Comparing Random Forest to Bayesian Networks as nitrogen management decision support systems
Bibliographic record
Abstract
Abstract Nitrogen (N) is notoriously difficult to manage and there are many approaches for fertilizer N rate recommendations. Existing fertilizer N rate recommendation systems can be improved by incorporating the effects of weather on sidedress economicoptimum N rates (EONR). In this study, we evaluated the performance of machine learning methods, a Bayesian Network (BN) and a Random Forest (RF) for estimating EONR for corn. BN draws relationships between variables based on assumptions about conditional independence, where the model is structured by an algorithm or, in this case, expert opinion. In contrast, RF determines model structure based on the input variables and model output. The models were trained and validated using a large database ( n = 324) of corn yield response to N fertilizer collected across southern Ontario. Sixty‐six of the 324 site‐years were used for validation with success assessed by the frequency that N rate predictions that produced net returns were within CAN$25 ha −1 of the observed EONR. The success rate was 64% and 48% for the BN and RF, respectively. Both models incorporated weather from planting to sidedress and outperformed a benchmark provincial N recommendation system. We argue that BN has advantages when some input variables are unknown or uncertain and for improving model structure with stakeholder feedback. Moreover, RF is easy to implement but the model structure must use point estimates instead of probabilities for uncertain parameter values such as future weather. BN represents a more flexible modeling approach than RF for incorporating both modeling and stakeholder input.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".