Harvesting value: Corporate strategies of data assetization in agriculture and their socio-ecological implications
Bibliographic record
Abstract
The global food system is characterized by market concentration and oligopoly. In our article, we focus on the most powerful input supply and machinery companies and analyze how these firms create value, both economic and otherwise, from big data. In digital capitalism, data is valorized across sectors; personal data is aggregated into large-scale datasets, a practice that feeds economic concentration and monopolization. Big data also has become central to the business model for agricultural companies; it is a claim made by the companies themselves. Yet, little is known about their specific strategies to do so. We aim to fill this gap, asking how is agricultural data transformed into value by the most powerful agribusinesses and ag-tech firms? Through the lens of assetization, we examine corporate strategies for transforming agricultural data into value. We draw on literature from food studies, specifically political economic analyses of the historical practices of agricultural corporations, as well as literature from critical data studies that investigates data as an asset. For our analysis, we rely on a variety of gray literature and public-facing documents: financial documents, sustainability and shareholder reports, terms of use, license agreements, and news articles. Our results contribute to the critical data studies literature on agricultural big data by identifying three main strategies of assetization: securing relationships and dependence, price-setting and data sharing, and product development and targeted marketing. The strategies have socio-ecological implications; our results indicate the reproduction of asymmetrical power relations in the agri-food system favoring corporations and the continuation of long-standing dynamics of inequalities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".