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Record W7023264322

Optimum nitrogen management of modern corn hybrids in Manitoba

2022· dissertation· en· W7023264322 on OpenAlexaboutno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2022
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicCrop Yield and Soil Fertility
Canadian institutionsnot available
Fundersnot available
KeywordsFertilizerNitrogenNitrogen fertilizerProfit (economics)Yield (engineering)NutrientCrop
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.585
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.197
Teacher spread0.180 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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