Estimation and prediction of Jatropha cultivation areas in China and India

The utilization of second-generation biofuel has been known as one of the sustainable solutions for energy challenges worldwide. Jatropha curcas L. (JCL), as a non-edible feedstock with high oil content fruits, has a promising potential for biodiesel production. JCL can grow in semi-arid and wasteland areas and has attracted many interests in recent years. Making efficient decisions for the production of JCL biofuels requires accurate forecasting of JCL cultivation areas. The present study develops an artificial neural network (ANN) model to predict the cultivation areas for JCL cultivation in China and India. Different structures of the ANN model are investigated for the ANN model, and the most appropriate one is determined. Eight input parameters were selected according to their impact on JCL development, including CO2 emission, Gross Domestic Product (GDP), oil price, fossil fuel energy consumption, renewable energy consumption, total energy use, population, and value-added agriculture. The mean absolute percentage error (MAPE) was used to validate the ANN models. The acquired results illustrate that the precision accuracy of the applied ANN model is 99.8%. Also, the results show that the JCL cultivation areas have increased by about 20% in China and have decreased by about 5% in India from 2010 to 2015. Finally, the achieved results from the ANN model are compared with different regression models to show the advantages of the ANN model. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

The study developed an artificial neural network (ANN) model to predict the cultivation areas for JCL cultivation in China and India with a precision accuracy of 99.8%. The results showed an increase of about 20% in JCL cultivation areas in China and a decrease of about 5% in India from 2010 to 2015. Additionally, the advantages of the ANN model over different regression models were demonstrated.