Platform-led or firm-led? An analysis of artificial intelligence development strategies in agricultural supply chains
Bibliographic record
Abstract
Agriculture experiences substantial yield losses due to pests and diseases, underscoring a need for advanced solutions such as artificial intelligence (AI). This study examines a contract farming supply chain with an agriculture firm and a platform, focusing on AI’s role in reducing these losses. Using game theory, we explore AI development conditions and compare two AI development modes: one where the agriculture firm develops AI (firm-led), and another where the platform undertakes AI development (platform-led). We also evaluate the effects of yield uncertainty, AI development efficiency, and firm’s planting effort efficiency on these strategies. Our findings reveal several key insights. First, in the firm-led mode, when AI enhances the firm’s planting effort efficiency, the firm always benefits, whereas only low AI development efficiency is beneficial to the platform. When AI reduces effort efficiency, only high development efficiency is advantageous to both parties. Second, in the platform-led mode, when AI improves planting effort efficiency, the outcomes are reversed compared to the firm-led mode. When AI reduces effort efficiency, only a moderate development efficiency is beneficial to the firm. Interestingly, in cases where AI significantly lowers effort efficiency, the platform consistently benefits. Third, the platform always tends to choose the platform-led mode, whereas the firm chooses this mode only when AI development efficiency is low. Additionally, the higher the average yield reduction rate, the greater the role of AI, the more likely the firm is to choose the firm-led mode. These insights contribute to a deeper understanding of AI development strategies in agricultural supply chains. • We investigate a contract farming supply chain that uses AI to mitigate yield losses. • We analyze two AI development modes: platform-led and agricultural firm-led modes. • An extremely high development efficiency can harm the platform in the firm-led mode. • Only platform-led mode can achieve win-win situation and raise AI development level. • Higher disaster occurrence probability, more likely firm is to choose firm-led mode.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".