Assessment of Pesticide Use Reduction Strategies for Thai Highland Agriculture
Combining Econometrics and Agent-based Modelling
Summary
Excerpt
Table Of Contents
- 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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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
Details
- Pages
- XXIII, 197
- Publication Year
- 2015
- ISBN (PDF)
- 9783653051346
- ISBN (MOBI)
- 9783653975215
- ISBN (ePUB)
- 9783653975222
- ISBN (Hardcover)
- 9783631657843
- DOI
- 10.3726/978-3-653-05134-6
- Language
- English
- Publication date
- 2015 (March)
- Keywords
- Pestizide Südostasien Hortikultur
- Published
- Frankfurt am Main, Berlin, Bern, Bruxelles, New York, Oxford, Wien, 2015. XXIII, 197 pp., 53 tables, 45 graphs
- Product Safety
- Peter Lang Group AG