Dry Matter Production and Harvest Index of Groundnut (Arachis hypogaea L.) Varieties Under Irrigation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Dry matter production for crops is generally influenced by the fertility status of the soil. Plant population may indirectly affect the amount of dry matter production due to its relationship with number of plants per unit area. An experiment to study the effect of plant population and basin size on dry matter production and other yield components of groundnut (Arachis hypogaea L.) varieties under irrigation was conducted during the dry seasons of 2004 to 2006 at the Irrigation Research Station of the Institute for Agricultural Research (IAR), Ahmadu Bello University at Kadawa. The treatments tested were three basin sizes (3m x 3m, 3m x 4m and 3m x 5m), three plant populations (50,000, 100,000 and 200,000 plants ha-1and three varieties (SAMNUT 23, SAMNUT 21 and SAMNUT 11). These were arranged factorally in a split plot design with plant population and variety assigned to the main plots and basin sizes in the sub plots. SAMNUT 23 had higher harvest index than the other varieties, however SAMNUT 11 recorded the highest dry matter plant-1. Dry matter production was significantly highest at 100.000 plants ha-1, while significantly highest harvest index was observed at 200,000 plants ha-1. SAMNUT 23 exhibited highest harvest index compared to the other varieties used in this study.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| 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 it