This article discusses the potential of BNs to complement the analytical toolkit of agricultural extension. Statistical modelling of the adoption of agricultural practices has tended to use categorical (logit/probit) regression models focusing on a single technology or practice, explained by a number of household and farm characteristics. Here, a Bayesian network (BN) is used to model household-level data on adoption of agrosilvopastoral practices in Tiby, Mali. We discuss the advantages of BNs in modelling more complex data structures, including (i) multiple practices implemented jointly on farms, (ii) correlation between probabilities of implementation of those practices and (iii) correlation between household and farm characteristics. This paper demonstrates the use of BNs for 'deductive' reasoning regarding adoption of practices, answering questions regarding the probability of implementation of combinations of practices, conditional on household characteristics. As such, BNs is a complementary modelling approach to logistic regression analysis, which facilitates exploring causal structures in the data before deciding on a reduced form regression model. More uniquely, BNs can be used 'inductively' to answer questions regarding the likelihood of certain household characteristics conditional on certain practices being adopted.