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Intensive breeding programs have increased rice yields, strongly contributing to increasing global food security during the post‐green revolution period. However, rice productivity has reached a yield barrier where further yield improvement is restricted by inadequate information on the association of yield components, and morphological and physiological traits with yield. We conducted a field experiment to evaluate (i) the contribution of morphological and physiological traits to yield and (ii) quantify the trade‐off effect between the yield components in rice, using a mini‐core collection of 362 rice genotypes comprising geographically distinct landraces and breeding lines. Our data point towards multiscale coordination of physiological and morphological traits associated with yield and biomass. Considerable trait variations across the genotypes in yield ranging from 0.5 to 78.5 g hill−1 and harvest index ranging from 0.7% to 60.7% highlight enormous diversity in rice across the globe. The natural elimination of trade‐off between yield components revealed the possibility to enhance rice yield in modern cultivars. Furthermore, our study demonstrated that genotypes with larger sink sizes could fix more carbon to achieve a higher yield. We propose that the knowledge thus generated in this study can be helpful for (a) trait‐based modeling and pyramiding alleles in rice‐breeding programs and (b) assisting breeders and physiologists in their efforts to improve crop productivity under a changing climate, thus harnessing the potential for sustainable productivity gains.

Continuous progression in genomics shows that repeats are important elements of genomes that perform many regulatory and other functions. Eventually, to date, many computational tools have been developed and frequently used for the identification and analysis of genomic repeats. A single tool cannot detect all different types of repeats in diverse species rather pipeline of tools is more effective. But, the choice of such rigorous and robust tools is highly challenging. A method has been implemented to select a set of optimal tools for finding all available classes of perfect and imperfect tandem repeats including microsatellites, minisatellites, and interspersed CRISPRs in genomes. A total of 11 tools have been shortlisted using rule-based selection and then ranked by analyzing rigorousness in searching in diverse species and execution time. Tool comparison shows consistency in perfect microsatellite detection performance but significantly differ for long and imperfect repeats. A web-server has been built which provides a generic platform for various classes of repeat identification from the diverse genome using multiple tools and comparison.