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Assessment of Pesticide Use Reduction Strategies for Thai Highland Agriculture

Combining Econometrics and Agent-based Modelling


Christian Grovermann

This study combines econometrics and agent-based modelling to evaluate the impacts of a range of pesticide use reduction strategies in the context of Thai highland agriculture. Pesticide productivity and pesticide overuse are quantified, while determinants of the adoption of innovations in pesticide use reduction are estimated. On that basis, the Mathematical Programming-based Multi Agent System (MPMAS), a bio-economic simulation model, is used to ex-ante assess the adoption of Integrated Pest Management (IPM) in combination with a series of market-based instruments that boost the transition to more sustainable pest control practices. The MPMAS simulation results demonstrate that, over five years, it is possible to bring down levels of pesticide use significantly without income trade-offs for farm agents. A proportional tax, increasing the price of synthetic pesticides by 50% on average, together with bio-pesticide subsidies for IPM proves to be the most cost-effective and practicable policy package. IPM practices are adopted by up to 75% of farm agents and pesticide use reductions reach up to 34%.
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6. Discussion and conclusion


6.1 Strength and weaknesses of the econometric analysis

A novelty of the econometric analysis shown in this paper is the inclusion of pesticide externalities when quantifying pesticide overuse levels. The PEA tool is straightforward to apply if farm-level data on active pesticide ingredients are available. Currently it appears to be the only available tool able to do this. However, several weaknesses in the methodology need to be considered, as discussed by Praneetvatakul et al. (2013), meaning there is room for improvements to this methodology.

The production function approach is based on standard micro-economic theory and assumes that farm decision-making is guided by a profit-maximizing motive. Nevertheless, in reality, there are other motivations for farm decision-making, but including these would make the calculation of economic optima very complex, and would require an unrealistically high amount of farm-level data. Therefore the idea of profit maximization is a necessary simplifying assumption for ease of computability.

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