The Effect of Including Legumes in Dairy Crop Rotations on Nitrous Oxide Emissions
Bibliographic record
Abstract
This study examined whether substituting legumes for corn in dairy crop rotations could reduce N2O emissions. This was done by measuring the surface N2O emissions of four different two-year crop rotation treatments: corn-corn, corn (+cover crop)-corn, soybean-corn, and alfalfa-alfalfa over an 18 month period (May 1, 2014-Oct. 31, 2015) on sandy loam and clay soils. Emissions of dissolved N2O in subsurface tile drains in the clay soil were also measured for eight months (March 20, 2015-October 31, 2015) to determine the contribution of dissolved N2O to total N2O emissions as well as the effect of crop rotations on dissolved N2O emissions. In the sandy loam soil, alfalfa and soybeans had the lowest growing season N2O emissions (9.6 and 10.6 g ha-1 d-1, respectively), and alfalfa had the lowest annual emissions over two years at 3.0 and 3.6 kg N2O-N ha-1 yr-1. In the clay soil, the alfalfa had 3-4 times lower soil-surface N2O emissions than the other treatments in the second growing season only. Non-growing season emissions accounted for 56% of annual N2O emissions. Subsurface emissions accounted for 0.4% of N2O emissions over an eight month period and followed the same treatment effects as the clay soil surface emissions. This study suggests it is possible to reduce N2O emissions by substituting legumes for corn, especially when combined with the appropriate soil type; however, reductions can be negated by management practices that increase non-growing season emissions.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".