The Economics of Annual Legume and Double Legume Cover Cropping in Southern Manitoba
Bibliographic record
Abstract
Plants require nitrogen for healthy growth and development.Legurne plants have a unique characteristic whereby they are able to naturally fix nitrogen from the atmosphere for their own growth and development.Some of this nitrogen then remains for use by subsequent crops.Using historical data from crop producing farms in southern Manitoba, this study quantifies the economic savings that could be realized by using legumes to supply nitrogen in a cereal-oilseed based rotation.Stochastic budgets are developed for four alternative crop rotations and the returns associated with each are evaluated using the utility-based risk ranking methods of stochastic dominance and stochastic efficiency.It is found that including a legume cover crop in a cereal-oilseed based rotation can reduce the amount of nitrogen required by a subsequent crop and in turn increase the net returns associated with the complete crop rotation.First and most importantly I would like to thank my family for supporting me and encouraging me throughout my entire education.You always believed in me and provided me with unconditional support, encouragement, and understanding without which I would have never been able to make it this far.Thank you to my supervisor, Dr. Jared Carlberg, for all your encouragement and guidance and always making time to help with whatever questions I had.Thank you to Dr. James Richardson
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".