scholarly journals AMMI VERSUS NONPARAMETRIC ANALYSIS FOR INVESTIGATION OF GE INTERACTION OF PLANT DISEASE EVALUATION

AGROFOR ◽  
2016 ◽  
Vol 1 (1) ◽  
Author(s):  
Naser Sabaghnia
Keyword(s):  
Disease Severity ◽  
Plant Disease Resistance ◽  
Plant Disease ◽  
Gray Leaf Spot ◽  
Multivariate Statistical Methods ◽  
Ge Interaction ◽  
Multivariate Statistical ◽  
Stability Parameters ◽  
Multivariate Procedures ◽  
Selection Of

In breeding for plant disease resistance programs, a large number of new improvedgenotypes are tested over a range of test pathogens or environments and theunderlying statistics used to model this system may be rather complicated. Due toordinal nature of most measured traits of disease responses, some nonparametricmethods used for analyzing genotype × environment (GE) interaction in twodatasets for disease severity of gray leaf spot of maize (with ten genotypes plantedin 10 and 11 environments). Usually, the presence of the GE interaction effectcomplicates the selection of the most favorable genotypes and there are severalstatistical procedures available to analyze these dataset including a range ofunivariate, nonparametric and multivariate procedures. Present analysis separatednonparametric methods based on dynamic concept from those which are based onthe static type indicated that RS statistic following to S6, NP2, NP3 and RSstatistics were found to be useful in detecting the non-complicated phenotypicstability in disease severity dataset. In complicated GE interaction, the ability ofAMMI stability parameters especially SPC1, SPCF, D1, DF, EV1, EVF and ASVstatistics were high in the detection of stability in complicated GE interaction. Ingeneral, nonparametric methods are useful alternatives to parametric methods andallow drawing valid conclusions with considerably better chances of detecting theGE interaction in experiments of plant pathology. Also, in some cases the GEinteraction structure is too complex to be summarized by only one parameter andso, it is essential to use multivariate statistical methods like AMMI.

scholarly journals How much do standard area diagrams improve accuracy of visual estimates of plant disease severity? A systematic review and meta-analysis

2021 ◽  
Author(s):  
Emerson Medeiros Del Ponte ◽  
Luis Ignacio Cazón ◽  
Kaique Santos Alves ◽  
Sarah J. Pethybridge ◽  
Clive H. Bock
Keyword(s):  
Systematic Review ◽  
Disease Severity ◽  
Plant Disease ◽  
Multiple Correspondence Analysis ◽  
Meta Analysis ◽  
Systematic Bias ◽  
Constant Bias ◽  
Systematic Biases ◽  
Visual Assessments ◽  
Selection Of

Plant disease severity is commonly estimated visually without or with the aid of standard area diagram sets (SADs). It is generally believed that the use of SADs leads to less biased (more accurate) and thus more precise estimates, but the degree of improvement has not been characterized in a systematic manner. We built on a previous review and screened 153 SAD studies published from 1990 to 2021. A systematic review resulted in a selection of 72 studies that reported three linear regression statistics for individual raters, which are indicative of the two components of bias (intercept = constant bias; slope = systematic bias) and precision (Pearson's correlation coefficient, r), to perform a meta-analysis of these accuracy components. The meta-analytic model determined an overall gain of 0.07 (r increased from 0.88 to 0.95) in precision. Globally, there was a reduction of 2.65 units in the intercept, from 3.41 to 0.76, indicating a reduction in the constant bias. Slope was least affected and was reduced slightly from 1.09 to 0.966, indicating marginally less systematic bias when using SADs. A multiple correspondence analysis suggested an association of less accurate, unaided estimates with diseases that produce numerous lesions and for which maximum severities of 50% are rarely attained. On the other hand, more accurate estimates were observed with diseases that cause only a few lesions and those diseases where the lesions coalesce and occupy more than 50% of the specimen surface. This was most pronounced for specimen types other than leaves. By quantitatively exploring how characteristics of the pathosystem and how SADs affect precision and constant and systematic biases, we affirm the value of SADs for reducing bias and imprecision of visual assessments. We have also identified situations where SADs have greater or lesser effects as an assessment aid.

scholarly journals Systemic Acquired Resistance Delays Race Shifts to Major Resistance Genes in Bell Pepper

2004 ◽  
Vol 94 (12) ◽  
pp. 1376-1382 ◽  
Author(s):  
A. M. Romero ◽  
D. F. Ritchie
Keyword(s):  
Disease Severity ◽  
Plant Disease Resistance ◽  
Plant Disease ◽  
Systemic Acquired Resistance ◽  
Field Experiments ◽  
Specific Resistance ◽  
Acquired Resistance ◽  
R Genes ◽  
Host Pathogen Interaction ◽  
Major Resistance Genes

The lack of durability of host plant disease resistance is a major problem in disease control. Genotype-specific resistance that involves major resistance (R) genes is especially prone to failure. The compatible (i.e., disease) host-pathogen interaction with systemic acquired resistance (SAR) has been studied extensively, but the incompatible (i.e., resistant) interaction less so. Using the pepper-bacterial spot (causal agent, Xanthomonas axonopodis pv. vesicatoria) pathosystem, we examined the effect of SAR in reducing the occurrence of race-change mutants that defeat R genes in laboratory, greenhouse, and field experiments. Pepper plants carrying one or more R genes were sprayed with the plant defense activator acibenzolar-S-methyl (ASM) and challenged with incompatible strains of the pathogen. In the greenhouse, disease lesions first were observed 3 weeks after inoculation. ASM-treated plants carrying a major R gene had significantly fewer lesions caused by both the incompatible (i.e., hypersensitive) and compatible (i.e., disease) responses than occurred on nonsprayed plants. Bacteria isolated from the disease lesions were confirmed to be race-change mutants. In field experiments, there was a delay in the detection of race-change mutants and a reduction in disease severity. Decreased disease severity was associated with a reduction in the number of race-change mutants and the suppression of disease caused by the race-change mutants. This suggests a possible mechanism related to a decrease in the pathogen population size, which subsequently reduces the number of race-change mutants for the selection pressure of R genes. Thus, inducers of SAR are potentially useful for increasing the durability of genotype-specific resistance conferred by major R genes.

scholarly journals A comparison of demographic behaviour between the CR regions using multivariate statistical methods

2008 ◽  
Vol 56 (6) ◽  
pp. 103-112
Author(s):  
Marie Prášilová ◽  
Jan Grosz ◽  
Pavla Hošková
Keyword(s):  
Human Reproduction ◽  
Agglomerative Clustering ◽  
Biological Structure ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Selection Of Variables ◽  
Solution Selection ◽  
And Migration ◽  
Demographic Behaviour ◽  
Selection Of

The inhabitants of separate CR regions show varying demographic behaviour that presents itself in the demographic measures´ values. The paper offers a comparison of the development of selected measures of size and movement of the population in the regions of CR in 1993 and 2006 years. Attention is paid to the changes in measures of economic and biological structure, life expectancy and some of the measures of human reproduction, patterns of growth and migration. Multivariate analysis methods have been employed for the solution. Selection of variables has been carried out in each year using factor analysis and similarity of the regions has been described by the hierarchic agglomerative clustering method. During the thirteen years, changes occurred in demographic behaviour of the regions. Currently the Capital Prague and the Středočeský Region differ significantly. All the regions remaining have been stabilized as concerns the demographic measures and they show homogeneity.

scholarly journals Application of multivariate techniques in the evaluation of melt granulation products

2011 ◽  
Vol 47 (4) ◽  
pp. 733-741
Author(s):  
Ana Teresa Ochoa Andrade
Keyword(s):  
Principal Component ◽  
Manufacturing Processes ◽  
Multivariate Techniques ◽  
Efficient Production ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Compaction Behavior ◽  
And Cluster Analysis ◽  
Melt Granulation ◽  
Selection Of

In this work, principal component analysis and cluster analysis were applied as helping tools to extract useful information in the development of formulations and manufacturing processes of melt granulates. Two melt granulation processes that differ in the shear stress applied to the solid bed during melting step were designed. These processes employ equipment frequently used in the local pharmaceutical industry. The selected binders include both hydrophilic and hydrophobic excipients, which were used alone or in binary mixtures. Granulates were characterized regarding their physicomechanical properties, including their compaction behavior. The resulting tablets were also evaluated. The selected multivariate statistical methods proved to be useful in facilitating the interpretation of the collected data and the study of the properties of granulates and tablets, as well as the selection of more efficient production processes.

scholarly journals Consideration of Nonparametric Approaches for Assessing Genotype-by-Environment (G × E) Interaction with Disease Severity Data

Plant Disease ◽  
2007 ◽  
Vol 91 (7) ◽  
pp. 891-900 ◽  
Author(s):  
L. V. Madden ◽  
P. A. Paul ◽  
P. E. Lipps
Keyword(s):  
Disease Severity ◽  
Plant Disease Resistance ◽  
Leaf Spot ◽  
Gray Leaf Spot ◽  
Published Data ◽  
Data Sets ◽  
Genotype By Environment ◽  
Ancillary Test ◽  
Wide Range ◽  
The Stability

Determination of host genotype-by-environment (G × E) interaction is needed to assess the stability of cultivar traits such as plant disease resistance and to reveal differences in aggressiveness or virulence of pathogen strains among locations. Here we explored the use of rank-based methodology to quantify the concordance (or discordance) of disease responses of host genotypes across environments, based on the Kendall coefficient of concordance (W) and ancillary test statistics, in order to determine the extent to which environment affected rankings of genotypes. An analysis of four data sets for disease severity of gray leaf spot of maize (with genotypes planted in as many as 11 locations in a given year) revealed highly significant concordance (P ≤ 0.001) overall, indicating that genotypes varied little in within-environment rankings. This suggests that the G × E interaction was small or nonexistent (in terms of rankings). A novel rank-based method by Piepho was evaluated to further elucidate the interaction (if any) through a test for variance homogeneity. The Piepho test statistic was not significant (P > 0.25) for any of the gray leaf spot data sets, confirming the stability of genotypes across environments for this pathosystem; however, analysis of published data sets for other pathosystems indicated significant results. The relationship shown by Hühn, Lotito, and Piepho between the ratio of genotype and residual variances of the original data and the rank-based W statistic was evaluated using Monte Carlo simulations. A more general functional relationship was developed that is applicable over a wide range of number of genotypes and environments in the analyzed studies. This confirms previously shown linkages between rankings of genotypes within environments and variability in the original (unranked) data, thus permitting ease of interpretation of parametric and nonparametric results.

Long-term variations in individual particle types in the aerosol of Phoenix, Arizona, as determined using automated electron microprobe analysis and multivariate statistical techniques

1993 ◽  
Vol 51 ◽  
pp. 742-743
Author(s):  
Karen A. Katrinak ◽  
James R. Anderson ◽  
Peter R. Buseck
Keyword(s):  
Electron Microprobe ◽  
Fine Fraction ◽  
Coarse Fraction ◽  
Motor Vehicles ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
X Ray ◽  
Mineral Particles ◽  
Anthropogenic Air Pollutants

Aerosol samples were collected in Phoenix, Arizona on eleven dates between July 1989 and April 1990. Elemental compositions were determined for approximately 1000 particles per sample using an electron microprobe with an energy-dispersive x-ray spectrometer. Fine-fraction samples (particle cut size of 1 to 2 μm) were analyzed for each date; coarse-fraction samples were also analyzed for four of the dates.The data were reduced using multivariate statistical methods. Cluster analysis was first used to define 35 particle types. 81% of all fine-fraction particles and 84% of the coarse-fraction particles were assigned to these types, which include mineral, metal-rich, sulfur-rich, and salt categories. "Zero-count" particles, consisting entirely of elements lighter than Na, constitute an additional category and dominate the fine fraction, reflecting the importance of anthropogenic air pollutants such as those emitted by motor vehicles. Si- and Ca-rich mineral particles dominate the coarse fraction and are also numerous in the fine fraction.

Pollen morphology of some Tamarix (Tamaricaceae) species of Iran

2020 ◽  
Vol 62 (1-2) ◽  
pp. 151-161
Author(s):  
T. Shagholi ◽  
M. Keshavarzi ◽  
M. Sheidai
Keyword(s):  
North Africa ◽  
Pollen Grains ◽  
Ecological Niches ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Qualitative And Quantitative ◽  
Petal Length ◽  
Electron Microscopes ◽  
High Level ◽  
Scanning Electron Microscopes

Tamarix L. (Tamaricaceae) is a halophytic shrub in different parts of Asia and North Africa. Taxonomy and species limitation of Tamarix is very complex. This genus has three sections as Tamarix, Oligadenia, and Polyadenia, which are mainly separated by petal length, the number of stamens, the shape of androecial disk and attachment of filament on the androecial disk. As there was no palynological data on pollen features of Tamarix species of Iran, in the present study 12 qualitative and quantitative pollen features were evaluated to find diagnostic ones. Pollen grains of 8 Tamarix species were collected from nature. Pollen grains were studied without any treatment. Measurements were based on at least 50 pollen grains per specimen. Light and scanning electron microscopes were used. Multivariate statistical methods were applied to clarify the species relationships based on pollen data. All species studied showed monad and tricolpate (except some individuals of T. androssowii). Some Tamarix species show a high level of variability, in response to ecological niches and phenotypic plasticity, which make Tamarix species separation much more difficult. Based on the results of the present study, pollen grains features are not in agreement with previous morphological and molecular genetics about the sectional distinction.

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