Assessing Different Plant‐Centric Water Stress Metrics for Irrigation Efficacy Using Soil‐Plant‐Atmosphere‐Continuum Simulation
Bibliographic record
Abstract
Abstract Understanding plant water stress (PWS) in the soil‐plant‐atmosphere‐continuum (SPAC) that connects water supply from soil, water demand from atmosphere, and plant self‐regulation is a prerequisite for efficient irrigation in response to water scarcity. Currently, PWS can be defined in various ways, for example, based on environmental factors and/or plant‐centric metrics. The environment‐based metrics usually do not take plants into consideration. Regarding the existing plant‐centric metrics, their interconnections and abilities to capture the physical water constraints from both soil water supply and atmospheric water demand are still unclear. This research investigates the theoretical foundations behind different PWS metrics, and assesses their efficacy and potentials for irrigation scheduling. This study first investigated the interconnections among different PWS metrics and the co‐regulation of soil moisture and vapor pressure deficit (VPD) on the plant‐centric metrics through an advanced process‐based model, ecosys . We then use ecosys to test different PWS metrics’ performance in guiding irrigation in terms of water use, maize yield, and economic profits. The case study was conducted at sites across a dramatic rainfall gradient in Nebraska, the largest irrigation state in the United States Corn Belt. The ecosys simulation indicates that canopy water potential and stomatal conductance ( g s ) are the most effective plant‐centric metrics in the SPAC system in indicating PWS. In addition, our findings show that using the plant‐centric metrics‐based irrigation schemes, which capture the co‐regulation of soil moisture and VPD, can improve producers’ economic profits through water savings.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| 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 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".