Measured and simulated water balances for agricultural fields under water table management
Bibliographic record
Abstract
In southern Quebec, uncertain rainfall and weather patterns can be detrimental to crop growth even though fresh water is an abundant resource. It is therefore desirable to have a good understanding of the water fluxes that occur in the soil profile of a drained agricultural field. Performing a soil water balance is a way to determine the water uses and losses within the soil profile. A seasonal study conducted in 2010 on a 4.2 ha agricultural field in Saint Emmanuel, Quebec aimed to quantify the water fluxes due to both lateral and vertical seepage in conventional (FD) and controlled drainage/subirrigation (CD) plots using the water balance method. Each component of the water balance was measured using high precision sensors, with the exception of surface runoff, which was assumed to be negligible. The water balance was replicated over three blocks, with each block consisting of paired FD and CD plots. The field observed water balance was then compared to a simulated water balance produced with the hydrological model DRAINMOD. The model was calibrated for the 2009 and 2010 seasons, and validated for the 2004 and 2005 seasons. The model performed satisfactorily with indices of agreement of 0.71-0.95 and 0.77-0.90 for FD and CD plots, respectively. Coefficients of determination, R2, were 0.37-0.88 and a narrower range of 0.44-0.77 for FD and CD, respectively. The field water balance determined vertical seepage depths for FD plots ranging from 109 to 149 mm entering the one metre soil profile. The water balance determined 46 mm entering the profile to 33 mm exiting the profile for CD plots. The DRAINMOD simulated seepage estimates for FD were 64 mm and 26 mm entering the profile for vertical and lateral seepage, respectively. Simulated seepage estimates for CD were 44 entering and 4 mm exiting for vertical and lateral seepage, respectively. These values represent both seepage as well as the cumulative error for all other components of the water balance. The results indicate more seepage is occurring in plots under controlled drainage and subirrigation. This concurs with literature and is valid since irrigation is applied and water tables are kept higher. While the crop benefits from irrigation, increased seepage can also result in nutrient loss from the field. A balance between irrigation applied and avoiding loss of nutrients is necessary to reduce input costs to the farmer while maintaining crop yield and minimizing nutrient contamination.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".