Effect of planting date on vegetative growth in greenhouse tomato crops.
Bibliographic record
Abstract
Leaf length, stem diameter and shoot growth in three June and two September, February and March plantings of tomato cv. Conqueror were measured every 14 days during the life of each crop. Crops were grown using a deep flow technique growing system in twin-skin polythene greenhouses. Leaf length and stem diameter readings were similar to Dutch guidelines for much of the life of the June plantings but were not in the other plantings. Shoot growth increments were similar to the Dutch guidelines. Possible causes of seasonal and between country differences are discussed. Additional key words: cv. Conqueror, deep flow technique growing system, twin-skin polythene greenhouses. Introduction Year round tomato (Lycopersicon esculentum L.) greenhouse production is achieved in the Auckland region and, in many other parts of New Zealand (HortNZ, 2007) through the use of a range of planting times. Thus, throughout a year, plants of different ages and crop loads are exposed to different light and temperature environments. We could, therefore, expect different patterns of vegetative growth to be seen at different planting times. In this study we recorded leaf length, stem diameter and shoot growth in a sample of plants of every planting of one cultivar in two neighbouring, greenhouses from June 2004. These measurements are used by consultants (including Canadian advisors) (Portree et al., 1996) and the Substratus Horticultural Crop Consultancy of Holland, one of the world’s largest greenhouse vegetable crop consultancy companies) and growers to monitor and compare tomato crops (but not widely in New Zealand). Our purpose was to retrospectively examine rather than monitor growth. We wished to identify differences in growth patterns at different planting times and to compare our results with the guidelines in Northern Hemisphere crop registration tools, e.g. those of the Substratus Horticultural Crop Consultancy (http://www.grodan.com/sw10039.asp). Results from winter, spring, late summer and early autumn plantings are described and discussed. Materials and Methods This study was conducted on plants in commercial crops growing in two double plastic covered greenhouses (termed houses A and B) at Waimauku (36 46’ S, 174 30’ E), in Rodney County, New Zealand). The greenhouses were similar in size (4,000 m), construction, growing systems (using deep flow technique), planting density, venting and heating set points, humidity settings, radiation received, irrigation treatments, electrical conductivity (EC), nutrition and crop management systems.
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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 | 0.001 | 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) | 0.000 | 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".