The Production and prediction of major chinese agricultural fruits using an econometric analysis and machine learning technique

This paper investigates and explores the relationship between agricultural gross domestic product (AGDP) and major fruit output: apple, citrus, pears, grapes and bananas in China. The ordinary least square (OLS) method and the augmented Dickey-Fuller (ADF) test were used to analyze the data, and the Johansen co-integration test was used to interpret the results. The machine learning technique was used to examine and to predict future agricultural productivity in China. Our study found that the coefficient of the apple fruit output has a significant or positive relationship with the AGDP. The results also show that the output of citrus, grapes and pears have coefficients that demonstrate a positive relationship with the AGDP, while the banana fruit output bears a negative relationship with China’s AGDP and is statistically insignificant. The use of an econometric analysis and machine learning technique to examine the relationship between the AGDP and the output from major fruits production in China makes the current study unique. A review of the literature suggests that only limited research has been conducted in this area. Key words: Major fruits, AGDP, ADF, production, machine learning technique.