Grain Legumes in Northern Great Plains: Impacts on Selected Biological Soil Processes
Bibliographic record
Abstract
Cropping systems in the Northern Great Plains have shifted from fallow‐based to legume‐based systems. The introduction of grain legumes has impacted soil organisms, including both symbiotic and nonsymbiotic N‐fixing bacteria, pathogens, mycorrhizae and fauna, and the processes they perform. These changes occur through effects of legume seed exudates, rhizosphere exudates, and decomposing crop residues. The legume– Rhizobium symbiosis results in dinitrogen (N 2 ) fixation that adds plant available N into the soil system. It is estimated that about 171 million kg N 2 was fixed by field pea ( Pisum sativum L.), lentil ( Lens culinaris Medik.), dry bean ( Phaseolus vulgaris L.), and chickpea ( Cicer arietinum L.) crops in the Canadian Prairies in 2004, representing 7% of the total fertilizer‐N (2580 million kg) used by Canadian prairie farmers in that year. Similarly, an estimated 40 million kg N 2 was fixed by field pea, lentil, and dry bean (including chickpea) crops in U.S. agroecosystems in 2004. Some of the fixed N 2 is recycled for the benefit of nonlegume crops grown after grain legumes. Many other associations benefit from the legume in a cropping system, including mycorrhizal associations that improve plant nutrient and water uptake, changes in the pathogen load and disease development, and overall changes in the soil community. Legumes contribute to greenhouse gas (N 2 O and CO 2 ) emissions during nitrification and denitrification of fixed N. However, because less fertilizer‐N is used in legume‐based cropping systems, overall greenhouse gas emissions are usually less than those in fertilized monoculture cereals. Therefore, grain legumes in Northern Great Plains have positive effects on agriculture by adding and recycling biologically fixed N 2 , enhancing nutrient uptake, reducing greenhouse gas emissions by reducing N fertilizer use, and breaking nonlegume crop pest cycles.
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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 | 0.001 |
| 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".