Effects of nitrogen fertilization and cutting height on the forage yield and feeding value of <i>Eleusine indica</i> in the dry season in Nepal
Bibliographic record
Abstract
The objective of this study was to determine the regrowth characteristics, crude protein (CP) content, and feeding value of Eleusine indica grass during the dry season in Nepal. The grass was cultivated using three different levels of N fertilization (0, 50, and 100 kg ha −1 ) and cutting heights (2, 4, and 6 cm above the ground) in a 3 × 3 factorial design. The forage yield, number of tillers per plant, and CP content of the grass were determined. A digestibility trial was conducted with six local, female Khari goats (15 ± 1.7 kg body weight) in a cross‐over design to compare the feeding value of E. indica with local forages. A comparatively higher forage yield, number of tillers per plant, and CP content were obtained with 100 kg N ha −1 . However, the forage yield and CP content were not significantly affected by the cutting height. With different levels of N fertilization and cutting heights, the cumulative forage yield in five harvests during the dry season (December to May) ranged from 1.83–3.82 t dry matter ha −1 . The digestibility coefficients of the dry matter, CP, crude fiber, and ether extract content of E. indica for goats were 0.54, 0.67, 0.70, and 0.54, respectively. The nutrient digestibility and palatability of E. indica were comparable to that of the mixed local forages. Hence, E. indica in conjunction with a sound N fertilization practise potentially can be utilized as an approach to overcome the problem of green forage scarcity, especially during the dry winter season in regions that have a tropical climate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".