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Record W3028226590 · doi:10.1139/cjps-2019-0276

Response of canola, wheat, and pea to foliar phosphorus fertilization at a phosphorus-deficient site in eastern Saskatchewan

2020· article· en· W3028226590 on OpenAlexaffvenueabout
Stephen Froese, J. Wiens, Thomas D. Warkentin, J.J. Schoenau

Bibliographic record

VenueCanadian Journal of Plant Science · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPhytase and its Applications
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCanolaPhosphorusHuman fertilizationAgronomyRandomized block designGrain yieldAnthesisCropPhosphateBiologyNitrogenField experimentAnimal scienceChemistryCultivar

Abstract

fetched live from OpenAlex

Foliar fertilization is a potential strategy to supplement phosphorus (P) requirements when conditions permit. In 2016 and 2017, canola, wheat, and pea were grown in a randomized complete block design trial near Pilger, SK, Canada. Each crop received a total P application of 20 kg P 2 O 5 ha −1 , with varying proportions of the P applied as seed-placed monoammonium phosphate (MAP) supplemented with foliar KH 2 PO 4 (0%, 25%, 50%, and 100%) applied prior to anthesis. Under field conditions, yield response decreased as the proportion applied as seed-placed MAP decreased. The 100% foliar-applied P treatment in canola was able to maintain significantly higher yield than the unfertilized control in the absence of seed-placed MAP, indicating some uptake and response. Of the crops evaluated, canola was most responsive to P fertilization. Phytate content ranged from 68% to over 90% of total seed P, with the highest proportions found in wheat grain. Foliar P application had limited effect on phytate and grain iron content, but there appeared to be an inverse relationship between seed-placed MAP and grain zinc concentration that was less evident when P was applied in foliar form. In this study, foliar P application was unable to substitute for seed-placed MAP and overall had a marginal effect on grain yield and P uptake as well as seed nutritional value.

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 score0.818
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.020
GPT teacher head0.208
Teacher spread0.189 · 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

Citations11
Published2020
Admission routes3
Has abstractyes

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