Comparison of Energy-use Efficiency for Lettuce Plantation under Nutrient Film Technique and Deep-Water Culture Hydroponic Systems
Bibliographic record
Abstract
Energy conservation opportunities in closed plant production systems have been widely discussed, however, a comparison of energy-use efficiency (EUE) for different types of hydroponic systems is lacking. This paper compares the EUE of two different hydroponic systems, namely nutrient film technique (NFT) and deep-water culture (DWC), within an aquaponics facility. The energy is monitored in a controlled environment using artificial lighting and its impact on the growth dynamics of the crops is measured, in this case, on a leafy green crop (Lactuca Sativa L. ‘Little Gem’). Offering better efficiency and reliability, light-emitting diode (LED) irradiation is used with a photosynthetic photon flux (PPF) of 140 µmol·s−1 and a photoperiod of 12-hours. The seeds are then placed in growth chambers, kept at an ambient temperature of 18°C for 21 days. These seedlings are then transplanted in rockwool cubes, followed by placement in NFT or DWC systems in equal numbers. Both systems are illuminated with LED irradiation having a PPF of 200 µmol·s−1. Continuous irradiation with a photoperiod of 16-hours is provided to both systems for 5 weeks. Crop growth parameters, such as leaf count and plant height, are measured in both systems resulting in similar numbers obtained, however, shoot fresh weight, leaf area, and root length are significantly different. Furthermore, the NFT system exhibited an EUE of 31.3 g. kWh−1 and outperformed the DWC system with an EUE of 24.53 g. kWh−1; indicating higher growth and better energy savings associated with NFT systems. These results suggest that NFT systems has a higher potential to offer better energy-use efficiency for producing crops in plant factories and aquaponics facilities.
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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 | 0.000 |
| 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".