Expert based model building to quantify risk factors in a combined aquaculture-agriculture system

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 Highlights • We converted knowledge from rice-shrimp farmers in the Mekong Delta into a probabilistic model• Key risks to production for both crops revolved around soil and water quality • On average, rice crop failure was perceived to be more likely than for shrimp • Capturing extant agricultural knowledge improves outcomes for research and practice

A Bayesian network model to explore practice change by smallholder rice farmers in Lao PDR

“…Useful examples of the applicability of Bayesian Networks include: environmental management (Low Choy, Stewart-Koster et al 2005;Henriksen, Rasmussen et al 2007;Ticehurst, Newham et al 2007;Stewart-Koster, Bunn et al 2010), reliability engineering (Trucco, Cagno et al 2008), software engineering (Fenton, Marsh et al 2004) and project management (Khodakarami and Abdi 2014). In fact, Bayesian Networks have been used for knowledge integration tools and systems analysis in an agricultural context within a number of studies (Henderson and Burn 2004;Bashari, Smith et al 2008;Florin, van Ittersum et al 2013;Stewart-Koster, Dieu Anh et al 2017).…”

Trends in Rice-Based Farming Systems in the Mekong Delta

“…Moreover, the Mekong Delta is highly vulnerable to the effects of climate change, including sealevel rise, increased flooding, and saline intrusion, particularly in the Coastal Complex and the Ca Mau Peninsula (Dasgupta et al 2007; Phạ m and Furukawa 2007; MONRE 2009). Recent evidence shows that saline intrusion is having adverse impacts on both rice and rice-shrimp farming systems (Mainuddin et al 2011(Mainuddin et al , 2013Ling et al 2015;Thuy and Anh 2015;USAID 2016;Leigh et al 2017;Stewart-Koster et al 2017).…”

Developing a Decision Support App for Computational Agriculture

“…BBNs have been applied to better understand many ecological, environmental and agricultural systems. Of particular relevance to the work described in this paper, they have been used to elicit expert knowledge from farmers attempting to simultaneously harvest rice and shrimp from the same combined aquacultureagriculture system [16].…”

“…In their paper, Stewart-Koster et al [16] described a combined aquacultureagriculture system in which farmers in the Mekong delta, in Vietnam, attempt to use the same fields to grow rice -on a central, raised platform -and shrimp in a surrounding ditch. While both are grown submerged in water, rice requires lower water salinity than shrimp which, of course, presents challenges for production of both crops, as attempted by farmers at some times during the year.…”