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Record W7030481534

Nutrient Manipulation and Media Selection in Decoupled Aquaponics for Growing Cannabis sativa

2022· other· en· W7030481534 on OpenAlexaboutno aff

Bibliographic record

VenueUA Campus Repository (The University of Arizona) · 2022
Typeother
Languageen
FieldAgricultural and Biological Sciences
TopicInnovations in Aquaponics and Hydroponics Systems
Canadian institutionsnot available
Fundersnot available
KeywordsAquaponicsClarifierHydroponicsNutrientSump (aquarium)AquacultureEffluentBiostimulationNutrient cycle
DOInot available

Abstract

fetched live from OpenAlex

Aquaponics combines aquaculture with water treatment engineering and hydroponics, a soilless nutrient delivery plant cultivation technique. This method has shown promising results to produce high-quality fish and crops in a controlled environment, where outside risks and effects associated with traditional agriculture are negated. Aquaponics’ ability to grow robust hemp and Cannabis plants provides an encouraging alternative to traditional production of these high value crops. A commercial-style, decoupled aquaponics test system was built for growing high-nutrient demand crops. The recirculating aquaculture system (RAS) component consists of two 1,100-liter fish tanks, a deep two-walled clarifier, a bead filter, a biofilter, two sump tanks, and two mineralization tanks. Nutrient-rich water from the fish tanks passes through a clarifier and bead filter, where most solids are removed from the system. After solids removal, water flows through a biofilter, before returning to the primary sump. From there treated fish effluent is pumped back to the fish tanks or can be added to three aerated, decoupled sumps as needed. Each decoupled sump feeds Dutch bucket hydroponic rows. Three different media types were tested: light expanded clay aggregate (LECA), coco coir, and a high-porosity peat-perlite mix donated by Berger (Saint-Modeste, QC, Canada.) An additional row of hemp was grown hydroponically in coco coir as a control group; nutrient solution was mixed daily to proper concentrations for hemp production. Sludge from the RAS is aggressively aerated in a two-stage mineralization process to enhance breakdown of solid waste into bioavailable forms for plant uptake, and the resulting supernatant is added back into the RAS. Temperature, pH, electrical conductivity, dissolved oxygen levels, water macro and micronutrient levels were tested to understand the health of the fish and plants more deeply. Results show the viability of hemp production in an aquaponics system, along with fish production and the ability to translate production for non-hemp cultivars of Cannabis sativa. While required initial costs, components, and expertise may be greater compared to traditional hydroponics, aquaponics is a nearly zero discharge and fully organic production method. The highest producing aquaponic row was the peat-perlite media with 76% of the biomass of the hydroponic production. The LECA treatment produced the highest quality buds with 20-21% higher CBD levels and 23-38% higher terpenes than the other treatments. Most notably, the aquaponic rows used 83-89% less water than the hydroponic row. After harvesting, flower biomass, terpene levels, CBD and THC levels were tested to aid the comparison of performance of each media. Based on macro and micronutrient statistical analysis, Al, B, Cu, Mg, and SO4 levels were not statistically different between media treatments and nutrient solutions (hydroponic versus aquaponic). There was no significant difference in magnesium, sodium, and zinc levels comparing media treatments. The LECA media treatment had higher calcium and molybdenum compared to the coco coir media treatment and peat-perlite media treatment. The peat-perlite media treatment had higher iron levels than other media treatments. The hydroponic solution compared with the aquaponic solution in coco coir media displayed significantly higher levels of Ca, Fe, Mg, Mo, and Zn.

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)0.000
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.503
Threshold uncertainty score0.523

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.008
GPT teacher head0.184
Teacher spread0.175 · 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 designNot applicable
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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

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