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Record W2135128766 · doi:10.4141/s00-036

Spring wheat (<i>Triticum aestivum</i>) yield and grain protein responses to N fertilizer in topographically defined landscape positions

2001· article· en· W2135128766 on OpenAlexvenueaboutno aff
F.L. Walley, D.J. Pennock, M. Solohub, G. Hnatowich

Bibliographic record

VenueCanadian Journal of Soil Science · 2001
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsAgronomyFertilizerYield (engineering)Grain yieldWheat grainSpring (device)AgricultureEnvironmental scienceSoil waterPoaceaeNitrogen fertilizerMathematicsBiologySoil scienceEcologyEngineering

Abstract

fetched live from OpenAlex

A 3-yr field study was initiated in 1996 to examine the different grain yield and grain protein responses of wheat to varied N fertilizer rates in a typical glacial till landscape in Saskatchewan, Canada. Our objective was to assess the agronomic and economic feasibility of variable rate fertilizer (VRF) N application for wheat. Results suggest that spring soil water status largely determined the yield and the protein content of wheat both within different years of the study and between different landscape positions within a given year. Although grain yield was strongly related to spring soil water and was predictable on that basis, the grain yield response of wheat to fertilizer N additions was highly variable due, in part, to the dual role that N played in determining both grain yield and grain protein content. As a consequence of the unpredictable nature of the varied response of wheat to N fertilizer additions, there was little economic rationale for using VRF strategies in the 3 yr of this study. However, in the long-term, we believe that VRF N application strategies can be employed to manage N inputs from the perspective of managing and replacing harvested N. Key words: Variable rate fertilizer application, precision farming, nitrogen application, N fertilizer, Saskatchewan

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)0.001
metaresearch head score (Gemma)0.001
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 score0.542
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.016
GPT teacher head0.214
Teacher spread0.198 · 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

Citations34
Published2001
Admission routes2
Has abstractyes

Explore more

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