Profitability of Supplemental Irrigation and Soil Dewatering for Potato Production in Atlantic Canada: Insights from Historical Yield and Weather Data
Bibliographic record
Abstract
Abstract Highly variable and unpredictable precipitation in humid regions makes water management important for consistent potato production. This study assessed the influence of supplemental irrigation (SI) and soil dewatering on potato productivity and profitability in Prince Edward Island, Canada. The average yields of Russet Burbank (RB), Shepody, Kennebec and Goldrush cultivars from 2000 to 2020 (excluding 2018) were conceptualized as the results of an un-replicated experiment with growing season (GS) precipitation representing water supply treatment. GS precipitation varied from 155 to 479 mm, with an average of 338 mm. Yield increased with increasing GS precipitation in the 155–257 mm range (Rainfall Zone 1; 3/20 seasons), became relatively insensitive to GS precipitation in the 258–425 mm range (Rainfall Zone 2; 12/20 seasons), and decreased as GS precipitation increased from 426 to 479 mm (Rainfall Zone 3; 5/20 seasons). Yields responded to GS precipitation following second-order polynomial regressions, with GS precipitation explaining 69%, 65%, 29% and 50% of yield variation for RB, Shepody, Kennebec and Goldrush cultivars, respectively. These yield regression equations predict that SI using a center-pivot system would produce a positive profit in the first half of Rainfall Zone 1 regardless of field size and in the second half of Rainfall Zone 1 in fields over 40 ha. SI would not produce a positive profit in Rainfall Zone 2 regardless of field size because precipitation was high enough that additional water supply would not have resulted in sufficient yield gains to offset the cost of SI. Soil dewatering would be beneficial for optimal production in Rainfall Zone 3 in which precipitation was excessive. The annual variation in precipitation, unpredictability of SI requirements, and unprofitability in most seasons, present a significant financial barrier for the widespread implementation of SI. On the other hand, the gross income gained from increased yields by soil dewatering would fully cover the cost of tile drain installation after only two to three extremely wet seasons, making tile drainage a good investment for consistent production. This study demonstrates that historical rain-fed yield and weather data can be used to assess the economics of potato production with SI and soil dewatering, and provides important insights on potato water management in a humid temperate climate.
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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 | 0.002 | 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".