Improvements in maize N recommendations decreased carbon dioxide equivalence without sacrificing yield
Bibliographic record
Abstract
Abstract Globally, agricultural scientists are challenged with creating, testing, and validating climate‐smart nutrient strategies that reduce greenhouse gas emissions while increasing food security. This study determined maize (Zea mays L.) N recommendations and bias for N‐rate studies conducted in South Dakota using models created for western Minnesota, Iowa, Eastern North Dakota, Nebraska, and South Dakota. From 2019 to 2021, 16 N rate studies were conducted in long‐term no‐tillage (>6 years) fields located in South Dakota. In the randomized block replicated study, the soils were mollisols that were derived in a semi‐arid frigid environment. The economic optimum N rates were calculated using four fertilizer‐to‐maize grain price ratios (4.11, 5.48, 6.85, and 8.23 [$ (kg N)−1] [$ (kg grain)−1]−1). Analysis showed that reducing the yield goal coefficient used in the South Dakota model from 21.4 to 17.9 kg N (Mg grain)−1 reduced the recommended N rate but did not reduce yield. The reduced yield goal coefficient that considered the fertilizer‐to‐maize price ratio also reduced model root mean square error (RMSE), bias, and the estimated partial carbon dioxide equivalence (CO2e) by at least 18%. Nitrogen recommendation models developed for western Minnesota, Iowa, and South Dakota had similar RMSE, bias, and fertilizer recommendations, and adjusting the recommendation based on expected fertilizer cost and maize selling price improved accuracy. This study suggests that yield was not sacrificed by reducing the coefficients from 21.4 to 17.9 kg N (Mg grain)−1 and that recommendations are improved by considering the fertilizer‐to‐maize grain price ratio.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 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 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".