Prioritization Methodology - Rwema25/AE-project GitHub Wiki
Dataset Prioritization Methodology
To ensure the selection of optimal climate datasets for agroecological research, we apply a structured prioritization approach. This process evaluates datasets based on criteria critical for agricultural applications, data integration, and scientific rigor.
Prioritization Criteria
- Relevance to Agroecology
- Does the dataset include variables essential for crop modeling and agricultural risk assessment (e.g., precipitation, temperature, soil moisture, evapotranspiration)?
- Spatial and Temporal Alignment
- Does the dataset’s spatial and temporal resolution meet the needs of farm-level or regional analysis (e.g., <10 km spatial resolution, daily or sub-monthly time steps)?
- Accessibility
- Is the dataset freely available for research use? Does it offer clear documentation and robust access methods (e.g., API, direct download, cloud platforms)?
- Integration Potential
- Are there established tools, scripts, or packages (e.g., Python/R libraries, Google Earth Engine scripts) to facilitate data retrieval, preprocessing, and analysis?
- Data Quality, Validation, and Uncertainty
- Has the dataset been peer-reviewed, validated against ground observations, or subject to uncertainty quantification? Is metadata comprehensive and transparent?
Example Scoring Approach
| Criterion | Weight (%) | Score (1–5) | Weighted Score |
|---|---|---|---|
| Relevance to Agroecology | 30 | 5 | 1.5 |
| Spatial/Temporal Alignment | 20 | 4 | 0.8 |
| Accessibility | 20 | 3 | 0.6 |
| Integration Potential | 20 | 4 | 0.8 |
| Data Quality & Validation | 10 | 4 | 0.4 |
| Total | 100 | 4.1 |
Priority Level:
Formula:
Weighted Score = (Weight as decimal) × Score
- High: Score ≥ 3.0 (Core dataset for agroecology)
- Medium: 2.0 ≤ Score < 3.0 (Supplementary or niche use)
- Low: Score < 2.0 (Limited utility for project)
References to Frameworks
- Climate Adaptation Prioritization:
- The methodology to prioritize climate adaptation measures in urban areas uses similar multi-criteria approaches.
- Sustainability 12(12), 4807 (2020).
Prioritized Dataset List
| Rank | Dataset | Weighted Score | Notes/Strengths |
|---|---|---|---|
| 1 | CHIRPS | 4.8 | Essential precip, high res, strong integration |
| 1 | AgERA5 | 4.8 | Key ag variables, high res, tailored for agriculture |
| 2 | TerraClimate | 4.7 | Water balance, high res, global, accessible |
| 2 | IMERG | 4.7 | High-res precip, global, accessible |
| 2 | TAMSAT | 4.7 | Africa-focused, very high res, accessible |
| 2 | CHIRTS | 4.7 | Essential temp, high res, strong integration |
| 3 | ERA5 | 4.2 | Broad variables, high res, strong integration |
| 4 | NEX-GDDP | 4.1 | Future projections, downscaled, accessible |
| 5 | NASA POWER | 4.0 | Solar, temp, global, but coarser res |
| 6 | CMIP6 | 3.9 | Future scenarios, coarser res, less direct for ag |
Indicator Prioritization Methodology
Indicators were prioritized using a multi-criteria matrix, scoring each index on relevance to key hazards, scientific consensus, crop/livestock specificity, data availability, computation feasibility, and management impact. Those with the highest total scores were selected as core indicators for our climate data tool. This approach ensures that our indicator set is robust, actionable, and grounded in both science and practical application.
Indicator Prioritization Matrix
We used a multi-criteria matrix to prioritize climate hazard indicators for inclusion in our climate data tool. Each indicator was scored from 1 (lowest) to 3 (highest) against the following criteria:
- Relevance to key agroecological hazards (drought, heat, waterlogging, compound events)
- Scientific consensus and prevalence in literature/operational use
- Crop/livestock specificity (can thresholds be tailored?)
- Data availability (open-access, spatial/temporal coverage)
- Computation feasibility (existing scripts, APIs, packages)
- Impact on management and decision-making
| Indicator | Relevance | Scientific Consensus | Crop/Livestock Specificity | Data Availability | Computation Feasibility | Management Impact | Total Score | Priority |
|---|---|---|---|---|---|---|---|---|
| SPI (Standardized Precipitation Index) | 3 | 3 | 1 | 3 | 3 | 2 | 15 | High |
| SPEI (Standardized Precipitation Evapotranspiration Index) | 3 | 3 | 1 | 3 | 3 | 2 | 15 | High |
| Soil Moisture Anomaly | 3 | 2 | 2 | 2 | 2 | 3 | 14 | High |
| NTX (Number of Hot Days) | 3 | 2 | 2 | 3 | 2 | 3 | 15 | High |
| THI (Temperature Humidity Index, Livestock) | 2 | 2 | 2 | 2 | 2 | 2 | 12 | Medium |
| VPD (Vapor Pressure Deficit) | 2 | 2 | 2 | 2 | 2 | 2 | 12 | Medium |
| Days Soil Moisture > Field Capacity | 2 | 1 | 2 | 2 | 2 | 2 | 11 | Medium |
Each criterion is scored from 1 (lowest) to 3 (highest). The total score is the sum across all criteria.
References
Hinkel, J. (2009). The PIAM Approach: A Framework for Multi-Criteria Selection of Indicators for Integrated Assessment. Ecological Indicators, 9(1), 89–103.