Luan Jingdong

An econometric analysis of major Chinese food crops: An empirical study

Abstract The basic objective of this study was to investigate and explore the relationship between major food crops of China and their relationship with agricultural gross domestic product (GDP) using an econometric analysis. Agriculture is considered an important sector of the Chinese economy as it accounted for about 10% of GDP. The total agricultural land of China covers 36% of the area of the world. In order to highlight the actual performance of the agricultural production and the output of major food crops, this study explored the relationship between agricultural GDP and the major crops output including wheat, cotton, rice, sugarcane, corn, and tubers in China over the period of 35 years from 1980 to 2015. The time series data were collected from the China Bureau of Statistics, Ministry of Agriculture China and various publications. Crop data were analyzed using the Ordinary Least Square Method and Augmented Dickey Fuller test and results were interpreted using the Johansen co-integration test. Our study found that output of wheat, cotton, sugarcane, corn, and tubers has positive and significant relation with the agricultural gross domestic product of China, while the output of rice crop has a negative but no significant relation with agricultural GDP of China. The study suggests that the Government of China should start new funding schemes for the development and better production of rice crops.

Prediction of Major Agricultural Fruits Production in Pakistan by Using an Econometric Analysis and Machine Learning Technique

ABSTRACT The 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.