Challenging Approaches to Nitrogen Fertilizer Recommendations in Continuous Cropping Systems in the Great Plains
Bibliographic record
Abstract
Cropping systems in the Great Plains have evolved over the past two decades from reliance on summer fallowing to continuous cropping under reduced or no‐tillage. Most N recommendation models were developed in fallow systems under conventional tillage and were based on average yield goal, with adjustments for soil profile N content. The objective of this review is to examine the impact of continuous cropping on N requirements. With high‐residue continuous cropping systems, N requirements may increase because of increased annualized production, reduced contribution of N mineralization, and increased immobilization and volatilization potential of surface‐applied fertilizer N. Mitigating these effects on N availability and supplemental N requirements are the reduction in yield per crop, reduced nitrate (NO3) leaching potential, increased N use efficiency (NUE), and increased rates of N mineralization due to higher soil organic matter (OM) content. Unfortunately, increased year‐to‐year yield variability with continuous cropping increases the difficulty in accurately estimating yield goals. Also, reducing the frequency and duration of fallow may reduce the usefulness of the preplant soil N tests in estimating N availability. Recent research has evaluated the use of optical sensors during the growing season to assess N stress and to estimate crop N requirements. If proved feasible for many crops, this would provide a drastic change for determining N recommendations. In the absence of a reasonable yield goal and known residual soil N content, a fertilizer N rate near 70 kg N ha−1 or less was generally sufficient to optimize small‐grain or oilseed yields in several continuous cropping studies.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".