Review of whole-farm models for accurate nitrogen budgets
Bibliographic record
Abstract
Whole-farm models describe the economic functioning and nutrient flows on farms including their interaction with the environment, e.g. through greenhouse gas emissions emissions. They range from simple decision-support tools to complex dynamic process-based models. The literature on whole-farm models in temperate climates was reviewed to identify gaps in knowledge, including development potential for the coupling with other models, within the context of the PREMIS project on primary farm data analysis and the Land-CRAFT center. Web of Science and Scopus databases were accessed using relevant keywords. Articles related to whole-farm models were then selected and the main attributes of the articles used were noted. Articles found from the ones selected, or from personal communication, were also added using the snowball approach. This resulted in a database of 192 articles and 116 different models which described either the whole farm or parts of it. Dynamic process-based models were the most used, particularly the Integrated Farm System, (IFSM, Rotz et al., 2007) and the Agricultural Production Systems sIMulator, (APSIM, McCown et al., 1996). These were followed by life cycle assessment (LCA) analyses that adhered to international standards set by the International Organization for Standardization (ISO) (ISO 2006a and b)). Dairy and beef farms were the most studied farm types, with most studies published from the U.S.A. and Australia, followed by New Zealand and Canada. Once the different models have been reviewed, consideration will be given to the developments necessary to fill any gaps in the existing ones and feed into landscape-level models.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".