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Record W3042314863 · doi:10.5539/jas.v12n8p1

Biochar Effects on Nutrients Retention and Release of Hydroponics Growth Media

2020· article· en· W3042314863 on OpenAlexvenueno aff
Mohammed Taha Haraz, Les Bowtell, Raed A. Al-Juboori

Bibliographic record

VenueJournal of Agricultural Science · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInnovations in Aquaponics and Hydroponics Systems
Canadian institutionsnot available
FundersUniversity of Southern Queensland
KeywordsBiocharNutrientLeaching (pedology)HydroponicsChemistryPotassiumAgronomyNitrateEnvironmental scienceSoil waterBiologySoil science

Abstract

fetched live from OpenAlex

Applying biochar (BC) to broad acre crops is an effective way to sequester carbon while improving soil fertility and reducing nutrient leaching. Adopting a similar approach in a hydroponic environment, BC may enhance nutrient availability and reduce natural fluctuations in the nutrient solution. This study monitored the effect of BC addition to peatmoss growth media with ratios of 0%, 5%, 25% and 50% on pH, electrical conductivity (EC) and macronutrient retention, over 15 days using hydroponic nutrient solution controls. Deionised water was used to measure nutrient releases in the closed loop hydroponic system, showing that biochar increased pH level in both retention and release stages. As expected, the pH increased by a maximum of 1.5 units with the highest biochar to growth media ratio due to the natural liming nature of the BC. The EC was also affected, as BC increased in the media EC reduced. With regards to retention of nutrients, BC recovered nitrate, phosphate, calcium and sulphate but did not affect magnesium. Potassium levels increased in solution with increasing BC ratio. In terms of outcomes relevant to global agriculture, the results show that the same level of production can nominally be obtained with around half the nutrient requirements.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.470
Threshold uncertainty score0.163

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.015
GPT teacher head0.211
Teacher spread0.196 · 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

Citations6
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Agricultural ScienceSame topicInnovations in Aquaponics and Hydroponics SystemsFrench-language works237,207