Impacts of Soil Phosphorus Drawdown on Snowmelt and Rainfall Runoff Water Quality
Bibliographic record
Abstract
Managing P export from agricultural land is critical to address freshwater eutrophication. However, soil P management, and options to draw down soil P have received little attention in snowmelt‐dominated regions because of limited interaction between soil and snowmelt. Here, we assessed the impacts of soil P drawdown (reducing fertilizer P inputs combined with harvest removal) on soil Olsen P dynamics, runoff P concentrations, and crop yields from 1997 to 2014 in paired fields in Manitoba, Canada. We observed that Olsen P concentrations in the 0‐ to 5‐cm soil layer were negatively correlated with the cumulative P depletion and declined rapidly at the onset of the drawdown practice (3.1 to 5.4 mg kg −1 yr −1 during 2007–2010). In both snowmelt runoff and rainfall runoff, concentrations of total dissolved P (TDP) were positively correlated with the concentrations of soil Olsen P. Soil P drawdown to low to moderate fertility levels significantly decreased mean annual flow‐weighted TDP concentrations in snowmelt runoff from 0.60 to 0.30 mg L −1 in the field with high initial soil P and from 1.17 to 0.42 mg L −1 in the field with very high initial soil P. Declines in TDP concentration in rainfall runoff were greater. Critically, yields of wheat ( Triticum spp.) and canola ( Brassica napus L.) were not affected by soil P depletion. In conclusion, we demonstrate that relatively rapid reductions in P loads are achievable at the field scale via managing P inputs and soil P pools, highlighting a management opportunity that can maintain food security while improving water security in cold regions. Core Ideas Reducing or ceasing soil P input decreased soil Olsen P rapidly. Soil Olsen P concentrations were negatively correlated with cumulative P depletion. Declining soil P led to decreases in flow‐weighted mean dissolved P concentrations. Improved water quality can be achieved with management of soil P. Agricultural productivity can also be maintained with lower soil P.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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) | 0.001 | 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 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".