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Record W6988994758

Agronomic practices to minimize lodging risk while maximizing yield and protein potential of spring wheat in the eastern Canadian Prairies

2022· dissertation· en· W6988994758 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
KeywordsCropCultivarGrowing seasonYield (engineering)Grain yieldCrop yieldSpring (device)
DOInot available

Abstract

fetched live from OpenAlex

Spring wheat is one of the most economically important crops grown on the Canadian prairies. Improvements in genetics and agronomy have resulted in higher yields obtained by producers, but have introduced challenges such as maintaining grain protein content and managing increased lodging risk. The objectives of this thesis research were to evaluate the effect of agronomic management practices such as N management, plant density, plant growth regulator (PGR) application and their interactions on spring wheat lodging risk, grain yield and protein content of spring wheat in the eastern Canadian prairies. This was done through two small plot field trials in south central Manitoba during the 2018 and 2019 growing seasons using cultivars common to, and widely grown across Manitoba. Early season N availability was critical for the development of yield components and allowed the crop to buffer against dry environmental conditions to produce grain protein. However, increased lodging risk associated with application of large amounts of N early in the season needs to be balanced with lodging management strategies. Low plant densities (150 plants m-2) and PGR applications improved the crop’s ability to resist lodging in this research. The lowest plant densities tested (150 plants m-2) allowed the crop to better resist both root and shoot lodging through increases in stem and anchorage strength and stem flexibility indicators compared to the highest densities tested (350 plants m-2). Low plant densities are often associated with decreased early season competitive ability against weeds and more variable crop maturity, neither of which are desirable. Therefore, the ability of PGRs to reduce lodging risk, through increased stem strength and reduced leverage, provides a critical tool for lodging management in spring wheat in regions with high yield potential and lodging pressure. Flexibility of application and yield increases, even in the absence of lodging, through increased kernels per spike, support a wide adoption of this technology as yields are likely to continue to rise in the future across the Canadian Prairies.

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 score0.179
Threshold uncertainty score0.285

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 science0.0000.000
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 head0.022
GPT teacher head0.198
Teacher spread0.176 · 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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