A Benchmark to Analyze On-Field Performances: A Case From an Irrigation Scheme in Tunisia
Bibliographic record
Abstract
Irrigation systems entail interacting processes that should be considered when analyzing the performances of irrigated areas. When analyzing the cause of performance gaps, one may consider the effect of one factor without taking into account the effect of other influential ones. This study addresses the performance of irrigated areas using a global approach. The analysis includes different factors of the production process and examines the relationship between irrigation and the economic performances of farmers’ irrigated fields. Results showed that technical efficiency (TE), considered as our economic indicator, evaluating the degree to which the inputs are used efficiently, was about 0.85 for fruit orchards, tomato, wheat and 0.66 for olive trees. The on-farm water distribution efficiency (Efarm) that evaluates water lost during its transport to plots was poor and can decrease to 36%. A great potential for improving water management exists. Efarm was not a significant factor for TE. So, Efarm causing substantial water waste has no significant impact on economic performance, the main concern of the farmer. However the easy access to water was a significant factor for TE (p-value < 0.032 in all the cases). The easy access to water is a possible lever for improvement.  We pointed out that irrigation performance in the studied area has no significant impact on economic performance. There is a divergence between the farmer’s interest (the economic efficiency) and the community’s objective to save water through better irrigation performance. Government efforts to provide incentives for farmers for better water management seem to have born no fruit. This study argues for the use of a bechmarking in building global representation adapted to the actual local context. The analysis approch suggests that more attention should be paid to the “water saving program” of Tunisia, by subsidising irrigation investments in a better way and focusing on subsidies that create the most of economic growth.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".