Planting Date and Cultivar Effects on Grain Yield in Dryland Corn Production
Bibliographic record
Abstract
Corn ( Zea mays L.) production is gradually spreading into the Sudan savanna zone of West Africa where production is limited by erratic and inadequate rainfall. To increase corn production, production practices should be properly designed to minimize the effects of low precipitation and high temperatures that characterize the zone. A study, to determine the performance of late (120 d), early (90 d), and extra‐early maturing (80 d) corn cultivars over a range of planting dates, was performed in the Sudan savannas of northeast Nigeria. Delaying planting generally increased days to flowering and the anthesis‐silking interval (ASI) and reduced dry matter production and yield and yield components. In Azir, planting of corn on 13 July reduced grain yield by 42% in 2006 because of a dry spell during crop establishment. Delaying planting to 21 and 28 July also reduced grain yield by 19 and 28.5%, respectively over the 2 yr. Averaged over the 2‐yr yield reduction was 29.5 and 42% when corn was planted on 21 and 28 July, respectively in Damboa. There was no interaction between planting date and corn cultivar for days to silking, ASI, and grain yield suggesting that the cultivars responded similarly to planting date. The extra‐early maturing cultivar, 95 TZEE‐W, produced highest dry matter, harvest index, and grain yield at all planting dates suggesting that this cultivar is the most suitable in both locations. To reduce risk of drought stress, extra‐early maturing corn cultivars should be planted in the Sudan savanna between the last week of June and the first week of July.
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 | 0.001 |
| 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".