Climate Change in Ethiopia: Implication on Human Capital in Rural Community - Case Study of Bilate Basin Agro-Pastoral Livelihood Zone of Sidama

Abstract

The objective of this study was to examine the trends of climate change and its subsequent impact on human capital development under the reference of human health and education of rural community of drought prone areas in Western Sidama (6.36°–7.14°N Latitude and 38.01°–38.56°E) of Ethiopia. Tropical Application of Metrology using SATellite (TAMSAT) data of both monthly rainfall and temperature was collected from Ethiopian National Meteorological Agency (NMA) for the period of 1987–2017. Data on perceived climate change; change driven impacts people experienced mainly on their health and education and copying or/and adaptation strategies affected community practiced was collected by employing both survey and participatory rural appraisal (PRA) techniques. In order to collect data from the household level, survey was employed for 400 households who were systematically sampled from 245,592 households of five drought prone administrative districts whereas PRA was employed to collect community level data. Mann–KENDALL TEST AND SEN’S SLOPE ESTIMATES (MAKESENS) and descriptive statistics were employed to analyze these data. The analysis result shows that there is increasing and decreasing trends of both temperature and rainfall, respectively. And increasing trend is statistically significant for temperature (α = 0.05; N = 31). Consequently, this change of climate variables has brought negative impacts on human capital mainly on health and education through various paths. Physiological inconvenience, prevalence of various diseases, and malnutrition were the main paths through which climate change impacts on human health were seen whereas students’ failure in standard exam attributed to a roll over impacts of climate change since early child hood, increasing school dropout rate and decreasing demand of the households to family education mainly owing to diminish in agricultural yields were the education dimension impacts of change in climate variables. Though a temporary migration to less stress adjacent areas, receiving aids and use of health extension services were a household level copying mechanisms observed, the first two were seen to reproduce unintended negative effects such as interethnic conflicts, forcing children to drop the school and aid dependency syndrome among receivers that the household themselves, aid organizations and government should work in consortium on building resilience both at household and community levels.

1. Introduction

Climate change is now more a reality [1] than a theory with a multiple implications on livelihood, health, and wealth of people across the globe [2]. According to the World Bank ([3], Xiii), as cited in Hameso [4], as the global climate is changed and the Earth is warmed, the rainfall pattern tends to shifts then by creating major climate extremes such as droughts, floods, and forest fires which collectively deny lives and livelihoods of millions. Poor people in Asia, Africa, and Latin America face prospects of tragic crop failure, agricultural productivity and, consequently, there happened increasing hunger, malnutrition, and disease [5]. Climate Change Impact [6], is any direct and/or indirect adversary impacts on one or more components of the small holders’ livelihood (human lives, culture, ecosystem, economy, social well-fair, and infrastructure) brought about by the average value variations on precipitations and temperature throughout the time under consideration. It refers to [2, 7, 8], both consequences and outcome of direct outcomes such as human and animal morbidity and mortality, loss of biota including crop failure, destruction of materials, disturbance of life systems, hungry, drought, and other hazards in addition to shocks while indirect consequence is the derivative inertial adversaries of these outcome in the form of residue. Adverse effects of climate change, on the other hand is, changes in the physical environment or biota resulting from climate change which has significant deleterious effects on the composition, resilience, or productivity of natural and managed ecosystems or on the operation of socio-economic systems or on human health and welfare [2]. There is high and increasing agreement among scientific society on the fact that [7, 8, 9] climate change affects all systems of the earth and its impact is greater in rain-fed agriculture of Africa.

Emerging literatures examine impacts of climate change on human capital development mainly on education by analyzing through different pathways [6, 10, 11], such as food and nutrition insecurity, infectious diseases (malaria, cholera, and diarrhea), and exposure to direct heat stress. The 2°C increase in temperature from normal is likely to reduce the schooling age of hotter environment children by 1.5 compared to the children of normal temperature environment. Hot days reduce performance on high stakes exams possibly by reducing the amount of learning achieved over the course of the school years and ultimately by reducing high school graduation rates [12]. On the other hand similar source indicates that above average temperature reduces education performance by up to 15% and lead to lasting impacts on educational attainment. Climate change affects food and nutrition security and subsequently this affects the cognitive development of the child even since the prenatal age, as pointed out by Dewey and Begum [10].

It is reported by the same authors that students drop the school at heat stress season. Families claim loss of farm owing to bad climatic conditions and thus, failed to send their children to school for financial constraints. This is reinforced by the report of UNDP-UNEP-UNCCD [13] as cited in IPCC [6] revealing that droughts can intensify the pressure to transfer children to the labor market in Ethiopia. With this path of climate change impacts in to health and education, although it is believed that formal education contributes to poverty reduction and economic development by fostering skills, intellectual ability, and employment opportunities, there is high and increasing confidence [7], among scientists that climate change affects education and earning from it then by keeping the poor in poverty trap. Further, although 57 million primary school-aged children worldwide (majority were in developing nations) remained out of school in 2015, in 2016, the United Nations released a set of more ambitious sustainable development goals (SDGs), one of which aims to achieve universal primary and secondary attainment by 2030 [14]. If climate change undermines educational attainment, this may have a compounding effect on underdevelopment that magnifies the direct impacts of climate change over subsequent times. On the other hand climate change affects human health and production capability through multiple and interactive paths. Climate change threatens [15], the health of people and communities by affecting their resilience and increasing their exposure, sensitivity, and adaptive capacity at all. On top of this, social determinants of health, such as those related to socioeconomic factors and health disparities, may amplify, or otherwise influence climate-related health effects, particularly when these factors simultaneously occur or close in time or space. All in all, climate change impact on human capital has been conceptualized by the interactive paths as depicted hereunder (Figure 1).

Figure 1.

Conceptualizing pathways of climate change impacts on human capital in rural community.

Although this theorized impacts of climate change on human capital stock and its capability, there is critical scientific studies’ gap generally at a national level in Ethiopia and specifically at a local level of the study area. This study therefore investigated the impacts of precipitation and temperature anomalies on the health and education of rural community. By doing so, as it is the first insight by its content and substituents, in addition to indicating the point of support oriented interventions to various civic and private institutions, it informs policy makings at local, regional and national levels. In addition, it could serve as reference to academia.

2. Study area

2.1 Description of the study area

This study was conducted within the 6.36°–7.14°N Latitude and 38.01°–38.56°E Longitude area in Ethiopia (Figure 2).

Figure 2.

Map of the study area.

The study area is located in Sidama administrative region in Ethiopia. According to annual statistical report of the Sidama zone—now Sidama regional state—Finance and Economic Development Sector [16], Sidama Administration Zone was one of the 14 zones in the SNNPRS region. It is located in the north eastern part of the region and bounded by Oromia in the North, East, and South East, with Gedeo Zone in the South, and Wolayta Zone in the West. Its geographic location of Sidama regional state lies between 6°14′–7°18′ North Latitude and 37°92′–39°14′ East Longitude.

The region consists 30/Thirty/woredas (administrative districts), which are: Hawassa Zuria, Malga, Wondo-genet, Gorche, Wonsho, [Aleta] Chuko, Loka-abaya, Bursa, Bona-zuriya, Chire, Shebedino, Dalle, Aleta-wondo, Dara, Hula, Aroresa, Bensa, A[Ha]rbegona, Boricha, Hawela, Darara, Bilate-Zuria, Dara-Otilicho, Teticha, Shafamo, Chirone, Chabe-Gambeltu, Bura, Daela, and Hokko Woreda. Whereas, Aleta wondo, Aleta Chuko, Yirgalem, Leku, Chuko, and Daye are City Administrations. Total population of Sidama [17], is projected to be 4,271,739. The average population density of Sidama region in the 2017/18 is 635 person per/km². It is one of the densely populated regions in the country. The total area of the region is 6981.9 km². Its western part tilts towards southern part of Great East African Rift Valley in Ethiopia [18].

2.2 Agro-ecological description

Typically, Sidama is classified in to three distinctive ago-ecological zones [19, 20]:

2.2.1 ‘Qolla’ (dry and hot tropical climatic zone)

It’s is semi-arid agro-ecological zone. This is one of agro-ecological zones this study considered. It is the low land and lies between 500 and 1500 m above sea level. Receiving mean annual 400–1000 mm and 20–30°C rainfall and temperature, respectively, this agro-ecological zone constitutes 30% of total land of Sidama. Economically, settlers of this agro-ecological zone practice agriculture. Here, farming is dominated by annual crops such as maize (known as Maize livelihood belt), sorghum, and haricot bean whereas coffee and chat are also practiced as cash crops in higher altitude (transitional zone to Woina Dega) of the zone. Furthermore, this agro-ecological zone is mainly known for its livestock production in Sidama. On the other hand, in Sidama the area is known for extended and persistent droughts and hence most of the households had been in chronic food insecurity [8, 21].

2.2.2 ‘Woina Dega’ (mid land, moist to humid, warm subtropical climate)

By constituting 54% of total land of Sidama, ‘Woina Dega’ agro-ecological zone lies within 1500–2500 masl. Climatically, it receives 1000–1800 mm mean annual rainfall and its mean annual temperature is known to be ranged within 15–20°C. The economic activity of this zone in Sidama is similar with that of the low land but here farming is dominated mainly by perennial crops such as coffee, ‘enset’, chat (Catha Adulis), tomato, and maize.

Although it is not uncommon to practice livestock economy in this zone, the farming dominance resulted in to shortage of grazing lands and made it problematic and hence it remained as only a supplementary sub-sector of the agriculture.

Though densely populated, this agro-ecological zone known to contribute higher share of cash crops from Sidama zone to local, regional, national, and international markets. Compared with the lower latitude zone, this agro-ecological zone, particularly to its higher altitude, is more food secured in terms of production and access dimensions thanks to ‘enset’, predominantly produced in this zone, and cash crops (mainly coffee, chat, and peen appeal) that are the sources of cash to access foods from market.

2.2.3 ‘Dega’ (high lands, wet and cool temperate climate)

Constituting 15% of the total area of Sidama land, this agro-ecological zone has the elevation of 2500–3500 m above sea level. Climatically, this this agro-ecological zone receives 1200–1800 mm mean annual rainfall and has mean annual temperature of 10–15°C. This is the agro-ecological zone where most Sidama’s rivers including the largest river, River Ganale, rises and tributes to Wabishabele River in Somalia.

2.2.3.1 Farming system and land use

The districts have bimodal rainfall pattern with two cropping seasons, short rainy season Spring (Belg) extending from February to May and the main rainy season summar (Kiremt) from June to September. The Belg rains are mainly used for planting short cycle crops such as maize production and seed bed preparation for the Kiremt crops.

The Kiremt rains are used for planting of cereal crops like some grains and vegetable crops and are also responsible for the growth and development of perennial crops such as enset, coffee, pineapple, and ch’at.

3. Material and methods

3.1 Data: source and type

This study employed data from secondary and primary sources. Secondary source dada include climate and agricultural data and primary source data include household survey and participatory rural appraisal (PRA; key informant, focused group discussion, and participant observation). Climate data for this study which encompassed the area of 6.36°–7.14°N Latitude and 38.01°–38.56°E Longitude was generated from National Meteorological Agency (NMA) on both monthly rainfall (mm) and daily maximum and minimum temperature for the period 1987–2017 which is 31 years’ time. The NMA processes and documents a TAMSAT (Tropical Application of Metrology using SATellite data and ground-based observation) data in collaboration with World Meteorological Organization (WMO). This form of data is functional [22], in Matewos and Tefera [23], where there is no well-organized, well calibrated, validated, and reliable spatiotemporal weather data on station based observation (Figure 3 and Table 1).

Figure 3.

Grid points of the study area.

No.	Study district	Total HHs (N)	Sampled farmers’ association	Total HHs of the sample site	Sample size (N)
1.	Hawassa Zuria	30,841	Rukessa Suke	1195	64
2.	Boricha	65,957	Shello Elancho	1697	91
3.	Loka Abaya	27,085	Falka	1397	75
4.	Aleta Chuko	44,888	Dibbicha	1994	107
5.	Dara	46,453	Safa	1185	63
	Total	215,224	5	7468	400

Table 1.

Population and samples.

Source: SAZFEDD (2017/18).

Agricultural data was collected from Bureau of Finance and Economic Development. Qualities of these data were controlled by identifying frontier outliers.

3.1.1 Household survey

This tool was employed to collect data from 400 respondents at a household level.

The sample size was determined based on Yemane [24]. Accordingly, the sample size will be as:

$$\mathit{n}=\mathit{N}/1+\mathit{N}\ast {\mathit{e}}^2,$$E1

whereas

n stands for sample

N stands for population

E stands for error term (error margin). Thus,

$$\mathit{n}=7468/1+7468{\left(0.05\right)}^2=>7468/18.665=>400$$

Structured and semi-structured questions were employed to collect data on major research issues such as experiences of farmers on the trend of changes and variability in local climate (temperature and rainfall), major indicators of the climate change and its driven impacts on health and education of their households.

3.1.2 Key informant interview (KII)

This tool was employed to collect data from a community level. For this purpose, the study included key informant interview to gain in-depth information about their experience and observations on the climate change as well as impacts of change on the health and education at a community level.

3.1.3 Focus group discussion (FGD)

With the intention to collect data from household to the community ranges, as an augmentation on KII data, one FGD was designed for each of five administrative districts (five FGD’s) under the study.

Normally, each of five groups had nine members who were from the composition of most elders, senior government representatives, the eldest household heading women, senior rural development agents, youth league leader in the structure of youth in local political arrangement, senior academician, religious society leader, senior health extension, and the representatives of civic societies who in one way or in other were expected to observe and/or feel the change and variability of climate and resulting impact on [their] farms and livestock within the society.

3.1.4 Participant observation

As another tool of PRA, this tool was employed to collect data on climate changes by observing various phenomena including bio-physical status of natural environments; personal physical condition and household assets (residences, farm, grazing fields and cattle on them and their physical statuses); and community’s asset (social service institutions such as the institutions of health and education; ecosystems and their interactions). Aerial photographs were captured as evidence on what were observed and documented along with their relevant data.

Survey was conducted through trained enumerators under the supervision of the researcher under the assistance of moderators. There were one enumerator and one moderator for each of five districts and totally, there were eight individuals involved for overall data collection. PRA was carried out by researcher with the assistance of moderators.

3.2 Methods of data analysis

3.2.1 Climate data analysis

Climate data was analyzed based on time series analysis. It carries insightful importance in business and policy planning. It’s applicable for a number of purposes including to study the past behavior of the phenomena under consideration; to compare the current trends with that in the past or the expected trends and to compare the performance of two different series of a different type for the same time duration. Mann–KENDALL TEST AND SEN’S SLOPE ESTIMATES (MAKESENS) was employed to test the trends the rainfall and temperature of the study area over last 31 years—1987–2017. Unlike a parametric analysis, in a regardless of the normal distribution of the population (N) of the study, the non-parametric regression (Mann–Kendall’s (MK) test) fits for temporal scale changes of the units corresponding to each times. Thus, it was employed to test the rainfall and temperature trends of all 31 years (372 months) of Western Sidama drought prone districts.

The Mann–Kendall (MK) trend test tool [25, 26, 27] is commonly used, from among other alternative models of trend analysis, to determine if a trend exists, and can handle seasonal patterns within the data.

For the time series x₁,…, x_n, the MK test uses the following statistic equation:

$$\mathrm{S}=\sum _{\mathbf{i}=\mathbf{1}}^{\mathbf{n}-\mathbf{1}}\sum _{\mathbf{j}=\mathbf{k}+\mathbf{1}}^{\mathbf{n}}\mathbf{\text{Sign}}\left(\mathbf{Xj}-\mathbf{Xi}\right)$$E2

If S > 0, then later observations in the time series tend to be larger than those that appear earlier in the time series and s = 0 implies no difference while the reverse is true if S < 0.

3.3 Standardized precipitation index (SPI)

The standardized precipitation index (SPI) is a widely used index to characterize meteorological drought on a range of timescales.

$$\mathrm{SPI}=\mathrm{X}–\mathrm{Xm}/\mathit{SD}$$E3

Where,

X is actual precipitation; Xm, mean precipitation; SD, standard deviation.

Weather conditions per SPI value (Table 2).

SPI value	Category
≥ +2.00	Extremely wet
+1.50 to +1.99	Very wet
+1.00 to +1.49	Moderately wet
−0.99 to +0.99	Mildly dry
−1.00 to −1.49	Moderately dry
−1.50 to −1.99	Severely dry
≤ −2.00	Extremely dry

Table 2.

Standardized precipitation index.

Source: McKee et al. (1993).

3.3.1 Survey data analysis

Survey data was analyzed by using descriptive statistics.

3.3.2 PRA data analysis

Data from this source was analyzed based on issue by issue and case by case augmentation in such a way to ensure triangulation.

4. Results and discussion

4.1 Rainfall and temperature trends

4.1.1 Rainfall trends

In terms of volume, as can be read from Figure 4, annual rainfall is seen decreasing with the magnitude of −3.8 mm/year which is not statistically significant (Table 3).

Figure 4.

Annual total rainfall trend of the study area.

Similar finding in the region was reported by Belay et al. [28], Matewos and Tefera [23], and Misrak et al. [29] that both short and long period rainfall has decreased.

4.1.1.1 Standardized precipitation index (SPI)

Of all 31 years, 15 (48.4% of times considered) years, the area has been receiving below normal amount of annual rainfall (Figure 5). The year 2016 was the driest year followed by its successor while 2006 was the wettest one followed by 1997. In a decadal wise, most (10 out of 15 or 67%) droughts were recorded within 1998–2010. This implies that the years of 2000s were the years of frequent and continuous drought.

Figure 5.

Standardize annual mean rainfall anomalies of the study area. Source: MAKESENS output.

Of 15 droughts recorded within the time series (1987–2017), 11 (74%)—Table 4—were mild with SPI ranges of −0.02 to −0.96; 2 (13%) were moderate with the SPI values of −1.38 and −1.10 recorded in 2000 and 2003, respectively and the rest 2 (13%) were severe droughts with the SPI values of −1.88 and −1.85 (Figure 5), recorded in 2016 and 2017, respectively. Similar findings were reported by other researcher such as Matewos and Tefera [23] and Misrak et al. [29].

Season	MK-test (z-test)	Trend	Significance	Sen’s slope (mm/year)
Annual total rainfall	−1.19	Decreasing	Insignificant	−3.481
Belg total rainfall	−1.46	Decreasing	Insignificant	−1.974
Kiremt total rainfall	−0.87	Decreasing	Insignificant	−1.165

Table 3.

Annual and inter seasonal rainfall trends.

Source: Output of NMA data.

SPI values	SPI values (drought severity)	Number frequency (N = 31)	% of occurrence	Probability of occurrence of such drought
−2 and less	Extremely sever	0	0	Never ever happened
−1.99 to −1.5	Severe	2	13	1 in 15.5 years
−1.49 to −1.00	Moderate	2	13	1 in 15.5 years
−0.99 to 0.00	Mild	11	74	1 in 2.8 years
		15	100

Table 4.

Annual drought frequency probability analysis.

Source: Own computation based on McKee et al. (1993) classification.

Though it is a stumbling block among scholars, definitions of the drought whatever it is climatological, atmospheric, meteorological, hydrological, agricultural, or be it is water resource management, commonly agree on that it is condition of insufficient moisture caused by insufficient (normal need) precipitation over a series of times.

Thus, according to McKee et al. [30] and Hayes et al. advices, precipitation (standardized) which is major factor for the sufficiency and insufficiency of water sources (ground water, stream flow, snowpack, and reservoir storage), time scale, and probability are vital variables to analyze climate born drought. While precipitation analysis enable to look at the supply side of water resource both for consumptive and usable demands, time scale points out to look the duration of sufficiency or deficit for these demand. Similarly, analysis on probability of occurrence of drought enables to inform stakeholder for further preparedness on the likelihood of associated risks. On top of this, extremely severe drought has not ever recorded for the last 31 years as meteorological data has revealed (Table 4).

On the other hand, in each of 15 years and 6 months, the study area experiences two droughts, one severe, and another moderate, while a mild drought occurs in every 2.8 years. The implication is the area is definitely prone to drought varying from most frequently occurring mild drought to severe drought through the moderate one (Table 4).

4.1.2 Temperature

Annual average maximum temperature has shown the increasing trend which is statistically significant (at α = 0.05; P = 0.012; Table 5). From Figure 6, it can be learnt that annual average maximum temperature ranges 30.81–31.77°C for the last 31 years which implies the raise by 0.96°C.

Maximum temperature	MK test (z-test)	Trend	Significance	Sen’s slope (mm/year)
Annual average	2.01	Increasing	**	0.012
Belg average	2.58	Increasing	***	0.028
Kiremt average	1.90	increasing	***	0.014

Table 5.

Annual, Belg, and Kiremt average maximum temperature.

^**

Significant trend at α = 0.05.

^***

Significant trend at α = 0.01.

Figure 6.

Annual average maximum temperature of the study area.

Throughout the study time series, 1987–2017, annual average maximum temperature is characterized by increase in the end of 1980s, fall in the beginning of 1990s, raise again in the late of 1990s, fall onset of 2000s, raise then again in the late of 2000s, abrupt down again in the early 2010s and the sharp rise again at the momentum which touched the highest (31.77°C) level in 2016 which as Figure 6 shows is, also, associated with SOI which is linked to strong El Niño event in 2015/16. With annual average maximum temperature of 31.77°C, the year 2016 is the hottest year where the 1996 is the wettest with annual average maximum temperature of 30.81°C of all 31 years. On the other hand, global land surface mean temperature rose in 1990s which is known as the hiatus or pause and then tended to decline then since 2000s, Parker, Wendy. Relatively, recent years have become warmer and warmer than the early years. This finding is similar with report by Ministry of Water Resources [31].

Thus, finding on local level temperature coincides with facts both at national and global levels.

4.2 Climate change impact on human health

Survey data revealed there is climate change impacts on human health recognized at a household and community level. The climate change impact on human health was explained through different paths.

The 77, 64, and 54% of the respondents believed that climate change has brought about negative impact on them and their family health through physiological inconveniences driven from extreme heat stress; occurrence of different diseases and increasing malnourishment rate (Table 6).

Indicators of impact	Number of respondent (N = 400)	Proportion
Occurrence of different human diseases	256	64
Heat stress inconvenience on human physiology	254	77
Malnourishment rate increased	180	54
Couldn’t afford education costs and thus cannot send children to the school	207	69
Children dropped the school for health attributed to heat stress and climate related health complications	194	66
Student failed standard test at the end b/c in one or another way of poor nutrition at the early age; poor attendance and follow at the lower grades	234	76

Table 6.

Impact of climate change on human capital.

On the other hand, it was learnt from FGD and KIIs that climate change has brought about clear impact on the health of households and communities at large.

Intolerable heat stress and chilling frosts; diarrhea, typhoid, typhus and malaria; food and nutrition insecurities were commonly mentioned indicators of climate change impacts on the households and communities of the study area. This finding is in a line with Smith et al. [32] that reported multichannel impact of climate change on human health including heat stress, food insecurity, malnutrition and infectious diseases. Moreover, participant in four (80%; Safa of Dara, Falka of Lokka Abayya, Shello Ellancho of Borricha, and Rukessa Sukke of Hawassa Zuria) of five FGD held in the study area pointed out that impact of heat stress is exacerbated by acute water shortage in the districts.

In a spatial wise, the shortage was more resounded in Borricha, Lokka Abbayya, and Hawassa Zuria districts. Added, apart from health extension information, health services at health posts were neither easily accessible nor were they competent which signifies institutional malfunction, as participants claimed. Similar finding was reported by other scientific researchers including Hameso [33] and Matewos [34] that despite the fact that 97% coverage of health posts in the study area, the services were not sufficient and hence, as the poor members of the community could not afford for far distanced privately owned health services which, mostly, were found in the cities, they silently remain vulnerable to climate induced health shocks and further morbidities. These claims were partly found to be acceptable by KIIs who were health extension and rural development agents in each of the districts.

Nutshell, data revealed that climate change has brought about negative impact on human health of the study area through the paths of generating physiological inconvenience (36%); mushrooming various climate driven human diseases (35%); and by exposing people to malnutrition (29%; Figure 7).

Figure 7.

Climate change impact on human health. Source: Survey 2020.

4.3 Climate change and responses from households and community at large

It was grasped from the participants of FGD and informants of KIIs that households and community at a large practice verities of copying mechanisms and adaptation strategies to withstand and to live with these climate shocks and reduce their impacts on their health. Receiving aids and transhumance were frequently mentioned practices to cope up climate shocks. Transhumance was being practiced to adjacent riverine localities of Bukito-Bura, Galade, Chiracha, Abaya Zuria, and Bilate Zuria. Despite participants and informants acknowledged the fact that this copying mechanism helped them to escape would be risks of droughts, at the same time, they also mentioned that they were exposed to get in to resource scarcity conflicts with pre-residing ethnic groups in the localities. According to their narrations, conflicts with Wolayita and Guji-Oromo ethnic groups were the typical ones. Thus, transhumance as a copying mechanism to family health impact of climate change offsets risks at hand while onsets other risks which is resource scarcity conflicts among rural community.

Furthermore, it was also raised by KIIs especially by school directors that, as long as households move with all the family members, students move with the transhumant family then by dropping out their school. Thus, children’s right to education was being denied.

Receiving aid as another copying mechanism was also recognized to support the sustenance of lives exposed to climatic hazards.

On top of this, survey data revealed that 57% of the respondents responded receiving aid in terms of cash and kind. Though so, 64% of them responded that responded that receiving the said aid has encouraged the culture of dependency (Figure 8; Table 7).

Figure 8.

Manifestations of aid dependency among rural households. Deindustriousness, decreasing industriousness; SkipFarmingS, skipping farming season; negligence, negligence to adopt adaptation strategies.

Issues	Obs	Mean	Std. Dev.	Min	Max
If a households receive aid	400	0.57	0.49	0	1
If receiving aid encouraged dependency culture	228	0.64	0.48	0	1

Table 7.

Aid receiving status of the households.

Further, data revealed that the dependency syndrome among aid receiving rural household is manifested through poor saving (73%); negligence to adopting adaptation options (66%); skipping farming season (62%); and decreasing industriousness (52%).

Sum up, though copying mechanisms peoples practiced in the study areas in a response to negative impacts of climate changes were recognized to save human lives that could have not alive otherwise, data at the same time, pointed out that these mechanisms reproduced unintended negative impacts on community’s peace and stability, child’s education, and individuals’ commitment to economic growth and sustainable development.

Thus, the aid organizations and government should work on antecedently to climate crisis so that to reduce further multiplying impacts on the households and on the community at large.

4.4 Climate change impact on education

Result of survey pointed out that climate change has brought about negative impacts on the education of the people in the study area. And it was identified that paths of impacts are also different. Negative impact of the climate change was claimed to deny the child’s right of access to education at rural settings. On top of this, 69% (Table 6) of the household believed that they had not been able to send their children to the school either at the onset of the school year or/and during the school course for not being able to afford for basic child provisions and facilities needed for the school.

This condition was further confirmed through all the group discussions held in the study area. As livestock was deteriorated in number; as crop yields remained only for hand to mouth which was even not sufficient for a household consumption, the groups narrated that, there was no source of finance to afford for all costs needed for school facilities of their children throughout the year and hence some household never enroll their children at the opening of the school while others’ children drop amid the semesters. Per the FGD, the more the drier a year is, the less likely that a household send their children to the school; most likely the child, if was send perhaps, to drops out the school and follow the household’s paths of copying mechanisms including temporary migration. This was affirmed through key informant interview with the school directors. Different adaptation strategies were being practiced at a community levels so as to reduce the impact of climate change on education including “School Feeding” program, aggressive afforestation at schools and digging boreholes in the schools so as to provide heat stressed and intolerably thirsted students with water at the school. On top of this, participants on focus group discussions and key informants (mainly school leaders) unambiguously disclosed that afforestation at the school has been contributing a lot so as to reduce heat irritations and inconveniences at schools. But at the same times, they did not report that as water tapes are running deeper following hydrological droughts in the areas, the boreholes they dug manually no more were being the sources of water for the schools; nor were there water pipelines nearby. This condition, therefore, requires the use of technology (which is beyond their affording capacities) such as water drilling machineries which dig up water from far deep and supply for the dehydrated students at the schools. This finding is alike with reports by Belay et al. [28] in the study of central rift valley of Ethiopia. On the other hand, according to key informant, particularly in three districts—Dara, Alata Chuko, and Boricha (Figure 9) there were some initiatives of “School Feeding” in some primary schools of the districts from the consortium of civic organizations and government to fill the food supply gaps of the households to their school children for which the FGD participants were very happy.

Figure 9.

School feeding at Alawo Arfe Primary School under the supply of Ebenezer Supporting and Development Association (ESDA) in Boricha district in 2018. Source: ESDA.

For its contribution to reduce school dropout rates and increasing better learning motivation of the children at the school, the “the School feeding” program was also recognized by the school directors and CEOs of the districts who were my key informants. Remarkably, from the report of year ended 2018 of ESDA, its program has contributed to 36% reduction of dropout rate while at the same time facilitated for better learning. However, both FGD participants and KIIs from the rest of two districts—Falka of Lokka Abayya and Rukessa Sukke of Hawassa Zuria—claim that there was no such kind of “School Feeding” interventions over their districts.

Linked to this, it was learnt from other key informant from the administrative zone level that the said program was not launched formally at the zone level and what was observed at two district level was that a pilot works for further implementation to which the informant was not sure. On the other hand, 66% of the household believed that children drop the school for health impairment attributed to heat stress and climate related morbidities (Table 6). Further, as the same table portrays, 76% of the respondent believed that student failed standard test at the end because in one or another way of poor nutrition at the early age; poor attendance and follow at the lower grades. This finding is in a line with what Park [35] reported that heat stress has a profound negative impact on both short and long term educational attainments and the impact increases with lower adaptive capacity of economically poor community. Indeed, the Intergovernmental Panel on Climate Change [6] Fifth Assessment and report predicted that climate-induced health and income shocks could in turn negatively affect educational outcomes if, for example, children experience poorer health and nutrition in early life, thereby impairing cognitive and physical development; if households are unable to pay school fees; or if children must participate in income generation activities during school ages.

All in all, climate change impact on education as explained through three above said components can be weighted as 36, 33, and 31%, respectively for failing for standard examination attributed to a roll over impacts of climate change; decreasing demand to education due to deteriorating purchasing power which as a consequence of crop failure and declining livestock and increasing dropout rate as a function of extreme heat stress and other health related complications (Figure 10).

Figure 10.

Education dimension of climate change impacts on human capital. Source: Survey 2019.

5. Conclusion

Data collected from secondary source and primary sources revealed that there is climate change that is defined through increasing temperature and decreasing rainfall over the study area. This change has brought about various impacts on rural community. Human capital (health and education) is one of livelihood dimensions that climate change has brought about negative impacts. To this end, the impact is 47% (Figure 11) for human health assessed through three sub-components (climate change impact driven diseases (physiological inconvenience (17%); prevalence of diseases (16%); and malnutrition (14%)) that are tracked in to in to one (health impact). On the other hand, the impact of climate change is 53% (Figure 11) for education assessed through other three sub-components (fail in standard exam attributed to a roll over impacts of climate change since early child hood (19%); increasing dropout rate (17%); and decreasing demand to education mainly owing to diminish in agricultural yield (17%)).

Figure 11.

Climate change impacts on health and education.

The latter is more linked to decrease in livestock and crop yields which are principal components and only the source of income for most of the households.

This diminution on the agricultural output resulted in to decline in food supply; diminish on their purchasing power (demand) of the households. Consequently, these households had not able to afford for education and education facilities of their children who either could not enroll to the school; or could not attend regularly; or drop it amid or fail to standard examinations of local and regional levels.

Thus, negative impact of climate change on human capital which is 53% for education and 47% for health implies, other things remain constant, that deteriorating future adaptive capacity of the rural community.

Data from survey and PRA also revealed that harmers in rural settings tried to cope up with and adapt climate change impacts on their health. It was also observed that there were some efforts of philanthropic and government organizations in supporting these endeavors the farmers. It was learnt these copying mechanism farmers practiced did contribute to save would be died lives. Nevertheless, it was also witnessed that some of these practices had led to unintended negative effects such as reinforcing interethnic; encouraging dependency syndrome and forcing children to drop the school.

Therefore, interventionists should plan to intervene before onset of climate change led crisis so as to reduce further mushrooming socio-economic problems during and crisis aftermaths.