Nutrient Considerations for Diversified Cropping Systems in the Northern Great Plains
Bibliographic record
Abstract
The impacts of recent changes in cropping systems in the northern Great Plains on nutrient dynamics are described in this review paper. Cropping intensity and diversity are increasing in the Great Plains because reduced tillage systems have improved water conservation and, subsequently, crop yields. Higher annualized crop yields increase nutrient removal, which must be balanced by increased nutrient inputs to ensure optimal crop yield and quality while avoiding soil depletion. Furthermore, diversification of crops alters the pattern and degree of nutrient removal and influences microbiological activity and soil quality. Although cropping intensification and diversification will influence most plant nutrients, N and P are the nutrients most commonly deficient for crop production in the Great Plains and most likely to be affected by management practices. Cropping intensification increases crop residue return to the soil, which in turn increases the soil organic C pool. This can lead to higher soil organic matter levels and a greater potential for nutrient cycling, an effect that will increase with time. Cropping systems that include legumes have the potential for contributing N to following crops and may moderate NO3 levels in the soil to avoid potential for NO3 leaching. Phosphorus availability may be influenced by depletion or accumulation of P, based on past cropping and fertilizer management. In addition, preceding crop may influence P availability through residue effects and impacts on vesicular‐arbuscular mycorrhizae activity. Synchrony of nutrient supply with crop demand is essential in order to ensure optimum crop yield and quality while avoiding negative environmental impacts.
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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".