Abstract:

Utilisation of plant resistance for insect pest management requires identification of resistant genotypes and development of cultivars combining high levels of resistance with other desirable characteristics. Consequently, a large number of genotypes are usually screened under suitable conditions to identify those with resistance to the insect pest in question using appropriate measures. A basic limitation to conventional selection methods is the choice of cut-off point(s) for efficient partitioning of genotypes into resistance/susceptible groups and the assumption that resistance traits contribute equally and independently to yield loss. This makes it difficult to effectively distinguish and/or delineate genotypes into resistance/susceptible groups and may thus hinder selection and consequently, delay or derail the development of resistant genotypes. This study considered objective statistical approaches to the classification and selection problems. The aim was to optimise the selection, by maximising the differences between the selected group and the other group(s) while minimising the differences within the selected group. Procedures for partitioning crop genotypes into resistance classes, based on observed resistance levels, were proposed and the potential for their use in selecting genotypes was evaluated. For the single-trait selection, the three categories of procedures
investigated include a graphical method where plots of ordered means against the corresponding normal scores were used to determine the optimum number of groups and their limits. The second is based on cluster analysis techniques. In the third procedure, classification models were formulated and hypothesis testing using the likelihood ratio and the quasi bayesian test statistics facilitated the identification of the most plausible points to subdivide the entries under study into distinct groups. Cluster analysis (CA) and likelihood ratio test (LRT) procedures were investigated for multi-trait selection, focusing on interrelationships among the resistance variables. Use of principal component analysis (PCA) generated indices (principal components (PCs)) was compared with some 3 commonly used indices, viz, rank summation index (RSI), Smith-Hazel index (SHI) and Dobie's index of susceptibility (I). The practical application of the procedures was investigated using two groups of real field data and the comparison of their relative performance was made through simulation studies. The results show that the hypothesis testing procedure, overall, performed best. The LRT statistic plots for the 2-group model, in addition to locating the optimum cut point for the model, gave a clear picture of the entire structure of the data, especially when distinct groups existed. Its power to detect the 1-group model, as shown from simulation studies, is an added advantage over the others. Use of the quasi bayesian test statistic gave poor results. Cluster Analysis, in general, gave fairly similar results to the LRT procedure. The method was fairly robust to distributional assumptions and easy to use. Normal score plots were fairly precise at determining the optimum number of groups in the data and, used along with other procedures, the plots gave satisfactory and useful results. The results further showed that, for multivariate classifications, the appropriate procedure and index to adopt depended heavily on intercorrelations among the variables. Simulation studies indicated that classification was greatly facilitated by the existence of high correlations among variables. In this case, promising results were obtained using PCs as the basic variables for classification, in comparison to RSI, SHI and I. Overall, the optimal conditions for all the classification procedures included equal groups sizes and high intercorrelations among variables. Application of the proposed procedures to real data demonstrated that the conventional methods of pre-setting selection conditions, in form of group limits, selection intensity, etc., could lead to enormous loss of useful materials. It was concluded that, other than being alternatives for solving the classification and subsequent selection problems, the procedures.