Design and evaluation of a plant-controlled atmometer for measuring crop evapotranspiration
Bibliographic record
Abstract
Irrigation is essential for optimal crop development and yield enhancement. Atmometers measure the evaporation rate through a porous surface to estimate the potential evapotranspiration (ETp), which is used to calculate the actual crop water consumption (ETa) using crop coefficients. When the soil is dry, however, plants transpire at a lower rate due to the increasing difficulty in drawing water from the soil. Therefore, the ET of a plant spans two phases: weather limiting Phase 1 and soil limiting Phase 2. The traditional atmometers presume that the crop is adequately irrigated and do not account for the drop in evapotranspiration caused by drier soils as in Phase 2. The objective of this project was to design and evaluate a plant-controlled atmometer capable of detecting crop water ET under both Phases 1 and 2. An atmometer with a variable evaporation rate determined by weather conditions and soil water status was built and tested. Experiments on the rate of evaporation through porous plates revealed that evaporation could be controlled by imposing a negative pressure on the water supply side to mimic the plants. Evaporation rates through porous ceramic plates were measured under different negative pressures to develop a relationship. This relationship was used to simulate the plant ET under different soil water contents and regulate the evaporation rates through the modified atmometer based on soil water content. In this experiment, an increase in ET rate led to increased negative pressure on the water supply side when the water supply was restricted. Water was released from the reservoir to the porous ceramic plate to match the weather conditions and the soil water content to mimic the plant. The change in water volume in the supply reservoir was used to calculate the ETa of the crop.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 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".