Impact of Slope Position and Hog Manure Injection on Soil P and N Distribution in an Undulating Landscape in Saskatchewan, Canada
Bibliographic record
Abstract
Concern for the Quality of Snowmelt Runoff (QSR) in the semiarid region of the CanadianPrairies has drawn much attention due to the fact that 80-85 percent of the annual runoff volumecomes from snowmelt. The QSR depends on the number of factors such as fall soil condition, soilnutrient levels, fertilizer type and application method, crop type, and characteristics of snow pack and snowmelt. These factors may vary with the landscape position. Among these factors, soil nutrient levels (i.e., soil phosphorus and nitrate) play a significant role in determining the quality of snowmelt runoff. Investigation of how soil phosphorus and nitrate are distributed over the landscape in the fall will provide vital information in researching how the quality of snowmelt runoff varies with landscape position and in identifying the Critical Source Areas (CSA), which contribute soil nutrients to snowmelt runoff. Therefore the primary objectives of this study are (1) to investigate how soil nutrients are distributed in the landscape in the fall before soil freeze-up and (2) to study how manure application affects this distribution. The study was conducted in a small watershed at Elstow, Saskatchewan, Canada. The site islocated in an undulating landscape and is internally draining to a small depression in the centre of the watershed. The average slope is 2.7%. Soils are moderate to fine textured. The watershed and adjacent fields received hog manure in the fall of 2001 and 2003. A Digital Elevation Model developed for the site was used to classify the landscape into different landform segments (i.e.,Shoulder, Backslope and Footslope). Six sampling transects, covering three landform segments,were established running radial from the centre of the watershed. Fall soil samples (2003 and 2004)collected along each transect were analyzed for soil moisture content, available soil phosphorous,soil nitrate and soil ammonium. For the landscape in this study, available fall soil phosphorus levels increased in the order of backslope < shoulder < footslope. Fall soil nitrate levels increased from shoulder to footslope for this landscape. Soil ammonium levels did not vary between shoulder and backslope, while the footslope positions had a significantly higher ammonium level than the other two landform segments. Manure application changed the above distribution patterns of available soil P, NO3- and NH4 +. However, one year after hog manure application, only available soil P still showed the effect of manure application. For this landscape, soil moisture did not vary with landform segments in the fall except the soil moisture level in footslope in fall 2004 which was significantly different from the other landformsegments.
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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 | 0.000 |
| 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".