Impacts of natural disasters on smallholder farmers: gaps and recommendations
Bibliographic record
Abstract
Here, we review the impacts of recent natural disasters in developing countries on rural agriculture and livelihoods with the objective of understanding gaps and providing recommendations. Lessons from these disasters demonstrate that national governments, aid agencies, and international/non-governmental organizations (I/NGOs) are effective primarily at distributing short-term products (e.g. food packages and tarpaulin) to cities. Such products are inexpensive, simple to procure, and easily quantifiable for donors. Unfortunately, the literature suggests that many national governments and foreign NGOs are ineffective at assisting rural farmers in the short and long term. Given that the global community is somewhat effective at distributing short-term products, we suggest that a similar strategy should be developed for rural areas, but focusing on products that can assist farm households. There appears to be a gap in knowledge of effective products that can target such households after a disaster. We propose an emergency sustainable agriculture kit (eSAK) framework for disaster relief in rural areas that involves a comprehensive list of products that can be combined into packages to address the needs of shelter, hunger, first aid, seeds, preservation of indigenous crop varieties, and post-disaster labour shortages. We also propose ideas on how to re-purpose relief products provided to urban areas to assist with farm needs. Products highlighted are rolls of agricultural-grade plastics, low-oxygen grain storage bags, waterproof gardening gloves, multi-use shovels, seeds of early maturing crops, fertilizers, inexpensive farming tools, temporary food support, and first-aid kits. These products are needed, inexpensive, labour efficient, compact, lightweight, available/procurable on a large scale, simple, and re- usable. Furthermore, correct use and re-purposing of the products can be explained using accompanying graphical illustrations, which is critical for rural illiterate households. As distribution to rural areas is a challenge, especially after a disaster, we propose the use of pre-existing alcohol/cigarette/snackfood distribution networks as a novel strategy for rural disaster relief. These efforts must be in partnership with local officials and grassroots organizations, with dedicated funding from governments and international aid agencies. It is hoped that global stakeholders will benefit from these recommendations to assist affected farmers after a crisis.
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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".