Towards an improved representation of the relationship between root traits and nitrogen losses in process-based models
Bibliographic record
Abstract
CONTEXT Nitrogen (N) application to crops is crucial to feed an increasing world population. Yet, much of this N is not taken up by crops, initiating a cascade of N losses with dire environmental and economic consequences. There is, therefore, a need to develop crops with traits that make them use N more efficiently, thereby reducing N losses. Process-based models have been used to design in-silico crops with desirable traits to maximize yield and increase climate resiliency, but few have been used with the perspective of reducing N losses. OBJECTIVE To examine the way process-based models capture interactions between root traits and N losses, and propose opportunities to improve model representation of observed relationships. METHODS We synthesize the current knowledge on the relationships between plant traits and N losses based on experiments reported in the literature, conduct a survey of process-based models simulating crop growth and N losses, and run a sensitivity analysis with selected models (DSSAT, APSIM, DNDCvCAN, Daisy). RESULTS AND CONCLUSIONS The results show that the relationships between root traits and N losses can be very strong in experiments, but model simulations do not capture the magnitude of these associations well. This is mainly due to the lack of a robust representation of the plant root mechanisms influencing N losses. Suggested model improvements include designing new functions to link root traits with key N-cycling processes supported by experimental evidence – such as root exudation of various compounds including biological nitrification inhibitors – and using easily observable morphological traits in process-based models as proxies to predict changes induced by plants on N-cycling by soil microbial communities. SIGNIFICANCE This work represents a key step towards designing novel root function-based ideotypes adapted to reduced fertilizer inputs while maintaining the same level of yield, and that is, therefore, potentially less harmful to the environment.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".