Spatio-temporal differences and factors influencing intensive cropland use in the Huang-Huai-Hai Plain

doi:10.1007/s11442-018-1533-6

Abstract

Abstract:

This study developed a comprehensive system to evaluate the intensity of cropland use and evolution of cropland use in the Huang-Huai-Hai Plain. Delphi-entropy methods were adopted to determine the weight of the index, and the GeoDetector model was established to explore the influencing factors. The results are summarized as follows: (1) The intensity of inputs, degree of utilization, and production increased continuously, but the intensity of continuous conditions experienced an overall decline followed by a rebound towards the end of the study period. The number of counties with high and moderately high intensity increased by 56.8% and 14.6%, respectively, from 1996 to 2011. The number of counties with moderately low and low intensity declined by 35.9 % and 11.9 %, respectively. Areas with significant increases in intensity were mainly distributed in northeast Hebei Province, northwest Shandong Province, and north Jiangsu Province. The intensity is high in northern Jiangsu and Anhui; the output effect remained above moderate intensity mainly near Beijing, Tianjin, Tangshan, and counties in the suburbs of Shijiazhuang. (2) Natural disasters, elevation, slope, and road networks were the main factors influencing the intensity of cropland use in this region, with influence values of 0.158, 0.143, 0.129, and 0.054, respectively. Areas with moderately high and high levels of intensity were distributed in low-lying areas. Uneven distribution of precipitation, seasonal drought, and flood disasters can directly affect the stability index of croplands and reduce the intensity of cropland use. Developed road networks are associated with moderately high intensity. Our results suggest recommendations such as promoting agricultural intensification and large-scale management, promoting the construction of road networks, improving early warning systems for drought and flood disasters, and promoting moderate and intensive use of arable land, and focusing on restoration and sustainable use of cropland.

Key words: intensive cropland use, spatio-temporal difference, influence mechanism, GeoDetector model, Huang-Huai-Hai Plain

Shuqin SHI, Yu HAN, Wentao YU, Yuqing CAO, Weimin CAI, Peng YANG, Wenbin WU, Qiangyi YU. Spatio-temporal differences and factors influencing intensive cropland use in the Huang-Huai-Hai Plain[J].Journal of Geographical Sciences, 2018, 28(11): 1626-1640.

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Target layer	Criterion layer	Index layer	Metering method	Effect	Weights
Intensive utilization of cultivated lands	Input intensity	Labor input per unit area	Population employed in agriculture per unit area of cropland	+	0.2565
		Fertilizer input per unit area	Application of fertilizers per unit area of cropland	+	0.2148
		Pesticide input per unit area	Application of pesticides per unit area of cropland	+	0.2850
		Power input per unit area	Total power of agricultural machinery per unit area of cropland	+	0.2437
	Utilization degree	Cultivation index	Area of cropland/Total area of all lands	+	0.2252
		Irrigation index	Area of effective irrigation per unit area of cropland	+	0.2235
		Stable yield index	Security areas/Seeded area	+	0.5515
	Output effect	Output value of every farmer	Total output value of agriculture/ Population of agricultural employment	+	0.5646
		The production of foodstuff	Total production of foodstuff/Seeded area	+	0.1613
		The production of foodstuff of every farmer	Total production of foodstuff/Population employed in agriculture	+	0.2740
	Continued situation	Non-agriculture index	Non-agriculture/Total population	-	0.6725
	Continued situation	Per capita net area of croplands	Total area of lands/Total population	+	0.3275

	Influencing factors	Code	Variables	Unit	Influence value	Proportion (%)
Natural resources conditions	Elevation	X1	Average elevation	m	0.143	23.55
	Slope	X2	Average slope	°	0.129	21.23
	Temperature	X3	Annual average temperature	℃	0.042	6.94
	Precipitation	X4	Annual average precipitation	mm	0.051	8.37
	Natural disasters	X5	Natural hazard rate	%	0.158	25.9
Social, economic, and policy conditions	Economic development state	X6	Gross domestic product	10,000	0.013	2.17
	Supportive policy	X7	Agricultural subsidy	10,000	0.006	1.03
	Urbanization extent	X8	Built-up area	Ha	0.012	2.03
	Transport condition	X9	Density of road network	m/ha	0.054	8.78