Assessment of pesticide use reduction strategies for Thai highland agriculture : combining econometrics and agent-based modelling
Pesticides Pests sähkökirjat Pesticides -- Thailand -- Econometric models Pests -- Integrated control -- Thailand -- Econometric models TECHNOLOGY & ENGINEERING / Environmental / General
Peter Lang
2015
First edition.
EISBN 9783653051346
Cover; 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 patterns2.3.2 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 probabilities3.2.1 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 diffusion3.3.7 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 scenarios4. 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
2.3.1 Description of cropping patterns2.3.2 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 probabilities3.2.1 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 diffusion3.3.7 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 scenarios4. 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
