The Role of Psychological Capital on Climate Change Adaptation Among Smallholder Farmers in the uMkhanyakude District of KwaZulu-Natal, South Africa

Abstract

Climate change and variability pose a challenge to the livelihoods of smallholder farmers. Previous studies on climate change in the context of smallholder farming have mainly focused on the influence of socio-economic factors in understanding farmers’ responses to climate change. However, little is known about the effect of psychological capital on climate change adaptation. There are calls for better empirical models and transdisciplinary approaches to understand the underlying drivers of climate change adaptation in smallholder farming systems. This study draws from behavioural decision research to assess psychological factors influencing climate change adaptation in the uMkhanyakude district of KwaZulu-Natal. This study adopted the Theory of Planned Behaviour to understand the effect of psychological capital on climate change adaptation. Data were collected from a sample of 400 smallholder farmers who were randomly selected from the uMkhanyakude district. Survey data were analysed using a multivariate probit regression model. The results of the multivariate probit regression model indicated that psychological capital (attitudes towards climate change, subjective norms, and trust) played an important role in influencing climate change adaptation. Climate change adaptation is also influenced by the gender of the farmer, education level, household size, and Tropical Livestock Units. These findings underscore the role of psychological capital in shaping climate change adaptation. This study recommends using transdisciplinary approaches (i.e., combining economics and psychology) in evaluating farmers’ responses to climate change.

1. Introduction

Climate change has a negative impact on the livelihoods of smallholder farmers and will hinder progress in attaining the 2030 Agenda on Sustainable Development [1]. Estimates from the Intergovernmental Panel on Climate Change (IPCC) indicate that by 2100, agricultural losses due to climate change in Southern Africa could range from 0.4 to 1.3% of Gross Domestic Product [2]. This is in the backdrop of an anticipated population increase in the region. Smallholder farmers in the Southern Africa region rely on agriculture for their livelihood and are vulnerable to climatic changes due to their high exposure and limited adaptive capacity [3]. Farmers’ heavy reliance on rainfed agriculture, coupled with inadequate technical and institutional capacity, further amplifies their vulnerability to climate change [4].

South Africa is characterised by arid conditions and a low rainfall index and receives an annual mean precipitation of less than 500 mm [5]. The country boasts the largest surface area under irrigation in the Southern African region, with 1.3 million hectares dedicated to irrigation [5]. Climate change impacts on South Africa primarily manifest through drought and floods. These climate-related disasters, as reported by [5], have caused infrastructure damage and, unfortunately, sometimes, loss of life.

Farm-level adaptation has been identified as an essential strategy for alleviating the adverse impacts of climate change [6]. Climate change adaptation involves a two-step process. The initial step necessitates that farmers recognise and perceive the occurrence of climate change, followed by the second step, which requires the implementation of appropriate adaptation strategies. Successful adaptation necessitates both the capacity and the willingness to implement adaptive measures. At the farm level, the decision to adapt to climate change is voluntary. Furthermore, studies have shown that farm-level climate change adaptation has failed to achieve local and conservation goals [7,8]. Consequently, there has been an increasing scholarly focus on understanding the factors influencing farmers’ pro-environmental behaviour [9]. Resource endowment is an important factor that determines farmers’ ability to adapt to climate change, while their willingness to adapt is shaped by their behaviour [10]. Climate change adaptation studies have largely focused on farmers’ tangible attributes (i.e., age, education, income, livestock ownership, access to land, etc.) while neglecting farmers’ intangible non-cognitive attributes (i.e., attitudes and personality traits) [10]. There is a growing recognition that appropriate climate change adaptation strategies must be tailored to the local context [11] and recognise the drivers of human behaviour, including attitudes and personality traits [12]. Climate change adaptation is a behavioural aspect that is influenced by one’s decision-making [13]. Individuals’ non-cognitive attributes play an important role in decision-making. In turn, decision-making is a psychological construct because it involves a non-cognitive process of applying scientific knowledge to determine a cause of action. Psychological capital represents a non-cognitive attribute that encapsulates an individual’s mindset, influencing their capacity to make sound decisions and life choices [10]. According to [14], psychological capital describes an individual’s mindset, which determines an individual’s motivation to make a certain choice or decision. Psychological capital is a mental endowment that possibly explains why individuals with the same level of resource endowment who face the same problem make different decisions [15]. Psychological capital serves as a key determinant in explaining why individuals with access to identical resources and operating within similar environments may exhibit varying levels of performance, a phenomenon frequently observed among smallholder farmers.

It is widely accepted that climate change adaptation in smallholder farming systems is influenced by social, biophysical, institutional, and economic factors [9]. Emerging empirical evidence suggests that psychological capital also plays an important role in influencing farmers’ behaviour [10,16,17,18,19]. Psychological capital affects not only farmers’ response to climate change but also their demand for adaptive strategies [20,21]. However, despite this evidence that both socio-economic and psychological factors influence climate change adaptation, very few studies have combined these factors in a vigorous way [22,23]. Therefore, the aim of this study is to close this research gap by adopting the theory of planned behaviour to estimate the effects of psychological capital on climate change adaptation while controlling for socio-economic and demographic factors.

2. A Literature Review

Psychological capital is defined based on two concepts from Social Cognitive Theory: perceived self-efficacy and locus of control [24,25]. Psychological capital is a form of non-cognitive skills that includes an individual’s mental attitude and inclination towards making the right decisions and choices in life [26]. Other scholars defined psychological capital as the part of human capital that is not captured by common IQ and achievement tests but rather represents “patterns of thought, feelings, and behaviours” [27,28,29]. Psychological capital consists of four dimensions: self-efficacy; hope; optimism; and resilience, which significantly influence farmers’ attitudes to the adoption of climate change mitigation measures, satisfaction, and innovative behaviours [30]. This implies that a farmer endowed with positive psychological capital is better placed to make a value judgment about the perceived probability and severity of a climate change threat. Several studies have demonstrated the importance of psychological capital for economic outcomes in developed and developing countries [31,32]. These studies found that psychological capital was positively associated with adaptations to environmental change [13,20] and that farmers with higher psychological capital were more likely to respond to the adversities of climate change by adopting a climate-smart technology [32]. This is because they perceived their costs of innovation adoption to be lower. Psychological capital is a relatively new concept in the field of climate change adaptation, and it is argued in this study that it should be accounted for in farm-level climate change adaptation studies. Research studies that fail to account for psychological capital miss a key factor that is critical in understanding farmers’ uptake of agricultural innovations.

3. Conceptual Framework

Environmental behavioural theories, including the Theory of Planned Behaviour (TPB), have been used in studies that examine the influence of psychological capital on farmers’ actions [33,34,35]. This study has adopted the TPB as a framework for assessing the psychological capital influencing farmers’ climate change adaptation behaviour. Moreover, there are no standardised indicators for assessing the non-cognitive abilities of smallholder farmers. The TPB has broad applicability as a theoretical framework for studying human action (Figure 1). This framework has been widely used in different disciplines, such as food consumption patterns [36]. However, its application to climate change adaptation remains limited, with only a few studies applying the theory in this context of climate change [16,37,38]. The TPB consists of three core components: attitudes towards the behaviour; subjective norms; and perceived behavioural control. When combined, these three factors influence an individual to adapt to climate change.

According to [26], attitudes are shaped by individuals’ beliefs about the possible outcomes or characteristics associated with performing a behaviour (behavioural beliefs), moderated by their assessments of these outcomes or attributes. Therefore, an individual who firmly believes that the behaviour will lead to favourable outcomes is likely to hold a positive attitude towards the behaviour. Conversely, an individual who firmly believes that the behaviour will result in unfavourable outcomes is likely to have a negative attitude towards it.

Similarly, an individual’s subjective norm is influenced by their normative beliefs, specifically the perception of whether significant referent individuals approve or disapprove of engaging in a particular behaviour, moderated by the individual’s motivation to conform to these referents [39]. An individual who believes that key referents expect them to perform the behaviour and who is motivated to meet those expectations will have a positive subjective norm. In contrast, an individual who perceives that these referents disapprove of the behaviour will hold a negative subjective norm, while a person less motivated to comply will exhibit a relatively neutral subjective norm.

According to [39], perceived behavioural control pertains to an individual’s perception of facilitators or barriers in the execution of a specific behaviour (i.e., control beliefs), moderated by their assessment of the relative strength or influence of each facilitator or barrier. The TPB posits that behavioural intentions, such as climate change adaptation, are influenced by individuals’ beliefs and attitudes towards the behaviour, subjective norms, and perceived behavioural control [40]. By identifying the underlying determinants of behaviour, the TPB aims to predict and comprehend behaviours associated with climate change, including climate change perception and adaptation.

Figure 1. The Theory of Planned Behaviour. Source: Adapted from [41].

Figure 1. The Theory of Planned Behaviour. Source: Adapted from [41].

4. Materials and Methods

4.1. Study Area

This study was conducted in the uMkhanyakude district of KwaZulu-Natal [42]. UMkhanyakude is located in the northern part of KwaZulu-Natal. UMkhanyakude has five local municipalities, namely, Jozini, Umhlabuyalingana, Mtubatuba, Hlabisa, and Big Five False Bay. Out of 55 municipalities, uMkhanyakude district is ranked as the 51st poorest municipality in South Africa. The population is largely young, unemployed, and depends on agriculture for its livelihood [43]. The district is arid and is severely affected by climate-induced changes [42]. In terms of geographic size, uMkhanyakude is the second-largest district in KwaZulu-Natal.

4.2. Data Collection

This study employed a mixed-method approach, incorporating both qualitative and quantitative data collection techniques. Quantitative data were collected through a survey by administering a questionnaire. A multi-stage sampling procedure was used to select participants. In the first stage, out of 11 districts in KwaZulu-Natal, the uMkhanyakude district was purposively selected. In the second stage, Jozini and uMhlabuyalingana local municipalities in the uMkhanyakude district were selected. These two local municipalities have the largest number of smallholder farmers in the uMkhanyakude district. UMkhanyakude is an arid district with a significant number of villages/communities engaged in smallholder farming. In the final stage, 400 smallholder farmers were randomly selected from Jozini and uMhlabuyalingana local municipalities. Data were collected between November and December 2020, when COVID-19 restrictions were eased. Data were collected through a survey questionnaire, which was designed to collect information on demographics, assets, crop production, livestock production, support services, climate change perception, climate change adaptation, and household food and nutrition security. The questionnaire was pre-tested on 10 non-sampled households. The aim of the pre-testing was to ensure reliability and coherence in the questionnaire. After pre-testing, the questionnaire was modified to make sure that it was easy to administer.

Focus Group discussions were conducted to obtain in-depth information about farmers’ experiences regarding climate change. In this study, the maximum number of participants per focus group was 12, as recommended by [28]. This group size was appropriate to facilitate maximum participation and encourage open and meaningful discussions. Focus groups were held outdoors to ensure proper ventilation; this ensured the safety of the farmers from the COVID-19 infection. The data collection tools for this study were approved by the University of the Free State’s ethics committee (protocol reference number: UFS-HSD2020/0632/2107). Informed consent was obtained from all farmers who participated in this study. Unique identifiers were removed from the dataset to ensure the confidentiality of the respondents.

4.3. Analytical Framework

Smallholder farmers often adopt multiple climate change adaptation strategies concurrently [5]. Traditional models, such as multinomial logit regression, are insufficient in capturing the interdependence between farmers’ choices of adaptation strategies [10]. This study uses the multivariate probit (MVP) model to analyse the influence of behavioural decision-making on climate change adaptation. The MVP model estimates multiple binary probit equations simultaneously, enabling the correlation of error terms across equations [7,29]. Following [44], an MVP model with five dependent variables can be expressed as follows:

$$\mathrm{Let}{P}_m={\beta }_m{x}_m+{\epsilon }_m,wherem=D_r,S_d,M_f,I_{c}and{M}_t$$

(1)

$$P_m=1if{P}_m>0,0otherwise$$

(2)

where $D_r$, $S_d$, $M_f$, $I_c$, and $M_t$ denote the climate change adaptation strategies, specifically the adoption of drought-resistant crops, the adjustment of planting dates, mixed farming practices, intercropping, and minimum tillage. $x_m$ represents the vector of explanatory variables, while ${\epsilon }_m$ represents the error terms. The error terms in equations 1 and 2 are interdependent and follow a multivariate normal distribution with a mean of 0 and a variance of 1. According to [9], the variance–covariance matrix for equation 1 is symmetrical, characterised by values of 1 along the leading diagonal, while the off-diagonal elements represent the pairwise correlations among the error terms of the five dependent variables $D_r$, $S_d$, $M_f$, $I_c$, and $M_t$). This assumption facilitates the joint estimation of the five dependent variables. The covariance matrix is as follows:

$$C=\left[\begin{array}{ccccc}1& \rho DrSd& \rho DrMf& \rho DrIc& \rho DrMt\\ \rho SdDr& 1& \rho SdMf& \rho SdIc& \rho SdMt\\ \rho MfDr& \rho MfSd& 1& \rho MfIc& \rho MfMt\\ \rho IcDr& \rho IcSd& \rho IcMf& 1& \rho IcMt\\ \rho MtDr& \rho MtSd& \rho MtMf& \rho MtIc& 1\end{array}\right]$$

(3)

The MVP model for this study is given as follows:

$$P_m={\beta }_0{\displaystyle {\sum }_{i=1}^k}{\beta }_m{H}_m+{\displaystyle {\sum }_{i=1}^k}{\beta }_m{I}_m+{\displaystyle {\sum }_{i=1}^k}{\beta }_m{D}_m+{\epsilon }_m$$

(4)

where $\beta$ represents the parameter estimates. $H_m$, $I_m$, and $D_m$ represent variables for household characteristics, institutional variables, and behavioural decision variables, respectively.

4.4. Independent Variables

Table 1. Variables used in the multivariate probit model.

Variable	Variable Description	Mean	Standard Deviation	Expected Sign
Age	Age of the household head in years	45	14.33	+
Gender	Gender of the household head (1 = male and 0 otherwise)	0.28	-	+
Education level	Number of schooling years	7.1	4.74	+
Adult equivalents	Factor representing household labour endowment (continuous)	7.6	3.4	+
Farm associations (Bridging capital)	Membership in farmers’ association (1 = yes and 0 otherwise)	0.36	-	+
Extension services	Access to extension services (1 = yes and 0 otherwise)	0.44	-	+
TLU	Tropical Livestock Units	9.3	13.8	+
Trust (Bonding capital)	Number of people that the household can revert to in time of need	4.7	5.3	+
PC_ATC	Principal component generated index for attitudes towards climate change	0.00	1	+
PC_SN	Principal component generated index for subjective norms	0.00	1	+
PC_PBC	Principal component generated index for perceived behavioural control	0.00	1	+

presents the socio-economic, institutional, and behavioural decision-making variables included in this model. Elements of behavioural decision-making are expected to positively impact farmers’ responses to climate change. This model includes behavioural decision-making variables such as attitudes towards behaviour, subjective norms, and perceived behavioural control. These factors help predict how individuals might respond to specific actions by considering their beliefs, social influences, and sense of control over the behaviour. Indicators were collected under each variable for behavioural decision-making and were then transformed using principal component analysis, following an approach suggested by [17]. In total, three principal components were constructed. The first principal component (PC_ATC) reflects farmers’ attitudes towards climate change and is hypothesised to positively influence their adaptation to climate change. The second principal component (PC_SN) represents subjective norms and is expected to positively influence climate change adaptation. The third principal component (PC_PBC), representing perceived behavioural control, is also anticipated to have a positive impact on climate change adaptation.

Socio-economic variables, including age, gender, education level, household size, and Tropical Livestock Units (TLU), were also incorporated into the multivariate probit (MVP) model to account for the heterogeneity of household characteristics. The sampled farmers had an average age of 45, indicating that most farmers in the study area were middle-aged. Age in this model was expected to have a positive effect on climate change adaptation. The majority of the sampled farmers were female. This further supports the widely held view in the literature that smallholder farming is dominated by women. On average, smallholder farmers attended school up to the 7th grade and did not complete secondary education. This has implications for the transfer of information since educated farmers can read and interpret climate information. Education is expected to have a positive effect on climate change adaptation. On average, each household has 7 members. The size of the household was used as a proxy for labour endowment. Households with a larger household size have more labour, and such labour can be used to drive climate change adaptation. On average, farmers have 9.3 TLU. Livestock acts as a supplementary enterprise to crop farming, helping farmers reduce risks related to adverse weather and possible crop failures. Incorporating livestock into farming systems enables farmers to diversify their risks and improve resilience against the impacts of climate change. Livestock ownership is expected to facilitate positive outcomes in climate change adaptation efforts.

Institutional variables, such as access to extension services and membership in farmer organisations, are expected to have a positive influence on climate change adaptation. Notably, around 44% of farmers reported having access to extension services. This result is in line with other studies conducted in South Africa [1,45]. About 44% of the farmers reported that they were members of farmer organisations. Membership in farmer organisations is anticipated to have a positive impact on climate change adaptation by facilitating the exchange of information regarding climate change and adaptation strategies among farmers, thereby promoting the adoption of these strategies. Trust was added as a predictor variable. Trust indicates social cohesion and belief in social systems, leading to collective action. On average, farmers can contact four community members in times of need. Trust was expected to have a positive effect on climate change adaptation.

5. Results

5.1. Climate Change Adaptation Strategies Adopted by Smallholder Farmers

To address climate risk, smallholder farmers in the uMkhanyakude district have implemented various adaptation strategies, as illustrated in Figure 2. Mixed farming is a widely used adaptation strategy. Figure 2 indicates that 16.5% of surveyed farmers adjust planting dates to mitigate climate change impacts. About 17.3% of smallholder farmers reported that they were planting drought-resistant crops as a response to the dry conditions. In focus group discussions, farmers mentioned planting crops such as cassava and sweet potatoes due to their low water requirements. Furthermore, irrigation is recognised as a vital strategy to bolster crop production by reducing dependence on rainfed agriculture; however, only 4.5% of smallholder farmers currently utilise irrigation. Approximately 24.5% of the surveyed farmers reported that they did not implement any adaptation strategy.

5.2. The Effect of Psychological Capital on Climate Change Adaptation

The multivariate probit (MVP) model results in

Table 2. Parameter estimates of the multivariate probit model.

Variable Code	Planting Drought Resistant Crops		Shifting Planting Dates		Mixed Farming		Intercropping		Minimum Tillage
	Coefficient	SE	Coefficient	SE	Coefficient	SE	Coefficient	SE	Coefficient	SE
Age	0.015	0.02	0.001	0.002	0.002	0.002	0.002	0.002	0.004	0.002
Gender	−0.079	0.053	−0.113 *	0.054	−0.049	0.052	−0.136 ***	0.047	−0.06	0.058
Education level	0.013 **	0.006	0.003	0.006	0.013 **	0.006	0.012 **	0.005	0.005	0.007
Household size	0.015 **	0.007	0.009	0.007	0.009	0.007	0.018 ***	0.006	−0.006	0.008
Farm associations	0.028	0.05	0.017	0.0.05	0.058	0.048	0.011	0.044	0.06	0.054
Extension services	0.007	0.005	0.006	0.005	-0.004	0.005	−0.004	0.004	−0.001	0.005
TLU	0.004 **	0.002	0.004 **	0.002	0.007 ***	0.002	0.005 ***	0.002	0.001	0.002
TRUST	0.009 **	0.005	0.007	0.005	0.009 *	0.004	0.01 **	0.004	0.008	0.005
ATC	0.063 **	0.025	0.049 *	0.025	0.07 ***	0.024	0.064 ***	0.022	0.064 **	0.027
SN	0.055 **	0.026	0.054 **	0.027	0.027	0.025	0.071 ***	0.023	0.046 **	0.029
PBC	0.029	0.026	−0.016	0.027	0.014	0.025	0.022	0.023	0.017	0.029
Number of observations = 400 Wald chi2(60) = 124.64 Prob > chi2 = 0.0000 Log likelihood = −821.85123

Notes: ***, **, and * mean significant at 1%, 5%, and 10% levels, respectively.

show that two out of the three dimensions of psychological capital have a significant positive effect on several climate change adaptation options. The dimensions influencing climate change adaptation include attitudes towards climate change and subjective norms, highlighting the significant role of psychological capital in adaptation decisions. The variable ATC (attitudes towards climate change) significantly and positively influence farmers’ decisions to adopt drought-resistant crops, adjust planting dates, practice mixed farming, intercropping, and adopt minimum tillage.

The variable for the subjective norm is significant and has a positive influence on farmers’ decisions to adopt drought-resistant crops, change planting dates, engage in intercropping, and use minimum tillage.

The variable TRUST, representing social capital, is significant and has a positive influence on the adoption of drought-resistant crops, mixed farming, and intercropping practices. This finding highlights the critical role that trust among farmers and within farming communities plays in facilitating the uptake of innovative agricultural strategies. The results indicate that female farmers are more inclined to adopt intercropping and change planting dates as strategies for climate change adaptation. This finding underscores the important role of gender in agricultural decision-making and highlights the adaptive capacities of female farmers in response to environmental changes. The results suggest that education is associated with planting drought-resistant crops, mixed farming, and intercropping. This finding supports the a priori expectation, as individuals with higher education levels are more environmentally conscious. The findings show a significant relationship between livestock ownership, measured in Tropical Livestock Units (TLU), and the adoption of diverse agricultural practices. These practices include growing drought-resistant crops, altering planting dates, engaging in mixed farming, and implementing intercropping. The observed positive relationship can be attributed to the benefits of livestock ownership in diversifying smallholder farmers’ agricultural activities. The results indicate that household size has a positive influence on the adoption of drought-resistant crops and intercropping. This result suggests that households with larger family sizes have more labour endowment, enabling them to effectively implement climate change adaptation strategies.

6. Discussion

Farmers in the uMkhanyakude district of KwaZulu-Natal have implemented various adaptation strategies to mitigate the negative effects of climate change. Similar practices have been documented in studies from South Africa [46,47]. However, some farmers did not adapt to climate change. In the focus group discussions, the most common reason given by farmers for not adapting to climate change was a lack of resources and knowledge.

The MVP model was used to estimate the psychological factors influencing climate change adaptation. Farmers’ positive attitudes towards climate change increase their propensity to adopt climate change adaptation strategies (planting drought-resistant crops, intercropping, shifting planting dates, and minimum tillage. These results are consistent with findings from studies conducted in other parts of the world. For example, In Malaysia, ref. [17] found that attitudes towards global warming influenced people’s pro-environmental behaviour. In Taiwan, ref. [16] found that food attitudes also influenced pro-environmental behaviour in diets. Farmers’ exposure to persistent drought may have shaped farmers’ attitudes to climate change. The uMkhanyakude district frequently experiences dry conditions, and most smallholder farmers practice rainfed agriculture [48]. In the focus group discussions, farmers reported that drought was a major problem in the area, negatively affecting crop and livestock productivity.

In the study area, societal norms have a positive bearing on the behaviour of community members. This implies that social pressure and perceptions from society and peers have an influence on climate change adaptation. This finding aligns with the existing literature, as social norms have consistently been identified as significant factors in TPB studies related to farmers’ decision-making processes [33,35]. This finding supports previous studies, suggesting that innovations tailored to farmers’ self-perceptions, socio-economic status, and the preservation of their key sources of social capital were more likely to succeed [14,33,35,49]. Such innovations not only resonate with farmers’ identities and values but also enhance their adaptive capacity in the face of challenges. By recognising and integrating these personal and contextual factors, stakeholders can foster a more conducive environment for the adoption of new agricultural practices, thereby promoting sustainable development and resilience within farming communities.

Our results suggest that interactions among community members facilitate the exchange of information and knowledge, thereby enhancing efforts towards adaptation. Interactions foster a supportive network where farmers feel more confident in trying new techniques, knowing that their peers are also engaged in similar efforts. These interactions represent a form of social learning that can empower farmers and bolster their self-confidence, ultimately facilitating adaptation to climate change. Ref. [35] observed that strong social networks could accelerate disaster response, improve adaptive capacity, and reduce exposure to external risks.

The results revealed that female smallholder farmers were more likely to adopt climate change adaptation strategies. This finding aligns with previous studies conducted in Kenya [36] and South Africa [37]. The higher adaptive capability of female smallholder farmers may be attributed to their heightened vulnerability to climate change, arising from factors such as limited off-farm activities, lower levels of education, and weaker social networks [50]. The observed gender disparity in the adoption of shifting planting dates underscores the necessity for targeted interventions and support for female farmers to strengthen their resilience to climate change.

The results revealed that education plays an important role in climate change adaptation. Educated individuals often have better access to information and resources, which enhances their understanding of climate change and its effects [51]. Education fosters critical thinking and encourages the exploration of sustainable agricultural practices. These practices can effectively reduce the negative impacts of climate change on agriculture. As a result, those with high levels of education are not only more aware of environmental issues but are also better equipped to implement strategies that promote sustainability and resilience in agricultural systems. This connection between education, environmental awareness, and practical knowledge highlights the importance of education in addressing climate-related challenges.

In smallholder farming systems, livestock plays an important role in climate change adaptation. Livestock serves as a supplementary enterprise to crop farming, allowing farmers to reduce risks related to adverse weather and potential crop failures. By integrating livestock into their farming systems, farmers can diversify their risks and improve their resilience to the impacts of climate change. This result aligns with previous empirical studies on climate change adaptation [52,53].

The results revealed that household size is associated with the adoption of drought-resistant crops and mixed cropping. This result is in consonance with the study by [54] that shows a positive relationship between household size and climate change adaptation. This association could be attributed to the ability of households to supply excess labour in farming activities.

7. Conclusions and Recommendations

The aim of this study was to assess the psychological determinants influencing climate change adaptation, applying the Theory of Planned Behaviour (TPB) as a framework of analysis. The climate change adaptation strategies evaluated include planting drought-resistant crops, intercropping, mixed farming, and shifting planting dates.

The primary psychological variables influencing farmers’ decisions to adopt climate change adaptation strategies were trust (in reliable support networks), attitudes towards adaptation, and subjective norms. This finding is particularly important for extension officers, policymakers, and other rural development practitioners. These findings also imply that climate change adaptation must be grounded in the local context. Programmes and policies designed to support smallholder farmers should take into account local norms and attitudes to ensure that messaging is targeted and effectively induces change. The findings of this study underscore the significance of local context and behavioural decision-making research in addressing societal challenges.

Although the findings of this study are specific to the immediate study area, they have broader implications for informing programmatic and policy efforts aimed at enhancing climate change adaptation on a larger scale. Future research can leverage this study and use the TPB methodology to apply it across study sides and scales. This will further enhance our understanding of human behaviour and climate change adaptation, which is essential for informing policy action at broader scales while considering the local context.