Impact of using different ET models in HYDRUS-1D on soil water dynamics and potato crop ET.
Bibliographic record
Abstract
Abstract Soil water content (SWC) plays a critical role in crop yield, irrigation scheduling, and water resources management. In the Canadian Prairies, the water content in the rootzone replenished by rainfall is rarely sufficient to satisfy crop water requirements. Thus, the need for robust and effective water management. Hydrologic modelling provides the opportunity to understand the underlying processes controlling and affecting soil water movement and distribution. Evapotranspiration (ET) is an important input of hydrologic models; thus, the estimation of ET could have significant consequences on modelling outcome and inference. The FAO Penman-Monteith (PM) is the recommended model for estimating the reference crop evapotranspiration (ETo). However, it is limited by requiring too many weather variables that are not readily available. Simple empirical ETo models have been developed as an alternative. In this study, six ETo models with different inputs were used to simulate soil water dynamics in a rainfed potato farm in Winkler, Manitoba, using the HYDRUS-1D model. The results showed that when used to simulate SWC, all the models followed a similar pattern, although a significant difference was observed at shallow depth (20 cm). Specifically, a significant difference (p < 0.05) was observed between observed and simulated SWC from Hargreaves Samani, Romanenko, Penman, and FAO-PM (missing) models. When used to simulate the crop evapotranspiration (ETc), there was no significant difference (p > 0.05) between observed and simulated ETc from FAO PM, Irmak, and Priestly – Taylor models. Hence, ETo models with fewer data inputs such as Irmak and Priestly – Taylor models can provide accurate and reliable results for water management in southern Manitoba.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".