中国柑橘市场预警研究
详细信息    本馆镜像全文|  推荐本文 |  |   获取CNKI官网全文
摘要
中国柑橘历经20多年的发展和规划,已经在中国长江以南的湖北、湖南、福建、广东、四川、江西等20个省市建立了柑橘生产基地,形成了赣南、桂北、湘南、长江上中游等4条优势柑橘带。加入WTO以来,中国柑橘产业无论是在生产、消费、进出口贸易、加工和储藏等多个方面均取得长足的进步。2011年中国柑橘产量为2944.04万吨,柑橘种植面积达2288.3千公顷,均居世界第一位。柑橘鲜果出口量也达到了90万多吨,柑橘罐头出口位居世界第一。柑橘加工量也有一定的发展,不少柑橘产区建立了冷藏、清洗、打蜡、包装和罐头橙汁加工的工厂。柑橘产业已经成为了中国国民经济的重要力量,为农民增收脱贫发挥着重要的作用。
     然而,在高速发展的同时,中国柑橘产业也面临着一系列的问题。在经过柑橘科研者的长期努力后,目前在柑橘产区,柑橘品种选育、栽培管理、病虫害防治已经不再是柑橘生产的主要困难,主要困难已经转变为销售困难和增收困难。柑橘价格长期在剧烈波动且实际价格不增反降,生产投资不断上涨、劳动力日益紧缺、柑橘品牌鱼目混珠、生产柑橘的利润不断地在下降。
     中国柑橘产业的规模是不是发展得过大了?中国柑橘需求量是不是有些不足?中国柑橘市场是否稳定?未来的发展态势又将如何?这些问题开始引起了柑橘学术界和产业界的注意和思考。本文正是基于这些思考,运用经济预期理论和局部调整模型,建立了中国柑橘市场预警模型,探寻中国柑橘产业发展中存在的问题,为柑橘产业的良性发展献计献策。
     中国柑橘市场预警指标体系的建立和模型的架构。在回顾中国柑橘产业的发展历史以及农产品市场预警的研究动态之后,结合农产品预警理论的一般要求,构建了中国柑橘市场预警指标体系和模型框架。提出了核心层、辅助层和制约层的三层指标体系共33个指标变量。指标涉及柑橘生产、需求、价格等多个方面,包含了柑橘供应、柑橘国内鲜果消费、进口、出口、柑橘价格等柑橘产业本身的变量,也包含了整个社会发展的宏观变量,例如市场化、城镇化、交通状况、人民币汇率、世界经济环境、是否加入WTO等。模型框架里包含了供给量模型、国内鲜果消费量模型、进口量模型、出口量模型、加工量模型等5个主模型和柑橘销售价格模型、进口价格模型、出口价格模型、加工价格模型等4个辅助模型。9个模型构成了一个相互依存相互制约的系统。
     中国柑橘市场预警理论模型的构建。运用经济学相关理论、经济预期理论和局部调整模型,选用对数线性模型推导出了带有残差信息的9个子模型的表达形式。为了解决这9个子模型中残差变量的经济信息,收集统计并整理了指标体系里33个变量的数据。运用斯皮尔曼等级相关系数以及经济规律,筛选出了影响柑橘供给量、柑橘国内鲜果消费量、柑橘进口量、柑橘出口量、柑橘销售价格、柑橘进口价格、柑橘出口价格、柑橘加工价格的影响因素。由于每个模型残差信息中带有的变量较多,考虑到模型拟合样本量不足以及模型中会存在严重的多重共线的情况,本文将主成分分析综合变量的方法引入到模型之中,对前面构造的理论模型进行了修正。
     中国柑橘市场预警实证模型的建立与应用。运用联立方程三阶段估计的方法,对整个模型进行了参数估计,结果发现,柑橘供给量模型的拟合精度达到了97%以上,柑橘国内鲜果消费量模型的拟合精度也达到了93%以上。其余模型的拟合精度均超过了80%以上。整个模型的拟合达到了预期的效果。运用模型研究了在收入增长下中国柑橘最大需求量的估计、控制种植面积下中国柑橘供需平衡的研究、城镇化进程对中国柑橘供给量的影响研究、人民币汇率变动对中国柑橘的出口的影响研究等4个问题,得出了相应的结果与结论:第一,按照当前的发展趋势,到2020年,中国柑橘供给量将达到5544万吨,柑橘需求量只达到4800万吨,柑橘供需缺口将进一步增大,供需矛盾更加突出,柑橘销售仍然是困难重重;第二,在居民可支配收入接近美国当前水平的2050年,中国柑橘需求量将达到6000万吨。此时,如果柑橘种植面积控制在3000千公顷以下,中国柑橘的供给量也只是达到5757万吨,那时中国柑橘产业将出现供不应求的局面;第三,加速城镇化是中国最近提出的发展规划。城镇化进程的加快有利于加快城乡融合、加快柑橘流通速度,提高柑橘供应能力;第四,人民币在今后若干年不断升值似乎成为了人们的共识。在其它条件不变的情况下,人民币升值给柑橘出口带来了负面的影响。
     主要研究结论:(1)中国柑橘产业已经进入供大于求的供需失衡状态。(2)中国居民可支配收入增长缓慢是致使供需失衡的主要原因。(3)中国柑橘销售价格呈现出明显的“蛛网模型”效应。(4)影响柑橘供给量与国内鲜果消费量的主要因素是柑橘销售价格。(5)中国柑橘的销售没有实现全年常态化,给了替代品太多的机会。(6)中国柑橘对外贸易处于劣势。(7)人民币不断升值和世界经济水平下滑严重制约了近几年的中国出口能力。(8)中国柑橘加工水平低,产业化链条不完整。(9)中国柑橘还是处在靠扩张面积增加产量的阶段。
     文章的最后,提出了对策建议:
     第一,建议政府出台相关政策,严禁毁粮田造柑橘园。对柑橘新建园实行严格的审批制度,实行建园配额报审制度,力争把柑橘种植面积控制在3000千公顷左右。
     第二,加大科技投入,努力提高柑橘单产水平与品质。加速柑橘加工业原料以及加工技术等方面的研究,努力增加附加值,提高市场竞争力。提高柑橘抗病虫害以及防冻、抗寒、抗旱等能力,努力提高柑橘单产。
     第三,政府决策者要考虑选择合适柑橘品种退出国内种植转嫁到东南亚国家进行种植,给予东道国相应的技术支持与帮助,并建立稳定的进口基地。这样既可以调节国内供需平衡,转移市场风险,也可以节省国内资源,提高土地利用率,还可以为国家创造一个良好的国际环境。
     第四,抓住国家城镇化建设的政策,努力改善柑橘产区交通状况,提高柑橘产区市场化水平。加强柑橘生产的山区与现代化的发达地区的融合。吸引高层次的技术人才、营销和管理人才。加强农业信息产业化,注重发展网络营销、旅游营销、休闲农业等新型营销模式,努力提高柑橘供应能力与需求能力。
     第五,‘改善贸易环境,制定相关政策,规范柑橘专业合作社的建设、鼓励建立各种营销组织,在人民币汇率不断升值的前提下,可以利用人民币结算等手段尽量降低汇率变动的影响,创造良好的营销国内国际环境。
     第六,通过应选宣传、增强柑橘产品的加工能力,研究橙汁加工的专有柑橘品种,扶植柑橘橙汁加工企业的发展,加强产品的品牌建设,柑橘的品种改良和栽培技术的研究,实现柑橘销售季节无缝对接,全年销售,努力提高柑橘需求量。
     第七,建立柑橘预警平台,就柑橘生产、供应、消费、进出口等进行信息预测预报,建立预警平台的反馈修复系统,形成高效精确的预警机制。加强市场信息服务体系建设,促进产销衔接。
     本文的创新之处在于:(1)将诸多宏观经济因素如城镇化进程、市场化水平、农业财政支持等引入到预警模型中,肯定了政府在柑橘市场预警中所起的作用,丰富了市场预警理论。(2)运用经济学相关理论、经济预期理论和局部调整模型,推导出了带有残差信息的9个子模型的表达形式。运用斯皮尔曼等级相关系数以及经济规律,筛选出了模型的解释变量。并将主成分分析方法融入到联立方程模型之中,丰富了联立方程组的构建思路。(3)本论文研究将预警理论与柑橘产业发展相联系。将供给、国内消费、加工、进出口以及价格通过联立方程模型结合起来,建立了中国柑橘市场预警模型,努力为柑橘产业发展提供建议。
After development and planning for more than20years, China has set up the producing areas of citrus in20provinces such as Hubei, Hunan, Fujian, Guangdong, Sichuan and Jiangxi, which are in the south of Yangtze River, and has formed four dominant citrus areas including south of Jiangxi province, north of Guangxi province, south of Hunan province and the middle and lower reaches of the Yangtze River. Since joining the WTO, Chinese citrus industry has achieved great progress in many aspects from production, consumption, import and export trade to processing and storage. In2011, the yield of citrus was29440400tons and the planting area was up to2288300hectares, ranking first in the world. Fresh citrus fruit exports reached more than900000tons, and canned citrus exports was also ranking the first in the world. What's more, citrus processing developed rapidly, and processing factories was built up in many producing areas, for refrigeration, cleaning, waxing, packaging and canned orange juice processing. Citrus industry has become an important force in China's national economy, and played a significant role in poverty alleviation of peasants.
     However, Chinese citrus industry is faced with a series of problems while with rapid development. After long-term efforts with citrus experts, variety breeding, cultivation management and pest control are no longer the major difficulties of citrus production. The main difficulty has become problems of production marketing and income increasing. While the price fluctuates in the stable pace for a long time, the productive investment is rising, the labor is increasingly scarce, the brand is passed fish eyes for pearls, and the profits is constantly on the decline.
     Is the industry scale of citrus in China too huge? Is Chinese demand for citrus inadequate? Is Chinese citrus market stable? How will the future development trend? These problems has attracted the attention and provoked reflective thoughts of both academia and industry. Based on these considerations, this paper establishes the Chinese citrus market early-warning model with the use of economic expectation theory and partial adjustment model, to explore the problems in the development of citrus industry, and propose reasonable suggestions for the benign development of citrus industry.
     Indicator System Establishment and Model Framework Construction of Chinese Citrus Market Early-warning. In reviewing the development history of Chinese citrus and research trends of agricultural market early-warning theory, indicator system and framework of Chinese citrus market early-warning model are constructed, combined with the general requirements of agricultural market early-warning theory. A total of33indicator variables are put forward in three layers of indicator system, which named core layer, auxiliary layer and constrained layer. These indicators involved many aspects such as aspects of production, demand and price of citrus, including not only variables about citrus industry, supply, fresh fruit domestic consumption, imports, exports, prices, for example, but also macro variables about entire social development, such as marketization, urbanization, traffic conditions, RMB exchange rate, world economic environment and whether to join the WTO. Model framework contains five main models which are supply model, fresh fruit domestic consumption model, import model, export model and processing capacity model, and four auxiliary models which are sales price model, import prices model, export prices model and processing prices model. These nine models constitute a system of mutually dependant and constrained.
     The Construction of The Theoretical Model of Chinese Citrus Market Early-warning.The expression forms of nine sub-models with residual information are then deduced through the application of logarithm linear model, using relevent economic theories, expectation theory and partial adjustment model. In order to deal with economic information of residual variables in these nine sub-models, data of33variables are collected and analyzed. Factors affecting the supply, fresh fruit domestic consumption, import, export, sales price, import prices, export prices and processing prices are screened out using the Spearman rank correlation coefficient and the economic laws, and are put into the residual variables of the model, to reconstruct the citrus market early-warning model.Since there are a lot variables within residual information in each model, and considering the model fitting sample size is inadequate and the problem of multicollinearity will be serious in the model, this paper introduces the method of principal component analysis comprehensive variables into the model, and modifies the previous theoretical model.
     After parameter fitting for the entire model through application of the method of three-stage estimated simultaneous equation, the result shows that fitting precision of citrus supply model reaches97%, and that of fresh fruit domestic consumption model reaches93%. The rest of the model fitting precision are more than80%. The overall model fitting has achieved the expected eftect.Then the model is used to study four questions, which are about the estimation of China's largest citrus demand under the condition of revenue growth, the research of the citrus supply and demand balance under the Chinese control of planting area, the study of the impact on the China's supply brought by the process of urbanization, and the impact on China's export of citrus brought by the RMB rate changes. Here are the relevant results and conclusions of the study:
     First of all, according to the current trends, by2020, the citrus supply in China will reach55.44million tons, while the citrus demand will only reach48million tons. The gap between supply and demand will increase further, and citrus sales will be still difficult.
     Second, in2050when the resident's disposable income reaches U.S. current level, the demand will reach60million tons. If the planting area is controled to less than3million hectares, the demand will only reach57.57million tons, and the Chinese citrus industry will appear in short supply at that time.
     Third, accelerated urbanization is a development plan that China proposes recently. The accelerated urbanization can be helpful to accelerate the integration of urban and rural, to increase the velocity of circulation of citrus, and to enhance the ability of supply of citrus.
     Fourth, RMB appreciation over the next several yeas seems to be the consensus. In the case of other conditions remain unchanged, the RMB appreciation has an negative impact on citrus exports.
     The main research conclusions is as following:
     (1) The Chinese citrus industry has been in a oversupply situation, which is a state of imbalance between supply and demand.
     (2) The slow speed of growth of Chinese residents'disposable income is the main reason causing the imbalance between supply and demand.
     (3) The sales price of Chinese citrus has an obvious'cobweb model'effect.
     (4) The main factors that has an impact on citrus supply and fresh citrus domestic consumption is the sales price of citrus.
     (5) The sale of Chinese citrus is not constant all year round, giving many sales opportunities to the alternatives.
     (6) The foreign trade of Chinese citrus is in disadvantage.
     (7) The RMB appreciation and the severe decline of world economy seriously restrict the ability of exports in recent years.
     (8) Chinese citrus has a low level of processing, and the industrial chain is incomplete.
     (9) Chinese citrus is still in the stage of using expansion of planting area to increase production.
     In the end, the paper puts forward seven countermeasures and suggestions.
     First, the government is recommended to introduce relevent policies to prohibit building citrus groves by ruining grainfield. The strict approval system and quote system should be used on the new-built citrus groves, making every effort to control the planting area to less than3million hectares or so.
     Second, the industry should continue to increase investment in science and technology to improve the yield per unit and quality of citrus. Research on raw material and technology of processing should be encouraged to increase the additional value and advance market competition capability. The ability of resisting plant diseases, insect pests, congealment, cold and drought ought to be enhanced to improve the yield per unit of citrus.
     Third, government policy makers is suggested to select proper breeds to exit domestic cultivation and be passed on to the Southeast Asian countries. These countries should be offered technology support and assistance, and be built stable base for imports. In this way, the balance of domestic supply and demand can be adjusted, market risk can be transfered, domestic resources can be saved, land utilization ratio can be improved, and a sound international environment can be established for China.
     Fourth, the industry is advised to grasp the national urbanization policy to improve the traffic condition in the citrus planting area and raise the level of marketization. The industry should strengthen the integration of mountain area producing citrus and modern developed regions, and should attract technology talents, marketing talents and management talents of high level. Also, it is suggested to strengthen the industrialization of agricultural information, pay attention to the development of new marketing patterns such as network marketing, tourism marketing and leisure agricultural, and improve the ability of demand and supply.
     Fifth, the government is recommended to improve the trading environment, formulate relevent policies, regulate the construction of the citrus specialized cooperatives and encourage the establishment of a variety of marketing organization. Under the premise of increasing RMB exchange rate, means such as RMB settlement can be used to minimize the effects of exchange rate fluctuations and establish a sound marketing environment both domestically and internationally.
     Sixth, propaganda system should be used to promote the processing ability of citrus products. The government should encourage research on proprietary citrus varieties for orange juice processing, foster the development of citrus juice processing enterprises, strengthen the brand construction of citrus products, encourage research on breed improvement and cultivation techniques, make efforts to achieve seamless joint of sales season to sale all year round, and increase the demand of citrus.
     Last, the citrus early-warning platform should be established to forecast information on citrus production, supply, consumption, import and export. Also, a feedback system of early-warning platform should be established to form the efficient and accurate early-warning mechanism. The market information service system should be strengthened to promote the cohesion of the production and marketing.
     Probable innovations of the paper can be summarized to the following three aspects:
     First, this paper creatively introduces many macroeconomic factors such as urbanization, marketization and agricultural financial support into the early-warning model, affirms the role of government in citrus market early warning system, enrich the theory of market early warning.
     Second,this paper uses relevent economic theories, expectation theory and partial adjustment model to deduce the expression forms of nine sub-models with residual information. The explanatory variables are screened out, using the Spearman rank correlation coefficient and the economic laws. Also, this paper applies the principal component analysis into simultaneous equation model to enrich the constructing idea of simultaneous equation.
     Third, this paper associates early-warning theory with the development of citrus industry, combines supply, domestic consumption, processing, import, export and price by using simultaneous equation model to establish the Chinese citrus market early-warning model and provide suggestions for the development of citrus industry.
引文
1.柏继云,孟军,吴秋峰.黑龙江省大豆生产预警指标体系的构建[J].东北农业大学学报,2007(4):568-572.
    2.柏继云.黑龙江省大豆生产预测预警研究与实证分析[硕士学位论文].哈尔滨:东北农业大学,2006.
    3.操张进.基于定性相空间的应急资源需求预测方法研究[硕士学位论文].合肥:中国科学技术大学,2011.
    4.曹明振.国家粮食安全预警决策支持子系统的设计与开发[硕士学位论文].北京:北方工业大学,2008.
    5.楚君.基于信号博弈的农产品加工服务研究[J].现代商贸工业,2013(4):18-20.
    6.冯飚,徐兆亮.城市蔬菜供需平衡问题的优化研究[J].西北师范大学学报,1995(1):53-56.
    7.高铁梅.计量经济分析方法与建模[M].北京:清华大学出版社,2006.
    8.高志刚,韩延玲.新疆棉花产业预警指标体系的构建[J].中国农垦经济,2004(12):28-31.
    9.贺京同,潘凝,建勋,桂章.基于模糊神经网络的宏观经济预警研究[J].预测,2000(4):42-45.
    10.虎晓红,马新明,席磊.粮食本底安全预警系统的设计及实现[J].曲阜师范大学学报,2006(4):118-120.
    11.黄德宏.影响河南农产品进出口原因分析[J].决策与信息,2009(53):89-90.
    12.姜秀华,任强,孙铮.上市公司财务危机预警模型研究[J].预测,2002(3):56-61.
    13.晋奇.河南省粮食生产预警系统研究[硕士学位论文].郑州:河南农业大学,2006.
    14.孔祥智,丁玉.我国农产品进出口贸易的特点及趋势:1998—2011[J].经济与管理评论,2013(1):103-112.
    15.李炳军,李秋芳,卢秀霞.灰色线性回归组合模型在河南省粮食产量预测中的应用[J].河南农业科学,2009(10):44-47.
    16.李红.农产品及其加工产品质量安全隐患与解决对策[J].中小企业管理与科技,2008(5):221-221.
    17.李建伟.我国进口价格的影响因素及政策建议[J].经济纵横,2011(10):1-6.
    18.李良波.农产品出口制约因素及预警指标体系构建[J].商业时代,2006(23):83-85.
    19.李启波,邬彬,吴丹等.从“菜贱伤农”看农产品市场预警机制的建立[J].中国农村小康科技,2007(10):33-35.
    20.李志斌.粮食生产安全预警研究—以东北三省为例[硕士学位论文].北京:中国农业科学院,2007.
    21.李子奈.计量经济学——方法与应用[M].北京:清华大学出版社,1992:52-56.
    22.刘传哲,张丽哲.金融危机预警系统及其实证研究[J].系统工程,1999,17(5):33-37.
    23.刘九从.中国棉花市场预警系统及指标体系研究[J].中国棉麻流通经济,2006(4):35-37.
    24.刘强,陈东东,杨盼.奶类消费需求组合预测—基于指数平滑法和灰色模型[J].中国商贸,2010(1):6-7.
    25.刘兴,顾海英.中国粮食产量周期波动测定及预警分析[J].陕西农业科学,2008(2):168-172.
    26.卢秀茹,代学钢,王健.基于信息技术的棉花风险预警系统及应用[J].农业工程学报,2007(9):159-163.
    27.陆胜民.世界柑橘生产、贸易、加工的历史、现状与发展趋势[J].食品与发酵科技,2010(6):63-68+71.
    28.吕新业.中国粮食安全及预警研究[博士学位论文].北京:中国农业科学院,2006.
    29.罗锋,牛宝俊.国际农产品价格波动对国内农产品价格的传递效应——基于VAR模型的实证研究[J].国际贸易问题,2009(6):16-22.
    30.马骥,张卫峰.组合预测方法在磷肥需求预测中的应用[J].知识丛林,2005(6):120-121.
    31.马九杰,张象枢,顾海兵.粮食安全衡量及预警指标体系研究[J].管理世界,2001(1):154-162.
    32.马腾.河北省棉花生产预测与预警研究[硕士学位论文].保定:河北农业大学,2008.
    33.马晓河,王为农,蓝海涛.入世后中国农产品供需平衡问题研究[J].宏观经济管理,2003(3):25-29.
    34.毛树春,李亚兵,王香河等.中国棉花产业经济预警指标的研究和应用—中国棉花生产景气指数(CCPPI)和中国棉花生长指数(CCGI)[J].中国农业科技导报,2005(4):55-58.
    35.穆维松,张小栓,刘雪,张领先,傅泽田.水果供给与需求关系组合分析模型的构建及应用[J],2005(11):139-144.
    36.祁春节,邓秀新.中美两国柑橘产业的比较[J].世界农业,2000(3):3-4.
    37.祁春节.中国柑橘产业的经济分析与政策研究[博士学位论文].武汉:华中农业大学,2001.
    38.秦鸣,何如海.苏南地区农产品出口影响因素的实证分析[J].湖北经济学院学报(人文社会科学版),2013(5):39-41.
    39.秦悦铭.我国大豆进口贸易影响因素分析[硕士学位论文].南京:南京航空航天大学,2012.
    40.阙树玉,王升.人民币汇率波动对中国农产品进口价格影响的研究[J].农业技术经济.2010(5):15-23.
    41.任斌,何俊杰.基于结构化神经网络挖掘的农产品产量预测方法[J].计算机工程与科学,2009,31(9):88-91.
    42.荣岩.人民币汇率传递效应的影响因素研究--基于不完全竞争的视角[博士学位论文].上海:复旦大学,2011.
    43.沈瑾,刘清.中国农产品加工预警体系构建的研究[J].农业工程技术(农产品加工业),2008(10):12-14.
    44.史峰,王辉,胡斐,郁磊.MATLAB智能算法30个案例分析[M].北京:北京航空航天大学出版社,2011.
    45.孙凤.粮食产需波动及预警系统[J].统计与决策,1997(5):19-20.
    46.谭向勇.中国主要农产品市场分析[M].北京:中国农业出版社,2001:256-266.
    47.陶建平,熊刚初,徐晔.我国水果消费水平与城镇化的相关性分析[J],中国农村经济,2004(6):18-24.
    48.田文娟.农产品加工存在的质量安全隐患与对策[J].农民致富之友,2012(16):27-27.
    49.汪晓银,刘大集,祁春节.中国蔬菜总产的主成分回归模型的构建及预测[J].农业系统科学与综合研究,2006,22(2):132-135.
    50.汪晓银,祁春节.中国蔬菜生产、消费与贸易研究—一个供需平衡的计量经济分析框架[硕十学位论文].武汉:华中农业大学,2004.
    51.汪晓银,谭劲英,谭砚文.城乡居民年人均蔬菜消费量长期趋势分析[J].湖北农业科学,2006,45(2):135-137.
    52.汪晓银,谭砚文,祁春节.中国省区农业生产条件差异的聚类分析[J].湖北大学学报(自然科学版),2004,26(4):350-353.
    53.汪晓银,赵玉,祁春节.化肥投入与蔬菜产出的边际分析[J].湖南农业大学学报(自然科学版),2004,30(4):348-350.
    54.汪晓银,周保平.数学建模与数学实验(第二版)[M].北京:科学出版社,2011.
    55.汪晓银,朱倩军,祁春节.中国蔬菜产出水平及其影响因素的主成分分析[J].华中农业大学学报(社会科学版),2004(5):16-18.
    56.王江,龚丽.构建中国农产品技术性贸易壁垒预警体系的框架[J].农业经济问题,2006(5):65-68.
    57.王文海.对完善中国农产品出口预警机制的思考[J].国际经济合作,2007(10):36-42.
    58.王志会.人民币升值对我国农产品进出口的影响[硕士学位论文].合肥:安徽大学,2011.
    59.吴金环,傅泽田.中国几种主要农产品的未来需求—从可变需求收入弹性看[J].西北农林科技大学学报(社会科学版),2004(6):33-35.
    60.吴璇.中国粮食价格预警系统研究[硕士学位论文].北京:中国农业大学,2003.
    61.席玉坤.调整农村结构,实现农产品供需平衡[J].中国商贸,2010(1):6-7.
    62.肖黎.中国农产品贸易逆差:格局、影响因素及其应对研究[博士学位论文].长沙:湖南农业大学,2012.
    63.肖培灵,马军海,耿立艳.基于灰色支持向量机组合模型的农产品产量预测[J].中国农机化,2010(1):44-47.
    64.薛文珑.海南农业经济预警系统研究[硕士学位论文].儋州:华南热带农业大学,2006.
    65.杨梅娟,陈亚军.变共轭梯度算法及其在农产品总产量预测中的应用[J].计算机应用,2006(11):2765-2772.
    66.杨艳涛.加工农产品质量安全预警与实证研究[博士学位论文].北京:中国农业科学院,2009.
    67.叶旭君,Kenshi Sakai,何勇.基于机载高光谱成像的柑橘产量预测模型研究[J].光谱学与光谱分析,2010(5):1295-1299.
    68.于平福,廖振钧,梁贤.广西农产品预警模型设计与开发研究[J].广西社会科学,2002(4):133-135.
    69.于平福,陆宇明,韦莉萍等.基于小波广义回归神经网络的粮食产量预测模型[J].湖北农业科学,2011(5):2135-2137.
    70.袁志清.广东省农业生产结构调整中的粮食供需平衡[J].南方经济,2001(1):66-68.
    71.张晶,李江风.耕地需求预测方法研究——以广西资源县为例[J].安徽农业科学,2006(6):1204-1206.
    72.张霞.农产品加工产业集群发展研究[博士学位论文].武汉:华中农业大学,2007.
    73.张勇,曾澜,吴炳方.区域粮食安全预警指标体系的研究[J].农业工程学报,2004(3):192-196.
    74.赵瑞莹,贾卫丽.农产品市场风险预警管理研究[J].农业现代化研究,2004(1):35-37.
    75.赵瑞莹,杨学成.农产品价格风险预警模型的建立与应用—基于BP人工神经网络[J].农业现代化研究,2008(2):172-175.
    76.赵瑞莹.农产品市场风险预警管理研究[博士学位论文].泰安:山东农业大学,2006.
    77.赵芝俊,张社梅.近20年中国农业技术进步贡献率的变动趋势[J].中国农村经济,2006(3):4-12+22.
    78.钟钰.中国农产品关税减让与进口的相互关系及经济影响[博士学位论文].南京:南京农业大学,2007.
    79.周方.关于‘规模收益不变’之假定及生产要素产出弹性系数的测算[J].数量经济技术经济研究,1995(6):40-50.
    80.周净,朱德开,方群.安徽农产品出口对农业经济增长的实证分析[J].特区经济,2008(6):180-181.
    81.朱丽萌.中国农产品进出口与农业产业安全预警分析[J].财经科学,2007(6):111-116.
    82.朱希刚.农业技术经济分析方法及应用[M].北京:中国农业出版社,1997.
    83. Adams RM. The benefits to Mexican agriculture of an El Nino-southern oscillation (ENSO) early warning system[J]. Agricultural and Forest Meteorology,2003 (115):183-194.
    84. Altman EI, Robert G, Haldeman, Narayanan P.Zeta analysis:a new model to identify bankruptcy risk of corporations[J]. Journal of Banking and Finance,1977, (9):29-54.
    85. Altman EI,Marco G, et al. Corporate distress diagnosis:comparisons using linear discriminant analysis and neural networks[J]. Journal of Banking and Finance,1994, (18):505-529.
    86. Altman El. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy[J]. Journal of Financial,1968,123 (24):589-609.
    87. Anna H. Using neural network for classification tasks:some experiments on data sets and practical advice[J]. Journal of Operation Research Society,1992,43:215-226.
    88. Aziz A, Emanuel D, Lawson G. Bank predicition:an investigation of cash flow based models[J]. Journal of management studies,1988:419-437.
    89. Boken VK. Forecasting Spring Wheat Yield Using Time Series Analysis:A Case Study for the Canadian Prairies[J]. Agronomy Journal,2000,92(6):1047-1053.
    90. Brockett PL, Cooper WW, Golden LL, Pitaktong U. A neural network method for obtaining an early warning of insurer insolvency[J]. The Journal of Risk and Insurance,1994,61 (3):402-424.
    91. Burkart O, Coudert V. Leading indicators of currency crises for emerging countries[J]. Emerging Markets Review,2002,3:107-133.
    92. Bustelo P. Novelties of financial crises in the 1990s and the search for new indicators[J]. Emerging Markets Review,2000,1:229-251.
    93. Carpio CE, Ramirez OA. Forecasting foreign cotton productiong:the case of India,Pakistan and Australia[A]. Atlanta, Georgia:Beltwide Cotton Conferences,2002.
    94. Fanning K, Cogger K. Neural network detection of management fraud using published financial[J]. International Journal of Intelligent System s in Accounting Finance and Management,1998,7 (1):21-41.
    95. Isengildina O, Irwin SH, Good DL. Empirical Confidence Intervals for WASDE Forecasts of Corn, Soybean and Wheat Prices[A], NCR-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management,2003.
    96. Isengildina O, Irwin SH, Good DL.USDA Interval Forecasts of Corn and Soybean Prices:Overconfidence or Rational Inaccuracy[A]? NCCC-134 Conference on Applied Commodity Price Analysis,2006.
    97. Jo H, Han I. Bankruptcy prediction using case-based reasoning, neural networks and discriminant analysis[J]. Expert System with Application,1997,13 (2):97-108.
    98. Kanji Yoshioka et al. Sources of Total Factor Productivity, Keio Economic Observatory, Keio University [A]. Monograph,1994 (5)
    99. Kantanantha N, Serban N, Griffin P. Yield and Price Forecasting for Stochastic Crop Decision Planning[J]. Journal of Agricultural, Biological, and Environmental Statistics,2010,15:362-380.
    100. Kolari J, Caputo M,Wagner D. Trait recognition:an alternative approach to early warming systems in commercial banking[J]. Journal of Business Finance & accounting,1996,23:9-10.
    101. Konandreas, P., Peter, B.& Richard, G. Estimation of Export Demand Functions for U.S. Wheat[J]. West J. Agr. Econ,1978,3:39-49.
    102. Kumar V. An early warning system for agricultural drought in an arid region using limited data[J]. Journal of Arid Environments,1998(40):199-209.
    103. Laitinen EK, Chong HG. Early-warning system for crisis in SMEs:Preliminary evidence from Finland and the UK[J]. Journal of Small Business and Enterprise Development,1999,6(1): 89-102.
    104. Longmire, J.& Morey, A. Strong Dollar Dampens Demand for U.S. Farm Products[R].FAER 193,U.S. Department of Agriculture, Economic Research Service,1983.
    105. Maskus, K.E. Exchange Rate Risk and U.S. Trade:A Sectoral Analysis[J].Federal Reserve Bank of Kansa City. Eco.Rev.,1986(3)16-28.
    106. Pick, D.H. Exchange Rate Risk and U.S. Agriculture Trade ows[J].American Journal of Agriculture Economics,1990,72(3):694-700.
    107. Salman AZ, Al-Karablieh EK. An early warning system for wheat production in low rainfall areas of Jordan[J]. Journal of Arid Environments,2001 (49):631-642.
    108. Sanders DR, Manfredo MR. Forecasting Basis Levels in the Soybean Complex:A Comparison of Time Series Methods[J]. Journal ofAgricultural and Applied Economics,2006,38:513-523.
    109. Schuh, G.E. The Exchange Rate and U.S. Agriculture[J].American Journal of Agriculture Economics,1974,56(1):1-13.
    110. Villani M. Bayesian prediction with cointegrated vector autoregressions[J]. International Journal of Forecasting,2001,17:585-605.
    111. Wanger WP, Otto J, Chung QB. Knowledge acquisition for expert systems in accounting and financial problem domains[J]. Knowledge Based Systems,2002,15:439-447.
    112. Xiaoyin Wang, Yuhong Li, Chunjie Qi. The Analysis of Technology Progress and Production of Chinese Citrus Fruit[J]. International Conference on Convergence & Hybrid Information Technology,2008.
    113. Yang BA, Li LX, Ji H, Xu J. An early warning system for loan risk assessment using artificial neural networks[J]. Knowledge-Based Systems,2001,(14):303-306.
    114. Yoon Y, Swales G. A comparison of discriminant analysis versus artificial neural networks[J]. Journal of Peratia Research Society,1993,44:51-60.