Simulating forage crop production in a northern climate with the Integrated Farm System Model
Bibliographic record
Abstract
Jégo, G., Rotz, C. A., Bélanger, G., Tremblay, G. F., Charbonneau, E. and Pellerin, D. 2015. Simulating forage crop production in a northern climate with the Integrated Farm System Model. Can. J. Plant Sci. 95: 745–757. Whole-farm simulation models are useful tools for evaluating the effect of management practices and climate variability on the agro-environmental and economic performance of farms. A few process-based farm-scale models have been developed, but none has been evaluated in northern regions with boreal and hemiboreal climates characterized by a short growing season and a long period with snow cover. The study objectives were to calibrate the grass sub-model of the Integrated Farm System Model (IFSM) and evaluate its predictions of yield and nutritive value of timothy and alfalfa, grown alone or in a mixture, using experimental field data from across Canada, andto assess IFSM's predictions of yield of major annual crops grown on dairy farms in eastern Canada using regional yield data from two contrasting regions. Several timothy and alfalfa datasets combining sites, years, harvests, and N fertilization rates were used to calibrate and evaluate the model. For timothy and alfalfa, the model's accuracy was globally satisfactory in predicting dry matter yield and neutral detergent fiber concentration with a normalized root mean square error (NRMSE)<30%. For N uptake, the scatter was a bit larger, especially for timothy (NRMSE= 49%), mainly because of a small range in the measured data. The model's accuracy for predicting the yield of annual crops was generally good, with an NRMSE<30%. Adding timothy and alfalfa to the grass sub-model of IFSM and verifying the model's performance for annual crops confirmed that IFSM can be used in northern regions of North America. In addition, the model was able to simulate the yield and nutritive value of a timothy–alfalfa mixture, which is the most common perennial mixture used in Canada.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".