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

机译：基于专家的模型构建，以量化水产养殖-农业联合生产系统中的风险因素

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In recent years, across tropical regions of the world, there has been an expansion of integrated farming systemsthat 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-watershrimp simultaneously during the wet season. Climatic variability has resulted in considerable crop losses in thissystem across many regions. Research has yet to identify the complete array of key risk factors, and their potentialinteractions, for integrated rice-shrimp farming. Consequently, different farming practices and environmentalfactors 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 therelationships that contribute to the risk of failure of both crops in integrated rice-shrimp farming systems duringthe wet season. We applied the approach in the Southern Mekong Delta, Vietnam, in the context of a broaderresearch program into the sustainability of the rice-shrimp farming system. The resulting network represents theexperts' perceptions of the key risk factors to production and the interactions among them. While both farmersand extension officers contributed to the identification of the processes included in the network, the farmersalone provided estimates of the probability of the relationships among them. The network identified the challengesto 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 thisprocess of model development, we were able to identify additional research questions for the broader researchteam and to identify simple steps the farmers could take to reduce the risk of crop failure. Integrating additionalempirical data into this network, as it becomes available, will help identify clear opportunities for improvementsin farming practices which should reduce the risk of crop failure into the future.
机译：近年来，在世界热带地区，结合稻米和虾类生产的综合农业系统得到了扩展。这些系统是作为作物轮作形式开发的（在雨季种植水稻，在旱季种植虾），一些农民在雨季同时种植水稻和咸淡水虾。气候变化导致该系统在许多地区造成相当大的农作物损失。研究尚未确定完整的关键风险因素及其潜在的相互作用，以用于水稻对虾的综合养殖。因此，需要澄清可能影响农作物产量的不同耕作方式和环境因素，以指导研究工作。我们基于专家知识应用了分阶段的迭代过程来开发概率贝叶斯信念网络，该知识网络描述了在雨季中综合水稻虾类养殖系统中两种作物均会遭受失败的风险的关系。在更广泛的研究计划中，我们在越南湄公河三角洲南部地区应用了该方法，以研究稻虾养殖系统的可持续性。由此产生的网络代表了专家对生产的主要风险因素及其之间相互作用的看法。虽然农民和推广人员都为确定网络中包含的过程做出了贡献，但农民自己提供了他们之间关系的可能性的估计值。该网络确定了将两种作物减产的风险降到最低的挑战，以及农民可以采取的缓解措施。总体而言，农民认为他们有更大的机会将虾而不是水稻的减产风险降到最低，而且机会有限似乎已经成功地生产了两者。通过让农民参与模型开发的这一过程，我们能够为更广泛的研究团队确定其他研究问题，并确定农民可以采取的减少作物歉收风险的简单步骤。将可用的其他经验数据整合到该网络中，将有助于发现改善耕作方式的明确机会，这将减少未来农作物歉收的风险。

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