Determinants of climate change adaptation strategies’ adoption among maize farming households: evidence from Malawi

Introduction: Climate change poses a serious threat to agricultural productivity and food security in Malawi, particularly among rural households that rely heavily on rainfed farming. This study examined the determinants of climate change adaptationv strategies among maize smallholder farmers in Chipoka EPA in Salima District, focusing on irrigation systems, zero tillage, and water harvesting.

Data and methods: Primary data were collected from 120 randomly selected households using a semi-structured questionnaire, and the multivariate probit model was employed to analyse factors influencing adoption.

Results: The results revealed that being male, higher education level, landholding size, income, access to extension service and perceptions of changes in rainfall and temperature significantly and positively influenced the adoption of adaptation strategies. The findings underscore the importance of both socioeconomic, institutional and agroecological factors in shaping farmers’ adaptive responses.

Conclusion: The study concludes that enhancing adaptive capacity requires policies that strengthen extension services, promote farmer training, address gender inequalities, and improve access to financial and land resources. Expanding irrigation and water harvesting systems, alongside reliable climate information services, is also essential. Collectively, these measures can increase resilience, improve agricultural productivity, and ensure sustainable food security among rural farming households in Malawi.

1 Introduction

Climate change refers to long-term shifts in weather patterns and average temperatures across the globe, primarily attributed to human activities that release greenhouse gases into the atmosphere (Filonchyk et al., 2024). It is one of the most pressing challenges of the present time, affecting food security, water resources, and human health. Climate change is expected to increase the frequency and intensity of natural disasters such as drought, floods, and storms, which have a significant impact on rural households. Rising temperatures and changing precipitation patterns have direct and indirect consequences for crop growth and productivity. Heat stress, for instance, can reduce crop yields and decrease the nutritional content of crops (Cohen et al., 2021; Lesk et al., 2022). Changes in rainfall patterns, including more frequent droughts and floods, can disrupt agricultural activities, leading to crop failures and decreased food production (Raza et al., 2024).

Additionally, climate change can disrupt the availability of natural resources crucial for improving household welfare in the food production spectrum. For example, changes in water availability can limit irrigation, which is vital for crop growth (Nechifor and Winning, 2019). Shifts in pest and disease patterns driven by climate change can also affect crop health and yield. Farah et al. (2025) examined the impacts of climate change on global agriculture and shows that climate change could lead to a decline in agricultural productivity by 14% compared to a scenario without climate change. This decline in productivity would have significant implications for food security, especially in regions that are already vulnerable to food insecurity.

Malawi is among the world’s most climate-vulnerable countries, ranking 169 out of 182 nations on the 2020 Notre Dame Global Adaptation Index (ND-GAIN) due to its high exposure and low adaptive capacity (Munthali et al., 2025). The country’s agricultural sector, which employs approximately 80% of the workforce is predominantly rain-fed and highly susceptible to climate variability. Historical data from the Malawi Department of Climate Change and Meteorological Services (DCCMS) indicate that mean annual temperatures have increased by 0.9 °C since 1970, while rainfall patterns have become increasingly erratic, with more frequent and intense droughts and floods (Government of Malawi, 2024). These changes have direct implications for food security in a nation where maize, the staple crop, faces yield reductions of 10%–20% under current climate projections (Bangelesa et al., 2023).

The unprecedented effects of climate change have called for climate change adaptation strategies as a solution to mitigate its negative impacts. Climate change adaptation strategies refer to practices, technologies, and policies that can help farmers and communities adapt to the impacts of climate change on agriculture and food systems (FAO, 2018). These strategies are critical for addressing these challenges and ensuring that rural households can continue to sustain their livelihoods. The level of implementation and adoption of adaptation strategies varies across the globe. In China, climate change has negatively affected their cereal yields by approximately 10% (Jin et al., 2025). Similar detrimental effects are noted in other countries, such as Australia and Brazil (Bailey et al., 2025; de Souza Batista et al., 2023). Meanwhile, national policies aimed at climate modelling and tapping into technologically sustainable agricultural practices to combat the adverse effects of weather are largely practiced by their farmers.

In the developing world context, climate change has a profound negative agricultural development in India. It’s emphasised by Singh et al. (2024) that cereal yields, which constitute the largest portion of a food basket in India, decreased due to extreme temperatures. Nonetheless, robust strategies, such as water treatment, groundwater recharge and irrigation for adapting to climate change have been rolled out across the country. The trends are also similar in other countries such as Qatar and Israel, in food production and output prices (Cai et al., 2024; Kan et al., 2023).

In Sub-Saharan African countries such as Ethiopia, Nigeria and Malawi, climate change indicates a detrimental effect on staple foods, resulting in food insecurity. Nonetheless, on-farm adaptation strategies such as zero tillage, irrigation and water harvesting are the most adopted by the respective countries’ farmers. The Government of Malawi has developed several policy frameworks to guide climate adaptation. The National Climate Change Management Policy (Government of Malawi, 2016) and Government of Malawi (2020) prioritise agricultural adaptation through five strategic pillars: (1) climate-smart agriculture, (2) sustainable water management, (3) climate information services, (4) social protection, and (5) institutional capacity building (Government of Malawi, 2020). Specific programs include the National Resilience Strategy (2020–2025), which targets 2.5 million smallholder farmers for adaptation support, and the Agriculture Sector Wide Approach (ASWAp).

Despite these policy commitments, implementation gaps persist. Shani et al. (2024) highlights that the adoption rates for recommended climate-smart practices in Malawi is 26% among the farmers, with adoption rates varying significantly by region, gender, and socioeconomic status. Successful adoption of the climate change adaptation technology can be kindled as a two-sided coin. One side is the existence of the technology itself, and the other side are the determinants that make the technologies necessary for adoption. It’s worth noting that, regardless of the existence of the climate adaptation technologies across the globe, determinants for adopting them remain underexplored, even in the Malawian context. Hence, this study fills this gap by assessing the household, institutional and agro-ecological determinants that influence the adoption of climate change adaptation strategies in Chipoka EPA in Malawi. Ultimately, this research answers the following question:

What are the household, institutional and agro-ecological determinants that influence the adoption of climate change adaptation strategies in Chipoka EPA in Malawi?

Different scholars have assessed the determinants of adopting climate change strategies across the globe using different methodological and theoretical frameworks. For instance, Solomon (2018) used household level data to assess the adoption determinants in Nigeria using a logistic regression. Solomon (2018) found that socio-economic factors such as age, gender, education level, credit access and access to extension are some of the significant determiners. Similarly, in Nigeria, Awe (2019) postulated that household size, access to timely weather information and farmers’ farming experience significantly influence a household to adopt the climate change adaptation strategy. Evidence from Ethiopia by Saguye (2016) using a logit regression concurs with Solomon (2018) on the socio-economic significant determinants of adoption, but Saguye (2016) adds on ecological, institutional and geographical factors. Specifically, he posits that labour constraints, inadequate access to productive farming and land tenure stimulates farmer’s ability to be innovative and adopt the climate adaptive strategies.

Another study (Marie et al., 2020) used a multinomial logistic regression to bring to light the socio-economic determinants of adopting the adaptive strategies in North Eastern Ethiopia. Among the adopted strategies, which include mixed cropping, mixed farming, using drought-resistant seed varieties and irrigation use, factors such as age, farm income and farm size were significant to positively influence the adoption decision. It’s worth mentioning that different adaptation strategies exist in the literature. Chidanti-Malunga (2011) documents that most farmers in Malawi practice irrigation, crop diversification, and crop residual management.

Empirical evidence from Guja and Bedeke (2025) indicates that most rural farming households in Malawi utilise risk reduction strategies such as tree planting, legume cropping and water conservation as part of their adaptation strategies. This can be kindled to as ex-ante climate preparation strategies, similarly echoed by Abid et al. (2020). It is imperative to note that other authors have attributed the adoption of adaptive strategies to a knowledge perspective. The authors emphasize that being knowledgeable about the present ex-ante and ex-post climate change adaptive strategies positively influences the adoption. This is also compounded by social capital roots, such as bonding social capital, linking social capital and bridging social capital (Abid et al., 2020; Shinbrot et al., 2019). Development theory posits that rural households utilise copying and pulling as ways of building resilience against shocks. As such, bonding social capital and bridging social capital have a high explanatory power in necessitating the adoption across and among rural farmer households in Salima district, as postulated by scholars (Han et al., 2022; Rust et al., 2023) respectively.

This inquiry has been motivated by scant literature regarding the subject matter in Malawi. Specifically, Malawi faces increasing climate vulnerabilities, and policymakers need evidence for targeted interventions. The study addresses gaps in local data and uses an advanced methodology that accounts for interrelated strategies, which previous studies might not have done. As a result, the findings of this study will help policymakers in developing policies tailored to Salima farmers on how best to improve their adaptation capacity. Again, this study will contribute to the academic discourse and will be helpful to the farmers themselves, as it will either recommend the adoption of the existing adaptation strategies or pave the way for new strategies.

This study focuses on three widely promoted climate adaptation strategies-irrigation systems, zero tillage, and water harvesting-selected for their effectiveness in enhancing water and soil management under variable climatic conditions in Malawi (Mvula et al., 2025; Nyirenda and Balaka, 2021). These strategies can be kindled as a wide array of investment and household labour requirements, allowing for an examination of how household resources influence adoption decisions. While other adaptation practices exist, these three are particularly relevant to the semi-arid agro-ecology of Chipoka EPA and are actively supported by the government and donor programmes as best-practice climate adaptation measures.

The rest of the study is organised as follows: Section 2 discusses the materials and methods, Section 3 presents and discusses the results and Section 4 concludes and provides recommendation.

2 Materials and methods

2.1 Study area

The study was conducted in Chipoka Extension Planning Area (EPA) in Salima, which is located to the east of Salima district under the Salima Agricultural Development Division (ADD). Chipoka EPA covers a large area with a total of 6,395 households (National Statistical Office, 2019). The main occupation of people in the EPA is farming activities and small-scale businesses, which include selling agricultural products such as maize, groundnuts, and beans. Chipoka EPA was chosen because it is one of the EPAs where climate vulnerability poses the risk of different climatic hazards such as temperature change, changing of rainfall patterns (FAO, 2019). Figure 1 shows the study area.

Figure 1

Figure 1. Map showing the study area (Chipoka EPA).

2.2 Data source

The study used primary data, which was obtained through a survey administered to randomly selected smallholder farmers within Chipoka EPA in the Salima district. The sample selection followed a multistage sampling technique. In the first stage of the study, we purposively selected Chipoka EPA. The second stage involved randomly selecting 120 farmers, who were determined using the Cochran (1977) formula (Equation 1). Equation 1 gives 118 respondents, but we just exceeded with 2 and collected data on 120 respondents.

$\begin{array}{ll}n=\frac{z^2\times p\times (1-p)}{{\in }^2}& (1)\end{array}$

Where z = 1.96 at 95% confidence level, p = 0.5 and $ϵ$ (margin of error) = 9%.

2.3 Survey instrument

The study used a semi-structured questionnaire to capture the demographics of the households, climate change perception and awareness, available climate change adaptation strategies and the challenges that are encountered when adopting or implementing the adaptation strategies. Before the data collection process, the instrument was pre-tested for methodological clarity, flow and ability to capture the study’s interests. Questionnaire pretesting has been emphasised by different authors (Hilton, 2017; Ikart, 2019) as it sets the baseline for the study to be used to compare with the post-survey results, and it potentially adds value to the study. The pretesting was done in Mkwinda EPA in Lilongwe District, and it involved 10 participants. Lilongwe District is 98 km away from the study’s setting, and it was selected to avoid contamination with the study’s actual respondents. We also checked for the language competency of the instrument. Specifically, we conducted a blind translation where we gave a Postgraduate Student (PS) at the Lilongwe University of Agriculture and Natural Resources to translate the questionnaire into the local language (Chichewa), and we also gave another PS to translate it back into English. We noted uniformity regarding the information which was required by the questionnaire. This back translation follows the principles suggested by Brislin (1970). The instrument was employed for field data collection from 22 January 2024 to 25 January 2024 during the peak growing season to ensure proper yield assessment, which is complementary to the adopted adaptation strategies.

2.4 Ethical consideration

Following Sileyew (2019), the study did not want to impose risk on the study’s respondents. Thus, anonymity during data collection was requested, and the informed consent was read out loud to explain the study’s intent and ask for their participation in the study. Furthermore, we assured the respondents that the responses would be solely for academic purposes, and they are free to leave the interview if they feel so.

2.5 Theoretical framework

2.5.1 The utility maximisation theory

In this study, the authors assume that a rational farmer would adopt the strategy if it maximises utility. Thus, farming households maximise utility by adopting adaptation strategies that yield higher returns (Kaphaika et al., 2023; Mgomezulu et al., 2023). However, it is imperative to note that utility is unobservable, and it behaves randomly under varying alternatives. Specifically, in discrete choice theory, the utility that a household derives from an adaptation strategy is not directly observable to the researcher. Instead, it is treated as a random variable, consisting of a deterministic component (which depends on observed household, institutional, and agro-ecological factors) and a stochastic component (which captures unobserved factors and measurement errors).

We assume an individual farming household, i, from a sample of N households chooses from a given set of adaptation strategies j = 1, 2, 3, namely (1) Irrigation systems; (2) Zero tillage; and (3) Water harvesting. Further, we assume that each farming household attaches a utility $U_{\mathit{ij}}$ to each adaptation strategy depending on institutional and agro-ecological factors ( ${\eta }_{\mathit{ij}}$ ) and household factors ( $h_i$ ). Therefore, the utility derived by an individual farming household i, from adopting practice j can be presented as follows:

$\begin{array}{ll}{U}_{\mathit{ij}}=({\eta }_{\mathit{ij}},{\mathrm{h}}_{\mathrm{i}})\text{and}\forall j=1,2,3& (2)\end{array}$

Where ${\eta }_{\mathit{ij}}$ constitutes factors such as access to extension services, changes in rainfall and changes in temperature, and $h_i$ constitutes factors such as age, gender, income level, household size among others.

Further letting $D_{\mathit{ij}}$ represent a discrete choice variable for each of the adaptation strategies, and assuming the absence of mutual exclusivity in the choices made by farming households, then $D_{\mathit{ij}}$ takes the value 1 if a household chooses adaptation strategy j and zero otherwise. The corresponding probability can be presented as follows;

$\begin{array}{ll}{P}_{\mathit{ij}}=Pr(U_{i1}>U_{i2}>U_{i3})& (3)\end{array}$

From Equation 3, the utility for the given choice of adaptation strategy is greater than the utility derived from the other adaptation strategies. Since the social, institutional, and agro-ecological features of the farming household are quite observable, the utility function (Equation 4) can then be modelled as follows:

$\begin{array}{ll}{U}_{\mathit{ij}}=V_{\mathit{ij}}+{\epsilon }_{\mathit{ij}}\text{and}\forall j=1,2,3& (4)\end{array}$

Where V_ij = δ_jX_ij is the representative farming household utility, and the X_ij is the vector of observed variables relating to household, institutional, and agroecological characteristics. ${{\rm E}}_{\mathit{ij}}$is the stochastic error term that captures the unobservable attributes like the farmer’s personal motivation, and δ_j is the vector of unknown parameters which are to be estimated.

2.5.2 Conceptual framework

In examining the determinants that influence adoption of different climate adaptation strategies, this study utilises the Sustainable Livelihoods Framework (hereafter, SLF) (Chambers and Conway, 1992). This framework provides a holistic understanding of the livelihood activities, assets and institutions that are available and utilised by resource-poor and marginalised households for development and poverty alleviation. Considering that Malawian smallholder farmers are resource-poor and trapped in transient poverty (Machira et al., 2023; Muyanga et al., 2020), this framework will be useful to understand the strategies that are used to better their welfare. It’s imperative to mention that the SLF comprises five assets, also called the asset pentagon, and these include financial capital, human capital, natural capital, social capital and physical capital. Different studies (Asif and Gill, 2025) that have also examined the determinants of climate change adaptation strategies across the globe, explicitly or implicitly using the SLF. In this study, we modified the SLF to capture the relationship between climate vulnerability, the adaptation strategies and the influencing factors as shown in Figure 2.

Figure 2

Figure 2. Conceptual framework.

2.6 Empirical model

2.6.1 The multivariate probit model

Greene (2018) and Wooldridge (2013) emphasize that more than two empirical choices can be estimated using a multivariate probit model. However, these models assume independence of irrelevant alternatives, which implies that the likelihood of choosing A or B will not change if another option C is introduced (Arad et al., 2023; Mittal and Mehar, 2016). Other scholars have stated that the error terms of the options should not be correlated. In our study, the farmers were subjected to interrelated choices, as adopting one adaptation strategy would not restrict the adoption of the other strategy. This entails that the error terms for the choices were correlated, and thus invalidates the multinomial probit model. It’s worth noting that different studies have modelled adoption using a multivariate probit model (Assaye et al., 2023; Donkoh et al., 2019; Kanyamuka et al., 2020; Khonje et al., 2022). Following Greene (2018), the multivariate probit model (Equation 5) in this study was specified as:

$\begin{array}{ll}{Y}_{\mathit{im}}^{\ast }={\beta }_m^{\prime }{X}_{\mathit{im}}+{\epsilon }_{\mathit{im}}\text{and}m=1,2,3& (5)\end{array}$

Where Y_im indicates the adaptation strategies consistently adopted by the smallholder farmer. The farmer in this case consistently adopts if $Y_{\mathit{im}}$ * > 0; $X_{\mathit{im}}$ presents the vector of socioeconomic and institutional factors. β presents the vector of parameters that were estimated. It should further be noted that the error term $\epsilon$ _im follows a multivariate normal distribution. This implies that the residuals have a zero expected value (E ( $\epsilon$ ) = 0) and a variance–covariance matrix (Equation 6), V, which has ones on the main diagonal and correlations ρ_jk = ρ_kj in the off-diagonal (Wooldridge, 2013).

$cov\left(\epsilon \right)=V=\left[\begin{array}{ccc}1& \dots & V_{13}\\ ⋮& \ddots & ⋮\\ V_{31}& \dots & 1\end{array}\right]\text{ (6)}$

The joint probabilities of adopting strategies are presented as follows,

$\begin{array}{ll}{Y}_{\mathit{im}}\mid X_{\mathit{im}}\text{for}i=1,2\dots .,n& (7)\end{array}$

These are assumed to form M-variate normal probabilities and are predicted using a likelihood function presented as follows;

$\begin{array}{ll}{L}_i={\varphi }_m\left(q_{i1}{X}_{i1}^{\prime }{\beta }_1\right),\dots \dots \dots \dots \dots \dots .q_{\mathit{im}}{X}_{\mathit{im}}^{\prime }{\beta }_m,R_{\mathit{jm}}^{\ast }& (8)\end{array}$

Where,

$\begin{array}{ll}{q}_{\mathit{im}}=2Y_{\mathit{imt}}-1& (9)\end{array}$

$\begin{array}{ll}{R}_{\mathit{jm}}^{\ast }=q_{\mathit{ij}}{q}_{\mathit{im}}{\rho }_{\mathit{jm}}& (10)\end{array}$

Thus, ρ_jm is the correlation coefficient among the pairs of the error terms of the equations, ϵ_j and ϵ_m. A correlation coefficient of greater than zero implies that the smallholder farmers indeed do not make independent decisions in adopting the three practices. Nonetheless, if ρ_jm = 0 then the multivariate probit model collapses to independent probit models as it implies independence in alternatives. Nonetheless, since farmers are in this scenario subjected to interrelated choices, the multivariate probit model is a better fit than the multinomial probit or logit model (Rahman and Chima, 2015). It’s imperative to mention that the model estimation and all other analyses were done using Stata 17 software.

2.7 Diagnostic tests

To check if the study’s variables were ideal for analysis, interpretation and triangulation with empirical literature, we checked for correlation between the adaptation strategies and multicollinearity. The study conducted the pairwise correlation tests among the adaptation strategies to check if they are correlated, and supported the use of Multivariate Probit. Regarding multicollinearity, the study involves many explanatory variables, such that a decision to adopt an adaptation strategy can emanate from one variable or be influenced by multiple joint variables. If the latter happens, the estimates become biased (Wooldridge, 2013), in such a way that the variable’s influence can be overestimated or underestimated.

2.7.1 Variables of the study and their expected signs

Table 1 reports the variables of the study, including their expected signs. The variables used in this study are supported by literature. Noteworthy, $X_1\mathit{\text{to}}{X}_7$ are household determinants, $X_8$ is the institutional determinant and $X_9\mathit{\text{to}}{X}_{10}$ are agro-ecological determinants.

Table 1

Table 1. Variables of the study and their expected signs.

3 Results and discussion

3.1 Descriptive statistics

Table 2 reports the descriptive statistics for the independent variables in our study. It’s imperative to mention that the p-values indicate if a statistical difference exists between the adopters and non-adopters for a particular variable. Notably, in Table 1, most household characteristics were not different between the adopters and the non-adopters’ group. For instance, males were more pronounced in both groups, and reproductive age had an effect ranging from 86% to 91% of the study participants. Furthermore, secondary school level had a large effect (approximately 48%) in influencing adoption, similarly noted among the non-adopters. This implies the decision-making capability of the household to choose to adopt or not. The joint decision-making power, which was evident in 65%–68% of the married households, influenced a household to either adopt an adaptation strategy or not.

Table 2

Table 2. Descriptive statistics for the study variables on pooled adoption.

This study found significant differences in terms of adoption by households’ access to extension services. Extension services can be kindled as a supporting pillar for the farming households to adopt and use the adaptation strategies. Again, a perception that temperature will change enabled approximately 22%–59% of the farmers to decide to adopt or not. We also noted that a perception of rainfall change enacted a decision to adopt an adaptation strategy or not in 46%–72% of the households. Regarding household size, adopters had higher household members compared to the non-adopters, on average. This suggests that human assets can enhance collective action and enable the adoption of adaptation strategies effortlessly. Noteworthy, adopters had income levels twice as the non-adopters, suggesting the advantage of financial assets at the household level. Lastly, land size among household adopters was larger (4.197 acres) relative to non-adopters. The urge to be productive on a large farm enabled the households to adopt an adaption strategy.

3.2 Climate change adaptation measures

As depicted in Table 3, the disaggregated adaptation measures reveal distinct socio-demographic, institutional and ecological patterns. With respect to gender, males were more pronounced in irrigation (61.04%) and zero tillage adaptation (56.45%), while females were common in water harvesting (44.29%), although the differences are not statistically significant (p > 0.05). Similarly, age categories showed no significant differences in the adoption of the study’s adaptation strategies (p > 0.05). Nonetheless, a higher proportion of the respondents in the productive age group was found to adopt an irrigation strategy (89.61%) compared to the elderly group which adopted zero tillage (12.90%) and water harvesting (12.86%).

Table 3

Table 3. Climate change adaptation measures employed by households.

Higher education attainment was associated with increased irrigation use amongst the three adaptation strategies, despite not showing any statistically significant differences. It is also noted that higher education level, i.e., secondary education, enhanced the use of water harvesting while tertiary education enhanced the adoption of zero tillage. Table 3 also shows that the irrigation strategy was common among married households (74.03%), whilst zero tillage and water harvesting were common among single-membered households, i.e., 17.74 and 17.14%, respectively.

In contrast, access to different extension services emerged as a strong and consistent determinant, which enabled the households to largely adopt all three adaptation strategies; irrigation (p < 0.000), zero tillage (p < 0.001), and water harvesting (p < 0.001). Furthermore, a perception about temperature and rainfall changes enabled farmers to have a mixed array of adaptation strategies. A perceived change in temperature was significantly associated with irrigation adoption (p < 0.001), but not with zero tillage (p = 0.944) or water harvesting (p = 0.454). Conversely, a perceived change in rainfall patterns significantly influenced the adoption of zero tillage (p < 0.001), while its association with irrigation (p = 0.961) and water harvesting (p = 0.774) remained insignificant. This study also found that households with a higher number of members practiced water harvesting efficiently, and a higher household income level was associated with irrigation use. Lastly, the study found that large farms, averaging 3.78 acres, were more likely to adopt irrigation. Notably, the differences across the adoption strategies are not statistically significant due to land size.

3.3 Determinants of climate change adaptation strategies among rural households

Before the multivariate probit model results, we present the model sufficiency tests to validate our findings. The model sufficiency results include the pairwise correlation matrix and the conditional index for multicollinearity.

3.4 Diagnostic tests

3.4.1 Pairwise correlation matrix

Table 4 presents the pairwise correlation matrix on the study dependent variables. The matrix was necessary to confirm if the climate change adaptation strategies are correlated. As indicated in Table 4, from the significance of the chi-square and p-values (Chi²(3) = 109.86, p-value = 0.000), the correlation coefficients indicate that the adaptation strategies are correlated, supporting the use of MVP.

Table 4

Table 4. Pairwise correlation matrix.

3.4.2 Multicollinearity

The study also assessed the presence of multicollinearity by using the conditional index methodology. According to Shrestha (2020), multicollinearity is present if a condition index is greater than 15. As evidenced in Table 5, the condition index for this study is 11.46, which is less than 15. This confirms that multicollinearity is not a problem in this study.

Table 5

Table 5. Multicollinearity check.

3.5 Model results

Table 6 reports the coefficients for the multivariate probit model specification. The Likelihood ratio test presented in Table 6 is significant, indicating that the adoption decisions are independent, thus rejecting the null hypothesis (rho21 = rho31 = rho32 = 0) that the covariance of the error terms across the equations are not correlated. Therefore, the application of the MVP, unlike the individual probit models is justified. Again, the value of the $\mathit{\text{Wald}}-{\mathrm{\chi }}^2$ indicated in the output shows that the model was specified correctly at 5%. The coefficients reported from the Multivariate Probit model indicate the direction of influence on the latent propensity to adopt. A positive (negative) and significant coefficient implies that the variable increases (decreases) the probability of adoption, ceteris paribus. The marginal effects are useful for quantifying the magnitude of the change in probability of a given variable. The following determinants were found to positively influence or negate the adoption of a particular adaptation strategy.

Table 6

Table 6. Multivariate probit results.

3.5.1 Gender

From the study, male smallholder farmers were found to significantly and positively adopt irrigation (β = 0.462, ME = 0.103, p < 0.000) and zero tillage adaptation strategies (β = 0.217, ME = 0.061, p < 0.000). However, the study noted that being male had a negative influence on using water harvesting (β = −0.165, ME = −0.039, p < 0.05). Our findings concur with the findings of other scholars (Fredriksson and Gupta, 2023; Hailemariam et al., 2024) but contrast with Hirpa Tufa et al. (2022). Zero tillage and irrigation are high-risk technologies that also require huge investments. On the other hand, water harvesting is a low-risk adaptation technology and requires little investment. Notably, this is a human asset aspect. These gendered disparities evidence that men generally have better access to resources and decision-making power in Malawi agriculture, similarly echoed by Mgalamadzi et al. (2024).

3.5.2 Age

In this study, it was found that age, which is a human asset, strongly predicts adoption patterns. Elderly farmers were less likely to use irrigation (β = −0.535, ME = −0.126, p < 0.000) but significantly more likely to adopt zero tillage (β = 1.866, ME = 0.304 p < 0.000) and water harvesting (β = 0.543, ME = 0.203, p < 0.000). This aligns with studies showing that older farmers may avoid labour-intensive strategies like irrigation due to physical constraints (Samuel, 2025), while opting for less labour-demanding methods such as zero tillage and water harvesting.

3.5.3 Education level

Education shows mixed effects. Farmers with primary education were significantly less likely to adopt irrigation (β = −0.546, ME = −0.072, p < 0.000) and, to a lesser extent, zero tillage (β = −0.372, ME = −0.058, p < 0.05). Secondary education significantly increased irrigation adoption (β = 1.730, ME = 0.367, p < 0.05). However, tertiary education level was associated with increased adoption of irrigation (β = 1.964, ME = 0.321, p < 0.05), and zero tillage (β = 1.822, ME = 0.212, p < 0.05). In Malawi, agriculture is largely subsistence, characterised with limited knowledge, and coupled with high risks, but low returns. The aforementioned traditional perspectives impose difficulties to adopt modernised technologies such as irrigation and zero tillage, which is more pronounced in primary level farmers. On the contrary, secondary and tertiary education shifts the perspective of a farmer from subsistence to commercial farming, and it provides contextualised training land management for employing adaptation strategies. As such, it is not surprising to note the use of irrigation and zero tillage among them. The findings of this human asset are consistent with Maguza-Tembo et al. (2017).

3.5.4 Marital status

Marital status played a significant role in adaptation decisions. Married households were significantly more likely to adopt irrigation (β = 1.152, ME = 0.323, p < 0.000), zero tillage (β = 1.142, ME = 0.106, p < 0.01), and water harvesting (β = 0.509, ME = 0.068, p < 0.000) relative to unmarried households. This supports the notion that marriage facilitates joint decision-making and labour pooling (Doss and Quisumbing, 2020). Divorced households, conversely, were less likely to adopt irrigation (β = −0.344, ME = −0.102, p < 0.000), highlighting the potential vulnerability of fragmented households. Marital status can be kindled as bonding social capital that improves trust for collective action. This has also been emphasised by Karakara (2025) that married households are more likely to implement activities that reduces their vulnerability to agricultural risk.

3.5.5 Extension service

Institutional support emerged as a decisive factor. Specifically, access to extension services consistently had positive coefficients across all three adaptation strategies ( $\beta =$0.573, p < 0.000) for irrigation, ( $\beta =$1.693, p < 0.000) for zero tillage, $(\beta =$0.781, p < 0.000) for water harvesting. This indicates a strong positive direction of influence on the adoption propensity. The marginal effects indicate a 13.0 percentage point higher probability for irrigation, 48.5 percentage points for zero tillage, and 26.8 percentage points for water harvesting when households access extension services. Within the Sustainable Livelihoods Framework, extension services enhance both human capital through technical knowledge transfer and social capital by creating institutional linkages, which explains their outsized effect on information-intensive practices like zero tillage. These findings align with Mapanje et al. (2021) and Pangapanga-Phiri and Mungatana (2021), who found extension as a key determinant of climate-smart technology adoption among Malawian smallholders, and with Assaye et al. (2023), who documented similar strong positive marginal effects in Ethiopian interrelated conservation practices.

3.5.6 Perception on temperature change

Farmers perceiving temperature changes were less likely to adopt irrigation (β = −0.626, ME = −0.192, p < 0.000) but more inclined towards zero tillage (β = 0.249, ME = 0.062, p < 0.000) and water harvesting. This may stem from water conservation and cost minimisation perspectives. This is especially true when a farmer considers that a temperature change will be accompanied with increased water evaporation from the farm. Hence, a higher likelihood of adopting zero tillage to prevent direct effect on the soil. Similarly, utilising irrigation will prove to be ineffective, and it will incur a sunk cost to the farmer. This perception is part of the human asset, and it has direct synergies with the financial assets of the farmer. It’s imperative to mention that our findings are also in line with other studies in empirical literature (Abid et al., 2019; Nyang’au et al., 2021; Reddy et al., 2022).

3.5.7 Perception of rainfall change

A perception that rainfall will either decrease or increase significantly influenced farmers to adopt irrigation (β = 0.585, ME = 0.129, p < 0.000) and water harvesting (β = 0.724, ME = 0.201, p < 0.000). This is especially true when a farmer’s perception exposes his vulnerability to climate, which translates to soil erosion, food insecurity and increased poverty. As a result, to minimise the occurrence of the aforementioned extreme climate events, farmers would either irrigate or collect more underground water. Different studies have also emphasised that rainfall perceptions influence the adoption of adaptation strategies (Asfaw et al., 2019; Al Mamun et al., 2021). This result has significant implications for bridging and bonding social networks that are available to farmers. Noteworthy, the perceptions emanating from fellow farmers, extension workers, community farmer clubs and other community-based organisations can influence the farmers to adopt the adaptation strategies.

3.5.8 Household size

Household resource endowments positively influenced the adoption of zero tillage (coefficient = 0.520, ME = 0.064, p < 0.000) and water harvesting (0.053, ME = 0.720, p < 0.000) technologies in this study. This may stem from the labour-intensive demands associated with zero tillage and water harvesting. Again, household size, which is an indicator of the available human capital at the household level, can be specialised for conservation-oriented practices. Our finding agrees with other scholars such as Jha et al. (2019) and Timothy et al. (2022).

3.5.9 Income level

The study found that income level was negatively associated with zero tillage (β = −1.98e-06, ME = −2.69e-07, p < 0.000) and water harvesting (β = −2.82e-06, ME = −8.37e-07, p < 0.000). This is because income level, which is a financial asset at the household level, influences the use of capital-intensive strategies like irrigation. It’s worth mentioning that our findings agree with other scholars such as Marie et al. (2020). This also aligns with the Sustainable Livelihoods Framework, where financial assets reduce liquidity constraints for technology adoption, consistent with Aboye et al. (2023).

3.5.10 Land size

In this study, landholding size significantly increased the likelihood of adopting irrigation (β = 0.246, ME = 0.074, p < 0.000), zero tillage (β = 0.517, ME = 0.053, p < 0.000), and water harvesting (β = 0.075, ME = 0.032, p < 0.05). Land size has physical asset characteristics, and it is a crucial determinant to invest, minimise risks and realise economies of scale, as far as adaptation strategies are concerned. Our findings, which are consistent with Pronti et al. (2024) confirms that landholdings enhance flexibility in experimenting with and sustaining adaptation strategies

4 Conclusion and policy recommendations

Climate vulnerability among rural smallholder farmers in Chipoka EPA induces them to adopt different adaptation strategies such as zero tillage, irrigation and water harvesting. In this study, we aimed to examine the determinants that influence the adoption of the aforementioned adaptation strategies using the Multivariate Probit Model (MVP) among maize smallholder farmers. The examination was supported by a modified version of the Sustainable Livelihoods Framework (SLF). This study shows that adoption of irrigation, zero tillage and water harvesting in Chipoka EPA is shaped by bundles of human, social, financial and natural capital, as well as farmers’ climate risk perceptions,

The results of the multivariate probit model underscore that the adoption of climate change adaptation strategies among rural households in Chipoka EPA is shaped by an interplay of socioeconomic, institutional, and ecological factors. It is imperative to note that male-headed households are significantly more likely to adopt irrigation (ME = 0.103, p < 0.000). Higher education particularly tertiary education positively influences the adoption of zero tillage (ME = 0.212, p < 0.000), while landholding size consistently increases the likelihood of adopting all three strategies, including irrigation (ME = 0.074, p < 0.000), zero tillage (ME = 0.053, p < 0.000), and water harvesting (ME = 0.032, p < 0.05). Access to extension services substantially enhances adoption, especially for irrigation (ME = 0.130, p < 0.000). In addition, perceptions of rainfall significantly promote the adoption of water harvesting (ME = 0.201, p < 0.000), whereas perceptions of temperature change positively influence the uptake of zero tillage (ME = 0.062, p < 0.000).

Male-headed, married, land-abundant, and extension-supported households were more likely to adopt adaptation strategies, while elderly farmers tended to avoid labour-intensive practices like irrigation but favoured less demanding conservation approaches such as zero tillage and water harvesting. Education exhibited mixed effects, with tertiary education positively influencing irrigation and zero tillage, while lower levels were adopting irrigation and water harvesting. Importantly, perceptions of rainfall and temperature variability significantly influenced strategy choice, highlighting the role of climate awareness in shaping adaptation.

The findings demonstrate that adoption decisions are not uniform but reflect farmers’ resource endowments, social positions, and environmental experiences. This suggests that effective policy interventions must be context-specific and farmer-centered, addressing the diverse constraints and opportunities that households face in adapting to climate change. Specifically, this study suggests:

a. Addressing social inequalities through equal access to resources among men and women. For instance, introduce gender-targeted irrigation financing schemes, such as matching grants or low-interest seasonal credit earmarked for women’s groups and female-headed households in EPAs with high climate risk, including Chipoka. This positions women with the ability to adopt high-risk and reliable adaptation strategies, such as irrigation.

b. Increasing farmers’ capacity through education. This empowers farmers to take calculated risks and adopt adaptation strategies that build stronger resilience. Again, scaling up farmer field schools and lead-farmer models in Chipoka EPA that simulate the decision environments faced by low-education farmers, using practical demonstrations and peer learning to reduce perceived risk and information barriers for irrigation and zero tillage

c. Continued access to climate change extension services to enable the adoption of resilient adaptation strategies for food security and poverty reduction.

d. Prioritise secure and tradable land rights (e.g., through systematic land registration and certificates of customary land estates) in EPAs like Chipoka, enabling farmers to use land as collateral to finance irrigation investments and other capital-intensive strategies. This can accelerate adoption of adaptation strategies and reduce climate risks.

5 Study limitations

Regardless of the valuable contribution of this study to the academic discourse, it has some limitations. Firstly, the study used cross-sectional data, which means that we can identify correlations but not establish causality between the determinants and adoption of the adaptation strategies. Secondly, the reliance on self-reported data on variables such as perception on climate vulnerability and adaptation strategies is susceptible to recall bias. Finally, the study assesses the adoption decision only, but does not evaluate the intensity of the adaptation strategies on development. Future research would focus on longitudinal study designs, the inclusion of more objective variables and assessing the actual impacts of the adopted adaptation strategies on household welfare and resilience.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving humans were approved by Lilongwe University of Agriculture and Natural Resources Research Ethics Centre. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation in this study was provided by the participants’ legal guardians/next of kin.

Author contributions

SN: Conceptualization, Formal analysis, Methodology, Software, Writing – original draft, Writing – review & editing. LK: Data curation, Formal analysis, Methodology, Writing – original draft, Writing – review & editing. MM: Supervision, Writing – review & editing.

Funding

The author(s) declared that financial support was not received for this work and/or its publication.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that Generative AI was not used in the creation of this manuscript.

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