An Overview of Land Degradation and Sustainable Land Management in the Near East and North Africa
Bibliographic record
Abstract
Land degradation and desertification (LDD) and climate change are having increased effects in the Near East and North Africa (NENA) impacting the livelihoods of about 410 million people. Agriculture is a vital sector, contributing on average 14% to the Gross Domestic Product (GDP) (excluding oil producing countries) and providing jobs and incomes for 38% of the region’s economically active population. Nevertheless, most NENA countries import at least 50% of the calories they consume. Furthermore, it is estimated that the total area that is desertified or is vulnerable to desertification cover 9.84 million km2 or about 86.7% of the total NENA region. Soil erosion by water, wind, and sand and dust storms (SDS) cause losses of about USD 13 billion of GDP each year. To confront these hardships, the region must endorse proper land use planning, prioritization of target areas for restoration and adoption of sustainable land and water management (SLWM) to reverse the situation. This paper analyses the inter-linkages between LDD, resource base management and food security under different scenarios and offers mitigation and remediation options. These include knowledge management and sharing; establishment of a regional platform to facilitate dialogue; public and private investment opportunities; provision of tools to scale-out sustainable land and water management options; and creation of a conducive enabling environment supported by policies and strategies. The paper provides policy and decision-makers with priority actions and options to enhance productivity, and combat land degradation to improve food security 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".