基于Census X12和HP滤波的海南蔬菜价格波动特征及原因分析

以海南省主要蔬菜品种2012年1月至2015年9月的月零售价格为研究对象,采用Census X12季节调整方法,把季节变动S分离出来,分析海南省各类蔬菜价格的季节性波动情况,进一步采用HP滤波法将趋势变动T、循环变动 C分离出来,分析海南省各类蔬菜价格的趋势和循环波动情况。然后对海南省蔬菜价格波动原因进行分析。     

我国天然橡胶产量波动的周期性分析——基于HP滤波的实证研究

利用HP滤波法测定1970～2010年间我国天然橡胶产量波动的周期,分析波动的周期性特征和成因,并分阶段研究了开割面积、单产波动与产量波动的关系,揭示了我国天然橡胶生产的波动规律。     

花生价格波动影响因素的实证分析——以山东省为例

近年来山东省花生价格波动频繁且幅度较大。本文基于1995-2014年山东省花生价格年度数据,在对山东省花生价格波动特征及影响因素分析的基础上,运用多元回归模型对花生价格波动的影响因素及程度进行了实证分析。结果表明:上期花生价格、上期花生产量、种植成本、大豆价格、居民收入对花生价格具有显著性影响。据此,提出了相应的政策建议。     

近年来荔枝价格疲软原因分析--以漳州市为例

本文主要以漳州为例分析近年来荔枝价格走向疲软的原因,并提出解决对策。     

中国对美国市场茶叶出口贸易波动成因分析——基于修正的CMS模型

本研究在分析中国对美国市场茶叶出口贸易现状的基础上,利用修正的恒定市场份额（CMS）模型分析了2000—2019年中国对美国市场茶叶出口贸易波动的成因。结果表明,美国市场茶叶进口需求的增加是中国茶叶出口增长的最主要拉动力,而竞争力效应和二阶效应对中国茶叶出口增长起到了较强的逆向拉动作用。显示性比较优势指数（RCA）结果显示,近年来中国对美国市场出口的各类茶叶产品的竞争力均呈现下降趋势,此外中国茶叶出口结构不符合美国市场茶叶需求结构、茶叶生产成本提高等也抑制了中国对美国市场茶叶出口的增长。     

基于DEA方法的旅游经济效率分析——以海南为例

采用DEA(Data Envelopment Analysis)方法对海南旅游经济效率进行评价,并提出海南旅游投入的调整策略,以期让海南旅游发展更加合理,为旅游投资规划和产业结构调整提供可参考的依据。     

基于传统茶品牌法律保护的案例分析——以王老吉凉茶为例

茶在我国是十分珍贵的文化遗产,有着悠久的发展历史,随着市场的不断变化,我国形成了各式各样的茶品牌,茶品牌销售的形式让更多的人了解茶文化。但是,在近几年我国开始出现许多关于茶品牌的商标侵权、包装装潢侵权等问题,以王老吉凉茶为例来说,王老吉属于我国的民族老字号品牌,在茶品牌市场中曾经有着十分有影响的地位,但在十九世纪时,王老吉的销售量开始面临阻碍,于是为了盘活资产便与广药集团达成了商标许可的协议,这使得王老吉茶品牌在后期销售时面临了一系列的法律问题。对此,本文基于传统茶品牌的法律保护问题进行分析,以王老吉凉茶为例展开讨论。     

基于主成分分析的橡胶种植适宜性评价——以海南省儋州市为例

选取土壤及气候资源,根据前人的研究成果及专家经验选择确定评价因子,运用模糊数学方法来量化评价因子,采用主成分分析方法对海南省儋州市的橡胶种植适宜性进行评价。结果表明：儋州市最适宜橡胶种植的面积为43 029.58 hm2,占农用地面积的14.99%;适宜面积为57 770.90 hm2,占20.12%,次适宜面积为40 934.9 hm2,占14.26%。该评价结果可以从宏观上实现儋州市的橡胶收益最大化以及为橡胶种植的区划提供决策参考。     

基于Landsat-8和LSMM的冬小麦面积提取研究——以皖北为例

美国国家航空航天局（NASA）成功发射了最新系列的陆地卫星（Landsat-8）,搭载的陆地成像仪（OLI）对波段进行了重新调整,从而避免了大气吸收干扰,能够更好地区分植被和无植被特征,为农业遥感提供了全新的高质量数据。本文选取皖北（阜阳、蚌埠、宿州）为研究区域,以Landsat-8遥感影像为研究数据,经缨帽变换得到遥感影像的主成分信息,并用其分量甄选出不同地物的端元,最后建立线性混合像元模型（LSMM）,并对皖北地区冬小麦进行提取。与历史统计数据对比,冬小麦种植面积的提取精度达到了90%以上,说明利用Landsat-8和LSMM开展大尺度范围的小麦种植面积提取具有明显的优势。     

我国南部茶叶对外贸易比较优势的经济学分析及措施——以海南为例

世界上最早的茶叶出现在中国,茶叶是我国的一种农产品,茶文化在中国有着十分悠久的历史。本篇文章重点围绕茶叶贸易进行分析,通过分析出的问题,制定出有针对性的整改措施,以促进我们南部茶叶贸易的可持续性发展。     

Projecting Distribution of the Overwintering Population of Sogatella furcifera (Hemiptera: Delphacidae), in Yunnan,China With Analysis on Key Influencing Climatic Factors

Sogatella furcifera (Horváth) is the most threatening migratory rice pest in Yunnan, China. S. furcifera overwinters in low- altitude basins and valleys in southern Yunnan and migrates northward in spring and summer of the following year, causing serious damage during migration. The overwintering distribution, areas, and spatial pattern of S. furcifera are relevant to the migration and outbreak of this pest. Based on a 4-yr field survey (2010–2013), this study projected areas suitable for S. furcifera to overwinter using a species distribution model, and analyzed the key influencing climatic factors using principal component analysis (PCA) and ecological niche factor analysis (ENFA). Our field survey showed that the northern latitudinal- and upper elevation limits of overwintering S. furcifera was 25.4° N and 1,608 m in western Yunnan and 24.2° N and 1,563 m in eastern Yunnan. The species distribution model produced a fragmented distribution pattern, with most of which in western Yunnan and only a few in eastern Yunnan. The PCA and ENFA analyses showed that the mean temperature of the driest quarter and the precipitation of the coldest quarter significantly influenced the distribution of S. furcifera in winter. The results suggested that the complex topography, spatial differences in winter temperatures, and host availability altogether determined the distribution of overwintering S. furcifera . Compared with previous surveys, the northern latitudinal- and upper elevation limits of overwintering S. furcifera were higher, while the population became rarer in some suitable areas due to change of farmland utilization in winter and possibly climate change.     

Decadal and Monthly Change of an Empirical Coefficient in the Relation between Solar Radiation and the Daily Range of Temperature in Japan: Implications for the Estimation of Solar Radiation Based on Temperature

Abstract: The need for solar radiation (R_s, MJ m^–2 d^–1) estimation remains a common concern for agronomists. Evaluation of crop productivity is primarily based on R_s data, which are difficult to collect because of cost and calibration requirements. Generally, historical R_s data are more difficult to obtain. This study focused on an estimation model based on the daily range of temperature and evaluated its accuracy from the viewpoint of crop productivity analysis. The variability of an empirical coefficient in the model (K_rs), which was derived from the relation between R_s and daily range of temperature (T_max – T_min) was analyzed using climatic data observed in Japan considering data availability and quality. K_rs had significant monthly differences, and it significantly increased from 1981 – 1985 to 2003 – 2007 at all 10 locations. Period-month interactions were not significant, except for in Utsunomiya, suggesting that the seasonal pattern did not change during the period. Weather data indicated that the increase in K_rs was caused not only by increased solar radiation but also by a decrease in T_max – T_min. The substantial differences in K_rs produced considerable bias for the estimated R_s when the estimation was conducted with a constant K_rs (0.16). Despite the bias, the model is considered to perform well given the present availability of R_s data. The results of this study suggest that the evaluation of the seasonal pattern of K_rs greatly improves the model accuracy.