The use of the land suitability rating system to assess climate change impacts on corn production in the lower Fraser Valley of British Columbia
Bibliographic record
Abstract
The land suitability rating system (LSRS) is a spatial modeling tool that generates a class rating for parcels of land for specific agricultural crops based on a soil–climate–landscape potential. We applied the LSRS module for corn suitability to the agricultural portion of the lower Fraser Valley of British Columbia (BC). We used data from six UN-IPCC AR4 projections covering a range of cold to hot and wet to dry scenarios for the time periods 2010–2039, 2040–2069, and 2070–2099 to assess the impacts of climate change on corn production. To obtain satisfactory spatial results, we linked high-resolution (400 m grid) monthly temperature and precipitation values to the individual polygons of a detailed (1 : 25 000 scale) soil map available for the study area. Of the six future climate scenarios evaluated, the Goddard Institute for Space Studies (GISS_EH-A1B/3) yielded the most favourable results whereby land suitability for corn without irrigation remained relatively stable through the 21st century. Conversely, the Hadley Centre Global Environmental Model (HadGEM-A1B/1) projected a large drop in land suitabililty for corn due to increased climatic and soil moisture deficits. The wide range of climate scenario inputs generated a similarly wide range of LSRS ratings. Most scenarios generated positive impacts for land suitability up to mid-century but negative impacts by late century. Overall, increased heat and aridity will produce earlier harvest dates for corn and likely mean significant changes to the types and timing of crop management practices in the region.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".