Prediction of Nitrogen Responses of Corn by Soil Nitrogen Mineralization Indicators
Bibliographic record
Abstract
Soil nitrogen mineralization potential (N min) has to be spatially quantified to enable farmers to vary N fertilizer rates, optimize crop yields, and minimize N transfer from soils to the environment. The study objectives were to assess the spatial variability in soil N min potential based on clay and organic matter (OM) contents and the impact of grouping soils using these criteria on corn grain (Zea mays L.) yield, N uptake response curves to N fertilizer, and soil residual N. Four indicators were used: OM content and three equations involving OM and clay content. The study was conducted on a 15-ha field near Montreal, Quebec, Canada. In the spring 2000, soil samples (n = 150) were collected on a 30- x 30-m grid and six rates of N fertilizer (0 to 250 kg N ha(-1)) were applied. Kriged maps of particle size showed areas of clay, clay loam, and fine sandy loam soils. The N min indicators were spatially structured but soil nitrate (NO3-) was not. The N fertilizer rate to reach maximum grain yield (N max), as estimated by a quadratic model, varied among textural classes and Nmin indicators, and ranged from 159 to 250 kg N ha(-1). The proportion of variability (R2) and the standard error of the estimate (SE) varied among textural groups and N min indicators. The R2 ranged from 0.53 to 0.91 and the SE from 0.13 to 1.62. Corn grain N uptake was significantly affected by N fertilizer and the pattern of response differed with soil texture. For the 50 kg N ha(-1) rate, the apparent N min potential (ANM) was significantly larger in the clay loam (122 kg ha(-1)) than in the fine sandy loam (80 kg ha(-1)) or clay (64 kg ha(-1)) soils. The fall soil residual N was not affected by N fertlizer inputs. Textural classes can be used to predict N max. The N min indicators may also assist the variable rate N fertilizer inputs for corn production.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 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".