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Record W2512927949 · doi:10.1139/cjps-2016-0185

Biological nitrogen fixation by pulse crops on the semiarid Canadian Prairie

2016· article· en· W2512927949 on OpenAlexaffvenueabout
Zakir Hossain, Xiaoyu Wang, Chantal Hamel, J. Diane Knight, Malcolm J. Morrison, Yongtao Gan

Bibliographic record

VenueCanadian Journal of Plant Science · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLegume Nitrogen Fixing Symbiosis
Canadian institutionsUniversity of SaskatchewanAgriculture and Agri-Food Canada
Fundersnot available
KeywordsCultivarSativumPhaseolusVicia fabaField peaBiologyNitrogen fixationAgronomyPisumGrowing seasonCropField experimentLegumeHorticulture

Abstract

fetched live from OpenAlex

Abstract: Pulses play a significant role in nitrogen cycling as they fix atmospheric N2 through symbiosis. However, it is unknown whether there are differences in the ability of biological nitrogen fixation (BNF) among pulse species and individual cultivars. Here, we quantified the BNF ability of selected pulse cultivars and determined the effect on crop yield. A total of 25 species-cultivar combinations of chickpea (Cicer arietinum L.), dry bean (Phaseolus vulgaris L.), faba bean (Vicia faba L.), field pea (Pisum sativum L.), and lentil (Lens culinaris Medik.) were tested in 2008–2010. Pulses had a higher BNF in the wetter 2010 season, and a lower BNF in the drier 2009 season. In 2010, faba bean and chickpea had the highest BNF at 106 kg N ha-1, followed by lentil, field pea, and dry bean at 87, 69, and 12 kg N ha-1, respectively. Across years, field pea had the most stable BNF ability, fixing 55 kg N ha-1 with an average seed yield of 2418 kg ha-1. There are large differences in BNF and yield among cultivars within a species and the magnitude of the difference varied with years. Large genetic variability in BNF and yield suggest the possibility that pulse cultivars with a higher N2-fixing ability and seed yield can be developed through selection of the N2-fixing trait.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.527
Threshold uncertainty score0.875

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.019
GPT teacher head0.194
Teacher spread0.175 · 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

Citations59
Published2016
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Plant ScienceSame topicLegume Nitrogen Fixing SymbiosisFrench-language works237,207