Effect of Cooling Root-Zone Temperature on Growth, Yield and Nutrient Uptake in Cucumber Grown in Hydroponic System During Summer Season in Cooled Greenhouse
Bibliographic record
Abstract
Optimum cool root zone temperature positively influences the production of greenhouse vegetables grown during summer/high temperature period under hydroponics system. Hence, the effect of root-zone temperature was investigated on the growth, yield and nutrient uptake of cucumber (Cucumis sativus L.) plants grown in pots filled with perlite medium under recirculating hydroponic system in greenhouse during summer period (June-August) in two consecutive years 2016/2017 and 2017/2018 using three cooling treatments-T1 (22 ºC), T2 (25 ºC) and T3 (28 ºC) and non-cooled treatment T4 (33 ºC) as control in Randomized Complete Design (RCD). All the treatments received the same nutrient concentrations. Significant (p < 0.05) differences were observed for all the characters viz. plant height, leaf number/m2, chlorophyll content, leaf area (cm2), fruit number /m2, yield (t/gh), fresh (g) and dry matter weight (g) of shoot and root at all cooled root-zone temperatures as compared to control in both the years. Plants at cooled root-zone temperature (RZT) of 22 ºC gave high number of fruits/m2 to the extent of 180 in 2016/2017 and 220 in 2017/2018 followed by that at 25 ºC (167, 221) and 28 ºC (178, 143) as compared to those in control (33 ºC) (101,133) in both the years. Similarly, highest fruit yields were found at cooled RZT of 22 ºC (5.0 t/gh) and 28 ºC (4.7 t/gh) in the first year and 22 ºC (6.1 t/gh) and 25 ºC (6.0 t/gh) in the second year. The plants at cooled RZT responded positively and significantly (p < 0.05) in the uptake of all nutrient elements in shoots and roots in comparison with those at non-cooled RZT in both years.
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 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 | 0.001 |
| 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".