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Record W4415353918 · doi:10.1021/cen-10321-buscon6

Corteva to split along seed and agrochemical lines

2025· article· en· W4415353918 on OpenAlexaboutno aff
Alexander Tullo

Bibliographic record

VenueC&EN Global Enterprise · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Physiology and Cultivation Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAgrochemicalCropAgriculturePosition (finance)Unit (ring theory)Quarter (Canadian coin)Crop protection

Abstract

fetched live from OpenAlex

In a reversal of a long-held industry practice, Corteva Agriscience plans to split its crop protection and seeds businesses into two separate companies next year.“We did not make this decision quickly or take it lightly,” Corteva CEO Chuck Magro said on a conference call with investors Oct. 1.The crop protection chemical business will continue to bear the Corteva name. The company projects that the unit will generate $7.8 billion in sales this year, 44% of its total. About half of its chemical sales come from herbicides and a quarter from insecticides. The crop protection business also has a growing presence in biologicals, which represent about 6% of its sales.The planned seed company, temporarily dubbed SpinCo, is home to the Pioneer brand and is expected to post $9.9 billion in sales this year. It is the North American leader in corn and soy and holds a number 2 position in cotton and canola.Magro will lead SpinCo. Corteva chair Greg Page will assume that role for Corteva after the separation.Corteva itself was established as part of the 2017 merger of Dow and DuPont, which combined the seed and crop protection businesses of the two big chemical companies. Corteva spun off on its own in 2019.Corteva’s split would buck industry norms. Agricultural peers such as Bayer, Syngenta, and BASF have both seed and crop protection chemical businesses and often pair certain chemicals and seeds as complete systems for farmers.But during the conference call, Magro said Corteva is outgrowing that model and that to

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.565
Threshold uncertainty score0.148

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 head0.006
GPT teacher head0.237
Teacher spread0.231 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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