Application of Statistical Tools for Data Analysis and Interpretation in Rice Plant Pathology
There has been a significant advancement in the application of statistical tools in plant pathology during the past four decades. These tools include multivariate analysis of disease dynamics involving principal component analysis, cluster analysis, factor analysis, pattern analysis, discriminant an...
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doaj-451bd59d3e804636b006eca090f5faef2020-11-24T23:16:39ZengElsevierRice Science1672-63082018-01-0125111810.1016/j.rsci.2017.07.001Application of Statistical Tools for Data Analysis and Interpretation in Rice Plant PathologyParsuram NayakArup Kumar MukherjeeElssa PanditSharat Kumar PradhanThere has been a significant advancement in the application of statistical tools in plant pathology during the past four decades. These tools include multivariate analysis of disease dynamics involving principal component analysis, cluster analysis, factor analysis, pattern analysis, discriminant analysis, multivariate analysis of variance, correspondence analysis, canonical correlation analysis, redundancy analysis, genetic diversity analysis, and stability analysis, which involve in joint regression, additive main effects and multiplicative interactions, and genotype-by-environment interaction biplot analysis. The advanced statistical tools, such as non-parametric analysis of disease association, meta-analysis, Bayesian analysis, and decision theory, take an important place in analysis of disease dynamics. Disease forecasting methods by simulation models for plant diseases have a great potentiality in practical disease control strategies. Common mathematical tools such as monomolecular, exponential, logistic, Gompertz and linked differential equations take an important place in growth curve analysis of disease epidemics. The highly informative means of displaying a range of numerical data through construction of box and whisker plots has been suggested. The probable applications of recent advanced tools of linear and non-linear mixed models like the linear mixed model, generalized linear model, and generalized linear mixed models have been presented. The most recent technologies such as micro-array analysis, though cost effective, provide estimates of gene expressions for thousands of genes simultaneously and need attention by the molecular biologists. Some of these advanced tools can be well applied in different branches of rice research, including crop improvement, crop production, crop protection, social sciences as well as agricultural engineering. The rice research scientists should take advantage of these new opportunities adequately in adoption of the new highly potential advanced technologies while planning experimental designs, data collection, analysis and interpretation of their research data sets.http://www.sciencedirect.com/science/article/pii/S1672630817300756statistical toolplant pathology data analysismultivariate analysisnon-parametric analysismicro-array analysisdecision theoryplant disease epidemicsrice |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Parsuram Nayak Arup Kumar Mukherjee Elssa Pandit Sharat Kumar Pradhan |
spellingShingle |
Parsuram Nayak Arup Kumar Mukherjee Elssa Pandit Sharat Kumar Pradhan Application of Statistical Tools for Data Analysis and Interpretation in Rice Plant Pathology Rice Science statistical tool plant pathology data analysis multivariate analysis non-parametric analysis micro-array analysis decision theory plant disease epidemics rice |
author_facet |
Parsuram Nayak Arup Kumar Mukherjee Elssa Pandit Sharat Kumar Pradhan |
author_sort |
Parsuram Nayak |
title |
Application of Statistical Tools for Data Analysis and Interpretation in Rice Plant Pathology |
title_short |
Application of Statistical Tools for Data Analysis and Interpretation in Rice Plant Pathology |
title_full |
Application of Statistical Tools for Data Analysis and Interpretation in Rice Plant Pathology |
title_fullStr |
Application of Statistical Tools for Data Analysis and Interpretation in Rice Plant Pathology |
title_full_unstemmed |
Application of Statistical Tools for Data Analysis and Interpretation in Rice Plant Pathology |
title_sort |
application of statistical tools for data analysis and interpretation in rice plant pathology |
publisher |
Elsevier |
series |
Rice Science |
issn |
1672-6308 |
publishDate |
2018-01-01 |
description |
There has been a significant advancement in the application of statistical tools in plant pathology during the past four decades. These tools include multivariate analysis of disease dynamics involving principal component analysis, cluster analysis, factor analysis, pattern analysis, discriminant analysis, multivariate analysis of variance, correspondence analysis, canonical correlation analysis, redundancy analysis, genetic diversity analysis, and stability analysis, which involve in joint regression, additive main effects and multiplicative interactions, and genotype-by-environment interaction biplot analysis. The advanced statistical tools, such as non-parametric analysis of disease association, meta-analysis, Bayesian analysis, and decision theory, take an important place in analysis of disease dynamics. Disease forecasting methods by simulation models for plant diseases have a great potentiality in practical disease control strategies. Common mathematical tools such as monomolecular, exponential, logistic, Gompertz and linked differential equations take an important place in growth curve analysis of disease epidemics. The highly informative means of displaying a range of numerical data through construction of box and whisker plots has been suggested. The probable applications of recent advanced tools of linear and non-linear mixed models like the linear mixed model, generalized linear model, and generalized linear mixed models have been presented. The most recent technologies such as micro-array analysis, though cost effective, provide estimates of gene expressions for thousands of genes simultaneously and need attention by the molecular biologists. Some of these advanced tools can be well applied in different branches of rice research, including crop improvement, crop production, crop protection, social sciences as well as agricultural engineering. The rice research scientists should take advantage of these new opportunities adequately in adoption of the new highly potential advanced technologies while planning experimental designs, data collection, analysis and interpretation of their research data sets. |
topic |
statistical tool plant pathology data analysis multivariate analysis non-parametric analysis micro-array analysis decision theory plant disease epidemics rice |
url |
http://www.sciencedirect.com/science/article/pii/S1672630817300756 |
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