Variable soil moisture responses to rainfall events in fields under different management practices
Bibliographic record
Abstract
Abstract Soil moisture response to rainfall is a key factor that dictates how well a landscape can support crop growth as well as its susceptibility to water runoff and leaching, however, few studies have investigated how agricultural management impacts this important soil function. This study compares two common agricultural soil treatments (cover crops and soil compaction) and their soil moisture response to rainfall in comparison to a control. In a humid temperate climate during March to November, individual rainfall events were delineated over two growing seasons and corresponding soil moisture responses were identified using in situ soil moisture sensors at four soil depths (20, 30, 40, and 60 cm). Results suggest that hydrological responses differed with both event type and management treatment. Not all rainfall events triggered a response: those that triggered responses at shallower soil depths were typically characterized by higher total event rainfall, and higher maximum and average rainfall intensity. In contrast, rainfall events triggering responses at deeper soil depths were characterized by longer event duration as well as higher 10‐day antecedent rainfall (AR). Soil moisture responses for the cover crop treatment were characterized by relatively lower initial and peak soil moisture at shallower depths but higher values at 60 cm depth, whereas soil moisture responses for the control and compacted soil treatments demonstrated the opposite. Matrix flow was most often generated for rainfall events with high magnitude and was not preferentially associated with any particular soil treatment. However, specific conditions were needed to generate vertical preferential flow, namely high total event rainfall for both horizons, or high AR for preferential flow in the Ap horizon, or high rainfall intensity for preferential flow in the Bt horizon. Our findings demonstrate the potential for detailed event‐based soil water process analysis using high‐frequency, multi‐depth soil moisture data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".