环青海湖地区草地蝗虫发生测报的气候指标研究

采用次序统计量方法,以1988～1999年环青海湖地区草地蝗虫发生程度资料和气候资料研究了蝗虫灾害等级与当地气候指标的定量关系,建立了春季和夏季预测模型,并以此对2000～2003年该地区蝗虫发生状况进行了预测试验。结果表明,敏感气候因子有:上年11～12月气温、3～6月温度及5～6月降水量等。春季预测模型中的指标为:(1)T11<-6℃;(2)T12<-10℃;(3)T3>-4℃;(4)T4>2℃。夏季预测模型中的指标为:(1)(T11+T12)/2<-8.5℃;(2)(T3+T4)/2>-1.5℃;(3)(T5+T6)>7.5℃;(4)(R5+R6)<48mm。当模型中有3个或4个指标满足,则当年可能发生严重蝗虫灾害,当没有或只有1～2个指标满足,则不会发生严重蝗虫灾害。     

Forecasting Infections of the Leaf Curl Disease on Peaches Caused by Taphrina deformans

An Israeli model forecasting leaf curl disease on peaches caused by Taphrina deformans was validated in the Emilia-Romagna region of northern Italy, during a three-year period (1996–1998), in 13 cases (year × location × cultivar). When the peach trees are susceptible to infection, the model uses mathematical functions to calculate the risk of infection on the basis of weather conditions (daily rainfall greater than 10mm, and maximum air temperature greater than 5°C), and it forecasts periods of possible symptom appearance based on the length of incubation. Peach trees became susceptible to infection between the end of January and mid March, when the first leaf buds attained phenological stage C, i.e. appearance of leaf apex. The trees remained susceptible for at least 9 weeks: the last infection occurred in mid-May.Since most of the leaf curl onsets observed in the orchards fell within the range of model forecasts, the model proved to be accurate in signalling both the first seasonal infection and repeated infections during the primary inoculum season. Few errors occurred, caused either by conditions of rainfall and temperature lower than the thresholds fixed in the model, or by discrepancies between forecast and actual length of incubation. Infection occurred also at 3.1–3.5°C, and with 9.6mm rainfall. Thus, thresholds should not be accepted too rigorously, and perhaps temperature should not be considered as a limiting factor for infection under the conditions of the present work. The length of incubation showed high variability: it was 23 days long on average, with a 95% confidence interval ranging from 20 to 27 days, and extreme values of 9 and 33 days.     

东亚飞蝗的长期预测预报技术研究

东亚飞蝗的发生面积、发生程度主要受残蝗面积、密度和降水条件的影响;库、湖蝗区的宜蝗面积主要受库、湖水位的影响。同时,东亚飞蝗的发生期主要受蝗卵发育期温度条件的影响。利用上述因子分别对东亚飞蝗的发生面积、发生程度及密度、3龄盛期进行预报,简便易行,准确可靠,时效性强,对指导防治工作十分有益。     

湿段天气在小麦赤霉病定量预报中的应用

通过本县小麦感病期内逐日气象观测资料统计分析,发现在该期内各日２：００、８：００、２０：００的空气相对湿度达８５％,１４：００达８２％,且在此湿度标准上持续３０ｈ以上,平均气温达１２℃的湿段天气累计持续时间长,小麦赤霉病发生重。依据湿段天气段在小麦感病的穗期开展赤霉病的短期定量预测,１０年次的回报病穗率误差为２个百分点左右,７年次的预报病穗率误差为０．２９￣５．２个百分点。     

杨小舟蛾发生规律与测报技术研究

杨上舟蛾［Ｍｉｃｒｏｍｅｌａｌｏｐｈａ ｔｒｏｇｌｏｄｙｔａ（Ｇｒａｅｓｅｒ）］中关中１年发生５代，以７、８月份的３、４代种群数量最大，危害最重。其天敌种类较多，多一年内种群数量消长影响较大，５～８月份降雨较多的年份发生较重。介绍了发育进度预测法，种群消长趋势指数预测法和黑光洒诱测法在杨小舟蛾测报中的应用。     

鞭角华扁时蜂滞育幼虫空间型测报抽样技术研究

鞭角华扁叶蜂滞育期幼虫通过空间分布型参数Ｋｕｎｏ指数、聚集指标Ｉ、ｍ／指数、扩散型指标Ｉσ及α、β值分析结果，滞育期幼虫呈聚集分布、个体群。应用空间分布型参数得抽样公式；序贯抽样累积虫量界限林间９０％抽样可靠性指标；５０条／ｍ２需样地４２块，２００条／ｍ２需样地２２块，３５０条／ｍ２需样地１９块。     

青杨虎天牛预测预报技术的研究

1993-1995年，对青杨虎天牛主要测报指标进行了研究。通过虫期发生量预测式，预测不同虫态的发生量，所预测的幼虫和成虫发生盛期与实查结果基本吻合。     

西北太平洋柔鱼渔情速报系统的开发

The neon flying squid, Ommastrephes bartrami, is one of the most important jig fisheries in the northwest Pacific Ocean. In order to understand the movement of O. bartrami fishing-ground better and supply O. bartrami fishing-ground information for Chinese fishing boats in the northwest Pacific ocean, the fishing condition analysis and forecasting system of O. bartrami was developed successfully. The system was based on established comprehensive database, which included the catch data of O. bartrami (total yields, count of total fishing boats, fishing position etc. ) and oceanic environmental information (SST, SST gradient etc. ). Artificial intelligent technology about case-based reasoning was also combined with GIS component technology successfully in the system. The process and function of system establishment are composed of four parts: setting up of case database for central fishing-ground and its environmental factors, knowledge reasoning of fishery information, GIS visualization analyzing as well as trend forecasting of central fishing-ground and information production mapping. At last as an example of the results, an experimental central fishing-ground forecasting of O. bartrami from 9 to 15 in July 2002 in the northwest Pacific Ocean was given in the paper. The results showed that through three class similar searching forecasting central fishing-ground would move west, and indicating that forecasting of the system for O. bartrami central fishing-ground was correct by comparing to real fishing-ground from 16 to 22 in July 2002. Consequently, artificial intelligent expert system technology about case-based reasoning is a useful method for fishing condition and fishing-ground forecasting.     

A structured approach to using biological and environmental parameters to forecast anchovy recruitment

Simulation studies have indicated that a valuable increase in mean annual yield could be achieved in the South African anchovy, Engraulis capensis , fishery if below-average recruitment could be forecast at the start of the fishing season, 6 months before recruitment can he reliably measured. This paper reports on initial investigations into biological and environmental indicator- which could be used to make such forecasts. Valid estimates of recruitment strength in the species are available from 1985 to 1992 and the environmental and biological indicators of interest have been monitored for periods starting from between 1984 and 1988, to the present. Most of the data were obtained from monitoring on anchovy spawner biomass surveys which have been undertaken each November since 1984.
A conceptual model of the factors influencing the recruitment process was constructed and the available data were examined for empirical evidence of the importance of each event included in the model. Fluctuations in copepod biomass and production on the spawning grounds, the incidence of oocyte atresia in adult females, the incidence of southerly winds at Cape Point and the distance offshore of the 16°C isotherm at Cape Columbine were associated with fluctuations in anchovy recruitment. It is suggested that particular attention should be paid to monitoring of these variables to investigate further these associations. If persistent relationships are found, it may be possible to construct an expert system to forecast recruitment from observed values of these variables at the start of a fishing season. A preliminary set of three decision rules is presented.     

组合预测模型在黑龙江省农机总动力预测中的应用

农机总动力是反映和评价农业机械化水平的一个重要指标.通过对黑龙江省农机总动力历史数据进行分析,建立了指数模型、GM（1,1）模型和BP神经网络模型3种预测模型,其次,应用基于离异系数法、二次规划法、Shapley值权重分配法分别构建组合预测模型.拟合结果表明,各种组合预测模型优于各单一模型.最后应用基于Shapley值权重分配法对黑龙江省农机总动力进行组合预测,为制定农机动力发展规划提供了依据.