Inter-row plant spacing effects on grain and fodder yields, growth performance, digestibility and manure quality of sheep
Bibliographic record
Abstract
ABSTRACT In Ghana, peanut (Arachis hypogaea L.) grain and fodder serve as important sources of protein for human and livestock nutrition, respectively. Experiments were conducted in four farming communities to determine the effects of planting annual peanut at four inter-row spacings of 30, 45, 65 and 75 cm on grain and fodder yields (experiment I), growth performance and manure quality (experiment II), and in situ digestibility (experiment III) of Djallonké sheep fed fodder from these plant spacings. Planting peanut at 30 cm inter-row spacing dually increased grain and fodder yields compared to planting at 60, and 75 cm. Peanut fodder from 30 cm inter-row spacing also had comparatively higher concentration of crude protein and lower concentrations of acid detergent fiber and acid detergent lignin, resulting in significant improvements in dry matter digestibility at 48 h and superior average daily weight gain of sheep. The concentration of N excreted in the manure of sheep fed the 30 cm fodder was greater than those fed peanut grown at 60, and 75 cm inter-row spacing. Planting peanut at an inter-row spacing of 30 cm therefore gave dual benefits of increasing grain and fodder yields as well as increasing the digestibility and growth performance of sheep fed peanut fodder as a supplementary diet to natural pasture for 70 days. Higher concentration of N in the manure of sheep fed 30 cm fodder could have additional benefits of improving soil fertility in smallholder farming systems where inorganic fertilizers are expensive and inaccessible to farmers.
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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 | 0.000 | 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".