Economic injury levels for western flower thrips (Thysanoptera: Thripidae) on greenhouse cucumber.

Low, medium and high densities of western flower thrips, Frankliniella occidentalis (Pergande), were established in three greenhouses at the Greenhouse and Processing Crops Research Centre, Ontario, Canada, in 1996 and 1998 to develop economic injury levels for thrips on greenhouse cucumber. Thrips densities were monitored weekly using yellow sticky traps and flower counts. Fruit was harvested twice a week, graded for size, weighed, and rated for thrips damage using three damage categories. Significant yield reduction was detected 4 wk after severe fruit damage was observed in the high and medium thrips density treatments in 1996 and 7 wk in 1998. Percentage of severe damaged fruit (P(F3)) has significant linear relationships with the adult thrips density (x) that was sampled by sticky traps 1 wk before harvest (P(F3) = -0.2533 + 0.0828x) and that was sampled by flower counts 2 wk before harvest (P(F3) = -0.2025 + 0.5490x). Based on the regression equations, economic injury levels, expressed as adult thrips per sticky trap per day or adult thrips per flower, were calculated for various combinations of control costs, yield potential and fruit prices. The economic injury levels for F. occidentalis ranged from 20 to 50 adults per sticky trap per day or 3 to 7.5 per flower as determined under average greenhouse production conditions in Ontario, Canada.

Sampling for IPM Decision Making: Where Should We Invest Time and Resources?

ABSTRACT Guides for making crop protection decisions based on assessments of pest abundance or incidence are cornerstones of many integrated pest management systems. Much research has been devoted to developing sample plans for use in these guides. The development of sampling plans has usually focused on collecting information on the sampling distribution of the pest, describing this sampling distribution with a mathematical model, formulating a sample plan, and sometimes, but not always, evaluating the performance of the proposed sample plan. For crop protection decision making, classification of density or incidence is usually more appropriate than estimation. When classification is done, the average outcome of classification (the operating characteristic) is frequently robust to large changes in the sampling distribution, including estimates of the variance of pest counts, and to sample size. In contrast, the critical density, or critical incidence, about which classifications are made, has a large influence on the operating characteristic. We suggest that rather than investing resources in elaborate descriptions of sampling distributions, or in fine-tuning sample size to achieve desired levels of precision, greater emphasis should be placed on characterizing pest densities that signal the need for management action and on designing decision guides that will be adopted by practitioners.

Assessment of a method for cultivar selection based on regional trial data.

In this study a method for analyzing regional trial data is investigated for its effectiveness in cultivar selection. The method is a synthesis of three procedures: (1) regression analysis for genotype × environment (GE) interaction, and subsequent cluster analysis for grouping cultivars for similarity of response; (2) superiority measure analysis of cultivar performance based on the distance mean square between the test cultivar and the maximum response; (3) type 4 stability analysis for three-way classification data (cultivar × location × year), to measure a cultivar's stability. Each of these three procedures is aimed at different aspects of the selection problem: the first obtains an overview of the types of cultivar response; the second measures a cultivar's general adaptability within the region; the third assesses a cultivar's ability to withstand unpredictable variation, namely that caused by year effects. Four sets of published data, each originally analyzed by a univariate or a multivariate approach, were used as examples. The results suggest that the present method provides direct and useful information for selection purposes. The superiority measure, which is the core of the method, can be used even if the data do not fit the linear model for GE interaction. Plotting the data with a fixed standard represented by the maximum response provides a simple visual tool for identifying environments where a cultivar performs well.

Genetic properties of four types of stability parameter.

The genetic properties of four types of stability parameter for individual genotypes were investigated using a set of diallel cross data (28 genotypes x four locations x 3 years). The specific parameters studied were: the variance of a genotype across environments (T1); the genotype x environment (GE) interaction effect for a genotype, squared and summed across all environments (T2); the residual mean square (MS) of deviations from the regression of a genotype on an environmental index (T3); and years within locations MS for a genotype, averaged over all locations (T4). Each stability statistic was fitted to the additive model, based on the assumption that if the stability parameter is heritable, stability of F1 is most likely to be the average stability of its parents. The results showed that T1 and T4 were additive, but T2 and T3 were not. A study of the consistency of stability rankings between two seeding rates over the same set of environments showed a similar pattern. It appears that stability parameters of types 1 and 4 are heritable, and thus useful for selection, while those of types 2 and 3 are nonheritable, and thus not useful.

Comparison of unpredictable environmental variation generated by year and by seeding-time factors for measuring type 4 stability.

Type 4 stability has been proposed to measure a cultivar's homeostatic property to resist unpredictable environmental variation. The requirement for calculating this stability is that the experiment must contain a time factor in addition to the cultivar x location factors. Of the two time factors, year and seeding-time, the latter is less attractive biologically because it represents only a part of the broader context of unpredictable variation represented by years, but it is attractive in terms of shortening the test period. Investigation of historical data from the Eastern Cooperative trials and the Ontario Production trials in Canada indicates that the unpredictable variation generated by seeding-time was about half that generated by year. Although type 4 stability measured by both factors appears to be the same, the stability measured by seeding-time is more prone to variation. The implication is that complete substitution of the year factor by seeding-time is not appropriate, but use of both factors in combination may be sensible.

A method of analyzing cultivar x location x year experiments: a new stability parameter.

Assessment of cultivar performance in a cultivar x location x year experiment is often difficult because of the presence of a location x year interaction. Our objective is to demonstrate a method on separation of environment effects (location x year) into predictable and unpredictabel components. The analysis consists of two parts: (1) a regression analysis based on location effects (averaged over years), assuming that the location means represent predictable environmental variation; and (2) the estimation of stability (denoted type 4) based on the years within location mean squares, assuming that years within location represent unpredictable environmental variation. From the regression analysis in (1), a breeder can determine the optimum range of locations in which a cultivar is well suited, and from (2) he can choose the most stable cultivars. The advantage of type 4 stability is that it is independent of the other cultivars included in the test and of the regression coefficient estimated for predictable variation. Three sets of published data are used to illustrate the analysis. Type 4 stability is compared with type 3 stability (deviation mean square from regression on environmental index) for genetic consistency. The analyses suggest that type 4 stability is consistent and is therefore a potential genetic parameter, but type 3 stability is not.

The use of regression methods to study genotype-environment interactions: extending Griffing's model for diallel cross experiments and testing an empirical grouping method.

A model combining features of Griffing's diallel cross analysis with regression analysis for genotype-environment interactions is introduced using carp data of Moav et al. (1975) as an example. An analysis of variance based on this model provides information on the combining abilities of genetic effects and the interactions of these effects with environments from which inferences can readily be made on heterosis and heterosis-environment interactions. Applying the empirical grouping method of Lin and Thompson (1975) to these data (ignoring their diallel cross structure) established groups which were remarkably consistent with their members' crossing backgrounds.