Modeling the Rice Land Suitability Using GIS and Multi-Criteria Decision Analysis Approach in Sindh, Pakistan
Bibliographic record
Abstract
The objective of this research was to evaluate rice land suitability in Sindh, Pakistan, by designing GIS-based Multi-Criteria Decision Analysis (MCDA) spatial model to aggregate interdisciplinary aspects including factors of soil physical and chemical properties, ground water quality, soil pH, agro-ecological zones, canal command area and temperature. A constraint map of water bodies was also utilized in this model. On the basis of these parameters,standardized raster maps were created, and then Pair-Wise Comparison Matrix (PWCM) of Analytical Hierarchy Process (AHP) was developed to calculate significant weights by means of Principal Eigen vector by Saaty’s method, with accepted Consistency Ratio (CR) of 0.10. Furthermore, Multi-Criteria Evaluation (MCE) employing Weighted Linear Combination (WLC) aggregated all the suitability maps to yield rice land suitability map. Final output map of this work demonstrated 30.2% increase in area suitable for rice cultivation with an increased production of 14,716,592.17 tonnes as compared to existing rice practices in Sindh. This increase in the area and production of the potential land shows the capability of our novel model and offers an opportunity to improve cultivation by providing the much required information at local level that could benefit farmers, vision scientists and decision makers to select appropriate cropping site and agricultural planning making the best use of available data.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".