Spatial Evolution, Driving Mechanism, and Patch Prediction of Grain-Producing Cultivated Land in China

Abstract

China has implemented strict policies for protecting cultivated land, and the Chinese government has focused on the non-grain production (NGP) of cultivated land. This study aimed to analyze the spatial evolution law of grain-producing cultivated land (GPCL) in China between 2000 and 2018, explore the mechanism of GPCL, and simulate the spatial characteristics of GPCL in 2036. We used the Geographic Information System (GIS) and a patch-generating land-use simulation model, a new model that proposes a land expansion analysis strategy by improving previous rule-mining methods. China’s grain production rate (GPrate) shows a gradual upward trend between 2000 (36.98%) and 2018 (47.18%). The mutual conversion of GPCL and non-grain-producing cultivated land (NGPCL) are the primary transfer types. The evolution of GPCL is driven by climatic, economic, and social factors, of which population density is the most important factor. GPCL expansion patches are distributed in densely populated, economically developed, and warm and humid plain areas. Further, the simulation results showed that the GPrate in 2036 is estimated to be 41.39%, with GPCL transfer-in significantly exceeding the amount transferred out. Our results further cultivated land evolution-associated research and provide a basis for formulating scientific land-use policies for cultivated land protection for other countries.

A Study on the Spatial–Temporal Evolution and Driving Factors of Non-Grain Production in China’s Major Grain-Producing Provinces

“…In this study, the non-grain production ratio was used as the explanatory variable. Concerning existing studies and data availability, 15 indicators were selected from natural conditions, resource endowment of grain production, agricultural science and technology level, urban–rural gap, agricultural production efficiency, social development, and economic development to perform the analysis of influencing factors ( Table 1 ) [ 27 , 58 , 59 , 60 ].…”

“…In terms of the mechanisms driving the phenomenon of non-grain production, some scholars have explored the analysis from the perspectives of cost and benefit [ 17 , 18 ], agricultural land transfer [ 19 ], industrial and commercial capital [ 20 ], policy understanding [ 21 ], farm household characteristics [ 22 ], labor transfer [ 23 ], cultivated land rental [ 24 ], and local government behavior [ 25 ]. Rising costs and declining returns of grain production [ 26 ], increased economic efficiency of non-grain economic crops [ 27 , 28 ], and dislocated government regulation [ 29 ] are important factors contributing to the serious phenomenon of non-grain production. In terms of the impact of non-grain production on cultivated land, different non-grain crops will have different impacts.…”

Spatio-Temporal Evolution and Multi-Scenario Simulation of Non-Grain Production on Cultivated Land in Jiangsu Province, China

“…The time interval for this study was 19 years, so we used CARS to predict the 2038 landscape based on the information and analysis obtained from the LEAS module. [39,40] Distance to highway [35] Distance to primary road [35,36] Distance to secondary road [35,36] Distance to railroad [35] Pontious et al [41,42] found that the Figure of Merit (FoM) index is superior to the Kappa coefficient in evaluating the accuracy of the simulated changes, so the FoM index was used for the accuracy evaluation of the simulations. Its calculation formula is:…”

Spatiotemporal Analysis and Prediction of Urban Land Use/Land Cover Changes Using a Cellular Automata and Novel Patch-Generating Land Use Simulation Model: A Study of Zhejiang Province, China

“…The LEAS module helps determine land use alteration using multiple patches obtained between the two nodes of LULC alteration. It utilizes a random forest (RF) algorithm in mining the potential variables of land use changes [53]. The modeling process requires overlaying two-time node land use data and extracting the spatial data for each LULC type.…”