A Farmer’s Account: Case Study on the Effect of Rainfall and Temperature to Grain Cultivation in Southwestern Saskatchewan, Canada
Bibliographic record
Abstract
The production of cereal, legume and oilseed crops on the prairie region of Canada is largely rainfed, with high variability in the accumulation and timing of precipitation. In turn, the fluctuation of climate imparts change in farming practice. The objective of the current study is to measure the effect of rainfall and temperature on grain yield, based on longitudinal data for multiple crops on a Saskatchewan farming operation. Adjustment was made for days to maturity, fertilizer management, crop inputs, and procedures (e.g., harvest method). Detailed and thorough records of rainfall and farming routine were obtained from a farm operator on different field plots over 33 consecutive growing seasons from 1986 to 2018. The efficacy of multiple adaptive farming practices to crop yield were also evaluated, and included seed treatment, swathing, desiccation, and in-crop spraying of fungicide or pesticide. Statistical models were formulated for the association of these factors to crop yield for canaryseed (Phalaris canariensis L.), canola (Brassica napus L.), lentil (Lens culinaris Medik.) and wheat (Triticum turgidum L.). Results from this study show that temperature and rainfall above the long-term average were negatively associated with wheat yield, although the effect modification between average temperature and cumulative rainfall was positively associated with wheat yield. Over 63% of the observed variation in crop yield was attributable to planting year on this farming operation. Crop diversification is key to mitigate the effects of extreme rainfall and temperature variation on yield in this agroregion.
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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 | 0.001 | 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 | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".