1 Introduction

One of the most serious threats brought by climate change is drought, which claims the lives of numerous human beings and livestock assets every year [1,2,3]. Lack of precipitation, decreased soil moisture and increased temperature are the main causes of drought. For instance, due to climate sensitivity, droughts contributed to a global reduction in agricultural production and productivity between 2001 and 2010 [1]. However, the response has been poorly managed and coordinated for formulating and implementing riskbased drought response policies [3]. As a result, the impacts of drought in Africa are getting worse because most farmers rely on rainfed agriculture and limited coping strategies. In this study, the term “drought coping strategies” refers to the short–and medium–term reactions to the negative effects of drought. During the major farming season, the Sub–Saharan African countries have been experiencing 10–40% failed rain seasons [4,5,6]. Even though, agriculture has been the most affected sector in the Sub–Saharan African countries, many other sectors (e.g., manufacturing industries) have also been experienced substantial decreases in their production due to lack of enough electricity. Drought has severely affected the economic activities of the countries. For example, the regions of Eastern and Southern Africa countries are primarily characterized by semi–arid and sub–humid climates with an extreme dry season [4]. The prolonged dry season has resulted in widespread crop failure, but also on livestock between 2013 and 2015 across Namibia, Somalia, and Kenya [3, 5]. This has significantly affected the livelihood of smallholder farmers. The drought in Uganda has also been a serious issue for about 6.3% of rural households, leading to food insecurity [7].

In the past three decades, Ethiopia has faced drought brought on by La Niña, which has affected the livelihood of more than 8 million people, killed 1.5 million animals, forced 500,000 children out of school, and put 10 million cattle at risk of death. This tragedy has also affected the country's economic and social growth. Approximately 98% of farmland relies on rainfall, which has an immense effect on agriculture and households’ food security [8]. During the 1984–1985 droughts, which are recorded as one of the most devastating drought periods in Ethiopia, the national GDP was dropped by 9.7%, while agricultural output was further declined by 21% [9]. The Southern zone of Tigray in northern Ethiopia has been one of the most vulnerable regions to drought. Gidey et al. [10] reported that the arid and semi–arid area of the region has been severely affected by the recurrent droughts due to late rainfall onset, early cessations, erratic and high dry spells. The major crops such as teff, barley, maize, wheat, sorghum and others yield have also truncated in the area due to drought. During the drought event, there will be a possibility of 21–100% losses of crop yield, but also livestock production and their products due to diseases and lack of fodder, and water access in the area. Studies indicated that the production of milk decreased during the drought event putting an end to the market for livestock products. The loss of pasture, depletion of water resources, loss of vegetation, and other important resources are good examples of drought impacts [11]. The majority of the study area was considerably impacted by drought between 2011 and 2015 [12].

The smallholder farmers of the study have adopted various drought adaptation and coping strategies. For instance, reducing food sales, reducing food intake, removing children from school and liquidating productive assets such as livestock, land, and trees are among others [4]. There is no single solution to drought as it takes an integrated strategy composed of several practices for exchanging knowledge, designing response strategies and implementing solutions [13]. Besides, there is limited study on drought coping strategies [14, 15]. The coping strategies vary from place to place based on the community's exposure to drought risk and socioeconomic context. Thus, it is incredibly important to analyze how smallholder farmers have responded to drought and its implications. This paper aimed to analyze the proactive and reactive farmers' drought adaptation and coping strategy at household level in the drylands of southern Tigray, Ethiopia. The findings of this study help to enhance the current drought response and coping strategies.

2 Methods

2.1 Study area

The study area is located in southern Tigray, Ethiopia. It includes Raya Chercher and Raya Azebo districts. It is found at 39°20′0'' and 40°0′0'' longitude east and 12°20′0'' and 12°50′0'' latitude north (Fig. 1). The total land mass of the area is 1777 km2, where 58% is in Raya Azebo district, and 42% is in Raya Chercher district. The area receives an annual average rainfall of 558 mm [1]. The study area experienced below–average seasonal rainfall, which can exacerbate drought because it is highly characterized by arid and semiarid climatic conditions [12]. The mean maximum and minimum temperatures were about 30.5 and 15.9 °C, respectively [1]. The altitude was between 913 to 3110 m above sea level. The primary means of subsistence and source of income in the study area were crop farming and livestock raising. The three main land cover types of the study area are cultivated land (1061 Km2), shrub/bush land (519 Km2), and barren lands or exposed rock surface (197 Km2), which account for 59.7, 29.2 and 11.1%, respectively. About 192,287 people living in the area. The estimated male population is 93,948 (48.9%), compared to the female population of 98,338 (51.1%). The average family size of the study area is 6.2.

Fig. 1
figure 1

Location of the study area

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2.2 Sampling procedure

Based on the Ethiopian Ministry of Agriculture and Rural Development agro–climatic zonation, the study area was stratified into Kolla or lowland (500–1500 m.a.s.l), Weyna–Dega or midland (1500–2300 m.a.s.l) and Dega or highland (2300–3200 m.a.s.l) (Table 1). The lowland areas and parts of the midlands and highlands experienced an increase in the frequency and severity of droughts [12]. A cluster sampling technique was employed to figure out the number of households living in each homogenous agro–climatology zones, and then the sample size was estimated based on [16] as follows (Eq. 1):

$$ n = \frac{{z^{2} \left( p \right)\left( q \right)}}{{\left( {d^{2} } \right)}} $$
(1)

where n = desired sample size, z = confidence level, 95per cent, p = proportion of the households to be included in the sample (20per cent), q = 1–0.2, i.e., 0.80, d = acceptable error 5 percent.

Table 1 Sample size determination and its allocation
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20% of households were sampled using the equation. The total number of households residing in a similar agro–climatic zone was then proportionally sampled using a systematic random sampling procedure. And then, the desired sample size (i.e., 246) was determined (Table 1).

2.3 Data collection

The socioeconomic data were collected from 246 households. Each household took an average of forty–five minutes to respond to the semistructured questions. A total of eleven focus group discussions consisting of eight team members, both female and male–headed households, were formulated to discuss how they are responding to drought and the coping strategies. We also conducted field observations to comprehend more about the biophysical and socioeconomic conditions of the study area. Moreover, earth observations (elevation, land cover, vegetation) at moderate spatial resolution have been obtained from Water and Land Resources Information System (WALRIS) archive at www.wlrc-eth.org, the National Geospatial Database System, Ethiopia.

2.4 Data analysis

The socioeconomic datasets were analyzed using Stata SE version 13. The most effective proactive and reactive drought coping strategies were assessed using a multinomial (polytomous) logit model (MNL). The MNL model is the best model to predict discrete choices because of the computational drawbacks of the multinomial probit (MNP) model [17, 18]. Several researchers have used this model to determine the optimal coping strategy or options for climate change adaptation [15]. We used the multinomial logit (MNL) model to analyze the various drought–coping techniques that impact smallholder farmers. To describe the MNL model, assume Y is the random response variable taking on the values {1, 2…, j} for choices j, a positive integer, and let X denote a set of explanatory variables. In this case, Y is the coping strategies measure the household heads chose. Here, we assumed that each farmer household selected a set of categorical discrete, disjoint choices of drought coping strategies and these strategies are assumed to depend on one or more factors of X (Gender, Age, Family size, farm size). The question shows how other variables changes in the factors of X that affects the response probabilities,\(P\left(Y=j|X=x\right)\), for j = 1, …., J. Since the probabilities must sum to unity,\(P\left(Y=j|X=x\right)\), is figured out once we know the probabilities for j = 1, 2…., J. Let X is a 1xK vector with first element unity, the MNL model has response probabilities given by (Eq. 2):

$$P\left( Y=j\text{ }\!\!|\!\!\text{ }X=x \right)=\frac{{{e}^{x{{\beta }_{j}}}}}{1+\mathop{\sum }_{k=1}^{J}{{e}^{x{{\beta }_{k}}}}},\quad \quad \quad j=1,...,J$$
(2)

where \({\beta }_{j}\) is K × 1 vector of coefficients, j = 1, ……, J

The response variables Y's for the on–farm drought coping strategies are: (I) Selection of drought tolerant crops, (II) Irrigation Construction of a flood, (III) Land preparation and use of compost, (IV) Soil and Water Conservation and (V) Use of weather predictions information. In addition, the response variables for the off–farm drought coping strategies are: (I) Looking for government support, (II) Migration of both animals and humans, (III) Borrow loans from microfinance, (IV) Selling of household assets (V) Daily Laborer (VI) Reduction of food consumption and (VII) feeding of roasted cactuses. The unbiased and consistent parameter estimate of the model Eq. (2) requires the assumptions of Independence of Irrelevant Alternatives (IIA) to hold. The IIA assumption requires that the probability of using a certain coping strategy (Collection and saving of pastures) selected by a given household head needs to be independent of the probability of choosing another coping strategy (i.e., \({\raise0.7ex\hbox{${{\text{P}}_{{\text{j}}} }$} \!\mathord{\left/ {\vphantom {{{\text{P}}_{{\text{j}}} } {{\text{P}}_{{\text{k}}} }}}\right.\kern-0pt} \!\lower0.7ex\hbox{${{\text{P}}_{{\text{k}}} }$}}\) The parameter estimates of the MNL model provide only the direction of the effect of the explanatory factor affects variables on the response variable, but estimates do not stand for either the actual magnitude of change or probabilities (Greene, 2003). The marginal effect has been computed to interpret the explanatory variables' effects on the probabilities. Is independent of the remaining probabilities). Differentiating equation–2 partially concerning the explanatory variables supplies marginal effects of the explanatory variables as shown below (Eq. 3).

$$ \frac{{\partial p_{j} }}{{\partial x_{k} }} = P_{j} \left[ {\beta_{jk} - \mathop \sum \limits_{j = 1}^{j - 1} P_{j} \beta_{jk} } \right] $$
(3)

The marginal effects or marginal probabilities are functions of the probability itself. They measure the expected change in probability of a particular choice being made concerning a unit change in an independent variable from their average, e.g., proactive and reactive coping strategies or on–off response. The explanatory variables of the study were households' socioeconomic, environmental, climatic and others, as hypothesized to have associations with the households' farmers coping strategies. Based on theory, empirical literature, and the researcher's best knowledge of the contextual setting, nineteen explanatory variables (Table 2) were used to determine the best coping strategies for drought for on–and off–response farm activities. Following the model, Variance Inflation Factor (VIF) and Contingency Coefficient (CC) were employed to detect multicollinearity for continuous and discrete variables. Breusch–Pagan test was conducted to assess the presence of heteroscedasticity or constancy of variance in the model. Finally, the model was evaluated for the validity of the Independence of Irrelevant Alternatives (IIA) assumptions by using Hausman's test. This test did not reject the null hypothesis of independence of the drought coping strategies, suggesting that the multinomial logit (MNL) specification is proper to model drought coping strategies of smallholder farmers in the study area. MNL model specification was used by several researchers to model drought coping strategies of smallholder farmers in Africa [19, 20]. The response variables in the empirical estimation were coping strategies that were chosen by the household farmers during the field survey (Table 2). In this case, various best coping strategies were selected to examine farmers' response to drought incidence, coping and impacts on natural resources (vegetation cover status), agricultural productions (crop and livestock).

Table 2 Selected variables used for drought coping strategies and impact assessment
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3 Results and discussion

3.1 Households characteristics

The household head's age is a crucial factor affecting drought coping strategies. The age of the household head is a proxy for farming experience, on the assumption that the household's knowledge of drought coping strategies and food security issues increases as the household head grows older and becomes more experienced [7]. The average age of our respondents was 47 years old. Our respondents' minimum and maximum ages ranged from 31 to 77 years old. About 36% of the male–headed household respondents were in the middle–aged adult category, while 9% were old adults of female–headed households. The remaining 6% of the respondents were elderly.

3.2 Educational status at the household level

Literacy is the principal factor or instrument for understanding facts (e.g., about drought) and changing our lifestyle and/or strengthening our resilience, coping and mitigation to various climate hazards. It is explained in terms of contribution to working efficiency, competency, income, adopting technologies and becoming visionary in creating a conducive environment to educate dependents with long–term targets to ensure better living conditions than illiterate ones [7]. Studies showed that the level of literacy could influence the selection of drought coping strategies due to the behaviors of respondents. About 61.38% of the respondents in the study area are illiterate, while 38.62% are literate. An illiterate male–headed household respondent, which covers about 68.8%, is higher than female–headed illiterate (31.4%) households (Table 3). Also, literate male–headed households cover only 88.4%, while the remaining 11.6% are female–headed households. Therefore, literate households can help generate innovative ideas or methods for understanding drought causes and improving existing coping strategies. Besides, literate households can reduce the chance of becoming food insecure [7].

Table 3 Literacy status of respondents at the household level
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3.2.1 Farm size at the household level

Male–headed households owned a maximum of 3.5 ha, while females owned 2.5 ha. Farm size is an important proxy for farmers' economic status and coping strategies [21]. The mean farm size owned by male–headed households (0.93 ± 0.74 ha) was higher than females (0.54 ± 0.75 ha) (Table 4). For example, about 2.03 percent of females and 6.5 percent of male–headed households in the study area were landless farmers. Farmers were renting plots for cultivation from other farmers who had serious health problems and economic challenges such as access to get or own oxen or labor to cultivate the land on time. Farmers with their farms are more likely to invest in coping strategies than landless farmers [21]. Studies show that if the cultivated land size increases the possibility that the household gets more yield is also high during the wet season ([7]. In this case, the household may have a better drought resilience mechanism. However, those households who live in marginal areas and whose livelihoods are highly dependent on natural resources are more susceptible to drought because they have limited drought coping ability [22].

Table 4 Comparison of farm size at the household level
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Furthermore, we compared the average farm size of male and female–headed households to assess the difference. Farm size significantly differed between the male and female–headed households in the study area (p < 0.001). One of the possible reasons for the difference is the demographic composition of the respondents.

3.3 Drought status at the household level

Understanding drought status at an early stage and household level is imperative for formulating effective mitigation measures [2] and coping strategies. For instance, between 2001 and 2015, the lowlands of the study area experienced drought about 10 to 11 times, the midlands 2 to 6 times, and the highlands 2 times [12]. Because of this, droughts occurred repeatedly in all agro–climatic zones of the study area. Therefore, appropriate drought mitigation measures help to reduce the adverse effects, especially for smallholder farmers who grow crops under unfavorable conditions, such as low and erratic precipitation and poor soils. About 24.8% of female–headed and 75.2% of male–headed respondents in the study area had experienced mild to extremely severe drought in the last three decades (Table 5). The severity may vary depending on the duration and recurrence of drought incidence. The association between the various drought severity levels in both household heads shows a significant association between the different drought severity levels and household heads (chi–square = 9.861, df = 3, p–value < 0.05). Tembo et al. [23] reported that about 92% of the farmers in the southeast of Tigray perceived that rainfall is significantly decreasing annually in the last three decades. This aggravates the incidence of drought and its impacts on natural resources, human and livestock.

Table 5 Drought severity level at the household level among male and female–headed households
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3.4 Smallholder farmers' drought coping strategies

Since 1983/85, the recurrent drought has regularly affected southern Tigray, particularly the natural resources (e.g., vegetation cover), and agricultural production and productivity (e.g., maize, teff) have declined in Raya Azebo and Raya Chercher districts. The major triggering factors were the lack of rainfall during the belg or short rainy season, which usually lasts from March–June and summer or the main rainy season (July–early September). This affects the area's education, health, and livelihood systems, leading to food insecurity and poverty. Besides, the persistent dryness may be brought on by the rise in Land Surface Temperature (LST) and poor vegetation cover (NDVI), which both significantly stress moisture [12]. The agricultural industry has been greatly impacted by rising temperatures and dry summers (e.g., seasonal deterioration in the quality of dryland fodder) [13]. The change in rainfall patterns and wind erosion, increased salinization, and decreased carbon mineralization as a result it aggravates the incidence of drought impacts [24]. Drought was one of the biggest obstacles to ensuring food security in the research area. Several attempts have been made to set up coping strategies to overcome drought stress in the study area. The proactive and reactive approaches were essential components of coping strategies [13]. The smallholder farmers have found multiple strategies to cope–up with the impact of drought. The selected strategies depend on the household heads farm size, family size, education, age, awareness, and severity of drought. The gender of respondents can influence the decision to select the best drought coping strategies because they have distinct roles [21, 25].

Table 6 shows the best on–farm drought coping strategies estimated using the MNL model that the smallholder farmers of the study area often used to cope with drought. Results of the multinomial logit (MNL) model show that the age of the household head, which represents experience, affects coping with drought positively and significantly in strategies number II and V (p–value < 5%). The relative risk ratio, which is the exponent of the coefficient of the estimate (\({\mathrm{e}}^{\upbeta }\)) shows that a change in the age of the household head results in a 3.5 and a 2.4 higher probability of choosing strategy number II and V compared to the base choice strategy (i.e., land preparation and use of compost (manure). The marginal effect of the age estimate shows that the probability of choosing strategy numbers II and V increased by 0.04% and decreased by 0.17%, respectively, compared to choosing the base choice strategy. Likewise, household size usually represents the size of the family living within the family, affecting coping with drought positively and significantly, both strategies numbers IV and V at p–value < 10%. The relative risk estimate of the family size estimate shows that a unit change in the family size of the household results in a 4 and 6.4 higher likelihood of choosing strategy number IV and V, respectively, compared to the choice of the base outcome. The average marginal effect of the family size estimate also shows that the likelihood of choosing strategy numbers IV and V decreased by 0.06% and increased by 0.04%, respectively, compared to the base outcome. Besides, farm size of the household, which is the share of cultivated land measured, affects coping with drought positively and significantly affects strategy number II (p–value < 10%). The relative risk estimate shows that a unit change in farm size of the household in choosing coping strategy II produces the same effect on coping to drought as compared to the base outcome. The average marginal effect of the estimate of the farm size shows that the probability of choosing strategy number II decreased by 6.63% as compared to the base outcome. Furthermore, soil and water conservation which represents the involvement of the farmers in preserving the cultivated land of the area under study affecting the coping to drought positively and significantly the choice of strategy II (p–value < 1%). The average marginal effect of the estimate shows that the probability of choosing strategy number II increases by 5.4% as compared to the base outcome. Moreover, the effects of drought on production, measured both in terms of complete loss, partial loss and pest and disease, the result shows that complete loss found to have significant effect in choosing the coping strategy number II (p–value < 5%). The marginal effect of this estimate also shows that the probability of choosing strategy number II increases by 18.1% as compared to the base outcome. Access to correct weather information also reduces the impacts of drought on both humans and livestock as ranchers can prepare for potential emergencies and serve as a valuable source of knowledge for researchers, land managers and policymakers. This may help to improve the existing response ability to drought in the form of preparedness strategies when drought strikes [5]. However, the farmers of the study area are not getting prompt information about the future drought due to lack of scientific drought monitoring and early warning systems. Now–a–days, high–quality information sources (e.g., weather forecast information) and peer–to–peer knowledge sharing on incoming drought risk improves the ability to respond to drought [3, 13]. Therefore, the community of the study area should get prompt information to strengthen their coping strategies and reduce the impact felt from drought.

Table 6 On–farm drought coping strategies in the study area
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In addition, Table 7 shows the MNL model estimates for the reactive drought coping strategy activities. The result reveals that male–headed household positively and significantly affects the choice of reactive drought coping for strategy number V (p–value < 10%). The relative risk ratio of the estimate shows that a male–headed household is 3.5 times higher in favoring the choice for strategy number V than the base choice strategy (i.e., migration to adjacent and remote areas). The marginal effect estimates of the male–headed households show that the probability of choosing strategy V increases by 0.77% compared to choosing the base outcome strategy. Besides, age of the household head, affecting the reactive drought coping strategy number I (p–value < 5%) and its relative risk estimate shows that the odds of choosing this strategy number I is same as compared to the base strategy. The marginal effect of the estimate of age shows that the likelihood of choosing strategy number I increased by 0.7% as compared to choosing base outcome strategy. Moreover, the family size of the household affecting the reactive drought strategy number III, positively and significantly (p–value < 10%) and strategy number V (p–value < 5%). The relative risk of the estimate shows that a unit change in size of the family has a 1.3 times higher effects in choosing strategy number III and 0.63 times less effect as compared to the base outcome. The marginal effect of the estimate for the family size tells us that the probability of choosing strategy number III is less likely to be selected as compared to the base outcome. This fact is true for all factors that have a significant influence on strategy number III.

Table 7 Off–farm drought coping strategies of the study area
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On the other hand, farm size affects the choice of strategy number I affect, coping with drought positively and significantly (p–value < 10%) and strategy number IV (p–value < 1%). The relative risk estimate of the farm size shows that a unit change in farm size is 0.45 and 0.31 times higher in selecting strategy numbers I and IV, respectively, compared to the base outcome. The marginal effect of the estimate shows that the probability of choosing strategy IV decreases by 9.7% and increases by 5.6% for strategy number I compared to the base outcome. Moreover, the belg and irrigation subcategory affects strategy number II's choice positively and significantly (p–value < 5%). The relative risk of the estimate shows that a unit change of the farming system used of belg and irrigation by the farmers have 16.6 times higher in choosing strategy I as compared to the base outcome. The marginal effect of the estimate shows that the probability of adopting strategy II decreases by 3.6% as compared to the base outcome. Furthermore, the effect of drought on crop production of complete loss affects the farmers choice of strategies I and V (p–value < 5%). The relative risk of the estimate shows that a change in the estimate changes the choice of strategy I and V by 5.5% and 5%, respectively as compared to the base outcome. The average marginal effect of the model, it is found out that in the present study the likelihood of choosing strategy number I and strategy number V would decrease (increase) the probability of selecting strategy I by 9.8% as compared to the base outcome.

Similarly, partial loss of the effect of drought on crop production affects the choice of strategy numbers I and V (p–value < 2%). The relative risk of the estimate shows that a change in the estimate changes the choice of strategy I and V by 3.4% and 2%, respectively, compared to the base outcome. The average marginal effect reveals that the probability of adopting strategies I and V would decrease the probability of selecting these two strategies as compared to the base's outcome. In addition, the effect of the drought on livestock shows that it positively and significantly affects the choice of strategy numbers I and V (p–value < 5%). The relative risk of the estimate of the factor changes the farmers' choice in strategies number I and V by 10.23 and 6.43, respectively, compared to the base outcome. The average marginal effect of the estimate reveals that the probability of choosing strategy number I and IV would decrease (increase), respectively, compared to the base outcome. Finally, access to credit affects the choice of strategy number II positively and significantly (p–value < 10%). The relative risk of the estimate shows that a change in the estimate would result in a change of about 7% in selecting strategy II as compared to the base outcome. The average marginal effect result reveals that the probability of choosing strategy II increases by 0.1% as compared to the base outcome.

Crop failures caused by drought could affect labor supply [26]. Farmers in Raya Azebo and Raya Chercher districts were migrating during the drought episodes to save their lives and livestock. This attitude severely affected the agricultural sector, and most households depend on government aid. Supplying drought relief may decrease self–reliance and raise dependency on government and donor organizations [3]. However, technological support may advance the farmer's drought coping strategy and farming system. Therefore, the attitude of farmers must be changed to build self–resilience and improve their drought coping. Besides, there is a need for a coordinated national drought policy that includes comprehensive monitoring, early warning and information systems, impact assessment procedures, risk management measures, drought preparedness plans, and emergency response programs to respond to drought effectively [3].

Similarly, Macon et al. [13] reported that recurrent droughts risk health economically and in rangeland. In the study area, several smallholder farmers sell their crops and buy livestock assets as a form of savings or insurances during the non–drought period. This practice may give relief to the households to cope–up with drought. However, during the periods of dry season and drought, most of the farmers reduce their food intake and sell their livestock at lower prices due to lack of fodder and livestock diseases [27]. For instance, diarrhea and mumps, skin infections, trypanosomiasis, worms and parasites, coughs and lung infections were some of the major diseases seen in the study area. Mehar et al. [21] and Speranza [5] were also reported similar cases in Bihar, India, and Kenya. This entails a substantial decrease in the existing production systems. It will also increase the livestock deaths, poor fertility and breeding, flea infestations and retarded growth [5]. Therefore, drought risk reduction policies and effective measures should be developed to diminish the impacts associated with droughts in the study area. Moreover, access to irrigation is an important strategy to reduce the vulnerability of agriculture to climate risks such as drought. Therefore, the farmers of Raya Azebo and Raya Chercher should have fair access to irrigation to enhance their strategies for coping with drought.

4 Conclusions

We analyzed farmers' proactive and reactive drought coping strategies at the household level in southern Tigray. Our research showed that the study area's most proactive drought–coping methods included gathering and storing pasture and hay, practicing soil and water conservation, and using weather prediction information. In addition, feeding of roasted cactus for livestock, taking out loans to start a small business, selling of household assets such as livestock, food for work and reduction of food consumption both in quantity and quality were identified as reactive or off–farm drought coping strategies in the study area. This result may give good insights to improve the current drought monitoring and early warning systems in the area. Besides, it helps to develop impact–based drought mitigation measures to save the lives of local community and their livestock assets to improve the production and productivity of the area.