Analysis of the factors influencing the implementation of climate smart agriculture technologies in a climate change hot spot in South Africa

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Agriculture has been greatly affected, resulting in food shortages. In response, climate-smart agriculture (CSA) has been embraced by many countries as a way of alleviating this problem. The extent of CSA implementation has not yet been established in many areas. The present study thus sought to evaluate CSA to determine the factors that influence its implementation by smallholder farmers in Ditsobotla local Municipality, which is a climate change hotspot in South Africa. Questionnaires, field observations, key informant interviews and focus group discussions were used to gather data from smallholder farmers. Ninety households were sampled via snowball and purposive sampling approaches. The quantitative data were analysed via the Statistical Package for the Social Sciences version 30. The results show that the main determinants of CSA implementation among smallholder farmers include source of income, access to inputs, and source of inputs coupled with the influence of the frequency of agricultural extension officers’ visits. Smallholder farmers acknowledged that the implementation of CSA technologies can positively reduce the impact of climate variability. However, there should be regular visits by agricultural extension officers to support the farmers, with equitable access to inputs, subsidies and grants. The government, private organisations and community members should scale up collaboration to promote the implementation of CSA technologies and food security. Climate Smart Agriculture technologies implementation smallholder farmers Ditsobotla Municipality South Africa Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Introduction Climatic conditions across the globe have been observed to be changing gradually, and changes are projected to continue throughout this century and beyond [ 1 – 3 ]. Noticeable changes across the globe include continuous extreme temperatures, frequent droughts, heat waves and longer frost periods. Climate projections, for example, indicate an increase in temperature by \(\:1.4℃\:\) by 2030 and \(\:2.1℃\:\:\) by 2070 [ 4 ]. Studies have highlighted that the livelihoods of smallholder communities worldwide have been threatened by the climate’s warming impacts as agricultural productivity has been negatively affected [ 5 ]. Climate variations are projected to affect the productivity of land suitable for agricultural production [ 5 ]. Consequently, efforts have been made to promote coping strategies against the challenges of climate change facing the world’s five hundred and ten million smallholder farmers [ 6 ]. Among these strategies is climate smart agriculture (CSA) implementation. Studies across the world have highlighted the attention given to CSA as a means to promote resilience against climatic variations and provide mitigation and adaptation measures that promote food security [ 2 , 7 , 8 ]. CSA is understood to have the ability to safeguard food security despite changes in climate conditions [ 9 ]. CSA practices include growing seeds and breeding livestock that are better adapted to drought conditions; storing rainwater for future use; and the sustainable application of manure, fertilisers and chemicals [ 5 ]. Furthermore, novel practices that can be easily integrated into CSA practices include weather forecasting and climate risk insurance [ 10 ]. In Africa, most smallholder farmers rely on international aid to augment their yields to abate food shortages caused by erratic and unreliable rainfall [ 11 ]. Notably, a third of the world’s food is produced by smallholder farmers [ 6 ]. Under these circumstances, most governments in Africa have started implementing CSA to improve agricultural activities to reduce the impacts of climate change and variability on smallholder agriculture [ 2 , 12 ]. Despite efforts to promote CSA among smallholder farmers, the rate of implementation of these technologies remains low, particularly in southern Africa [ 7 ]. According to [ 13 ], although conservation agriculture has been widely used in southern Africa, its implementation remains limited. [ 14 ] further highlighted that studies that were performed in South Africa, Malawi and Zimbabwe revealed that the rates of adoption and implementation of CSA remain low. It is important to understand the determining factors behind the adoption of CSA practices. This understanding is necessary to inform such efforts to enhance the implementation of the desired technologies. The literature shows that socioeconomic and environmental conditions should be considered during the implementation of CSA practices [ 15 ]. Moreover, local knowledge should be integrated on the basis of stakeholder consultations. South Africa has been experiencing global warming, and this phenomenon has been observed through the increase in annual temperatures compared with those in previous years [ 16 ]. The Ditsobotla local municipality is in the North Western province of South Africa. This area is a climate change hotspot. Climate change hotspots are determined by a combination of factors such as the geography of an area and social, economic and infrastructure conditions [ 17 , 18 ]. Moreover, hotspots experience severe effects of climate change, such as rising temperatures, increased frequency of extreme weather events and continued disruptions to ecosystems and agriculture [ 18 ]. Climate hotspot regions are mostly found in low-lying coastal regions, islands, or close to poles. Interestingly, they are vulnerable to rising sea levels, storm surges and melting ice [ 18 , 19 ]. Fragile ecosystems such as coral reefs and wetlands may be susceptible to the impacts of climate change. According to [ 20 ], the Amazon rainforest is an example of a climate hotspot since it is vulnerable to deforestation and climate shifts. Poorly marginalised communities in most developing countries are hotspots because they are vulnerable to climate change impacts and lack the resources to adapt to those impacts [ 21 ]. Studies along the Eastern African coast have highlighted that areas that heavily depend on agriculture, fishing and tourism may be at risk if climate change disrupts these industries through droughts, temperature changes or damage to ecosystems. [ 22 ]. The production of maize and sunflower in Ditsobotla has been declining due to climate variations [ 23 ]. Additionally, livestock farming has been projected to increase vulnerability due to increased temperatures, which consequently affects open grazing areas [ 24 ]. The effects of changes in climate are anticipated to negatively affect the food security industry, which contributes immensely to the gross domestic product of South Africa [ 25 ]. Studies have shown that over the next 50 years, maize production could fall between 10% and 20% [ 26 ]. The literature has shown that the existence of recurring droughts in Ditsobotla local municipality has been a problem, and more needs to be done to address the consequences that follow [ 26 ]. If the problem is not adequately addressed, smallholder farmers in Ditsobotla are bound to continue being at risk of food shortages due to prolonged droughts and extreme temperature conditions. Additionally, there is a need to implement practical, indigenous knowledge systems that are people centered, together with enhanced policies and institutional and technological innovations, to mitigate climate variations [ 10 ]. Although studies have highlighted the advantages of climate-smart technologies, such as improving agricultural production and incomes and increasing food security, the degree of implementation in Ditsobotla is low [ 27 ]. It has been argued that more farmers need to implement these practices to bring meaningful transformation among smallholder farmers [ 5 ]. On the basis of this background, the present study aimed to analyse the factors that influence the implementation of climate-smart agriculture technologies in Ditsobotla local municipality, which is a climate change hotspot. The main research questions were as follows: i. What are the socioeconomic characteristics of the smallholder farmers in Ditsobotla local municipality? ii, what are the CSA practices available in Ditsobotla Local Municipality that smallholder farmers have implemented? iii, which factors determine the implementation of CSA practices by farmers in the area? Information about the factors that affect adoption and implementation is important for informing efforts to promote the implementation of CSA practices in Ditsobotla local municipality and other areas with similar circumstances. This study also provides valuable evidence-based data for policy makers. The findings are likely to confer to policy development to fill any existing gaps that enhance resilience among smallholder farmers. Moreover, the results obtained from the study may be useful in reviewing existing policies and promoting innovations that increase the implementation of CSA practices. 2 Materials and methods 2.1 Description of the study area The research was performed in the Ditsobotla local municipality, which is located within the Ngaka Modiri Molema district, which is geographically located in the North West, South Africa (see Fig. 1a and 1b). Ditsobotla is part of the five local municipalities in the Ngaka Modiri Molema district and is 1728 meters above sea level between latitudes \(\:{26}^{^\circ\:}{32}^{{\prime\:}}{51}^{{\prime\:}{\prime\:}\:}S\) and \(\:25^\circ\:\:{59}^{{\prime\:}}\:{13}^{{\prime\:}{\prime\:}}\:S\) and longitudes \(\:25^\circ\:\:{2}^{{\prime\:}}\:{42}^{{\prime\:}{\prime\:}\:}E\:and\:25^\circ\:\:{56}^{{\prime\:}}\:{53}^{{\prime\:}{\prime\:}}\) . The area of the municipality is 6 387 \(\:{km}^{2}\) , with a population of 181 865 [ 28 ]. The municipality consists of two main towns, Lichtenburg, Coligny and four townships, surrounded by numerous villages, including some commercial farming areas. Below are maps showing the provinces of South Africa and the Ngaka Modiri Molema District Municipality. The Ditsobotla local municipality is located within a grassland biome whose terrain is mainly flat and rolling. Very few trees are found in the area, and geophytes are common. This characteristic promotes crop production and livestock production. Ditsobotla is a climate change hotspot characterised by average annual rainfall between 300 mm and 600 mm [ 22 ]. Most of the rainfall is received in summer, whereas less than 5 mm is received in winter [ 22 ]. Winter temperatures are as low as \(\:\text{7,8}℃\) , and summer temperatures can reach \(\:\text{40,6}℃\) [ 22 ]. 2.2 Method of data collection Data were collected in Ditsobotla district municipality in Northwest Province, which has a population of 932 smallholder farmers [ 30 ]. The smallholder farmers were sparsely placed in terms of their geographical location. Therefore, the research incorporated a mixed methodology utilising both snowball and purposive sampling to allow flexibility and collection of data that are information rich [ 31 ]. Snowball sampling relies mostly on referrals, whereby the initial participants nominate other respondents willing to take part in the survey [ 32 ]. Purposive sampling uses a population of interest, such as smallholder farmers, who have the knowledge and experience of farming [ 31 ]. Moreover, the population presented a high level of farming knowledge and volunteered to participate and share their opinions about the use of CSA practices [ 33 ]. The advantage of sampling a population of interest is that a homogeneous sample is used, resulting in a reduced variance that makes it easy to obtain statistical significance in the study [ 34 ]. According to [ 35 ], mixing sampling methods in research promotes triangulation, resulting in better results. Furthermore, the efficiency and cost effectiveness of the research were enhanced [ 35 ] The sample size for the households interviewed was determined via the formula of Yamane (1967): \(\:n=\frac{N}{[1+N({e)}^{2}]}\) [ 36 ] where n = the sample size. N = Population size e = Level of precision (10%) n = \(\:\frac{932}{[1+932({\text{0,1})}^{2}]}\) = 90 From the study area, 90 smallholders were sampled. Structured questionnaires were distributed to smallholder farmers in the area to gather quantitative data. Twenty people were used to pre-test the questionnaire, and feedback was used to clarify all the questions that were not clearly worded. Moreover, questionnaires were tailored to suit their intended recipients [ 37 ]. The questionnaires were distributed via the random walk method among farmers in the area [ 38 ]. To ensure confidentiality, the questionnaires excluded identifiable information such as name, phone number, and address. All questions were standardised to ensure uniformity among recipients, and consent was obtained before questionnaires were administered. The questionnaires were interpreted as Tswana to accommodate farmers who could not read and write. Key informants, which included two extension officers, two district agricultural officials, two village headmen, and three ward councillors were interviewed to obtain in-depth information. Key informants played a significant role by sharing their experiences with farming. Field observations were further implemented to verify agricultural practices in the area to gain insights into farmers’ experiences and performance [ 39 ]. To confirm the data from the households, discussions were administered with the smallholder farmers in the community hall to consolidate the findings. Three groups were used because the population was homogeneous and knowledgeable about farming. The focus group discussions lasted two hours per session, with two breaks of fifteen minutes and refreshments in between to foster concentration and comfortability. Naturalistic observations were made whereby the researcher observed farmers in their natural environment (fields) and learned more about their practices. Naturalistic observations offered more reliable results because of the natural settings in which they were carried out. Second hand, information about the effectiveness of climate-smart agriculture and shortfalls in other areas was obtained from published reports such as journals, CSA source books, published dissertations, web sources, conference papers, and government departments such as the Department of Agriculture, Forestry and Fisheries (DAFF), North West Community Survey 2016 reports, and the Department of Agriculture, Land Reform and Rural Development and Statistics South Africa (DALRRD) in the form of figures, reports, policies and acts. All the data sets were aligned to the study. 2.3 Data Analysis The data were interpreted via the Statistical Package for the Social Sciences (SPSS) version 30 software program. Descriptive statistics such as frequencies and percentages were used to interpret socioeconomic characteristics, characterise technologies implemented by smallholder farmers and assess the contribution of CSA to communities as a way of adapting to climate change impacts. The outcomes of the study are presented in tables, bar graphs, pie charts, and figures. A binary logistic regression model was used to analyse factors influencing the use of CSA practices in the Ditsobotla local municipality. The model best suited the study because the dependent dichotomous variable used provided an understanding of the factors that influenced the results of the study. Frequencies and means were used to analyse data on the frequency of implementation of climate-smart agricultural practices (CSPs). Qualitative data analysis was performed to obtain a deeper understanding that surpasses numbers and statistical inference [ 40 ]. Data generated from focus groups, interviews, documents, and observations were prepared and organised for thematic analysis. Data logging was performed to capture major ideas and identify potential biases that may influence the interpretation of data [ 41 ]. The data were coded, and consequently, conclusions were derived from the patterns formed during the study. 3. Research Results 3.1 Socioeconomic characteristics of the participants The respondents were asked about their socioeconomic characteristics, and the results in Table 1 show that 56% of the sampled smallholder farmers were males, whereas 44% were females. The results indicated that there are more males actively involved in agriculture to provide a livelihood for their families. Most of the respondents (40%) were aged between 50 and 59 years. Interestingly, more than three quarters of the farming population are elderly people. Younger farmers represented 16% (40–49 years) and 11% (30–39 years) of the sample, respectively. Half of the respondents in the study area had a primary education. Only 38% of the respondents matriculated, and 11% had diploma certificates. Most of the farmers’ revenue stems from the sale of farm produce (60%), followed by pensions (29%) and, finally, salaries (11%). Married people in Ditsobotla constitute 54.4% of the population, followed by widowed people, with a population of 29%. The single population contributed 17% of the total population. The household size of the majority of the respondents was between 4% and 6%, accounting for 44% of the sample. The results revealed that medium-sized households prevailed in the area. There are high levels of farming experience in the study area. Category 16 and above took the lead at 44%, followed by categories 11–15 at 31%. The 6–10 category has the least experience, with 11.1%. Table 1 Socioeconomic characteristics of the respondents Variable Number of respondents Percentage (%) Gender Male 50 56 Female 40 44 Total 90 100 Age 30–39 10 11 40–49 14 16 50–59 36 40 60 and above 30 33 Total 90 100 Educational Background Primary 46 51 Matric 34 38 Diploma 10 11 Total 90 100 Source of Income Sale of farm produce 54 60 Salary 10 11 Pension 26 29 Total 90 100 Marital Status Single 15 17 Married 49 54 Widowed 26 29 Total 90 100 Household Size 0–3 17 19 4–6 40 44 7–10 33 37 Total 90 100 Farming Experience 0–5 13 14 6–10 10 11 11–15 28 31 16 and above 39 44 Total 90 100 Source: Survey results 3.2 Climate-smart agricultural technologies implemented by smallholder farmers in the area The farmers were asked to indicate the various CSA technologies they implemented in the area. Table 2 shows the CSA practices implemented in Ditsobotla municipality. Livestock breed improvement was ranked first, with an average of 3.28, followed by the use of drought-resistant seed varieties (2.58), crop rotation (2.56), minimum tillage (2.46), chemical fertilisers (2.44), mulching (2.26), organic manure (2.08), water harvesting (1.82), intercropping (1.58) and agroforestry (1.21). Livestock breeding in the form of adaptable breeds has been widely implemented due to the dry conditions that prevail in the area. Table 2 CSA practices implemented in the area (n = 90) CSA Practices Never Rarely Often Always Mean Ranking Livestock breed improvement 8(8.9%) 2(2.2%) 37(41.1%) 43(47.8%) 3.28 1 Drought resistant seeds 3(3.3%) 38(42.2%) 29(32.2%) 20(22.2%) 2.73 2 Crop rotation 10(11.1%) 28(31.1%) 44(48.9%) 8(8.9%) 2.56 3 Minimum tillage 7(7.8%) 41(45.6%) 36(40.0%) 6(6.7%) 2.46 4 Chemical fertiliser 8(8.9%) 43(47.8%) 30(33.3%) 9(10.0%) 2.44 5 Mulching 20(22.2%) 29(32.2%) 39(43.3%) 2(2.2%) 2.26 6 Organic Manure 18(20.0%) 51(56.7%) 17(18.9%) 4(4.4%) 2.08 7 Water harvesting 37(41.1%) 32(35.6%) 21(23.3%) 0.00% 1.82 8 Intercropping 43(47.8%) 42(46.7%) 5(5.6%) 0.00% 1.58 9 Agroforestry 76(84.4%) 10(11.1%) 3(3.3%) 1(1.1%) 1.21 10 Source: Survey results 3.3 Analysis of factors influencing the use of CSA technologies The study assumed that socioeconomic factors influenced the implementation of CSA technologies. The factors influencing the use of CSA technologies were analysed via a binary logistic regression model. The findings shown in Table 3 revealed that the influence of the source of agricultural income ( \(\:\beta\:\) = -2.853, p = 0.024) was a significant barrier to the implementation of CSA technologies. Moreover, the influence of access to inputs ( \(\:\beta\:\) = -4.096, p = 0.016) also hindered the implementation of CSA technologies. The influence of the source of the provision of inputs ( \(\:\beta\:\) = 9.486, p = 0.009) positively impacted the acceptance of CSA technologies. Finally, the influence of the frequency of agricultural extension officers ( \(\:\beta\:\) = -8.214, p= 0,016) retarded the acceptance of CSA technologies. Table 3 Analysis of factors influencing the use of CSA technologies via the binary regression model Independent Variables β S. E Wald D f Sig Exp(β) Gender 4.158 2.124 3.832 1 0.050 63.922 Age 0.128 1.007 0.16 1 0.899 1.137 Income source -2.853 1.267 5.071 1 0.024 0.058 Marital status 0.570 1.275 0.200 1 0.655 1.768 Household size 0.114 1.070 0.11 1 0.915 1.121 Land tenure 0.364 0.995 0.134 1 0.715 1.439 Farm size 0.657 1.041 0.399 1 0.528 1.930 Information source 0.09 0.711 0.00 1 0.990 1.009 Membership group -16.05 12482.4 0.000 1 0.999 0.000 Access to inputs -4.096 1.706 5.763 1 0.016 0.017 Inputs provision 9.486 3.637 6.803 1 0.009 13173.9 Extension frequency -8.214 3.424 5.754 1 0.016 0.17 Constant 24.268 12482.48 0.000 1 0.998 34613619 -2Log Likelihood \(\:{22.864}^{a}\) Cox& Snell R square 0.476 Nagelkerke R square 0.802 p < 0.05 Source: Survey results 3.4 Sources of agricultural inputs, subsidies and grants Farmers were asked about their sources of agricultural inputs, subsidies and grants Most respondents highlighted that they secured inputs by themselves using money obtained from selling farm produce and pension money (39%). As highlighted in Fig. 2 , government subsidies and grants contributed 24%, and the percentage of respondents who received inputs from NGOs was 17%. North West Kooperate (NWK), a private institution, offered 17% of the subsidies and grants to farmers in the area. 3.5 Frequency of extension officers in Ditsobotla Municipality The respondents were asked about the frequency of extension officers in the study area, and the results revealed that extension officers occasionally visited the area, as indicated in Fig. 3 . The highest percentage of respondents (46%) noted that visits were occasional, whereas 24% of the respondents indicated that extension officers rarely visited the area. The highlighted situation prevents smallholder farmers from receiving adequate information about CSA practices [ 5 ]. Moreover, the distribution of inputs, subsidies and grants becomes compromised. There is a need for extension officers to visit farmers on a regular basis to disseminate information on new technologies better suited to the changing climate to promote food security in the area. 3.6 Level of use of CSA technologies in Ditsobotla Municipality The respondents were asked about the level of use of CSA technologies in the area, and the results highlighted in Fig. 4 revealed that the level of CSA in the area remained low at 54%. Medium users constituted 24%, and high users constituted 21%. 3.7 Perceptions of the contribution of CSA to adaptation to the impacts of climate change The respondents were asked how they perceived the contribution of CSA in adapting to climate change impacts. The results in Fig. 5 revealed that 64% of the respondents agreed that the implementation of CSA technologies contributed positively to their adaptation to the impacts of climate change. However, 36% disagreed with the idea stated above for various reasons. Most farmers highlighted that there was a need for educational guidelines on agricultural practices that are linked to climate change. 4. Discussion Most of the smallholder farmers actively involved in agriculture were males aged between 50 and 59 years. This situation can be attributed to local cultural and social factors whereby females have been historically marginalised to access land and resources [ 42 ]. Studies from West Africa further highlighted that gender norms and beliefs play crucial roles in women’s participation in agriculture [ 43 ]. Interestingly, more than three-quarters of the farming population are elderly people. This scenario could hinder the implementation of smart agricultural practices, as the majority of them want to keep their traditional methods such as monoculture. The results further revealed that young adults are not keen on agricultural activities. According to [ 44 ], most South African rural areas are constantly affected by rural to urban migration. Such movements force the aging population to continue working in fields without the help of the younger population. There is an obligation for young adults to learn more about farming and bring new innovations to make agriculture sustainable for future generations. Half of the respondents in the study area have primary education, and most of the farmers derive their income from the sale of farm produce. The results indicate that agriculture plays a significant role in the livelihoods of most farmers in Ditsobotla. However, some farmers augmented their source of income by selling fresh vegetables and chickens. The average household size consists of 4 to 6 people. Household size is an important determinant of the availability of labour. Livestock breeding in the form of adaptable breeds has been widely implemented due to the dry conditions that prevail in Ditsobotla. A similar scenario was observed by [ 45 ], who highlighted that livestock farming has been widely implemented in many rural areas of South Africa as a way of improving food security in a changing climate environment. Moreover, from the observations during data collection, it was noted that livestock farming was rampant in the area. Drought-resistant seed varieties in the form of sunflowers are prevalent in Ditsobotla because sunflowers have a root system that can withstand water loss due to high temperatures [ 46 ]. Crop rotation is of paramount importance, as it enables the soil to maintain its fertility [ 47 ]. The application of chemical fertilisers has been observed in sunflower and maize production because both crops are sensitive to nutrient deficiencies [ 46 , 48 ]. Sunflower and maize production can be enhanced by applying fertilisers and manure in the correct proportions at the right time and place. Organic manure is rarely applied by farmers because they do not have enough manure to cover their fields. Instead, chemical fertilisers are commonly applied, depending on their availability. Water harvesting has been performed in Ditsobotla for years because of drought conditions. A number of boreholes, JoJo tanks and water bowsers were observed during data collection. Intercropping has rarely been implemented because of the rampant theft of crops in Ditsobotla. Agroforestry was the least common method to be implemented due to low levels of awareness of the practice in Ditsobotla. In South Africa, agroforestry is not well established in many rural areas despite the numerous benefits it has. This idea has been supported by [ 49 ] in his study of the adoption of agroforestry in Tsolo and Lusisiki in Eastern Cape. A necessity exists to intensify education and training on agroforestry practices [ 49 ]. A number of socioeconomic factors were found to influence the implementation of CSA technologies. Among them was the source of agricultural income. Most of the farmers in Ditsobotla do not have enough money to support their farming activities. Notably, they relied on the sale of farm produce to provide inputs for the next farming season. Farmers in Ditsobotla should engage with various farming enterprises to increase their income base and promote food security. This observation is in line with the findings of [ 50 ] in their study in southern Mali, where multiple sources of income were encouraged to promote sustainable agriculture among smallholder farmers. Access to inputs was found to influence the implementation of CSA technologies. Similar studies on the accessibility of inputs have been conducted in several countries, including Malawi and Zambia, and the input subsidy programs have helped encourage farmers to implement CSA technologies [ 51 ]. Most farmers receive subsidised drought-resistant seeds and inorganic fertilisers, and consequently, increased productivity ensues, resulting in a surplus for sale [ 51 ]. Limited sources of provision of farm inputs were found to have an effect on the implementation of CSA technologies. Having multiple sources of input provisions helps to accelerate the implementation of CSA immensely. Studies in China, Vietnam, Nepal, Uganda and Kenya have shown that governments, private sectors, financial institutions, civil society and NGOs have been facilitating the implementation of CSA technologies through the provision of tree seedlings, stress-resilient seeds, fertilisers and irrigation technologies, resulting in accelerated implementation [ 52 ]. Finally, the frequency of agricultural extension officers in Ditsobotla was found to influence CSA implementation. Notably, implementation was found to be retarded by the low visibility of agricultural extension officers in the area. A study by [ 53 ] in Lushoto Northeast, Tanzania, revealed that access to agricultural information is positively related to the probability of implementing CSA technologies. Furthermore, the idea concurs with the conclusions of [ 54 ] in their study of the role and perspective of CSA in Africa. They concluded that continuous learning accelerated information acquisition and fostered acceptance and implementation of CSA technologies in most African countries. 5. Conclusion This study analysed the factors influencing the implementation of CSA by smallholder farmers in Ditsobotla local municipality. The findings revealed that most of the respondents were elderly males with mostly primary education. Most farmers’ income is derived from the sale of farm produce. High levels of farming experience exist among the farmers in the area. However, there is a call for extension officers to educate farmers on modern methods of farming that incorporate CSA technologies to foster implementation. Many households struggle to obtain assistance from the government. The government should ensure an equitable distribution of inputs to accelerate the implementation of CSA technologies. Livestock breed improvement was observed to be mostly implemented in the area; however, agroforestry was the least common improvement to be implemented due to erratic rainfall. The level of CSA in the area remains low, and more attention is needed to enhance the full implementation of technologies for future generations to increase the level of food sufficiency in a changing environment. The source of agricultural income, access to inputs, source of provision of inputs and frequency of agricultural extension officers in the area have been highlighted as the most important factors that affect the implementation of CSA technologies in the area. This study recommends that young adults learn more about farming and bring new innovations to make agriculture sustainable for future generations. Incentives should be channelled towards agriculture to attract the interests of the younger generation. Timeous visibility by extension officers should be accelerated to disseminate information and guide farmers on how to continuously implement new information regarding CSA technologies. Moreover, they should provide proper guidance on the fair distribution of inputs to reduce corruption and nepotism in the area. The government should work together with NGOs, NWK and other financial institutions in the area to help farmers with inputs and credit to increase food production in the area. Policymakers should consider factors such as the source of agricultural income, access to inputs, source of provision of inputs and frequency of agricultural extension officers to greatly influence the implementation of CSA technologies in a bid to speed up their uptake. Declarations Acknowledgements The author gratefully acknowledges all the respondents in Ditsobotla Municipality who voluntarily participated in this study. Author contributions M Mtuwa: Project conception, methodology, data collection and analysis, draft and final manuscript: M Chitakira: Project conception, methodology, project supervision, draft and final manuscript. Funding University of South Africa financially supported this research project Data availability Full data for this study can be obtained from the authors upon request and in accordance with the applicable data governing policies and procedures. Ethics approval Ethical clearance was obtained from the ethical committee of the University of South Africa (College of Agriculture and Environmental Sciences, Health Research Ethics Committee). The research was carried out in accordance with their guidelines and regulations. 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In: Sea Level Rise and Ocean Health in the Context of Climate Change [Working Title]. IntechOpen; 2023: https://www.intechopen.com/online-first/88259 . Pienaar A, Coetzee C, Nemakonde L. Ongoing climate crises and obstacles to adaptation: Observations from the Ditsobotla Local Municipality, South Africa. The Journal for Transdisciplinary Research in Southern Africa. 2021;17(1): http://www.td-sa.net/index.php/td/article/view/1089 . DEA. South African Department of Environmental Affairs (DEA). (2015). Climate support programme-Vulnerability assessment. Final report for North West province, Department of Environmental Affairs, Pretoria. 2015. Oduniyi O. Prioritisation on cultivation and climate change adaptation techniques: A potential option in strengthening climate resilience in South Africa. Agron Colomb.2019;37(1):62–72. Oduniyi OS, Tekana SS. Adoption of agroforestry practices and climate change mitigation strategies in North West province of South Africa. IJCCSM. 2019;11(5):716–29. 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Handbook of Research Methods and Applications in Experimental Economics. Edward Elgar Publishing; 2019. Lester JN, Cho Y, Lochmiller CR. Learning to Do Qualitative Data Analysis: A Starting Point. Human Resource Development Review. 2020;19(1):94106. Creswell J. Research design: Quantitative, Qualitative and Mixed methods Approaches. Thousand Oaks, Califonia; SAGE Publications, Inc.; 2014. Gebre GG, Isoda H, Rahut DB, Amekawa Y, Nomura H. Gender differences in agricultural productivity: evidence from maize farm households in southern Ethiopia. Geo Journal 2021;86(2):843–64. Zossou E, Arouna A, Diagne A, Agboh-Noameshie RA. Gender gap in acquisition and practice of agricultural knowledge: case study of rice farming in West Africa. Ex Agric. 2017;53(4):56677. Maka L, Ighodaro ID, Ngcobo-Ngotho GPT. Capacity development for scaling up Climate-Smart Agriculture (CSA) innovations: agricultural extension’s role in mitigating climate change effects in Gqumashe community, Eastern Cape, South Africa. S Afr Jnl Agric Ext. 2019 ;47(1). Molieleng L, Fourie P, Nwafor I. Adoption of Climate Smart Agriculture by Communal Livestock Farmers in South Africa. Sustainability. 2021;13(18):10468: https://www.mdpi.com/2071-1050/13/18/10468 Stoicea P, Chiurciu I, Soare E, Iorga A, Dinu T, Tudor V, et al. Impact of Reducing Fertilisers and Pesticides on Sunflower Production in Romania versus EU Countries. Sustainability. 2022;14(14):8334. Ojo TO, Kassem HS, Ismail H, Adebayo DS. Level of adoption of climate smart agriculture among smallholder rice farmers in Osun State: does financing matter? Scientific African. 2023;21: e01859. Zamanian M, Yazdandoost M. The effects of biological and chemical fertiliser sources on the production and quality of sunflower. Indones J Agric Sci. 2022;23(1):15. Zerihun MF. Agroforestry Practices in Livelihood Improvement in the Eastern Cape Province of South Africa. Sustainability. 2021;13(15):8477. Dembele B. Understanding the multiple sources drivers of agricultural income amongst smallholder farmers in Southern Mali. Raae. 2018; 21(2):32–40. Jayne TS, Sitko NJ, Mason NM, Skole D. Input Subsidy Programs and Climate Smart Agriculture: Current Realities and Future Potential. In: Lipper L, McCarthy N, Zilberman D, Asfaw S, Branca G, editors. Climate Smart Agriculture. Cham: Springer International Publishing; 2018. p. 251–73. (Natural Resource Management and Policy; vol. 52). Vincent A, Balasubramani N. Climate-smart agriculture (CSA) and extension advisory service (EAS) stakeholders’ prioritisation: a case study of Anantapur district, Andhra Pradesh, India. Journal of Water and Climate Change. 2021;12(8):3915–31. Nyasimi M, Kimeli P, Sayula G, Radeny M, Kinyangi J, Mungai C. Adoption and Dissemination Pathways for Climate-Smart Agriculture Technologies and Practices for Climate-Resilient Livelihoods in Lushoto, Northeast Tanzania. 2017;5(3):63. Abegunde VO, Obi A. The Role and Perspective of Climate Smart Agriculture in Africa: A Scientific Review. Sustainability. 2022;14(4):2317. Additional Declarations No competing interests reported. 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Chitakira","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYDACCcYGECnHwABmEKelEajUwpgULWClFYnEqmdg4J/d3P7gZ45E+obbzc0ffjDYyTPwH36A35I7Bxsbe7dJ5G64c7BNsoch2bCB4ZgBXi0GEomNDbwgLTcS2xh4GJgTgA4lrKXx7zaJdIMbic0f/zDUJzAws38gqKUZaEsCUEuDNA/D4QQGNh78tkjcSGycLbtNwnAm0GHSMgbHDdt4eArwauGfkf7g49ttdfJ8N9Iff3xTUS3Pz398A14t6O5kYGAjRf0oGAWjYBSMAuwAAKdLRQ7ojF/xAAAAAElFTkSuQmCC","orcid":"","institution":"University of South Africa","correspondingAuthor":true,"prefix":"","firstName":"Munyaradzi","middleName":"","lastName":"Chitakira","suffix":""}],"badges":[],"createdAt":"2025-05-13 21:53:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6658799/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6658799/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84806532,"identity":"f2b5aebe-38f8-4d09-bd75-795cc6c6ba6c","added_by":"auto","created_at":"2025-06-17 14:10:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":164195,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea \u003c/strong\u003eProvinces of South Africa\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb \u003c/strong\u003eNgaka Modiri Molema District\u003cstrong\u003e \u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSource:\u003cstrong\u003e[29]\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6658799/v1/26e33fe70ee254d756a46557.png"},{"id":84806530,"identity":"ac998e5a-66f7-4c75-84f7-64a3760694eb","added_by":"auto","created_at":"2025-06-17 14:10:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":55910,"visible":true,"origin":"","legend":"\u003cp\u003eSources of agricultural inputs, subsidiesor grants\u003c/p\u003e\n\u003cp\u003eSource: Survey results\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6658799/v1/d2d1c00f209be27c46043743.png"},{"id":84806971,"identity":"0c8bf894-b309-4096-b69c-e5b7900b2fe2","added_by":"auto","created_at":"2025-06-17 14:18:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":12267,"visible":true,"origin":"","legend":"\u003cp\u003eFrequencies of extension officers in Ditsobotla municipality\u003c/p\u003e\n\u003cp\u003eSource: Survey results.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6658799/v1/19239db15f2f34cb98523046.png"},{"id":84805510,"identity":"3741e80c-cced-4cf2-a4e5-b75034241d0e","added_by":"auto","created_at":"2025-06-17 14:02:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":12433,"visible":true,"origin":"","legend":"\u003cp\u003eLevel of use of CSA technologies\u003c/p\u003e\n\u003cp\u003eSource: Survey results\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6658799/v1/b2af36019aec3d6ca6309df2.png"},{"id":84805513,"identity":"01a8c0cc-6d74-4a19-940a-06e5a5c99dc3","added_by":"auto","created_at":"2025-06-17 14:02:00","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":49998,"visible":true,"origin":"","legend":"\u003cp\u003ePerceived contribution of CSA to adaptation to climate change\u003c/p\u003e\n\u003cp\u003eSource: Survey results.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6658799/v1/8004107b18526399e6887b7c.png"},{"id":84808415,"identity":"0da0ca7e-b937-4b32-af98-537fd271918c","added_by":"auto","created_at":"2025-06-17 14:34:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1262031,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6658799/v1/8c372071-b648-47ba-bccc-148c121a70b5.pdf"},{"id":84805509,"identity":"d5b34c26-bae8-40bd-9535-e7a328a17299","added_by":"auto","created_at":"2025-06-17 14:02:00","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17699,"visible":true,"origin":"","legend":"","description":"","filename":"PARTICIPANTINFORMATIONSHEET.docx","url":"https://assets-eu.researchsquare.com/files/rs-6658799/v1/e944ef47cf3984d7cd40a3d1.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis of the factors influencing the implementation of climate smart agriculture technologies in a climate change hot spot in South Africa","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eClimatic conditions across the globe have been observed to be changing gradually, and changes are projected to continue throughout this century and beyond [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Noticeable changes across the globe include continuous extreme temperatures, frequent droughts, heat waves and longer frost periods. Climate projections, for example, indicate an increase in temperature by \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:1.4℃\\:\\)\u003c/span\u003e\u003c/span\u003e by 2030 and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:2.1℃\\:\\:\\)\u003c/span\u003e\u003c/span\u003eby 2070 [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Studies have highlighted that the livelihoods of smallholder communities worldwide have been threatened by the climate\u0026rsquo;s warming impacts as agricultural productivity has been negatively affected [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Climate variations are projected to affect the productivity of land suitable for agricultural production [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Consequently, efforts have been made to promote coping strategies against the challenges of climate change facing the world\u0026rsquo;s five hundred and ten million smallholder farmers [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Among these strategies is climate smart agriculture (CSA) implementation.\u003c/p\u003e \u003cp\u003eStudies across the world have highlighted the attention given to CSA as a means to promote resilience against climatic variations and provide mitigation and adaptation measures that promote food security [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. CSA is understood to have the ability to safeguard food security despite changes in climate conditions [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. CSA practices include growing seeds and breeding livestock that are better adapted to drought conditions; storing rainwater for future use; and the sustainable application of manure, fertilisers and chemicals [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Furthermore, novel practices that can be easily integrated into CSA practices include weather forecasting and climate risk insurance [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn Africa, most smallholder farmers rely on international aid to augment their yields to abate food shortages caused by erratic and unreliable rainfall [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Notably, a third of the world\u0026rsquo;s food is produced by smallholder farmers [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Under these circumstances, most governments in Africa have started implementing CSA to improve agricultural activities to reduce the impacts of climate change and variability on smallholder agriculture [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Despite efforts to promote CSA among smallholder farmers, the rate of implementation of these technologies remains low, particularly in southern Africa [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. According to [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], although conservation agriculture has been widely used in southern Africa, its implementation remains limited. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] further highlighted that studies that were performed in South Africa, Malawi and Zimbabwe revealed that the rates of adoption and implementation of CSA remain low. It is important to understand the determining factors behind the adoption of CSA practices. This understanding is necessary to inform such efforts to enhance the implementation of the desired technologies.\u003c/p\u003e \u003cp\u003eThe literature shows that socioeconomic and environmental conditions should be considered during the implementation of CSA practices [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Moreover, local knowledge should be integrated on the basis of stakeholder consultations. South Africa has been experiencing global warming, and this phenomenon has been observed through the increase in annual temperatures compared with those in previous years [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The Ditsobotla local municipality is in the North Western province of South Africa. This area is a climate change hotspot. Climate change hotspots are determined by a combination of factors such as the geography of an area and social, economic and infrastructure conditions [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Moreover, hotspots experience severe effects of climate change, such as rising temperatures, increased frequency of extreme weather events and continued disruptions to ecosystems and agriculture [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Climate hotspot regions are mostly found in low-lying coastal regions, islands, or close to poles. Interestingly, they are vulnerable to rising sea levels, storm surges and melting ice [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Fragile ecosystems such as coral reefs and wetlands may be susceptible to the impacts of climate change. According to [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], the Amazon rainforest is an example of a climate hotspot since it is vulnerable to deforestation and climate shifts. Poorly marginalised communities in most developing countries are hotspots because they are vulnerable to climate change impacts and lack the resources to adapt to those impacts [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Studies along the Eastern African coast have highlighted that areas that heavily depend on agriculture, fishing and tourism may be at risk if climate change disrupts these industries through droughts, temperature changes or damage to ecosystems. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe production of maize and sunflower in Ditsobotla has been declining due to climate variations [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Additionally, livestock farming has been projected to increase vulnerability due to increased temperatures, which consequently affects open grazing areas [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The effects of changes in climate are anticipated to negatively affect the food security industry, which contributes immensely to the gross domestic product of South Africa [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Studies have shown that over the next 50 years, maize production could fall between 10% and 20% [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The literature has shown that the existence of recurring droughts in Ditsobotla local municipality has been a problem, and more needs to be done to address the consequences that follow [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. If the problem is not adequately addressed, smallholder farmers in Ditsobotla are bound to continue being at risk of food shortages due to prolonged droughts and extreme temperature conditions. Additionally, there is a need to implement practical, indigenous knowledge systems that are people centered, together with enhanced policies and institutional and technological innovations, to mitigate climate variations [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough studies have highlighted the advantages of climate-smart technologies, such as improving agricultural production and incomes and increasing food security, the degree of implementation in Ditsobotla is low [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. It has been argued that more farmers need to implement these practices to bring meaningful transformation among smallholder farmers [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOn the basis of this background, the present study aimed to analyse the factors that influence the implementation of climate-smart agriculture technologies in Ditsobotla local municipality, which is a climate change hotspot. The main research questions were as follows: i. What are the socioeconomic characteristics of the smallholder farmers in Ditsobotla local municipality? ii, what are the CSA practices available in Ditsobotla Local Municipality that smallholder farmers have implemented? iii, which factors determine the implementation of CSA practices by farmers in the area? Information about the factors that affect adoption and implementation is important for informing efforts to promote the implementation of CSA practices in Ditsobotla local municipality and other areas with similar circumstances. This study also provides valuable evidence-based data for policy makers. The findings are likely to confer to policy development to fill any existing gaps that enhance resilience among smallholder farmers. Moreover, the results obtained from the study may be useful in reviewing existing policies and promoting innovations that increase the implementation of CSA practices.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Description of the study area\u003c/h2\u003e \u003cp\u003eThe research was performed in the Ditsobotla local municipality, which is located within the Ngaka Modiri Molema district, which is geographically located in the North West, South Africa (see Fig.\u0026nbsp;1a and 1b). Ditsobotla is part of the five local municipalities in the Ngaka Modiri Molema district and is 1728 meters above sea level between latitudes \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{26}^{^\\circ\\:}{32}^{{\\prime\\:}}{51}^{{\\prime\\:}{\\prime\\:}\\:}S\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:25^\\circ\\:\\:{59}^{{\\prime\\:}}\\:{13}^{{\\prime\\:}{\\prime\\:}}\\:S\\)\u003c/span\u003e\u003c/span\u003e and longitudes \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:25^\\circ\\:\\:{2}^{{\\prime\\:}}\\:{42}^{{\\prime\\:}{\\prime\\:}\\:}E\\:and\\:25^\\circ\\:\\:{56}^{{\\prime\\:}}\\:{53}^{{\\prime\\:}{\\prime\\:}}\\)\u003c/span\u003e\u003c/span\u003e. The area of the municipality is 6 387\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{km}^{2}\\)\u003c/span\u003e\u003c/span\u003e, with a population of 181 865 [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The municipality consists of two main towns, Lichtenburg, Coligny and four townships, surrounded by numerous villages, including some commercial farming areas. Below are maps showing the provinces of South Africa and the Ngaka Modiri Molema District Municipality.\u003c/p\u003e \u003cp\u003eThe Ditsobotla local municipality is located within a grassland biome whose terrain is mainly flat and rolling. Very few trees are found in the area, and geophytes are common. This characteristic promotes crop production and livestock production. Ditsobotla is a climate change hotspot characterised by average annual rainfall between 300 mm and 600 mm [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Most of the rainfall is received in summer, whereas less than 5 mm is received in winter [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Winter temperatures are as low as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{7,8}℃\\)\u003c/span\u003e\u003c/span\u003e, and summer temperatures can reach \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{40,6}℃\\)\u003c/span\u003e\u003c/span\u003e [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Method of data collection\u003c/h2\u003e \u003cp\u003eData were collected in Ditsobotla district municipality in Northwest Province, which has a population of 932 smallholder farmers [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The smallholder farmers were sparsely placed in terms of their geographical location. Therefore, the research incorporated a mixed methodology utilising both snowball and purposive sampling to allow flexibility and collection of data that are information rich [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Snowball sampling relies mostly on referrals, whereby the initial participants nominate other respondents willing to take part in the survey [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Purposive sampling uses a population of interest, such as smallholder farmers, who have the knowledge and experience of farming [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Moreover, the population presented a high level of farming knowledge and volunteered to participate and share their opinions about the use of CSA practices [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The advantage of sampling a population of interest is that a homogeneous sample is used, resulting in a reduced variance that makes it easy to obtain statistical significance in the study [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. According to [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], mixing sampling methods in research promotes triangulation, resulting in better results. Furthermore, the efficiency and cost effectiveness of the research were enhanced [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe sample size for the households interviewed was determined via the formula of Yamane (1967): \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:n=\\frac{N}{[1+N({e)}^{2}]}\\)\u003c/span\u003e\u003c/span\u003e [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/p\u003e \u003cp\u003ewhere n\u0026thinsp;=\u0026thinsp;the sample size.\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;Population size\u003c/p\u003e \u003cp\u003ee\u0026thinsp;=\u0026thinsp;Level of precision (10%)\u003c/p\u003e \u003cp\u003en = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{932}{[1+932({\\text{0,1})}^{2}]}\\)\u003c/span\u003e\u003c/span\u003e = 90\u003c/p\u003e \u003cp\u003eFrom the study area, 90 smallholders were sampled.\u003c/p\u003e \u003cp\u003eStructured questionnaires were distributed to smallholder farmers in the area to gather quantitative data. Twenty people were used to pre-test the questionnaire, and feedback was used to clarify all the questions that were not clearly worded. Moreover, questionnaires were tailored to suit their intended recipients [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The questionnaires were distributed via the random walk method among farmers in the area [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. To ensure confidentiality, the questionnaires excluded identifiable information such as name, phone number, and address. All questions were standardised to ensure uniformity among recipients, and consent was obtained before questionnaires were administered. The questionnaires were interpreted as Tswana to accommodate farmers who could not read and write.\u003c/p\u003e \u003cp\u003eKey informants, which included two extension officers, two district agricultural officials, two village headmen, and three ward councillors were interviewed to obtain in-depth information. Key informants played a significant role by sharing their experiences with farming. Field observations were further implemented to verify agricultural practices in the area to gain insights into farmers\u0026rsquo; experiences and performance [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. To confirm the data from the households, discussions were administered with the smallholder farmers in the community hall to consolidate the findings. Three groups were used because the population was homogeneous and knowledgeable about farming. The focus group discussions lasted two hours per session, with two breaks of fifteen minutes and refreshments in between to foster concentration and comfortability. Naturalistic observations were made whereby the researcher observed farmers in their natural environment (fields) and learned more about their practices. Naturalistic observations offered more reliable results because of the natural settings in which they were carried out.\u003c/p\u003e \u003cp\u003eSecond hand, information about the effectiveness of climate-smart agriculture and shortfalls in other areas was obtained from published reports such as journals, CSA source books, published dissertations, web sources, conference papers, and government departments such as the Department of Agriculture, Forestry and Fisheries (DAFF), North West Community Survey 2016 reports, and the Department of Agriculture, Land Reform and Rural Development and Statistics South Africa (DALRRD) in the form of figures, reports, policies and acts. All the data sets were aligned to the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data Analysis\u003c/h2\u003e \u003cp\u003eThe data were interpreted via the Statistical Package for the Social Sciences (SPSS) version 30 software program. Descriptive statistics such as frequencies and percentages were used to interpret socioeconomic characteristics, characterise technologies implemented by smallholder farmers and assess the contribution of CSA to communities as a way of adapting to climate change impacts. The outcomes of the study are presented in tables, bar graphs, pie charts, and figures. A binary logistic regression model was used to analyse factors influencing the use of CSA practices in the Ditsobotla local municipality. The model best suited the study because the dependent dichotomous variable used provided an understanding of the factors that influenced the results of the study. Frequencies and means were used to analyse data on the frequency of implementation of climate-smart agricultural practices (CSPs).\u003c/p\u003e \u003cp\u003eQualitative data analysis was performed to obtain a deeper understanding that surpasses numbers and statistical inference [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Data generated from focus groups, interviews, documents, and observations were prepared and organised for thematic analysis. Data logging was performed to capture major ideas and identify potential biases that may influence the interpretation of data [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The data were coded, and consequently, conclusions were derived from the patterns formed during the study.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Research Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Socioeconomic characteristics of the participants\u003c/h2\u003e \u003cp\u003eThe respondents were asked about their socioeconomic characteristics, and the results in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e show that 56% of the sampled smallholder farmers were males, whereas 44% were females. The results indicated that there are more males actively involved in agriculture to provide a livelihood for their families. Most of the respondents (40%) were aged between 50 and 59 years. Interestingly, more than three quarters of the farming population are elderly people. Younger farmers represented 16% (40\u0026ndash;49 years) and 11% (30\u0026ndash;39 years) of the sample, respectively. Half of the respondents in the study area had a primary education. Only 38% of the respondents matriculated, and 11% had diploma certificates.\u003c/p\u003e \u003cp\u003eMost of the farmers\u0026rsquo; revenue stems from the sale of farm produce (60%), followed by pensions (29%) and, finally, salaries (11%). Married people in Ditsobotla constitute 54.4% of the population, followed by widowed people, with a population of 29%. The single population contributed 17% of the total population. The household size of the majority of the respondents was between 4% and 6%, accounting for 44% of the sample. The results revealed that medium-sized households prevailed in the area. There are high levels of farming experience in the study area. Category 16 and above took the lead at 44%, followed by categories 11\u0026ndash;15 at 31%. The 6\u0026ndash;10 category has the least experience, with 11.1%.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSocioeconomic characteristics of the respondents\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of respondents\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60 and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational Background\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMatric\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiploma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource of Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSale of farm produce\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSalary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u0026ndash;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarming Experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u0026ndash;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16 and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSource: Survey results\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Climate-smart agricultural technologies implemented by smallholder farmers in the area\u003c/h2\u003e \u003cp\u003eThe farmers were asked to indicate the various CSA technologies they implemented in the area. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the CSA practices implemented in Ditsobotla municipality. Livestock breed improvement was ranked first, with an average of 3.28, followed by the use of drought-resistant seed varieties (2.58), crop rotation (2.56), minimum tillage (2.46), chemical fertilisers (2.44), mulching (2.26), organic manure (2.08), water harvesting (1.82), intercropping (1.58) and agroforestry (1.21). Livestock breeding in the form of adaptable breeds has been widely implemented due to the dry conditions that prevail in the area.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCSA practices implemented in the area (n\u0026thinsp;=\u0026thinsp;90)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCSA Practices\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRarely\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOften\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAlways\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRanking\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLivestock breed improvement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8(8.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2(2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e37(41.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e43(47.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrought resistant seeds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3(3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38(42.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29(32.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20(22.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrop rotation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10(11.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28(31.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e44(48.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8(8.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimum tillage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7(7.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41(45.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36(40.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6(6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemical fertiliser\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8(8.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43(47.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30(33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9(10.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMulching\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20(22.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29(32.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39(43.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2(2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrganic Manure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18(20.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51(56.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17(18.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4(4.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater harvesting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37(41.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32(35.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21(23.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercropping\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43(47.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42(46.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5(5.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgroforestry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76(84.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10(11.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3(3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1(1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSource: Survey results\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Analysis of factors influencing the use of CSA technologies\u003c/h2\u003e \u003cp\u003eThe study assumed that socioeconomic factors influenced the implementation of CSA technologies. The factors influencing the use of CSA technologies were analysed via a binary logistic regression model. The findings shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e revealed that the influence of the source of agricultural income (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e = -2.853, p\u0026thinsp;=\u0026thinsp;0.024) was a significant barrier to the implementation of CSA technologies. Moreover, the influence of access to inputs (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e = -4.096, p\u0026thinsp;=\u0026thinsp;0.016) also hindered the implementation of CSA technologies. The influence of the source of the provision of inputs (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e = 9.486, p\u0026thinsp;=\u0026thinsp;0.009) positively impacted the acceptance of CSA technologies. Finally, the influence of the frequency of agricultural extension officers (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e= -8.214, p= 0,016) retarded the acceptance of CSA technologies.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of factors influencing the use of CSA technologies via the binary regression model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndependent Variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS. E\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWald\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eD f\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSig\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eExp(β)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e63.922\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.137\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome source\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.768\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.121\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand tenure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.439\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarm size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.930\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInformation source\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMembership group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-16.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12482.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccess to inputs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-4.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInputs provision\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13173.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtension frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-8.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12482.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34613619\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-2Log Likelihood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{22.864}^{a}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCox\u0026amp; Snell R square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNagelkerke R square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSource: Survey results\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Sources of agricultural inputs, subsidies and grants\u003c/h2\u003e \u003cp\u003eFarmers were asked about their sources of agricultural inputs, subsidies and grants Most respondents highlighted that they secured inputs by themselves using money obtained from selling farm produce and pension money (39%). As highlighted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, government subsidies and grants contributed 24%, and the percentage of respondents who received inputs from NGOs was 17%. North West Kooperate (NWK), a private institution, offered 17% of the subsidies and grants to farmers in the area.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Frequency of extension officers in Ditsobotla Municipality\u003c/h2\u003e \u003cp\u003eThe respondents were asked about the frequency of extension officers in the study area, and the results revealed that extension officers occasionally visited the area, as indicated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The highest percentage of respondents (46%) noted that visits were occasional, whereas 24% of the respondents indicated that extension officers rarely visited the area. The highlighted situation prevents smallholder farmers from receiving adequate information about CSA practices [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Moreover, the distribution of inputs, subsidies and grants becomes compromised. There is a need for extension officers to visit farmers on a regular basis to disseminate information on new technologies better suited to the changing climate to promote food security in the area.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Level of use of CSA technologies in Ditsobotla Municipality\u003c/h2\u003e \u003cp\u003eThe respondents were asked about the level of use of CSA technologies in the area, and the results highlighted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e revealed that the level of CSA in the area remained low at 54%. Medium users constituted 24%, and high users constituted 21%.\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Perceptions of the contribution of CSA to adaptation to the impacts of climate change\u003c/h2\u003e \u003cp\u003eThe respondents were asked how they perceived the contribution of CSA in adapting to climate change impacts. The results in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e revealed that 64% of the respondents agreed that the implementation of CSA technologies contributed positively to their adaptation to the impacts of climate change. However, 36% disagreed with the idea stated above for various reasons. Most farmers highlighted that there was a need for educational guidelines on agricultural practices that are linked to climate change.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eMost of the smallholder farmers actively involved in agriculture were males aged between 50 and 59 years. This situation can be attributed to local cultural and social factors whereby females have been historically marginalised to access land and resources [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Studies from West Africa further highlighted that gender norms and beliefs play crucial roles in women\u0026rsquo;s participation in agriculture [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Interestingly, more than three-quarters of the farming population are elderly people. This scenario could hinder the implementation of smart agricultural practices, as the majority of them want to keep their traditional methods such as monoculture.\u003c/p\u003e \u003cp\u003eThe results further revealed that young adults are not keen on agricultural activities. According to [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], most South African rural areas are constantly affected by rural to urban migration. Such movements force the aging population to continue working in fields without the help of the younger population. There is an obligation for young adults to learn more about farming and bring new innovations to make agriculture sustainable for future generations.\u003c/p\u003e \u003cp\u003eHalf of the respondents in the study area have primary education, and most of the farmers derive their income from the sale of farm produce. The results indicate that agriculture plays a significant role in the livelihoods of most farmers in Ditsobotla. However, some farmers augmented their source of income by selling fresh vegetables and chickens. The average household size consists of 4 to 6 people. Household size is an important determinant of the availability of labour.\u003c/p\u003e \u003cp\u003eLivestock breeding in the form of adaptable breeds has been widely implemented due to the dry conditions that prevail in Ditsobotla. A similar scenario was observed by [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], who highlighted that livestock farming has been widely implemented in many rural areas of South Africa as a way of improving food security in a changing climate environment. Moreover, from the observations during data collection, it was noted that livestock farming was rampant in the area. Drought-resistant seed varieties in the form of sunflowers are prevalent in Ditsobotla because sunflowers have a root system that can withstand water loss due to high temperatures [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Crop rotation is of paramount importance, as it enables the soil to maintain its fertility [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. The application of chemical fertilisers has been observed in sunflower and maize production because both crops are sensitive to nutrient deficiencies [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Sunflower and maize production can be enhanced by applying fertilisers and manure in the correct proportions at the right time and place.\u003c/p\u003e \u003cp\u003eOrganic manure is rarely applied by farmers because they do not have enough manure to cover their fields. Instead, chemical fertilisers are commonly applied, depending on their availability. Water harvesting has been performed in Ditsobotla for years because of drought conditions. A number of boreholes, JoJo tanks and water bowsers were observed during data collection. Intercropping has rarely been implemented because of the rampant theft of crops in Ditsobotla. Agroforestry was the least common method to be implemented due to low levels of awareness of the practice in Ditsobotla. In South Africa, agroforestry is not well established in many rural areas despite the numerous benefits it has. This idea has been supported by [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] in his study of the adoption of agroforestry in Tsolo and Lusisiki in Eastern Cape. A necessity exists to intensify education and training on agroforestry practices [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA number of socioeconomic factors were found to influence the implementation of CSA technologies. Among them was the source of agricultural income. Most of the farmers in Ditsobotla do not have enough money to support their farming activities. Notably, they relied on the sale of farm produce to provide inputs for the next farming season. Farmers in Ditsobotla should engage with various farming enterprises to increase their income base and promote food security. This observation is in line with the findings of [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] in their study in southern Mali, where multiple sources of income were encouraged to promote sustainable agriculture among smallholder farmers.\u003c/p\u003e \u003cp\u003eAccess to inputs was found to influence the implementation of CSA technologies. Similar studies on the accessibility of inputs have been conducted in several countries, including Malawi and Zambia, and the input subsidy programs have helped encourage farmers to implement CSA technologies [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Most farmers receive subsidised drought-resistant seeds and inorganic fertilisers, and consequently, increased productivity ensues, resulting in a surplus for sale [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLimited sources of provision of farm inputs were found to have an effect on the implementation of CSA technologies. Having multiple sources of input provisions helps to accelerate the implementation of CSA immensely. Studies in China, Vietnam, Nepal, Uganda and Kenya have shown that governments, private sectors, financial institutions, civil society and NGOs have been facilitating the implementation of CSA technologies through the provision of tree seedlings, stress-resilient seeds, fertilisers and irrigation technologies, resulting in accelerated implementation [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFinally, the frequency of agricultural extension officers in Ditsobotla was found to influence CSA implementation. Notably, implementation was found to be retarded by the low visibility of agricultural extension officers in the area. A study by [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] in Lushoto Northeast, Tanzania, revealed that access to agricultural information is positively related to the probability of implementing CSA technologies. Furthermore, the idea concurs with the conclusions of [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] in their study of the role and perspective of CSA in Africa. They concluded that continuous learning accelerated information acquisition and fostered acceptance and implementation of CSA technologies in most African countries.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study analysed the factors influencing the implementation of CSA by smallholder farmers in Ditsobotla local municipality. The findings revealed that most of the respondents were elderly males with mostly primary education. Most farmers\u0026rsquo; income is derived from the sale of farm produce. High levels of farming experience exist among the farmers in the area. However, there is a call for extension officers to educate farmers on modern methods of farming that incorporate CSA technologies to foster implementation. Many households struggle to obtain assistance from the government. The government should ensure an equitable distribution of inputs to accelerate the implementation of CSA technologies.\u003c/p\u003e \u003cp\u003eLivestock breed improvement was observed to be mostly implemented in the area; however, agroforestry was the least common improvement to be implemented due to erratic rainfall. The level of CSA in the area remains low, and more attention is needed to enhance the full implementation of technologies for future generations to increase the level of food sufficiency in a changing environment.\u003c/p\u003e \u003cp\u003eThe source of agricultural income, access to inputs, source of provision of inputs and frequency of agricultural extension officers in the area have been highlighted as the most important factors that affect the implementation of CSA technologies in the area.\u003c/p\u003e \u003cp\u003eThis study recommends that young adults learn more about farming and bring new innovations to make agriculture sustainable for future generations. Incentives should be channelled towards agriculture to attract the interests of the younger generation. Timeous visibility by extension officers should be accelerated to disseminate information and guide farmers on how to continuously implement new information regarding CSA technologies. Moreover, they should provide proper guidance on the fair distribution of inputs to reduce corruption and nepotism in the area. The government should work together with NGOs, NWK and other financial institutions in the area to help farmers with inputs and credit to increase food production in the area. Policymakers should consider factors such as the source of agricultural income, access to inputs, source of provision of inputs and frequency of agricultural extension officers to greatly influence the implementation of CSA technologies in a bid to speed up their uptake.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003eThe author gratefully acknowledges all the respondents in Ditsobotla Municipality who voluntarily participated in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003eM Mtuwa: Project conception, methodology, data collection and analysis, draft and final manuscript: M Chitakira: Project conception, methodology, project supervision, draft and final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eUniversity of South Africa financially supported this research project\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003eFull data for this study can be obtained from the authors upon request and in accordance with the applicable data governing policies and procedures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u0026nbsp;\u003c/strong\u003eEthical clearance was obtained from the ethical committee of the University of South Africa (College of Agriculture and Environmental Sciences, Health Research Ethics Committee). The research was carried out in accordance with their guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u0026nbsp;\u003c/strong\u003eThe researcher obtained informed consent from all the participants before data collection was carried out. The willingness of the participants to withdraw from providing responses during data collection was guaranteed\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u0026nbsp; \u0026nbsp;The authors declare that they have no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhmad MM, Yaseen M, Saqib SE. Climate change impacts of drought on the livelihood of dryland smallholders: Implications of adaptation challenges. 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Sustainability. 2022;14(4):2317.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-environment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Environment](https://www.springer.com/44274/)","snPcode":"44274","submissionUrl":"https://submission.nature.com/new-submission/44274/3","title":"Discover Environment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Climate Smart Agriculture technologies implementation, smallholder farmers, Ditsobotla Municipality, South Africa","lastPublishedDoi":"10.21203/rs.3.rs-6658799/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6658799/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWorldwide, food shortages have occurred due to increased drought conditions associated with climate variability. Agriculture has been greatly affected, resulting in food shortages. In response, climate-smart agriculture (CSA) has been embraced by many countries as a way of alleviating this problem. The extent of CSA implementation has not yet been established in many areas. The present study thus sought to evaluate CSA to determine the factors that influence its implementation by smallholder farmers in Ditsobotla local Municipality, which is a climate change hotspot in South Africa. Questionnaires, field observations, key informant interviews and focus group discussions were used to gather data from smallholder farmers. Ninety households were sampled via snowball and purposive sampling approaches. The quantitative data were analysed via the Statistical Package for the Social Sciences version 30. The results show that the main determinants of CSA implementation among smallholder farmers include source of income, access to inputs, and source of inputs coupled with the influence of the frequency of agricultural extension officers\u0026rsquo; visits. Smallholder farmers acknowledged that the implementation of CSA technologies can positively reduce the impact of climate variability. However, there should be regular visits by agricultural extension officers to support the farmers, with equitable access to inputs, subsidies and grants. The government, private organisations and community members should scale up collaboration to promote the implementation of CSA technologies and food security.\u003c/p\u003e","manuscriptTitle":"Analysis of the factors influencing the implementation of climate smart agriculture technologies in a climate change hot spot in South Africa","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-17 14:01:55","doi":"10.21203/rs.3.rs-6658799/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-27T23:40:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-01T11:51:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-26T10:07:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"265864175479930325007989533685097805225","date":"2025-07-24T07:53:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"325221843252439854430004009156062606400","date":"2025-07-16T19:37:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"292978746832273454083935344094727283965","date":"2025-06-19T17:38:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"298998648139217817366893980869339977584","date":"2025-06-17T18:58:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-13T14:04:52+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-04T09:59:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-20T06:56:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-20T06:50:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Environment","date":"2025-05-13T21:37:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-environment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Environment](https://www.springer.com/44274/)","snPcode":"44274","submissionUrl":"https://submission.nature.com/new-submission/44274/3","title":"Discover Environment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2a41f8a7-8658-438c-a1bd-790f4f22d69c","owner":[],"postedDate":"June 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-16T17:38:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-17 14:01:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6658799","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6658799","identity":"rs-6658799","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}