Douthwaite, B. (author), Ellis-Jones, J. (author), Schulz, S. (author), Hussaini, M.A. (author), Oyewole, B.D. (author), Olanrewaju. A.S. (author), and White, R. (author)
Format:
Journal article
Publication Date:
2004
Published:
Nigeria
Location:
Agricultural Communications Documentation Center, Funk Library, University of Illinois Document Number: C24052
10 pages., Researchers investigated empowerment in the context of two strategies, Integrated Weed Management and Integrated Pest Management. Findings suggested: "With the rise of chemical resistance, the agricultural industry has placed considerable emphasis on the need to accelerate and achieve farmer adoption if IWM and IPM, but our evidence suggests that greater emphasis should be given to understanding the socio-cultural factors that affect farmer decision making. Farmer empowerment emerged as a core concept from the data."
Beal, George M. (author), Bohlen, Joe M. (author), and Lingren, Herbert G. (author)
Format:
Report
Publication Date:
1966
Published:
USA
Location:
Agricultural Communications Documentation Center, Funk Library, University of Illinois Box: 15 Document Number: B01824
Notes:
#980, Harold Swanson Collection. Claude W. Gifford Collection., Ames, IA : Agricultural and Home Economics Experiment Station, Cooperative Extension Service, Iowa State University of Science and Technology, 1966. 24 p. (Special Report No.49)
29 pages, Agent-based models are important tools for simulating farmers’ behaviour in response to changing environmental, economic or institutional conditions and policies. This article introduces an agent-based modelling approach that combines behavioural factors with standard bio-economic modelling of agricultural production. More specifically, our framework integrates the cumulative prospect theory and social interactions with constrained optimisation decisions in agricultural production. We apply our modelling approach to an exemplary bio-economic model on the assessment of weed control decisions. Results show the effects of heterogeneous farm decision-making and social networks on mechanical weed control and herbicide use. This framework provides a generic and conceptually sound approach to improve the scope for representing farmers’ decision-making and allows the simulation of their decisions and recent advances in behavioural economics to be aligned with existing bio-economic models of agricultural systems.
