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.

Delineation of Genotype-by-Environment interactions for identification and validation of resistant genotypes in mungbean to root-knot nematode ( Meloidogyne incognita ) using GGE biplot

Abstract: Susceptibility to root-knot nematodes (Meloidogyne spp.) is one of the major factors limiting mungbean production in South and South-East Asia. Host-pest-environment interaction in mungbean and root-knot nematode (M. incognita) was investigated in multi-location field evaluation using 38 promising mungbean genotypes extracted from initial evaluation of 250 genotypes under sick plots considering second stage freshly hatched juvenile as inoculants. The extent of environmental and genotype-by-environment interactions (GGE) was assessed to comprehend the dynamism of resistance and identification of durable resistant mungbean genotypes. Among environmental factors, nematode activity was highly influenced by rainfall and minimum temperature. The GGE biplot and multiple comparison tests detected a higher proportion of genotype × environment (GE) interaction followed by genotype and environment on number of nematode galls, gall index and reproduction factor. The first two principal components (PCs) explained 64.33% and 66.99% of the total variation of the environment-centered gall scoring and reproduction factor data, respectively. The high GE variation indicated the presence of non-cross over interactions which justify the necessities of multi-location testing. Detection of non-redundant testing locations would expedite optimum resource utilization in future. The GGE biplot analysis identified genotypes such as PM-10-12, IPM-410-3 and NVL-641 as the outperforming and desirable genotypes with durable resistance against M. incognita which can be exploited in mungbean breeding programmes globally. On the contrary, the highest gall scoring and reproduction factor were recorded in genotype IPM-9901-8. Computation of confidence interval (CI) at 95% level through bootstrapping increased precision of GGE biplot towards genotype recommendation. Furthermore, total phenol content, ascorbic acid, phenlylalanine ammonia lyase (PAL) and polyphenol oxidase (PPO) activities were also higher in identified resistant genotypes and this information would be useful for devising mungbean breeding strategies in future for resistance against root-knot nematodes.

Bug prediction modeling using complexity of code changes

Abstract: Researchers have proposed and implemented a plethora of bug prediction approaches in terms of different mathematical models for measuring the reliability growth of the software and to predict the latent bugs lying dormant in the software. During the last four decades, software reliability growth models (SRGM) have been successfully used to measure the reliability growth of closed source software. The SRGM developed were based on either calendar time or on testing effort. In late 90s, due to the advancement in communication and internet technologies, the development of open source software gets an edge and is proven to be very successful in different fields. Recently, researchers have measured the latent bugs in the open source software using an SRGM which has been developed for closed source software and concluded that the existing SRGM can well predict the latent bugs, but, still, it needs more investigation. In open source software, the source codes are frequently changes (the complexity of code changes) to meet the new feature introduction, feature enhancement and bug repair. In this paper, we have developed two complexity of code changes/entropy based bug prediction models namely (i) time vs entropy and (ii) entropy vs bugs. We have compared the proposed models with the existing time vs bugs SRGM. The empirical work has been carried out using three subsystems of Mozilla project. The statistical significance of different approaches has been tested using a non-parametric Kolmogorov–Smirnov (K–S) test. The bug prediction approaches have been compared on the basis of various performance measures namely R-Square (R2), Adjusted R-Square (adj. R2), Bias, variation and root mean square prediction errors. We found that the potential complexity of code changes based bug prediction approach i.e. time vs entropy is better over the time vs bugs and entropy vs bugs on the basis of different comparison criteria and statistical test.

Biplot evaluation of test environments and identification of lentil genotypes with durable resistance to fusarium wilt in India

Abstract: Fusarium wilt (caused by Fusarium oxysporum f. sp. lentis) is the most crucial limiting variable for decreasing yield levels of lentils (Lens culinaris Medik.) around the world. A set of 20 diverse lentil genotypes comprising breeding lines and released varieties was evaluated, along with susceptible controls, for resistance to fusarium wilt through natural incidence for two continuous years (2010–11 and 2011–12) in six diverse lentil-growing environments in India. Analysis of variance showed that the effect of genotype (G) and environment (E) for disease incidence was highly significant. Among the three sources of variation, the biggest contribution in disease occurrence was accounted for by environment (54.68%), followed by G × E interaction (17.32%). The high G × E variation necessitated assessment of the genotypes at different locations (environments). GGE biplot analysis of the studied genotypes revealed that genotype PL 101 and released cultivar L 4076 had low levels of disease incidence. The sources of resistance to fusarium wilt have great potential for use in lentil-breeding programs. Another biplot of relationships among environments demonstrated that, among the test locations, Sehore and Faizabad, were the most effective for differentiation of genotypes. On the basis of discriminating ability and representativeness, the Sehore location appeared an ideal testing site for natural incidence of F. oxysporum f. sp. lentis.

Identification, characterization, validation and cross-species amplification of genic-SSRs in Indian Mustard (Brassica juncea)

Abstract: Brassica juncea is an economically important oilseed crop worldwide. It has limited genomic resources at present. We generated 47,962,057 expressed sequence reads which were assembled into 45,280 unigenes. A total of 4108 SSR loci (≥10 bp) were identified in these unigenes. Trinucleotide was the most frequent repeat unit (59.91 %) followed by di- (38.66 %), tetra - (0.71 %), hexa - (0.49 %) and pentanucleotide repeats (0.24 %). Primers were designed for 2863 SSR loci among which 460 were selected for primer synthesis. A total of 339 loci amplified successfully of which 134 (39.5 %) exhibited polymorphism among six B. juncea genotypes with PIC values ranging from 0.18 to 0.81. Further, 25 polymorphic SSRs were used for analysis of genetic variability in 25 genotypes of Brassicas and their wild relatives. Two to five alleles with PIC values 0.22–0.66 were detected at these loci. The dendrogram grouped the genotypes according to their known pedigree/systematic position.