Research on early warning and control measures for arable land resource security

Abstract

Liaoning Province is an important commercial grain production base with abundant arable land resources and total grain yield; however, most of the arable land is of low-quality grade, and less than one-third is highly productive. This study utilized a distinct region in Liaoning Province as the study area and introduced a pressure-state-response model using data related to the quantity, quality, ecology and socio-economics of the arable land. This study constructed an early warning system for arable land resource security and used machine learning to understand the early warning state of arable land resource security. Finally, this study analyzed the spatial and temporal change characteristics of arable land resource security warning and proposed a regulation and control scheme for arable land resource security in the study area. It is essential to achieve equal sustainability of food security, economic development and ecological protection and guarantee the safe and smooth operation of arable land resources. The research results showed: (1) The arable land resource security values in the study area from 1999 to 2018 showed an increasing yearly trend from 0.3 to 0.7. The predicted total arable land resource security values from 2019 to 2023 gradually increased; therefore, the future situation faced by the arable land resource security in the study area will improve. (2) The number of medium and heavy alarms for arable land resource security in the study area from 1999 to 2018 showed a decreasing yearly trend, and the spatial pattern showed a change from concentrated to dispersed. (3) The key driving factors in the study area were gross domestic product per capita and natural population growth rate, which fundamentally drove the changes in arable land resource security alerts. National regulations and the approval process for urban construction land must be strictly enforced to avoid the rapid growth of the urban market economy.