The Impact of Agricultural Finance on Adopting Climate Change Mitigation Practices: Comparative Approach Evidence From Jordan
Bibliographic record
Abstract
This study aimed to demonstrate the impact of agricultural financing on the adoption of climate change mitigation practices. The study sample consisted of two categories: 151 tomato producers in the Deir Alla region of the Jordan Valley (Jordan) who received agricultural financing, and another 175 tomato producers who did not receive such financing in the same area. To achieve its objectives, the study adopted the descriptive analytical approach by showing the current situation of tomato crop production in Jordan and the study area. A comparative approach was adopted in the study. The level of climate change mitigation practices adoption and the values of some important financial indicators at the farm level for the study sample categories were determined. The gross margin (GM), the net farm income (NFI), trends of inputs and outputs, and farm financial efficiency indicators were calculated. The study results revealed that the profit margin and net farm income for farms that received agricultural financing were better than those that did not. The trend of inputs and outputs indicated that the financial efficiency of the farms that received agricultural financing was at a moderate sustainable level compared to a low level of sustainability for the farms that did not receive such financing. The results also showed that the debt-to-asset ratio in the farms that received financing was at a sufficient degree of financial sustainability that enables them to continue their activities without being affected by the risks of not paying their debts, compared to the farms that did not receive financing. The study concluded that agricultural financing has a significant impact on covering the costs of practices to mitigate the potential effects of climate change, which reflects positively on improving the productive performance of the agricultural activities and transferring them to a better level of financial sustainability. The study recommended the need to take the necessary measures to facilitate access to finance for farmers, especially smallholder farmers, to cover the costs of measures to face the unexpected risks arising from climate change.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.003 | 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".