Yields and Quality of Italian Ryegrass (Lolium multiflorum) and Common Vetch (Vicia sativa) Grown in Monocultures and Mixed Cultures under Irrigated Conditions in the Highlands of Madagascar
Bibliographic record
Abstract
A field experiment was conducted under irrigated conditions in the highlands of Madagascar to assess the potential of intercropping Italian ryegrass with common vetch for improving yield and quality of forage. Seed proportions studied were ryegrass-vetch 100:0; 0:100; 50:33; 50:50; 50:66; 75:33 and 75:66. Mixtures were sown in alternate rows and the sowing rates of pure stands of ryegrass and vetch were 20 and 60 kg per hectare, respectively. The results showed that all mixtures achieved yield advantage over pure stands with the highest land equivalent ratio value for dry matter yield obtained from the mixture of 75:66 (1.47) followed by 50:50 (1.35). Slight increase of crude protein content and protein digested in the small intestine when rumen-fermentable nitrogen is limiting (PDIN) content were obtained from mixtures compared with pure stand of ryegrass. Vetch rate in dry matter yield of mixtures increased with the increase of vetch seed proportion and ranged from 31 to 44%. Agressivity and competitive ratio indices showed that ryegrass was slightly competitive than vetch. Intercropping Italian ryegrass with common vetch at the seed proportions of 75:66 or 50:50 could be a more sustainable alternative cropping to alleviate dry season feed shortages of dairy livestock in the highlands of Madagascar.
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 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) | 0.000 | 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) | 0.000 | 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 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".