Crop yield impacts from rotations in Manitoba
Bibliographic record
Abstract
Primary agriculture and agriculture manufacturing, as one of the dominant industries in Manitoba, generates jobs, agricultural products, and boosts economic output. Crop rotation, swapping out different crops over seasons, is an agricultural practice with both economic and ecological benefits for both crop production and the environment. Previous research by Manitoba Agriculture Service Corporation (MASC) studied the relative yield responses of crops grown in Manitoba on large fields using only the previous crop as the influential factor, which might be biased. Thus, there is a need to explore the 'rotation effect' after taking more factors into the crop yield models. The purpose of this research is to explore how crop rotations influence crop yields in Manitoba. A yield model given inputs of fertilizers, climatic and regional factors, seeding dates, regional effects, yearly dummies, soil qualities, and crop rotation effects were developed in the analysis. Yield responses through the coefficients of previous crops were examined through the yield model at both provincial and regional levels. The results were consistent with previous findings that monoculture leads to negative yield potential and rotating crops with soybeans is likely to help boost crop yields. The impacts from the previous crops on yields in this study are more precisely estimated than Kubinec's since it contains more influential factors, which in return helps farmers better estimate their profits. Also, the effects of seeding dates, regional levels, fertilizers, precipitation and temperature on crop yields were identified. These new results provide farmers with a more thorough understanding of crop rotations so they can make better choices about crop selections for production in Manitoba.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".