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Smart agriculture (SmartAg) has emerged as a rich domain for AI-driven decision support systems (DSS); however, it is often challenged by user-adoption issues. This paper reports a case-based reasoning (CBR) system, PBI-CBR, that predicts grass growth for dairy farmers, that combines predictive accuracy and explanations to improve user adoption. PBI-CBR's key novelty is its use of Bayesian methods for case-base maintenance in a regression domain. Experiments report the tradeoff betweendoi:10.24963/ijcai.2020/649 dblp:conf/ijcai/ChenSHLML20 fatcat:cjn73bwpqzdcheoctkkl62fokq