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
Papers
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TL;DR: In this article, the steady state behavior of a discrete time, first come first in, limited space, queueing problem with S heterogeneous groups each having A parallel channels is considered.
1 citations
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05 Jan 2020
TL;DR: In this paper, small area estimation (SAE) approach was used to produce the small area estimates of the total basal cover (m2/ha) for trees, shrubs and herbs for the state of Maharashtra in India.
Abstract: This chapter describes small area estimation (SAE) approach to produce the small area estimates of the total basal cover (m2/ha) for trees, shrubs and herbs for the state of Maharashtra in India. All seven forest types are defined as small areas. The analysis uses the data of survey conducted by Tropical Forest Research Institute, Jabalpur, India during the Indian Council of Forestry Research and Education’s revisiting of forestry types of India in the year 2011–12. The nested quadrats of 10 m × 10 m, 3 m × 3 m and 1 m × 1 m size for tree, shrub and herb layers respectively are the sampling units. The auxiliary data, percentage of forest cover at small area level is available from India’s State of Forest Report 2009 (FSI 2009). The results show that forest type-wise estimates of total basal cover for trees, shrubs and herbs generated by SAE approach are reliable as compared to direct survey estimates. Such disaggregate level estimates are invaluable policy information for state forest department and local resource managers.
1 citations
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TL;DR: In this article, a test based on ordered observations for testing the null hypothesis of equality of two variances has been given, which is a function of the sum of ranks assigned to smaller size sample.
Abstract: A method of analysis for comparing the variability of two samples drawn from two populations has been developed. The method is also suitable for the nonnumeric form of data. A test based on ordered observations for testing the null hypothesis of equality of two variances has been given. The test statistic is a function of the sum of ranks assigned to smaller size sample. Ranking procedure has been modified to depict the variability in the data by the sum of ranks. The null distribution of the test-statistic has been worked out for small samples and it turns out to be chi-square distribution for large samples. The analytical procedure has been explained by a numerical example on the productivity and production of rice and wheat in India from 1950–51 to 1983–84.
1 citations
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TL;DR: In this article, the authors review the evolution of tractor use in India in the past few decades, and supplement this with a panel model analysis using factors associated with state-level tractor density growth.
Abstract: This study reviews the evolution of tractor use in India in the past few decades, and supplements this with a panel model analysis using factors associated with state-level tractor density growth. Growth in tractor use in India, unlike that in the United States and Japan, has occurred at relatively low wage rates and with a substantial majority of the workforce remaining in the agricultural sector. Considerable growth in domestic manufacturing has contributed to growth in tractor densities. Tractor density across the 14 major states in India between 1982 and 2012 was positively affected by income per capita, cropping intensity, and the average size of farmland holdings. Tractor intensity grew at a fast pace even in low-wage regions of India, indicating that relatively lower labor wages might not have been a binding factor for diffusion of farm machinery and tractors among smallholding farmers in India.
1 citations
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04 Mar 2016
TL;DR: The overall codon usage analysis showed that codons ending with G and C are preferred more in the rhizobium genome than codon ending with A and T, which revealed that compositional constraints along with translational selection are the major cause ofcodon usage bias.
Abstract: Bacteria from genus Rhizobium have ability to fix atmospheric nitrogen in symbiosis with leguminous plants resulting in formation of root nodules. They act as an alternate source of nitrogenous fertilizers. The study of codon usage patterns of Rhizobium species is gaining increasing attention over the times. In the present study three strains of Rhizobium namely Sinorhizobium meliloti 1021, Bradyrhizobium japonicum USDA110 and Rhizobium tropici CIAT899 whose complete genome sequence are available were retrieved from NCBI for the analysis of codon usage. The overall codon usage analysis showed that codons ending with G and C are preferred more in the rhizobium genome than codon ending with A and T. ENc plot revealed that compositional constraints along with translational selection are the major cause of codon usage bias. Correspondence analysis (COA) showed that the variation in codon usage is accounted mainly by the first two axes. From the Pearson correlation analysis significant correlation was identified among the first axis of COA and Codon adaptation index (CAI) and other factors of codon usage bias. 17 optimal codons were identified that were shared among these three strains.
1 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 |