Effect of Mineral Nitrogen and Legume Intercrops on Maize (Zea Mays L.) Nitrogen Uptake, Nutrient Use Efficiency and Yields in Chitedze and Zomba, Malawi
Bibliographic record
Abstract
Nitrogen (N) is the most critical plant nutrient. Maize has a high nitrogen demand. Low maize (Zea mays L.), yields in smallholder farms of Malawi, the country’s staple, is attributable to declining N fertility aggravated by the ever increasing price of fertilizer. Little effort has been made to establish the best nitrogen rate in a maize- cowpea and maize-bean intercrop under variable soil conditions as a way of improving production, and was the objective of the current study. Field experiments were conducted at Chitedze Agricultural Research Station in Lilongwe and Makoka Agricultural Research Station in Zomba during the 2016/17 growing season. A split plot layout in a randomized complete block design, with three replicates was used. The main plots were; sole maize, sole bean, sole cowpea, bean/maize and cowpea/maize intercrop systems. The sub plots were N fertilizer rates (0, 52.5, 78.75 and 105 kg N ha-1), applied as urea. The data collected was subjected to analysis of variance using SAS software version 9.3 (SAS Institute Inc.) at P<0.05. Means were separated using Duncan Multiple Range (DMRT) test at 95% significance level. The results showed that application of N increased maize N uptake and grain yield. NUE however decreased with increasing N fertilizer rate.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".