Smallholder farmers vulnerability to climate change in Tigray, Ethiopia

ABSTRACT

Smallholder farmers in the Tigray region are highly vulnerable to climate change-induced shocks due to their heavy reliance on rain-fed agriculture. This study aims to assess the multidimensional livelihood vulnerability of smallholder farmers in the Tigray region to climate change using the framework provided by the Intergovernmental Panel on Climate Change (IPCC). The study used a mixed methods research design composed of qualitative and quantitative research methods. Data were collected from 120 randomly selected households, Focus Group Discussions (FGDs) and Key Informant Interviews (KIIs). The findings reveal that more than 30% of the Atsbi Womberta district and 28% of the Tahtay Koraro district are classified as highly vulnerable to climate change. In Atsbi, the primary contributing factor to the High Vulnerability Index (HVI) is exposure, accounting for approximately 37% (mean value: 0.579 ± 0.111), followed by adaptive capacity at 33% (0.523 ± 0.104), and sensitivity at 30% (0.467 ± 0.160). Similarly, in Tahtay Koraro, exposure is the dominant contributing factor, comprising 38% (mean value: 0.618 ± 0.176), followed by low adaptive capacity at 32% (0.553 ± 0.113), and sensitivity at 30% (0.524 ± 0.167). Both districts indicate low adaptive capacity and high exposure to climate variabilities. To address the high vulnerability of smallholder farmers to climate change in the Tigray region, policymakers must prioritize disaster risk management strategies. These strategies should aim to reduce exposure to climate variabilities and strengthen the adaptive capacity of farming households.

KEYWORDS:

• Adaptive capacity
• climate change
• exposure
• sensitivity
• vulnerability indices

1. Introduction

Climate change is a global phenomenon that is affecting the world at an alarming rate, with impacts that are proving to be more significant than previously estimated. Developing countries, especially vulnerable populations, bear the brunt of this phenomenon (Thomas et al., 2019; Winsemius et al., 2018)., Roughly 3.3 billion individuals residing in these countries are highly susceptible to climate change impacts (IPCC, 2022). Poor agrarian communities face higher vulnerability due to limited adaptive capacity and restricted access to alternative modes of production (Eriksen et al., 2021; Huong et al., 2019). However, it is crucial to recognize that the impacts vary across regions, countries, sectors, and communities (Thao et al., 2019; Wu et al., 2016)

Ethiopia, being a developing nation, is facing an escalating vulnerability to climate-induced risks, such as increasing temperatures and unpredictable rainfall patterns (Demem, 2023; Dendir & Simane, 2019; Hilemelekot et al., 2021; Mekonnen et al., 2021; Sertse et al., 2021). Within Ethiopia, the Tigray region stands out due to its diverse topography, fragile ecosystems, and marginalized communities, leaving it particularly susceptible to the impacts of climate change (Sertse et al., 2021; Skjeflo, 2013; Tesfaye et al., 2022).

Smallholder farmers in Tigray, Ethiopia are highly vulnerable to climate change and variability (Demem, 2023; Gebru et al., 2020). The vulnerability of smallholder farmers is affected by factors like exposure to climate change, sensitivity to its impacts, and the ability to adapt (Belay et al., 2017; Ebrahim et al., 2022; Gebru et al., 2020; Tofu et al., 2022). These farmers heavily rely on rain-fed agriculture and natural resources, and their limited capacity to adapt makes them highly vulnerable to climate change (Gebru et al., 2020; Georgieva et al., 2022).

Studies indicate a continuous rise in mean atmospheric temperature and rainfall variability, posing a significant threat to the agricultural sector, particularly primary staple food crops (Asmamaw et al., 2020; Demem, 2023; DiFalco & Veronesi, 2014; Hilemelekot et al., 2021). Consequently, the food security and livelihoods of millions of rural households are at risk (Thornton et al., 2019). Additionally, non-climate stressors such as inadequate infrastructure, population growth, and armed conflicts amplify the region’s susceptibility to natural disasters and climate change impacts (Kidane et al., 2021; Tesfaye et al., 2022). Despite limited research on climate change vulnerability in Northern Ethiopia (Abeje et al., 2019; Addisu et al., 2019; Alemayehu & Bewket, 2016; Zeleke et al., 2021), there is a lack of information regarding the vulnerability of smallholder farmers and the variations in vulnerability across different communities within the study region.

Investigating the vulnerability of smallholder farmers to climate change impacts in Tigray is crucial for developing effective adaptation strategies and policies to support communities in the face of climate change and variability (Ebrahim et al., 2022; Shukla et al., 2021; Tofu et al., 2022; Yimam & Holvoet, 2023). Climate change vulnerability assessments provide valuable insights into the expected impacts of climate change, enabling decision-makers and programs to implement strategies that mitigate risks associated with climate change (Huong et al., 2018; Panthi et al., 2016). Moreover. Climate-related vulnerability and risk assessments play a pivotal role in the identification of adaptation options and measures (Estoque et al., 2023).

While different methods exist to measure vulnerability, many researchers adopt the IPCC’s working definition, which considers vulnerability as a function of exposure (E), sensitivity (S), and adaptive capacity (AC) (Estoque et al., 2023; Keshavarz et al., 2017). Previously, vulnerability assessment studies primarily focused on the physical impacts and negative consequences of disasters (Huong et al., 2018). However, it became evident that a comprehensive assessment should consider the interactions between humans and their physical, social, economic, and political environments (Hahn et al., 2009; IPCC, 2022). Therefore, this study was aimed to investigate the smallholder farmers vulnerability to climate change induced problems considering the f (E, S, AC).

2. Methodology

2.1. Study site

The study took place in the two districts of Tahtay_Koraro and Atsbi Womberta, located in the Tigray region of Ethiopia. Geographically, these districts can be found between 12° and 15° N latitude and 36° 30′–40° 30′ E longitude (Figure 1).

Figure 1. Map of study of sites in Tigray regional state.

Tahtay_Koraro district sits at an average altitude of around 1883 meters above sea level. It encompasses different agroecological zones, with midland/‘Weyna_dega’ covering 75% of the area, highland/‘Dega’ covering 2%, and lowland/‘Kolla’ covering 23%. In terms of land coverage, the district spans approximately 66,214 square kilometers, with 28% designated as farmland, 16% as rangeland, 20% as marginal land (settlement + bare land), 21% as exclosures, and 15% as natural forest. The average landholding size in Tahtay_Koraro ranges from 1 to 1.6 hectares. As of 2018, the district had a population of approximately 16,500, experiencing a population growth rate of around 3% from 2010 to 2012. The major crops cultivated in Tahtay_Koraro include Tef (44%), Maize (23%), Finger Millet (23%), and Sorghum (18%). The district receives an average annual total rainfall of 900 mm, with mean monthly temperatures ranging from 10.4 to 22.6 ºC. The agricultural season in Tahtay_Koraro revolves around the Kiremt rains, which span from mid-May to mid-September, with the primary rainfall occurring between mid-June and mid-September.

Atsbi Womberta is characterized by intense, short-duration rainfall. The district covers an area of approximately 146,096 hectares, with 9% designated as crop land, 3.3% as rangeland, and 61% as woodland. The estimated population of Atsbi Womberta is around 130,575, with 62,070 males and 68,505 females. This district is susceptible to drought conditions, with annual rainfall ranging from 500 mm to 624 mm. It experiences bimodal rainfall, known as Belg (short rains) from November to March and Kiremt (long rains) from June to September.

2.2. Sampling and data collection

Mixed methods research design composed of qualitative and quantitative research methods were used for the study. The qualitative research methods were used to collect suggestions, opinions, and perceptions of participants related to rural households’ livelihood vulnerability to climate variability and extreme and analyze. Quantitative research methods were used to collect and analyze socioeconomic data. Two specific communities (Tahtay_Koraro and Atsbi Womberta) from Tigray regional state, were purposively chosen from the Eastern and northwestern zones. The data collection process involved both primary and secondary data. Primary data was obtained through a combination of methods including household surveys, Focus Group Discussions (FGDs), Key Informant Interviews (KIIs), and transect walks.

For the household survey, a total of 120 randomly selected respondents were surveyed. Roughly 60 households were surveyed in each commune. The sample size was determined based on the sampling procedures outlined by Kothari (2004).

$\mathrm{n}=\frac{{\mathrm{z}}^2.\mathrm{N}.{\sigma }^2}{\left(\mathrm{N}-1\right){\mathit{e}}^2+{\mathrm{z}}^2.{\sigma }^2}$

Where, n = sample size, N = pop size, e = acceptable error (the precision), σ² = standard deviation of the population, z = standard variate at a given confidence level.

The sample size for the household survey was determined using a confidence interval of 95% (with z = 1.96), a margin of error (e) of 0.5, and a variance (σ²) of 3. This calculation yielded a required sample size of 138. However, during the analysis, 18 questionnaires were discarded due to incomplete recording of responses. Thus, the final sample size used for analysis was 120 households.

A total of 5 FGD discussions involving between 6 and 8 participants were organized in each district. Of this total, two were female and 3 were male FGDs. Participants of the FGD were selected from various social groups based on their social status, education, age and religious leader. Key informant interviews ranging from 7 to 10 per district were also conducted by involving development agents, elders, model farmers, bureau representative, experts and other individuals who have better knowledge on the present and past environmental, social and economic status of the study area.

2.3. Data analysis

2.3.1. Calculating the Livelihood Vulnerability Index (LVI)

The Livelihood Vulnerability Index (LVI) employed in this study comprised eight critical components: demographic profile, livelihood strategies, social networks and infrastructure, health, water resources, ecosystem, natural disasters, and climate variability (as shown in Appendix 1). These components were carefully selected based on their suitability for data collection through household surveys, ensuring the practicality and relevance of the LVI in assessing livelihood vulnerability. The detailed explanation provided in Appendix 1 clarifies how each sub-component was quantified, including the specific survey questions used and their sources.

Calculating LVI indicators involved a four-step process as outlined by Hahn et al. (2009). Firstly, the raw data was transformed into appropriate measurement units to ensure consistency and comparability. Subsequently, each sub-component was standardized using a specific equation (Equation 1), which allowed for meaningful comparisons and aggregation of the indicators. This standardization step is crucial in facilitating a comprehensive understanding of the overall livelihood vulnerability level.

(1) $\mathrm{Inde}{\mathrm{x}}_{\mathrm{s}}=\mathit{\hspace{0.17em}}\left(\mathrm{observed}\mathit{\hspace{0.17em}}-\mathrm{minimum}\right)/\mathit{\hspace{0.17em}}\left(\mathrm{maximum}\mathit{\hspace{0.17em}}-\mathit{\hspace{0.17em}}\mathrm{minimum}\right)$(1)

After each was standardized, the subcomponents were averaged using Equation (2) for calculating the standardized scores of each main component:

(2) $\mathrm{M}=\frac{{\sum }_{i=}^n\mathit{inde}{x}_{\mathit{si}}}{\mathit{n}}$(2)

where M is one of the major six components; index_si represents the subcomponents; indexed by i, that make up each major component; and n is the number of subcomponents in each major component. Finally, LVI score was generated by combining the weighted averages of all the major components (Equation (3). To ensure that all main components contribute equally to the overall LVI, the weights of each main component are determined by the number of subcomponents of which it is comprised (Sullivan, 2002).

(3) $\mathrm{LVI}=\frac{{\sum }_{i=1}^6{w}_{Mi{M}_i}}{{\sum }_{i=1}^6{w}_{\mathit{Mi}}}$(3)

where LVI is the vulnerability index for one of the communes, equals the weighted average of the 6 major components; wMi the weights of each major component, which are determined by the number of subcomponents that make up each major component. The range of LVI lies between from 0 (least vulnerable) to 0.5 (most vulnerable).

2.3.2. Calculating the LVI-IPCC: IPCC framework approach

The Equations (1)–(3) were used to calculate the LVI—IPCC. The major components are combined into the LVI—IPCC factors and calculated by following equation:

(4) $\mathrm{CFd}=\frac{{\sum }_{i=1}^n{w}_{\mathit{Mi}{\mathit{M}}_{\mathit{di}}}}{{\sum }_{i=1}^n{w}_{\mathit{Mi}}}$(4)

where CFd is an IPCC defined contributing factor (exposure, sensitivity, or adaptive capacity) for community d, Mdi are major components for community d indexed by i, wMi is the weight of each major component, and n is the number of major components in each contributing factor. One exposure, sensitivity, and adaptive capacity were calculated, the three contributing factors were combined using the formula developed by Hahn et al. (2009).

(5) $\mathrm{LVI}-\mathrm{IPCCd}=\left(\mathrm{exposure}\mathrm{index}\text{-}\mathrm{adaptive}\mathrm{capacity}\mathrm{index}\right)\text{break}\times ,\mathrm{sensitivity}\mathrm{index}$(5)

where LVI—IPCCd is the LVI for commune d expressed using the IPCC vulnerability framework. LVI—IPCCd was scaled from −1 (denoting least vulnerable) to 1 (denoting most vulnerable.

3. Results and discussion

3.1. Exposure

The examination of exposure disclosed that both communities were significantly susceptible to the impact of climate change and variations, with average values of 0.595 ± 0.144 (mean ± standard deviation) as displayed in Table 1. The difference in average values between the two communities did not show any significant statistical difference (p < 0.05) (Table 1).

Table 1. Mean LVI—IPCC contributing factors calculation for Tahtay Koraro and atsbi wombert districts, Tigray

Vulnerability class	Tahtay Koraro (n = 60)	Atsbi Wombert (n = 60)	Total (n = 120)	P_Value
Exposure	0.618 ± 0.176^a	0.579 ± 0.111^a	0.595 ± 0.144	0.131
Adaptive capacity	0.553 ± 0.113^a	0.523 ± 0.104^a	0.538 ± 0.109	0.064
Sensitivity	0.524 ± 0.167^a	0.467 ± 0.160^a	0.496 ± 0.166	0.064
LVI—IPCC	0.045 ± 0.108^6	0.030 ± 0.075^a	0.03 ± 0.112	0.386

Different letters indicate significant differences between the two communities and similar letters show no significant at p < 0.05.

The Atsbi Womberta community in Ethiopia faces significant challenges due to climate change. The primary concern reported by 19% of households is the frequent occurrence of drought, which poses a threat to both livestock and crop production. This is closely followed by 18.8% of households citing unpredictable rainfall and 18.6% noting delayed rainfall. These observations align with previous studies that highlight the impact of long-term climate changes on precipitation patterns, rainfall fluctuations, and temperature (Asfaw et al., 2018; Asmamaw et al., 2020; Berhe et al., 2023; Yisehak et al., 2021). As a result, there has been an escalation in the frequency of droughts in Tigray regional state. Key informant interviews and focus group discussions confirm a decrease in the frequency and intensity of rainfall, coupled with an increase in temperature.

The community farmers observed a shift in the traditional farmers Kiremt season, which used to start in May but now begins in June or July and has become unpredictable. This shift in rainfall season has also been scientifically demonstrated at the Atsbi station and reported in the highlands of Ethiopia (Berhe et al., 2023; Gashaw et al., 2023; Tarkegn & Jury, 2020). It has resulted in the loss of specific local crop varieties, including ri’e, Atena barley, and wheat. Other studies in Ethiopian highlands have also reported this shift in crop varieties (Alemayehu & Bewket, 2016; Gebresamuel et al., 2022). Moreover, it has been noted in studies that highland crops like wheat and barley are particularly susceptible climate change effects, with even minor alterations in climate having a significant impact on their yield (Chen et al., 2016; Gebresamuel et al., 2022). Similar findings also indicate exposure to changing trends in temperature and rainfall over the past few decades in Ethiopia, which are pivotal for crop production (Berhe et al., 2023; Dendir & Simane, 2019).

In the community of Tahtay Koraro, various climate-related exposures have been identified. These include delays in rainfall (14.2%), erratic rainfall (14%), drought affecting livestock and crops (13%), and strong winds (11.2%). A significant number of small-scale farming households in this district have reported crop failure due to the delayed onset of rainfall. Previously, farmers were able to cultivate Wanzie and white Sorghum, but due to the shift in the start of the rainy season from May to June/July, they are now unable to cultivate these crops. Similar irregularities in rainfall timing have been reported in other parts of the country and in India as well (Asfaw et al., 2018; Gebresamuel et al., 2022; Sathyan et al., 2018). It should be noted that not only the delay in rainfall but also the variation in its amount and intensity poses a significant threat to crop production. In Tigray, approximately 80% and 69% of respondents reported a reduction in the amount and intensity of rainfall, respectively. Focus group discussions also highlighted that crop production is impacted not only by rainfall but also by increases in temperature, higher wind speeds, and hailstorms. These findings align with previous research indicating that the impacts of climate change are more pronounced in the northern, northeastern, and eastern lowlands of the country (Aragie, 2013; Berhe et al., 2023; Gashaw et al., 2023). Furthermore, scholars argue that persistent environmental challenges, such as drought, heatwaves, flooding, erosion, coupled with socioeconomic shocks, including loss of social networks, unemployment, and market failures, can worsen the vulnerability of livelihoods to climate-related disasters (Buhaug & Uexkull, 2021; Estoque et al., 2023; Gerlitz et al., 2017).

3.2. Adaptive capacity

The research findings indicate that the mean adaptive capacity values of the community varied from 0.553 ± 0.113 (Tahtay Kraro) to 0.523 ± 0.104 (Atsbi Womberta), but this difference is not statistically significant (p < 0.05) (Table 1). When examining Atsbi Womberta specifically, the domains that contribute the most to its adaptive capacity, listed in descending order, are social network and infrastructure, water resource availability, demographic profile, and livelihood strategy (Figure 2).

Figure 2. Subcomponents of adaptive capacity in Atsbi Womberta.

Livelihood is the major concern in the community, as income from crop production, land holding size, and livestock production contribute less than 4%. This poses a serious threat to the community’s survival. Crop production was identified as the most vulnerable aspect by the majority of respondents in the district, with 74.6% expressing concern about high temperatures and 91.7% expressing concern about rainfall variability These findings are supported by similar studies that highlight a strong association between low income from crop production and factors such as drought exposure, delayed rainfall, and small land holdings (Berhe et al., 2023; Ruwanza et al., 2022; Singh et al., 2020; Tofu et al., 2022). In addition, it was found that the average landholding size for respondents is 0.35 ± 0.25 ha, with some respondents owning no land and the majority of the younger generation being landless. Consequently, the survey revealed that 100% of the respondents reported the migration of youth from their community to support their families.

The survey revealed a significant finding regarding household reliance on crop production as their main source of income. More than 86% of households in the community depend on crop production, which is now at risk. Previously, the community benefitted from two cropping seasons, but now it has become increasingly difficult to have even one successful cropping season. This observation aligns with previous studies that have also reported a notable decline in the Belg season (Asfaw et al., 2018; Berhe et al., 2023). Frost has also negatively affected crop production, particularly peas in Atsbi Wonberta. Additionally, frequent drought has resulted in the loss of livestock, exceeding the current Total Livestock Unit (TLU) by 40%. Consequently, farmers have been forced to sell their livestock at low prices.

In Atsibi Womberta and Tahtay Koraro, a significant proportion of the population is illiterate, with 54% and 42% respectively unable to read and write. This lack of literacy makes them more vulnerable to climate change-induced shocks as they struggle to understand extension services and apply alternative options during such events (Asmamaw et al., 2020; Dendir & Simane, 2019). In Atsbi Wonberta, the educational level of the household head also affects adaptive capacity, with the majority being illiterate (more than 54%). Water resources also play a role in adaptive capacity, as many households still rely on natural springs for drinking water, which are also used by livestock. Additionally, less than 6% of the population has access to irrigated water. The existing water systems have technical problems that the poor community cannot afford to fix. This aligns with research suggesting that water vulnerability in rural areas is primarily impacted when the agricultural sector heavily relies on water sources and the infrastructure is inadequate.

In Tahtay Koraro, attention should be given to the livelihood strategy and demographic characteristics of the community (Figure 3).

Figure 3. Subcomponents of adaptive capacity in Tahtay Koraro.

The adaptive capacity of the communities in the district varies from kebele to kebele. Based on our observations and focused group discussions, Selam kebele has lower adaptive capacity compared to Beles kebele, mainly due to lower productivity and soil depth, which limits their ability to cope with and adapt to climate change challenges. On the other hand, Beles kebele shows higher adaptive capacity due to better agricultural productivity and soil conditions. Despite this, crop and livestock production only contribute less than 4% to the woreda’s economy, highlighting the need to improve income from these sectors in the community. Therefore, efforts should be focused on improving income from crop and livestock production in the community. Over the past 10 years, the community has lost almost half of its current Total Livestock Unit (TLU) due to climate change. The impact of climate change on livestock loss is also reported in other studies (Ali et al., 2020; Gomez-Zavaglia et al., 2020; Mihiretu et al., 2021). While the overall infrastructure in the district is satisfactory, market access in Selam kebele requires attention as residents have to travel a two-hour journey on foot to reach the nearest town. Market chain issues further affect the livelihoods of small groups of households producing vegetables, leading to low selling prices for their produce. Improved market access is crucial as it enables communities to diversify their livelihood strategies, exchange market information, and better mitigate and adapt to climate change-induced shocks (Dedehouanou & McPeak, 2020; Destaw & Fenta, 2021; Gupta et al., 2020; Tesso et al., 2012). Other studies have shown that inadequate physical structures significantly affect access to basic services like credit facilities and healthcare, contributing to socioeconomic marginalization (Dong et al., 2020; Gerlitz et al., 2017; Shah et al., 2020).

3.3. Sensitivity

The sensitivity analysis revealed that Tahtay Koraro displayed marginally higher values (0.524 ± 0.167) compared to Atsbi Womberta (0.467 ± 0.160), without reaching statistical significance at a significance level of p < 0.05. Among the factors studied in Tahtay Koraro, the ones contributing the most to sensitivity to climate change and variability were the reliance on rainfed crop cultivation (0.864), followed by the dependence on inferior livestock breeds (0.757) and insufficient crop varieties (0.707). In Atsbi Womberta, the community exhibited high sensitivity to climate change, predominantly driven by the reliance on rainfed crop cultivation (0.930), inadequate crop varieties (0.725), and suboptimal farming landscape quality. Previous studies have also emphasized the impact of climate change on rainfed agriculture, as well as the significance of poor crop and livestock varieties (Asfaw et al., 2018; Dumenu & Obeng, 2016; Sathyan et al., 2018; Tofu et al., 2022). The sensitivity assessment indicates that the living standard of the community remains relatively low, highlighting the need for increased government support and intervention to uplift their conditions. The sensitivity assessment aligns with similar studies conducted both within the country and abroad (Asfaw et al., 2021; Eshetu, 2014; Sujakhu et al., 2018; Tesso et al., 2012; Thao et al., 2019)

3.4. Livelihood Vulnerability Index (LVI)-IPCC

Our study, guided by the IPCC framework, revealed that the LVI (Livelihood Vulnerability Index) of the communities varied between 0.021 and 0.34, with an average value of 0.037 ± 0.094. Significantly, at a p-value of less than 0.05, there was no discernible difference in the LVI-IPCC between the two communities. Consequently, the findings highlight that a significant proportion of households in both communities are susceptible to the detrimental effects of climate change and variability (Figure 4). Furthermore, our findings are substantiated by additional studies conducted within our country, as well as in other countries across Sub-Saharan Africa (Bedeke, 2022; Connolly-Boutin & Smit, 2015; Gebrehiwot & van der Veen, 2013; Herslund et al., 2016).

Figure 4. The total mean LVI—IPCC by households.

In Atsbi Womberta, over 30% of households were marked as highly vulnerable, whereas approximately 30% of households in Tahtay Korar were classified as less vulnerable. Likewise, in Tahtay Koraro, 28% of households were found to be highly vulnerable, while 25% of households in Atsibi Womberta were categorized as less vulnerable (Figure 5).

Figure 5. LVI_IPCC by households of the two community.

Vulnerability is influenced by exposure and sensitivity, while adaptive capacity plays a role in reducing vulnerability (Asmamaw et al., 2020; Dendir & Simane, 2019; Huong et al., 2018). The average effect of these three dimensions contributed to the household vulnerability indices (LVI-IPCC) (Figure 6).

Figure 6. Vulnerability triangle diagram of the contributing factors of LVI_IPCC.

This research study discovered that exposure accounted for more than 37% of the household vulnerability index in both communities, closely followed by adaptive capacity and sensitivity, as depicted in Figure 6. These findings align with previous studies emphasizing the susceptibility of Ethiopia to climate-induced weather extremities and its limited ability to cope with climate change e (Conway & Schipper, 2011; Stige et al., 2006; Tofu et al., 2022).

Through community discussions and interviews, it became evident that the study area faces significant exposure to recurring droughts, floods, heatwaves, erosion, and land degradation. Deforestation, hillside cultivation, agricultural expansion, overgrazing, and unsustainable forest practices were identified as contributing factors to these challenges. Previous research also highlights floods, hailstorms, droughts, unpredictable rainfall, locust infestations, armyworm outbreaks, and landslides as climate-related shocks impacting smallholder agricultural production in Ethiopia (Deressa et al., 2009; Tofu et al., 2022).

4. Conclusion and recommendations

This study reveals that smallholder farmers in the Atsbi Womberta and Tahtay Koraro communities, Tigray regional state, Ethiopia, are highly vulnerable to the impacts of climate change. Exposure to recurrent droughts, floods, heatwaves, erosion, and land degradation, coupled with inadequate adaptive capacity and high sensitivity, puts the livelihoods of these farmers at risk. The study shows that exposure is the largest contributor to household vulnerability indices, followed by adaptive capacity and sensitivity. While this study has limitations in the subjective selection of sub-components, future research conducted at different times could provide valuable insights into how the adaptive capacity, exposure, and sensitivity of smallholder farmers in these districts change as adaptation practices are implemented. Conducting similar studies in the same location over time would enhance understanding and inform effective strategies to improve the resilience of smallholder farmers in Tigray regional state, Ethiopia.

To improve the resilience of smallholder farmers, comprehensive measures are recommended. Government support and intervention must be enhanced through the provision of climate change adaptation measures, such as extension services, drought-resistant seeds, and irrigation facilities. Promoting alternative livelihood strategies and improving infrastructure, including market access and basic services, will further enhance their adaptive capacity. Education and awareness programs should be implemented to empower farmers with knowledge on climate change impacts and adaptation strategies. Lastly, conducting thorough research and monitoring will provide valuable insights for targeted interventions and policies.

Author contributions

AM: Conceived the study; collected data; performed the analysis and writing of the manuscript.

Availability of data and materials

All data generated or analyzed during this study can be obtained from the corresponding author.

Acknowledgements

I acknowledge the financial support provided by Environment, Forest and Climate Change Commission (EFCCC) for field survey. I also acknowledge to Mr. Berihu Tesfamariam and Mr. Tamrat Lolaso who participated during the data collection.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/27658511.2024.2345452

Appendix

Appendix:

Indexed major Components and sub-components for LVI-IPCC selected districts

Table

Major components	Subcomponents	Community
		Tahtay Koraro	Atsbi Womberta
Demographic profile	Dependency ratio	0.136	0.151
	Age of household head	0.588	0.545
	Sex household head	0.567	0.517
	Educational level HH	0.250	0.167
	Family size	0.751	0.713
Livelihood strategies	Farming experience	0.393	0.406
	Land size	0.010	0.004
	Land certification status	0.057	0.053
	Practice of livestock rearing	0.833	0.817
	Practice of crop production	0.932	0.950
	Livestock size	0.067	0.071
	Crop income	0.049	0.021
	Access to safety net program	0.644	0.492
Infrastructure and social networks	Access to market information	0.433	0.517
	Access to credit and saving service	0.967	0.883
	Women’s participation in decision making process	0.850	0.864
	Access to school	1.000	0.983
Water resources	Access to clean drinking	0.831	0.667
	Availability of irrigation water	0.300	0.339
Degree of sensitivity	Dependence on rain fed crop cultivation	0.864	0.930
	Dependence on poor livestock breads	0.757	0.597
	Dependence on forest products	0.509	0.506
	Dependence on poor variety crops	0.707	0.725
	Farming landscape quality	0.660	0.667
	Settlement area sensitive to flood	0.339	0.268
	Farmland area sensitive to flood	0.429	0.305
	Farmland area sensitive to landslide	0.217	0.128
	Settlement area sensitive to landslide	0.196	0.116
Level of exposure	Drought on crops and livestock productivity	0.763	0.902
	Damage by erosion and flooding	0.616	0.454
	Strong wind on crops	0.661	0.610
	Hailstorm on crops	0.515	0.644
	Extreme heat on livestock health	0.497	0.170
	Delay on rainfall onset	0.836	0.856
	Erratic nature of rainfall	0.825	0.864