Impacts of Crop Rotation and Tillage Practices on Potato Yield and Farm Revenue
Bibliographic record
Abstract
Core Ideas The two‐step regression simulation approach was the most appropriate model to evaluate all important explanatory variables on mean potato yield. Potato yield increased when the frequency of potato increased in the rotation. Rotations with lower potato frequency had higher net revenue for the potato year; however, rotations with higher potato frequency had higher overall net revenues. The overall financial impacts of rotations with lower potato frequency were negative. Other incentives for adopting beneficial management practices are necessary. To reduce the impacts of intensive potato ( Solanum tuberosum L.) production systems on the environment, beneficial management practices (BMPs) (e.g., crop rotation and conservation tillage) have been promoted in Atlantic Canada. However, quantitative information on the impacts of BMPs on yield and farm revenue is still lacking. Direct assessment of BMPs in operating farms is difficult because producers adjust other inputs and practices when implementing BMPs. A dataset with 5 yr of crop rotation, farm inputs, and soil and climate data was obtained from farms in New Brunswick, Canada. Two‐step regression with a stochastic production function method was used to assess the impacts of three crop rotations and two tillage practices on potato yield. Potato yield increased when potato frequency increased in the rotation. This effect was largely caused by increased fertilizer and other inputs on rotations with a higher potato frequency. Spring moldboard plowing had negative effects on yield compared with fall moldboard plowing. The two‐step yield model was used with different scenarios to estimate the effect of crop rotation on farm revenue net of input costs. Rotations with lower potato frequency had higher net revenue for the potato year, largely because of reduced input costs. However, rotations with higher potato frequency had higher overall (multiyear) net revenues because of the more valuable potato years in the rotation. Therefore, without considering the long‐term impacts on the environment, the overall financial impacts of rotations with lower potato frequency were negative. This suggests that other incentives for adopting BMPs are necessary.
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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 | 0.000 | 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 | 0.000 |
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 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".