Progress on quantitative assessment of the impacts of climate change and human activities on cropland change

doi:10.1007/s11442-016-1272-5

Abstract

Abstract:

It is important to study the contributions of climate change and human activities to cropland changes in the fields of both climate change and land use change. Relationships between cropland changes and driving forces were qualitatively studied in most of the previous researches. However, the quantitative assessments of the contributions of climate change and human activities to cropland changes are needed to be explored for a better understanding of the dynamics of land use changes. We systematically reviewed the methods of identifying the contributions of climate change and human activities to cropland changes at quantitative aspects, including model analysis, mathematical statistical method, framework analysis, index assessment and difference comparison. Progress of the previous researches on quantitative evaluation of the contributions was introduced. Then we discussed four defects in the assessment of the contributions of climate change and human activities. For example, the methods were lack of comprehensiveness, and the data need to be more accurate and abundant. In addition, the scale was single and the explanations were biased. Moreover, we concluded a clue about quantitative approach to assess the contributions from synthetically aspect to specific driving forces. Finally, the solutions of the future researches on data, scale and explanation were proposed.

Key words: climate change, human activities, contributions, quantitative, cropland pattern

Xiaoli SHI, Wei WANG, Wenjiao Shi. Progress on quantitative assessment of the impacts of climate change and human activities on cropland change[J].Journal of Geographical Sciences, 2016, 26(3): 339-354.

Figures/Tables 1

Table 1

Methods for identifying the contributions of climate change and human activities to cropland change"

Methods	Study areas	Periods	Models	Literatures
Model analysis	China	2000-2005	CLUE-S, Dinamica EGO	Gao and Yi, 2012
	Europe	2000-2050	ROIMPEL, SFARMMOD	Audsley et al., 2006
	Global	2000-2080	AEZ, BLS	Tubiello and Fischer, 2007
	Vietnam	2007-2030	CLUE, MAGNET	Rutten et al., 2014
Mathematical statistical method	Jamaica	1942-2010	Logistic regression	Newman et al., 2014
	Mississippi, USA	1938-2010	Stepwise logistic regression	Schweizer and Matlack, 2014
	Taibus Banner, China	2008-2009	Logistic regression	Hao et al., 2010
	Jiangsu Province, China	2000-2008	Principal component analysis, linear regression	Du et al., 2014
	Amazon, Brazil	2001-2012	Fixed effects panel regressions	Gollnow and Lakes, 2014
	Fuyang City, China	1999-2006	Random effects model, fixed effects model	Zhong et al., 2011
	Ireland	1993-2007	Random effects model, spatial autoregressive random effects model	Upton et al., 2014
	Bohai Rim	2010	Linear regression, spatial error model, spatial lag model, geographically weighted regression model	Wu et al., 2014
	Jiangxi Province	1988-2005	Mechanism model of land conversion	Zhan et al., 2010
	China	1996-2011	STIRPAT model	Zhang and Chen, 2014
Framework analysis	Swiss	1930-2000	System definition, system analysis, and system synthesis framework	Hersperger and Bürgi, 2009
Framework analysis	Portugal, Sweden	1950-2010	Pressures-Frictions-Attractors-Triggers	Beilin et al., 2014
Index assessment	Northern Iran	1967-2002	Land use change area ratio	Kelarestaghi and Jeloudar, 2011
Index assessment	Swiss Alps	Past 120 years	Rates of landscape change	Schneeberger et al., 2007
Difference comparison	China	1980-2000	Constant eco-region in the 1980s	Gao and Liu, 2006
	Sahel	1982-2003	Residuals trend of NDVI (NDVI RESTREND)	Herrmann et al., 2005
	South Africa	1985-2003	Rain-Use Efficiency, NDVI RESTREND	Wessels et al., 2007
	Ordos region, China	1981-2000	NPP RESTREND	Xu et al., 2009

Table 1

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