Jessour for diversified and resilient agroecological systems to ensure food security and sustainable livelihoods in arid ecosystems
Bibliographic record
Abstract
Poster presented at the 5th World Congress on Agroforestry: “Transitioning to a Viable World”. Québec, Canada, July 17-20, 2022: For agricultural production, exploiting mountain slopes for rainwater runoff collection is a low-cost practice that supports sustainable agroecological systems and increases yield. To this day, people in rural communities continue to use an ancient and well-known system called Jessour to strengthen agricultural productive capacity and diversify their livelihoods. However, some effort is needed to maintain these systems and they require careful planning and engineering. A Jessour is composed of three parts, a sloping ground for collection, a terrace and an earth dyke. Jessour are mainly used for cultivating olive trees and sometimes dates, figs and almond trees. During rainy years, cereals (barley, wheat) and legumes (peas, lentils, broad beans) are cultivated between the trees. Once these crops are harvested, the crop residues are used as fodder for grazing livestock. Crop residues help fill feeding gaps, especially during the dry summer season. Livestock is continuously moved between trees, which allows rangelands to rest before winter dormancy. In arid areas of Southern Tunisia, Jessour are a vital agroecological system that support orchard plantation, annual crops and livestock, contribute to resilient, help sustain livelihoods for the majority of households and play an important role in ensuring food security under climate change and water scarcity. Therefore, greater attention is needed to establish and strengthen mechanisms that can make this proven technology more effective while conserving agrobiodiversity.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.080 | 0.015 |
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".