Spatiotemporal Variation of Cultivated Land Security and Its Drivers: The Case of Yingtan City, China

doi:10.5814/j.issn.1674-764x.2021.02.014

Abstract

Abstract:

Maintaining an adequate security level of cultivated land is essential for the healthy and sustainable survival of China’s large and growing population. We constructed a cultivated land security evaluation index system, combined with an improved TOPSIS method by taking into account the balance and stability of quantitative, qualitative, and ecological security. We applied this improved method to an evaluation of the state of cultivated land security and analyzed its spatiotemporal variation in Yingtan City (Jiangxi Province, China) from 1995 to 2015. The drivers of the changes in cultivated land security were investigated via a spatial regression model, which can eliminate the effect of spatial autocorrelation. The results showed that cultivated land security decreased rapidly from 1995 to 2005, although it tended to rise slowly in the subsequent period from 2005 to 2015. Areas deemed to be in a highly dangerous state were mainly distributed in the Yuehu District, while those that were secure appeared primarily in the southern mountainous area, with the area in a generally dangerous state extending to the west in the same direction as urban development. Among the examined drivers, social-economic factors and policy factors significantly influenced the cultivated land security. Our work suggests that government managers should take appropriate measures to improve cultivated land security according to its spatiotemporal variations and the underpinning drivers in this region.

Key words: cultivated land security, driving forces, spatiotemporal variation, Yingtan City

KUANG Lihua, YE Yingcong, GUO Xi, XIE Wen, ZHAO Xiaomin. Spatiotemporal Variation of Cultivated Land Security and Its Drivers: The Case of Yingtan City, China[J]. Journal of Resources and Ecology, 2021, 12(2): 280-291.

Figures/Tables 9

References 66

1	Anselin L . 1988. Spatial econometrics: Methods and models. Dordrecht, Netherlands: Springer.
2	Bagherzadeh A, Gholizadeh A . 2016. Parametric-based neural networks and TOPSIS modeling in land suitability evaluation for alfalfa production using GIS. Modeling Earth Systems and Environment, 3(1): 1‒11.
3	Bernués A, Tello-García E, Rodríguez-Ortega T , et al. 2016. Agricultural practices, ecosystem services and sustainability in high nature value farmland: Unraveling the perceptions of farmers and nonfarmers. Land Use Policy, 59: 130‒142.
4	Chen J, Zhang Y L, Chen Z Y , et al. 2015. Improving assessment of groundwater sustainability with analytic hierarchy process and information entropy method: A case study of the Hohhot Plain, China. Environmental Earth Sciences, 73(5): 2353‒2363.
5	Chen L L, Song G, Meadows M E , et al. 2018. Spatio-temporal evolution of the early-warning status of cultivated land and its driving factors: A case study of Heilongjiang Province, China. Land Use Policy, 72: 280‒292.
6	Chen R S, Cai Y L . 2010. Progress in the study of scale issues in land change science. Geographical Research, 29(7): 1244‒1256.
7	Cheng Q W, Jiang P H, Cai L Y , et al. 2017. Delineation of a permanent basic farmland protection area around a city centre: Case study of Changzhou City, China. Land Use Policy, 60: 73‒89.
8	Cumming G S, Buerkert A, Hoffmann E M , et al. 2014. Implications of agricultural transitions and urbanization for ecosystem services. Nature, 515(7525): 50‒57.
9	Deng X Z, Gibson J, Wang P . 2017. Management of trade-offs between cultivated land conversions and land productivity in Shandong Province. Journal of Cleaner Production, 142: 767‒774.
10	Díaz S, Demissew S, Carabias J , et al. 2015. The IPBES conceptual framework—Connecting nature and people. Current Opinion in Environmental Sustainability, 14: 1‒16.
11	Eckersten H, Kornher A, Bergkvist G , et al. 2010. Crop yield trends in relation to temperature indices and a growth model. Journal of Climate Research, 42(2): 119‒131.
12	Elhorst J P . 2003. Specification and estimation of spatial panel data models. International Regional Science Review, 26(3): 244‒268. DOI URL
13	Fang C L, Li G D, Wang S J . 2016. Changing and differentiated urban landscape in China: Spatiotemporal patterns and driving forces. Environmental Science & Technology, 50(5): 2217‒2227.
14	Foley J A, DeFries R, Asner G P , et al. 2005. Global consequences of land use. Science, 309(5734): 570‒574. DOI URL PMID
15	Foley J A, Ramankutty N, Brauman K A , et al. 2011. Solutions for a cultivated planet. Nature, 478(7369): 337‒342.
16	Gao H D, Li Z B, Jia L L , et al. 2016. Capacity of soil loss control in the Loess Plateau based on soil erosion control degree. Journal of Geographical Sciences, 26(4): 457‒472.
17	Hernández Á, Arellano E C, Morales-Moraga D , et al. 2016. Understanding the effect of three decades of land use change on soil quality and biomass productivity in a Mediterranean landscape in Chile. Catena, 140: 195‒204. DOI URL
18	Hong H K, Liao H P, Wei C F , et al. 2015. Health assessment of a land use system used in the ecologically sensitive area of the Three Gorges reservoir area, based on the improved TOPSIS Method. Acta Ecologica Sinica, 35(24): 8016‒8027. (in Chinese).
19	Islam M R, Hassn M Z . 2013. Losses of agricultural land due to infrastructural development: A study on Rajshahi District. International Journal of Scientific & Engineering Research, 4(11): 391‒397.
20	Kuang L H, Ye Y C, Zhao X M , et al. 2018. Evaluation and obstacle factor diagnosis of cultivated land system security in Yingtan City based on the improved TOPSIS method. Journal of Natural Resources, 33(9): 1627‒1641. (in Chinese).
21	Lambin E F, Geist H J, Lepers E . 2003. Dynamics of land-use and land-cover change in tropical regions. Annual Review of Environment and Resources, 28: 205‒241.
22	Li H, Wu Y Z, Huang X J , et al. 2017a. Spatial-temporal evolution and classification of marginalization of cultivated land in the process of urbanization. Habitat International, 61: 1‒8.
23	Li W B, Wang D Y, Li H , et al. 2017b. Urbanization-induced site condition changes of peri-urban cultivated land in the black soil region of northeast China. Ecological Indicators, 80: 215‒223.
24	Lichtenberg E, Ding C R . 2008. Assessing farmland protection policy in China. Land Use Policy, 25(1): 59‒68.
25	Liu J P, Guo Q B . 2015. A spatial panel statistical analysis on cultivated land conversion and Chinese economic growth. Ecological Indicators, 51: 20‒24.
26	Liu J B, Zhang Y, Wang H Y , et al. 2018. Study on the prediction of soil heavy metal elements content based on visible near-infrared spectroscopy. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 19(9): 9‒43.
27	Liu L, Liu Z J, Gong J Z , et al. 2019. Quantifying the amount, heterogeneity, and pattern of farmland: Implications for China’s requisition-com pensation balance of farmland policy. Land Use Policy, 81: 256‒266.
28	Liu T, Liu H, Qi Y J . 2015. Construction land expansion and cultivated land protection in urbanizing China: Insights from national land surveys, 1996-2006. Habitat International, 46: 13‒22.
29	Liu X L, Wang Y, Li Y , et al. 2017. Changes in arable land in response to township urbanization in a Chinese low hilly region: Scale effects and spatial interactions. Applied Geography, 88:24-37. DOI URL
30	Liu Y, Huang X J, Yang H , et al. 2014. Environmental effects of land-use/ cover change caused by urbanization and policies in Southwest China Karst area-A case study of Guiyang. Habitat International, 44: 339‒348. DOI URL
31	Liu Y S, Zhang Y Y, Guo L Y . 2010. Towards realistic assessment of cultivated land quality in an ecologically fragile environment: A satellite imagery-based approach. Applied Geography, 30(2): 271‒281. DOI URL
32	Long A H, Liu J J, Ni C Y , et al. 2006. Assessment on the characteristic of heavy metals contaminated farmland soil around Guixi Smeltery Jiangxi Province. Chinese Journal of Soil Science, 37(6): 1212‒1217.
33	Lu N C, Wei H J, Fan W G , et al. 2018. Multiple influences of land transfer in the integration of Beijing-Tianjin-Hebei region in China. Ecological Indicators, 90: 101‒111. DOI URL
34	Mitsuda Y, Ito S . 2011. A review of spatial-explicit factors determining spatial distribution of land use/land-use change. Landscape and Ecological Engineering, 7(1): 117‒125. DOI URL
35	Negasa T, Ketema H, Legesse A , et al. 2017. Variation in soil properties under different land use types managed by smallholder farmers along the toposequence in southern Ethiopia. Geoderma, 290: 40‒50. DOI URL
36	Niu H P, Fang G Y, Gao H Q , et al. 2011. Cultivated land quantity niche regulation and its environmental effect. Transactions of Nonferrous Metals Society of China, 21: s699‒s705. DOI URL
37	Shi K, Yang Q Y, Li Y Q , et al. 2019. Mapping and evaluating cultivated land fallow in Southwest China using multisource data. Science of the Total Environment, 654: 987‒999. DOI URL
38	Song W . 2014. Decoupling cultivated land loss by construction occupation from economic growth in Beijing. Habitat International, 43: 198‒205. DOI URL
39	Song W, Chen B M, Shi W J , et al. 2011. Evaluation for cultivated land resources security of China in 2007. Progress in Geography, 30(11): 1449‒1455. (in Chinese). DOI URL
40	Song W, Pijanowski B C, Tayyebi A . 2015. Urban expansion and its consumption of high-quality farmland in Beijing, China. Ecological Indicators, 54: 60‒70. DOI URL
41	Song W, Wu K, Zhao H , et al. 2019. Arrangement of high-standard basic farmland construction based on village-region cultivated land quality uniformity. Chinese Geographical Science, 29(2): 325‒340. DOI URL
42	Sterling S M, Ducharne A, Polcher J . 2013. The impact of global land-cover change on the terrestrial water cycle. Nature Climate Change, 3(4): 385‒390. DOI URL
43	Sun B, Zhang L X, Yang L Z , et al. 2012. Agricultural non-point source pollution in China: Causes and mitigation measures. AMBIO, 41(4): 370‒379. DOI URL
44	Takoutsing B, Weber J, Aynekulu E , et al. 2016. Assessment of soil health indicators for sustainable production of maize in smallholder farming systems in the highlands of Cameroon. Geoderma, 27(6): 6‒64.
45	Tan R, Beckmann V, van den Berg L , et al. 2009. Governing farmland conversion: Comparing China with the Netherlands and Germany. Land Use Policy, 26(4): 961‒974. DOI URL
46	Wang J Y, Xin L J, Wang Y H . 2020a. How farmers’ non-agricultural employment affects rural land circulation in China? Journal of Geographical Sciences, 30(3): 378‒400.
47	Wang Q R, Liu R M, Men C , et al. 2018. Application of genetic algorithm to land use optimization for non-point source pollution control based on CLUE-S and SWAT. Journal of Hydrology, 56(0): 0‒86.
48	Wang Y H, Li X B, Xin L J , et al. 2020b. Farmland marginalization and its drivers in mountainous areas of China. Science of the Total Environment, 719:135132. DOI: 10.1016/j.scitotenv.2019.135132.
49	Wang Z, Wang L M, Xu R N , et al. 2012. GIS and RS based assessment of cultivated land quality of Shandong Province. Procedia Environmental Sciences, 12: 823‒830. DOI URL
50	Wiik L, Ewaldz T . 2009. Impact of temperature and precipitation on yield and plant diseases of winter wheat in southern Sweden 1983‒2007. Crop Protection, 28(11): 952‒962.
51	Xie H L, He Y F, Xie X . 2017. Exploring the factors influencing ecological land change for China’s Beijing-Tianjin-Hebei Region using big data. Journal of Cleaner Production, 142: 677‒687.
52	Xie H L, Yao G R, Liu G Y . 2015. Spatial evaluation of the ecological importance based on GIS for environmental management: A case study in Xingguo County of China. Ecological Indicators, 51: 3‒12.
53	Xu D D, Cao S, Wang X X , et al. 2018. Influences of labor migration on rural household land transfer: A case study of Sichuan Province, China. Journal of Mountain Science, 15(9): 2055‒2067.
54	Yoon K P, Kim W K . 2017. The behavioral TOPSIS. ExpertSystems with Applications, 89: 266‒272.
55	Yu B H, Zheng X Q . 2003. The blindness of farmland crisis and urbanization process. Urban Problem, 3: 58‒60. (in Chinese)
56	Yu W H, Zang S Y, Wu C S , et al. 2011. Analyzing and modeling land use land cover change (LUCC) in the Daqing City, China. Applied Geography, 31(2): 600‒608.
57	Zareie A, Sheikhahmadi A, Khamforoosh K . 2018. Influence maximization in social networks based on TOPSIS. Expert Systems with Applications, 108: 96‒107. DOI URL
58	Zdruli P, Calabrese J, Ladisa G , et al. 2014. Impacts of land cover change on soil quality of manmade soils cultivated with table grapes in the Apulia Region of south-eastern Italy. Catena, 12(1): 1‒13.
59	Zhang J Z, He C X, Chen L , et al. 2018. Improving food security in China by taking advantage of marginal and degraded lands. Journal of Cleaner Production, 171: 1020‒1030.
60	Zhang X, Davidson E A, Mauzerall D L , et al. 2015. Managing nitrogen for sustainable development. Nature, 528(7580): 51‒59.
61	Zhang Y M, Zhou C H, Zheng C H , et al. 2006. Spatial land use patterns in Guyuan County: Simulation and analysis at multi-scale level. Resources Science, 28(2): 88‒9. (in Chinese)
62	Zhang Z F, Zhao W, Gu X K . 2014. Changes resulting from a land consolidation project (LCP) and its resource-environment effects: A case study in Tianmen City of Hubei Province, China. Land Use Policy, 40: 74‒82.
63	Zhao H B, Ma Y J . 2014. Spatial-temporal pattern and obstacle factors of cultivated land ecological security in major grain producing areas of Northeast China: A case study in Jilin Province. Chinese Journal of Applied Ecology, 25(2): 515‒524. (in Chinese)
64	Zheng H W, Zhang R, Meng Z , et al. 2015. Diagnosis on cultivated land ecological security based on the PSR model and set pair analysis. China Land Sciences, 29(12): 42‒5. (in Chinese)
65	Zhong T Y, Qian Z, Huang X J , et al. 2018. Impact of the top-down quota-oriented farmland preservation planning on the change of urban land-use intensity in China. Habitat International, 77: 71‒79.
66	Zhou J, Liang J N, Hu Y M , et al. 2018. Exposure risk of local residents to copper near the largest flash copper smelter in China. Science of the Total Environment, 630: 453‒461.

Criteria layer	Indicator layer		Indicator description	Weight
Quantitative security	Cultivated land retention (n₁)		The area of cultivated land in the year / the area of cultivated land in 2015	0.3254
	Per capita cultivated area (n₂)		Cultivated land area / population (ha per person)	0.3186
	Cultivated land supplement coefficient (n₃)		Increased area of cultivated land in the same year / reduced area of cultivated land	0.3560
Qualitative security	Natural qualities	Effective soil thickness (n₄)	Reflecting the depth of soil effective tillage	0.0817
		Soil texture (n₅)	Reflecting soil physical properties	0.0686
		Soil pH (n₆)	Reflecting the acidity and alkalinity of cultivated soil	0.0883
		Soil organic matter (n₇)	Reflecting the soil fertility level of cultivated land	0.1972
	Farming conditions	Irrigation guarantee rate (n₈)	Reflecting the ability of irrigation and water conservancy	0.1750
		Drainage condition (n₉)	Reflecting the ability of arable land to drain water	0.1051
		Plot flatness (n₁₀)	Reflecting the steepness of the cultivated land surface	0.0431
		Road density (n₁₁)	Reflecting road accessibility, Road length / spot area (m ha^‒1)	0.1606
		Cultivated land concentration (n₁₂)	The concentration of cultivated land in space	0.0804
Ecological security	Forest coverage (n₁₃)		Forest area / total land area	0.3381
	Proportion of soil erosion area of cultivated land (n₁₄)		Soil erosion area / total cultivated area	0.1362
	Fertilizer load per unit area (n₁₅)		Fertilizer application rate / cultivated area (kg ha^‒1)	0.1957
	Pesticide load per unit area (n₁₆)		Pesticide application rate / cultivated area (kg ha^‒1)	0.1587
	Heavy metal pollution (n₁₇)		Reflecting the degree of damage by heavy metals	0.1713

Criteria layer	Indicator layer		Indicator description	Weight
Quantitative security	Cultivated land retention (n₁)		The area of cultivated land in the year / the area of cultivated land in 2015	0.3254
	Per capita cultivated area (n₂)		Cultivated land area / population (ha per person)	0.3186
	Cultivated land supplement coefficient (n₃)		Increased area of cultivated land in the same year / reduced area of cultivated land	0.3560
Qualitative security	Natural qualities	Effective soil thickness (n₄)	Reflecting the depth of soil effective tillage	0.0817
		Soil texture (n₅)	Reflecting soil physical properties	0.0686
		Soil pH (n₆)	Reflecting the acidity and alkalinity of cultivated soil	0.0883
		Soil organic matter (n₇)	Reflecting the soil fertility level of cultivated land	0.1972
	Farming conditions	Irrigation guarantee rate (n₈)	Reflecting the ability of irrigation and water conservancy	0.1750
		Drainage condition (n₉)	Reflecting the ability of arable land to drain water	0.1051
		Plot flatness (n₁₀)	Reflecting the steepness of the cultivated land surface	0.0431
		Road density (n₁₁)	Reflecting road accessibility, Road length / spot area (m ha^‒1)	0.1606
		Cultivated land concentration (n₁₂)	The concentration of cultivated land in space	0.0804
Ecological security	Forest coverage (n₁₃)		Forest area / total land area	0.3381
	Proportion of soil erosion area of cultivated land (n₁₄)		Soil erosion area / total cultivated area	0.1362
	Fertilizer load per unit area (n₁₅)		Fertilizer application rate / cultivated area (kg ha^‒1)	0.1957
	Pesticide load per unit area (n₁₆)		Pesticide application rate / cultivated area (kg ha^‒1)	0.1587
	Heavy metal pollution (n₁₇)		Reflecting the degree of damage by heavy metals	0.1713

Feature class	Driver	Instruction	Predicted influence direction
Natural factors	The effective accumulated temperature (X₁)	Sum of the average temperature>10 ℃ per year	+
Natural factors	Precipitation (X₂)	Unit: mm	+
Socio-economic factors	Per capita income of farmers (X₃)	Changes in farmers’ income levels	+
Socio-economic factors	Agricultural mechanization level (X₄)	Unit: ha per agricultural machine	+
Policy factors	Investment in environmental governance as a proportion of GDP (X₅)	Environmental governance investment / GDP	+
	Protection of cultivated land (X₆)	Increased area of cultivated land / reduced area of cultivated land	+
	Input of agricultural technicians (X₇)	Unit: ha per person	+

Feature class	Driver	Instruction	Predicted influence direction
Natural factors	The effective accumulated temperature (X₁)	Sum of the average temperature>10 ℃ per year	+
Natural factors	Precipitation (X₂)	Unit: mm	+
Socio-economic factors	Per capita income of farmers (X₃)	Changes in farmers’ income levels	+
Socio-economic factors	Agricultural mechanization level (X₄)	Unit: ha per agricultural machine	+
Policy factors	Investment in environmental governance as a proportion of GDP (X₅)	Environmental governance investment / GDP	+
	Protection of cultivated land (X₆)	Increased area of cultivated land / reduced area of cultivated land	+
	Input of agricultural technicians (X₇)	Unit: ha per person	+

Security state	1995	2005	2015
Generally secure	8.01	6.14	5.95
Critically secure	91.32	48.96	60.72
Generally dangerous	0.67	37.55	27.38
Highly dangerous	0	7.36	5.95