Explorations of a Bayesian Belief Network for the Simultaneous Farming of Rice and Shrimp Crops

Abstract

Efficiencies in farming practice in many parts of South East Asia can make substantial, positive differences to villages and communities. The use of automated decision-assistance tools such as Bayesian Belief Networks (BBNs) can help to accomplish this. For the problem described herein, farmers attempt to grow both rice and shrimp crops in the same physical area. The motivation becomes one of finding a set of conditions that minimises the probabilities of crop failures. In this work, we explore an existing BBN and determine a range of likely environmental scenarios and the factors that farmers can control to help improve the likelihood of harvesting successful rice and shrimp crops.