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Assessment of Pesticide Use Reduction Strategies for Thai Highland Agriculture

Combining Econometrics and Agent-based Modelling

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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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4. Model verification and validation

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Verification and validation is a crucial part of the overall MPMAS modelling process. It ensures that model inputs based on the lottery as well as model outcomes correspond to reality. In fact, validation is considered one of the most important steps in any quantitative modelling approach (Kaiser and Messer, 2012), e.g. farm level decision-making models. A review of 48 bio-economic farm models by Janssen and van Ittersum (2007) however showed that of the reviewed applications only 23 were validated, four of these quantitatively. The MPMAS application used here is based on established decision-making, crop growth and innovation diffusion modelling methods, while the model’s coefficients are estimated based on comprehensive and reliable survey data. This approach guarantees the construct validity of the model. In the following section, the verification and validation applied to the model’s results are explained in more detail. It should be noted however, that due to the nature of the model and the data collection process, the simulated and observed data are not independent.

4.1 Verification of asset allocations

Verification in the context of MPMAS implies checking that the resources allocated to agents are consistent with the observed resources available to farmers.

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