Detecting Disturbance to Agricultural Productivity from Historical Armed Conflict in Afghanistan: The Panjshir Offensives, 1980-1985
Bibliographic record
Abstract
A particular challenge threatening global food security is the threat of armed conflict. In particular, the Panjshir valley of northeastern Afghanistan continues to experience acute food insecurity due to intense armed conflict. In this rural valley, conflict driven displacement leads to agricultural land abandonment and decreases in crop yields. These decreases in local food production have an outsized impact on food security, due to the region’s dependence on subsistence agriculture. Despite the consensus that armed conflict has a significant negative impact on the population’s food security, the exact mechanics of how conflict impacts food security remains unclear. To quantify armed conflict’s impact on local food production, I compare trends in vegetation health between agricultural plots in high-conflict and no-conflict landscapes with similar altitudinal gradients. I focus on the period during the Soviet occupation of Afghanistan from 1980-1989, which saw nine major military offensives occur in the Panjshir valley. I use Landsat 5 (1984-2012) to obtain the Normalised Difference Vegetation Index (NDVI) values for agricultural plots that have been designated as control (no conflict) and treatment (high conflict). These plots are delineated using HEXAGON KH-9 declassified spy imagery, and assigned conflict intensity designations based on explosive ordnance disposal (EOD) data from The HALO Trust, a non-governmental organisation which carries out unexploded ordnance clearance in Afghanistan. Residual Trend analysis (RESTREND) is applied to Landsat NDVI values to distinguish between the shifts in vegetation health that are anthropogenically and climatically driven. This research provides a deeper understanding of how past conflict has acted as a driver of food insecurity in the region. Additionally, it allows for future work to build off of these findings and predict how current and future conflict might have an impact. These findings can inform humanitarian and development aid policy, while the methodology can be applied to other contexts where conflict is present.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".