试论"三元"立交林业技术经营体系(理论问题)

根据国内外关于林业经营体系的论述,提出了“生态为先－市场取向－区域特色”三元立交林业技术经营体系的理念和内涵表述。其基本表述为,生态为先是指新的技术经营体系必须在指导思想上始终把建设生态林业作为原则和目标;市场取向是指在市场经济大环境中建设生态林业,必须重视市场在资源配置,经营运作,矛盾调节中的作用;区域特色是指新的林业技术经营体系能主动适应资源条件和生态位要求。三者的关系是,生态为先是持续发展的灵魂体现,市场取向是活力源泉,区域特色是发展根基。其定位是,适应社会主义市场经济,确保生态效益主导需求,通过模式运作,保障和促进不同生态经济类型区,流域,区域社会经济,特别是山区农村经济协调发展。在此基础上建立具有区域特色,包括产供销,贸工林等内容,及其运作机制的技术,经济,管理经营等运作内容,并对它们进行了分析。     

油松高世代种子园营建技术

通过对3批142个油松优树半同胞子代进行测定,选出36个优良家系,树高、胸径、材积平均增产15.4%、26.9%、73.3%,遗传增益分别为8.2%、8.5%、22.2%;再从优良家系内选择263个优良单株,树高、胸径、材积平均增产27.2%、41.1%、125.9%,预期遗传增益分别为13.3%、11.7%、37.2%;用选出的优良单株营建高世代无性系种子园,并提出了高世代种子园建立技术和种子高产关键技术.     

板栗高产优质栽培技术模式试验

采用修剪、追肥、喷药、喷施叶面肥等综合技术措施，对凤城市包营子、四家子、永兴、和睦等地的板栗进行优质栽培技术模式试验，试验证明，可大幅度提高板栗果实的产量，红光栗、辽丹58号单株产量增加7．12kg和8．85kg，每公顷栽植750株可增加栗实产量达1500—5340kg，比对照增产50％。186％，每公顷增加产值7500-26700元，效果极其显著。     

谈应用Photoshop cs软件绘制林业规划设计图的方法

介绍利用Photoshop CS软件绘制林业规划设计图,方法时尚、便于掌握、色彩鲜艳、成本低和效率高。在使用中,注意电脑系统、相关设备的配置;绘制方法和手段要正确;掌握一些技巧,才能充分发挥该软件的优越性,得到高质量的林业规划设计图。     

日本林地施肥技术

本文介绍日本林地的施肥技术和施肥效果,并对中国林地施肥技术进行了探讨。     

日本松干蚧防治技术综述

本文对1952年以来国内外松干蚧的营林技术防治、检疫措施、生物防治、化学防治等具体防治技术进行了综述及评价。     

吉林市园林灌木抑茵效果分析

园林植物是城市园林景观系统的重要组成部分，是发挥园林功能和绿化效益的重要因子，在城市绿化景观建设中具有不可替代的作用。园林植物体能挥发出多种不同的挥发性物质，它能杀灭空气中细菌或者抑制其生存空间，从而达到净化空气的作用。因此，为探求吉林市内现有植物造景中不同园林灌木的抑菌效果，本文采用了自然沉降法对吉林市内园林灌木的抑菌效果进行了较为系统的分析研究，以便为植物造景、园林绿化等设计中提供理论参考。     

光皮木瓜栽培技术

本文总结了光皮木瓜的育苗、苗木定植和果园管理技术,并对病虫害防治技术进行了初步探讨。     

木荷芽苗切根移栽育苗技术

木荷是我省造林的主要阔叶树种，木荷芽苗地奶移栽可使苗木粗壮，均匀，侧须、根发达；有利造林成活和林木速生丰产。     

Temporal development of crown condition of beech and oak as a response variable for integrated evaluations

The evaluation uses the longest available time series for beech and oak defoliation in Germany. The data from Hesse, starting from 1984, show a typical pattern: for the first 12 years, a continuous increase in defoliation was observed ranging from an average value of 14% in 1984 to a peak value of 30%. This was followed by a subsequent decrease in the loss of foliage accompanied by a high variability, until the last monitoring in 2003, where an average value of 25% defoliation was observed. For both tree species, the years of trend reversal were identical. The same pattern was observed in the German federal states: Rhineland-Palatinate, North Rhine-Westphalia, and Bavaria. The year of trend reversal was identical in Hesse and Rhineland-Palatinate. In North Rhine-Westphalia, it occurred 1 year earlier and in Bavaria 3 years earlier. Whereas defoliation trends were clearly demonstrated, tree mortality did not appear on a large scale. The sample trees were grouped into four discrete clusters according to their annual defoliation values from 1984 to 2003. In 1996, the clusters represent 15, 25, 35, and 50% defoliation values. Regarding beech in Hesse, there was no overlap in the defoliation curves observed among the different clusters. These four clusters having different degrees of defoliation over the whole time span of 20 years were used for a further detailed statistical analysis. For discrete variables like crown spacing and—in the case of beech—fruit bearing, mosaic plots were applied in order to visualize relations of low dimensional contingency tables, with defoliation trends being used as the response variable. The data show for beech a very clear relation between defoliation and age, relative crown spacing, stand composition, and fruit bearing. Regarding oak, besides age and relative crown spacing, the years with significant appearance of biotic stress factors—leaf eating insects—show a clear relation to trends of defoliation. The statistical model used in this study—logistic regression—allows applying a multinomial response variable and a number of continuous or categorical explanatory variables. With this approach, an iterative optimized selection of effect variables was used to test the relevance of different variables on the defoliation pattern of the same four clusters mentioned above. For this, the variables were grouped in an iterative process with five steps, starting with a few basic variables of tree and site information, and ending with a total of more than 20 variables in the fifth step. The process selects first the variables which are of significance on the defoliation, and calculates the possible errors in the grouping of the different trees to the four clusters. In this analysis of beech, the basic tree and stand variables: age, relative crown spacing, stand composition and fruit bearing proved to be the most relevant group of parameters, with the other variables explaining the variation of defoliation only to a minor extent. More complex model levels do not change any basic selected variables; however, Cation Exchange Capacity (CEC), C/N-ratio, Al- and Ca-proportion of CEC are additionally selected and give a hint of the relevance of soil conditions. Regarding beech, the errors of the statistical model are lower compared to oak.