Evaluation of the soil moisture sensors for irrigation scheduling of strawberries
Bibliographic record
Abstract
Horticultural producers are in need of efficient and timely techniques for determining crop water requirements. The question of when and how much to irrigate, termed irrigation scheduling, is particularly important for high-value crops such as strawberries (Fragaria ananassa). During the growing season, irrigation scheduling decisions are influenced by climatic variables such as rainfall, temperature and humidity, which directly impact soil moisture levels. A field study was therefore conducted to evaluate two soil moisture sensors for irrigation scheduling of commercial strawberries on a farm in Simcoe, Southern Ontario. Strawberries were grown on raised beds with plastic mulch under two management practices – open field and plastic high tunnels. For each practice, two soil moisture sensors based on time domain reflectometry (TDR) were evaluated. The sensors, Campbell Scientific's water content reflectometer (WCR) and ESI's Gro-point (GP) monitored soil moisture continuously over the growing season (May to October 2007). Soil samples were collected to obtain volumetric water content as a unit of reference for the purpose of comparison and evaluation of the two sensors. Equivalent water depths (EWD) were calculated for an effective strawberry rooting depth of 0.3 m. The calculated EWDs were compared with the grower's irrigation scheduling practices. The study found that the WCR and GP reliably recorded continuous trends in soil moisture throughout the growing season. For the WCR sensor, gravimetric analyses of soil samples showed excellent correlation, resulting in R2 of 0.94 and 0.97 for the open field and plastic high tunnel, respectively. The R2 for the GP sensor was good at 0.88 for the open field but poor for the plastic high tunnels, due to a malfunctioning sensor. The EWDs for the two plots were calculated to be 699 mm for the open field and 711 mm for the plastic high tunnels. A significant fin
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".