Bioeconomic model for optimal control of the invasive weed Zea mays subspp. (teosinte) in Spain

Abstract

Teosinte is an invasive weed which emerged recently in Northeastern Spain, an important corn-growing region in Western Europe. It is causing substantial agronomic and economic damages and is threatening the availability of corn in the region. Farmers and regulatory agencies can choose from a number of strategies to control for teosinte infestations including adoption of specific cultural practices such as manual control constructing false seedbeds, as well as adopting corn rotations with other annual and perennial crops. In spite of the potential negative impacts of this weed, little is known about what the optimal control strategies are, both from the private (e.g. the farm) and social (e.g. regulatory agencies) perspectives. In response, we develop a dynamic optimization model to identify the sequence of control strategies that minimize private and social costs under low- and high-infestation level scenarios, for a fifteen-year planning horizon. We calibrate the model using biological data from experimental trials and economic parameters collected from farmers in the region. Our results suggest the economic losses of teosinte infestation can reach up to 7444 and 8421 €/ha for low- and high-infestation scenarios if nothing is done to control it. In addition, results show that optimal private and social strategies are different. For example, under high-infestation levels, private losses are minimized at 26.5% by not controlling in years 1-2, use false seedbeds in year 3, planting alfalfa in years 4-8, and planting corn thereafter in the total area. In contrast, social cost are minimized at 27.9% by adopting rotations starting year, return to corn mono-cropping in half the area after year four. Results show false seedbed and manual controls, currently recommended by the regulatory agency in lowinfestation cases, are not socially optimal.