Keywords:-

Keywords: Agriculture, Technology, Forecast, Adoption, Mathematical Modeling.

Article Content:-

Abstract

In this paper, we proposed a simple mathematical model for farmers in the Ranchi district to adopt and implement agricultural technologies. We used the Fisher-Pry model, which is a very effective model in the study of technological adoption. The model's output is a Sigmoid curve, or S-shaped curve, that develops exponentially at first, then approximately linearly, and lastly asymptotically. We have studied the models of technology spread using a basic case study and brought in a real-world situation. Mathematically, it is possible to predict that adoption will rise to the number m/2 when 50% of the farmers have adopted the technology, after which it will slow down. In a real-life situation, the point of inflection might occur before or after m/2.We also collected data on some of the food crops, and net irrigated area of Ranchi district from 2011 to 2020, and evaluated it graphically/mathematically. We examined the advantages of technology in agriculture using utility functions, by employing basic scenarios and selecting between different technologies for adoption

References:-

References

Marcus Barla, ‘The impact of new agricultural technology on tribal farming: A study of Ranchi district of Ranchi Jharkhand’, Journal of Economic & Social Development, Vol - IX, No. 1, 2013 ISSN 0973 - 886X .

Singh & Mishra, ‘A Mathematical Modeling approach to study growth rate of grassroots technological innovations’, IJRRAS 3 (2), May 2010.

Dr. Umesh Kumar Gupta, ‘A competitive mathematical modeling of technological innovation diffusion’, International Journal of Statistics and Applied Mathematics 2017;2(6):118-121.

Kapur JN, ‘Fascination world of Mathematical sciences volume Ⅺ, Mathematical sciences Trust society India, 1992.

Carrillo, M. (2003). Growth, Life Cycle and Dynamic Modelling. Mathematical and Computer Modelling of Dynamical Systems, 9(2), 121–136.

Anand, A., Agarwal, M., Aggrawal, D. et al. Successive generation introduction time for high technological products: an analysis based on different multi-attribute utility functions. Environ Dev Sustain (2022).

https://doi.org/10.1007/s10668-022-02357-9

M.A. Akudugu, E. Guo, S.K.N. Dadzie, Adoption of modern agricultural production technologies by farm households in Ghana: what factors influence their decisions? J. Biol. Agric. Healthcare 2 (2012) 1–13.

C.A. Wongnaa, D. Awunyo-Vitor, J.E.A. Bakang, Factors affecting adoption of maize production technologies: a study in Ghana, J. Agric. Sci.-Sri Lanka 13(2018) 81–99,

https://doi.org/10.4038/jas.v13i1.8303.

E. Martey, P.M. Etwire, W. Adzawla, W. Atakora, P.S. Bindraban, Perceptions of COVID-19 shocks and adoption of sustainable agricultural practices in Ghana, J. Environ. Manag. 320 (2022), 115810, https://doi.org/10.1016/j..

S.K. Kriesemer, P.A. Gr¨otz, Fish for all? The adoption and diffusion of small-scale pond aquaculture in Africa with special reference to Malawi, 2008A.K. Barak, S.

Hill, L., & Kau, P. (1973). Application of multivariate probit to a threshold model of grain dryer purchasing decisions. American Journal of Agricultural Economics, 55(1), 19-27.

Barak, A. K., & Barak, M. S. (2016). Impact of abnormal weather conditions on various reliability measures of a repairable system with inspection. Thailand Statistician, 14(1), 35-45.

Downloads

Citation Tools

How to Cite
Bhuinyan, R., & Mahato, A. K. (2024). Forecasting Technology Adoption Behaviour and Agricultural Production Growth. International Journal Of Mathematics And Computer Research, 12(6), 4264-4270. https://doi.org/10.47191/ijmcr/v12i6.01