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1. School of Business, Hunan University of Technology, Zhuzhou 412007, China; 2. College of Economics and Trade, Hunan University of Technology, Zhuzhou 412007, China

1 Introduction

Since China’s entry into WTO, the degree of opening up to the outside world has been enlarged, and the competitiveness of Chinese agriculture has been promoted continuously. For example, China’s agricultural exports have risen from 27.91 billion USD in 2001 to 187.56 billion USD in 2015, ranking among the top three in the world. However, the situation of agricultural industry development is not optimistic because of its inherent weakness and vulnerability to domestic and foreign factors. For example, in recent years, the "Goldman Sachs Pig Raising Incident" and "Zhang Yu A Incident" and the "decapitation strike" conducted by other multinational companies against China’s leading agricultural companies, have formed a huge threat to the safety of the agricultural industry. Therefore, the in-depth study of the issue of agricultural industry safety, not only helps to clarify the problems and difficulties facing agricultural development, but also certainly provides useful reference for the construction of agricultural modernization.

At present, the research on the safety of agricultural industry is mainly focused on three aspects. (i) Research on the connotation of the safety of the agricultural industry. Zhu Xiaofeng defines agricultural safety as the stable position of agriculture in all industries as a basic industry, with a sustainable and healthy development ability[1]. Xu Jiexiang and Xing Xiaobing believe that independent production capacity, independent control of agricultural production capacity, and international competitiveness constitute the "iron triangle" of agricultural industry safety[2]. Zhu Limeng focuses on the elaboration of agricultural safety from withstanding external threats and risks and other aspects[3]. Ni Hongxing finds that the safety of agricultural industry is the synthesis of the realization of the theme function, the control forces of the industry and the ability of the sustainable development of the industry[4]. Ren Dapeng, Zhu Qizhen and Li Ping believe that the issue of resource allocation is the core of agricultural safety[5]. (ii) Assessment of the safety of the agricultural industry. Xu Fang and Liu Dianguo, Sun Rong and Liu Dianguo use AHP to measure, respectively[6-7]. Ning Xuemin uses data envelopment analysis (DEA) model for empirical analysis[8]. Jin Saimei and Cao Qiuju adopt factor analysis to measure, and find that China’s agricultural industry was unsafe from 1995 to 2009[9]. Zhang Shurong and Wei Xiufen use the principal component analysis method to evaluate the safety of cotton industry by selecting the specific agricultural industrycotton industry as a sample[10]. Xiao Wenxing selects fuzzy comprehensive evaluation method to analyze[11]. Dong Yinguo, Liang Gen and Shang Huiqin use entropy weight method to estimate the safety of agricultural industry in China from 2002 to 2012[12]. (iii) Analysis of influencing factors of agricultural industry safety. The study of Vandana Shiva shows that the monopoly power of large agricultural multinational enterprises in developed countries is negatively correlated with agricultural safety in developing countries[13]. Wang Xinlan and Jiang Hong believe that China’s unequal trade status and treatment will threaten the safety of the agricultural industry[14]. The research results of Yang Wei show that foreign investments do not produce a significant structural effect and technology spillover effect, but lead to increased risk of China’s agricultural industry[15]. Ni Hongxing finds that trade in agricultural products will have a dual impact on the safety of agricultural industry. Ren Dapeng, Zhu Qizhen and Li Quan believe that agricultural labor force is a long-term factor affecting the safety of agricultural industry.

From the existing research results: (i) There is a lack of researches on the safety of agricultural industry in Hunan Province, it is only scattered, if any, discussion in the study, there is no systematic research framework. As a major agricultural province, Hunan has been in the top ten in the country in terms of total output value of farming, forestry, fishing and animal husbandry. Then, with China’s accession to the WTO, is the agricultural industry in Hunan Province safe? What factors will have an important impact on it? How to maintain and improve the safety of agricultural industry in Hunan Province? This is an important subject that economists need to study in depth. (ii) The research on the connotation of agricultural industry safety is not complete and systematic enough, and how to build an accurate, comprehensive indicator evaluation system needs to be further studied. (iii) Fuzzy comprehensive evaluation method has become the mainstream method to measure the safety of agricultural industry due to its clear and systematic results, but on the setting of the rank membership function, the existing results are too simple to reflect the real economic situation.

Based on the knowledge of industry economics, development economics, fuzzy mathematics and safety theory, and combined with the existing research results, this paper will construct a new evaluation system of agricultural industry safety"four-force" model. In this paper, a new grade membership function is put forward, and the fuzzy comprehensive evaluation method is used to analyze the agricultural industrial safety of Hunan Province since China’s entry into WTO, so as to provide some pertinent recommendations for Hunan to accelerate the agricultural modernization and maintain the agricultural industrial safety.

2 Constructionofnewsafetyindexsystemofagriculturalindustry

Table 1 presents the evaluation index systems of agricultural industry safety constructed by some domestic scholars such as He Weida and He Chang[16]. It can be seen that most of these systems are established based on the theory of industrial economics, taking the Chinese economy as the research sample and taking a specific industry as the research object. Although some valuable research conclusions have been obtained, the connotation and extension of these index systems are still not clear, and there is too much emphasis on industrial control, competitiveness and external dependence, not reflecting the requirements of industrial generation environment, recovery ability and so on.

Table1Safetyevaluationsystemofdomesticagriculturalindustry

RepresentativefiguresIndexsystemConclusionsHeWeidaandHeChang(2002)[16]Internationalcompetitiveness，externaldependenceofindustry，industrialcontrolBasicallysafeHeWeidaandHeDan(2007)[17]Agriculturalindustrydevelopmentspeed，internationalcompetitiveness，grainself?suffi?ciencyrate，agriculturalindustryimportandexportdependencedegreeBasicallysafeZhuLimeng(2007)[3]Agriculturalindustryinternationalcompetitiveness，grainself?sufficiencyrate，foreigndependenceofagriculturalindustryimportsandforeigndependenceofagriculturalindus?tryexportsBasicallysafetounsafeSunRong，LiuDianguo(2009)[7]Industrialenvironment，organizationstructure，vitality，recoveryBasicallysafeXiaoWenxing(2012)[11]Industrialcontrol，externaldependenceofindustry，industrialcompetitivenessBasicallysafe

In view of this, based on industrial economics, development economics and industrial safety theory, this paper puts forward the "four-force" model of the index system of agricultural industrial safety. It includes four parts, that is, agricultural industry generation force, agricultural industry competition force, agricultural industry control force and agricultural industry dependence force.

Table2The"four-force"modelofthenewindexsystemofagriculturalindustrialsafety

PrimaryindexSecondaryindexSpecificdefinition(quantifiableindex)IndextypePositiveindexInverseindexGenerationforceIndustrialpolicyTheproportionofagriculturalfinancialexpendituretothetotalfinancialex?penditure√LaborcostAveragewageofemployedpersonsinagriculture√Capitalcost1?yearloanbenchmarkinterestrate√Capacityforinnova?tionDomesticagriculturalpatentapplicationvolumeper10000persons√R&DcapacityNumberofagriculturalR&Dpersonnelper10000persons√CompetitionforceDomesticshareofindustryTheproportionofthetotalagriculturaloutputvaluetothetotalagriculturaloutputvalueofChinasprovinces√LaborproductivityTheaddedvalueoftheagriculturalindustrydividedbythenumberofagricul?turalworkers√

(To be continued)

(Continued)

PrimaryindexSecondaryindexSpecificdefinition(quantifiableindex)IndextypePositiveindexInverseindexInternationalshareofindustryTheproportionofagriculturalexporttoworldagriculturalexport√Tradecompetitionindex(Agriculturalexports?agriculturalimports)/(Agriculturalexports+agricultur?alimports)√Dominantcompara?tiveadvantageindex(Agriculturalexports/totalexportsofHunan)/(Worldagriculturalexports/totalworldexports)√ControlforceTechnicalcontrolforceTheproportionofeffectivepatentsofforeign?fundedagriculturalenterprisestoeffectivepatentsofagriculturalenterprises√Foreignequitycon?trolforceTheratioofregisteredcapitalofforeign?fundedenterprisestoregisteredcap?italofallenterprises√DependenceforceForeigndependenceofindustrialexportTheratioofthetotalexportsofagriculturalproductstothetotaloutputvalueofagriculturalproducts√ForeigndependenceofindustrialimportTheratioofthetotalimportsofagriculturalproductstothetotaloutputvalueofagriculture√ExternaldependenceofindustrialcapitalTheratioofforeigncapitalstockofagriculturalindustrytototaloutputvalueofagriculture√

Note: Positive index indicates that the higher the index value, the higher the safety of agricultural industry; inverse index indicates that the higher the index value, the lower the safety of agricultural industry.

The agricultural industry generation force includes five secondary indexes: industrial policy, labor cost, capital cost, innovation ability and research and development ability. The labor cost and capital cost are the inverse indexes. The agricultural industry competition force includes five secondary indexes: domestic share of agricultural industry, labor productivity, international share of agricultural industry, trade competition index and dominant comparative advantage index. The agricultural industry control force includes two secondary indexes, technical control force and foreign equity control force, and these two indexes are all inverse indexes. The agricultural industry dependence force includes the degree of external dependence of three variables (industrial export, industrial import and industrial capital), and the three indexes are all inverse indexes. The specific definition of secondary indexes is shown in Table 2.

3 EmpiricalanalysisofagriculturalindustrysafetyinHunanProvince

3.1SelectionofvariablesanddatasourcesAccording to the index system of "four forces", this paper selects 15 economic variables such as industrial policy and labor cost. To test the quality of the data used in this paper, we observe the basic characteristics of each variable and eliminate the impact of the abnormal data on subsequent analysis. Table 3 reports the descriptive statistical re-sults of the main variables. There is obvious incompatibility among the indexes, and direct calculation will lead to the distortion of the evaluation results. Therefore, the necessary data processing willfollow. First, the inverse index is converted into positive index by reciprocal method, and then the averaging method is used. Although there is a lot of lit-erature using the normalization method, the average value of each index after normalization is 0, and the standard deviation is 1, so it is difficult to compare the variation degree among the indexes. The averaging method not only retains the difference of the degree of variation of indexes, but also contains the information of the difference of the degree of influence of indexes, and the dimensionless processing is performed on the indexes. The original data mainly come fromChinaStatisticalYearbook,ChinaRuralStatisticalYearbook,ChinaAgriculturalStatisticalYearbook,ChinaScienceandTechnologyStatisticalYearbook,InternationalStatisticalYearbook,ChinaForeignTradeStatisticalYearbook,HunanProvinceStatisticalYearbook,HunanScienceandTechnologyStatisticalYearbook, and Wind database from 2001 to 2014. For partial default data, the mean value is used instead.

Table3Statisticaldescriptionofkeyindexes

MeanMedianMaximumMinimumStandarddeviationObservedvalueIndustrialpolicy9．4989．99212．4996．7352．02613Laborcost1209810632234415332616413Capitalcost5．9285．8507．2535．3100．68013Innovationability0．0450．0380．1020．0090．03313R&Dcapacity5．0844．8147．4293．5591．30113Domesticshareofindustry9．0059．1099．8678．1650．49513Laborproductivity92558605169114011475113Internationalshareofindustry0．0630．0320．4260．0190．10913Tradecompetitionindex0．3430．3380．678-0．0340．17913Dominantcomparativeadvantageindex0．8180．8021．2620．4890．25213Technicalcontrolforce11．18511．46321．3137．0223．67813Foreignequitycontrolforce45．39843．39056．24230．2309．86813Foreigndependenceofindustrialexport0．9860．9671．1410．8840．08413Foreigndependenceofindustrialimport0．5180．4371．0720．1840．25213Foreigndependenceofindustrialcapital1．3441．2581．8760．9370．35113

3.2SettingofweightforindexesThe setting of index weight is usually divided into two types: subjective assignment and objective assignment. The subjective assignment is arbitrary and the objective evidence is insufficient, so the common factor analysis method in objective assignment in this paper is used to determine the weight. Through factor analysis, the eigenvalues and factor scoring coefficients of principal components can be obtained. The weight is usually between 0 and 1, and the factor score coefficient may be negative. To this end, this paper uses the following formula to process:

(1)

whereaijrepresents the scoring coefficient andλjthe eigenvalue of the principal component.

By factor analysis and formula (1), we can obtain the weight of each index of Hunan agricultural industry safety (Table 4).

Table4WeightofHunanagriculturalindustrysafetyindexsystem

OverallgoalPrimaryindexandweight SecondaryindexandweightAgriculturalindustrysafetyAgriculturalindustrygenerationforceIndustrialpolicy0．0228(0．3748)Laborcost0．0990Capitalcost0．0019CompetitionforceInnovationability0．2226(0．4778)R&Dcapacity0．0286Domesticshareofindustry0．0014Laborproductivity0．1113Internationalshareofindustry0．1938ControlforceTradecompetitionindex0．1246(0．0282)Dominantcomparativeadvantageindex0．0468DependenceforceTechnicalcontrolforce0．0051(0．1192)Foreignequitycontrolforce0．0231Foreigndependenceofindustrialexport0．0018Foreigndependenceofindustrialimport0．0880Externaldependenceofindustrialcapital0．0295

3.3SettingofthemodelPeople’s judgment of industrial safety mainly comes from previous experience and perception, so there is no clear line between safety and unsafety. Therefore, compared with other methods, the fuzzy comprehensive evaluation method based on fuzzy mathematics theory is more accurate and more effective. The concrete steps of the fuzzy comprehensive evaluation method are as follows:

First of all, the indexes undergoing the dimensionless processing previously are transformed to 0-100. The transformation formula is:

(2)

Next, there is a need to set the membership function. There is a fuzzy interval in people’s evaluation of safety. For example, between 0 and 20 points, it is certainly "extremely unsafe", but between 20 and 30 points, it may swing between "extremely unsafe" and "unsafe". Similarly, between 40 and 50 points may be in the "basically safe" range, or in the "unsafe" range. Therefore, the membership function of equal group distance used by most scholars can not accurately reflect the real situation. In view of this, this article uses the following ways to set membership functions.

The membership functionA1(x) is an "extremely unsafe" hierarchical membership function, defined as follows:

(3)

The membership functionA2(x) is an "unsafe" hierarchical membership function, defined as follows:

(4)

The membership functionA3(x) is a "basically safe" hierarchical membership function, defined as follows:

(5)

The membership functionA4(x) is a "relatively safe" hierarchical membership function, defined as follows:

(6)

The membership functionA5(x) is a "very safe" hierarchical membership function, defined as follows:

(7)

According to the membership function (3)—(7), we can get the membership degree of each secondary index relative to the evaluation set under the four primary indexes (agricultural industry generation force, agricultural industry competition force, agricultural industry control force, agricultural industry dependence force) from 2001 to 2013. Then according to the weight of each index, the corresponding membership degree of the primary index can be obtained by applying the corresponding fuzzy transformation, and each level can be evaluated according to the class.

3.4EvaluationresultsofHunanagriculturalindustrysafety

Table 5 presents the results of fuzzy comprehensive evaluation of Hunan agricultural industry safety. From this, we can see that the probability of Hunan agricultural industry safety at different levels in 2001 was 0, 023 54, 0.382 1, 0.368 8, 0.013 7, respectively. According to the principle of maximum membership, the Hunan agricultural industry in 2001 was "basically safe ". According to this principle, from 2001 to 2009, Hunan agricultural industry was "basically safe"; in 2010 and 2011, Hunan agricultural industry was "safe"; in 2012, Hunan agricultural industry was "basically safe"; in 2013, Hunan agricultural industry was "very safe". Table 5 shows that the agricultural industry in Hunan Province has shown an upward trend from "basically safe" to "very safe", especially since 2011, the level of safety of agricultural industry has increased significantly. But at the same time, we must also see that the state of Hunan agricultural industry in the "safe" or "very safe" range is not stable. Especially under the background of the slow recovery of the world economy, the shrinking demand of the international market and the rapid transformation of the economic structure of our country, the safety of Hunan agricultural industry still has some risks.

According to the weight of the index system of Hunan agricultural industry safety and the fuzzy comprehensive evaluation method, the evaluation results of four primary indexes of the agricultural industry safety can also be obtained (Table 6). Details are given below.

Table5FuzzycomprehensiveevaluationresultsofHunanagriculturalindustrysafety

IndexesVeryunsafeUnsafeBasicallysafeSafeVerysafe200100．23540．38210．36880．0137200200．23960．45690．30350．0000200300．17800．59760．22270．0017200400．31640．52060．16300．0000200500．01950．85450．12610．0000200600．13350．77800．08850．0000200700．00190．85490．14320．0000200800．01830．70970．25800．0139200900．02290．81570．15950．0019201000．01930．44610．53460．0000201100．02930．38170．57690．0121201200．13660．42990．35780．0758201300．13120．25730．27780．3336

Note: The values in the table show the probability of agricultural industry safety at different levels.

(i) The index of agricultural industry generation force has changed from "unsafe" to "safe", showing a better trend. Since China joined the WTO, China has increased its financial support for agriculture, continuously introduced and improved foreign advanced technology, so that the national innovation ability, R&D investment and technology absorption and learning ability have been improved rapidly. Hunan has always regarded solving the problem of "agriculture, rural areas and farmers" as the top priority of all the work, and has strictly implemented tax concessions, financial guidance and support, and strengthened the financial loan. In 2014, the contribution rate of agricultural scientific and technological progress was over 60%, 4.4 percentage points higher than that of the whole country. As a result, the agricultural production capacity of Hunan Province has been continuously improved. (ii) The index of agricultural industry competition force is in the "basically safe" range, with the exception of the "very safe" level in 2013. After joining the World Trade Organization, the domestic and international share of Hunan agricultural product export has been increasing, but there are less than 20 leading agricultural enterprises with output value of more than one billion yuan in Hunan Province. The technological content of agricultural products for export is still low, and there are few large brands, and the international competitiveness still needs to be improved. Therefore, in most years, the index of agricultural industry competition force in Hunan is in the "basically safe" range. (iii) The trend of the index of agricultural industry control force is opposite to the trend of the former two indexes, changing from "safe" in 2001 to "basically safe" and "unsafe", which shows that the agricultural industry control force in Hunan Province is declining, and the introduction of foreign capital has produced a significant negative impact on the agricultural industry control force. Since China’s entry into WTO, the merger and acquisition activities of multinational companies to Chinese agricultural enterprises have increased. For example, 64 of the 97 large-scale oil and fat enterprises in China have been held by transnational grain merchants, accounting for up to 66% of the total shares. As a result, in recent years, China’s agricultural industry control force has been declining, falling to unsafe state. (iv) The index of agricultural industry dependence force fluctuates frequently, but is in the range of "basically safe" and above in most years. In recent years, the import and export dependence of Hunan Province is declining, but due to the dependence on foreign capital to form inertia, this will be a serious constraint on the sustainable development of agricultural industry.

Table6TheevaluationresultsofprimaryindexesofHunanagriculturalindustrysafety

IndexesGenerationforceCompetitionforceControlforceDependenceforce2001SafeBasicallysafeSafeSafe2002UnsafeBasicallysafeSafeSafe2003BasicallysafeBasicallysafeSafeSafe2004BasicallysafeBasicallysafeSafeUnsafe2005BasicallysafeBasicallysafeBasicallysafeBasicallysafe2006BasicallysafeBasicallysafeBasicallysafeUnsafe2007BasicallysafeBasicallysafeBasicallysafeBasicallysafe2008BasicallysafeBasicallysafeBasicallysafeSafe2009BasicallysafeBasicallysafeBasicallysafeSafe2010SafeBasicallysafeUnsafeSafe2011SafeBasicallysafeUnsafeSafe2012SafeBasicallysafeUnsafeBasicallysafe2013SafeVerysafeUnsafeBasicallysafe

4 Conclusionsandpolicyrecommendations

Based on the construction of the "four-force" index system, this paper uses the method of factor analysis and fuzzy comprehensive evaluation to make empirical calculation on the degree of safety of agricultural industry in Hunan Province. The results show that from 2001 to 2013, the degree of safety of agricultural industry presented a trend of changing from "basically safe" to "very safe", but it was not stable, and it is still necessary to be vigilant to the safety risk of agricultural industry. Among the four primary indexes, the indexes of agricultural industry generation force and agricultural industry competition force share almost the same evaluation results as the overall index. The index of agricultural industry dependence force fluctuates frequently, in the vicinity of "basically safe", and the index of agricultural industry control force shows an obvious trend of deterioration, changing from "safe" to "unsafe", indicating that foreign capital merger and acquisition and holding of agricultural enterprises have formed a significant threat to the safety of agricultural industry in Hunan Province, which must be paid enough attention to.

Under the background of marketization and globalization, in order to maintain the safety of agricultural industry in Hunan Province, the government needs to further optimize the domestic and foreign economic environment, accelerate the innovation process of agricultural industry, and improve the generative power of agricultural industry; refine the export structure of agricultural products, improve the export quality of agricultural products, and further enhance the domestic and international competitiveness of agricultural industry; avoid introducing foreign capital and technology blindly, and establish a more perfect market access mechanism; establish and tighten the market mechanism for foreign equity participation and holding in agricultural enterprises, improve the independent control of agricultural industry, and reduce the external dependence of agricultural industry, so as to ensure the sustainable, healthy and high-quality development of Hunan agricultural industry.

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