Prospects of LDN in the SWANA region through SLM and micro water harvesting
Bibliographic record
Abstract
Sustainable Land Management (SLM) is an important consideration for dryland regions within the Southwest Asia and North Africa (SWANA) region, since conservation agriculture, water management, and biodiversity enhancement can build organic carbon stocks in soils. The current study considers dryland strategies and recommends top strategies of Low Emissions Development (LED) for dryland agricultural policies. Data compilation for this study is based on published regional data in reputable sources used to substantiate agricultural management strategies that support the accumulation of organic carbon in soils. Based on the results, SLM strategies that support carbon buildup should be adopted by governments. More specifically, governments should consider revising their National Adaptation Plans (NAPs) and Nationally Determined Contributions (NDCs) in the agricultural sector to consider LED by addressing existing examples from practice evident in dryland regions. Specific evidence-based recommendations include (1) policies to reduce overgrazing and intensive tillage, since this disturbs soils and affects soil erosion and productivity; (2) policies that encourage the use of micro harvesting and recycled (brackish) water/ wastewater; and (3) policies that encourage maintaining a ground cover and biodiversity, such as retaining crop residues and diversifying crops by using crop rotations, including halophytic and glycophytic species.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".