State of the global adoption and spread of Conservation Agriculture
Bibliographic record
Abstract
Chapter 2 in volume 1 provided a detailed account of the global adoption and spread of Conservation Agriculture (CA) up to the year 2015/16. This chapter provides an update of the global adoption and spread of CA for the year up to 2018/19. In 2008/09, global CA cropland area was 106.5 M ha spread across 36 countries. In 2013/14, the global area of CA cropland was 156.7 M ha, spread across 55 countries. In 2015/16, the global area of CA cropland was 180.4 M ha, spread across 79 countries. In 2018/19, the CA area increased to 205.4 M ha (14.7% of global cropland), spread across 102 countries. Thus, CA increased by some 50 M ha of cropland for each of the two five-year periods, 2008/09 to 2013/14, and 2014/15 to 2018/19. About 50% of the global CA area is located in the Global South and 50% in the Global North. At the regional level, 4% of the CA area in 2008/09 was in Europe (including Russia and Ukraine), Asia and Africa whereas in 2018/19, it was 16%. Since 2008/09, greater percentage gains in CA area have been recorded for Europe, Asia and Africa regions. At the national level, countries that have increased their CA areas significantly are Brazil, Argentina, Paraguay and Uruguay in South America; the USA and Canada in North America; Russia and Ukraine; Spain, France, the UK, Italy and Romania in Europe; China, India, Kazakhstan, Pakistan and Iran in Asia; South Africa, Zambia and Ghana in Africa, and Australia. CA systems have an important role to play in addressing the global burden of environmental crises and in meeting the Sustainable Development Goals. The 8th World Congress on Conservation Agriculture agreed to work towards a notional goal of transforming 50% of global cropland area or 700 M ha into CA by 2050. CA global community must continue its effort to improve the quality and performance of CA systems by incorporating biological or organic CA practices. Equally important for the future is the need to support smallholder farmers transform their conventional systems to CA systems with support from sustainable mechanization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".