Collaboration and optimization in farmland exchanges
Bibliographic record
Abstract
Abstract To keep businesses competitive in world markets, the number of farms in most countries is decreasing while farm size is increasing, and parcels making up the farm area are extremely fragmented. It is common for farms to expand faster than available arable land near the farm center, leading to longer distances between the farm center and field areas. Consequently, logistic costs increase and affect the farm's profitability and the environment. Moreover, there is often substantial overlap between different farms, which implies that it is possible for farmers to collaborate with each other. We developed and proposed three optimization models to describe different levels of collaboration and sharing restrictions. Many practical considerations need to be weighed, such as the soil type and crops used. We collected detailed information on farms, parcels, and cost drivers in seven large instances for our case study in Sweden. One instance had 1431 farms and 32,091 parcels with 96 crop types. The resulting optimization models were very large; special pre‐processing and optimization techniques were developed for their solutions. The theoretical potential for collaboration in logistic cost reduction was very large, ranging from approximately 10% to 50% depending on the form of the collaboration. Because a large part of costs comes from less use of diesel for transport resources, the reduction in cost is directly related to a reduction in CO2 emissions.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".