The Effect of Producers' Risk Attitudes on Sizing the Harvest and On‐farm Drying System
Bibliographic record
Abstract
A simulation model of an on‐farm grain harvest and drying system was used to determine net returns associated with alternative sized systems. The physical processes of corn and soybean planting, growth, harvest and drying were simulated under historical weather conditions for 33 years. Weather data determined the number of field days, physiological maturity date, harvest grain moisture, yield losses and dryer energy costs. Guidelines developed by agricultural engineers and agricultural economists were used to initially size the combine and dryer for three sizes of farms. Capacities of the combine and dryer were increased and decreased by 10, 20, 30, 40 and 50%, independently. Net returns to labor and management, reflecting yield and price correlations, were determined for each of the equipment combinations. Stochastic dominance was used to evaluate the distributions of net revenue and determine the risk efficient combine and dryer sets. It was found that there were small differences in net returns for systems that completed harvest and drying in the 27B32 day range for all three farm sizes. For a given size of farm, risk averse producers were found to prefer smaller capacity combines and dryers. Les auteurs se sont servis d'un modèle simulant la récolte et le séchage du grain à la ferme pour calculer le revenu net de systèmes de production de taille variable. Les aspects physiques de la plantation, de la croissance, de la récolte et du séchage du maïs et du soja ont été reproduits en fonction des données météorologiques relevées pendant 33 ans. Ces données ont permis d'établir le nombre de jours de croissance, la date de la maturité physiologique, la teneur en eau du grain, les pertes de rendement et le coût de l'énergie nécessaire au séchage. Ensuite, les auteurs ont suivi les recommandations des agronomes et des économistes agricoles pour déterminer les dimensions initiales de la moissonneuse et du séchoir pour trois exploitations de taille différente. La capacité de la moissonneuse et celle du séchoir ont été augmentées ou réduites indépendamment de 10, de 20, de 30, de 40 et de 50 pour cent, puis on a calculé le revenu net venant du travail et de la gestion du producteur en tenant compte de la corrélation entre le rendement et les prix pour chaque combinaison. La dominance stochastique a permis d'estimer la distribution du revenu net et d'identifier les ensembles moissonneuse/séchoir rentables en fonction du risque. Les systèmes où la récolte et le séchage sont terminés au bout de 27 à 32 jours entraînent de légères variations du revenu net, pour les trois types d'exploitation. Peu importe la taille de la ferme, l'aversion pour le risque incite le producteur à choisir une moissonneuse et un séchoir de dimensions plus modestes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".