Evaluation of Oats (<i>Avena sativa</i>) Varieties for Adaptability Performances and Their Nutritional Value in the Highland of Masha, South West Ethiopia
Bibliographic record
Abstract
In Ethiopia, feed is the major production inputs that affect the production and productivity of animal. In this regard, One of the possible option to alleviate feed shortage is introduction and utilization of improved forage crops for the given production system. The study was conducted on 11 oat varieties during 2017 and 2018 main cropping season at Masha highland of south-western Ethiopia to evaluate their adaptability and identify high dry matter yield and good nutritional quality producing oat varieties for highland agro-ecological areas of south-west Ethiopia. The experiment was conducted using randomized complete block design replicated three times. Data were taken for days to 50% flowering, plant height, leaf to stem ratio, dry matter yield, grain yield and their nutritional contents. The data were analyzed using the general linear model procedures of SAS and the least significance difference was used for mean separation. The result of the combined analysis indicated that most of the agronomic traits were significantly (p<0.01) affected by varieties. The mean leaf to stem ratio of 79AB3849Tx) (80SA95) had the highest value (1.5) followed by PI-1706 (1.4). The tested oat varieties show significantly (p<0.01) different among varieties in their dry matter yield. Among the evaluated oat varieties, PI-1706 gave the highest dry matter yield (12.7 ton/ha) followed by 79AB3849Tx) (80SA9) (12.0 ton/ha) whereas Clintland60MN16016 gave the lowest (5.4 ton/ha) dry matter yield. The result of grain yield of these two varieties were consistent with dry matter yield. Based on the chemical compositions, PI-1706, KY7078394Canada and 79AB3849Tx) (80SA95) were the best varieties in their crude protein contents. Thus, from the results of the present study it can be concluded that PI-1706, 79AB3849Tx) (80SA95) and KY7078394Canada were best adapted and high yielder oat varieties and can be demonstrated on farm condition for wider use in the highlands of Bench-maji and Masha areas and in similar agro-ecological zones of south-western Ethiopia.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".