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.

Discovery of Putative Herbicide Resistance Genes and Its Regulatory Network in Chickpea Using Transcriptome Sequencing

Abstract: Background Chickpea (Cicer arietinum L) contributes 75% of total pulse production Being cheaper than animal protein, makes it important in dietary requirement of developing countries Weed not only competes with chickpea resulting into drastic yield reduction but also creates problem of harbouring fungi, bacterial diseases and insect pests Chemical approach having new herbicide discovery has constraint of limited lead molecule options, statutory regulations and environmental clearance Through genetic approach, transgenic herbicide tolerant crop has given successful result but led to serious concern over ecological safety thus non-transgenic approach like marker assisted selection is desirable Since large variability in tolerance limit of herbicide already exists in chickpea varieties, thus the genes offering herbicide tolerance can be introgressed in variety improvement programme Transcriptome studies can discover such associated key genes with herbicide tolerance in chickpea Results This is first transcriptomic studies of chickpea or even any legume crop using two herbicide susceptible and tolerant genotypes exposed to imidazoline (Imazethapyr) Approximately 90 million paired-end reads generated from four samples were processed and assembled into 30803 contigs using reference based assembly We report 5759 differentially expressed genes (DEGs), of which 3037 were regulated by 980 miRNAs, 1528 transcription factors associated with 897 DEGs, 47 Hub proteins, 3540 putative Simple Sequence Repeat-Functional Domain Marker (SSR-FDM), 13778 genic Single Nucleotide Polymorphism (SNP) putative markers and 1174 Indels Randomly selected 20 DEGs were validated using qPCR Pathway analysis suggested that xenobiotic degradation related gene, glutathione S-transferase (GST) were only up-regulated in presence of herbicide Down-regulation of DNA replication genes and up-regulation of abscisic acid pathway genes were observed Study further reveals the role of cytochrome P450, xyloglucan endotransglucosylase/hydrolase, glutamate dehydrogenase, methyl crotonoyl carboxylase and of thaumatin-like genes in herbicide resistance Conclusion Reported DEGs can be used as genomic resource for future discovery of candidate genes associated with herbicide tolerance Reported markers can be used for future association studies in order to develop marker assisted selection (MAS) for refinement In endeavour of chickpea variety development programme, these findings can be of immense use in improving productivity of chickpea germplasm

A New Algorithm for Extreme Vertices Designs for Linear Mixture Models

Abstract: A new algorithm for obtaining extreme vertices designs for linear mixture models is proposed. The algorithm generally produces designs that are as efficient as those produced by the XVERT algorithm of Snee and Marquardt (1974) but with less computational effort. Use of the algorithm in obtaining designs is also described.

Mitigation of yield-scaled greenhouse gas emissions from irrigated rice through Azolla, Blue-green algae, and plant growth–promoting bacteria

Abstract: Irrigated transplanted flooded rice is a major source of methane (CH4) emission. We carried out experiments for 2 years in irrigated flooded rice to study if interventions like methane-utilizing bacteria, Blue-green algae (BGA), and Azolla could mitigate the emission of CH4 and nitrous oxide (N2O) and lower the yield-scaled global warming potential (GWP). The experiment included nine treatments: T1 (120 kg N ha−1 urea), T2 (90 kg N ha−1 urea + 30 kg N ha−1 fresh Azolla), T3 (90 kg N ha−1 urea + 30 kg N ha−1 Blue-green algae (BGA), T4 (60 kg N ha−1 urea + 30 kg N ha−1 BGA + 30 kg N ha−1 Azolla, T5 (120 kg N ha−1 urea + Hyphomicrobium facile MaAL69), T6 (120 kg N ha−1 by urea + Burkholderia vietnamiensis AAAr40), T7 (120 kg N ha−1 by urea + Methylobacteruim oryzae MNL7), T8 (120 kg N ha−1 urea + combination of Burkholderia AAAr40, Hyphomicrobium facile MaAL69, Methylobacteruim oryzae MNL7), and T9 (no N fertilizer). Maximum decrease in cumulative CH4 emission was observed with the application of Methylobacteruim oryzae MNL7 in T7 (19.9%), followed by Azolla + BGA in T4 (13.2%) as compared to T1 control. N2O emissions were not significantly affected by the application of CH4-oxidizing bacteria. However, significantly lower (P<0.01) cumulative N2O emissions was observed in T4 (40.7%) among the fertilized treatments. Highest yields were observed in Azolla treatment T2 with 25% less urea N application. The reduction in yield-scaled GWP was at par in T4 (Azolla and BGA) and T7 (Methylobacteruim oryzae MNL7) treatments and reduced by 27.4% and 15.2% in T4 and T7, respectively, as compared to the T1 (control). K-means clustering analysis showed that the application of Methylobacteruim oryzae MNL7, Azolla, and Azolla + BGA can be an effective mitigation option to reduce the global warming potential while increasing the yield.