Prediction of Major Agricultural Fruits Production in Pakistan by Using an Econometric Analysis and Machine Learning Technique |
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| Authors: | Abdul Rehman Zhang Deyuan Imran Hussain Muhammad Shahid Iqbal Yang Yang Luan Jingdong |
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| Institution: | 1. Research Center of Agricultural-Rural-Peasants, Anhui University Hefei, Chinaabdlrehman@ahu.edu.cn zayan78611@yahoo.com;3. Research Center of Agricultural-Rural-Peasants, Anhui University Hefei, China;4. Allama Iqbal Open University Islamabad, Pakistan;5. School of Computer Science and Technology, Anhui University Hefei, China;6. College of Economics &7. Management, Anhui Agricultural University Hefei, China |
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| Abstract: | ABSTRACTThe aim of this study was to use an econometric analysis to investigate the relationship between agricultural gross domestic product (AGDP) and variables such as apple, citrus, pears, grape and banana in Pakistan; data were explored from 1980 to 2015; we used time series data collected from secondary sources, including the Pakistan Bureau of Statistics, Statistical Year Books and the Economic Survey of Pakistan. Data were analyzed by using the Ordinary Least Square (OLS) method and Augmented Dickey Fuller (ADF) test, and results were interpreted by using the Johansen co-integration test. The machine learning technique was used to examine and predict the future agricultural productivity in Pakistan. We found that output of banana, citrus and pears had a positive and significant influence on AGDP, whereas apples and grapes had a negative but insignificant influence on AGDP. |
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| Keywords: | Fruits production AGDP ADF agricultural development machine learning technique |
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