Spatiotemporal Patterns and Driving Factors of Non-Grain Cultivated Land in China’s Three Main Functional Grain Areas

Abstract

Food security, fundamental to national security, is challenged by the non-grain conversion of cultivated land. Based on the social and economic statistical data in China, this paper explores the spatiotemporal patterns and driving factors of non-grain cultivated land nationwide and in China’s three main functional grain areas during 2000–2020 with the help of the GIS Spatial Analysis and Spatial Metrology Model. The results show, first, that non-grain conversion initially increased but later decreased, with the non-grain level increasing from 30.61% in 2000 to 34.78% in 2003 and then decreasing to 30.28% in 2020; vegetables, fruits, and medicinal herbs were the main non-grain crops in most areas. Second, the non-grain levels showed an obvious spatial agglomeration state; the regions with low non-grain levels were located in the main grain-producing areas, and although the non-grain conversion levels in the main grain-producing areas decreased, the non-grain levels in these areas were clearly lower in the north than in the south. Moreover, the non-grain conversion levels in the main grain-sales areas and the grain production and sales balance areas increased. Third, rural population size, per capita GDP, the proportion of primary industry, and the land transfer rate are important drivers of the non-grain conversion of cultivated land nationally, but there are also significant spatial differences in the influence of these driving factors in different functional grain areas. This paper provides a new research perspective for analyzing the influencing mechanisms of cultivated non-grain land from the three functional grain areas. At the same time, it reveals the roots of the government’s predicament in the governance of non-grain cultivated land. It provides a reference for the government to formulate new policies for managing non-grain cultivated land.

Impact of global warming on labor productivity in the Chengdu-Chongqing economic circle, China

“…Future development planning for the CCEC must consider the risk of heat and reduce its impact on labor productivity. Heat has the greatest impact on agriculture and the CCEC is an important food production area in China [33]. Therefore, the development of climate-smart agricultural technologies and management systems that can cope with heat is a direction of development.…”

Spatial Characteristics of the Non-Grain Production Rate of Cropland and Its Driving Factors in Major Grain-Producing Area: Evidence from Shandong Province, China

“…However, when detecting the NGPR of cropland, the existing literature usually considers the cropland as a whole and does not classify the cropland in detail [49]. In addition, most of the NGPR calculations in previous research were based on the statistical data or land use transfer data, which may lead to uncertainties [15,19] The level of the NGPR of cropland is closely correlated to global food security and the green development of society because it is one of the key parameters that can determine grain production [50]. The regional food security is under threat if a region has high level of the NGPR of cropland, regardless of the size of the existing area of cropland [51,52].…”

“…However, when detecting the NGPR of cropland, the existing literature usually considers the cropland as a whole and does not classify the cropland in detail [49]. In addition, most of the NGPR calculations in previous research were based on the statistical data or land use transfer data, which may lead to uncertainties [15,19]. Therefore, this research classified the cropland into paddy land, irrigated land, and dry land on account of the actual condition of the cropland use in the case region.…”

“…The existing methods for calculating of the NGP of cropland have these two problems: (1) using the statistical yearbook data or household survey data can enable large-scale research but it cannot reflect the detailed information for the cropland; (2) the machine learning algorithm can collect the NGP information at the patch scale; however, assessing its accuracy is difficult [15,17]. Moreover, several studies explored the driving factors of the NGP of cropland with various methods, for instance, the logistic regression model [18], the spatial Durbin model [12], the multiple regression model [19], and the geographical detector model [20]. These studies persistently have a research gap in exploring the spatial heterogeneity of the potential driving factors on the NGP of cropland based on the detailed survey data of the land patch.…”

Progress and Prospects of Non-Grain Production of Cultivated Land in China

“…In some cases, researchers also use the proportion of income spent on food or food expenditures as a share of total income [34]. Additionally, scholars have utilized various methods to investigate the driving factors of NGPCL, including the logistic regression model [35], spatial Durbin model [3], multiple regression model [28,36], geographical detector model [37], spatial econometric model [29], spatial autocorrelation analysis [38], and Tobit model [26]. These research methods and findings have provided valuable insights for understanding and studying the issue of NGPCL.…”