Organic Inputs on Maize (Zea mays) Yield and Chemical Properties of Two Ultisols
Bibliographic record
Abstract
Effects of organic inputs (OIs) on maize grain yield and chemical properties of Ultisols were assessed at Msekera and Misamfu Agricultural Research Stations in the medium and high rainfall regions respectively of Zambia. The OIs included biomasses of the leguminous species Cajanus cajan, Tephrosia vogelii, and Crotalaria juncea, in-situ composted native grasses and shrubs called fundikila, modified fundikila using Mucuna pruriens, and composted cattle manure, with chemical fertilizer as a control. After two crop growing seasons, cattle manure significantly increased soil pH, while modified fundikila significantly increased levels of soil organic matter. At Msekera, OIs increased levels of total N by 300%. At Misamfu, fundikila and Cajanas cajan increased total N by 35%. The OIs did not significantly increase available P and K at both sites. In the first season at Msekera, OIs had a higher mean maize yield (6075±368 kg/ha) than chemical fertilizer (3567±715 kg/ha). Maize yields for OIs in the second season did not differ significantly from those of the first season. At Misamfu the leguminous OIs Cajanas cajan and Tephrosia vogelii, had a lower combined mean maize yield (5405±242 kg/ha) than chemical fertilizer (7426±430 kg/ha) in the first season. A 70% decline in maize yield occurred on plots with leguminous OIs in the second season compared to the first season. Leguminous OIs generally performed better at Msekera than at Misamfu. At both sites, the traditional OIs, cow manure at Msekera, and fundikila at Misamfu had higher maize yields than leguminous OIs. We concluded that effects of OIs on soil chemical properties and maize yield vary with soil and climatic conditions and that these need to be considered when selecting OIs for use by farmers.
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 | 0.000 | 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".