Agroforestry Risk & Cost-Benefit Analysis

This repository provides tools and workflows for building, evaluating, and analyzing agroforestry systems—focusing on shade trees, risk assessment, and cost–benefit trade-offs—using the climate-risk modeling framework CLIMADA.

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Repository Structure

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├── agroforestry_systems/                # Typical agroforestry plot generation
│   ├── Build_typical_agroforest_plot.ipynb  # Generate typical agroforestry plots
│   └── *.xlsx                           # Excel outputs of typical plots
├── agroforest_risk/                     # Risk analysis workflows
│   ├── get_point_locations.ipynb        # Gather species occurrence points
│   ├── suitability_typical_agroforest_plots.ipynb  # Run suitability models
│   ├── risk_agroforest_system.ipynb     # Compute risk (suitability loss + extreme weather)
│   ├── species_thresholds.json          # Thresholds for suitability analysis
│   ├── utils_agroforestry.py            # Agroforestry helper functions
│   ├── utils_hazards.py                 # Hazard helper functions
│   └── utils_suitability_modelling.py   # Suitability modelling functions
├── cost_benefit/                        # Cost–benefit evaluation workflows
│   ├── make_canopy_alternatives.ipynb   # Generate canopy composition scenarios
│   ├── utils_cb.py                      # Helpers for cost–benefit routines
│   └── CostBenefit_Canopy.ipynb         # Main cost–benefit analysis notebook
├── climate_data/                        # Climate datasets and preprocessing
├── experiments/                         # Exploratory / test notebooks
├── config.py                            # Central configuration (paths for data, outputs)
├── README.md                            # This documentation file

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Workflow Overview

1. Build Typical Plots
   Run Build_typical_agroforest_plot.ipynb to generate representative agroforestry plots.
   Results are exported as Excel files into agroforestry_systems/.

2. Risk Analysis (agroforest_risk/)

   • get_point_locations.ipynb: collect occurrence points for coffee, cacao, and associated species.
   • suitability_typical_agroforest_plots.ipynb: apply suitability models to the typical plots.
   • risk_agroforest_system.ipynb: compute risk by combining impacts from suitability loss and extreme weather.
   • Supporting files: species_thresholds.json generated in the suitability modelling, plus utility modules (utils_agroforestry.py, utils_hazards.py, utils_suitability_modelling.py).

3. Cost–Benefit Analysis (cost_benefit/)

   • CostBenefit_Canopy.ipynb: perform cost–benefit evaluation.
   • Utility: utils_cb.py.

4. Experiments
   If you are brave enough, those are experiments that we tried. We do not guarantee that it works or that they are well documented.

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Dependencies

This project builds on CLIMADA (Climate Adaptation), a powerful open-source Python framework designed for probabilistic climate risk assessments and adaptation analysis.

• As of March 2025, the latest stable version is CLIMADA v6.0.1.
• A full installation guide is available here:
  CLIMADA Installation Guide — Read the Docs :contentReference[oaicite:1]{index=1}

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License & Citation

• CLIMADA is licensed under GPL-3.0. Please ensure compliance when distributing or adapting code.
• If using this repository in publications or presentations, please cite both CLIMADA and your project appropriately.