Modelling the effects of climate change, agricultural inputs, cropping diversity, and environment on soil nitrogen and phosphorus: A case study in Saskatchewan, Canada
Bibliographic record
Abstract
The relative impact of climate change, agricultural inputs, crop diversity, and environment on soil nitrate-N (NO3-N) and labile soil phosphorus (P) has seldom been assessed in the scientific literature. Furthermore crop management of plant nutrients, based on a combination of agricultural inputs and crop diversity, has not been assessed with respect to adaptation to climate change. This modeling study assessed soil NO3-N leaching and labile P simulated with the Environmental Policy Integrated Climate (EPIC) model for historical and future climate scenarios for the Alternative Cropping Systems (ACS) study (1994–2013) in North-Western Saskatchewan, Canada. The EPIC model was updated with 19 years of field management information from the ACS study. The field study was a combination of the three levels of agricultural inputs [organic (ORG), reduced (RED), and high (HI)] and three levels of cropping diversity [low (LOW), diversified annual grains (DAG), and diversified annual & perennial (DAP)]. Recursive partitioning with multivariate analyses of agricultural inputs, cropping diversity, precipitation, growing degree days, and terrain were used to assess changes in NO3-N and P for each climate change scenario. This is the first analysis, with the EPIC model in the Canadian Prairies, of the effects of climate change on NO3-N losses in agricultural runoff, and soil P content in the context of different agricultural input systems in combinations with diversified rotations. NO3-N losses increased by 28% (from 27.1 to 34.7 kg ha−1 y−1), while labile soil P decreased by 12% (from 24.7 to 21.6 kg ha−1 y−1) under climate change, compared to historical weather. Summer precipitation explained 12% of total variation in future NO3-N losses. Combined, input and diversity explained 23% and 20% of variation in NO3-N losses and labile P, respectively. Cropping diversity was most significant, with reduced NO3-N leaching and labile P under climate change, accounting for 22% and 13% of total variation, respectively. Combined, RED inputs and DAG diversity reduced the impact of climate change on NO3-N losses and soil P and may provide a sustainable, adaptive solution for farming with regards to upcoming seasonal variations in temperature and precipitation. The scientific community, decision and policy makers will use this information to develop conceptual and practical farm- and field-scale technologies for producers, in order to adapt to the impact of climate change on agricultural production and the environment, with methodology which can be applied in Canada and other countries.
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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 | 0.002 |
| 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".