The Effectiveness of Alternative Marketing Strategies for Ontario Corn and Soybean Producers
Bibliographic record
Abstract
Marketing decisions can be some of the most difficult decisions facing grain and oilseed producers. There is a lack of information about the marketing strategies that are most effective, and there is also a lack of consensus in the literature about whether some types of marketing strategies can consistently perform better than others. This paper uses a simulation model based on daily cash and futures prices to compare returns and risk over time from specific marketing strategies for corn and soybean producers in Ontario. This paper also examines whether there are differences in the relative effectiveness of strategies between higher‐price years and lower‐price years. The results indicate that preharvest marketing strategies for both corn and soybeans tend to generate prices that are much higher than selling everything at harvest (the baseline strategy), particularly for the higher‐price years; however, these differences are not always statistically significant. Preharvest strategies are also found to reduce downside risk relative to the baseline. Les décisions commerciales peuvent représenter certaines des décisions les plus difficiles auxquelles les producteurs de céréales et d’oléagineux sont confrontés. Il existe un manque d’information sur les stratégies commerciales les plus efficaces et un manque de consensus dans la littérature quant à l’existence ou non de stratégies commerciales capables de donner régulièrement de meilleurs résultats que d’autres. Dans le présent article, nous avons utilisé un modèle de simulation fondé sur les prix au comptant et les prix à terme quotidiens afin de comparer, au fil du temps, les rendements et les risques des stratégies commerciales qui sont spécifiques aux producteurs de maïs et de soja de l’Ontario. Nous avons aussi tenté de déterminer s’il existe ou non des écarts d’efficacité relative des stratégies entre les années où les prix sont élevés et les années où les prix sont faibles. Nos résultats indiquent que, dans le cas du maïs et du soja, la stratégie commerciale qui consiste à vendre la production avant la récolte tend à dégager des prix plus élevés que la stratégie commerciale qui consiste à vendre toute la production au moment de la récolte (stratégie de base), particulièrement dans le cas des années où les prix sont élevés. Cependant, ces écarts ne sont pas toujours statistiquement significatifs. Les stratégies de vente avant récolte contribueraient également à réduire le risque de perte en cas de baisse comparativement à la stratégie de base.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".