The economics of potato crop rotations in southern Manitoba
Bibliographic record
Abstract
Rapid expansion of the potato industry in Manitoba has led to increased potato production in Manitoba. While the introduction of potatoes into current cropping systems may provide an opportunity for producers to increase the profitability of their farming operation, development of economically and environmentally sustainable production systems is key to the long-term success of the industry. Currently, little information is available about the economic and environmental impacts of such crop rotations with respect to potato production in Canada. Recognizing the importance of sustainable production systems, in 1998, a potato crop rotation study was established at Manitoba Crop Diversification Center (MCDC) at Carberry to develop recommendations for irrigated potato management in southern Manitoba. Six crop rotations ranging from two to four years in duration, and containing potatoes in combination with oilseed, cereal and/or legume crops, were included in this study. Each phase of each rotation was present in each year making a total of 18 treatments. This paper discusses the economic return of these six different crop rotations. The data with respect to all the production practices, including storage, transport and marketing for the period of 1998 to 2001 were collected and analyzed using a computer model developed for this purpose. Econometrics View software was used to develop this model, and the model takes into account the whole system for each rotation when analyzing the data. The preliminary results suggest that, for the period of 1999 through 2000, both potato-canola and potato-oat-wheat rotations appeared to be promising rotations. However, the poor potato yield of the potato-oatwheat rotation in 2001 resulted in low average net income for that rotation. The higher net benefit of the potato-canola rotation during the time period analyzed was primarily the result of a higher proportion of the tuber yield being of marketable size, not for higher gross tuber or canola yields. It is, however, difficult to make firm conclusions at this point of time as to which rotation will be most profitable in the long term. This is mainly because of the rotation treatments have only been in place since 1998 and, as such, observed differences among rotations may be a function not only of the rotation but also of environmental conditions and management within a given year.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".