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Influence of Nutrient Management Practices on Growth, Flowering and Yield Attributes of Cucumber (Cucumis sativus)

2022· article· en· W4286699366 on OpenAlex
Priyanka Sahu, P. Tripathy, G. S. Sahu, S. K. Dash, S. K. Pattnayak, S. Sarkar, R. K. Nayak, Nisikanta Nayak, S. Mishra

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Environment and Climate Change · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Physiology and Cultivation Studies
Canadian institutionsNutrition International
Fundersnot available
KeywordsNutrient managementCucumisBiofertilizerVermicompostAzotobacterVineNutrientHorticultureFertilizerField experimentBiologyMathematicsVeterinary medicineAgronomyMedicine

Abstract

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Field experiments were conducted at AICRP on Vegetable Crops, operating under Odisha University of Agriculture and Technology, Bhubaneswar, Odisha, India during summer season of 2017 and 2018 to find out the impact of various nutrient management practices on growth, yield attributes and yield of cucumber (Cucumis sativus L.). Twelve nutrient management practices such as, T1 (Absolute Control), T2 (RDF through Fertilizer (100:60:60 NPK ha-1), T3 (½ RDF + Biofertilizer consortia (BF) i.e., Azospirillum, Azotobacter and PSB @ 4 kg ha-1 in 1:1:1), T4 (Vermicompost @ 4 tha-1), T5 (VC @ 2 tha-1+ BFs), T6 (½ RDF + VC @ 2 tha-1+ BFs), T7 (RDF+ VC @ 2 tha-1+ Biofertilizer consortia), T8 (FYM @ 20 tha-1), T9 (FYM @ 10 tha-1+ BFs), T10 (½ RDF + FYM @ 10 tha-1+ BFs), T11 (RDF+ FYM @ 10 tha-1+ BFs) and T12 (½ RDF + FYM @ 10 tha-1 + VC @ 2 tha-1+ BFs), were evaluated by adopting RBD replicated thrice. The pooled results over two years revealed significant variations among the nutrient management practices for all the characters under study. Invariably, INM practices recorded significantly better vegetative growth, earliness in flowering, fruit yield and yield attributing parameters over inorganic, organic sources, BFs and absolute control. The results revealed integrated application of ½ RDF+FYM @ 10tha-1+VC @ 2tha-1+BFs recorded significantly higher maximum vegetative growth parameters (i.e., vine length of 296.4 m with 4.1 primary branches vine-1), induced earliness in flowering (i.e., days to appearance of male flowers : 30.2, days to appearance of female flowers : 31.7, sex ratio of : 12.8, fruit yield attributing parameters (i.e., fruit girth : 15.0 cm, fruits vine-1 : 8.6, days to 1st fruit harvest : 45.3), days to final harvest : 80.1, yield i.e., marketable yield (12.6 kg plot-1, 156.0 q ha-1, 15.6 tha-1) and total fruit yield (13.9 kg plot-1, 172.2 q ha-1, 17.2 tha-1 ). Thus it may be concluded that integrated application of nutrients from inorganic, organic with soil inoculation of biofertilizer consortia not only increased significantly increased growth, flowering and fruit yield in cucumber.

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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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.631
Threshold uncertainty score0.121

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 head0.050
GPT teacher head0.249
Teacher spread0.198 · 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