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I am writing this article with the intent to offer some insights on Biometrical Techniques in Plant Breeding. This article is an attempt to provide you with an idea about biometrical techniques in plant breeding pdf download. I will share my thoughts on the subject of biometrical techniques in plant breeding pdf download and hope that it will be of benefit to you. The need for improved optimization of crop production given mounting food insecurity, climate change, and land degradation has pushed scientists to explore new avenues for improved crop production. Biometric analysis provides a means of quantifying variation within populations by analyzing localized traits or genes that are exhibited at that point or locus. The most common biometric techniques are Discriminant Function Analysis, Principal Component Analysis, Regression, Cluster Analysis, Statistical Classification and Neural Networks. These techniques are widely used in plant breeding to predict hybrid performance which is of great importance in determining the likely success of a new crop variety. Many crop traits are quantitative which are influenced by more than one gene. Using biometrical techniques, crop breeders can identify the major genes that influence each trait of interest. The best approach for evaluating several traits is to express them using different units rather than on the same scale even if the units are comparable. Cluster analysis is effective for detecting major genes that influence an unrelated group of traits. Regression analysis can be used to identify individual quantitative traits for which there are strong genetic determinants. Regression analysis is also useful for identifying genetic determinants of complex traits. Neural networks are widely used in plant breeding because of their easy implementation, flexibility and accuracy which are not available with other biometric techniques. Neural networks have been successfully applied to identify genetic determinants of both qualitative and quantitative traits. Discriminant Function Analysis is a statistical classification technique that is used to distinguish between groups of individuals using different traits. Another statistical classification method is Statistical Classification which makes use of both individual and group characteristics. In order to make meaningful predictions, standard error must be minimized as well as global error maximized. When the standard error is minimized, the variability across samples is lower but the mean for each sample may be higher or lower than that of the population. The opposite occurs when global error maximization or minimization is used where a good measure of complex traits can be achieved. The process to maximize global error may also result in a large variance within a category if one breed was not tested against itself. Principal Component Analysis (PCA) is a statistical technique developed by Pearson and Kendall and it is often used to reduce the number of variables needed to represent a known set of observations. In machine learning applications, they have been successfully used to learn from examples when there are few labeled examples. Discriminant functions are important tools in plant breeding which uses mathematical techniques to predict the probable performance of a variety. The purpose of this article has been to offer some insights on biometrical techniques in plant breeding pdf download..

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