Optimizing Soil Moisture and Nitrogen Use Efficiency of Some Maize (Zea mays) Varieties under Conservation Farming System
Bibliographic record
Abstract
In Zambia, small holder farmers depend on producing maize (Zea mays), which is a major staple food for many Zambians. Maize productivity among the smallholder farmers is quite low, giving only 2.3 tons per hectare. The low yields are attributed to insufficient and erratic rain fall, low soil fertility, and poor farming practices. Therefore, the objective of this study was to evaluate the performance of maize genotypes for nitrogen use efficiency and soil moisture utilisation under conservation farming system. The trials were carried out at two sites with different soil types. Three maize varieties i.e. ZMS 606, GV 640 and GV 635 were evaluated in maize – cowpea rotation. Four cowpea varieties used for rotation with maize, i.e. Bubebe Lutembwe, BB 14-16-2-2 and LT 11-3-3-12. BB 14-16-2-2 and LT 11-3-3-12 are mutation-derived lines of Bubebe and Lutembwe cowpea parent varieties respectively. The experimental design used was split plot with three replications. The NUE was significantly (P< 0.05) higher in CF and accounted for 27 % and 15% more than conventional farming system which valued 17% and 3% at Chisamba and Batoka, respectively. while soil moisture content was higher at Batoka than Chisamba in CF system. ZMS 606 and GV 640 varieties were superior over GV 635 for NUE. Cowpea variety BB 14-16-2-2 significantly increased NUE of maize varieties. Therefore, smallholder farmers in Zambia can increase maize productivity in maize - cowpea rotation system due to the increased NUE. Recommendations are made for farmers to select improved nitrogen efficient maize varieties to optimize productivity of maize in conservation farming system.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".