Climate Change and Shrimp Farming in Andhra Pradesh, India: Socio-economics and Vulnerability
Bibliographic record
Abstract
Approximately 70% of shrimp consumed globally is farmed. India is ranked among the top five shrimp farming countries globally, and occurs mainly in the eastern coastal state of Andhra Pradesh (AP). More than 90% of the farms are less than 2 ha and are farmer owned, operated and managed. The objective of this study was to increase our understanding of climatic and socio-economic factors influencing this sector, through a survey of 300 shrimp farmers in AP in 2009/10. The farming communities were divisible into two groups: members of a society/cooperative and those operating individually. The latter were large scale adopting more intensive practices. The average production cost was Indian Rupees (IRS) 80,186 ha-1 and net income in summer and winter was IRS 221,901 and IRS 141,715, respectively. The mean technical efficiency estimated using Stochastic frontier function was 7% and 54%. The present study attempts to explain the difference in efficiencies using socio-economic and climatic variables, the latter being a novel approach. Among socio-economic variables, farming experience and membership in society were found to have a significant influence to improve technical and economic efficiencies. Further improvements in identifiable facets of the practices and a consequent increase in technical efficiency will make the sector less vulnerable to climatic change impacts.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".