摘要：Many past studies have recognised the importance of a shift in the supply of a commodity as a measure of the impact of research. Several of these have discussed a range of options for mathematically representing this shift in an aggregate supply function. Among those, the most realistic assumption would be ‘supply shift as parallel’ and subdividing the production area in to homogenous regions in terms of the impact of the innovation in question on yield and production costs. But, very few researchers have focused on the importance of understanding the theoretical linkages underlying these possible shifts across homogenous Production Environments (PE). It is also crucial to disaggregate the total aggregate welfare estimates based on different categories of adopters as well as production environments (PEs). In general, the normal aggregate estimates masks the range of important implications of research impacts by hiding the exceeded welfare gains of favourable environments with that of lower benefits to the non-favourable environments. There is an equal chance of committing significant empirical error in over measuring the welfare changes by ignoring the different production environments. The detailed understanding of different production environments and technology adoption process facilitates incorporation of each component of the story/activity in its appropriate form rather than developing an additional set of hypothetical assumptions. It is obvious that the corresponding unit cost reductions will not be the same across heterogeneous production environments for a given specific technology in particular region. This research paper will present empirical results of chickpea improved cultivars adoption in Andhra Pradesh state of India and provides a deeper understanding about dis-aggregated production environments and matching unit cost reductions in measuring welfare changes due to research.
关键词：dis-aggregation of welfare estimates;different production environments;unit cost of reduction and welfare assessment
