Resources Management and Risk Efficiency of Crop Rotation Systems in Sudan Gezira Scheme
Bibliographic record
Abstract
Risk is an important factor in crop rotation systems and cropping system management studies. The study uses stochastic simulation techniques and Stochastic Efficiency with Respect to Function (SERF) to evaluate five crop rotations risk-efficiency and economic sustainability in Sudan Gezira Scheme. Price and yield risk for five crops were simulated to calculate whole-scheme net return. The analysis shows with the present irrigation system capacity 4 course rotation is the most preferred at lower (ARAC) and the 5 Course rotation (B) with 53% land use intensity achieve water distribution equity and is the most risk efficient crop rotation at upper (ARAC). It is downside risk oriented and resilience crop rotation. The area allocated in 5 Course rotations (B) for cotton crop is 17%, wheat 7%, sorghum 60%, groundnut 4% and fodder crops 12%. Fodder can be grown two times in summer and winter season without creating water shortage problems. However, this will increase net return and increase soil fertilities within the selected crop rotation. The result also shows that return pack to night storage irrigation system needs a risk premium of 36 Million (SDG). The techniques used in this study could be used with any distribution estimates for the uncertain variables to incorporate new crop varieties and research recommendation packages. They also could be modified to account for new information contribution during the decision process and account for dynamic effects and policy adjustment and modification.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".