Cost Benefit Analysis of Growing Cucumbers in Greenhouse at Different Cooling of Nutrient Solution Temperatures in Closed Hydroponic System in Oman
Bibliographic record
Abstract
Oman is a country that is mostly dry and hot, with daily maximum temperatures easily reaching 40°C or more during summer. The Oman weather therefore renders conventional open field agriculture almost impossible. The sustainable future of agriculture in Oman and other similar desert countries will therefore depend very much on the adoption of land and water saving technologies such as greenhouses and soilless culture or hydroponics. Soilless culture (Hydroponics) is the technique of growing plants without soil with their roots immersed in nutrient solution. Among factors affecting hydroponic production systems, is the nutrient solution temperature which is considered to be one of the most important determining factors of crop yield and quality. The aim of this research is therefore to investigate the economic effect of cooling nutrient solutions temperature technique on cucumber output. Four nutrient solutions temperatures are investigated and a Cost Benefit Analysis is undertaken. Results indicate that all the four cooling nutrient temperature yields positive returns (benefits) above variable and total costs for the two years of this experiment. Cooling nutrient temperature (22ºC) yields higher returns than the other treatments followed by treatment (25ºC), (28ºC) and then the CONTROL treatment. Returns for the second year are higher than the first year. Therefore treatment (22ºC) was observed to be the best overall producing the highest return above variable and total costs. It is therefore considered the best alternative for cucumber growers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| 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) | 0.000 | 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 teacher head, 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".