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 \neffective tool to optimize irrigation in this crop. However, the potential economic and agronomic \nbenefits of this technology are still unclear, especially over varying agricultural landscapes. We \nhypothesize that VRI technology will be a valuable tool when used on potato fields that have a \nhigh degree of soil and topographic heterogeneity. This hypothesis is tested using field-level data \nfrom 2019, 2020 and 2021 collected in the Lethbridge region.\nWe define a site-specific irrigation-yield production function based on the water balance and \nphysical properties of unique management zones within a field, specifically soil texture and \ntopographic variability. The function is used in an economic optimization model to determine the \nbenefits of VRI technology relative to uniform irrigation.\nAfter calibrating the optimization model with field level data, we conclude that heterogeneity \nbetween management zones has a large impact on the net present value (NPV) of an investment \nin VRI. Generally, as heterogeneity between management zones increases, the NPV of an \ninvestment in VRI goes from being negative (a poor investment) to being positive (a strong \ninvestment). Our study is one of the first to apply observational field data in an economic \noptimization model to estimate the benefits of VRI relative to uniform irrigation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 teacher head, 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".