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Record W4200108656 · doi:10.5539/jas.v14n1p1

Effect of Row Spacing and Plant Density on the Yield of Faba bean L. Under Very Differentiated Humidity Conditions

2021· article· en· W4200108656 on OpenAlexvenueno aff
J. Prusiński

Bibliographic record

VenueJournal of Agricultural Science · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetic and Environmental Crop Studies
Canadian institutionsnot available
Fundersnot available
KeywordsYield (engineering)Plant densityAgronomyRelative humidityHumidityAir temperatureHorticultureBiologySowingGeographyMeteorology

Abstract

fetched live from OpenAlex

The research covered Polish low-tannin Faba bean ‘Albus’, and its yielding depending on the row spacing and plant density under very different humidity conditions across the research years. The mean multi-year faba bean seed yield was 4.01 t ha-1 and it was most correlated with the total rainfall in August and in June-August. Due to a considerable variation in the total rainfall and air temperature, the faba bean seed yield ranged from 0.69-2.14 t ha-1 in dry years (2018-2019) with high air temperature, from 6.64 to 6.59 ha-1 in humid years (2016-2017) with a considerably higher total rainfall and lower air temperature in June-August. There was no significant effect of the row spacing and plant density on the faba bean yielding, except for a significantly lower seed yield in 2019 following the application of the lowest plant density. Out of all the yield structure components, only the number of pods per plant for 60-75 plants per m2 was significantly lower than for 45 plants. The other yield structure components did not differ significantly due to the factors studied either, except for the LAI, the value of which for a narrow row spacing was significantly higher. There was found a significant correlation between all the plant traits and with the faba bean yield.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.631
Threshold uncertainty score0.219

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.018
GPT teacher head0.204
Teacher spread0.186 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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