Please wait a minute...
Journal of Arid Land
Journal of Arid Land  2020, Vol. 12 Issue (2): 199-214    DOI: 10.1007/s40333-020-0060-3     CSTR: 32276.14.s40333-020-0060-3
Research article     
Coupling between the Grain for Green Program and a household level-based agricultural eco-economic system in Ansai, Shaanxi Province of China
LI Yue1, WANG Jijun1,2,*(), HAN Xiaojia2, GUO Mancai1, CHENG Simin2, QIAO Mei1, ZHAO Xiaocui1
1 Institute of Soil and Water Conservation, Northwest A&F University, Yangling 712100, China
2 Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling 712100, China
Download: HTML     PDF(442KB)
Export: BibTeX | EndNote (RIS)      

Abstract  

The implementation of the Grain for Green Program (GGP) has changed the development track of the agricultural eco-economic system in China. In response to the results of a lag study that investigated the coupling between the GGP and the agricultural eco-economic system in a loess hilly region, we used a structural equation model to analyze the survey data from 494 households in Ansai, a district of Yan'an City in Shaanxi Province of China in 2015. The model clarified the direction and intensity of the coupling between the GGP and the agricultural eco-economic system. The coupling benefits were derived through linkages between the program and various chains in the agricultural eco-economic system. The GGP, the agroecosystem of Ansai and their potential coupling effects were in a state of general coordination. The agroecosystem directly affected the coupling effect, with the standardized path coefficient of 0.87, indicating that the agroecosystem in Ansai at this stage provided basic material support for the coupling between the GGP and the agricultural eco-economic system. The direct path coefficient of agroeconomic system impacted on the coupling effect was -0.76, indicating that partial contradictions occurred between the agroeconomic system and the coupling effect. Therefore, although the current agroecosystem in Ansai should be provided sufficient agroecological resources for the benign coupling between the program and the agricultural eco-economic system, agricultural development failed to effectively transform agroecological resources into agricultural economic advantages in this region, which resulted in a relative lag in the development of the agricultural economic system. Thus, the coupling between the GGP and the agricultural eco-economic system was poor. To improve the coupling and the sustainable development of the agricultural eco-economic system in cropland retirement areas, the industrial structure needs to be diversified, the agricultural resources (including agroecological resources, agricultural economic resources and agricultural social resources) need to be rationally allocated, and the chain structure of the agricultural eco-economic system needs to be continuously improved.

Key words： Grain for Green Program      agroecosystem      agroeconomic system      agrosocial system      coupling effect      household      structural equation model     
Received: 11 January 2019      Published: 10 March 2020
Corresponding Authors:
About author: *Corresponding author: WANG Jijun (E-mail: jjwang@ms.iswc.ac.cn)
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
Yue LI
Jijun WANG
Xiaojia HAN
Mancai GUO
Simin CHENG
Mei QIAO
Xiaocui ZHAO
Cite this article:

LI Yue, WANG Jijun, HAN Xiaojia, GUO Mancai, CHENG Simin, QIAO Mei, ZHAO Xiaocui. Coupling between the Grain for Green Program and a household level-based agricultural eco-economic system in Ansai, Shaanxi Province of China. Journal of Arid Land, 2020, 12(2): 199-214.

URL:

http://jal.xjegi.com/10.1007/s40333-020-0060-3     OR     http://jal.xjegi.com/Y2020/V12/I2/199

Fig. 1 Overview of the study area
Latent variable category Observable variable Definition and calculation method for the observable variable
GGP
(ξ1)		 Eco-economic forest structure x1 (total score for each item: 1-6) The structure of an eco-economic forest reflects the implementation process of the GGP and the allocation and proportion of ecological forests and economic forests after the implementation. The aim is to determine the suitability of the composition of ecological forests and economic forests for the regional natural conditions and whether their ratio is within the threshold set by the state.
Scale: moderate ratio between economic forests and ecological forests (1, No; 2, Generally; 3, Yes).
Forest and grass quality of cropland retirement areas x2 (total score for each item: 0-3) Forest and grass quality of a cropland retirement area reflects the level of ecological restoration of the converted forest and grassland. If the area of cropland retirement reflects the level of ecological restoration on the basis of scale, the quality reflects the intensity of the restoration, and the community structure and tree species diversity are the cores of characterization.
Scale: structure of the community is reasonable (0 No; 1 Yes)+there is a diverse range of species (0, No; 1, Yes)+there is a balance between economic and ecological benefits (0, No; 1, Yes).
Agroecosystem
(η1)		 Forest and grass area rate y1 (%) Forest and grass area rate is the sum of woodland and grassland areas divided by the area of available land.
Scale: percentage of directly calculated results.
Per capita basic cropland y2 Per capita basic cropland refers to the basic cropland area divided by the total population.
Scale: percentage of directly calculated results.
Agroeconomic system (η2) Agricultural labor ratio y3 (%) Agricultural labor ratio refers to the agricultural labor divided by the sum of agricultural labor and non-agricultural labor.
Scale: percentage of directly calculated results.
Agricultural commodity rate y4 (%) Agricultural commodity rate refers to the commodity volume of agricultural products divided by the agricultural output.
Scale: percentage of directly calculated results.
Agrosocial system (η3) Renewal of values y5 (total score for each item: 0-2) The renewal of values reflects the changes in people's production and management concept, the regulation of their own behavior, and the degree of changes in self-awareness caused by the return of cropland to forestland with project implementation.
Scale: degree of understanding of important national policies and guidelines (1, Do not know; 2, Not comprehensive; 3, Comprehensive understanding)+changes in the concept of democracy and rule of law (1, No; 2, Slightly; 3, Exist)+technology training activities (1, No; 2, Slightly; 3, Exist)+changes in market concept before and after the return of cropland to forestland (1, Weakened; 2, No; 3, Enhanced)+proportion of basic living consumption and cultural education consumption after the return of cropland (1, Life consumption proportion is larger; 2, Basically the same; 3, Cultural education consumption is larger).
Life satisfaction y6 (total score for each item: 4-12) Life satisfaction reflects people's cognition, perception and self-satisfaction and is an evaluation of the core elements of social life after the GGP.
Scale: changes in social order and social atmosphere after the return of cropland (1, Exist; 2, No; 3, Slightly)+satisfaction with current social security situation (1, Dissatisfied; 2, Basic satisfaction; 3, Satisfied)+degree of improvement in social order and social improvement after the return of cropland (1, No; 2, Not obvious; 3, Significantly improved)+frequency of participation in social activities and village collective activities increased after the return of cropland to forestland (1, No; 2, Small increase; 3, Significantly increased).
Coupling effect
(η4)		 Relevance of the agricultural industry resource chain y7 (total score for each item: 1-9) Relevance of the agricultural industry resource chain is the support and correlation between related agricultural industries, which primarily reflects the relationship between leading industries and related industries and follow-up industries.
Scale: 1, extensive cultivation and reclamation; 2, extensive cultivation; 3, single grain; 4, agricultural fruit, agriculture and animal husbandry; 5, agricultural fruit development, forestry and animal husbandry; 6, mainly industry; 7, formation of related industries; 8, organic and unified relationship among industries; 9, a virtuous combination of ecological, economic and social systems.
Land use structure for agriculture, forestry and animal husbandry reflects the allocation and proportion of various land resources in the process of land use.
Land use structure for agriculture, forestry and animal husbandry y8 (%) It is difficult to determine in the actual analysis process, and therefore, the process is expressed as the ratio of cultivated land to forestland and grassland area.
Per capita net income y9 Per capita net income is: total revenue-production cost/population.
Scale: actual value of the calculation.
Table 1 Interpretation of system variables in the synergistic mechanism linking the Grain for Green Program (GGP) and the agricultural eco-economic system
Fig. 2 Conceptual model of the coupling relationships between the Grain for Green Program (GGP) and the agricultural eco-economic system in Ansai. Agroecosystem represents the agricultural ecosystem; agroeconomic system represents the agricultural economic system; and agrosocial system represents the agricultural social system. Latent variables are shown in ellipses and observed variables are shown in boxes; e1-e16 are the residual variables, which indicate the part of the endogenous variable that cannot be interpreted by the exogenous variable; e→ is the measurement error of a variable, and the details of which are shown in Table 1. All path coef?cients are shown as standardized coef?cients to compare the direct effects among variables.
X2/df RMSEA GFI TLI CFI IFI PNFI PCFI
Reference standard <3.00 <0.10 >0.90 >0.90 >0.90 >0.90 >0.50 >0.50
Evaluation result 2.99 0.07 0.91 0.90 0.91 0.90 0.51 0.51
Table 2 Fitting indices for the structural equation model
ξ1 η1 η2 η3 η4
η1 0.12 0.00 0.00 0.000 0.00
η2 0.24 0.85 0.00 0.000 0.00
η3 -0.03 0.04 0.56 0.000 0.00
η4 -0.07 0.22 -0.74 0.038 0.00
x1 0.68 0.00 0.00 0.000 0.00
x2 0.55 0.00 0.00 0.000 0.00
y1 -0.05 -0.40 0.00 0.000 0.00
y2 0.10 0.79 0.00 0.000 0.00
y3 0.13 0.48 0.57 0.000 0.00
y4 -0.03 -0.10 -0.12 0.000 0.00
y5 -0.01 0.01 0.12 0.214 0.00
y6 -0.02 0.03 0.38 0.685 1.31
y7 0.00 -0.01 0.02 -0.001 -0.02
y8 -0.13 0.38 -1.26 0.064 1.70
y9 0.00 -0.01 0.03 -0.002 -0.04
Table 3 Overall standard influence coefficients
Fig. 3 Coupling relationships between the GGP and the agricultural eco-economic system function model in Ansai. Latent variables are shown in ellipses and observed variables are shown in boxes; e1-e16 are the residual variables, which indicate the part of the endogenous variable that cannot be interpreted by the exogenous variable. Estimates are standardized regression weights of B→A and correlations between B?A (A and B mean any parameters in Fig. 3). The path coefficient greater than 0.00 means that the direct and indirect impacts of the exogenous variable on the endogenous variable are positive; however, the path coefficient lower than 0.00 means that the direct and indirect impacts of the exogenous variable on the endogenous variable are negative.
Paths P
Agroecosystem (η1) ← GGP (ξ1) 0.01
Agroeconomic system (η2) ← GGP (ξ1) 0.04
Agroeconomic system (η2) ← Agroecosystem (η1) ***
Agrosocial system (η3) ← Agroecosystem (η1) 0.03
Agrosocial system (η3) ← Agroeconomic system (η2) 0.03
Agrosocial system (η3) ← GGP (ξ1) 0.04
Coupling effect (η4) ← Agrosocial system (η3) 0.03
Coupling effect (η4) ← Agroeconomic system (η2) 0.01
Coupling effect (η4) ← Agroecosystem (η1) ***
Table 4 Standardized model coefficients of the corrected model
Compound paths P
Coupling effect
(η4)		 ← Agroecosystem
(η1)		 ← GGP (ξ1) 0.01
Coupling effect
(η4)		 ← Agroeconomic system (η2) ← GGP (ξ1) 0.05
Couplingeffect
(η4)		 ← Agrosocial system
(η3)		 ← GGP (ξ1) 0.08
Coupling effect (η4) ← Agrosocial system (η3) ← Agroecosystem
(η1)		 ← GGP (ξ1) 0.08
Coupling effect (η4) ← Agrosocial system (η3) ← Agroeconomic system (η2) ← GGP (ξ1) 0.11
Table 5 Standardized model coefficients for the compound paths of the corrected model
Total income Agricultural income Non-agricultural income
Income of interviewed 494 households (×104 CNY) 2961.39 897.21 2064.18
Proportion (%) 100.00 30.30 69.70
Table 6 Total income derived from the agricultural and non-agricultural incomes based on surveyed data in Ansai
Total labor force Agricultural labor force Non-agricultural labor force
Labor force of interviewed 494 households (people) 1637 771 866
Proportion (%) 100.00 47.10 52.90
Table 7 Sizes and proportions of the household agricultural and non-agricultural labor force based on the surveyed data in Ansai
Rationality of community structure Rationality of tree species diversity Moderate proportion of eco-economic forest
Effective interviewed 494 households (households) 394 389 310
Proportion (%) 79.80 78.90 62.10
Table 8 Forest and grass quality of cropland retirement areas based on the household survey
[1]   Aderson J C, Gerbring D W. 1988. Structural equation modeling in practice: A review and recommended two-step approach. Pyschological Bulletin, 103(3): 411-423.
doi: 10.1007/s00128-019-02649-3 pmid: 31203410
[2]   Asah S T. 2008. Empirical social-ecological system analysis: From theoretical framework to latent variable structural equation model. Environmental Management, 42: 1077-1090.
doi: 10.1007/s00267-008-9172-9
[3]   Bennett M T. 2008. China's sloping land conversion program: Institutional innovation or business as usual? Ecological Economics, 65(4): 699-711.
doi: 10.1086/691993 pmid: 29805178
[4]   Bertalanffy L V. 1987. General System Theory-Foundation Development, Applications (Reversion edition). New York: George Beazitler, 189-239.
[5]   Bollen K A. 1989. Structural Equations with Latent Variables. New York: Wiley, 55-70.
[6]   Brahim N, Blavet D, Gallali T, et al. 2011. Application of structural equation modeling for assessing relationships between organic carbon and soil properties in semiarid Mediterranean region. International Journal of Environmental Science & Technology, 8(2): 305-320.
[7]   Byrne B M. 1998. Structural Equation Modeling with LISREL, PRELIS, and SIMPLIS; Basic Concepts, Applications, and Programming. Mahwah: Lawrence Erlbaum Associates, 66-74.
[8]   Byrne B M. 2001. Structural equation modeling with AMOS, EQS, and LISREL: Comparative approaches to testing for the factorial validity of a measuring instrument. International Journal of Testing, 1(1): 55-86.
doi: 10.1207/S15327574IJT0101_4
[9]   Deng L, Liu S, Kim D G, et al. 2017. Past and future carbon sequestration benefits of China's grain for green program. Global Environmental Change, 47: 13-20.
doi: 10.1016/j.gloenvcha.2017.09.006
[10]   Duncan T E, Duncan S C, Strycker L A. 2006. An Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues, and Applications. Mahwah: Lawrence Erlbaum Associates, 93-105.
[11]   Gough L, Grace J B. 1999. Effects of environmental change on plant species density: Comparing predictions with experiments. Ecology, 80(3): 882-890.
doi: 10.1890/0012-9658(1999)080[0882:EOECOP]2.0.CO;2
[12]   Grace J B, Anderson T M, Scheiner O S M. 2010. On the specification of structural equation models for ecological systems. Ecological Monographs, 80(1): 67-87.
doi: 10.1890/09-0464.1
[13]   Grosjean P, Kontoleon A. 2009. How sustainable are sustainable development programs? The case of the sloping land conversion program in China. World Development, 37(1): 268-285.
doi: 10.1016/j.worlddev.2008.05.003
[14]   Hung N T, Asaeda T, Manatunge J. 2007. Modeling interactions of submersed plant biomass and environmental factors in a stream using structural equation modeling. Hydrobiologia, 583(1): 183-193.
doi: 10.1007/s10750-006-0526-0
[15]   Jia X Q, Fu B J, Feng X M, et al. 2014. The trade off and synergy between ecosystem services in the Grain-for-Green areas in Northern Shaanxi, China. Ecological Indicators, 43: 103-113.
doi: 10.1016/j.ecolind.2014.02.028
[16]   Kalaitzandonakes N G, Monson M J. 1994. An analysis of potential conservation effort of crp participants in the state of missouri: a latent variable approach. Journal of Agricultural & Applied Economics, 26(1): 200-208.
[17]   Li Q R, Wang J J, Guo M C. 2012. Coupling relationship of ecological agricultural system of commodities in Ansai county based on structural equation model. Transactions of the Chinese Society of Agricultural Engineering, 28(16): 240-247. (in Chinese)
[18]   Li Q R, Amjath-Babu T S, Zander P, 2016a. Role of capitals and capabilities in ensuring economic resilience of land conservation efforts: A case study of the grain for green project in China's Loess Hills. Ecological Indicators, 71: 636-644.
[19]   Li Q R, Liu Z, Zander P, et al. 2016b. Does farmland conversion improve or impair household livelihood in smallholder agriculture system? A case study of Grain for Green project impacts in China's Loess Plateau. World Development Perspectives, 2: 43-54.
doi: 10.1016/j.wdp.2016.10.001
[20]   Li Q R, Amjath-Babu T S, Sieber S, et al. 2018. Assessing divergent consequences of payments for ecosystem services (PES) on rural livelihoods: A case-study in China's Loess Hills. Land Degradation & Development, 29(10): 3549-3570.
[21]   Li Y, Wang J J, Liu P L, et al. 2018. Study on the synergic relationship between grain for green and agricultural eco-economic social system—A case study in Ansai county. Journal of Natural Resources, 33(7): 1179-1190. (in Chinese)
doi: 10.31497/zrzyxb.20180107
[22]   Liu C, Wu B. 2010. Grain for Green Programme in China: Policy making and implementation? Policy Briefing Series, 60: 1-17. The University of Nottingham, China Policy Institute, Nottingham.
doi: 10.1016/j.scitotenv.2016.09.026 pmid: 27623527
[23]   Liu J G, Li S X, Ouyang Z Y, et al. 2008. Ecological and socioeconomic effects of China's policies for ecosystem services. Proceedings of the National Academy of Sciences of the United States of America, 105(28): 9477-9482.
[24]   Liu J X, Li Z G, Zhang X P, et al. 2013. Responses of vegetation cover to the Grain for Green Program and their driving forces in the He-Long region of the middle reaches of the Yellow River. Journal of Arid Land, 5(4): 511-520.
doi: 10.1007/s40333-013-0177-8
[25]   Maruyama G M. 1998. Basics of Structural Equation Modeling. Thousand Oaks: Sage.148-149.
[26]   McCune B, Grace L B. 2002. Analysis of Ecological Communities Gleneden Beach. Oregon: MjM Software Design, 1-300.
[27]   Persson M, Moberg J, Ostwald M, et al. 2013. The Chinese Grain for Green Programme: Assessing the carbon sequestered via land reform. Journal of Environmental Management, 126: 142-146.
doi: 10.1016/j.jenvman.2013.02.045
[28]   Ren J Z, He D H, Wang N, et al. 1995. Models of coupling agro-grassland systems in desert-oasis region. Acta Prataculturae Sinica, 2: 11-19. (in Chinese)
[29]   Rong T S.2009. AMOS and Research Methods. Chongqing: Chongqing University Press, 25-33. (in Chinese)
[30]   Smith J, Scherr S J. 2003. Capturing the value of forest carbon for local livelihoods. World Development, 31(12): 2143-2160.
doi: 10.1016/j.worlddev.2003.06.011
[31]   State Forestry Administration of the People's Republic of China. 2016. China forestry development report 2016, Beijing, China. [2016-12-09]. . (in Chinese)
[32]   Steiger J H. 1990. Structural model evaluation and modification: An interval estimation approach. Multivariate Behavioral Research, 25(2): 173-189.
doi: 10.1207/s15327906mbr2502_4 pmid: 26794479
[33]   Sutton-Grier A E, Kenney M A, Richardson C J. 2010. Examining the relationship between ecosystem structure and function using structural equation modelling: A case study examining denitrification potential in restored wetland soils. Ecological Modeling, 221(5): 761-768.
doi: 10.1016/j.ecolmodel.2009.11.015
[34]   Tomarken A J, Waller N G. 2005. Structural equation modeling: strengths, limitations, and misconceptions. Annual Review of Clinical Psychology, 1(1): 31-65.
doi: 10.1146/annurev.clinpsy.1.102803.144239 pmid: 17716081
[35]   Uchida E, Xu J, Rozelle S. 2005. Grain for green: cost-effectiveness and sustainability of China's conservation set-aside programme. Land Economics, 81(2): 247-264.
doi: 10.3368/le.81.2.247
[36]   Uchida E, Rozelle S, Xu J. 2009. Conservation payments, liquidity constraints, and off-farm labor: Impact of the Grain-for-Green Program on rural households in China. American Journal of Agricultural Economics, 91(1): 70-86.
doi: 10.1111/ajae.v91.1
[37]   Wang J J, Guo M C, Jiang Z D, et al. 2010. The construction and application of an agricultural ecological-economic system coupled process model. Acta Ecological Sinica, 30(9): 2371-2378. (in Chinese)
[38]   Wang J J, Wang Z S, Cheng S M, et al. 2017. Eco-economic thinking for developing carbon sink industry in the de-farming regions. Chinese Journal of Applied Ecology, 28(12): 4109-4116. (in Chinese)
doi: 10.13287/j.1001-9332.201712.013 pmid: 29696909
[39]   Wang X H, Shen J X, Zhang W. 2014. Emergy evaluation of agricultural sustainability of Northwest China before and after the grain-for-green policy. Energy Policy, 67: 508-516.
doi: 10.1016/j.enpol.2013.12.060
[40]   Werner C, Schermelleh-Eagel K. 2009. Structural equation modeling: Advantages, challenges, and problems. Introduction to Structural Equation Modeling with LISREL. Goethe University, Germany.
[41]   Wright S. 1934. The method of path coefficients. Annals of Mathematical Statistics, 5(3): 161-215.
doi: 10.1214/aoms/1177732676
[42]   Xu Z, Xu J, Deng X, et al. 2006. Grain for Green versus grain: conflict between food security and conservation set-aside in China. World Development, 34(1): 130-148.
doi: 10.1016/j.worlddev.2005.08.002
[43]   Yao S, Guo Y, Huo X. 2010. An empirical analysis of the effects of China's land conversion program on farmers' income growth and labor transfer. Environmental Management, 45(3): 502-512.
doi: 10.1007/s00267-009-9376-7
[1] LEI Hangyu, DUAN Dantong, CHEN Yi, GUO Huifeng, LI Jiangtao, LI Xiang. Effects of landscape fragmentation of plantation forests on carbon storage in the Loess Plateau, China[J]. Journal of Arid Land, 2024, 16(2): 266-281.
[2] BAI Haihua, YIN Yanting, Jane ADDISON, HOU Yulu, WANG Linhe, HOU Xiangyang. Market opportunities do not explain the ability of herders to meet livelihood objectives over winter on the Mongolian Plateau[J]. Journal of Arid Land, 2020, 12(3): 522-537.
[3] Shaofei JIN, Xiaohong TIAN, Hesong WANG. Hierarchical responses of soil organic and inorganic carbon dynamics to soil acidification in a dryland agroecosystem, China[J]. Journal of Arid Land, 2018, 10(5): 726-736.
[4] HAO Haiguang, ZHANG Jiping, LI Xiubin, ZHANG Huiyuan, ZHANG Qiang. Impact of livelihood diversification of rural households on their ecological footprint in agro-pastoral areas of northern China[J]. Journal of Arid Land, 2015, 7(5): 653-664.
[5] JianXiang LIU, ZhiGuang LI, XiaoPing ZHANG, Rui LI, XianChun LIU, HanYang ZHANG. Responses of vegetation cover to the Grain for Green Program and their driving forces in the He-Long region of the middle reaches of the Yellow River[J]. Journal of Arid Land, 2013, 5(4): 511-520.