Understanding Variable Rate Irrigation under Situations of Field Heterogeneity
Bibliographic record
Abstract
Due to the water sensitive nature of potatoes, variable rate irrigation (VRI) technology may be an effective tool to optimize irrigation in this crop. However, the potential economic and agronomic benefits of this technology are still unclear, especially over varying agricultural landscapes. We hypothesize that VRI technology will be a valuable tool when used on potato fields that have a high degree of soil and topographic heterogeneity. This hypothesis is tested using field-level data from 2019, 2020 and 2021 collected in the Lethbridge region. We define a site-specific irrigation-yield production function based on the water balance and physical properties of unique management zones within a field, specifically soil texture and topographic variability. The function is used in an economic optimization model to determine the benefits of VRI technology relative to uniform irrigation. After calibrating the optimization model with field level data, we conclude that heterogeneity between management zones has a large impact on the net present value (NPV) of an investment in VRI. Generally, as heterogeneity between management zones increases, the NPV of an investment in VRI goes from being negative (a poor investment) to being positive (a strong investment). Our study is one of the first to apply observational field data in an economic optimization model to estimate the benefits of VRI relative to uniform irrigation.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".