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http://krishi.icar.gov.in/jspui/handle/123456789/69930
Title: | Technology Forecasting for Envisioning Bt Technology Scenario in Indian Agriculture |
Other Titles: | Not Available |
Authors: | Mrinmoy Ray Ramasubramanian V. K. N. Singh Santosha Rathod Ravindra Singh Shekhawat |
ICAR Data Use Licennce: | http://krishi.icar.gov.in/PDF/ICAR_Data_Use_Licence.pdf |
Author's Affiliated institute: | ICAR::Indian Agricultural Statistics Research Institute ICAR::Indian Institute of Rice Research |
Published/ Complete Date: | 2022-02-15 |
Project Code: | AGENIASRISIL201604900086 |
Keywords: | Scientometrics Grey model Cross impact analysis (CIA) technique Activity Index (AI) Genetic Algorithm (GA) TOPSIS MICMAC CIAT |
Publisher: | NAAS |
Citation: | Ray, M., Ramasubramanian, V., Singh, K.N. et al. Technology Forecasting for Envisioning Bt Technology Scenario in Indian Agriculture. Agric Res (2022). https://doi.org/10.1007/s40003-022-00612-z |
Series/Report no.: | Not Available; |
Abstract/Description: | For scoping the future prospects of Bacillus thuringiensis (Bt) technology in Indian agricultural scenario, case studies of three quantitative/quasi-quantitative techniques of Technology Forecasting tools viz., activity index (AI)-based scientometric analysis, Grey modeling and cross impact analysis (CIA) techniques have been done. Under AI-based scientometric analysis, information relating to abstract, keywords, authors, affiliation, etc., relevant to research publication on applications of Bt technology in India vis-à-vis three other competing country regions—China, USA cum Canada and European countries were collected from ScienceDirect database for the period 1997–2017. AI has been constructed for seven domains viz. Bt Cotton, Bt Maize, Bt Mustard, Bt Brinjal, Bt Soybean, Bt Sunflower, Bt Rice, and ‘Bt related but not crop specific’ under these four regions considered. From the values of AI, it has been found that India’s research effort is higher only in Bt Cotton and Bt Mustard than the other regions considered. Secondly, for Grey modeling, its conventional version as well as Grey model improved by Genetic Algorithm (GA) were fitted using yearly Bt cotton yield of India (2002–2003 to 2016–2017) obtained from Cotton Advisory Board of India. Only the first 11 years were utilized for model fitting and the rest were utilized for validation purposes. The results revealed that Grey model improved by GA performed better. Lastly, for employing CIA technique to study the direct as well as indirect cross impacts of Bt technology, 14 factors were considered. Three types of CIA techniques viz., Direct Classification, Cross-Impact Matrix Multiplication Applied to Classification, and CIA with Time Consideration have been attempted. The ranking of the factors obtained by three methods was combined using Technique for Order Preference by Similarity to an Ideal Solution approach. The analysis suggested that factors viz., Government policy, Bt seed sector, and technological interventions came out to be mainly responsible for future prospects of Bt technology in India. |
Description: | Not Available |
ISSN: | Not Available |
Type(s) of content: | Article |
Sponsors: | Not Available |
Language: | English |
Name of Journal: | Agricultural Research |
NAAS Rating: | 5.95 |
Volume No.: | Not Available |
Page Number: | Not Available |
Name of the Division/Regional Station: | Not Available |
Source, DOI or any other URL: | https://doi.org/10.1007/s40003-022-00612-z |
URI: | http://krishi.icar.gov.in/jspui/handle/123456789/69930 |
Appears in Collections: | AEdu-IASRI-Publication |
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