A Framework for Evaluating Irrigation Impact on Water Balance and Crop Yield Under Different Soil Moisture Conditions
Bibliographic record
Abstract
ABSTRACT In agricultural regions, irrigation is a fundamental component of the hydrological cycle that is essential to maintain crop yields but also exerts pressure on water resources. Various irrigation practices and schedules are typically implemented within a watershed, and each may have profound implications for the water balance and plant productivity. However, there is a major paucity of irrigation taking and application data on a fine‐grained spatio‐temporal scale, which poses challenges for water resource managers to assess the impact of irrigation practices under different moisture conditions. Here, we present a novel framework to evaluate the influence of different irrigation practices on a Lake Erie Basin watershed through a suite of irrigation scheduling models (ISMs) that can be implemented into the Soil and Water Assessment Tool (SWAT) model. We also provide a simple modification to SWAT's AUTOIRR function that bypasses known bugs and enables reliable representation of irrigation. This framework can be easily modified and extrapolated to other watersheds with diverse climates, crop rotations and environmental conditions. We found that a range of ISMs with varying irrigation methods (i.e., drip irrigation vs. sprinkler and surface irrigation), irrigation scheduling (i.e., continuous vs. event‐based), irrigation trigger mechanism (i.e., soil moisture threshold vs. no threshold) and water use efficiencies consistently resulted in improved corn and soybean yields, enhanced evapotranspiration and reduced flow discharge. In contrast, the same ISMs result in significant declining trends of evapotranspiration, soil water content, water yield and baseflow when we emulate conditions of prolonged droughts, which suggest that the benefits from irrigation are unsustainable regardless of the water use efficiency. Our study highlights the need for fine‐grained monitored irrigation, evapotranspiration and soil moisture content data to more accurately assess the influence of irrigation on watershed hydrology. Importantly, we stress the need to develop adaptive water resource management practices to maintain crop yields and water stores under a changing climate in the Great Lakes Basin and beyond.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".