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Record W2909878594 · doi:10.5539/jas.v11n2p71

Maize Response to Chemical and Microbial Products on Two Tanzanian Soils

2019· article· en· W2909878594 on OpenAlexvenueno aff
Kiriba Deodatus, Thuita Moses, Ernest Semu, Ikerra Susan, M. Msanya Balthazar, Cargele Masso

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgronomic Practices and Intercropping Systems
Canadian institutionsnot available
FundersBill and Melinda Gates Foundation
KeywordsNutrientAgronomyFertilizerSoil fertilityPhosphorusGreenhouseEnvironmental scienceBiomass (ecology)Soil waterBiologyChemistryEcology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.782
Threshold uncertainty score0.239

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.234
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2019
Admission routes1
Has abstractyes

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