 Expert based model building to quantify risk factors in a combined aquaculture-agriculture systemBen Stewart-Koster, Nguyen Dieu Anh, Michele A. Burford, Jason Condon, Nguyen Van Qui, Le Huu Hiep, Doan Van Bay and Jesmond Sammut Agricultural Systems, 2017, vol. 157, issue C, 230-240 Abstract: In recent years, across tropical regions of the world, there has been an expansion of integrated farming systems that combine rice and shrimp production. While these systems were developed as a form of crop-rotation – growing rice in the wet season and shrimp in the dry season – some farmers grow both rice and brackish-water shrimp simultaneously during the wet season. Climatic variability has resulted in considerable crop losses in this system across many regions. Research has yet to identify the complete array of key risk factors, and their potential interactions, for integrated rice-shrimp farming. Consequently, different farming practices and environmental factors that may affect crop production need to be clarified to guide research efforts. We applied a staged, iterative process to develop a probabilistic Bayesian belief network based on expert knowledge that describes the relationships that contribute to the risk of failure of both crops in integrated rice-shrimp farming systems during the wet season. We applied the approach in the Southern Mekong Delta, Vietnam, in the context of a broader research program into the sustainability of the rice-shrimp farming system. The resulting network represents the experts' perceptions of the key risk factors to production and the interactions among them. While both farmers and extension officers contributed to the identification of the processes included in the network, the farmers alone provided estimates of the probability of the relationships among them. The network identified the challenges to minimise the risk of failure for both crops, and the steps farmers can take to mitigate some of them. Overall, farmers perceived they have a better chance to minimise risk of failure for shrimp rather than rice crops, and limited opportunities appear to exist for successful production of both. By engaging the farmers in this process of model development, we were able to identify additional research questions for the broader research team and to identify simple steps the farmers could take to reduce the risk of crop failure. Integrating additional empirical data into this network, as it becomes available, will help identify clear opportunities for improvements in farming practices which should reduce the risk of crop failure into the future. Keywords: Bayesian belief network; BBN; Rice-shrimp farming; Expert knowledge; Mekong Delta (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:agisys:v:157:y:2017:i:c:p:230-240 DOI: 10.1016/j.agsy.2017.08.001 Access Statistics for this article Agricultural Systems is currently edited by J.W. Hansen, P.K. Thornton and P.B.M. Berentsen More articles in Agricultural Systems from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.