Maize Response to Chemical and Microbial Products on Two Tanzanian Soils
Bibliographic record
Abstract
Low soil fertility has been a major factor to low maize yields in smallholder farms in sub-Saharan Africa. Technologies have been proposed including inorganic, fertilizers and plant growth promoting microorganisms. A study was conducted under greenhouse and field conditions to evaluate the effects of liquid inorganic fertilizer and microbiological products on growth, nutrient uptake and yield of maize. Products evaluated were Teprosyn (nitrogen, zinc phosphorus), BioSoil Crop Booster (BSCB) (Pseudomonas fluorescens), and Bio Soil Nitro plus (BSN+) (Acetobacter sp.). Treatments were: products alone (low and high rate), product + half rate phosphorus (10 kg P ha-1), half rate P, full rate P (20 kg P ha-1) and Control. All products were analysed for quality. None of the products met the label claims in nutrient/organism concentration. An increase of biomass was observed in the greenhouse for half rate P + BSCB low rate and high rates for BSCB and BSN+ compared to Control. Half rate P + BSN+ low rate gave the highest grain yield which was similar full rate nitrogen and P. BSCB and BSN+ at low rates with P half rate resulted in an increase in biomass yield in the greenhouse. Efficacy of low rate BSN+ + half rate P was demonstrated when applied at the recommended rates and combined with half rates of N and P. A package of inorganic and Bio-fertilizers should be developed based on soil fertility status, and the quality of the inputs verified to ensure that they are conform to the label guarantee analysis.
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".