Nutrient Removal by Corn Grain Harvest
Bibliographic record
Abstract
Effective nutrient management requires an accurate accounting of nutrients removed from soils in the harvested portion of a crop. Because the typical crop nutrient values that have historically been used may be different under current production practices, a study was conducted to measure nutrient uptake in grain harvested in 1998 and 1999 from 23 site‐years in the Mid‐Atlantic region of the USA. There were 10 hybrids included in the study, but each site grew only one hybrid each year. Corn (Zea mays L.) production practices followed local state extension recommendations. Minimum, maximum, and mean corn grain yields were 4.9, 16.7, and 10.3 Mg ha−1. Nutrient concentrations were determined on grain samples oven‐dried at 70°C for 24 h. Minimum, maximum, and median nutrient concentration values were as follows: 10.2, 15.0, and 12.9 g N kg−1; 2.2, 5.4, and 3.8 g P kg−1; 3.1, 6.2, and 4.8 g K kg−1; 0.13, 0.45, and 0.28 g Ca kg−1; 0.88, 2.18, and 1.45 g Mg kg−1; 0.9, 1.4, and 1.0 g S kg−1; 9.0, 89.5, and 33.6 mg Fe kg−1; 15.0, 34.5, and 26.8 mg Zn kg−1; 1.0, 9.8, and 5.3 mg Mn kg−1; 1.0, 5.8, and 3.0 mg Cu kg−1; and 2.3, 10.0, and 5.5 mg B kg−1. Median nutrient uptake values found in this study are similar to commonly used book values, but there was considerable variation among samples of corn grain. Concentrations of P and K in grain were positively associated with yield level, and concentrations of grain P were positively correlated with Mehlich‐3 soil test P. The variability in nutrient removal values seen in this study, even for the same hybrid, raises questions about the usefulness of average values for estimating crop nutrient removal across a range of cropping conditions. Research is needed to identify or develop a means to correct for the sources of variability.
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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 | 0.000 |
| 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) | 0.001 | 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".