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Table of contents
General introduction
1. Key unknowns in nitrogen budget for oil palm plantations: A review
1.1. Introduction
1.2. N budget within oil palm management
1.2.1. Standard oil palm management
1.2.2. Application of N budgets to fertiliser management
1.2.3. System boundaries and accounted fluxes
1.3. N fluxes and variability in plantations: state-of-the-art
1.3.1. Inputs
1.3.2. Internal fluxes
1.3.3. Outputs
1.4. Important fluxes and critical conditions for N losses
1.4.1. The most important and most uncertain fluxes
1.4.2. Critical conditions for N losses
1.5. Discussion and key research needs
1.6. Conclusions
Acknowledgments
2. Quantifying nitrogen losses in oil palm plantation: models and challenges
2.1. Introduction
2.2. Material and methods
2.2.1. Model selection and description
2.2.2. Description of comprehensive models
2.2.3. Description of sub-models
2.2.4. Model runs and sensitivity analysis
2.3. Results
2.3.1. Comparison of the 11 comprehensive models
2.3.2. Comparison of the 29 sub-models
2.3.3. Sensitivity analysis
2.4. Discussion
2.4.1. Relevance of model comparisons and flux estimates
2.4.2. Challenges for modelling the N budget in oil palm plantations
2.4.3. Implications for management
2.5. Conclusions
Acknowledgements
3. Yield and nitrogen losses in oil palm plantations: main drivers and management tradeoffs determined using simulation
3.1. Introduction
3.2. Material & methods
3.2.1. Study sites and datasets
3.2.2. Inputs, outputs and parameters
3.2.3. Morris sensitivity analysis
3.3. Results
3.3.1. Outputs of the simulations
3.3.2. Influential parameters
3.3.3. Trade-off between yield and N losses
3.4. Discussion
3.4.1. Relevance of the simulation built-up and outputs
3.4.2. Study limitations
3.4.3. Implications for managers, experimentalists, and modellers
3.5. Conclusions
4. IN-Palm: an agri-environmental indicator to assess potential nitrogen losses in oil palm plantations
4.1. Introduction
4.2. Materials and methods
4.2.1. INDIGO® method and fuzzy decision tree modelling approach
4.2.2. Modelled processes
4.2.3. Data used for design, calibration, reference values and validation
4.2.4. Validation of the R-leaching module
4.2.5. Scenario testing
4.3. Results and discussion
4.3.1. General structure and outputs
4.3.2. Calculation of the 17 modules
4.3.3. Validation of the R-Leaching module against field data
4.3.4. Scenario testing and management for N loss reduction
4.4. Conclusion
Acknowledgements
General discussion
6.1. Potential management options to reduce N losses in oil palm
6.2. Future use and development of IN-Palm
6.3. Future field measurements to reduce knowledge gaps in N loss estimates
6.4. INDIGO® framework and life cycle assessment
General conclusion
Appendices
Appendix 1. Permissions of reproduction of published journal articles in this thesis
Appendix 2. Parameter ranges for the Morris’ sensitivity analysis of chapter 2
Appendix 3. IN-Palm technical report
1. User instructions
2. Advantages and computation of fuzzy decision tree models
3. Structure of the 17 modules
Appendix 4. Pictures of fields to help the user in IN-Palm
Appendix 5. Summary of all parameters of IN-Palm
References
