Influence of electric conductivity management on greenhouse tomato yield and fruit quality
Bibliographic record
Abstract
The osmotic and ionic effects of the electrical conductivity (EC) of the nutrient solution and its interactions with climatic factors and cultural practices on tomato yield and fruit quality are reviewed. Adjusting the salinity of the nutrient solution allows growers to modify water availability to the crop and hence improve fruit quality. At some point, however, increases in salinity limit marketable yield. Under high ECs, fruit size is inversely related to EC while the dry matter content of the fruit is linearly increased by the EC. The exact rate of yield decline varies with interactions between cultivars, environmental factors, composition of the nutrient solution, and crop management. According to different studies and growth conditions, salinities higher than 2.3-5.1 mS$\cdot$cm$^{-1}$ result in an undesirable yield reduction, while ECs of 3.5-9.0 mS$\cdot$cm$^{-1}$ improve tomato fruit quality. Manipulating the indoor climate such as humidity, temperature and ambient CO$_2$ level may offset the negative effect of high salinity on yield and fruit quality such as blossom-end rot. The light intensity received by the plant directly affects the quantity of photoassimilates available to the fruit, it also increases their sugar: acid ratio, and influences the transpiration rate and the water uptake by the plant, which in turn, affect the EC around the root. Increasing the EC with NaCl reduces titratable acids, potassium and nitrogen in the fruit but also increases their sodium content. NaCl enhances the sweetness of tomato fruit and improves the overall flavour intensity. Depending upon the composition of the saline solution, ion toxicities or nutritional deficiencies may arise because of a predominance of specific ion or competition effects among cations and anions. Keeping the proper nutrient levels and ratios between all the nutrients in the root environment for each growth stage of a crop should be targeted in order to achieve high yields and high quality products throughout the cropping season. Several EC and fertigation management regimes could improve fruit quality and are presented in this review.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".