Factors Influencing the Adoption of Climate-Smart Agricultural Practice by Small-Scale Farming Households in Wondo Genet,Southern Ethiopia

Abstract

Climate change is a global event that poses one of humanity’s most serious threats. Consequently, climate-smart agriculture (CSA) provides a once-in-a-lifetime opportunity to adapt to the effects of global climate change while lowering greenhouse gas emissions. The purpose of this study was to investigate the factors that influence farmers’ adoption of CSA practices in the Wondo Genet Woreda in southern Ethiopia. The study employed a mixed-methods approach with 213 randomly selected households (HHs). In the study, descriptive statistics, a composite score index, and an ordered logit regression model were used. Farmers’ awareness of CSA practices was high, which aided them in increasing farm income and farmland productivity. Tree planting, the use of organic manure, and the use of irrigation systems were the most CSA practices in the study area. Furthermore, the findings revealed that education, HH size, income, climate change perception, and farmland size all had statistically significant effects on farmers’ adoption of CSA. Meanwhile, the distance between the farm and the homestead had a negative and significant effect on the level of CSA adoption. Finally, socioeconomic factors should be considered when developing and implementing CSA programs for farmers.

Keywords

adoption climate change climate-smart agriculture ordered logit regression Wondo Genet

Introduction

Climate change is a worldwide event that poses one of the most serious risks mankind has ever faced, generating floods and droughts and hurting farmers’ livelihoods by influencing ecosystems, water supplies, food security, settlements, and human health (Food and Agriculture Organization of the United Nations [FAO], 2016). Climate and agriculture are strongly interrelated universal processes and thus variations in climate influence agricultural activities. Improving the accumulations of carbon dioxide (CO₂) will have a lot of prospective effects on plants and may also have a lot of indirect threats on herbivores and all other food chain members. Dangerous climate conditions such as influential rainstorms, high wind pressures, and high temperatures have much influence on agricultural activities (Amin et al., 2015).

According to IPCC, in several regions of the world, climate change commonly affects agriculture production more negatively than positively and unindustrialized countries are highly vulnerable to further adverse impacts. To minimize these adverse impacts, there are two options: mitigation and adaptation. Mitigation is a reduction of greenhouse emissions and it is mainly the responsibility of developed countries (Adger et al., 2005). Adaptation is a modification of social, ecological, and economic systems in reply to observed or anticipated climate variability through changes in the processes, practices, or structures to moderate detrimental consequences and enhances beneficial ones (Adger et al., 2005; The Energy and Resources Institute, 2006).

To incorporate this approach, CSA intends to solve the overlapping difficulties of climate change and food security (Lipper et al., 2014). The FAO and the World Bank pioneered the concept of “Climatic Smart Agriculture,” claiming that lowering greenhouse gas (GHG) emissions, adapting crops to grow in changing climate conditions, and increasing agricultural yields could result in “triple wins” (Actionaid International, 2014). CSA, on the other hand, is not a novel production scheme; rather, it’s a technique for guiding actions to convert and redirection systems of agriculture to successfully and sustainably improve development, environment, and food security in a changing climate (Kanamaru et al., 2013; Lipper et al., 2014).

No nation is protected from the effects of climate change, and poor countries, which contributed the least to the problem’s occurrence, are the most vulnerable and least adaptable to its consequences (Yesuf et al., 2008). The level of vulnerability depends on environmental and socioeconomic factors (Adger et al., 2005). Environmental factors include climate conditions, quality of soil, topographic exposure, and availability of natural resources, whereas the level of technological development, infrastructure, income, political setup, institutional strength, credit availability, and access to international markets are socioeconomic factors (Adger et al., 2003; Sarun et al., 2018).

Research and development must take into account how well communities and organizations respond to CSA initiatives. Survival necessitates a transformational and reorienting strategy for agricultural development in light of the new realities of climate change (Lipper et al., 2014). As a result, a proactive platform for government agencies, non-governmental organizations (NGOs), donors, the commercial sector, and civil society organizations in Ethiopia is essential if we are to close gaps and improve collective action on CSA.

Climate change and erraticism exist major challenges to agricultural production and the rural livelihood of smallholder farmers (Murray et al., 2016). The contemporary predictions show that the worldwide average annual rainfall through the end of the century is anticipated to rise, while changes in the amount and intensity of rainfall will vary significantly by region (Harvey et al., 2014; IPCC, 2014). GHG absorptions in the atmosphere will continue to increase unless the billions of tons of our annual emissions shrinkage substantially. These climate changes will affect agriculture in many ways and mostly adversely (Arora, 2019; IPCC, 2014).

Currently, approximately 48% (450 million) of Africans live in life-threatening poverty, earning fewer than the US $1.25 per day, with 63% of the continent’s poor living in rural areas and relying on agriculture for a living. At the same time, the continent is experiencing a rapid increase in population and urbanization (Williams et al., 2015). Ethiopia is Africa’s second-most populous country, trailing only Nigeria. Agriculture is the backbone and a cornerstone of the Ethiopian economy, accounting for 50% of GDP, 84% of exports, and 80% of job opportunities (Simane et al., 2016; WorldBank, 2008). Despite having a large contribution to the country’s economy, its production is generally low (Bezabih et al., 2014). To sustainably feed the world’s expanding population in the face of climate change, the agricultural output must increase. The answer to this problem should include climate-smart agriculture (CSA).

Ethiopia’s agriculture is characterized by its reliance on rainfall, and climate change and variability most obviously exhibited in chronic, overwhelming droughts and floods that shook the country’s economic growth (Belete, 2006). The contrary impact of climate variability in Ethiopia is significant (Gebrehiwot, 2013). The expertise of erratic precipitation, which occasionally results in extremely severe droughts; the impact of which, in addition to land degradation, human population growth, and climate change, has significantly harmed the country’s economic, social development, and food security status (FAO, 2016). Ethiopia is thus defined by food insecurity caused by environmentally damaging situations, as well as other structural and institutional factors (Kim, 2014; Simane et al., 2016).

The Ethiopian government has implemented a slew of policies, strategies, and laws aimed at bolstering climate change mitigation and adaptation, as well as sustainable development. Ethiopia has a ministry of environmental protection, forestry, and climate change, which is practiced in only a few countries around the world. To address the effects of climate change, the country has several climate-related policies and strategies in place, including the climate-resilient green economy strategy (FDRE/NAP, 2019). However, it is not well embedded in the local communities, lacks detailed guidelines, manuals, and action plans, and is not sufficiently mainstreamed into existing programmers and projects (FAO, 2011; Simane et al., 2016).

There is a scarcity of satisfactory investigation results on CSA practices in Ethiopia for various agroecology, soil type, precipitation/rainfall pattern, farming system, temperature, and moisture ranges (Jirata et al., 2016). As a result, CSA research projects should be supported. At all levels, data on CSA and conservation agriculture, in particular, are insufficient. Ethiopia, in particular, lacks knowledge and skills in CSA and conservation agriculture. Furthermore, there is no database of climate-smart agricultural practices and technologies that work well and where, allowing farmers, extension agents, or policymakers to decide which practices to scale up and how. An inclusive capacity development approach based on a thorough assessment of the needs of all stakeholders is required for knowledge distribution. Using data collected from small-scale farmers in southern Ethiopia Wondo Genet, this study aims to fill a knowledge gap on CSA adoption in small-scale agriculture by assessing the factors influencing the level of CSA practices; identifying major CSA practices, and local farmer’s perception of CSA. Recognizing this issue, the study used a composite score index and an ordered logit regression (OLR) model to jointly analyze farmers’ level of adoption of CSA practices and factors influencing CSA practice adoption.

Materials and Methods

Description of the Study Area

The study was conducted in the Wondo Genet Woreda which is located in the Northeastern direction from the capital city of the Sidama regional state, Hawassa. It lies about 260 km from Addis Ababa between 38° 37′ to 38° 42′ longitude east and 7° 02′ to 7° 07′, latitude North (Negash & Kelboro, 2014). The area comprises 18 kebeles namely Chuko, Baja, Gemicho-Baja, Watara-Gendo, Babo-Chororo, Gike-Gina, Awaye, Gike-Atoye, Wotera-Kechema, Wosha-Soyoma, Edo, Aruma, Yuwo, Kela 01, Busa, Entaye, Shasha-Kekele, and Gotona-Omuwa (Ango & Bewket, 2009). Reported that the landscape of the study area varies with an altitude ranging between 1,600 and 2,580 m.a.s.l (Figure 1).

Figure 1.

Map of the study area.

Sampling Techniques and Sample Size

To determine sample population, multistage sampling techniques were used. In the first stage, three kebeles were chosen purposively from a total of 18 kebeles based on their agricultural potential, proximity to the nearest forest, and agroecology condition. These kebeles were Baja Fabrika, Wosha Soyama, and Eddo. In the second stage, by using a simple random sampling technique, 10% of the households (HH), a total of 213 HHs, were taken from each kebele, thus, 89 HHs from Baja Fabrika, 64 HHs from Eddo, and 60 HHs from Wosha Soyama kebele respectively. The main reason for using the random sampling technique was that all farmers are practicing similar agricultural activities.

Data Sources and Data Collection Tools

Sources of Data

Primary data was collected and attained for a specified purpose, and it was gathered from the original sources, which were respondents. A household survey, key informant interviews, a focused group discussion, and field observations were utilized to collect primary data. Secondary data was acquired based on the actual sources, including thorough desk reviews of peer-reviewed journals, books, conference papers, dissertations, and research reports, as well as extensive desk reviews of both published and unpublished literature.

Household Survey Questionnaire

Cross-sectional data were distributed to sample households via a questionnaire survey once the respondents agreed on research ethics. This method was used to collect data on demography, socioeconomics, biophysics, productivity, selection of technologies, and adoption of CSA practices. Earlier than the final survey was implemented pre-testing was carried out with 25 households. This sample size is consistent with Moore et al. (2011) and Connelly (2008) who recommend 10% of the actual sample size for a pilot study. Following the pretest and focus group discussions, the survey questionnaire was refined and translated into the local language (Sidamegna) to facilitate communication with respondents. A random selection approach was employed to generate samples of individual households from the total households. This approved in obtaining a more representative respondent from the total households’ with equal likelihood, hence increasing efficiency. Formal household interviews were done under the direction of the researcher and/or professional enumerators.

Key Informant Interview (KII)

Key informants from the sample kebeles and Woreda were interviewed to get more qualitative data on the adoption of CSA practices. The purpose of the interview was to promote open interaction among the interviewers involved. About 15 key informants (5 from each kebele) were selected and interviewed for this purpose with the help of kebele leaders.

Focus Group Discussions (FGDs)

FGDs were used to collect information on group nuances and to allow a small group of respondents to be guided by a professional moderator to focus on the core issue of the study topic. The focus group discussant was selected using a purposive sampling technique based on knowledge of the study area’s current and historical environmental, social, and economic status. FGDs were held with a diverse range of participants, including household heads, women-headed households, development agents, and youngsters. In each kebeles, three focus group sessions with five to seven participants each were organized (one focus group discussion in each kebeles). The primary goal of the FGD was to better comprehend the adoption of CSA practices, as well as people’s perceptions about climate change and its causes, and their responses to it. Community knowledge of CSA, the consequences of climate change, and the problems of crop and livestock production, as well as hurdles to efficiently implementing them, were among the key discussion topics.

Field Observation

Field observation is a supporting system for collecting data that can be used to supplement or contextualize data obtained through other means. The researcher went on field trips to validate and supplement the information gathered from the primary and secondary data collection methods. Field observations were used to explore the area’s biophysical, and socioeconomic conditions. While in the study area, the researcher looks at vegetation coverage, topography, herd management, significant development interventions, people’s awareness, and other associated elements.

Data Analysis Methods

The survey data was analyzed using descriptive statistics and econometric models. Descriptive statistics include the mean, frequency, standard deviation, percentages, distribution, and graphs.

Econometric Model Specification

The factors influencing households in the study area’s adoption of CSA practices were investigated using the composite score index and the Generalized OLR Model. The composite score was calculated using the farmers’ replies regarding the extent or level to which they adopted CSA techniques. The farmers were then classified as high, medium, or low users according to a composite score. The respondents are then classified as high, medium, or low users based on a composite score (Abegunde et al., 2020; Ojoko et al., 2017). The ordering of the farmers based on the composite score is used as a dependent variable in a logit model, with hypothesized factors as independent variables (Abegunde et al., 2020).

In rating the farmers’ responses, a binary scale with 1 indicating yes and 0 indicating no is used, with 1 indicating yes and 0 indicating no use of the previously specified CSA practices. With the 11 statements/responses, a respondent can only have 11 and 0 as maximum and minimum points.

High users = Individuals with a point total between 11 and M + SD.

Medium users = Individuals who fall somewhere between the upper and lowest categories.

Low users = those with a point total of between 0 and M − SD.

Generalized OLR can estimate models with fewer restrictions than ordinary OLR, which frequently violates assumptions, but non-ordinal approaches like multinomial logistic regression are less parsimonious and difficult to interpret (Abegunde et al., 2020; Long & Freese, 2001; Williams, 2006). Equation (1) represents the generalized OLR model:

\begin{array}{l} P (Y i > j) = g (X β j) \\ = e x p (a j + X i β j \frac{e x p (a j + X i β j}{1 + \begin{matrix} e x p (a j + X i β j) \end{matrix}}, \\ j = 1, 2, 3 \dots \dots \dots N - 1 \end{array}

(1)

Where N is the number of categories of the ordinal regressand. From the equation stated above, the probabilities that Y will take on each of the values 1. . ., N are equal to

P (Y i - 1) = (X β j)

(2)

\begin{array}{l} P (Y i - 1) = (X β j - 1) - g (X i B j) \\ j = 2, \dots N - 1 P (Y i = N) = g (X i β N - 1 \end{array}

(3)

Y i = β o + β 1 X 1 + β 2 X 2 + β 2 X 2 + \dots \dots \dots β n X n + μ i

(4)

The dependent variable Yi = level of usage of CSA practices (high user = 3; medium user = 2; low user = 1). X1. . . . . .Xn indicates the explanatory variables; $β 1 \dots \dots . β n$ represents the parameters of the explanatory variables; and $β 0$ represents the intercept, while m $i$ represents the error term. However, before using the OLR (equation (1)) to investigate the effect of explanatory variables on CSA practice, the variance inflation factor (VIF) was used to determine whether continuous explanatory variables were collinear. It was calculated as follows:

V I F = \frac{1}{1 + R i^{2}}

(5)

Where R_i² is the coefficient of determination in the regression of one explanatory variable (X), besides explanatory variables (X_j). The value VIF is 1 if there is no collinearity between regressors. When a variable’s VIF value surpasses 10, as it does when R_i² exceeds .90, the variable is considered to be very collinear (Gujarati, 2004). To determine the degree of correlation between the dummy explanatory variables, a contingency coefficient is calculated. A score of 0.75 or higher shows that the two variables have a stronger association (Healey, 2010). The contingency coefficient (CC) was computed as:

C C = \sqrt{\frac{χ 2}{N + χ 2}}

(6)

Where

CC = coefficient of contingency,

χ² = chi-square test, and

N = total sample size

Description of the Generalized Ordered Logit Explanatory Variables

Table 1 summarizes the explanatory variables used in the model to analyze the determinants of the level of CSA practices among the sampled households.

Table 1.

Variables Name, Expected Signs, Definitions, and Coding.

Variables	Description and measurement type	Expected sign
Age	Age of respondents in years	+/−
Gender	Respondents gender (male = 1; female = 0)	+/−
Education status	The education level (years of schooling)	+
Marital status	Marital status (marred = 1; single = 0)	+/−
Household size	The number of people living in the house	+/−
Income	Total monthly income of the respondent	+
Farm experience	Length of time spent cultivating	+
Distance of farm to homestead	Distance from home to the farm locationin kilometers	+/−
Size of farmland to homestead	The size of farmland in hectares	+/−
Contact with development agency	Number of yearly visits of extension agents	+
Perception of the effect of climate change	Perception of the respondent on the impact of climate change (perceived = 1; otherwise = 0)	+
Number of livestock	Tropical livestock unit (in number)	+

Results and Discussion

Socioeconomic Characteristics of the Respondents

The results from (Table 2) showed the socio-economic characteristics of the respondents. The majority of respondents (40.8%) fell within the age range of 41 and 50, which corresponds to the farming active years. As a result, they are healthy enough to engage in agricultural activities. This finding is comparable with Demissie (2019), Kom et al. (2020), and Tekeste (2021); study in Climate-smart agriculture practice (CSAP) and its impact on food security in Siyadebrina Wayu Woreda North Shewa Ethiopia, and Ojoko et al. (2017) study in factors influencing the level of use of CSAPs in Sokoto state, Nigeria.

Table 2.

Socioeconomic Characteristics of the Respondents.

Variables	Frequency	Percentage (%)
Gender
Male	182	85.46
Female	31	14.55
Education
Uneducated	19	8.92
First cycle primary school (Grades 1–4)	65	30.52
Second cycle primary school (Grades 5–8)	51	23.94
High school (Grades 9 and 10)	44	20.66
Preparatory (Grades 11 and 12)	21	9.86
TVET and above	13	6.10
Marital status
Single	51	23.94
Marriage	162	76.06
Household size
1–5	36	16.90
6–10	130	61.03
11–15	42	19.72
16–20	5	2.35
Age
18–30	20	9.4
31–40	41	19.2
41–50	87	40.8
51–60	31	14.6
61–70	16	7.5
71–80	12	5.6
>80	6	2.8
Farm size
0	30	14.08
0.25–1.00	141	66.20
1.01–1.5	27	12.68
>1.5	15	7.04
Farming experience
1–15	32	15.02
16–30	121	56.81
31–45	46	21.60
>46 and above	14	6.57

Source. Own survey result, 2019/2020.

In Wondo Genet Woreda, Ethiopia, women are only permitted to participate in activities such as harvesting, winnowing, and processing off-farm produce, so 85.46% of the respondents are men. During a group discussion with key informants, participants stated that the majority of farm-related activities were controlled by men, with women only having the right to participate in farm harvesting, winnowing, and postharvest activities. This finding is analogous to Demissie (2019) and Tadesse et al. (2021). These results contradict the findings of Mfundo (2013) and Pujiwidodo (2016), who also perceived that small-scale farming in South Africa is experienced mainly at a local level by elder females. About 30.52% of the respondents only had Grades 1 to 4 education followed by 23.94% with Grades 5 to 8. From the total sampled respondents only 8.92% of the respondents were uneducated. This shows that, when compared to illiterate farmers, the majority of smallholder farmers have sufficient formal knowledge to understand and execute the CSA technology presented in the area.

Results of the farming experience revealed that the majority of the rural farmers (56.81%) had farming experience of 16 to 30 years. Farming experience is very important in farming activities; more experienced farmers are more likely to choose these technologies as it helps the farmer in the area of proper farm management to maximize profit. The study is consistent with Khatri-Chhetri et al. (2017) and Ojoko et al. (2017). Most of the rural farmers’ size of their farmland holdings range between 0.25 and 1.0 ha was 66.20% and on average a farmer owns 0.6 ha of land.

Income Sources of the Respondents

The respondents in the study area were engaged in the production of annual crops, cash crops, and livestock (Dessie & Kinlund, 2008; Mellisse et al., 2018). They also took part in activities away from the farm. With a minimum of 658.33 and a maximum of Birr 11,000, the studied households’ combined monthly revenue from on- and off-farm activities was calculated to be Birr 350,860. On average, the income of the surveyed respondents was estimated to be Birr at 6,106.2 per month. Taking the average family size of 9, the average per capita income was Birr 678.46 per month. This is approximately 10 times higher than Birr 65.83 monthly average per capita income reported by the international monetary fund at the country level (International Monetary Fund, 2019). Higher average per capita incomes in the study area could be due to the production of cash crops.

The majority of key informants and focus group participants claimed that on-farm activities were the main source of income for the area’s residents, notably the money made from the cultivation of cash crops including khat, avocado, papaya, and other fruits (Table 3). Similarly, the findings of the household survey revealed that the total monthly income of these cash crops and other crops was calculated to be Birr 313,873. Specifically, khat and sugarcane products provided 25.3% and 18.9% of average monthly income to 192 (90.14%) and 98 (46%) of the sampled households, respectively. The remaining 55.8% of monthly income came from other cash and annual crops (See Table 3).

Table 3.

The Incomes of the Households.

Major crop type	Number HH practiced	Purpose of production	Income	Percentage (%)	Rank
Khat	192	1	79,324.04	25.3	1st
Avocado	97	3	46,076.34	14.7	4th
Papaya	101	3	9,492	3.0	8th
Banana	93	3	15,684.66	5.0	6th
Cabbage	62	3	7,602	2.4	9th
Enset	206	2	14,000	4.5	7th
sugarcane	98	1	62,204.58	19.8	2nd
Coffee	87	3	49,004.02	15.6	3rd
Maize	92	2	21,933.34	7.0	5th
Potato	69	2	7,000	2.2	10th
Haricot bean	73	2	840	0.3	11th
Carrot	69	3	712	0.2	12th
Total income			313,873

Source. Household survey data, 2018. Own survey result, 2019/2020.

Note. Purpose of production: sales = 1, consumption = 2, and both = 3.

On the other hand, the monthly income of households from off-farm activities (employment in other farms, trade) was calculated to be Birr 24,982. The fact that off-farm income contributes the least to total family income explains why the majority of the surveyed households can rely primarily on agricultural activities with relatively small landholding sizes for a living. It also shows that a sizable proportion of households engaged in on-farm income-generating activities to meet their family’s livelihood needs. Data on livestock owned by respondents was also gathered. According to the survey results, the surveyed households have more than one cow and less than one other animal (Table 4). Selling milk products earned the respondents Birr 12,005 per month.

Table 4.

Number of Livestock Per Household and TLU.

Livestock types	Total livestock	Numbers
Oxen	88	0.41
Bulls	99	0.46
Cows	489	2.30
Calves	249	1.17
Donkeys	47	0.22
Chicken	671	3.15
Sheep	105	0.49
Goat	112	0.53
Total		8.73

Source. Own survey result, 2019/2020.

Farmers’ Awareness About CSA Practices

Out of the total 213 sample households, approximately 96.5% had knowledge of CSA and are well-informed about what a CSA is and its functions, while 3.5% of participants were unaware of what a CSA is and its functions. Those who were aware of the CSA were also asked for their thoughts on the significance of CSA practice. In the case of awareness, farmers’ responses were revealed on a three-point continuum scale (fully aware, aware, and not aware), with scores of 3, 2, and 1, respectively. Following that, the frequency was multiplied by the score (3, 2, or 1) to obtain the total weighted score, which was then divided by the total sample households (213) to obtain the weighted mean score (WMS), and rank order was assigned based on the weighted mean score.

Table 5 reveals that CSA helps to enhance farm revenue, which scored first in terms of the weighted mean score (WMS) at 2.47, is followed by CSA helps to boost production, CSA helps to maintain the health of the soil, and CSA helps to adopt animal/plant species and CSA helps to escape drought second, third, and fourth with WMS 2.36, 2.23, and 1.79 respectively. Whereas, CSA helps to escape drought and CSA helps to reduce GHG emissions, ranked fifth, and sixth with WMS 1.62 and 1.6, respectively. This result is in line with Kumar et al. (2016) and Rohila et al. (2018). Furthermore, participants in the focus group discussion, stated that local communities have practiced and are well aware of CSA practices such as physical soil and water conservation activities such as mulching, cultivating improved varieties, applying animal dungs, and water harvesting through the use of ponds.

Table 5.

Farmers’ Awareness on CSAP.

	Fully aware (%)	Aware (%)	Not aware (%)	Total weighted score	Mean weighted score	Rank
CSAP helps to sustain the soil health	92 (43.19)	78 (36.62)	43 (20.19)	475	2.23	3
CSAP helps to escape drought	41 (19.25)	49 (23.00)	123 (57.7)	344	1.62	5
Productivity is increased via CSAP	111 (52.11)	68 (31.92)	34 (15.96)	503	2.36	2
CSA helps to enhance farm revenue	123 (57.75)	67 (31.46)	23 (10.80)	526	2.47	1
CSAP helps to reduce GHG emissions	42 (19.72)	44 (20.66)	127 (59.6)	341	1.60	6
CSAP helps Adopt animal/plant species	28 (13.15)	112 (52.5)	73 (34.27)	381	1.79	4

Source. Own survey result, 2019/2020.

Perception of Respondents on a Climate Change Impact

Rainfall distribution and patterns, heat intensity, and temperature were the major influencing factors of CSA practices, which determined the level of productivity of agricultural practices. Furthermore, according to a large number of participants in the focus group discussion, temperature, heat intensity, and rainfall amount distribution and patterns were the major influencing factors for agricultural practices, and their influence has gradually deteriorated. In the previous three decades, households were polled about their impressions of temperature, heat intensity, rainfall amount, distribution and patterns, and extreme event-changing trends. About 94.2% of households supposed an “increase” in temperature, 1.5% of households perceived a “decrease” in temperature, 1.77% of households perceived “no change” in temperature, 2.53% of households said they don’t know about the change. On the other hand, 83.25% of households reported an increase in heat intensity; 1.95% of respondents reported a decrease in heat intensity; 11.3% of respondents claimed no change in heat intensity; and 3.5% of respondents claimed they were unaware of temperature changes, as shown in (Figure 2).

Figure 2.

Households’ perceptions on change.

The majority of the surveyed households reported precipitation changes, the extent of rainfall, and/or distribution, in the study area over the past 30 years. A substantial percentage of respondent households (92.6%) perceived the change in the amount of rainfall. Out of 92.6% of households that reported a change in rainfall amount, 80.46% reported a decrease, while the remaining 4.8% reported an increase; on the other hand, 5.1% reported no change in rainfall; and 7.73% of those polled did not pay enough attention to the trend in rainfall volume.

The result also revealed that the mainstream of the respondent households (87.16%) saw a shift in the timing of the rainfall, in particular, 89% shorter wet seasons have been noted, and 3.5% noticed prolonged wet seasons; 3.7% of the responders reported that the rainy season remained unchanged. About 3.8% did not give attention to the situation of the rain seasons.

The two climatic factors that have the most effects on farming activities in the study area are temperature and rainfall. Rainfall is vital to farming because it signals long-term climate system changes. This is in line with the studies of Limantol et al. (2016) and Saguye (2017). The rainfall pattern, on the other hand, is more important to farmers. Farmers may suffer greatly if the correct quantity of rain occurs, followed by a protracted dry spell before the next storm.

Climate Smart Agricultural Practices

In the past, smallholder farmers in the study area have unintentionally implemented CSA as part of their normal agricultural practices. The survey revealed that many of the households were already aware of the CSA practices shown to them (see Figure 2). Widely adopted climate-smart practices were tree planting, effective use of nitrogen fertilizer, irrigation, crop diversity, crop rotation, intercropping, use of organic manure, minimal tillage, and use of terraces, livestock breed improvement, and diversification and mulching.

Results from the analysis of the frequency of use of the CSA practices among the sampled farmers reveal that the use of tree planting, use of nitrogen fertilizer, and irrigation were the most popular practices among the households in the study area. In contrast, the Use of terraces, Mulching, and livestock breed improvement and diversification were the least popular CSA practices.

(Table 6) the degree of use of CSA practices among the respondents in the research area was used to order the farmers into three classes: low, medium, and high users based on the frequency of usage of CSA activities (Figure 3). According to (Table 6), the category of CSA practice users adopted by the sampled households is based on the proportion of CSA practices they use relative to the overall number of CSA activities.

Table 6.

Category of Users of CSA Practices Among Small-Scale Farmers.

Category of users of CSA practices	Frequency	Percentage (%)
Low	54	25.4
Medium	52	24.4
High	107	50.2
Total	213	100

Source. Own survey result, 2019/2020.

Figure 3.

Frequency of use of CSA practices in the study area.

Consequently, the finding of this study, half (50.2%) of the sampled respondents fell into the category of the high user of CSA practices. About 25.4% and 24.4% were in the low and medium user categories respectively.

Factors Affecting Climate-Smart Agricultural Practices Adoption

The ordered logit model is a return model for a variable having an ordinal answer. A linear function of the covariates with regression coefficients consistent in each cumulative probability is predicted by the model, which is based on the cumulative probabilities of the response variable (Michaels, 1989). In this case, the model’s independent variable is the user’s category of CSA practice (low, medium, and high), where the medium user category serves as the control category in the model.

Before running the order logistic regression model, the independent variables were tested for multicollinearity and degree of association using the Variance inflating factor (VIF), and Contingency coefficient (CC). According to the VIF, the data did not have a significant problem with multi-collinearity. That is, the VIF of the continuous variables was below 10 (Table 7), indicating that the whole continuous variables were included in the regression analysis. The CC was also used to calculate the degree of association between dummy variables. The CC analysis reveals a weak relationship among the dummy explanatory variables (Table 8). As a result, the regression analysis included all of the dummy variables.

Table 7.

Variance Inflating Factor of the Continuous Explanatory Variable.

Variables	VIF	1/VIF
Age	3.37	0.296736
Education	3.65	0.273973
Household size	1.16	0.862069
Income	1.16	0.862069
Farm experience	1.11	0.900901
Size of farmland	1.08	0.925926
Size of farmland	1.07	0.934579
Tropical livestock unit	1.06	0.943396

Source. Own survey result, 2019/2020.

Table 8.

Contingency Coefficients for Dummy Explanatory Variables.

Variables	Gender	Marital status	Contact with the development agent	Perception of climate change
Gender	1.0000
Marital status	−0.0132	1.0000
Contact to the Development agent	−0.0295	0.0882	1.0000
Perception of climate change	0.0563	0.0347	0.0857	1.0000

Source. Own survey result, 2019/2020.

The goodness-of-fit of the models was also measured using pseudo-R². The pseudo-R² was computed at .1406 in lower to medium interaction and .143 in high to medium interaction. Each of the logit regression models passed the goodness of fit tests recommended by Pallant and Tennant (2007) and Iyer et al. (1991).

In addition to checking the model’s goodness of fit, a diagnostic test was run to ensure that the proportionate odds assumption was met in the models utilized. Because the proportional odds assumption is violated, a generalized ordered logistic model is preferable to an OLR model or an ordered probit regression model. A test was run for the model fit for the low and high levels of usage of CSAPs in the study area. The null hypothesis is that there is no difference in the coefficients between models. Results on the approximate likelihood-ratio test of proportionality of odds across the response categories for low levels of usage of CSAPs (prob > χ² = .001), and high label usage of CSAPs (prob > χ² = .0048) indicate that there are significant differences in the coefficients in the models (Table 9). This finding suggests that the proportional odds assumption or the parallel regression assumption has been violated.

Table 9.

Order Logit Model Results of the Factors Influencing the Level of CSAP.

variable/category	Low			High
variable/category	Coef.	SE	p > /z/	Coef.	SE	p > /z/
Constant	1.07044	1.13698	.346	.031	0.9731	.031
Age	−.00579	0.01942	.765	−.0036	0.0135	.791
Gender	.27605	0.3805	.468	.0221	0.0881	.802
Education	.03616	0.0303	.002 ***	.2093	0.0870	.016**
Marital status	.55942	0.2323	.160	.0969	0.4208	.818
Household size	.00080	0.0022	.725	0.0277	0.0641	.045**
Income	.0398	0.0250	.112	.8258	0.5620	.014**
Farm experience	.0005	0.0010	.996	.0765	0.0791	.333
Distance—homestead	−.00340	0.0016	.038**	−.0098	0.0203	.626
Size of farm land	.04504	0.1007	.028**	.2068	0.2776	.002**
Contact with DA	.0630	0.0466	.176	.1658	0.2783	.551
Perception of climate change	.0239	0.1432	.867	.0247	0.4873	.024**
Tropical livestock unit	.112	0.1238	.9	.043	0.0477	.91
Number of observations	212			212
LR χ² (12)	111.18			89.9
Prob > χ²	.00			.0048
Pseudo R²	.1406			.143
Log likelihood	−216.54			−90.88

Note. The reference category was created using the medium user category. Data from a survey, 2019/2020.

***

, **, and * signify statistical significance at the levels of 1%, 5%, and 10%.

(Table 9) shows that, the education of the household head was significant and positively influences the level of adoption of CSA practice in the lower and higher user categories at a 5% (p = .002 and .016) level of statistical significance respectively. This shows that farmers are more comfortable using current CSA practices and advances the more educated they are. This is consistent with research from (Abegunde et al., 2020; Akudugu et al., 2012; Ojoko et al., 2017). Those authors stated that educated farming households would easily adopt modern agricultural production technologies and strategies (Abid et al., 2015; Ade & Bosede, 2016; Sebatta et al., 2014; Ume et al., 2020) study reveals that education has a positive effect on smallholder farmers’ CSA practice. This implies that education allows a farmer to make educated judgments and identify farm-related opportunities for profit maximization where they exist. Therefore, the use of CSA practices and their propagation among rural farmers depend on this factor.

The lower degree of CSA adoption is negatively correlated with the respondents’ farmland’s distance from their homestead, which is statistically significant. As a result, farmers who cultivate distant from their homesteads are probable to use CSA practices less frequently. This might be due to the difficulties they would have in practicing good and effective management due to the stress presented by distance as compared to their peers who live nearer to their farms. Apart from affecting market accessibility (Abegunde et al., 2020; Teklewold et al., 2013), distances can also affect important aspects of agriculture, such as access to technology, knowledge, and financial institutions.

Another important factor that influenced the use of CSA practices in the research area was the size of farmland at (p = .028) and (p = .045), respectively, the size of the farmland statistically and positively affected the degree of adoption of CSA techniques in both the lower and higher user groups. This study suggests that farmers with larger farmlands adopted more CSA adoption techniques, indicating that encouraging farmers to adopt CSA practices is a good thing. This outcome is in line with that of Kom et al. (2020) and Teklewold et al. (2013). The findings support the findings of Abegunde et al. (2020), who found that land fragmentation may be a barrier to CSA implementation. Land is a vital resource in agricultural output, and farmer households who have access to land and other resources will be able to host the novelties or techniques required for a fruitful agricultural endeavor.

The sample household’s perception of the consequence of climate change was statistically significant at 5% (p = .024) at a higher level user category. Farmers perceive climate change to harm agricultural production in the study area, especially for high users. Additionally, this research indicates that farmers who believe that climate change has a major negative impact on agricultural production would use more CSA practices. This is associated with the finding of Abegunde et al. (2020), Justin et al. (2017), and Teklewold et al. (2013). Farmers who observed an increase in floods and fluctuations in moisture levels in their area were more likely to implement more CSA techniques, according to the study. This outcome is unsurprising, given those farmer respondents who recognize the threat associated with severe weather, and changing climatic tendencies recognize the importance of CSA practices in improving their resilience to climatic impacts. The findings on farmers’ perceptions of climate change imply that a sufficient degree of knowledge of climate change’s negative impact on agricultural output among farmers will prospectively boost CSA adoption in the area’s farming system.

Conclusion and Recommendation

In response to the challenge posed by global climate change, farming operations are now succeeding thanks to CSA. Few studies have been done to look into the variables affecting the level of CSA adoption among small-scale farmers in Ethiopia. This study closes that gap by identifying the factors influencing the extent to which small-scale farming households in Wondo Genet areas, south-central Ethiopia, adopt climate-smart agricultural practices. Farmers in the study area generally embraced local CSA techniques like tree planting, nitrogen fertilizer use, and irrigation.

This research investigated that factors like household size, income, education, homestead distance from the farm, farm size, and perception of climate change/variability are more likely to be significant variables in CSA adoption within the study area. The most important socioeconomic and demographic factors (education, income Size of farmland perception of temperature change that is positively correlated with CSA practice) should be considered in CSA practice. This implies that policy options for developing the main idea of local people’s contribution should take these variables into account.

As a result, the study concluded that to achieve any effective adoption action in these communities and the district as a whole, existing institutions must be strengthened. Similarly, research investigation, supportive programs, and policy should emphasize the adoption of CSA practices, particularly those that are not widely used by farmers, as well as strengthening plus enhancing the adoption of the most important CSA practices. To boost domestic productivity, policymakers should encourage and provide technical assistance to households that practice agricultural practices that promote soil fertility and productivity, as well as encourage farmers to participate in larger CSAs packages. Because the research was time-limited and budget-constrained, such research should be conducted at the landscape level to provide policymakers and practitioners with a comprehensive picture of CSA practices-related information at the landscape level.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Bamlaku Ayenew Kassa

References

Abegunde

V. O.

Sibanda

Obi

(2020). Determinants of the adoption of climate-smart agricultural practices by small-scale farming households in King Cetshwayo district municipality, South Africa. Sustainability, 12(1), 195. https://doi.org/10.3390/SU12010195

Abid

Scheffran

Schneider

U. A.

Ashfaq

M. J. E. S. D.

(2015). Farmers’ perceptions of and adaptation strategies to climate change and their determinants: The case of Punjab province, Pakistan. Earth System Dynamics, 6(1), 225–243.

Actionaid International. (2014) “Climate smart agriculture” causes confusion (pp. 1–4). Author. https://actionaid.org/sites/default/files/climate_smart_agriculture_confusion_4pages.pdf

Ade

A. T.

Bosede

S. J.

(2016). Ordered probit analysis of factors influencing farmers’ contributions to climate change adaptation decisions in southwest Ni[1]geria. Trends in Agricultural Economics, 10, 1–11. https://doi.org/10.3923/tae.2017.1.11

Adger

W. N.

Arnell

N. W.

Tompkins

E. L.

(2005). Successful adaptation to climate change across scales. Global Environmental Change, 15(2), 77–86. https://doi.org/10.1016/j.gloenvcha.2004.12.005

Adger

W. N.

Huq

Brown

Conway

Hulme

(2003). Adaptation to climate change in the developing world. Progress in Development Studies, 3(3), 179–195. https://doi.org/10.1191/1464993403ps060oa

Akudugu

Guo

Dadzie

(2012). Adoption of modern agricultural production technologies by farm households in Ghana: What factors influence their decisions? Journal of Biology, Agriculture and Healthcare 2(3).

Amin

Mubeen

Hammad

H. M.

Nasim

(2015). Climate smart agriculture: An approach for sustainable food security. Agricultural Research Communication, 2(3), 13–21. www.aspublisher

Ango

Bewket

(2009). Challenges and prospects for sustainable forest management in wondo genet area, Southern Ethiopia. Ethiopian Journal of Development Research, 29(2), 27–64. https://doi.org/10.4314/ejdr.v29i2.42709

10.

Arora

N. K.

(2019). Impact of climate change on agriculture production and its sustainable solutions. Environmental Sustainability, 2(2), 95–96. https://doi.org/10.1007/s42398-019-00078-w

11.

Belete

(2006). Current experience on existing small scale irrigations. Best Practices and Technologies for Small Scale Agricultural Water Management in Ethiopia, 3–11.

12.

Bezabih

Falco

S. D.

Mekonnen

(2014). On the impact of weather variability and climate change on agriculture: Evidence from Ethiopia. Environment for Development Discussion Paper-Resources for the Future (RFF), 14–15.

13.

Connelly

L. M.

(2008). Pilot studies. Medsurg Nursing, 17(6), 411–412. https://doi.org/10.1145/3081016.3081020

14.

Demissie

T. K.

(2019). Center for food security studies climate smart agricultural practices and its implications to food security in siyadebrina wayu Woreda, North Shewa, Ethiopia. Climatesmart Agricultural Practices, and its Implications to Food Security in Siyadebrina Wayu.

15.

Dessie

Kinlund

(2008). Khat expansion and forest decline in wondo genet, Ethiopia. Geografiska Annaler, Series B: Human Geography, 90(2), 187–203. https://doi.org/10.1111/j.1468-0467.2008.00286.x

16.

The Energy and Resources Institute. (2006). Adaptation to Climate Change in the Context of Sustainable Development (Background Paper) (pp. 1–29). https://sustainabledevelopment.un.org/content/documents/1490adaptation_paper.pdf

17.

FDRE/NAP. (2019). Ethiopia’s climate resilient green economy strategy. EA/IRENA Renewables Policies Database (pp. 1–147). Author.

18.

Food and Agriculture Organization of the United Nations. (2011). Ethiopia country programming framework 2012–2015. Office of the FAO Representative in Ethiopia to AU and ECA (p. 60.), Author.

19.

Food and Agriculture Organization of the United Nations. (2016). Climate change and food security: Risks and responses. Author. https://doi.org/10.1080/14767058.2017.1347921

20.

Gebrehiwot

(2013). Assessing the evidence of climate variability in the northern part of Ethiopia. Journal of Development and Agricultural Economics, 5(3), pp. 104–119. https://doi.org/10.5897/jdae12.056

21.

Gujarati

D. N.

(2004). Basic econometrics (4th ed.). McGraw-Hill Companies.

22.

Harvey

C. A.

Chacón

Donatti

C. I.

Garen

Hannah

Andrade

Bede

Brown

Calle

Chará

Clement

Gray

Hoang

M. H.

Minang

Rodríguez

A. M.

Seeberg-Elverfeldt

Semroc

Shames

Smukler

Wollenberg

(2014). Climate-smart landscapes: Opportunities and challenges for integrating adaptation and mitigation in tropical agriculture. Conservation Letters, 7(2), 77–90. https://doi.org/10.1111/conl.12066

23.

Healey

J. F.

(2010). The ettential of statistics (2nd ed.). Wadsworth Cengage Learning.

24.

International Monetary Fund. (2019). IMF Annual Report 2019: Our Connected World. Editorial and Publications Division of the IMF’s Communications department (pp. 10–12).

25.

IPCC. (2014). Climate change 2014 synthesis report summary chapter for policymakers (p. 31). Author.

26.

Iyer

Hosmer

D. W.

Lemeshow

(1991). Applied logistic regression: The statistician. Wiley. https://doi.org/10.2307/2348743

27.

Jirata

Grey

Kilawe

(2016). Ethiopia climate-smart agriculture scoping study (p. 54). FAO.

28.

Justin

C. O.

Wiliams

C. E.

Vera

T. S.

(2017). Understanding the factors affecting adoption of subpackages of CSA in Southern Malawi. International Journal of Agricultural Economics and Extension, 5(2), 259–265. www.internationalscholarsjournals.org

29.

Kanamaru

Hancock

Bockel

Braimoh

Förch

Grewer

Hiepe

Jonsson

Krishnamurthy

Marais

Medeiros

Matteoli

Seeberg-Elverfeldt

Thornton

P. K.

Touchemoulin

Vantwout

(2013). Module 18: Assessment, monitoring and evaluation. In FAO. Climate-smart agriculture source book (pp. 493–534). Food and Agriculture Organization of the United Nations (FAO).

30.

Khatri-Chhetri

Poudel

Shirsath

(2017). Assessment of climate-smart agriculture (CSA) options in Nepal. LIBIRD and CCAFS.

31.

Kim

(2014). Determinants of household food security in Rural Ethiopia: An empirical analysis. Journal of Rural Development/Nongchon-Gyeongje, 37(2), 1–29.

32.

Kom

Nethengwe

N. S.

Mpandeli

N. S.

Chikoore

(2020). Determinants of small-scale farmers’ choice and adaptive strategies in response to climatic shocks in Vhembe District, South Africa. GeoJournal, 87, 677–700. https://doi.org/10.1007/s10708-020-10272-7

33.

Kumar

Shehrawat

P. S.

Rohila

A. K.

Ghanghas

B. S.

Kumar

(2016). Knowledge status of masumbi (Citrus sinensis) cultivating farmers of Haryana (India): A sustainable fruit crop. Indian Journal of Ecology, 43(2), 599–601.

34.

Limantol

A. M.

Keith

B. E.

Azabre

B. A.

Lennartz

(2016). Farmers’ perception and adaptation practice to climate variability and change: A case study of the Vea catchment in Ghana. SpringerPlus, 5, 830. https://doi.org/10.1186/s40064-016-2433-9

35.

Lipper

Thornton

Campbell

B. M.

Baedeker

Braimoh

Bwalya

Caron

Cattaneo

Garrity

Henry

Hottle

Jackson

Jarvis

Kossam

Mann

McCarthy

Meybeck

Neufeldt

Remington

. . .Torquebiau

E. F.

(2014). Climate-smart agriculture for food security. Nature Climate Change, 4(12), 1068–1072. https://doi.org/10.1038/nclimate2437

36.

Long

J. S.

Freese

(2001). Regression models for categorical dependent variables using STATA. Stata Press.

37.

Mellisse

B. T.

van den Ven

G. W. J.

Giller

K. E.

Descheemaeker

(2018). Home garden system dynamics in Southern Ethiopia. Agroforestry Systems, 92(6), 1579–1595. https://doi.org/10.1007/s10457-017-0106-5

38.

Mfundo

M. M.

(2013). The effects of infrastructural and institutional services on food security in Ntambanana rural area [Doctoral dissertation] (pp. 1–129). University of Zululand.

39.

Michaels

(1989). Let patients see their medical records, Ontario college says. CMAJ, 141(10), 1077. https://doi.org/10.1007/978-94-007-0753-5

40.

Moore

C. G.

Carter

R. E.

Nietert

P. J.

Stewart

P. W.

(2011). Recommendations for planning pilot studies in clinical and translational research. Clinical and Translational Science, 4(5), 332–337. https://doi.org/10.1111/j.1752-8062.2011.00347.x

41.

Murray

Gebremedhin

Brychkova

Spillane

(2016). Smallholder farmers and climate smart agriculture: Technology and labor-productivity constraints amongst women smallholders in Malawi. Gender, Technology and Development, 20(2), 117–148. https://doi.org/10.1177/0971852416640639

42.

Negash

Kelboro

(2014). Effects of socio-economic status and food consumption pattern on household energy uses: Implications for forest resource degradation and reforestation around wondo genet catchments, South-Central Ethiopia. Eastern Africa Social Science Research Review, 30(1), 27–46. https://doi.org/10.1353/eas.2014.0001

43.

Ojoko

Akinwunmi

J. A.

Yusuf

S. A.

Oni

O. A.

(2017). Factors influencing the level of use of climate-smart agricultural practices (CSAPs) in Sokoto state, Nigeria. Journal of Agricultural Sciences, Belgrade, 62(3), 315–327. https://doi.org/10.2298/jas1703315o

44.

Pallant

J. F.

Tennant

(2007). An introduction to the Rasch measurement model: An example using the Hospital Anxiety and Depression Scale (HADS). British Journal of Clinical Psychology, 46(1), 1–18. https://doi.org/10.1348/014466506X96931

45.

Pujiwidodo

(2016). The role of small-scale agriculture in poverty reduction in Cezwana Area Jozini Local Municipality (Kz 272), South Africa [Doctoral dissertation]. University of Zululand.

46.

Rohila

A. K.

Shehrawat

Malik

(2018). Awareness, constraints and prospects of climate-smart agricultural practices (CSAP). Journal of Agrometeorology, 20, 167–171.

47.

Saguye

T. S.

(2017). Assessment of farmers’ perception of climate change and variability and its implication for implementation of climate-smart agricultural practices: The case of Geze Gofa District, Southern Ethiopia. Journal of Geography & Natural Disasters, 7(1), 1–9. https://doi.org/10.4172/2167-0587.1000191

48.

Sarun

Ghermandi

Sheela

A. M.

Justus

Vineetha

(2018). Climate change vulnerability in a tropical region based on environmental and socio- economic factors. Environmental Monitoring and Assessment, 190(12), 1–7. https://doi.org/10.1007/s10661-018-7095-3

49.

Sebatta

Mugisha

Katungi

Kashaaru

Kyomugisha

(2014). Smallholder farmers’ decision and level of participation in the potato market in Uganda. Modern Economy, 5(8), 895–906. https://doi.org/10.4236/me.2014.58082

50.

Simane

Beyene

Deressa

Kumie

Berhane

Samet

(2016). Review of climate change and health in Ethiopia: Status and gap analysis. Ethiopian Journal of Health Development, 30(1), 28–41. https://doi.org/10.4314/ejhd.v30i1

51.

Tadesse

Simane

Abera

Tamene

Ambaw

Recha

J. W.

Mekonnen

Demeke

Nigussie

Solomon

(2021). The effect of climate-smart agriculture on soil fertility, crop yield, and soil carbon in Southern Ethiopia. Sustainability, 13(8), 1–11. https://doi.org/10.3390/su13084515

52.

Tekeste

(2021). Climate-smart agricultural (CSA) practices and its implications to food security in Siyadebrina Wayu District, Ethiopia. African Journal of Agricultural Research, 17(1), 92–103. https://doi.org/10.5897/ajar2020.15100

53.

Teklewold

Kassie

Shiferaw

Köhlin

(2013). Cropping system diversification, conservation tillage and modern seed adoption in Ethiopia: Impacts on household income, agrochemical use and demand for labor. Ecological Economics, 93, 85–93. https://doi.org/10.1016/j.ecolecon.2013.05.002

54.

Ume

C. O.

Opata

P. I.

Onyekuru

A. N. J.

(2020). Gender and climate change adaptation among rural households in Nigeria. In African handbook of climate change adaptation (pp. 1–17). Springer International Publishing.

55.

Williams

(2006). Generalized ordered logit/partial proportional odds models for ordinal dependent variables. Stata Journal, 6(1), 58–82. https://doi.org/10.1177/1536867x0600600104

56.

Williams

T. O.

Mul

Cofi

(2015). Unlocking Africa’s agricultural potentials for transformation to scale of climate-smart agriculture in the African context [Conference session]. Abdou Diouf International Conference Center; Dakar, Senegal. https://www.afdb.org/fileadmin/uploads/afdb/Documents/Events/DakAgri2015/Climate_Smart_Agriculture_in_the_African_Context.pdf

57.

WorldBank. (2008). Managing water resources to maximize sustainable growth: A World Bank water resources assistance strategy for Ethiopia. World Bank Other Operational Studies, (13). https://openknowledge.worldbank.org/bitstream/handle/10986/11758/447350BRI0Box31IC10PNOTE131Ethiopia.pdf?sequence=1

58.

Yesuf

Di Falco

Deressa

Ringler

Kohlin

(2008). The impact of climate change and adaptation on food production in low-income countries: Evidence from the Nile Basin, Ethiopia. IFPRI Discussion Paper, 828, 24. http://www.ifpri.org/publication/impact-climate-change-and-adaptation-food-production-low-incomecountries%5Cnhttp://www.ifpri.org/sites/default/files/publications/ifpridp00828.pdf