Economic Impact of Climate Change on Food Crop Production using Ricardina Approach: A Case of Kellem Wollega Zone, Ethiopia
Bibliographic record
Abstract
Background: Historical records and increased scientific consensus provide strong evidence that the global climate is changing. Future climate change would lead to an increase in climate variability and the frequency and intensity of extreme events. Agriculture that is dependent on climate conditions is inherently sensitive to climate changes. Today’s world agriculture has gone through challenges from population growth and has adapted to changing economic conditions, technology and resource availabilities. But uncertainty remains concerning the ability of agricultural systems to adapt to climate change. Thus, climate change adaptation of the essence for a resilient food crop production system requires economic transformation by arrangement of institutional and technology. Methods: This study uses the Ricardian model to examine the economic impact of climate change on agriculture in Kellem Wollega Zone, Ethiopia. The net farm revenue is regressed against climate variable (temperature and precipitation), soil and socio-economic variables to help determine the factors that influence variability in net farm revenues. The study was based on the data from a survey of 400 smallholder farming households interviewed across the zone. Result: The Ricardian model analysis shows the coefficients of summer, autumn and winter temperature are positive whereas the coefficient of spring temperature is negative. Regarding the precipitation, the coefficients of summer and spring precipitation are positive while coefficients of autumn and winter precipitation are negative. The marginal impact analysis results show an increase in summer and spring temperatures has mostly negative effects on net farm revenues implying that further temperature increases would be harmful to agricultural activities while increases in autumn temperatures increase net farm revenues in the study area. The summer and spring precipitation would increase the net farm revenue but the autumn precipitation reduces the farm revenue. The elasticity results show that net farm revenues are highly sensitive to changes in climate and the elasticity is relatively high for both summer temperature and precipitation. The impacts of climate change under the three special Reports on Emission Scenarios, (Canadian General Circulation Model, Hadley Centre for Climate Prediction and Research and Parallel Climate Model) predicted that by 2100 net farm revenues would decrease across all farms per hectare by US$ 942.83, US$ 1048.16 and US$ 1024.32 respectively. The finding suggests there is a great need for the concerned bodies to provide up-to-date information about climate change and rainfall patterns in the forthcoming season so that the farmers make informed decisions and develop adaptation strategies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".