Water management of canola under tile drainage in the Canadian Prairies
Bibliographic record
Abstract
An effective water management system is necessary to address global food shortages, water scarcity, and increasing climate extremes. Southern Manitoba is a major crop production area with highly productive soils, flat topography, and seasonally high water table. The objectives of this research were to (i) determine the impacts of controlled drainage (CD), free drainage (FD), and no drainage (ND) on canola yield and quality, (ii) simulate water table depth (WTD) under canola production using the DRAINMOD model, (iii) evaluate the potential impacts of climate change on the hydrology and canola yield using the DRAINMOD model and (iv) assess the performance of the standard ET model under limiting conditions and fourteen empirical ET models in the region. The result shows large differences in yield between the years, suggesting that canola yield is significantly influenced by weather variables. In 2019 with normal average temperature and rainfall close to the long-term average, CD plots consistently yielded higher yields than FD and ND, with a significant difference with FD. As rainfall decreased and temperature increased in the following years, the impact of drainage, especially CD, becomes diminished, with no significant differences between the treatments. Also, the prevailing weather may have masked the oil quality parameters and nutrient dynamics across the soil profile, as there were no significant differences between the treatments. The WTD collected from the PESAI (Prairies East Sustainable Agriculture Initiative) site in Arborg was used to test the ability of the DRAINMOD model to predict WTD using the 2019 and 2020 canola growing seasons. Statistical analysis and graphical plots showed close agreement between the measured and simulated WTD. Economic analysis using the simulation results suggests that the 10 m drain spacing maximized the return on investment. The parameterized DRAINMOD model was thereafter run with downscaled climate model projections from CANESM2 for historical (1981-2010), midcentury (2041-2070), and late-century (2071-2100) periods under three representative concentration pathways (RCP2.6, RCP4.5, and RCP 8.5). Results showed an overall decrease in average relative canola yield under FD and CD in the future. The ET (evapotranspiration) is an essential input to hydrologic models. However, the weather variables required to compute the recommended ET model (FAO PM) equation are not readily available. The results showed that the FAO-PM model decreased with increased missing data, yielding average to poor results. The best performing models were Valiantzas-1, Valiantzas-3, Irmak, Valiantzas-2, and Priestly-Taylor models. New empirical coefficients were also developed to improve selected empirical ET models. Overall, this research is important for developing optimal drainage design variables for increased crop yield.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".