Effect of climate change on fruit by co-integration and machine learning

Abstract

Purpose The purpose of this paper is to assess the impacts on production of five fruit crops from 1961 to 2018 of energy use, CO2 emissions, farming areas and the labor force in China. Design/methodology/approach This analysis applied the autoregressive distributed lag-bound testing (ARDL) approach, Granger causality method and Johansen co-integration test to predict long-term co-integration and relation between variables. Four machine learning methods are used for prediction of the accuracy of climate effect on fruit production. Findings The Johansen test findings have shown that the fruit crop growth, energy use, CO2 emissions, harvested land and labor force have a long-term co-integration relation. The outcome of the long-term use of CO2 emission and rural population has a negative influence on fruit crops. The energy consumption, harvested area, total fruit yield and agriculture labor force have a positive influence on six fruit crops. The long-run relationships reveal that a 1% increase in rural population and CO2 will decrease fruit crop production by −0.59 and −1.97. The energy consumption, fruit harvested area, total fruit yield and agriculture labor force will increase fruit crop production by 0.17%, 1.52%, 1.80% and 4.33%, respectively. Furthermore, uni-directional causality is correlated with the growth of fruit crops and energy consumption. Also, the results indicate that the bi-directional causality impact varies from CO2 emissions to agricultural areas to fruit crops. Originality/value This study also fills the literature gap in implementing ARDL for agricultural fruits of China, used machine learning methods to examine the impact of climate change and to explore this important issue.

Deep Learning-Based Growth Prediction System: A Use Case of China Agriculture

“…This study used the image data of 5000 fruits and predicted the accuracy of more than 90% of them. Similarly, Khan et al [55] finds effect of cimate change on fruit by cointegration and machine learning methods with an accuracy of 90.00 ± 2%. Dang et al [56] presented a used convolution neural network (CNNs) and Efficient Net architecture for fruit recognition using the 360 fruit dataset.…”