Grain Yield and Yield Components of Quality Protein Maize Genotypes as Influenced by Irrigation and Plant Population in the Nigerian Savannah
Bibliographic record
Abstract
An experiment was conducted to assess the yield and yield components of QPM genotypes to plant population under irrigated conditions in a semi arid ecology of Northern Nigeria. Field trials were conducted at the Irrigation Research Station, Institute for Agricultural Research, Kadawa (11° 39'N, 08° 20'E) and 500 m above sea level) during dry seasons 2007, 2008 and 2009 to study the effect of (Zea mays L.) genotypes (TZE-W Pop X 1368, EV-DT W99 STR and DMR-ESRW), four plant population (33333, 44444, 55555 and 66666 plants ha-1) and three irrigation scheduling (40, 60 and 80 centibars soil moisture tension) on the growth and yield of quality protein maize. A split plot design was used with combinations of genotypes and irrigation regimes assigned to the main plot and plant population assigned to the sub-plot. The treatments were replicated three times. The study revealed that genotype EV-DT W99 STR had significantly higher weight of ears per plant, cob length, cob diameter, number of rows per cob, 100 grain weight, grain yield, shelling percentage and harvest index than the other two genotypes used in the trial. Irrigating at 40 and 60 centibars significantly increased weight of ears per plant, while delayed irrigation significantly depressed total dry matter production. Based on the results obtained in this study, it can be concluded that the use of genotype EV-DT W99 STR, at 60 centibars irrigation scheduling and population of 55,555 plants ha-1 had resulted in good agro-physiological characters of QPM at Kadawa.
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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) | 0.000 | 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 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".