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.

Performance evaluation of gypsum block wireless sensor network system for real time irrigation scheduling

Abstract: Majority of farmers in India are irrigating their crops without using any irrigation scheduling criteria. Consequently, the application of excess irrigation water causes water logging, wastage of p...

Determination of window size and identification of suitable method for prediction of donor splice sites in rice ( Oryza sativa ) genome

Abstract: Accurate prediction of the gene structure depends upon the accurate prediction of splice sites. The conserved feature in splicing junction has been successfully used for the prediction of eukaryotic splice sites. In eukaryotes, though the di-nucleotide GT is conserved at 5′ splice sites, the pattern surrounding the conserved di-nucleotide varies from species to species. Most of the work related to splice site analysis has been extensively done in Homo sapiens and Arabidopsis thaliana. However, such works are yet to be fully explored in Oryza sativa and other species of grass family. In this study, statistical techniques have been applied to discriminate the real splice sites from pseudo splice sites in rice, maize and barley genomes and based on this a suitable window size is determined for the prediction of donor splice sites. Depending upon the determined window size, appropriate methods for predicting donor splice sites in rice have been considered and compared in terms of prediction accuracy. The results revealed that a window size of 9 base pair (3 bp at the exon end and 6 bp at the intron start including the conserved di-nucleotide GT at the beginning of intron) is an effective window size in all the three species of grass family for the prediction of donor splice sites. Further, the Maximum Entropy Model based method is found as best among the short sequence based prediction methods for donor splice sites with the 9 base pair window size.

Jackknife variance estimation under two-phase sampling

Abstract: Two new Jackknife methods, as the counterparts of two existing Bootstrap methods of variance estimation under two-phase sampling, have been proposed. A simulation study has been conducted under both design-based and Conditional inference frameworks by generating two-phase samples from an infinite population for comparison of the proposed methods with five existing Jackknife and Bootstrap methods. The first method, the two-phase post-stratified Jackknife, reduces to an existing Jackknife variance estimation method considered under sampling from infinite population set up. The performance of the second method, the two-phase proportionate Jackknife, was better than two existing Jackknife methods while performing at par with another Jackknife method as well as with the two Bootstrap methods considered.

Identification of Novel Marker-Trait Associations for Lint Yield Contributing Traits in Upland Cotton (Gossypium hirsutum L.) Using SSRs.

Abstract: Improving the yield of lint is the main objective for most of the cotton crop improvement programmes throughout the world as it meets the demand of fiber for textile industries. In the current study, ninety-six genotypes of Gossypium hirsutum were used to find novel simple sequence repeats marker-based associations for lint yield contributing traits by linkage disequilibrium. Extensive phenotyping of 96 genotypes for various agronomic traits was done for two consecutive years (2018 and 2019) in early, normal, and late sown environments. Out of 168 SSR markers screened over the ninety six genotypes, a total of 97 polymorphic markers containing 293 alleles were used for analysis. Three different models i.e. mixed linear model (MLM), compressed mixed linear model (CMLM), and multiple locus mixed linear model (MLMM) were used to detect the significant marker-trait association for six different environments separately. A total of 38 significant marker-trait associations that were common to at least two environments were considered as promising associations and detailed annotation of the significant markers has been carried out. Twenty-two marker-trait associations were found to be novel in the current study. These results will be very useful for crop improvement programmes using markers-assisted cotton breeding.

Response to Selection Under Non‐Random Mating I. Partitioning Genetic Variance

Abstract: In analogy to the concept of breeding value defined for random mating equilibrium populations, the “transmittable genetic value” of an individual is defined as the average value of its expected progeny for any system of mating. The genotypic value is then characterised in terms of transmittable and residual genetic values and components of genetic variance redefined which can be estimated by the conventional procedure based on resemblance between relatives.