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Record W2336806432 · doi:10.14288/1.0098729

Effects of seeding rate and time of nitrogen fertilizer application on winter wheat in south coastal British Columbia

2010· article· en· W2336806432 on OpenAlexaboutno aff
Hoseah Kibett Tarus

Bibliographic record

VenuecIRcle (University of British Columbia) · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCrop Yield and Soil Fertility
Canadian institutionsnot available
Fundersnot available
KeywordsSeedingAgronomyFertilizerNitrogenNitrogen fertilizerEnvironmental scienceWinter wheatBiologyChemistry

Abstract

fetched live from OpenAlex

A two year field experiment was carried out in Delta Municipality approximately 30 km south of Vancouver, British Columbia on a Crescent silty clay loam. The study was designed to investigate the effects of seeding rate and time of N application on intensively managed winter wheat (Triticum aestivum L.) cv. Monopol. In 1988-89, seeding rates were: 200, 250 and 300 seeds m⁻², while in 1989-90 they were 150, 300 and 450 seeds m⁻². Nitrogen treatments consisted of 0 or 225 kg N ha⁻¹ as ammonium nitrate split-applied at Zadoks growth stages (GS) 22,31 or 37 (0/0/0; 0/125/100; 25/125/75; 50/125/50 kg N ha⁻¹). The number of established plants significantly increased with increasing seeding rate in both years. Shoot counts (main stem + tillers) were not significantly influenced by seeding rate in 1988-89, but a linear relationship existed in 1989-90. Shoot mortality, however, was higher at the high seeding rate and commenced earlier in the season. Time of N application had no effect on shoot production, nevertheless, N application enhanced shoot survival. Without N, disease incidence was higher at the lower seeding rate in 1988-89, while N application increased disease incidence over the control in 1989-90. Grain yield and yield components were not significantly influenced by seeding rate in 1988-89. In 1989-90, grain yield and grains m⁻² significantly decreased with increasing seeding rate, while number of heads m⁻² and number of grains head⁻¹ were not significantly affected. In both years, thousand grain weights were maximized at the lower seeding rate with added N. Grain yield and yield components, except for number of grains head⁻¹ in 1988-89, were significantly increased with N application over the control in both years, however, time of N application had no significant effect. Dry matter yields at GS 31 in 1988-89 and GS 37 in 1989-90 linearly increased with increasing seeding rate. At the lowest seeding rate, dry matter yield increased with N3 compared with N2 at GS 37 in 1989-90. Delayed N significantly reduced dry matter yields at GS 37 in both years and at GS 69 in 1989-90. With added N, dry matter yields significantly decreased with increasing seeding rate at GS 85 and at GS 92 in 1989-90. N uptake linearly increased with increasing seeding rate at GS 31 in 1988-89. Delayed N significantly reduced N uptake at GS 31 in 1988-89 and at GS 37 in both years. Harvest index was not significantly influenced by seeding rate in 1988-89, but it decreased significantly with increasing seeding rate in 1989-90. In both years, harvest index was increased with N application and in 1988-89 it was maximized with delayed N. While grain protein was increased with N application in both years, it was maximized in 1989-90 by delaying the first N application until GS 31. N application significantly increased soil mineral N at GS 37 in 1988-89 and throughout the growth period in 1989-90. Low levels of mineral N, as calculated by the difference method, remained in the top 0-50 cm of soil at the end of each season.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.298
Threshold uncertainty score0.599

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.004
GPT teacher head0.152
Teacher spread0.147 · 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 designBench or experimental
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
Published2010
Admission routes1
Has abstractyes

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