Country-Level Bio-Economic Modeling of Agricultural Technologies to Enhance Wheat-Based Systems Productivity in the Dry Areas
Bibliographic record
Abstract
<p>Conservation Agriculture (CA) have a large potential for enhancing cereal yields in the semi-arid areas through better management of soil moisture. The objective of the current paper is to quantify, at national level, the impact of CA adoption in wheat-based agricultural systems in Syria. A country-level bio-economic approach was used for this purpose. Different CA technical packages (TPs) were first developed and simulated through APSIM crop modeling software, in order to estimate the long-term yields of wheat under different CA TPs for the period 2015-2039. The considered CA packages are a combination of zero-tillage, mulching, raised bed, fertilizer doses, and planting dates. The simulated yields are then introduced into IMPACT model while assuming that TPs will be adopted on 35% of the wheat areas in the countries. Results show that the comparative advantages of CA TPs on overcoming the effect of climate change will only be significant after 2030. In 2039, the effect of different TPs on average wheat yields in Syria will be 4% to 12% (depending on the TP) higher than the average yields under climate change and no CA technology adoption. These yield enhancements may reduce the wheat trade deficit with 30 up to 140%, also depending on the technical package. The combination of mulching techniques, together with average nitrogen dose of 30kg/ha, and late planting date of wheat provides the best prospective for the wheat sector in Syria.</p>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".