Abstract
Due to the effects of climate change, farmers in Kajiado County have embraced different climate change adaptation strategies including the use of indigenous knowledge (IK) and scientific approaches. The objective of this study was to assess the determinants of farmers' IK practices influencing the uptake of Climate Change Adaptation Strategies (CCAS) in Kajiado County, Kenya. Using the Model of Private Proactive Adaptation to Climate Change (MPPACC), IK-related contextual factors that constituted the socio-demographic, economic, and geo-ecological variables were tested against the CCAS variable on Pearson Coefficient Correlation in determining associations. Multi-stage sampling was done and data were collected using questionnaires, key informant interviews, focus group discussions (FGDs), and observation checklists while data analysis involved the use of both descriptive and inferential statistics. The findings show CCAS were likely to be adapted to by those with higher levels of education and those with higher levels of monthly income while those unwilling were more likely males, older, with larger household sizes. and those who owned land. The findings also showed that effective approaches including IK climate change adaptation practices and the CCAS can be applied in a complimentary manner to achieve the desired results in regions that possess diverse climatic and geophysical conditions.
HIGHLIGHTS
IK adherence depends on the farmer's individual socio-demographic factors.
IK is entrenched among farmers as it is learned through a social-oriented mechanism.
Type of farming determines IK applications hence influencing CCAS uptake differently.
Geo-ecological factors influence the types of locally developed agricultural technologies.
Integration of local farming techniques with CCAS can enhance the latter's uptake.
INTRODUCTION
Effects of climate change on agricultural productivity globally have elicited significant responses from agronomists, hydrologists, and agriculturalists among other scientists who have collaborated with the aim of coming out with mitigation measures to alleviate the resultant adverse effects (Piontek et al. 2014). However, despite all these efforts, there is a growing concern since previous studies indicate that only limited adoption of climate-smart agricultural (CSA) practices by farmers is realized (Sain et al. 2018).
Studies conducted on farmers' climate change adaptation approaches reveal that there are contextual factors that determine the practices that local small-scale farmers would prefer (Grothmann & Patt 2005; Hailegiorgis et al. 2018). These factors go a long way in shaping the farmers' climate change risk perceptions, subsequently influencing their adaptation efficacy, perceived self-efficacy, and perceived cost efficacy. The relevant literature that was reviewed showed that IK practiced by local farmers in relation to their socio-demographic profiles as well as the peoples' knowledge attitudes and perceptions either slowed or enhanced their uptake of new farming technologies (Dhanush et al. 2015; Meijer et al. 2015; Nyale et al. 2019). Socio-demographic and economic characteristics such as age, sex, household size, level of education, poverty indices, and sources of income were considered as key in determining the perceived adaptation efficacy – a farmer's evaluated effectiveness of his/her adaptation measures to avert climate change risks.
Globally, farmers, especially in developed economies have been encouraged to adopt new farming technologies not just for purposes of increasing productivity, but more recently, for their products to be competitive in the international markets (Macfadyen et al. 2018; Heeb et al. 2019). Agricultural production has a myriad of objectives including operating at optimal levels of production while meeting the current stipulated millennium sustainability goals (OECD 2001). For example, within Western Europe countries, there has been a purposive initiative facilitated and sponsored by the European Union to adopt active management of agricultural practices by the farmers, focusing on applying appropriate technologies and practices, such as ‘Precision Agriculture’, that decrease greenhouse gas (GHG) emissions while increasing agriculture productivity and income (European Union 2019).
There is no gainsaying by stating that a critical body with a mandate to devise mitigation and response mechanisms toward addressing adverse effects of the escalating global warming trends still grapples with sharp contradictions created by different perceptions from researchers on scientifically designed approaches to dealing with climate change. In its recent report the Intergovernmental Panel on Climate Change (IPCC) in regard to Bio-energy with Carbon Capture Storage (BECCS), is believed to keep the global temperature low, yet other insights from the same body believe that the concept can easily surpass the sustainable levels in the land domain-land being the key resource for agricultural production (Creutzig et al. 2021).
In Sub-Saharan Africa, most of the local farmers tend to stick to their traditional coping mechanisms for climate variability, rather than taking up the recommended strategies developed by the experts. Response by farmers in adapting to new farming strategies is rather slow (Ndjeunga & Bantilan 2005), hampering the implementation of climate change farming policies. It has been observed that even in the cases where these agricultural innovations have been implemented by the farmers, they are soon abandoned particularly in Sub-Saharan Africa (Dahlquist et al. 2007; Kiptot et al. 2007; Meijer et al. 2015). Moreover, many traditional communities especially in Africa find the transferability of indigenous agricultural knowledge easier than trying out more conventional scientific farming techniques (Tanyanyiwa 2019). Other studies have also indicated that there are underlying socio-cultural factors that could be hindering the adoption of CSA technologies by local rural farmers (Jellason et al. 2021).
In Kenya, and more specifically, Kajiado County, a recent and relevant study carried out in the county on the indigenous knowledge (IK) practices being utilized by farmers to cope with and adapt to adverse climate change impacts revealed in its findings that 98% of the respondents still apply IK in managing their farms (Manei et al. 2016). In full appreciation of the importance of the adverse effects of climate change on agriculture, the Kenya Government, through the Ministry of Environment and Mineral Resources (MoEMR), developed the 2010 National Climate Change Response Strategy (NCCRS). The Kajiado Profile prepared by the International Center for Tropical Agriculture (CIAT) shows that its agriculture sector has encountered persistent climatic challenges, especially, drought (CIAT 2018). This has led to massive crop failure and livestock losses and has subsequently occasioned severe food shortages for years.
The indigenous practices employed in agriculture by local communities in affected areas are deeply rooted and the successful introduction of new technologies requires sufficient training and sensitization. The current study examined certain characteristic factors that embody the IK among several local communities which may influence the adoption of scientific climate change farming strategies. Additionally, IK most commonly has an accepted assertion from its proponents, that the term is associated with a particular place (Tatira 2000; Aryal et al. 2018; Tanyanyiwa 2019). This assertion is buttressed by general consensus among scientists on which Studley (1998) avers that this knowledge ‘is linked to a specific place, culture or society; is dynamic in nature; belongs to groups of people who live in close contact with natural systems and contrasts with “modern” or western formal scientific knowledge.’
Equally, the socio-cognitive progression within which farming-oriented climate change adaptation occurs tends to take place in a localized socio-ecological environment. Moreover, it is argued by Mitter et al. (2019) that farm and regional characteristics can also significantly modify the way a farmer will appraise climate change-related risks, hence seeking to apply the knowledge that is ‘locally’ understood in response to the adverse effects of climate change. This study, therefore, focused on examining the nature of these IK-oriented socio-cultural, economic, and ecological characteristics that may influence the local farmers' uptake of CCAS in Kajiado County, Kenya.
CONCEPTUAL FRAMEWORK
Model of Private Proactive Adaptation to Climate Change (Source:Grothmann & Patt 2005).
Model of Private Proactive Adaptation to Climate Change (Source:Grothmann & Patt 2005).
It is used to demonstrate how climate change characteristics relate to other proximal factors that influence an individual farmer's cognition enabling him/her willingness to adopt the CCAS. The agricultural contextual factors which consist of farmers' socio-economic, demographic, and regional farm characteristics, existing IK as well as the farmer's knowledge, attitudes, and perceptions all play a part in their climate change risks, opportunities, and adaptation appraisals. This model was therefore used to illustrate succinctly how these predictor variables influenced the willingness of the farmers in Kajiado County to adopt the CCAS.
MATERIALS AND METHODS
Study area
Location of Kajiado County showing the study sites (Source:GOK 2019).
Research design
This study adopted a mixed methods research approach in which both qualitative and quantitative methods were used, highlighting and examining the subjectivity and objectivity aspects of matters under investigation. The descriptive research design was therefore used to determine the nature of farmers' IK practices influencing the uptake of climate change adaptation strategies in Kajiado County, Kenya.
Sampling strategy
The study focused on collecting responses from the representatives of households as principal respondents. The selection criteria were based on the household's major occupation or livelihood activity being farming (crop, livestock, or both). The primary sampling units were sub-counties, sub-locations or villages in a multi-stage sampling process, as per the 2019 national census data. In stage 1, three sub-counties were purposively sampled namely Kajiado West, Kajiado Central and Loitokitok sub-counties. The choice of Loitokitok is informed by the fact that it is the region with much of the crop farming that is rain-fed. On the other hand, Kajiado West is largely arid and semi-arid with pastoralism being the major livelihood activity. Kajiado Central has a bit of crop farming but is extensively inhabited by pastoralists who live in large semi-arid areas that constitute the sub-county (County Government of Kajiado 2019).
The 382 respondents participated in this study, representing a response rate of 99.5%, as shown in Table 1 overleaf.
Summary of sampling methods and sample size for the study population
Sub-counties . | Sampling method . | No. of households (KNBS) . | Sample size . |
---|---|---|---|
Kajiado West | Multi-stage Random, Simple random and Proportionate | 42,774 | 110 |
Kajiado Central | Multi-stage Random, Simple random and Proportionate | 37, 238 | 131 |
Loitokitok | Multi-stage Random, Simple random and Proportionate | 47,058 | 141 |
TOTAL | 127,070 | 382 |
Sub-counties . | Sampling method . | No. of households (KNBS) . | Sample size . |
---|---|---|---|
Kajiado West | Multi-stage Random, Simple random and Proportionate | 42,774 | 110 |
Kajiado Central | Multi-stage Random, Simple random and Proportionate | 37, 238 | 131 |
Loitokitok | Multi-stage Random, Simple random and Proportionate | 47,058 | 141 |
TOTAL | 127,070 | 382 |
Data collection
The determination of data collection instruments was done to illustrate the methodology and the data types that were utilized in this study. Both quantitative and qualitative data were required for the purposes of obtaining the desired information in Kajiado County. A questionnaire survey was used to capture data on the farmers' level of awareness of climate change adaptation strategies, their familiarity with the adaptation practices and knowledge of climate change-related risks. In addition, the instrument was also used to collect information on the farmers' perceptions and attitudes in regard to applying IK as compared with CCAS in responding to the impacts of climate change.
Key informant interview schedules were developed and used to obtain data from government experts in the ministry of agriculture, livestock, departments of meteorology and professionals from non-governmental organizations who were specifically engaged in addressing the issues of climate change impacts on agricultural productivity within the county. In regard to the FGD, there were three discussant groups which were each made up of eight participants and were clustered under youths, women and selected small-scale farmers and pastoralists (mainly elders). The discussion guides were predetermined and semi-structured to avoid digression hence safeguarding the quality of data collected.
Data analysis and presentation techniques
This study mainly queried the uptake of climate change adaptation strategies by local farmers as influenced by IK practices, the latter being the independent variable and the former, the dependent variable. The independent variables were examined through the prism of the nature of these practices, defined by farmers' socio-demographic, economic and geo-ecological factors. The analysis and presentation techniques are summarized in Table 2.
Summary of methods of data analysis and presentation techniques
Objective . | Independent variables . | Dependent variables . | Research design . | Methods of data analysis . | Presentation . |
---|---|---|---|---|---|
Assess the determinants of farmers' indigenous knowledge practices influencing the uptake of climate change adaptation strategies in Kajiado County, Kenya.. |
|
| Descriptive, correlational and cross-sectional survey designs | Descriptive statistical analysis, qualitative analysis, Likert scale, Spearman rank order correlation and chi-squared test | Frequency tables, pie charts, graphs, narratives |
FORMULA: Test of association using Pearson correlation coefficient | |||||
where r represents correlation coefficient; x2 represents values of the socio-demographics or economic variable in sample; ![]() ![]() ![]() |
Objective . | Independent variables . | Dependent variables . | Research design . | Methods of data analysis . | Presentation . |
---|---|---|---|---|---|
Assess the determinants of farmers' indigenous knowledge practices influencing the uptake of climate change adaptation strategies in Kajiado County, Kenya.. |
|
| Descriptive, correlational and cross-sectional survey designs | Descriptive statistical analysis, qualitative analysis, Likert scale, Spearman rank order correlation and chi-squared test | Frequency tables, pie charts, graphs, narratives |
FORMULA: Test of association using Pearson correlation coefficient | |||||
where r represents correlation coefficient; x2 represents values of the socio-demographics or economic variable in sample; ![]() ![]() ![]() |
The frequency tables, charts, graphs, and narratives have been used to present the findings of this study. Inferential statistics were done through the Likert scale, Spearman rank order correlation, and Chi-square calculations using SPSS version 20.
RESULTS AND DISCUSSIONS
Socio-demographic and economic characteristics of the farmers
As it was found in this study, the unanimous response from women in one of the FGDs is that women are the ones who mainly work on farms. One of them stated:
‘Sisi wanawake ndio tunafanya kazi shambani. Wazee wetu wamezoea kupeleka mifugo malishoni mbali. Watoto wetu siku hizi wanaenda shuleni, na wale walimaliza shule wameenda mjini kutafuta vibarua. [It is we women who work on farms. Our husbands focus on taking livestock for grazing in far places. Our children go to school, while those that are through with school, migrate to urban areas to look for employment].’
The above assertion underscores the importance of household size as a measure of labor in carrying out farming activities at a household level. The narrative supports an argument that IK-related farming practices being highly labor intensive (Altieri et al. 2012), they are easier facilitated by larger household sizes which have more individuals available to ease the constraints on either the grazing work or on the overworked women in the farms. Larger families are therefore likely to find it easier to carry out IK-oriented agricultural activities.
This study also sought to determine the influence of the household's farm size on the farmer's willingness to adapt to climate change agricultural technologies. The responses were grouped categorically into six; those with land under 5 ha, 5–9, 10–19, 20–49, 50–100, and those with over 100 ha. The farm sizes according to those who responded in the affirmative to the question as to whether they own land and who were 269 in number, is illustrated by results in Table 3 overleaf. Although a majority of those who owned land were those with farms of between <1 and 5 ha (45%), and were found in more wet areas of Loitokitok where crop farming is most common, the farmers with bigger lands (51–100 ha) were found in the more arid areas of Kajiado West where livestock pastoralism was the most common form of agriculture. As shown in Table 3, the majority of the respondents had land of <5 ha at 45.8% yet those with land between 10 and 19 ha represented only 4.8%.
Households’ farm sizes
. | Kajiado West (n = 67) . | Kajiado Central (n = 91) . | Loitokitok (n = 111) . | Total (n = 269) . |
---|---|---|---|---|
Below 5 h | 4.5 | 16.5 | 92.8 | 45.0 |
5–9 ha | 6.0 | 16.5 | 4.5 | 8.9 |
10–19 ha | 6.0 | 7.7 | 1.8 | 4.8 |
20–49 ha | 19.4 | 24.2 | 0.9 | 13.4 |
51–100 ha | 31.3 | 19.8 | 0.0 | 14.5 |
Over 100 ha | 32.8 | 15.4 | 0.0 | 13.4 |
. | Kajiado West (n = 67) . | Kajiado Central (n = 91) . | Loitokitok (n = 111) . | Total (n = 269) . |
---|---|---|---|---|
Below 5 h | 4.5 | 16.5 | 92.8 | 45.0 |
5–9 ha | 6.0 | 16.5 | 4.5 | 8.9 |
10–19 ha | 6.0 | 7.7 | 1.8 | 4.8 |
20–49 ha | 19.4 | 24.2 | 0.9 | 13.4 |
51–100 ha | 31.3 | 19.8 | 0.0 | 14.5 |
Over 100 ha | 32.8 | 15.4 | 0.0 | 13.4 |
Field data, 2020.
These results indicate that the majority of participants in this study were small-scale farmers. Their farming technologies are largely traditional and characterized by what they have socially learned over the years as IK.
Farmers’ socio-demographic influences
The findings of this study show that the socio-demographic characteristics of a farmer can determine how his/her IK practices influence the uptake of modern CC adaptation technologies as shown in Table 4 overleaf. The results indicate that the majority of the socio-demographic variables, namely, level of income, level of education, type of livelihood activity /source of income household size, size of land owned, age, land ownership and type of land ownership all have a significant relationship with the uptake of CCAS. In the MPPAC model (Figure 1) that was used in this study, these socio-demographic and economic characteristics were the contextual factors that were able to predict the adaptation intentions of farmers in Kajiado County.
The relationship between background factors and level of uptake of modern agricultural practices index score
. | No uptake of modern agricultural practices (n = 178) . | Uptake of modern agricultural practices (n = 204) . | Spearman rho . | p-value . |
---|---|---|---|---|
Sub-county | ||||
Kajiado West | 14.1 | 14.7 | 0.163** | 0.002 |
Kajiado Central | 13.6 | 20.7 | ||
Loitokitok | 18.8 | 18.1 | ||
Sex of the respondents | ||||
Male | 28.8 | 31.9 | 0.057 | 0.271 |
Female | 17.8 | 21.5 | ||
Age group of the respondents | ||||
18–24 | 6.0 | 1.8 | 0.340** | 0.006 |
25–34 | 9.2 | 8.1 | ||
35–49 | 11.8 | 22.8 | ||
50–64 | 14.9 | 14.9 | ||
65–86 | 4.7 | 5.8 | ||
Household size | ||||
1–3 members | 9.2 | 6.0 | 0.519** | 0.000 |
4–7 members | 22.3 | 29.6 | ||
8–11 members | 8.6 | 13.4 | ||
11 and above | 6.5 | 4.5 | ||
Household farm in hectares recorded (n = 269) | ||||
Below 5 ha | 21.2 | 23.8 | 0.451** | 0.000 |
5–9 ha | 1.9 | 7.1 | ||
10–19 ha | 1.1 | 3.7 | ||
20–49 ha | 4.1 | 9.3 | ||
51–100 ha | 8.6 | 5.9 | ||
Over 100 ha | 2.6 | 10.8 | ||
Household average monthly income | ||||
<3,000 | 19.4 | 2.6 | 0.581** | 0.000 |
3,000 – <10,000 | 14.9 | 14.4 | ||
10,000 – <20,000 | 6.0 | 14.7 | ||
20,000 – <30,000 | 4.2 | 8.1 | ||
>30,000 | 2.1 | 13.6 | ||
Education level of the respondents | ||||
Illiterate | 25.4 | 12.3 | 0.562** | 0.000 |
Adult education | 2.6 | 1.3 | ||
Primary education | 11.3 | 11.3 | ||
Secondary education | 7.3 | 20.4 | ||
College plus | 0.0 | 8.1 | ||
PPI Index | ||||
Poorest | 4.5 | 1.0 | 0.314** | 0.000 |
Poor | 18.6 | 17.8 | ||
Medium | 23.0 | 31.2 | ||
High | 0.5 | 3.4 | ||
Are you originally from this village?, No (migrated) | 6.8 | 10.2 | 0.136** | 0.000 |
The reason for your movement (n = 65) | ||||
Marriage | 26.2 | 41.5 | 0.164 | 0.191 |
Search for pasture | 13.8 | 10.8 | ||
Due to land | 3.1 | 4.6 | ||
Land ownership (Do you own land?), yes | 27.7 | 42.7 | 0.309** | 0.000 |
Type of land ownership? (n = 269) | ||||
Private | 19.7 | 46.1 | 0.290** | 0.000 |
Communal | 13.8 | 10.0 | ||
Public | 4.1 | 0.7 | ||
Leased | 1.9 | 3.7 | ||
Source of income | ||||
Livestock and agricultural products and produce | 34.8 | 31.7 | 0.555** | 0.000 |
Employment salary | 2.1 | 8.6 | ||
Remittances | 2.1 | 4.5 | ||
Other sources of income | 7.6 | 8.6 |
. | No uptake of modern agricultural practices (n = 178) . | Uptake of modern agricultural practices (n = 204) . | Spearman rho . | p-value . |
---|---|---|---|---|
Sub-county | ||||
Kajiado West | 14.1 | 14.7 | 0.163** | 0.002 |
Kajiado Central | 13.6 | 20.7 | ||
Loitokitok | 18.8 | 18.1 | ||
Sex of the respondents | ||||
Male | 28.8 | 31.9 | 0.057 | 0.271 |
Female | 17.8 | 21.5 | ||
Age group of the respondents | ||||
18–24 | 6.0 | 1.8 | 0.340** | 0.006 |
25–34 | 9.2 | 8.1 | ||
35–49 | 11.8 | 22.8 | ||
50–64 | 14.9 | 14.9 | ||
65–86 | 4.7 | 5.8 | ||
Household size | ||||
1–3 members | 9.2 | 6.0 | 0.519** | 0.000 |
4–7 members | 22.3 | 29.6 | ||
8–11 members | 8.6 | 13.4 | ||
11 and above | 6.5 | 4.5 | ||
Household farm in hectares recorded (n = 269) | ||||
Below 5 ha | 21.2 | 23.8 | 0.451** | 0.000 |
5–9 ha | 1.9 | 7.1 | ||
10–19 ha | 1.1 | 3.7 | ||
20–49 ha | 4.1 | 9.3 | ||
51–100 ha | 8.6 | 5.9 | ||
Over 100 ha | 2.6 | 10.8 | ||
Household average monthly income | ||||
<3,000 | 19.4 | 2.6 | 0.581** | 0.000 |
3,000 – <10,000 | 14.9 | 14.4 | ||
10,000 – <20,000 | 6.0 | 14.7 | ||
20,000 – <30,000 | 4.2 | 8.1 | ||
>30,000 | 2.1 | 13.6 | ||
Education level of the respondents | ||||
Illiterate | 25.4 | 12.3 | 0.562** | 0.000 |
Adult education | 2.6 | 1.3 | ||
Primary education | 11.3 | 11.3 | ||
Secondary education | 7.3 | 20.4 | ||
College plus | 0.0 | 8.1 | ||
PPI Index | ||||
Poorest | 4.5 | 1.0 | 0.314** | 0.000 |
Poor | 18.6 | 17.8 | ||
Medium | 23.0 | 31.2 | ||
High | 0.5 | 3.4 | ||
Are you originally from this village?, No (migrated) | 6.8 | 10.2 | 0.136** | 0.000 |
The reason for your movement (n = 65) | ||||
Marriage | 26.2 | 41.5 | 0.164 | 0.191 |
Search for pasture | 13.8 | 10.8 | ||
Due to land | 3.1 | 4.6 | ||
Land ownership (Do you own land?), yes | 27.7 | 42.7 | 0.309** | 0.000 |
Type of land ownership? (n = 269) | ||||
Private | 19.7 | 46.1 | 0.290** | 0.000 |
Communal | 13.8 | 10.0 | ||
Public | 4.1 | 0.7 | ||
Leased | 1.9 | 3.7 | ||
Source of income | ||||
Livestock and agricultural products and produce | 34.8 | 31.7 | 0.555** | 0.000 |
Employment salary | 2.1 | 8.6 | ||
Remittances | 2.1 | 4.5 | ||
Other sources of income | 7.6 | 8.6 |
Field data, 2020. **Statistical bivariate significance at p < 0.01 at 99% confidence level.
A more succinct interpretation from these findings is that on a reducing scale level of income, level of education and type of land ownership were significant factors that influenced a farmer who practices IK in their farming activities to adopt new climate change-oriented technologies. It can be deduced that a farmer with a high income, may easily opt to adopt new technology, should this have any cost implications. On the other hand, a farmer's low level of education may hinder him/her to internalize quickly the benefits of scientific-developed adaptation strategies. Lastly, the type of land ownership is a critical component, especially for the nomadic pastoralists who own huge tracks of land and consider migration as the best option during extended droughts.
To answer the question as to what degree and direction the relationships between these factors and the CCAS approaches exhibited, Pearson Correlation tests were conducted, and the results are summarized in Table 5. A comparative analysis was done on the socio-demographic and economic characteristics of the participant farmers; results show that these factors have low degree correlations with most of the CCASs except for the age which had a moderate degree relationship (r = −0.337). Compared to the farmers who were willing to do irrigation, those that were unwilling were more likely males, more likely older, more likely with larger household sizes, and more likely those who own land.
Summary of correlation between socio-demographic factors and CCAS approaches
. | Tests . | Increased acreage of land under irrigation farming . | Increased numbers of superior cattle breeds . | Farmer's involvement in agricultural development planning . | Practicing artificial insemination for livestock . | Fenced off and reseeded natural pasture . | Better pasture establishments . | Increased cultivation of drought resistant crops . | Water resource management practices (e.g. water harvesting) . |
---|---|---|---|---|---|---|---|---|---|
Sex of the respondent In favor of male | Pearson Correlation | −0.160 | −0.102 | 0.038 | −0.154 | −0.161 | −0.143 | 0.008 | 0.072 |
Sig. (two-tailed) | 0.000 | 0.041 | 0.458 | 0.001 | 0.000 | 0.005 | 0.869 | 0.162 | |
Age | Pearson Correlation | −0.161 | −0.337 | 0.008 | −0.218 | −0.095 | −0.168 | 0.021 | −0.143 |
Sig. (two-tailed) | 0.002 | 0.000 | 0.871 | 0.000 | 0.063 | 0.002 | 0.685 | 0.005 | |
Household size | Pearson Correlation | −0.113 | −0.187 | −0.052 | −0.028 | −0.046 | −0.042 | −0.006 | −0.056 |
Sig. (two-tailed) | 0.028 | 0.001 | 0.312 | 0.584 | 0.367 | 0.414 | 0.904 | 0.278 | |
Education level | Pearson Correlation | 0.284 | 0.196 | 0.074 | 0.172 | 0.355 | 0.061 | 0.125* | 0.458 |
Sig. (two-tailed) | 0.000 | 0.000 | 0.147 | 0.001 | 0.000 | 0.235 | 0.015 | 0.000 | |
Land ownership (Do you own land?) in Favor of yes | Pearson Correlation | −0.192 | −0.198 | −0.039 | −0.101 | −0.144 | −0.038 | −0.084 | −0.208 |
Sig. (two-tailed) | 0.003 | 0.000 | 0.453 | 0.049 | 0.005 | 0.458 | 0.100 | 0.000 | |
Type of land ownership? In Favor of communal | Pearson Correlation | −0.121 | −0.149 | −0.069 | −0.188 | −0.315 | −0.054 | 0.049 | −0.214 |
Sig. (two-tailed) | 0.017 | 0.002 | 0.257 | 0.000 | 0.000 | 0.379 | 0.428 | 0.000 | |
What is your farm size in acres? | Pearson Correlation | −0.254 | −0.272 | −0.240 | −0.008 | −0.141 | −0.227 | −0.030 | 0.129 |
Sig. (two-tailed) | 0.000 | 0.000 | 0.000 | 0.900 | 0.021 | 0.000 | 0.623 | 0.034 | |
Household average monthly income | Pearson Correlation | 0.218 | 0.145 | 0.028 | 0.250 | 0.394 | 0.199 | 0.075 | 0.474 |
Sig. (two-tailed) | 0.000 | 0.004 | 0.581 | 0.000 | 0.000 | 0.000 | 0.142 | 0.000 |
. | Tests . | Increased acreage of land under irrigation farming . | Increased numbers of superior cattle breeds . | Farmer's involvement in agricultural development planning . | Practicing artificial insemination for livestock . | Fenced off and reseeded natural pasture . | Better pasture establishments . | Increased cultivation of drought resistant crops . | Water resource management practices (e.g. water harvesting) . |
---|---|---|---|---|---|---|---|---|---|
Sex of the respondent In favor of male | Pearson Correlation | −0.160 | −0.102 | 0.038 | −0.154 | −0.161 | −0.143 | 0.008 | 0.072 |
Sig. (two-tailed) | 0.000 | 0.041 | 0.458 | 0.001 | 0.000 | 0.005 | 0.869 | 0.162 | |
Age | Pearson Correlation | −0.161 | −0.337 | 0.008 | −0.218 | −0.095 | −0.168 | 0.021 | −0.143 |
Sig. (two-tailed) | 0.002 | 0.000 | 0.871 | 0.000 | 0.063 | 0.002 | 0.685 | 0.005 | |
Household size | Pearson Correlation | −0.113 | −0.187 | −0.052 | −0.028 | −0.046 | −0.042 | −0.006 | −0.056 |
Sig. (two-tailed) | 0.028 | 0.001 | 0.312 | 0.584 | 0.367 | 0.414 | 0.904 | 0.278 | |
Education level | Pearson Correlation | 0.284 | 0.196 | 0.074 | 0.172 | 0.355 | 0.061 | 0.125* | 0.458 |
Sig. (two-tailed) | 0.000 | 0.000 | 0.147 | 0.001 | 0.000 | 0.235 | 0.015 | 0.000 | |
Land ownership (Do you own land?) in Favor of yes | Pearson Correlation | −0.192 | −0.198 | −0.039 | −0.101 | −0.144 | −0.038 | −0.084 | −0.208 |
Sig. (two-tailed) | 0.003 | 0.000 | 0.453 | 0.049 | 0.005 | 0.458 | 0.100 | 0.000 | |
Type of land ownership? In Favor of communal | Pearson Correlation | −0.121 | −0.149 | −0.069 | −0.188 | −0.315 | −0.054 | 0.049 | −0.214 |
Sig. (two-tailed) | 0.017 | 0.002 | 0.257 | 0.000 | 0.000 | 0.379 | 0.428 | 0.000 | |
What is your farm size in acres? | Pearson Correlation | −0.254 | −0.272 | −0.240 | −0.008 | −0.141 | −0.227 | −0.030 | 0.129 |
Sig. (two-tailed) | 0.000 | 0.000 | 0.000 | 0.900 | 0.021 | 0.000 | 0.623 | 0.034 | |
Household average monthly income | Pearson Correlation | 0.218 | 0.145 | 0.028 | 0.250 | 0.394 | 0.199 | 0.075 | 0.474 |
Sig. (two-tailed) | 0.000 | 0.004 | 0.581 | 0.000 | 0.000 | 0.000 | 0.142 | 0.000 |
Field data, 2020.
Similarly, those that were unwilling to rear other cattle breeds were more likely males, more likely older, more likely those with larger household sizes, and more likely those who own land. On the other hand, farmers willing to embrace irrigation were more likely to be those with higher levels of education and more likely those with higher monthly incomes.
Although the custodianship of IK farming practices in most of the African traditional settings is collectively exercised among the older members of the society (both men and women) (Barigye & Siraje 2019), this study found that among the Maasai community, this knowledge is mainly exercised at the goodwill of men. One of the focus group participants among the eight selected women commented on this:
‘Our customs and traditions concerning what should be done on the land as well as our cattle is mainly decided by our husbands or male elders. As women, we are mainly mandated to implement the decisions already made. For example, whatever is going to be planted on the land in one given season, the size of the land to be utilized, which animals to be sold are all decisions that are made by the man of the house – my husband.’
The above assertion therefore still underscores the decision-making aspect of gender in adapting to new farming technologies among farmers. However, as the findings further reveal in this study, the more rural and traditional Kajiado West sub-county, farming communities have their decision-making processes influenced by these socio-demographic factors.’
Socio-demographic profiles have been proven to considerably predict the uptake of new agricultural technologies by farmers that operate within traditional rural settings (Badu et al. 2018; Melesse 2018; Sunny et al. 2018). Similar to documented study findings, these results tend to affirm that socio-demographic profiles have been proven to considerably predict the uptake of new agricultural technologies by farmers that operate within traditional rural settings (Badu et al. 2018; Melesse 2018; Sunny et al. 2018).
Geo-ecological influences
The findings show in regard to regional variances of prevailing ecological and climatic conditions that communities tend to adapt to climate change effects differently; each region using unique IK practices. When asked about what their likely response mechanisms would be in cases of adverse effects of climate change, 95, 82, and 10% of the respondents in Kajiado West, Kajiado Central and Loitokitok, respectively, opted for migration in search for water and pastures. Overall, 56% of the respondents in Kajiado County opted for migration in search of water and pastures illustrated in Table 6.
Farmers’ response mechanisms to climate change effects per sub-county
Response mechanism to CC effects . | Migration % . | |||
---|---|---|---|---|
Yes . | No . | Totals . | ||
Sub-county | Kajiado West | 95 | 5 | 100 |
Kajiado Central | 82 | 18 | 100 | |
Loitokitok | 10 | 90 | 100 | |
Overall | 56 | 44 | 100 | |
. | . | Indigenous knowledge practices % . | ||
Response mechanism to CC effects . | Yes . | No . | Totals . | |
Sub-county | Kajiado West | 53 | 47 | 100 |
Kajiado Central | 39 | 61 | 100 | |
Loitokitok | 6 | 94 | 100 | |
Overall | 29 | 71 | 100 | |
. | . | Wait for humanitarian assistance from NGOs, civil societies, etc. (%) . | ||
Response mechanism to CC effects . | Yes . | No . | Totals . | |
Sub-county | Kajiado West | 5 | 95 | 100 |
Kajiado Central | 11 | 89 | 100 | |
Loitokitok | 58 | 42 | 100 | |
Overall | 29 | 71 | 100 |
Response mechanism to CC effects . | Migration % . | |||
---|---|---|---|---|
Yes . | No . | Totals . | ||
Sub-county | Kajiado West | 95 | 5 | 100 |
Kajiado Central | 82 | 18 | 100 | |
Loitokitok | 10 | 90 | 100 | |
Overall | 56 | 44 | 100 | |
. | . | Indigenous knowledge practices % . | ||
Response mechanism to CC effects . | Yes . | No . | Totals . | |
Sub-county | Kajiado West | 53 | 47 | 100 |
Kajiado Central | 39 | 61 | 100 | |
Loitokitok | 6 | 94 | 100 | |
Overall | 29 | 71 | 100 | |
. | . | Wait for humanitarian assistance from NGOs, civil societies, etc. (%) . | ||
Response mechanism to CC effects . | Yes . | No . | Totals . | |
Sub-county | Kajiado West | 5 | 95 | 100 |
Kajiado Central | 11 | 89 | 100 | |
Loitokitok | 58 | 42 | 100 | |
Overall | 29 | 71 | 100 |
Field Data, 2020.
Crosstabs showing relationship between farmer's sub-county and climate change response mechanisms
Independent variable . | Dependent variable . | Pearson Chi-Square . | df . | Asymp. Sig. (2-sided) . | Significance . |
---|---|---|---|---|---|
Farmer's location of residence (sub-county) | Climate change response | ||||
Resign to our fate and do nothing | 79.467 | 2 | 0.000** | Significant | |
Appeal to government for help through local administration | 9.887 | 2 | 0.007** | Significant | |
Wait for humanitarian assistance from NGOs, civil societies etc. | 111.514 | 2 | 0.000** | Significant | |
Use traditional IK to survive | 66.604 | 2 | 0.000** | Significant | |
Seek help within social networks; relatives, clan, neighbors | 6.908 | 2 | 0.032 | ||
Migration | 231.267 | 2 | 0.000** | Significant |
Independent variable . | Dependent variable . | Pearson Chi-Square . | df . | Asymp. Sig. (2-sided) . | Significance . |
---|---|---|---|---|---|
Farmer's location of residence (sub-county) | Climate change response | ||||
Resign to our fate and do nothing | 79.467 | 2 | 0.000** | Significant | |
Appeal to government for help through local administration | 9.887 | 2 | 0.007** | Significant | |
Wait for humanitarian assistance from NGOs, civil societies etc. | 111.514 | 2 | 0.000** | Significant | |
Use traditional IK to survive | 66.604 | 2 | 0.000** | Significant | |
Seek help within social networks; relatives, clan, neighbors | 6.908 | 2 | 0.032 | ||
Migration | 231.267 | 2 | 0.000** | Significant |
Field Data, 2020. **Statistical bivariate significance at p<0.01 at 99% confidence level.
These same variations in response were noted when they were asked about composite IK practices as an option, with Kajiado West, Kajiado Central, and Loitokitok posting 53, 39, and 6%, respectively. Overall, 39% of the respondents in Kajiado County opted for IK practices. The results therefore confirm that; based on the regional variances of prevailing ecological and climatic conditions, communities tend to adapt to climate change effects differently. The cooler temperatures and relatively heavy amounts of rainfall that are (and adequately predictable) experienced in Loitokitok sub-county; which also has rich volcanic soils, has its farmers adopting a largely sedentary form of agriculture which is mainly characterized by crop and dairy farming technologies.
This can be demonstrated from the study findings shown in Table 6, where 58% of the farmers in Loitokitok sub-county would likely look up to expert assistance from NGOs, civil societies, and research institutions among others, to address the effects of climate change as compared to only 5% of the respondents in more arid Kajiado West sub-county. Overall, 29% of the respondents in Kajiado County waited for humanitarian assistance from NGOs, and civil societies.
These findings therefore reinforce the applicability and significance of the credence that, the socio-cognitive progression within which farming-oriented climate change adaptation occurs, tends to take place in a localized socio-ecological environment. Such an environment as Mitter et al. (2019) avers is characterized by ‘social and institutional support, cultural values and norms, regional characteristics, and climate-related trigger events’.
Crosstab tests done between the existing likely climate change response approaches from farmers and the farmer's current location, the pro-IK practice of migration was the most preferred option that was influenced by where a farmer resided (CI = 99%, χ2 = 231.27, p < 0.000) as shown in Table 7. Similarly, dependence on humanitarian assistance from an organization that more often offers development programs that are CCAS-oriented is an option that is significantly associated with the location of a farmer.
The type of farming is regionally unique to the prevailing climatic and geophysical conditions and is a likely determinant of which climate change adaptation approach a farmer would prefer; whether IK practice or conventional CCAS.
CONCLUSIONS AND RECOMMENDATIONS
Conclusions
This study investigated the extent to which socio-demographic, economic, and geo-ecological factors influence farmers to either favor applying IK practices or the conventional CCAS in their agricultural activities in Kajiado County in Kenya. On socio-demographic characteristics of farmers, the level of education played a key role in the CC adaptation approach revealing a slower uptake among less learned farmers which could be attributed to a gap in terms of inadequate CCAS sensitization and awareness efforts considering that the majority of them were illiterate. Based on the findings on the gender and age of a farmer, it was manifestly clear that adaptation programs that would target the youth and women by increasing their involvement in agriculture would boost CCAS uptake.
The IK practices that are largely labor intensive, are more sustained by traditional families that have larger numbers of members. Economically, the study results show that farmers view the adoption of CCAS as having some financial implications on their incomes. It would therefore mean that CCAS programs that reflect envisaged improved returns for new technologies are more likely to be received well by farmers as revealed by increased uptake among those with higher incomes. As shown in the study results, farmers that resided in a particular location with a unique climatic and geophysical profile are involved in a specific type of farming that can thrive in the prevailing conditions. This is an indication that an effective approach that includes the IK climate change adaptation practices and the CCAS can be applied in a complimentary manner to achieve the desired results in a region that possesses diverse climatic and geophysical conditions.
The socio-demographic and economic characteristics of farmers in determining the farmer's tendency to practice IK ranked differently in terms of their significance. These factors therefore correctly fitted in the MPPACC model as contextual factors that played a significant role in influencing a farmer's appraisals, subsequently shaping his/her decision to either adapt to new agricultural CCAS or stick to their IK practices.
Recommendations
There is a need to prioritize the development of climate change adaptation and development plans that conform to the geo-ecological and socio-cultural characteristics through the direct involvement of all stakeholders, including the local farmers of Kajiado County.
DATA AVAILABILITY STATEMENT
All relevant data are included in the paper or its Supplementary Information.
CONFLICT OF INTEREST
The authors declare there is no conflict.