Integrated Multi-criteria Land Suitability Evaluation and Mapping for Scaling Malt Barley Varieties in Rain-Fed Production Areas of Ethiopia
Bibliographic record
Abstract
Information on variety specific land suitability analysis was not available in Ethiopia. Therefore, integrated multi-criteria land suitability analysis and mapping for contrasting malt barley varieties was carried out to identify where and how much potentially suitable land exists in the country. The main factors considered for analysis include rainfall and temperature during the growing period, length of growing period, digital elevation models, (altitude and slope data) and soil characteristics (types, pH, depth, texture and drainage). The malt barley varieties included are late maturing Bekoji-1, EH1847 and Holker; and early maturing Grace, IBON 174/03 and Sabini. For classification of the data layers according to the degree of suitability for each variety, various reports and other relevant information were reviewed and used in defining the limits of the suitability ranges of malt barley varieties. The overall suitability was computed by multiplying the selected criteria weight by the assigned sub-criteria score and summing these values in the ArcGIS Model Builder. The analysis showing the extent and patterns of suitable land area available for the selected malt barley varieties are presented in the form of tabular data and maps. Highly suitable areas for these varieties include: 125,332 ha for Bekoji-1; 124,004 ha for EH1847; 775,312 ha for Grace; 125,356 ha for Holker; 1,677,388 ha for IBON 174/03; and 307,952 ha for Sabini. The results suggest that current improved malt barley varieties can be targeted for scaling out in the identified land suitability classes in the highlands of Ethiopia. Results also suggest that future research and development works should give priority for developing early maturing, acidic and waterlogging soil tolerant malt barley varieties. The results can be useful for policy and decision making to ensure land resources are used in the most productive and sustainable ways and solve the mismatches between current land use and land suitability for malt barley varieties in the country.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".