Assessment of Growth and Yield Characteristics of a Greenhouse Hybrid and a Dual-Purpose Tomato Cultivar under Different Microclimatic Conditions
Bibliographic record
Abstract
Tomato (Solanum lycopersicum L., family Solanaceae) represents one of the most cultivated horticultural crops worldwide. It contains various nutrients, providing health benefits. For better crop management strategies, especially with global warming, it is crucial to investigate how varieties or cultivars bred for different purposes respond to the changing macro or microenvironmental conditions. This research aimed to assess the growth, reproductive development and selected quality parameters of two tomato varieties, ‘Sylviana’ (a greenhouse hybrid) and ‘Bolseno’ (a dual-purpose variety), under a greenhouse environment. The study was conducted in the mid-country wet zone of Sri Lanka where the intensive-control greenhouse (T1) maintained a favourable air temperature (<33°C) for crop growth compared to high daytime temperatures of semi-intensive (T2) and less-intensive (T3) greenhouses (up to 36°C). In ‘Bolseno’, vegetative growth parameters were significantly affected (P<0.05) by different environments but not in ‘Sylviana’ (P>0.05). T1 resulted in the highest mean fruit weight and mean fruit diameter in both varieties. Since ‘Sylviana’ performed well in all three growing environments, the fruit quality was assessed by measuring the antioxidant activity (AOX) and total phenolic content (TPC) using 1, 1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging assay and Folin–Ciocalteu method, respectively. Significantly higher AOX (P<0.05) was reported in T1 while T2 had a higher TPC. The results revealed that different tomato genotypes respond differently to changes in micro environmental conditions in terms of growth and some reproductive traits, and the same variety grown in different conditions had different TPC and AOX.
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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".