Effects of Number and Rate of Goat Manure Application on Soil Properties, Growth and Yield of Sweet Maize (Zea mays L. saccharata Strut)
Bibliographic record
Abstract
Alternative sources of plant nutrients have now become highly imperative especially for vegetable crop production in Nigeria. Due to the escalating costs, environmental and health problems associated with excessive use of inorganic fertilizers on continuously cropped fields, there is a need for more research on the use of organic manures and residues. A field experiment was conducted in the late growing season from September to December, 2012 in Calabar, a humid forest agroecology in south eastern Nigeria to evaluate the effects of two types of goat manure (GM) application (single and double split doses), five rates of GM (0, 5, 10, 15 and 20 t ha-1) and 400kg ha-1 NPK fertilizer (120:60:60 kg ha-1) rate on soil chemical properties and agronomic performance of sweet maize (Zea mays L. saccharata Strut). Factorial combinations of the treatments were fitted into a randomized complete block design with three replications. The application of GM significantly (P ? 0.05) increased soil pH, organic matter (OM) content, total N, available P, exchangeable K, Ca, Mg and the cation exchange capacity (CEC) status of the soil. Soil exchangeable acidity (EA) was reduced from 1.76 to 0.64 cmol kg-1 at 20 t ha-1 GM rate. The 20 t ha-1 also recorded the highest values for soil pH, OM, P, Ca, Mg and CEC, while the values for residual N and K peaked at the NPK fertilizer treatment. The double split application of GM recorded higher values for growth and yield attributes, and increased soil properties than the single application. Growth and yield parameters such as plant height, number of leaves, leaf area index (LAI), total dry matter (TDM), number and weight of grains/ear and total grain yield were significantly (P < 0.05) increased by GM and NPK fertilizer treatments. The values obtained for all growth and yield parameters except LAI at the 20 t ha-1 GM rate were not significantly different from those at the NPK fertilizer treatments. The application of 5, 10, 15 and 20 t ha-1 GM, and NPK fertilizer significantly increased TDM by 11.9, 74.3, 91.9, 106.2 and 104.6%; weight of grains/ear by 16.5, 54.6, 61.4, 100.6 and 94.4% and total grain yield by 46.9, 111.7, 121.0, 127.2 and 140.1% respectively, compared with the control treatment. The interactions between number of applications and rates showed that split applying GM at 20 t ha-1 maximized TDM, weights of whole and dehusked green ears and total grain yield compared to other GM rates, hence it is recommended.
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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 | 0.001 |
| 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 | 0.001 |
| 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".