The Role of Agroforestry in Poverty Alleviation: A Case Study from Nujiang Prefecture, Southwestern China
Bibliographic record
Abstract
Agroforestry has gained increasing attention as a sustainable land use mode to ensure food security, mitigate global climate change, and improve farmers’ livelihoods. Likewise, agroforestry plays a key role in alleviating poverty, mitigating climate change and achieving the Sustainable Development Goals (SDGs) in China. Lanxangia tsaoko, as a typical agroforestry species in Nujiang Prefecture, plays a vital role in improving farmers’ livelihoods. After years of development, the Lanxangia tsaoko industry (LTI) in Nujiang Prefecture has made remarkable achievements and accumulated useful experiences. Taking the development of LTI as an example, this paper analyzes the impact of agroforestry on farmers’ livelihoods and its mechanism through field survey and theoretical deduction. First, by investigating the willingness of households to choose LTI, we found that most farmers have a positive attitude toward LTI, and they actively participate in cooperative organizations. Then, by analyzing the development stage and mode of LTI in Nujiang Prefecture, we found that the industry has made significant progress under the external and internal effects. We also found that the mechanism by which agroforestry affects farmers’ livelihoods is a process in which various stakeholders, including government, enterprises, farmers, etc., participate in industrial development with their production components and advantages. Although agroforestry is an effective way to improve farmers’ livelihoods and promote sustainable agricultural development, it is also necessary to pay attention to risk prevention. This paper discusses the role of agroforestry in farmers’ livelihoods, which provides a reference for lower-income forest areas.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".