Modeling tillage and manure application on soil phosphorous loss under climate change
Bibliographic record
Abstract
Abstract Phosphorus (P) losses from non-point sources into receiving water bodies play a significant role in eutrophication. Given their failure to adequately control eutrophication in the Lake Erie, conservation recommendations for agricultural watersheds should be reconsidered, particularly under climate change. Using the Environmental Policy Integrated Climate model, the potential impacts on crop yield, surface runoff, tile drainage, and relevant dissolved reactive phosphorus (DRP) losses from manure-amended corn-soybean rotation plots in the Lake Erie basin were estimated for six tillage methods with different mixing efficiencies and manure broadcast application. These were investigated under twelve different regional and global future climate simulations. Tillage alone proved to have only a minor impact on mean corn yield (± 2%). Climate change led to large uncertainties under the single tillage treatment. As a result of the combined effects of biogeochemical processes (e.g., supply) and hydrological (e.g., transport), strong negative relationships (R 2 = 0.98) were found between tillage mixing efficiency and DRP loss in surface runoff, tile drainage, and total DRP loss. The impacts of combined manure application (broadcast) and tillage on crop yield and flow volume were similar as those of tillage alone. With respect to total DRP losses, the effects of labile P content change outweighed those of surface runoff or tile drainage change (hydrologic). This resulted in a change in total DRP losses ranging from − 60% to + 151%, with being closely correlated with decreasing tillage mixing efficiency (R 2 = 0.94) from moldboard to no-till. Therefore, rotational tillage should be considered for DRP loss reduction and energy saving.
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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 | 0.001 | 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".