Characterising the impact of drought on Jowar (Sorghum spp) crop yield using bayesian networks
Intelligent Systems Design and Applications
School of Science
Drought is a complex, natural hazard that affects the agricultural sector on a large scale. Although the prediction of drought can be a difficult task, understanding the patterns of drought at temporal and spatial level can help farmers to make better decisions concerning the growth of their crops and the impact of different levels of drought. This paper studied the use of Bayesian networks to characterise the impact of drought on jowar (Sorghum spp) crop in Maharashtra state on India. The study area was 25 districts on Maharashtra which were selected on the basis of data availability. Parameters such as rainfall, minimum, maximum and average temperature, potential evapotranspiration, reference crop evapotranspiration and crop yield data was obtained for the period from year 1983 to 2015. Bayes Net and Naïve Bayes classifiers were applied on the datasets using Weka analysis tool. The results obtained showed that the accuracy of Bayes net was more than the accuracy obtained by Naive Bayes method. This probabilistic model can be further used to manage and mitigate the drought conditions and hence will be useful to farmers in order to plan their cropping activities.