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Record W2339635239 · doi:10.2134/agronj2015.0497

Winter Wheat Cropping System Response to Seed Treatments, Seed Size, and Sowing Density

2016· article· en· W2339635239 on OpenAlexafffundabout
Brian L. Beres, T. Kelly Turkington, H. R. Kutcher, B. Irvine, Eric N. Johnson, John T. O’Donovan, K. Neil Harker, Christopher B. Holzapfel, Ramona M. Mohr, Gary Peng, Dean Spaner

Bibliographic record

VenueAgronomy Journal · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWheat and Barley Genetics and Pathology
Canadian institutionsAboriginal Affairs Northern Dev CanadaUniversity of AlbertaUniversity of SaskatchewanAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food CanadaCenters for Disease Control and Prevention
KeywordsSeedingSowingAgronomySeed treatmentBiologyCropping systemCrop yieldCropGrowing seasonYield (engineering)Germination

Abstract

fetched live from OpenAlex

Poor stand establishment resulting in lower yield is a major constraint to expanding winter wheat ( Triticum aestivum L.) across western Canada. To address this issue, we conducted a study totaling 26 site‐years over three growing seasons (2011–2013) to observe crop responses to system manipulations involving seeding rate (200 and 400 seeds m −2 ), seed size as a proxy for plant vigor (light/thin, moderate, heavy/plump), and a dual fungicide/insecticide seed treatment, on crop establishment, yield, and seed quality. Fall and spring plant density was about 10 plants m −2 greater for heavy seed vs. light seed. Seed treatment improved fall and spring plant density slightly more than 10 plants m −2 . The dual seed treatment improved yield and test weight for thin seed size. Hypothesized weakest agronomic systems (low seeding rate and untreated, light seed) that often included the 200 seeds m −2 seeding rate were the poorest performing (suboptimal responses and highly variable) systems; however, a favorable response to seed treatments allowed for partial compensation and grain yield comparable to systems with high seeding rates. Greater instability was generally observed in weak systems irrespective of seed treatment. The economic advantage of the seed treatment was more apparent with thinner winter wheat stands as it resulted in greater gross (CAN+$31 ha −1 ) and net (+$22 ha −1 ) returns. This study reaffirms the importance of a strong and integrated agronomic system and indicates seed treatments can help offset weak systems comprised of poor stand establishment and lower yield performance. Core Ideas Poor stand establishment resulting in lower yield is a major constraint to expanding winter wheat acreage across western Canada. Hypothesized weakest agronomic systems (low seeding rate and untreated, light seed) that often included the 200 seeds m −2 seeding rate were the poorest performing (sub‐optimal responses and highly variable) systems; however, a favorable response to seed treatments allowed for partial compensation and grain yield comparable to systems with high seeding rates. Winter wheat crop establishment and grain yield results indicated that producers that intentionally use low seeding rate should use a dual fungicide/insecticidal seed treatment; however, the inherent variability is not entirely overcome with seed treatment. Inclusion of a dual fungicide/insecticide seed treatment provided the highest gross returns for both levels of sowing density; however, the added input cost of the seed treatment relative to the magnitude of change for grain yield reduced overall net returns at the 400 seeds m −2 seeding rate compared to the check (–$11 ha −1 ). This study reaffirms the importance of a strong and integrated agronomic system and indicates seed treatments can help offset weak systems that tend to have poor stand establishment and lower yield performance.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.923
Threshold uncertainty score0.266

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 head0.013
GPT teacher head0.207
Teacher spread0.194 · 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

Citations39
Published2016
Admission routes3
Has abstractyes

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