OECD-FAO Agricultural Outlook 2019-2028 (Summary in Portuguese)
Bibliographic record
Abstract
Global agriculture has evolved into a highly diverse sector, with operations that range from small subsistence farms to large multinational holdings.Farmers' products are sold fresh in local markets, but also across the world through sophisticated and modern value chains.Beyond their traditional role of providing humankind with food, farmers are important custodians of the natural environment and have become producers of renewable energy.In order to meet the high expectations society places on agriculture, public and private decision makers require reliable information on the likely trends of global demand, supply, trade and prices and the factors driving them.To this end, the OECD-FAO Agricultural Outlook is an annual reference that provides a comprehensive medium-term baseline scenario for agricultural commodity markets at national, regional and global levels.In addition to providing a plausible baseline scenario for agriculture markets in the coming decade, the Outlook identifies a widening set of risks to agricultural markets that can help policy makers better anticipate and manage them.These include the spread of plant and animal diseases and the growing risk of extreme climatic events, as well as possible supply disruptions from growing trade tensions.This OECD-FAO Agricultural Outlook 2019-2028 foresees that the demand for agricultural products will grow by 15% over the coming decade.The way in which this demand is met will determine the sector's impact on the natural resource base, notably land, water, and biodiversity.Rising food production also comes with higher greenhouse gas emissions, with nearly one quarter of all emissions coming from agriculture, forestry and land use change.Unsurprisingly, there are now mounting pressures on agriculture to reduce its carbon footprint, and to help mitigate climate change.At the same time, roughly two billion people derive their livelihoods from agriculture.Many of the world's poorest people will continue to live in rural areas and will depend on agriculture for an important share of their incomes.Some 820 million people worldwide remain undernourished, while millions suffer from other forms of malnutrition, such as micronutrient deficiencies and obesity.This report supports the work of our Members in their efforts to end hunger, achieve food security, improve nutrition, and promote sustainable agriculture by 2030, as committed under the Sustainable Development Goals (SDGs) and in the 2015 UN Framework Convention on Climate Change Paris Agreement.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.048 | 0.041 |
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".