Climate Change Awareness in Mpumalanga Province, South Africa
Bibliographic record
Abstract
Climate change is one of the most important environmental issues facing the world today. The impact of climate change is a reality and it cuts across all climate-sensitive sectors including the Agriculture sector. It is well documented by several scientists, Intergovernmental Panel on Climate Change and other experts that climate change threatens sustainable economic development and the totality of human existence. This study will enable small scale maize farmers in Mpumalanga province to understand the challenges and the threat posed by climate variability and climate change. The study was conducted in Nkangala District, Mpumalanga province. Mpumalanga province remains the largest production region for forestry and the majority of the people living in Mpumalanga are farmers and they have contributed immensely to promote food security. However, due to the threat by climate variability and change, sectors such as the Agriculture, Water etc are experiencing the following pattern: (a) Putting livelihoods and food production at serious risks due to extreme climatic events, climate variability and change. It was noted that there is a need for climate change awareness across the agriculture sector. Currently, there is enough evidence that shows that climate change is affecting different elements of agriculture such as crops and livestock. Random sampling technique was used to select two hundred and fifty farmers to be interviewed. The questionnaires were administrated to household head farmers and included matters relating to household general information, climate change awareness, land characteristics, observation on climate change and agronomic practices including maize production. Data was analysed using the statistical for social sciences (SPSS version 20). Descriptive statistics was used to describe data and Univariate regression analysis was conducted to demonstrate the relationship and association of variables. It was noted that the majority of farmers in this province need capacity building and also climate change awareness initiatives which would assist these farmers to build the adaptive capacity, increase resilience and reduce vulnerability. By coming up with these kind of interventions it is believed that some of these farmers would be able to change their farming methods, diversify their cropping systems and also introduce drought tolerant crops in order for them to have good yields and also be able to generate good income.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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".