Application of Statistical Tools for Data Analysis and Interpretation in Rice Plant Pathology

作     者：Parsuram NAYAK Arup Kumar MUKHERJEE Elssa PANDIT Sharat Kumar PRADHAN 

作者机构：Indian Council of Agricultural ResearchNational Rice Research Institute 

出 版 物：《Rice science》 (水稻科学（英文版）)

年 卷 期：2018年第25卷第1期

页      面：1-18页

核心收录：

学科分类：0710[理学-生物学] 0831[工学-生物医学工程（可授工学、理学、医学学位）] 09[农学] 0901[农学-作物学] 0836[工学-生物工程] 0902[农学-园艺学] 

主　　题：statistical tool plant pathology data analysis multivariate analysis non-parametric analysis micro-array analysis decision theory plant disease epidemics rice 

摘      要：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 h