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Optimizing the rate of phosphorus to enhance grain yield and quality in two Camelina sativa (L.) crantz accessions

2021· article· en· W3181742959 on OpenAlexaboutno aff
Muhammad Mansoor Javaid, Muhammad Saeed, Hasnain Waheed, Muhammad Nadeem, Muhammad Faizan Ahmad, Ahsan Aziz, Allah Wasaya, Masood Iqbal, Athar Mahmood, Rashad Mukhtar Balal

Bibliographic record

VenueSemina Ciências Agrárias · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNitrogen and Sulfur Effects on Brassica
Canadian institutionsnot available
Fundersnot available
KeywordsCamelina sativaRandomized block designYield (engineering)AgronomyPhosphorusCamelinaMathematicsCropFertilizerCrop yieldHorticultureBiologyChemistry

Abstract

fetched live from OpenAlex

Camelina sativa (L.) Crantz is an emerging oil seed crop and research information on its response to different levels of phosphorous (P) fertilizer is lacking. The two years study was performed to investigate the response of C. sativa to various rates of P fertilizer. The experiments were laid out in Randomized Complete Block Design (RCBD) with factorial arrangement having four replications. The P was applied in soil at the rate of 0, 30, 40 and 60 kg ha-1 to two C. sativa accessions namely Canadian and Australian. Soil applied phosphorus rates had significant effects on the growth, yield and quality of C. sativa and two accessions were varied to each other. Australian accession performed better in terms of quality traits and Canadian was superior in terms of seed yield. An increase in P rate improved growth, yield and quality and 60 kg P ha-1 resulted in maximal crop growth rate (6.79), seed yield (1239 kg ha-1), total P uptake (0.67%) and oil contents (39.8%). The regression model estimated that each increment in P rate increased the seed yield by 11.5 and 11.2 kg ha-1 in Canadian and Australian accessions, respectively. Conclusively, increases in P rates (0 to 60 kg ha-1) impart a positive impact on C. sativa accessions and 60 kg P ha-1 was most effective to achieve optimum yield and profitability.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.588

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 communication0.0000.000
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 head0.015
GPT teacher head0.322
Teacher spread0.307 · 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 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

Citations8
Published2021
Admission routes1
Has abstractyes

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