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Record W2789620785

Evaluation of soybean (Glycine max) planting dates and plant densities in northern growing regions of the Northern Great Plains

2017· dissertation· en· W2789620785 on OpenAlexaboutno aff
Cassandra Tkachuk

Bibliographic record

VenueMspace (University of Manitoba) · 2017
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicSoybean genetics and cultivation
Canadian institutionsnot available
Fundersnot available
KeywordsSowingGlycineAgronomyGeographyAgroforestryForestryEnvironmental scienceBiology
DOInot available

Abstract

fetched live from OpenAlex

Soybean (Glycine max L. Merr.) planting date and plant density are agronomic decisions made simultaneously at the beginning of the growing season that can be used to maximize yield and economic return. Research on these basic soybean agronomic decisions must be conducted to support the expansion of soybean production in northern growing regions of the Northern Great Plains (NGP). The objectives of this study were to evaluate the effects of planting dates based on soil temperature on soybean emergence, maturity, and yield for short and long season varieties in Manitoba, and to determine optimum soybean plant density for early to very late planting dates in northern growing regions of the NGP. In the first experiment, calendar date had a greater influence than soil temperature at planting on soybean yield. Soybean yield declined with later planting rather than increasing soil temperature at planting. The earliest planting dates resulted in the greatest soybean yields. In the second experiment, soybean yield-density relationships were responsive to planting date. Yield-density relationships formed early/mid (May 4 to 26) and late/very late (June 2 to 23) planting date groups for combined site years. Early/mid planting dates resulted in greater maximum yields. According to the yield-density model, true yield maximization did not occur for any planting dates and site years within the range of plant densities tested in this field study. Soybean economic optimum seed densities (EOSDs) were much lower than predicted plant densities that maximized yield. Soybean EOSDs were identified as 492,000 and 314,000 seeds ha-1 by marginal cost analysis for early/mid and late/very late planting, respectfully. These values were sensitive to changes in soybean grain price and seed cost. Thus, growers need to adjust EOSDs for changes in price and cost. A combined analysis of soybean yields from both experiments using similar target plant densities determined that a significant negative linear relationship existed between soybean yield and planting date. The greatest soybean yields resulted from early planting and declined by 16 kg ha-1 for each one-day delay in planting from Apr 27 to June 16. However, yield responses varied among site years. The overall recommendation from this study would be to plant soybeans during the month of May at a profit-maximizing seed density, accounting for fluctuating grain price and seed cost.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.969
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.033
GPT teacher head0.210
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 source (direct Gemma or distilled Codex), 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

Citations6
Published2017
Admission routes1
Has abstractyes

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