Assessment of Pesticide Use Reduction Strategies for Thai Highland Agriculture

Combining Econometrics and Agent-based Modelling

by Christian Grovermann (Author)
Thesis XXIII, 197 Pages


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%.

Table Of Content

  • Cover
  • Title
  • Copyright
  • About the author(s)/editor(s)
  • About the book
  • This eBook can be cited
  • Acknowledgements
  • Summary
  • Zusammenfassung
  • Table of Contents
  • List of Tables
  • List of Figures
  • Abbreviations
  • 1. Introduction
  • 1.1 Problem statement
  • 1.2 State of the art and research gaps
  • 1.2.1 Optimal pesticide use and pesticide overuse
  • 1.2.2 Diffusion and adoption of innovations to reduce pesticide use
  • 1.2.3 Assessment of pesticide use reductions
  • 1.3 Research objectives
  • 1.4 Pesticide policy background
  • 1.5 Structure of the thesis
  • 2. Materials
  • 2.1 Study area selection and data collection
  • 2.2 Farm characteristics in the study area
  • 2.3 Land-use in the study area
  • 2.3.1 Description of cropping patterns
  • 2.3.2 Categorisation and selection of land-uses
  • 2.4 Pest pressure, pest management and pesticide use in the study area
  • 2.5 Vegetable IPM, the Royal Project and sustainable intensification
  • 3. Methods
  • 3.1 Quantification of pesticide productivity and pesticide overuse from farmer as well as from societal points of view
  • 3.1.1 Conceptual frame
  • 3.1.2 Specification of the production functions
  • 3.1.3 Econometric estimation of pesticide productivity
  • 3.1.4 Quantification of the external costs of pesticide use
  • 3.2 Innovation diffusion and adoption probabilities
  • 3.2.1 Agricultural technologies and the theory of innovation diffusion
  • 3.2.2 Specification of the adoption regression model
  • 3.2.3 Innovativeness ranking and categorisation
  • 3.2.4 Econometric estimation of adoption probabilities
  • 3.3 Model description of the MPMAS Mae Sa watershed application
  • 3.3.1 The methodological context of MPMAS
  • 3.3.2 Model set-up and dynamics
  • 3.3.3 Asset allocation to create the agent population
  • 3.3.4 Random spatial allocation of plots and other spatial inputs
  • 3.3.5 The decision-making component
  • 3.3.6 Investment objects and innovation diffusion
  • 3.3.7 Innovativeness ranking and adopter categorisation of agents
  • 3.3.8 Perennial crops
  • 3.3.9 Crop water demand and yields
  • 3.3.10 Irrigation water supply
  • 3.3.11 Farmgate selling, input prices and other input data
  • 3.3.12 Tax collection and compensation payments
  • 3.3.13 SWAT-based pesticide use constraints – chlorothalonil and cypermethrin
  • 3.4 Scenario specifications of simulation experiments
  • 3.4.1 Pesticide taxes
  • 3.4.2 IPM access and pesticide taxes
  • 3.4.3 IPM access and adoption incentives
  • 3.4.4 Policy mixes
  • 3.4.5 SWAT-based pesticide use regulation scenarios
  • 4. Model verification and validation
  • 4.1 Verification of asset allocations
  • 4.2 Validation of outcome variables
  • 4.3 Testing of innovation diffusion and adoption process
  • 5. Results
  • 5.1 Private and social levels of optimal pesticide use and overuse
  • 5.2 Adoption of GAP standard
  • 5.3 Simulation experiments
  • 5.3.1 The baseline scenario
  • 5.3.2 Impact of tax interventions
  • 5.3.3 Impact of IPM adoption with and without pesticide taxes
  • 5.3.4 Impact of IPM adoption with adoption incentives
  • 5.3.5 Impact of intervention mixes
  • 5.3.6 SWAT-based reductions scenarios for chlorothalonil and cypermethrin
  • 5.4 Key lessons learned for policy-making
  • 6. Discussion and conclusion
  • 6.1 Strength and weaknesses of the econometric analysis
  • 6.2 Strength and weaknesses of the MPMAS application
  • 6.3 Implications for pesticide policy-making
  • References
  • Annex
  • Annex I: Percentage of IPM adopters in the agent population
  • Annex II: Flat tax for 3 selected scenarios + 2 additional scenarios with higher tax rates
  • Annex III: Land-use shares in the different scenarios
  • Annex IV: Segmented cumulative distribution functions for innovativeness determinants
  • Annex V: Selected spatial inputs

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List of Tables

Table 1: Factors leading to pesticide overuse

Table 2: Possible policy interventions for pesticide use reduction

Table 3: Farm household (hh) characteristics and assets in the Mae Sa watershed villages, 2010

Table 4: Structure of farms in the Mae Sa watershed villages

Table 5: Average production data for important crops (standard deviations in brackets)

Table 6: Production data for IPM vegetable rotations as practiced by farmers at Doi Angkhang (2012, n = 34)

Table 7: Summary statistics of variables used in the analysis

Table 8: Determinants of innovativeness

Table 9: Sub-divisions of the agent population

Table 10: Crop data selected for the MP matrix (standard deviations in brackets)

Table 11: Simplified matrix overview of the MP decision-making model applied to pesticide use reduction strategies in northern Thailand

Table 12: Example of objects in the network

Table 13: Innovation segments

Table 14: Data of perennials crops in the model

Table 15: Part of the MP model showing simplified implementation of IPM vegetables as perennial crops

Table 16: Meteorological data for the CropWat model

Table 17: Annual crop data as specified in the CropWat MPMAS input

Table 18: Irrigation water supply by month and by sector, in m3/second

Table 19: Summary statistics of farmgate selling prices and input prices used in the model

Table 20: MP Matrix of the tax collection agent

Table 21: Key physico-chemical properties and application data of chlorothalonil and cypermethrin

Table 22: Implementation of the reduction of chlorothalonil and cypermethrin use in the MP matrix

Table 23: Overview of policies at different intervention levels

Table 24: Pesticide tax and tax compensation scenarios simulated by MPMAS

Table 25: IPM access and IPM access in conjunction with pesticides tax scenarios simulated by MPMAS ← XIX | XX →

Table 26: IPM access in conjunction with supportive policy scenarios simulated by MPMAS

Table 27: Mixed policy scenarios simulated by MPMAS

Table 28: Number of days per year when simulated daily concentrations exceed NOEC and PNEC thresholds

Table 29: Chlorothalonil and cypermethrin pesticide use reduction scenarios simulated by MPMAS

Table 30: Goodness of fit and robustness – MPMAS asset allocation

Table 31: Validation results for three outcome variables across all seed values

Table 32: Validation at the cluster level – summary statistics of goodness of fit parameters

Table 33: Comparison of simulated and observed innovation diffusion and adoption

Table 34: Production function estimates with abatement specification

Table 35: Private and social levels of optimal pesticide use and overuse

Table 36: Probit regression with sample selection – Output

Table 37: Marginal effects of the probit model within the sample selection

Table 38: Land use, pesticide use and incomes over time

Table 39: Simulated changes in pesticide use and income levels for the different tax scenarios

Table 40: Simulated changes in land-use for the different tax scenarios

Table 41: Evaluation of interventions for tax scenario impacts when compared to the baseline

Table 42: Simulated changes in pesticide use and income for IPM + tax interventions


XXIII, 197
ISBN (Hardcover)
Publication date
2015 (March)
Frankfurt am Main, Berlin, Bern, Bruxelles, New York, Oxford, Wien, 2015. XXIII, 197 pp., 53 tables, 45 graphs

Biographical notes

Christian Grovermann (Author)

Christian C. W. Grovermann holds an MSc in Sustainable Resource Management from the Technische Universität München and holds a PhD in Agricultural Economics from the University of Hohenheim (Germany). He currently works as an Associate Agricultural Officer at the Food and Agriculture Organization (FAO) of the United Nations. His main research interests include agri-environmental policy, impact assessment, sustainable agriculture and agricultural innovation systems.


Title: Assessment of Pesticide Use Reduction Strategies for Thai Highland Agriculture