A Spatial Multi-Criteria Decision-Making Approach to Evaluating Homogeneous Areas for Rainfed Wheat Yield Assessment
Bibliographic record
Abstract
Rainfed wheat plays a vital role in global food security, particularly in regions where water availability is a limiting factor. Identifying homogeneous areas with a similar yield potential is essential for optimizing resource allocation, improving agricultural sustainability, and enhancing water resource management. Unlike previous studies that primarily focused on cropland suitability, this study presents an integrated approach to delineate homogeneous areas for the rainfed wheat yield using advanced mechanistic analysis and multi-criteria decision-making techniques. Additionally, it examines the homogeneity of these areas in terms of the actual yield relative to the potential yield. Kurdistan province in Iran was selected as the study area. Key phenological stages of wheat growth—germination, flowering, and seed filling—were determined using a day-growth model. A set of four primary criteria—precipitation, temperature, soil properties, and topography—along with twenty sub-criteria were selected based on expert knowledge and previous research. The Fuzzy-AHP method was employed to assign weights to each factor, and a weighted linear combination approach was used to generate a final classification map. The results categorized the study area into five suitability classes: currently unsuitable (N2 and N1), somewhat suitable (S3), moderately suitable (S2), and very suitable (S1), in accordance with the FAO standard framework. These classifications highlighted significant yield variations among the zones. The findings revealed that the highest and lowest average rainfed wheat yields were observed in classes S1 and N2, respectively, with yield-to-potential yield ratios ranging from 75% in S1 to 20% in N2. This research underscores the potential of spatial analysis in enhancing precision agriculture and water resource management, contributing to more resilient food production systems in water-scarce regions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".