Perceptions and Practices in Understanding Climate Change and Sustainable Agriculture in Jordan
Bibliographic record
Abstract
The research focuses on a key question: How aware are farmers in Jordan of impacts of climate change and agricultural adaptation strategies? To answer this question, this study employs a descriptive methodology, collecting data through a questionnaire administered via face-to-face interviews. Participants’ responses are assessed using a Likert scale, and demographic data is analyzed using the chi-square test. The sample consists of 60 farmers selected randomly. Accordingly, the study aims to analyze farmers’ perceptions, attitudes, and behaviors regarding climate change, as well as to identify factors influencing their adoption of adaptation strategies. These findings are expected to contribute to raising awareness and participation in climate action efforts and building more sustainable and resilient agricultural communities in Jordan. Analytically, results show that education is more strongly correlated with farm size than with age or type of agricultural activity, thus highlighting its role in improving production management. Furthermore, there is a clear awareness of climate change and its agricultural impacts, despite a limited understanding of its causes related to agricultural activities. Concerns center on weather, production costs, and water scarcity. Field crops are the most affected sector, while forests are the least impacted. In this context, the most significant impacts include increased costs and the degradation of resources and rangelands. Farmers demonstrate a clear awareness of importance of sustainable practices, with animal disease control, soil fertility maintenance, and efficient water management receiving the highest ratings, followed by rainwater harvesting techniques, improved varieties, and agricultural insurance. A preference for organic fertilizers and a reduction in chemical use are evident, reflecting a general trend toward sustainable agriculture, despite some individual variations. In conclusion, this study emphasizes that education plays a pivotal role in improving production management and increasing farmers’ awareness of climate change, even with their limited understanding of its agricultural causes. This coincides with a growing awareness of importance of sustainable practices for mitigating risks. Therefore, this study recommends strengthening agricultural extension programs, using modern resource management techniques, and developing effective agricultural insurance systems to ensure the sustainability of agricultural sector and its ability to adapt to climate change.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| 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".