Characterising the Impact of Drought on Jowar (Sorghum spp) Crop Yield Using Bayesian Networks
| Autor: | Leisa Armstrong, Shubhangi S. Wankhede |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
010504 meteorology & atmospheric sciences
biology business.industry Crop yield Bayesian network 04 agricultural and veterinary sciences Agricultural engineering Sorghum biology.organism_classification 01 natural sciences Naive Bayes classifier Agriculture Natural hazard Evapotranspiration 040103 agronomy & agriculture 0401 agriculture forestry and fisheries business Cropping 0105 earth and related environmental sciences Mathematics |
| Zdroj: | Advances in Intelligent Systems and Computing ISBN: 9783319763477 ISDA |
| Popis: | 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 Naive 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. |
| Databáze: | OpenAIRE |
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