A Bayesian Approach to Evaluating the Dynamics of Rice Production in Madagascar

Abstract

In Madagascar, domestic rice production does not meet the local demand. Thus, increasing productivity is crucial for ensuring food security for a booming population. The last two decades have been marked by technological improvements in support of a vision of agricultural development. The main objective of the present study is to evaluate rice productivity in Madagascar based on changes in technology and the planted area during the period from 1961 to 2017. To conduct our analysis, we construct a set of statistical models involving time-varying parameters that capture the changes in productivity and progress in rice production technology. To estimate these time-varying parameters, we apply Bayesian methods based on the smoothness prior approach. The estimates for variances in system noise show that the proposed model is well fitted to the data. In addition, the results provide the interesting finding that technological change is estimated to be elastic, with values increasing from 1 to 8 during the six decades of the study period. However, the planted area estimates are inelastic, despite positive values fluctuating around 0.9–1. Thus, rice productivity in Madagascar is highly dependent on technology, although more time is required before a positive response is seen.