Long-term crop rotation diversification enhances maize drought resistance through soil organic matter
Bibliographic record
Abstract
Abstract Climate change adaptation requires building agricultural system resilience to warmer, drier climates. Increasing temporal plant diversity through crop rotation diversification increases yields of some crops under drought, but its potential to enhance crop drought resistance and the underlying mechanisms remain unclear. We conducted a drought manipulation experiment using rainout shelters embedded within a 36-year crop rotation diversity and no-till experiment in a temperate climate and measured a suite of soil and crop developmental and eco-physiological traits in the field and laboratory. We show that diversifying maize-soybean rotations with small grain cereals and cover crops mitigated maize water stress at the leaf and canopy scales and reduced yield losses to drought by 17.1 ± 6.1%, while no-till did not affect maize drought resistance. Path analysis showed a strong correlation between soil organic matter and lower maize water stress despite no significant differences in soil organic matter between rotations or tillage treatments. This positive relationship between soil organic matter and maize water status was not mediated by higher soil water retention or infiltration as often hypothesized, nor differential depth of root water uptake as measured with stable isotopes, suggesting that other mechanisms are at play. Crop rotation diversification is an underappreciated drought management tool to adapt crop production to climate change through managing for soil organic matter.
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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) | 0.012 | 0.002 |
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; both teacher heads agree on what is shown here.
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".