Variety screening trial of Brassica carinata as a summer intermediate crop in Northern Italy
Bibliographic record
Abstract
Intermediate oilseed crops serve a dual purpose by providing feedstock for the biofuel industry while also offering ecosystem services and additional income to farmers. Winter intermediate crops, such as camelina ( Camelina sativa L. ), have been identified as a suitable strategy for northern Italy, meanwhile, there remains a complete lack of possible alternatives for summer intermediate crops. Nonetheless, winter cereals continue to spread across a larger growing area. This study aimed to evaluate the agronomic performance of 13 different varieties of carinata ( Brassica carinata A. Braun) provided by Nuseed (Canada), and grown as an intermediate summer crop in 2021 and 2022 in northern Italy. Carinata was planted in early June and harvested by the end of September. Key meteorological parameters were also recorded. In 2021, limited precipitation led to early maturity of carinata, reaching 2130 Growing Degree Days (GDD) in 101 days, from sowing to harvest. In contrast, in 2022, a wetter growing season resulted in seed maturity at 2885 GDD over 147 days. Carinata seed yield varied greatly across varieties, between 0.24 Mg ha −1 up to 1.92 Mg ha −1 , a range likely influenced by genetic factors. This study demonstrates that carinata presents a promising intermediate summer crop, particularly when seed yield exceeds 0.96 Mg ha −1 , which represents the break-even yield to fully compensate for cultivation costs. To enhance carinata productivity, selection of the most suitable variety for each pedo-climatic area can help to overcome damages caused by abiotic (i.e., heat and drought) and biotic (i.e., Altica oleracea, Nysius cymoides ) stresses.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".