Cost Benefit Analysis of Climate Change Adaptation Strategies on Crop Production Systems: A Case of Mpolonjeni Area Development Programme (ADP) in Swaziland
Bibliographic record
Abstract
Prolonged drought and floods as a result of climate change are a serious problem for households at Mpolonjeni ADP because their livelihood is mainly rainfedfarming. This is evident as there is high level of food insecurity, crop failure, poverty and hunger, which has forced many households to abandon farming and survive by food aid. The study was a descriptive survey aimed to identify private adaptation strategies to climate change and conduct a cost benefit analysis for the identified adaptation strategies. A stratified random samplingtechnique was used to select 350 households. Personal interviews were conducted using structured questionnaires. Data were analysed using descriptive statistics and cost benefit analysis where net present value (NPV) and internal rate of return (IRR) were used as decision rules. Adaptation strategies used were; drought resistant varieties, switching crops, irrigation, crop rotation, mulching, minimum tillage, early planting, late planting and intercropping. Switching crops had the highest NPV, where maize (E14.40) should be substituted with drought tolerant crops such as cotton (E1864.40), sorghum (E283.30) and dry beans (E292.20). The study recommends that households should grow drought tolerant crops such as cotton, sorghum and dry beans instead of maize. The government should provide irrigation infrastructure, such as dams, strengthen extension services and subsidise farm inputs in order to improve crop production.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".