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Record W2583836192 · doi:10.1139/cjps-2016-0330

Potato Yield and Phosphorus Use Efficiency of Two New Potato Cultivars in New Brunswick, Canada

2017· article· en· W2583836192 on OpenAlexaffvenueabout
Judith Nyiraneza, Benoît Bizimungu, Aimé J. Messiga, Keith Fuller, Sherry Fillmore, Yefang Jiang

Bibliographic record

VenueCanadian Journal of Plant Science · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPotato Plant Research
Canadian institutionsUniversity of FrederictonAgriculture and Agri-Food Canada
Fundersnot available
KeywordsCultivarHuman fertilizationYield (engineering)PhosphorusRandomized block designHorticultureMathematicsPlateau (mathematics)AgronomyAnimal scienceBiologyChemistry

Abstract

fetched live from OpenAlex

This 2-yr (2013 and 2014) study evaluated the variation in tuber yield and selected indices of phosphorus (P) use efficiency of two new potato cultivars [AAC Alta Cloud (AC) and AAC Alta Strong (AS)] versus Russet Burbank (RB), the french fry industry standard. Initial P levels and P saturation index [(P/Al) × 100] ranged from 85 to 89.5 mg P kg−1 and from 4.6% to 5.6%, respectively. A complete randomized block design for each variety was laid out in three adjacent plots with six P fertilization rates. The average tuber yield ranged from 29.06 to 36.81 Mg ha−1 for AC, from 27.56 to 41.50 Mg ha−1 for AS, and from 38.15 to 42.02 Mg ha−1 for RB, with the highest yield observed in 2014. Tuber yield response to P application was described by the quadratic-plus-plateau model for AC and linear-plus-plateau model for AS with a mean critical (above which the yield response to P applications is unlikely) P rate of 40 kg P ha−1. Russet Burbank did not respond to P application. The trends of P uptake efficiency (PUE) for AC and AS mirrored that of tuber yield, while RB had a linear increase in PUE with P rate. The study highlights that current P recommendations for potatoes can be reduced without affecting yield, thus increasing farmers’ economic returns. It also reveals the need to test optimal fertilization levels for new cultivars.

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 score0.073
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.040
GPT teacher head0.249
Teacher spread0.209 · 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

Citations15
Published2017
Admission routes3
Has abstractyes

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