Evaluation of STICS model performance for long-term simulation of biomass production and nitrogen nutrition of spring barley and timothy cultivated in two important agricultural regions in Quebec (Canada)
Bibliographic record
Abstract
Spring barley (Hordeum vulgare L.) and timothy (Phleum pratense L.) are crops of prime economic importance for the province of Quebec and other regions with cold, humid continental climate (North America, Nordic countries, etc.). Soil-crop models are powerful tools for calculating, a wide range of agronomic and environmental variables, since they are designed to simulate the complex interactions between crops, water, and soil nitrogen (N) in the soil–plant–atmosphere continuum. Among other models, STICS is a process-based soil-crop model developed initially for temperate agropedoclimatic conditions. However, it can be adapted to conditions of other agrosystems.The objectives of this thesis were to analyze and extend the scope of application of STICS to these two economically important crops grown under agropedoclimatic conditions in the Province of Quebec, and to evaluate the model's predictive performance on long–term simulations. This thesis is a contribution to the study of the genericity of STICS for Quebec agrosystems. In addition to the climatic context, the originality of this work lies in the crops studied – spring barley and timothy – and the number of successive years of continuous simulations (without annual reinitialization). The predictive performances of STICS were analyzed for aboveground biomass production, N content and N export for i) a 31-year spring barley monoculture grown under two tillage systems and fertilized with two N sources (mineral N and liquid dairy manure); and ii) an 8-year timothy grassland, fertilized each year with four application N rates (0, 60, 120, 180 kg N ha-1). We used databases from two experimental field trials conducted by Agriculture and Agri-Food Canada.For the barley monoculture, the STICS calibration procedure required the adjustment of cultivar parameters in particular, thus confirming the genericity of most plant parameters defined in STICS. There is a good agreement between observed and predicted variables of interest with the various tillage systems and N sources during the 31 successive barley cropping years, but with greater dispersion for the N nutrition. Predictions of crop attributes were more accurate in years with rainfall close to the long-term average. For timothy grassland grown over 8 years, the agreement between observed and predicted values was satisfactory for the first harvest. STICS correctly simulated the positive effect of the N application rates on biomass production and plant N nutrition. Nevertheless, the predicted values were overestimated by the model in the absence of N fertilization. Except for this very specific situation, which is not representative of agronomic practices, STICS performed satisfactorily in the context of the two field experiments studied. In addition, STICS reproduced well decreasing trend in timothy productivity observed with the age of the sward. The results showed that this decrease in yield over time is strongly correlated with the reduction in metabolic reserve in the perennial organs.In conclusion, this thesis work has demonstrated the applicability and reliability of the STICS model for the long-term simulation of biomass production and N nutrition of spring barley and timothy under agropedoclimatic conditions in the Province of Quebec.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".