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Record W2905044221 · doi:10.5539/jas.v11n1p112

Effect of Boron Supplement on Yield of Wheat Grown in Calcareous Soils of Different Textural Classes under Arid Conditions

2018· article· en· W2905044221 on OpenAlexvenueno aff
Baydaa H. A. Al-Ameri, Suad A. Al-Saedi, Ibrahim B. Razaq

Bibliographic record

VenueJournal of Agricultural Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Micronutrient Interactions and Effects
Canadian institutionsnot available
Fundersnot available
KeywordsLoamCalcareousSoil waterAgronomyStrawAridSoil textureCalcareous soilsBiocharEnvironmental scienceChemistrySoil scienceGeologyBotanyBiology

Abstract

fetched live from OpenAlex

Boron sufficiency supply to plant in calcareous soils of arid regions is severely reduced under arid climatic conditions. Therefore, this study was conducted to determine the effect of Boron (B) fertilization on yield of Wheat grown in calcareous soils of arid regions. Boron was added at two rates namely 0 and 0.5 mg B kg-1 soil to three most common textured class’s soils. Straw and grain yield was determined on oven dried basis. B content of plants was determined as well. Straw and grain yield of wheat was significantly increased by (67.0 and 87.1%), (24.5 and 82%) and (64.5 and 48%) under the addition of 0.5 mg B Kg-1 soil over that of no B addition to clay, loam and sandy loam soil, respectively. Results also showed that wheat grown on the coarse-textured soil had the least B uptake per pot compared to loam and clay textured soils. B content in straw and grain was increased by (77.4 and 121%), (81.2 and 157%) and (184 and 96.9%) under B addition compared to those of zero B addition to clay soil, loam soil and sandy loamy soil, respectively. Response to B addition, significantly, increased in all soils due to increase of available B content in soils which may suggest the importance of adding adequate rate of B application under cropping system of arid regions.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.406
Threshold uncertainty score0.180

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 head0.014
GPT teacher head0.261
Teacher spread0.247 · 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
Published2018
Admission routes1
Has abstractyes

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