Nonlinear Effects of Agricultural Technology on Sustainable Grain Production in China
Bizhen Chen
1343700050@qq.comMinnan University of Science and Technology (China)
https://orcid.org/0009-0000-8559-9996
Dehong Sun
Minnan University of Science and Technology (China)
Abstract
Grain production is an important element of the United Nations Sustainable Development Goals, regarding livelihoods and social stability. This article uses data on agricultural technology, social factor and grain production in China from 2011 to 2022, and uses the Generalized Additive Model (GAM) to deeply explore the nonlinear impact of agricultural technology and social factor on grain production. The results of the study show that (1) China’s grain output is generally on a growing trend, but the growth rate is declining and fluctuating significantly. There is a significant difference in grain production before and after the COVID-19 epidemic. Moreover, the output in the northern region is significantly higher than that in the south. (2) Except for Consumption expenditure per capita, all other agricultural technology and social factor variables are positively correlated with grain out. (3) The impact of agricultural technology and social factor on grain output shows significant non-linear characteristics, and its impact effect varies in different intervals. Specifically, When the value of the agricultural meteorological observation service station is 20-25, the effective irrigation area is greater than 1800, consumption expenditure per capita greater than 17000 and the total sowing area of crops is 7500, it can significantly increase grain yield. On the contrary, if the emission value of chemical oxygen demand exceeds 130, it has a significant inhibitory effect on grain yield. Furthermore, the effect on grain yield peaks when the total power of agricultural machinery, GDP, and the number of unemployed people in cities approach 3000, 10000, and 20, respectively. The results of the study provide an important basis for optimizing the allocation of agricultural resources and enhancing the efficiency of grain production. Finally, some practical policy recommendations are put forward.
Keywords:
grain production, sustainable development, machine learning, generalized additive models, agricultural technology, social factorReferences
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Authors
Bizhen Chen1343700050@qq.com
Minnan University of Science and Technology China
https://orcid.org/0009-0000-8559-9996
Authors
Dehong SunMinnan University of Science and Technology China
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