Meta-analysis in plant pathology: synthesizing research results

ABSTRACT Meta-analysis is a set of statistical procedures for synthesizing research results from a number of different studies. An estimate of a statistical effect, such as the difference in disease severity for plants with or without a management treatment, is collected from each study along with a measure of the variance of the estimate of the effect. Combining results from different studies will generally result in increased statistical power so that it is easier to detect small effects. Combining results from different studies may also make it possible to compare the size of the effect as a function of other predictor variables such as geographic region or pathogen species. We present a review of the basic methodology for meta-analysis. We also present an example of meta-analysis of the relationship between disease severity and yield loss for foliar wheat diseases, based on data collected from a decade of fungicide and nematicide test results.     

Bayesian analysis in plant pathology

ABSTRACT Bayesian methods are currently much discussed and applied in several disciplines from molecular biology to engineering. Bayesian inference is the process of fitting a probability model to a set of data and summarizing the results via probability distributions on the parameters of the model and unobserved quantities such as predictions for new observations. In this paper, after a short introduction of Bayesian inference, we present the basic features of Bayesian methodology using examples from sequencing genomic fragments and analyzing microarray gene-expressing levels, reconstructing disease maps, and designing experiments.     

Meta-analysis for evidence synthesis in plant pathology: an overview

Meta-analysis is the analysis of the results of multiple studies, which is typically performed in order to synthesize evidence from many possible sources in a formal probabilistic manner. In a simple sense, the outcome of each study becomes a single observation in the meta-analysis of all available studies. The methodology was developed originally in the social sciences by Smith, Glass, Rosenthal, Hunter, and Schmidt, based on earlier pioneering contributions in statistics by Fisher, Pearson, Yates, and Cochran, but this approach to research synthesis has now been embraced within many scientific disciplines. However, only a handful of articles have been published in plant pathology and related fields utilizing meta-analysis. After reviewing basic concepts and approaches, methods for estimating parameters and interpreting results are shown. The advantages of meta-analysis are presented in terms of prediction and risk analysis, and the high statistical power that can be achieved for detecting significant effects of treatments or significant relationships between variables. Based on power considerations, the fallacy of na?ve counting of P values in a narrative review is demonstrated. Although there are many advantages to meta-analysis, results can be biased if the analysis is based on a nonrepresentative sample of study outcomes. Therefore, novel approaches for characterizing the upper bound on the bias are discussed, in order to show the robustness of meta-analysis to possible violation of assumptions.     

The interdependent effects of solar disinfestation and compost maturity level on soil microbial activity

The combined effects of soil solarization and application of compost of various maturity levels upon soil microbial activity were studied under field conditions during 2010–2012. Eight treatments were divided into solarized and non-solarized treatments, and each was either non-amended or annually amended with compost at 6 kg (DW) m^-2. The composts were mature, partially mature, or immature. In all three tested parameters of microbial activity (respiration rate, heat output, and dehydrogenase activity) the immature compost showed higher activity than the other compost types. Soil samples were collected weekly in order to assess microbial activity, which was evaluated from measurements of soil respiration rate, heat output, and dehydrogenase activity. Significant and year-to-year reproducible differences in all three parameters were observed between soils amended with the three compost types. Some residual (carry-over) effects of previous-year treatments were obtained. Microbial activity decreased in both solarized and non-solarized treatments during the experimental period, probably because of increasing summer temperatures, but the decline was sharper in the solarized treatments probably due to oxygen deficiency under the tarp. Significantly higher activity was found in the treatments amended with the immature compost, both in the solarized and non-solarized treatments. This could be a result of the high level of dissolved organic matter in the immature compost, which enhanced microbial activity.     

Extension plant pathology: strengthening resources to continue serving the public interest

Extension plant pathologists deliver science-based information that protects the economic value of agricultural and horticultural crops in the United States by educating growers and the general public about plant diseases. Extension plant pathologists diagnose plant diseases and disorders, provide advice, and conduct applied research on local and regional plant disease problems. During the last century, extension plant pathology programs have adjusted to demographic shifts in the U.S. population and to changes in program funding. Extension programs are now more collaborative and more specialized in response to a highly educated clientele. Changes in federal and state budgets and policies have also reduced funding and shifted the source of funding of extension plant pathologists from formula funds towards specialized competitive grants. These competitive grants often favor national over local and regional plant disease issues and typically require a long lead time to secure funding. These changes coupled with a reduction in personnel pose a threat to extension plant pathology programs. Increasing demand for high-quality, unbiased information and the continued reduction in local, state, and federal funds is unsustainable and, if not abated, will lead to a delay in response to emerging diseases, reduce crop yields, increase economic losses, and place U.S. agriculture at a global competitive disadvantage. In this letter, we outline four recommendations to strengthen the role and resources of extension plant pathologists as they guide our nation's food, feed, fuel, fiber, and ornamental producers into an era of increasing technological complexity and global competitiveness.     

New applications of statistical tools in plant pathology

ABSTRACT The series of papers introduced by this one address a range of statistical applications in plant pathology, including survival analysis, nonparametric analysis of disease associations, multivariate analyses, neural networks, meta-analysis, and Bayesian statistics. Here we present an overview of additional applications of statistics in plant pathology. An analysis of variance based on the assumption of normally distributed responses with equal variances has been a standard approach in biology for decades. Advances in statistical theory and computation now make it convenient to appropriately deal with discrete responses using generalized linear models, with adjustments for overdispersion as needed. New nonparametric approaches are available for analysis of ordinal data such as disease ratings. Many experiments require the use of models with fixed and random effects for data analysis. New or expanded computing packages, such as SAS PROC MIXED, coupled with extensive advances in statistical theory, allow for appropriate analyses of normally distributed data using linear mixed models, and discrete data with generalized linear mixed models. Decision theory offers a framework in plant pathology for contexts such as the decision about whether to apply or withhold a treatment. Model selection can be performed using Akaike's information criterion. Plant pathologists studying pathogens at the population level have traditionally been the main consumers of statistical approaches in plant pathology, but new technologies such as microarrays supply estimates of gene expression for thousands of genes simultaneously and present challenges for statistical analysis. Applications to the study of the landscape of the field and of the genome share the risk of pseudoreplication, the problem of determining the appropriate scale of the experimental unit and of obtaining sufficient replication at that scale.     

绿色荧光蛋白在植物病理学研究中的应用

绿色荧光蛋白（green fluorescent protein, GFP）是生命科学研究中的一种重要报告基因,本文在论述GFP的发现、结构和发光原理的基础上,从抗病基因的亚细胞定位、启动子活性分析、病原菌 植物互作研究和基因表达分析等方面讨论GFP在植物病理学研究中的应用。     

植物病理学中常见数据的统计分析方法

 数据统计分析不充分或不正确,是导致中国科研工作者在国际期刊投稿中遭拒的主要原因之一。过去20年,统计方法学及计算机软件在统计应用方面已有重大进展。但是,大多数中国植物病理学家仍然使用传统的方差分析或普通的分析方法去分析一些数据,而这些数据其实可用更合适的方法分析。本文简要介绍一些适于植物病理学常见数据的统计分析方法,希望对广大科研工作者有所帮助,也希望加强与应用统计学家的科研合作。