Atouts et besoins en innovations du tournesol pour une agriculture durable.
Bibliographic record
Abstract
For the last 20 years, sunflower world acreages have largely increased. In 2010, they reached 24 million hectares. Two thirds are located in Europe (4 million hectares), in Russia and Ukraine (11.3 million hectares) where the acreage can still expand a lot. This situation is favourable for research, breeding, seeds production, exchange…all the more that sunflower price seems well oriented and that its oil is globally well appreciated. In France, sunflower has other assets: oleic varieties on more than 50% surfaces, well organized supply chain, main seeds companies research centres, involvement of INRA, good adaptation to the environmental requirements. But, for 20 years, mean seed yields have lightly increased which contributed to a large fall of the acreages (now stabilized to 700 000 hectares) and to their concentration in two regions: South-West and West-Atlantic. Productivity improvement seems essential: it first needs the breeding of high yielding varieties with a good disease tolerance but also the setting of complete crop management system (variety, date of sowing, planting density…) adapted to different situations. Because of the environmental context, it seems necessary to carry out studies on the new systems for weed control with post emergence herbicides, mechanical weed control, wheatsunflower intercrop cultivation and adaptation to climate change. As far as oilseed processing and outlets are concerned, several research points should be considered to ensure the sustainability of this crop.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.041 | 0.008 |
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".