Crop sequences, nitrogen fertilizer and grazing intensity in relation to wheat yields in rainfed systems
Bibliographic record
Abstract
SUMMARY The Mediterranean region is experiencing unrelenting land-use pressure, largely driven by population growth. Long-term cropping system trials can guide crop and soil management options that are biophysically and economically sustainable. Thus, an extensive cereal-based rotation trial (1983–98) was established in northern Syria, to assess various two-course rotations with durum wheat (Triticum turgidumDesf.). The alternative rotations were: continuous wheat, fallow, chickpea (Cicer arietinum), lentil (Lens culinaris), medic (Medicagospp.), vetch (Vicia sativa) and watermelon (Citrullus vulgaris) as a summer crop. Ancillary treatments were: nitrogen (N) fertilizer application to the cereal phase (0, 30, 60 and 90 kg N/ha) and variable stubble grazing management (zero or stubble retention, moderate and heavy grazing). Both phases of the rotation trial occurred each year. The soil is a fine clay, thermic Calcixerollic Xerochrept. Seasonal rainfall was the dominant factor in influencing overall yields. Rotations significantly influenced yields, being highest for fallow (2·43 t/ha), followed by watermelon (similar to fallow), vetch, lentil, medic and chickpea, and least for continuous wheat (1·08 t/ha). Overall, yields increased consistently with added N, but responses varied with the rotation. The various stubble grazing regimes had little or no effect on either grain or straw yields. While the trial confirmed the value of fallow and the drawbacks of continuous cereal cropping, it also showed that replacing either practice with chickpea or lentil, or vetch for animal feed, was potentially a viable option. Given favourable economics, legume-based rotations for food and forage could contribute to sustainable cropping throughout the Mediterranean region.
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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 | 0.000 | 0.001 |
| 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 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".