Cover crop performance under a changing climate in continuous corn system over Nebraska
Bibliographic record
Abstract
Abstract Fall‐planted cover crop (CC) within a continuous corn (Zea mays L.) system offers potential agroecosystem benefits, including mitigating the impacts of increased temperature and variability in precipitation patterns. A long‐term simulation using the Decision Support System for Agrotechnology Transfer model was made to assess the effects of cereal rye (Secale cereale L.) on no‐till continuous corn yield and soil properties under historical (1991–2020) and projected climate (2041–2070) in eastern Nebraska. Local weather data during the historical period were used, while climate change projections were based on the Canadian Earth System Model 2 dynamically downscaled using the Canadian Centre for Climate Modelling and Analysis Regional Climate Model 4 under two representative concentration pathways (RCP), namely, RCP4.5 and RCP8.5. Simulations results indicated that CC impacts on corn yield were nonsignificant under historical and climate change conditions. Climate change created favorable conditions for CC growth, resulting in an increase in biomass. CC reduced N leaching under climate change scenarios compared to an average reduction of 60% (7 kg ha−1) during the historical period. CC resulted in a 6% (27 mm) reduction in total water in soil profile (140 cm) and 22% (27 mm) reduction in plant available water compared to no cover crop during historical period. CC reduced cumulative seasonal surface runoff/soil evaporation and increased the rate of soil organic carbon buildup. This research provides valuable information on how changes in climate can impact the performance of cereal rye CC in continuous corn production and should be scaled to wider locations and CC species.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.001 | 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 source (direct Gemma or distilled Codex), 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".