Indian Agricultural Statistics Research Institute

About: Indian Agricultural Statistics Research Institute is a facility organization based out in New Delhi, India. It is known for research contribution in the topics: Population & Small area estimation. The organization has 454 authors who have published 870 publications receiving 7987 citations.

Utilization of Molecular, Phenotypic, and Geographical Diversity to Develop Compact Composite Core Collection in the Oilseed Crop, Safflower (Carthamus tinctorius L.) through Maximization Strategy.

Abstract: Carthamus tinctorius L. is a dryland oilseed crop yielding high quality edible oil. Previous studies have described significant phenotypic variability in the crop and used geographical distribution and phenotypic trait values to develop core collections. However, the molecular diversity component was lacking in the earlier collections thereby limiting their utility in breeding programs. The present study evaluated the phenotypic variability for twelve agronomically important traits during two growing seasons (2011-12 and 2012-13) in a global reference collection of 531 safflower accessions, assessed earlier by our group for genetic diversity and population structure using AFLP markers. Significant phenotypic variation was observed for all the agronomic traits in the representative collection. Cluster analysis of phenotypic data grouped the accessions into five major clusters. Accessions from the Indian Subcontinent and America harboured maximal phenotypic variability with unique characters for a few traits. MANOVA analysis indicated significant interaction between genotypes and environment for both the seasons. Initially, six independent core collections (CC1 – CC6) were developed using molecular marker and phenotypic data for two seasons through POWERCORE and MSTRAT. These collections captured the entire range of trait variability but failed to include complete genetic diversity represented in 19 clusters reported earlier through Bayesian Analysis of Population Structure (BAPS). Therefore, we merged the three POWERCORE core collections (CC1 – CC3) to generate a composite core collection, CartC1 and three MSTRAT core collections (CC4 – CC6)to generate another composite core collection, CartC2.The mean difference percentage, variance difference percentage, variable rate of coefficient of variance percentage, coincidence rate of range percentage, Shannon’s diversity index and Nei’s gene diversity for CartC1 were 11.2, 43.7, 132.4, 93.4, 0.47 and 0.306, respectively while the corresponding values for CartC2 were 9.3, 58.8, 124.6, 95.8, 0.46 and 0.301. Each composite core collection represented the complete range of phenotypic and genetic variability of the crop including nineteen BAPS clusters. This is the first report describing development of core collections in safflower using molecular marker data with phenotypic values and geographical distribution.These core collections will facilitate identification of genetic determinants of trait variability and effective utilization of the prevalent diversity in crop improvement programs.

Micronutrients (B, Co, Cu, Fe, Mn, Mo, and Zn) content in made tea (Camellia sinensis L.) and tea infusion with health prospect: A critical review.

Abstract: Tea (Camellia sinensis L.) is a perennial acidophilic crop, and known to be a nonalcoholic stimulating beverage that is most widely consumed after water. The aim of this review paper is to provide a detailed documentation of selected micronutrient contents, viz. boron (B), cobalt (Co), copper (Cu), iron (Fe), manganese (Mn), molybdenum (Mo), and zinc (Zn) in made tea and tea infusion. Available data from the literature were used to calculate human health aspect associated with the consumption of tea infusion. A wide range of micronutrients reported in both made tea and tea infusion could be the major sources of micronutrients for human. The content of B, Co, Cu, Fe, Mn, Mo, and Zn in made tea are ranged from 3.04 to 58.44 μg g−1, below detectable limit (BDL) to 122.4 μg g−1, BDL to 602 μg g−1, 0.275 to 13,040 μg g−1, 0.004 to 15,866 μg g−1, 0.04 to 570.80 μg g−1 and 0.01 to 1120 μg g−1, respectively. Only 3.2 μg L−1 to 7.25 mg L−1, 0.01 μg L−1 to 7 mg L−1, 3.80 μg L−1 to 6.13 mg L−1, 135.59 μg L−1...

A Random Effect Block Bootstrap for Clustered Data

Abstract: Random effects models for hierarchically dependent data, for example, clustered data, are widely used. A popular bootstrap method for such data is the parametric bootstrap based on the same random effects model as that used in inference. However, it is hard to justify this type of bootstrap when this model is known to be an approximation. In this article, we describe a random effect block bootstrap approach for clustered data that is simple to implement, free of both the distribution and the dependence assumptions of the parametric bootstrap, and is consistent when the mixed model assumptions are valid. Results based on Monte Carlo simulation show that the proposed method seems robust to failure of the dependence assumptions of the assumed mixed model. An application to a realistic environmental dataset indicates that the method produces sensible results. Supplementary materials for the article, including the data used for the application, are available online.