Fertilizer and Genotype Effects on Maize Production on Two Soils in the Northern Region of Ghana
Bibliographic record
Abstract
Soils in the Guinea Savanna agro-ecological zone of Ghana are depleted in major nutrients by continuous cropping and residue removal, resulting in low maize yields. While many studies have assessed the fertilizer requirements for maize, most did not account for the role of the soil type and maize genotype. A study was conducted on Plinthosol and Lixisol in the Tolon district of the Northern Region of Ghana to assess fertilizer and genotype effects on maize productivity. Two maize genotypes (i.e. Obatanpa -110 days to maturity) and (Dodzie - 75 days to maturity) were compared at three fertilizer application rates (i.e. 0-0-0, 60-15-35 and 90-25-50 kg ha-1 N, P K) in a randomized complete block design using four replications, with genotype allocated to the main plots and fertilizer levels to the subplots. Soils were characterized, revealing very low total N and available P concentrations in the top layers. Grain yield was significantly affected by maize genotype, irrespective of the soil type. The longer-duration (Obatanpa) tended to out yield the short duration genotype (Dodzie), and generally outperformed Dodzie in all yield parameters except for the harvest index on Plinthosol. Inorganic fertilizers significantly (P<0.001) increased yield and all yield parameters over the control on both soils, with yield increases of 84 and 90% at 60-15-35 and 90-25-50 kg ha-1 N, P K, respectively. Genotype by fertilizer interaction was highly significant (P<0.001) for grain yield on both soils. We conclude that farmers in the Guinea Savannah agro-ecological zone of Ghana need to supply nutrients to enhance grain yields of maize, irrespective of the prevailing soil type.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".