Spatial Distribution Patterns of Soil Water Availability as a Tool for Precision Irrigation Management in Histosols: Characterization and Spatial Interpolation
Bibliographic record
Abstract
Lettuce ( Lactuca sativa L.) production in organic soils is important in Quebec, Canada. Lettuce is highly sensitive to tip burn, a physiological disorder that can lead to significant yield losses. Tip burn losses have been linked to various factors, such as root water uptake deficits. A precision irrigation approach using local applications of water based on lettuce requirements and soil water available capacity (SWAC) reduces the occurrence of tip burn but may need mapped spatial information of SWAC for proper irrigation management. The objectives of this study were (i) to determine a rapid, efficient, and reliable method for interpolating SWAC and (ii) to use this interpolation method in precision irrigation simulations in management zones to demonstrate the importance of using SWAC maps. The methods for SWAC interpolation used in this study were inverse distance weighting (IDW), thin plate splines (TPS) and kriging with external drift (KED). The simulation used a calculation procedure for mass balance that contained SWAC maps, evapotranspiration (ET) and precipitation. A comparison of each interpolation method and multiple statistical criteria revealed that IDW and KED were the most precise methods, depending on the study site. Simulations of precision irrigation showed that in many cases, local irrigation management in seven to eight zones must account for the spatial distribution of SWAC to attain an 80% irrigation adequacy for lettuce. Hence, using SWAC maps as a tool for managing irrigation would allow growers to save water and to apply an accurate amount of water in appropriate areas.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".