Estimating Extent of Vulnerability of Agriculture and Livelihoods to Climate Change
Bibliographic record
Abstract
Vulnerability assessments can play a vital role in designing appropriate adaptation and mitigation policies targeted towards climate change and its impacts on ecosystems, and those who depend upon these resources for their livelihoods. This paper assesses climate change in Karnataka, the agriculture and livelihoods contexts of vulnerability. Karnataka state has the second largest area under rainfed agriculture in India and several studies have shown that climate change is happening in the state. Agriculture sector is vulnerable to climate variability and change across its three regions: coastal, hilly and plains.Long-period monthly rainfall data (1901-2016) at district, region and state level was collected from India Meteorological Department and other sources and changes in seasonal and annual rainfall are analyzed. A Principal Component Analysis (PCA) was run on a dataset of seven variables for agricultural vulnerability and ten variables for livelihood vulnerability across thirty districts. The PCA generated three components for each index that broadly represented the underlying themes of agriculture and livelihood vulnerability present in the larger data set. Two vulnerability indices i.e., agricultural vulnerability index and livelihood vulnerability index were developed for all districts.Long-period rainfall analysis showed a small decreasing trend in annual rainfall at the state level. South-eastern region is becoming slightly wetter, while parts of hilly region becoming drier. In one district, rainfall reduced by 460 mm and in the neighbour it raised by 250 mm. Decadal meteorological drought analysis indicated an increasing trend in moderate droughts in north interior Karnataka. Indicators like cropping intensity, gross area irrigated and commercial crop area are the major drivers in determining the agricultural vulnerability. Livelihood index indicators like per capita income, population density, percentage of literacy rate and livestock units are major drivers for livelihood vulnerability. Agricultural vulnerability index analysis indicates four districts and livelihood vulnerability index analysis shows five districts as most vulnerable.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".