Optimum nitrogen management of modern corn hybrids in Manitoba
Bibliographic record
Abstract
Nitrogen (N) fertilizer applications are often necessary to achieve maximum profit in annual crop production. This research was meant to assist producers to achieve maximum profits by optimizing N application practices, as N is often a yield-limiting nutrient and also a significant cost of production. The research is focused on the 4Rs of nutrient stewardship to determine the right rate, right source, right timing, and right place of N fertilizer applications. Using 17 site-years of data collected from the 2018 and 2019 growing seasons, this research answers common questions such as how much N is required to produce a high-yielding corn crop, and are there benefits to split applying nitrogen. This research also investigates more technical questions such as are there advantages to using enhanced efficiency fertilizers (EEFs), and how consistent are in-season and post-season tests at evaluating crop N sufficiency. Thirteen of 17 site-years had a statistically significant response to N fertilizer application. Twelve of those sites (plus the 4 unresponsive sites) obtained their statistically greatest yield at fertilizer N application rates of 90 kg N ha-1 or less. According to quadratic response models for maximum return to nitrogen (MRTN) and accounting for spring soil nitrate, lower yielding sites (<8150 kg grain corn ha-1) required 0.0298 kg N kg-1 corn produced and sites yielding >8150 kg of corn ha-1 were more efficient users of soil and fertilizer N, requiring 0.0224 kg N kg-1 corn. The source and placement comparisons showed no statistically significant differences in yield between three separate sources of EEF and conventional urea when applied at the same rate. Comparisons of application timings at planting, at V4, and V8 growth stage showed no significant yield increases by delaying N application and at sites with very small reserves of residual nitrate-N there was a yield penalty for delayed application. Results of the pre-side dress nitrate test, stalk nitrate test, and post-harvest soil test were within the range of the current decision guidelines for some site-years; however, across the site-years there was no consistency between the observed test values at the economic optimum fertilizer rates.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.001 | 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".