Reprint from Agricultural Institute of Canada Sustainable Futures Spring 2011 Food Scarcity – A Myth?
Bibliographic record
Abstract
The year 2011 started off with low world stocks and improving commodity prices. The rising prices are largely due to challenging weather anomalies around the world reducing yield and quality. Combine this with an increasing world population, improved living standards and growing demands for livestock products which requires more grain production – and the future for Canada’s agricultural commodities looks pretty good. Similar to 2008, some market analysts are bullish on long-term commodity price increases. Sustained profitability is predicted based on the belief that global food demand will outstrip supply capacities. On the other hand, I’ve run across commentaries recently that question the prediction of “food scarcity”. There are several reasons why the minority of analysts believes the world will not face food scarcity challenges and is, in fact, optimistic about increasing supply and food availability. First of all, consider the real price of Canadian wheat over a 105 year period (Figure 1). Despite some annual market fluctuations, it is on a slow decline. Given the capacities of 21 st century technology, innovation and communication to increase production with improved efficiencies, why would this long-term trend reverse? There are management and technology innovations emerging that are already reducing the amount of fossil-fuel based fertilizers and crop protection products per ton of grain or kilo of livestock. This is critical to the transformation towards a sustainable global food system. Secondly, there are substantial areas of quality farm land that are currently under-utilized; some obvious spots The UK Government’s 2011 Foresight Report estimates that the application of existing knowledge, management techniques and technology could increase average yields two to threefold in many parts of Africa, and twofold across the former Russian Federation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.173 | 0.075 |
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".