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Assessment of the state and prospects of rapeseed production in Ukraine and in the world

2023· article· en· W4386562988 on OpenAlexaboutno aff
Oleksii Zabarnyi, О. Demyanyuk

Bibliographic record

VenueAgroecological journal · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Biological Studies
Canadian institutionsnot available
Fundersnot available
KeywordsRapeseedTonneAgricultural economicsAgricultural scienceChinaAgricultureProduction (economics)MealBusinessGeographyEconomicsEnvironmental scienceAgronomyBiologyFood science

Abstract

fetched live from OpenAlex

The article analyses the statistical data of the United States Department of Agriculture on the main producers, exporters, importers and consumers of rapeseed, oil and meal and establishes that the EU, Canada and China are the leaders in terms of rapeseed production in the world. According to the forecasts, in 23/24 MY almost half of all imported seeds will be from the EU (5.1 mln tonnes) and China (3.0 mln tonnes). Canada is the world’s largest exporter of rapeseed and rapeseed products, and it is forecast that about 8.6 mln tonnes of the seeds will be sold to other countries, including the EU and China. Canada has been the largest exporter of rapeseed for many years. According to analysts’ forecasts, 8.6 mln tonnes of seeds, which is 48.6% of the total world exports, will be sold to other countries. Canada will export 3.1 mln tonnes of oil and 5.25 mln tonnes of rapeseed meal. In 23/24 MY, the EU countries are forecast to use 25.4 mln tonnes of rapeseed and its products for domestic consumption, while China — 18.4 mln tonnes. According to the State Statistics Service of Ukraine, in 2013, 0.95 mln ha of winter rapeseed were harvested, the gross harvest amounted to 2.28 mln tonnes, and the average yield was 2.40 t/ha. In 2022, the gross harvest of winter rapeseed was 3.25 million tonnes. At the same time, the harvested area was 1.13 mln ha, and the average yield in the country increased to 2.87 t/ha. Thus, over the past 10 years, the sown area in Ukraine has increased by 19%, and the gross seed harvest by 42.4%. The increase in sown areas, gross seed harvest and average yields was driven by improvements in certain elements of winter rape growing technology and the introduction of new varieties and hybrids. The State Register of Plant Varieties Suitable for Distribution in Ukraine includes 350 varieties and hybrids of winter rape. Over the past 10 years, 306 varieties and hybrids of winter rape have been included in the Register, which is 87.4% of the total. At the same time, 20 varieties and 6 hybrids of winter rape of Ukrainian breeding and 16 varieties and 264 hybrids of foreign breeding were registered.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.021
GPT teacher head0.254
Teacher spread0.233 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2023
Admission routes1
Has abstractyes

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