Submitted to: American Journal of Agricultural Economics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/25/2008
Publication Date: 7/10/2008
Citation: Richards, T.J., Eaves, J., Manfredo, M., Naranjo, S.E., Chu, C.C., Henneberry, T.J. 2008. Spatial-Temporal Models of Insect Growth, Diffusion and Derivative Pricing. DOI: 10.1111/j.1467-8276.2008.01170.x. American Journal of Agricultural Economics 90(4) 962-978
Interpretive Summary: The sweetpotato whitefly is a serious pest of numerous agricultural crops throughout the world. It’s feeding disrupts plant productivity and quality and it acts as a vector of over 100 plant viruses. The insect is highly mobile and with its ability to feed on a number of plants pest management is complex and challenging. Here the concept of insect derivatives for reducing the risk of economic impact is developed and formulated for this pest species attacking cotton. This extends on a previous system of derivatives by incorporating both the temporal and spatial components of the insect’s population dynamics. We show that insect derivatives can play an important risk management role in mitigating whitefly damage in cotton. Beyond developing a new risk management instrument, the key methodological contributions of this paper lies in pricing derivatives with stochastic or variable properties in both space and time dimensions.
Technical Abstract: Insect derivatives represent an important innovation in specialty crop risk management. An active over-the-counter market in insect derivatives will require a transparent pricing method. The paper develops an econometric model of the spatio-temporal process underlying a particular insect population and develops a pricing model based on this process. We show that insect derivatives can play an important risk management role in mitigating B. tabaci (whitefly) damage in cotton. Beyond developing a new risk management instrument, the key methodological contributions of this paper lies in pricing derivatives with stochastic properties in both space and time dimensions.