Bibliographic record
Abstract
Understanding the soil water dynamics in cultivated organic soils is crucial for achieving sustainable agriculture on histosols, preserving ecosystem health, and mitigating the impacts of climate change. There has been extensive research on the soil-hydraulic properties of mineral soils, yet there is a limited understanding of such characteristics in cultivated organic soils. This limitation arises because of the high organic matter content, significant porosity, high compressibility, high tortuosity, low shear strength, and the tendency of organic soils to undergo shrinkage and swelling. To bridge this knowledge gap, in this dissertation, a laboratory column experiment was designed, and a computer model was employed to analyze soil water retention curves (SWRCs) of organic soils. Intact soil columns (length: 60 cm, diameter: 20 cm) were collected from intensively cultivated organic soil in the Napierville region of Quebec, Canada. These soil columns were subjected to wetting and drying cycles for 112 days, and the effect on soil water status was monitored by measuring matric potential at different depths (10 cm, 26 cm, and 48 cm) using automated matric potential sensors. Based on the lab data, the Hydrus 1D model was then used to simulate SWRCs at the three soil depths. Initial hydraulic parameters, as found in the van Genuchten-Mualem model (pore size distribution index, and bubbling pressure), were drawn from the literature on similar soil types. An inverse modelling approach within Hydrus 1D was executed to optimize the hydraulic parameters by comparing the model’s output with observed matric potential data. In the laboratory experiment, the matric potential gradually decreased from saturation to 277.52 kPa, -181.2 kPa, and -89.93 kPa at depths 10 cm, 26 cm, and 48 cm, respectively, during the first drying period (52 days), indicating a larger decrease at lesser depths. The soil columns were then subjected to a second drying period (19 days), and the matric potential decreased from -50.01 kPa, -60.02 kPa, and -44.1 kPa to -263.89 kPa, -172.61 kPa, and -109.76 kPa at the same depths. The results were similar to those reported in other studies conducted on peat soil at corresponding depths. The Hydrus 1D inverse modelling showed that simulated matric potential steadily decreased from saturation to -267.92 kPa, -189.95 kPa, and -85.31 kPa at depths of 10 cm, 26 cm, and 48 cm, respectively, during the first drying period. Likewise, during the second drying period, the simulated matric potential decreased from -85 kPa, -68.1 kPa, and -65.6 kPa to -222.12 kPa, -162.3 kPa, and -90.81 kPa at the same depths. Model accuracy was validated for the second drying period with a coefficient of determination and Nash-Sutcliffe Efficiency: 0.971 and 0.915, 0.928 and 0.879, and 0.69 and -0.365 at depths of 10 cm, 26 cm, and 48 cm, respectively. The optimized soil hydraulic parameters for SWRC in Hydrus 1D inverse modelling were suitable for accurately describing the behaviour of peat soils in the study area. Based on the simulated SWRCs, the moisture contents at field capacity were 0.6 cm³/cm³, 0.696 cm³/cm³, and 0.49 cm³/cm³, while the wilting points were 0.165 cm³/cm³, 0.107 cm³/cm³, and 0.14 cm³/cm³ for soil depths of 10 cm, 26 cm, and 48 cm, respectively. The weighted available water content was 264 mm for a soil depth of 60 cm, which is the typical root zone depth for crops such as carrots, lettuce, celery, and onions. This amount is lower than the crop water requirement of 375-525 mm per year for organic soil in Quebec. Therefore, additional irrigation water is necessary for these organic soils. The present results can assist researchers, land managers, and agricultural practitioners in deciding water management practices for the intensive cultivation of histosols
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".