5.8 Knowledge on Mitigation and Adaptation held by Farmers
5.8.1 Farmers’ Awareness and Understanding
Farmers were asked to state their awareness regarding climate change and variability (cf.
questions d18 in Appendices 1 and 2). Findings showed most farmers are aware of the concept of climate change and variability. Study findings indicated that 78 (93%) of farmers were aware of climate change and variability and only 6 (7%) were not aware. The in-depth interviews with district agricultural officers indicated that farmers were aware of climate change and variability. These results are supported by the DOI of Rogers (2003) which indicates that communication channels have a significant impact on awareness and knowledge creation. The study findings, which indicate that most farmers are aware of climate change and variability, reflect farmers’ exposure to change agents, among them experts and extension officers from the CCAA training. Figure 5.6 summarises these findings.
Figure 5.6: Climate Change and Variability Awareness by Farmers
In spite of farmers’ awareness, the in-depth interviews with agricultural extension officers emphasised the need for more education and awareness campaigns for farmers. The extension officers stated that adoption of innovations, change of attitude and behavioural
78; 93%
6; 7%
Yes No
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change required time before being assimilated by farmers to impact farming practices.
Findings from the FGDs suggest that the majority of farmers were aware of the effects of climate changes on the environment.
Despite the study findings from the semi-structured interviews with farmers indicating that more (4 or 66.7%) respondents in Chibelela and fewer (2 or 33.3%) respondents from Maluga village were not aware of climate change and variability, a cross-tabulation could not ascertain any significant difference in awareness between Maluga and Chibelela villages.
This indicates that fewer respondents not being aware might be thanks to the sensitisation and training farmers had undergone. The findings from cross-tabulation on awareness between the two study villages indicated a Pearson Chi-square value of 0.239 and the significance value of 0.696 at 0.05 probability level significance. (See Table 5.10.)
Table 5.3: Cross-Tabulation between Study Villages and Climate Change and Variability Awareness
Chi-Square Tests
Value df Asymp. Sig.
(2-sided)
Exact Sig. (2- sided)
Exact Sig. (1- sided)
Pearson Chi-Square 0.239a 1 0.625
Continuity Correctionb 0.004 1 0.951
Likelihood Ratio 0.245 1 0.621
Fisher's Exact Test 0.696 0.483
Linear-by-Linear
Association 0.236 1 0.627
N of Valid Casesb 84
a. 2 cells (50.0%) have an expected count less than 5. The minimum expected count is 2.57.
b. Computed only for a 2x2 table
Awareness of climate change and variability was cross-tabulated with gender, age, level of education and wealth. Findings suggest no direct correlation between gender, age and level of education and farmers’ awareness about climate change and variability. Findings from the
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cross-tabulation between sex and climate change and variability awareness shows a Pearson Chi-square value of 0.617, a significance value of 0.661 and a 0.05 probability level of significance. This test shows no direct relationship between the two variables. (See Table 5.11.)
Table 5.4: Cross-Tabulation between Sex and Climate Change and Variability Awareness
Chi-Square Tests
Value df
Asymp. Sig.
(2-sided)
Exact Sig. (2- sided)
Exact Sig. (1- sided)
Pearson Chi-Square 0.617a 1 0.432
Continuity Correctionb 0.107 1 0.743
Likelihood Ratio 0.686 1 0.407
Fisher's Exact Test 0.661 0.393
Linear-by-Linear
Association 0.610 1 0.435
N of Valid Casesb 84
a. 2 cells (50.0%) have expected counts less than 5. The minimum expected count is 1.86.
b. Computed only for a 2x2 table
The cross-tabulation between age and climate change and variability awareness did not ascertain any direct association between the variables. The findings from the cross-tabulation shows a Pearson Chi-square value of 9.812, a significance value of 0.081 and a 0.05 probability level of significance. This result suggests that there is no direct relationship between age and climate change and variability awareness. See Table 5.12 for more details.
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Table 5.5: Cross-Tabulation between Age and Climate Change and Variability Awareness
Chi-Square Tests
Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 9.812a 5 0.081
Likelihood Ratio 7.329 5 0.197
Linear-by-Linear
Association 3.216 1 0.073
N of Valid Cases 84
a. 7 cells (58.3%) have an expected count of less than 5. The minimum expected count is 0.14.
The study did not find a direct relationship between level of education and awareness of climate change and variability. Findings from the cross-tabulation indicate the Pearson Chi- square value of 0.509, the significance value of 0.917 at the 0.05 probability level of significance. This result illustrates no direct relationship between the two variables. (See Table 5.13)
Table 5.6: Cross-Tabulation between Level of Education and Climate Change and Variability Awareness
Chi-Square Tests
Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 0.509a 3 0.917
Likelihood Ratio 0.522 3 0.914
Linear-by-Linear
Association 0.003 1 0.959
N of Valid Cases 84
a. 5 cells (62.5%) have expected counts of less than 5. The minimum expected count is 0.07.
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There was no significant relationship between farmers’ income levels and farmers’ climate change and variability awareness. Findings from the Chi-square test show a calculated Pearson Chi-square value of 8.346 and significance value of 0.08 at the 0.05 probability level of significance. These findings are supported by Diffusion of Innovations model of Rogers (2003), which shows wealth is not the major factor which influences awareness in the adoption of innovations. Table 5.14 gives more details.
Table 5.7: Cross-Tabulation between Wealth and Climate Change and Variability Awareness
Chi-Square Tests
Value df
Asymp. Sig. (2- sided)
Pearson Chi-Square 8.346a 4 0.080
Likelihood Ratio 6.958 4 0.138
Linear-by-Linear
Association 4.408 1 0.036
N of Valid Cases 84
a. 7 cells (70.0%) have an expected count of less than 5. The minimum expected count is 0.29.
Farmers were asked to explain their understanding of climate change and variability. Content analysis showed that most farmers interviewed described climate change and variability as changes in weather, an increase in temperature and inadequate rainfall. The FGD findings indicated that farmers expressed their understanding of climate change and variability as a change of the environment and vegetation cover, reduced availability of water, increased drought, deforestation, disappearance of endemic tree species and increased wind. Some of the above findings were supported by farmers from Chibelela village who stated that “In the past, we used to throw seeds such as tomato, peas and maize on the soil without adding fertilizer or pesticide and have a hefty harvest, but these days one cannot plant that way and expect harvest”.
Farmers described other factors which are directly associated with climate change and variability and which are seen to be contributing factors to climate change and variability.
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These are environmental pollution, increased population, reduced soil fertility, more diseases, insects and use of pesticide in farming, and increased carbon dioxide pollution from factories.
The respondents were describing how they understand climate change. Therefore, these are their views on how they understand climate change and variability and it should not be linked literally with the level of pollution produced by factories in the villages.
The findings from the FGDs showed variation between farmers’ levels of understanding of the concept of climate change and variability. Farmers who had been exposed to advanced training, workshops and who hold a higher level of education could explain more explicitly their understanding of the contributing factors to climate change and variability. Unlike those with a limited level of education, farmers who had a higher level of education were more able to explain their understanding of carbon dioxide pollution related to factories. These findings confirm those by Rogers (2003: 171,172,222,288-291), who observed that awareness can affect an individual’s ability to acquire knowledge.
Despite farmers’ ability to describe their understanding of climate change and variability, findings from both the interviews and FGDs demonstrate that many farmers still failed to interpret and contextualise the meaning of climate change and variability. It was evident from the interviews that farmers could not associate a direct relationship between the causes of climate change and variability.
During the focus group discussion in Chibelela village, a farmer, UK, stated, “You know, Mr.
Facilitator, despite education and awareness interventions programmes done still many farmers are not in a position to understand and interpret issues related to climate change and variability”. The complexities of farmers’ understanding of climate change and variability emerged when another farmer, DM, in a FGD, asked rhetorically, “why are there areas with forests but still the same areas don’t receive enough rainfall?”
Findings from the content analysis of both the interviews and FGDs showed that most farmers associated climate change and variability with erratic rainfall and drought/famine.
Farmers designated a good and bad year based on the amount of rainfall, drought and harvesting, which, to a greater extent, described their understanding of climate change and variability. The research findings revealed that farmers’ awareness, attitude and adaptation strategies towards climate change and variability were influenced by the incidence of
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drought, food insecurity, water scarcity, reward from an innovation, NGOs and government interventions to lessen the severity of the effects of climate change and variability.
Most farmers’ in their response on factors that contribute to erratic rainfall patterns indicated that deforestation is the major factor perceived for climate change and variability.
Deforestation occurs as a result of increased human activities, such as tree cutting for charcoal, firewood and building. Other factors highlighted by farmers included climate change and variability, overgrazing, farm expansion and population increase as major contributing factors which cause unpredictable rainfall.
Agricultural extension officers were asked to explain their understanding of climate change and variability (cf. question e24 in Appendix 3). The agricultural extension officers described climate change and variability as change in rainfall onset, increased temperature and increased incidence of pests and diseases. They gave the causes of climate change and variability as industrial gases, deforestation, shifting cultivation, high livestock populations and the destruction water sources.
Thus, despite farmers’ difficulties in interpreting climate change and variability, local indicators improved their understanding of new trends on changes in rainfall, temperature and wind. In section 5.8.2., the study presents findings on farmers’ use of indigenous knowledge on weather prediction.