Yield, Nitrogen Uptake and Nitrogen Leaching of Sensor-Based Fertigation-Cultured Tomato in a Shallow Groundwater Region: Effect of Shallow Groundwater on Tomato Irrigation
Bibliographic record
Abstract
Fertigation with surface drip has been introduced and broadly applied for vegetable cultivation in the Eastern China, which presents high precipitation and always has shallow groundwater. To estimate the influence of high groundwater level on the tomato nitrogen (N) and water use efficiency and develop new sensor-based fertigation technology, experiments were executed in plastic greenhouse in the experimental farm of Yangzhou University located in the suburban of Yangzhou city during 2016-2017 growing seasons using a block randomization with three replications. Three N dosages and 4 watering treatments were carried out in this experiment. The data indicated that irrigation threshold of -35 kPa was optimum to get the maximum production of tomato. In this treatment, the value of estimated plant evapotranspiration (ETc) was much higher than total applied water volume, suggesting high groundwater table had a significant contribution on the tomato ETc and a sensor-based irrigation strategy should be more accurate than the simulated ETc irrigation method to calculate the water demand under this condition. In addition, our results indicated that high groundwater level had a positive effect to alleviating N leaching. Finally, we can conclude that fertigation technology enhanced the N use efficiency (NUE) and water use efficiency (WUE) and three fourths of the calculated N dosage (according to a traditional nutrient equation) was sufficient to optimize tomato yield.
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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".