Meghana Singh Rajotia, Dev Vart, Abhay Dashora, Rao Pankaj, Harshdeep, Jyoti, Neeraj Kharor and S.K. Pahuja
Abstract
Pearl millet (Pennisetum glaucum L.), commonly known as bajra, is a critical cereal crop, ranking fourth globally in cultivation area after rice, wheat, maize, and sorghum. It is extensively grown in arid and semiarid regions, providing food and fodder for millions. Research on pearl millet often involves analyzing large datasets with numerous genotypes and agronomic traits, such as yield, drought resistance, and pest tolerance. However, the high intercorrelation among variables complicates data analysis and interpretation, necessitating the use of robust data reduction techniques. Principal Component Analysis (PCA), introduced by Pearson (1901) and refined by Hotelling (1933), is a widely used method for dimensionality reduction. PCA simplifies complex datasets by transforming correlated variables into a smaller set of uncorrelated principal components, capturing the majority of the variation. This enables researchers to identify the most influential variables and genotypes while addressing challenges like multicollinearity and redundancy. In the context of pearl millet, PCA facilitates the evaluation of key traits and patterns, improving the efficiency of crop improvement programs. By prioritizing critical variables, PCA supports sustainable agricultural practices and aids breeders in optimizing strategies for developing superior genotypes, enhancing productivity, and ensuring food security in vulnerable regions.