Institution
Indian Agricultural Statistics Research Institute
Facility•New Delhi, India•
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.
Papers published on a yearly basis
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TL;DR: It seems that the interplay of the genotype-phenotype relationship for quantitative variation is not only complex but also requires a dialectical approach for its understanding in which ‘parts’ and ‘whole’ evolve as a consequence of their relationship and the relationship itself evolves.
Abstract: Most characters of economic importance in plants and animals, and complex diseases in humans, exhibit quantitative variation, the genetics of which has been a fascinating subject of study since Mendel’s discovery of the laws of inheritance. The classical genetic basis of continuous variation based on the infinitesimal model of Fisher and mostly using statistical methods has since undergone major modifications. The advent of molecular markers and their extensive mapping in several species has enabled detection of genes of metric characters known as quantitative trait loci (QTL). Modeling the high-resolution mapping of QTL by association analysis at the population level as well as at the family level has indicated that incorporation of a haplotype of a pair of single-nucleotide polymorphisms (SNPs) in the model is statistically more powerful than a single marker approach. High-throughput genotyping technology coupled with micro-arrays has allowed expression of thousand of genes with known positions in the genome and has provided an intermediate step with mRNA abundance as a sub-phenotype in the mapping of genotype onto phenotype for quantitative traits. Such gene expression profiling has been combined with linkage analysis in what is known as eQTL mapping. The first study of this kind was on budding yeast. The associated genetic basis of protein abundance using mass spectrometry has also been attempted in the same population of yeast. A comparative picture of transcript vs. protein abundance levels indicates that functionally important changes in the levels of the former are not necessarily reflected in changes in the levels of the latter. Genes and proteins must therefore be considered simultaneously to unravel the complex molecular circuitry that operates within a cell. One has to take a global perspective on life processes instead of individual components of the system. The network approach connecting data on genes, transcripts, proteins, metabolites etc. indicates the emergence of a systems quantitative genetics. It seems that the interplay of the genotype-phenotype relationship for quantitative variation is not only complex but also requires a dialectical approach for its understanding in which ‘parts’ and ‘whole’ evolve as a consequence of their relationship and the relationship itself evolves.
17 citations
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TL;DR: The first appearance of white rust disease (Albugo candida) on leaves and pods (staghead formation) of Indian mustard occurred between 36 and 131 days after sowing (d.a.s.) as mentioned in this paper.
Abstract: Experiments were laid out at Bharatpur, New Delhi and Kangra with Indian mustard (Brassica juncea) cvs ‘Varuna’ and an important one in the locality sown on 10 dates at weekly intervals. First appearance of white rust disease (Albugo candida) on leaves and pods (staghead formation) of mustard occurred between 36 and 131 days after sowing (d.a.s.), 60 and 123 d.a.s., respectively. Severity of white rust disease on leaves was favoured by >40% afternoon (minimum) relative humidity (RH), >97% morning (maximum) RH and 16–24°C maximum daily temperature. Staghead formation was significantly and positively influenced by 20–29°C maximum daily temperature and further aided by >12°C minimum daily temperature and >97% morning (maximum) RH. Regional and cultivar specific models devised could predict, at a few weeks after sowing, the crop age at which white rust first appeared on the leaves, as staghead, the highest rust severity on leaves, staghead numbers and the crop age at peak rust severity on leaf, highest staghe...
17 citations
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TL;DR: In this paper, the identification of suitable renewable energy technologies to satisfy the energy requirement of both tea plantation and industry for north-eastern states and the southern part of India are reviewed extensively and the technological barriers are delineated.
Abstract: In tea production, India ranks second largest in the world, after China. Indian tea industry is one of the largest in the world and with over 13,000 gardens and produces 1350 million kg of tea leaves. Tea production and processing require electrical and thermal energy in various processes such as irrigation, withering, rolling, fermentation, drying, sorting/grading, and packaging. To produce one kg of tea requires thermal and electrical energy in the range of 4.45–6.84 kWh and 0.4–0.7 kWh respectively. In tea gardens, diesel generators are commonly used for irrigational needs in off-grid areas. In tea industry, fossil fuels such as coal, low sulphur diesel are mostly used to encounter the thermal energy needs and these energy sources heavily pollute the environment. This is a serious cause of concern for all including national and international agencies. These conventional fuels may be replaced by suitable renewable energy resources to meet the energy demand of tea plantations and industries. The identification of suitable renewable energy technologies to satisfy the energy requirement of both tea plantation and industry for north-eastern states and the southern part of India are reviewed extensively and the technological barriers are delineated.
17 citations
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17 citations
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TL;DR: This study discusses two wavelet-based neural network approaches envisaging monthly wholesale onion price of three markets, namely Bangalore, Hubli, and Solapur, and found to be highly proficient in denoising and capturing the inherent pattern of the series through a distinctive approach.
Abstract: An agriculture-dominated developing country like India has been always in need of efficient and reliable time series forecasting methodologies to describe various agricultural phenomenons, whereas agricultural price forecasting continue to be the challenging areas in this domain. The observed features of many temporal price data set constitute complex nonlinearity, and modeling these features often go beyond the capability of Box–Jenkins autoregressive integrated moving average methodology. Moreover, despite the popularity and sheer power of traditional neural network model, the empirical forecasting performance of this model has not been found satisfactory in all cases. To address the problem, wavelet-based modeling approach is recently upsurging. Present study discusses two wavelet-based neural network approaches envisaging monthly wholesale onion price of three markets, namely Bangalore, Hubli, and Solapur. Wavelet-based decomposition makes it possible to describe the useful pattern of the series from both global as well as local aspects and found to be highly proficient in denoising and capturing the inherent pattern of the series through a distinctive approach. Besides, wavelet method can also be used as a tool for function approximation. The improvement upon time-delay neural network also be made up to a great extent through using wavelet-based approaches as exhibited through proper empirical evidence.
17 citations
Authors
Showing all 462 results
Name | H-index | Papers | Citations |
---|---|---|---|
Sunil Kumar | 30 | 230 | 3194 |
Atmakuri Ramakrishna Rao | 21 | 109 | 1803 |
Charanjit Kaur | 20 | 80 | 4320 |
Anil Rai | 20 | 208 | 1595 |
Ranjit Kumar Paul | 17 | 93 | 875 |
Hukum Chandra | 17 | 75 | 825 |
Sudhir Srivastava | 17 | 69 | 1123 |
Krishan Lal | 16 | 68 | 1022 |
Ashish Das | 15 | 146 | 1218 |
Eldho Varghese | 15 | 127 | 842 |
Deepti Nigam | 14 | 29 | 812 |
Mir Asif Iquebal | 14 | 88 | 604 |
Rajender Parsad | 13 | 98 | 799 |
Deepak Singla | 13 | 32 | 422 |
Prem Narain | 13 | 80 | 503 |