施肥水平和种植密度对有机油菜产量的影响

为摸清有机油菜生产的肥料与密度对产量的影响,解决有机油菜栽培中的种植密度与施肥水平,利用有机种植采用的农福旺有机肥,对有机油菜施肥量和密度的最优栽培模式进行探索。采用裂区设计,研究施肥量与密度对有机油菜的产量的影响,结果表明：从有机油菜产量构成因素分析单株有效角果在高肥高密时出现最高,荚粒数在高肥低密下粒数最高;同一种植密度下,单株有效角果和荚粒数有所增加,但各处理间均不明显。施肥量3000~5250 kg/hm2对有机油菜产量的影响无差异,有机油菜受种植密度的影响产量差异显著,两者之间互作无效应。综合各方面因素,以施有机肥3750 kg/hm2,种植密度30万株/hm2为有机油菜适宜施用量和密度。     

Assessment of likelihood of failure using limited visual,basic, and advanced assessment techniques

Tree risk assessments can be categorized by their time, training, and equipment requirements. In arboriculture and urban forestry, practitioners must select a risk assessment method that is appropriate for the tree or trees to be assessed, available resources, and management objectives. While more detailed advanced risk assessment levels are believed to provide more accurate information regarding likelihood of failure (a component of tree risk), it is not clear how this additional information influences risk ratings. For this experiment we compared likelihood of failure ratings for trees assessed by 70 arborists using Level 1 (limited visual), Level 2 (basic), and Level 3 (advanced) risk assessment methods. Mean ratings did differ by level of assessment (P < 0.001). Mean likelihood of failure ratings for limited visual assessments were lower than the basic and advanced assessment techniques used. However, the differences between the basic and advanced assessment methods tested were less pronounced. Additionally, no level of assessment consistently reduced variability in ratings among arborists.     

Long-term Lysimeter Experiments - Findings from Brandis Lysimeter Station 2nd Communication

The goal of these studies was to evaluate lysimeter experiments performed over a number of years on the nitrogen cycle of different soil types to estimate the potential hazard of various types of farming usage resulting from N-leaching losses into the groundwater. The studies were carried out in monolithic lysimeters measuring 1m2 with a depth of 3m located in Brandis (near Leipzig, Saxony, Germany). The soils were four pedohydrotopes (Top a-d) characterised by increasing depth, usable field capacity (nFK) and sorption capacity. The average values calculated for the experiments lasting 21 years were as follows for the extreme pedohydrotopes a and d respectively: annual nitrogen losses - 85 and 185kg/ha; annual nitrogen leaching -51 and 5kg/ha; and leachate nitrate levels - 100 and 39mg/l. Viewed on a year-by-year basis, effects due to weathering and soil type outweighed the usage-related leaching risk. Organic farming usually reduces N leaching and the leachate N level below the recommended limits. However, ploughing in clover (usually carried out in autumn to improve the supply of nutrients in biologically dynamic organic agriculture) and also spreading stable manure combined with winter black fallow raised the level of nitrate in leachate above the maxima. Hence N fertilisation as prescribed by the computer program BEFU within environmentally sustainable land use does not appear to be sufficient to significantly reduce N leaching.     

Crown condition as a function of soil,site and tree characteristics

One of the aims of this work is to describe how the target variable “tree vitality” in terms of needle loss is affected by other explanatory variables. To describe such a relationship in a realistic way, we use generalized additive mixed models (GAMMs) which allow to take spatial correlation of the data into account and in addition allow the inclusion of explanatory variables as predictors with the possibility of having non-linear effects. The GAMMs are fitted in a Bayesian framework using Markov chain Monte Carlo techniques. Data are available for two years 1988 and 1994. We select a set of best explanatory variables from a large set of variables including tree-specific variables, such as species, age, nutrients in the needles and site-specific variables such as altitude, relief type, soil depth and content of different nutrients in the top soil. In the two models for 1988 and 1994, different sets of explanatory variables were selected as best predictors. In both models, the effects of explanatory variables allowed a plausible interpretation. For example, the site-specific variables such as relief and soil depth were significant predictors, since these factors determine how well water and nutrient supply is balanced at a specific site. The selected sets of explanatory variables differed between 1988 and 1994, giving an indication of a possible change in the main causes of forest deterioration between 1988 and 1994. From the set of nutrient variables measured in the soil and in the needles, in 1988 altitude a.s.l. and magnesium supply were among the explanatory variables, in 1994 a combination of Al in the soil and the N/K-ratio (in the needles) was selected in the model. In 1988 altitude a.s.l. was among the most important predictors in the model. This is in contrast to 1994 where altitude was not selected. This may have to do with the fact that in the early phase of forest health monitoring (1988) one of the main causes of forest deterioration was magnesium deficiency. Later on this may have changed to a combination of soil acidification and nitrogen eutrophication. Thus by using an adequate model such as the GAMM, sets of explanatory variables for needle loss may be identified. By fitting two GAMMs, with different sets of “best” predictors, at two time points 1988 and 1994, we can detect changes in these sets of “best” predictors over time. This allows us to use the monitoring data with the tree vitality indicator crown condition/needle loss as a tool for forest health management, which may involve decisions about concrete counter measures like e.g. forest liming.