Evaluation of Sorghum-Cowpea Intercrop Productivity in Savanna Agro-ecology using Competition Indices
Bibliographic record
Abstract
The competitive behaviour of sorghum-cowpea intercrops at different planting patterns were studied at the Abubakar Tafawa Balewa University Teaching and Research farm, Bauchi in the northern Guinea savanna of Nigeria during the 2005 and 2006 growing seasons. The treatments comprised of three row arrangements of sorghum/cowpea mixtures; one row of sorghum for one row of cowpea (1S:1C), two rows of sorghum for one row of cowpea (2S:1C) and one row of sorghum for two rows of cowpea (1S:2C), respectively. Sole crops of sorghum and cowpea were included as check to compare yields of intercropped mixtures. Results indicated that grain and straw yields of both sorghum and cowpea were higher in sole cropping than in the intercropping mixtures. However, the 2S:1C planting arrangement exhibited higher LER, competitive indices values, SPI and MAI (N7857.11) compared to the other planting arrangements and the sole crops. From this study, it is inferred that intercropping of sorghum with cowpea at 2S:1C planting pattern will give higher income, better land use efficiency and thus enhancing sustainability of crop production than sole culture of each crop species.
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 | 0.001 | 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".