Soil Water Storage in Soybean Crop Measured by Polymer Tensiometers and Estimated by Agrometeorological Methods
Bibliographic record
Abstract
The estimation of soil water status in cropped areas continues to be challenging for soil and climate scientists. This study contributes to this issue estimating soil water storage by the water balance of Thornthwaite and Mather, Rijtema and Aboukhaled, and Dourado and de Jong van Lier, combined with crop potential evapotranspiration estimated by Penman-Monteith, to compare them with soil water storage values calculated from polymer tensiometer data of a soybean crop field experiment. The experiment was conducted in Piracicaba, SP, with tensiometers installed at 0.05, 0.15 and 0.3 m depths. Results show that the tensiometers presented good performance to measure soil water pressure head in the whole range of the available water capacity for the crop. The tensiometer presents the advantage of allowing measurements of soil water storage in layers, in contraposition to climatologic water balance calculations which assume one single layer. Rijtema and Aboukhaled presented the best correlation with the water storage estimated from tensiometer data.
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 | 0.000 |
| 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 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".