Future perspective of Bt technology in Indian agriculture.

Abstract

The future of Indian agriculture is very much affected by the emerging scenario in the field of science and technology effected by thrust areas of research. Use of what is called, Technology Forecasting (TF), can aid in understanding the underlying trends in the key factors of technologies so that they can be influenced to achieve the required needs. Technology refers to the collection of production possibilities, techniques, methods, machinery and processes by which resources are actually transformed by humans to meet their wants. Forecasting is the scientific and systematic process of computation/ prediction or giving a statement of what is expected to happen in the future in relation to a particular event or situation. TF is thus distinct from the usual forecasting where technology plays a role but not the central issue. It enables us with qualitative and/or quantitative prediction of technologies at stated level of confidence with a specific time frame and specified level of support such as policy, capital, human resource and infrastructural needs. Modern TF involves a systemic, quantitative and comprehensive analysis of future technologies. It tries to determine what technologies should be developed at different times in the future to meet the specific needs of our population and also as to what technologies are likely to be available at different times in the future in view of the present rate of growth of scientific innovation. For scoping the future perspectives of Bt technology in Indian agricultural scenario, case studies of four quantitative/ quasi-quantitative techniques of TF technology forecasting tools viz., Trend Impact Analysis (TIA), Scientometric analysis, Grey modeling and Cross impact analysis (CIA) techniques applied for Bt technology in agricultural sector have been employed. In TIA a modified Delphi approach has been proposed based on GOS tree approach which has the ability to combine the expert opinions after first round survey. Next based on the collected data time series intervention model has been employed for envisioning crop yield scenarios of maize, potato, rice, tomato, okra, cabbage, mustard at All-India level considering the impact of Bt technology. while employing CIA technique to study the direct as well as indirect cross impacts of Bt technology, the following factors were considered: increased productivity, contribution to national income, efficient overall agricultural sector, reduced input costs, improved quality of produce, increased farmer's income, reduced product costs to consumers, government policy, ethical and legal concerns, health issues, environmental implications, societal impacts, technological interventions, Bt seed sector. Three types of CIA techniques viz., direct classification, Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) and CIA with Time consideration (CIAT) have been attempted. The ranking of the factors obtained by three methods were combined using Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) approach. The analysis suggested that Govt. policy, Bt seed sector and technological interventions are the main three factors for future perspective of Bt technology in India. For bibliometrics analysis specific information relating to abstract, key words, 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. Activity Index (AI) has been constructed for seven domains viz. Bt Cotton, Bt Maize, Bt Mustard, Bt Brinjal, Bt Soyabean, Bt Sunflower, Bt Rice and ‘Bt related but not crop specific’ under these four regions. From the values of AI, it has been found that India’s research effort is higher only in Bt Cotton and Bt Mustard than other regions considered. Conventional version of Grey model as well as Grey model improved by genetic algorithm were fitted using yearly Bt cotton yield of India (2002-03 to 2016-17) obtained from Cotton Advisory Board of India. Only the first 11 years were utilized for model fitting and the rest utilized for validation purposes. The results revealed that Grey model improved by genetic algorithm performed better. In addition to this an improved version of intervention model based on genetic algorithm has been proposed. The proposed model was employed for quantifying the impact of cotton yield due to introduction of Bt cotton.