan & Kim Hua Tan & Ajay Kumar, 2024. "A decision support system based on an artificial multiple intelligence system for vegetable crop land allocation problem," Annals of Operations Research, Springer, vol. 342(1), pages 621-656, November.
    4. 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).
    5. 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).
    6. Guilherme Jesus & Martim L. Aguiar & Pedro D. Gaspar, 2022. "Computational Tool to Support the Decision in the Selection of Alternative and/or Sustainable Refrigerants," Energies, MDPI, vol. 15(22), pages 1-20, November.
    7. Ke Liu & Matthew Tom Harrison & Haoliang Yan & De Li Liu & Holger Meinke & Gerrit Hoogenboom & Bin Wang & Bin Peng & Kaiyu Guan & Jonas Jaegermeyr & Enli Wang & Feng Zhang & Xiaogang Yin & Sotirios Ar, 2023. "Silver lining to a climate crisis in multiple prospects for alleviating crop waterlogging under future climates," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    8. Bushra Ahmed Alhammad & Mahmoud F. Seleiman & Matthew Tom Harrison, 2023. "Hydrogen Peroxide Mitigates Cu Stress in Wheat," Agriculture, MDPI, vol. 13(4), pages 1-15, April.
    9. Guo, Shibo & Zhao, Jin & Zhao, Chuang & Guo, Erjing & Liu, Zhijuan & Harrison, Matthew Tom & Liu, Ke & Zhang, Tianyi & Yang, Xiaoguang, 2024. "Adapting crop land-use in line with a changing climate improves productivity, prosperity and reduces greenhouse gas emissions," Agricultural Systems, Elsevier, vol. 217(C).
    10. Michael Gbenga Ogungbuyi & Juan P. Guerschman & Andrew M. Fischer & Richard Azu Crabbe & Caroline Mohammed & Peter Scarth & Phil Tickle & Jason Whitehead & Matthew Tom Harrison, 2023. "Enabling Regenerative Agriculture Using Remote Sensing and Machine Learning," Land, MDPI, vol. 12(6), pages 1-25, May.
    11. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sahar Shahpari & Janelle Allison & Matthew Tom Harrison & Roger Stanley, 2021. "An Integrated Economic, Environmental and Social Approach to Agricultural Land-Use Planning," Land, MDPI, vol. 10(4), pages 1-18, April.
    2. Lundström, Christina & Lindblom, Jessica, 2018. "Considering farmers' situated knowledge of using agricultural decision support systems (AgriDSS) to Foster farming practices: The case of CropSAT," Agricultural Systems, Elsevier, vol. 159(C), pages 9-20.
    3. 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).
    4. 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.
    5. Jotham Akaka & Aurora García-Gallego & Nikolaos Georgantzís & Jean-Christian Tisserand, 2021. "Decision support systems adoption in pesticide management," Working Papers 2021/08, Economics Department, Universitat Jaume I, Castellón (Spain).
    6. Prost, Lorène, 2021. "Revitalizing agricultural sciences with design sciences," Agricultural Systems, Elsevier, vol. 193(C).
    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. Dolinska, Aleksandra & Hassenforder, Emeline & Loboguerrero, Ana Maria & Sultan, Benjamin & Bossuet, Jérôme & Cottenceau, Jeanne & Bonatti, Michelle & Hellin, Jon & Mekki, Insaf & Drogoul, Alexis & Va, 2023. "Co-production opportunities seized and missed in decision-support frameworks for climate-change adaptation in agriculture – How do we practice the “best practice”?," Agricultural Systems, Elsevier, vol. 212(C).
    9. Harrison, Matthew T. & McSweeney, Chris & Tomkins, Nigel W. & Eckard, Richard J., 2015. "Improving greenhouse gas emissions intensities of subtropical and tropical beef farming systems using Leucaena leucocephala," Agricultural Systems, Elsevier, vol. 136(C), pages 138-146.
    10. Meyer-Aurich, Andreas & Karatay, Yusuf Nadi, 2019. "Effects of uncertainty and farmers' risk aversion on optimal N fertilizer supply in wheat production in Germany," Agricultural Systems, Elsevier, vol. 173(C), pages 130-139.
    11. Gary Bentrup & Michael G. Dosskey, 2022. "Tree Advisor: A Novel Woody Plant Selection Tool to Support Multifunctional Objectives," Land, MDPI, vol. 11(3), pages 1-23, March.
    12. So Pyay Thar & Thiagarajah Ramilan & Robert J. Farquharson & Deli Chen, 2021. "Identifying Potential for Decision Support Tools through Farm Systems Typology Analysis Coupled with Participatory Research: A Case for Smallholder Farmers in Myanmar," Agriculture, MDPI, vol. 11(6), pages 1-20, June.
    13. Charné Viljoen & Janke van der Colf & Pieter Andreas Swanepoel, 2020. "Benefits Are Limited with High Nitrogen Fertiliser Rates in Kikuyu-Ryegrass Pasture Systems," Land, MDPI, vol. 9(6), pages 1-20, May.
    14. Martin, G. & Duru, M. & Schellberg, J. & Ewert, F., 2012. "Simulations of plant productivity are affected by modelling approaches of farm management," Agricultural Systems, Elsevier, vol. 109(C), pages 25-34.
    15. Sterk, B. & van Ittersum, M.K. & Leeuwis, C. & Rossing, W.A.H. & van Keulen, H. & van de Ven, G.W.J., 2006. "Finding niches for whole-farm design models - contradictio in terminis?," Agricultural Systems, Elsevier, vol. 87(2), pages 211-228, February.
    16. McPhee, Malcolm J. & Evered, Mark & Andrews, Todd & Pacheco, David & Dougherty, Holland C. & Ingham, Aaron B. & Harden, Steven & Crean, Jason & Roche, Leslie & Eastburn, Danny J. & Oltjen, James W. & , 2019. "Beef production simulation of nitrate and lipid supplements for pasture and rangeland fed enterprises," Agricultural Systems, Elsevier, vol. 170(C), pages 19-27.
    17. Sophia Xiaoxia Duan & Santoso Wibowo & Josephine Chong, 2021. "A Multicriteria Analysis Approach for Evaluating the Performance of Agriculture Decision Support Systems for Sustainable Agribusiness," Mathematics, MDPI, vol. 9(8), pages 1-16, April.
    18. Moglia, Magnus & Alexander, Kim S. & Thephavanh, Manithaythip & Thammavong, Phomma & Sodahak, Viengkham & Khounsy, Bountom & Vorlasan, Sysavanh & Larson, Silva & Connell, John & Case, Peter, 2018. "A Bayesian network model to explore practice change by smallholder rice farmers in Lao PDR," Agricultural Systems, Elsevier, vol. 164(C), pages 84-94.
    19. Mace, Karen & Morlon, Pierre & Munier-Jolain, Nicolas & Quere, Lionel, 2007. "Time scales as a factor in decision-making by French farmers on weed management in annual crops," Agricultural Systems, Elsevier, vol. 93(1-3), pages 115-142, March.
    20. Kaini, S. & Harrison, M. T. & Gardner, T. & Nepal, Santosh & Sharma, A. K., 2022. "The impacts of climate change on the irrigation water demand, grain yield, and biomass yield of wheat crop in Nepal," Papers published in Journals (Open Access), International Water Management Institute, pages 1-14(17):27.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:agiwat:v:257:y:2021:i:c:s0378377421004388. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/agwat .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.