Climate-Smart Agriculture Technologies and Determinants of Farmers’ Adoption Decisions in the Great Rift Valley of Ethiopia

Abstract

Agriculture is a sector that is very vulnerable to the effects of climate change while contributing to anthropogenic greenhouse gas (GHG) emissions to the atmosphere. Therefore, applying Climate-Smart Agriculture (CSA) technologies and practices (referee hereafter as CSA technologies) that can sustainably boost productivity, improve resilience, and lower GHG emissions are crucial for a climate resilient agriculture. This study sought to identify the CSA technologies used by farmers and assess adoption levels and factors that influence them. A cross-sectional survey was carried out gather information from 384 smallholder farmers in the Great Rift Valley (GRV) of Ethiopia. Data were analyzed using percentage, chi-square test, t test, and the multivariate probit model. Results showed that crop diversification, agroforestry, and integrated soil fertility management were the most widely practiced technologies. The results of the chi-square and t tests showed that there are differences and significant and positive connections between adopters and non-adopters based on various attributes. The chi-square and t test results confirmed that households who were older and who had higher incomes, greater credit access, climate information access, better training, better education, larger farms, higher incomes, and more frequent interactions with extension specialists had positive and significant associations with CSA technology adopters. The model result showed that age, sex, and education of the head; farmland size; livestock ownership; income; access to credit; access to climate information; training; and extension contact influenced the adoption of CSA technologies. Therefore, considering barriers to the adoption of CSA technologies, in policy and action is anticipated to support smallholder farmers in adapting to climate change while lowering GHG emissions.

1. Introduction

One of the most vulnerable nations to the effects of climate change and variability is Ethiopia [1,2]. The nation’s low capability for adaptation, few options for generating income, climate-sensitive industry (agricultural), and land subject to land degradation and desertification all contribute to its vulnerability to climate change [2]. Farmers’ vulnerability is additionally increased by situations such as drought, flooding, hailstorms, and landslides, as well as the recurrent appearance of pests and diseases [1,2,3,4]. The mainstay of Ethiopia’s economy is agriculture, which is primarily a subsistence production sector [5,6,7]. The performance of the nation’s agricultural system is far below its potential as a result of numerous obstacles (climatic, technological, socio-cultural, policies, and institutional) [3,5,6]. Long-term data show that severe food insecurity has frequently resulted from rainfall failures; the frequency of these natural shocks has increased recently and will continue in the future [8,9,10,11].

The agriculture sector is a significant contributor to climate change due to its generation of a high amount of greenhouse gas (GHG) emissions as well as its mitigation potential [6,12,13,14]. To counter this threat, the Ethiopian government developed a policy and strategy document. The paper includes a green economy strategy and a strategy for climate resilience, which aims to improve the nation’s ability to endure the adverse consequences of climate change through diverse initiatives in several national economic sectors [8].

As a result, the sector must evolve in order to accomplish goals for food security, agricultural development, climate change adaptation, and reduced emission intensities per output [7,15,16]. Climate-smart agriculture (CSA), which is a type of agriculture that sustainably increases productivity, enhances resilience(adaptation), and reduces/removes GHGs (mitigation) where possible, enhances achievement of national food security and development goals; it is, therefore, accepted nationally as an approach to transform the sector [7]. Currently promoting CSA technologies has become a top policy priority in most African countries including Ethiopia [10]. Conservation agriculture, integrated soil fertility management, small-scale irrigation, agroforestry, and crop diversification are the common CSA technologies practiced at smallholder farming systems in Ethiopia [17,18]. Climate smart agriculture (CSA) contributes to agricultural production, resilience to climate change, and climate change mitigation [14,19,20,21,22,23,24]. Crop yield has been shown to increase by 30–45% under CSA practices [11]. However, the generation, transfer, and adoption of improved agricultural technologies in general and CSA technologies in particular is still much less than what is necessary to meet the need for food production and the co-benefit of GHG emission reduction in the country [10].

The Ethiopian Great Rift Valley (GRV) is used as a case study region. Given the effects of climate change and harsh weather, it is an environmentally susceptible place [10,11,23]. For the local smallholder farmers, further current and future climate change and variability could be a significant problem [5]. According to studies, smallholder farmers in the GRV are aware of the changing climate and are using adaption strategies and coping mechanisms [17,25]. Studies on the perspectives of farmers and the factors that influence their decisions to adapt have also been conducted by [23,26]. The adoption of CSA technologies and the factors influencing farmers’ decisions to employ the technologies, however, have not yet been the subject of any studies. Therefore, it is crucial and timely to identify the CSA technologies and assess the adoption factors that influence them. The results of this study could add to the body of knowledge about CSA technologies used by smallholder farmers and the key elements that influence their adoption. The goals of this study are to identify the CSA practices used by smallholder farmers in the study region and to analyze the factors that influence farmers’ decisions to adopt particular CSA practices.

2. Materials and Methods

2.1. The Study Area

Three districts in the GRV of Ethiopia, namely Adama Zuria (8°33′350 N–8°380 46 N latitudes and 39°100 570 E–39°30′150 E), Arsi Negele (7.15° to 7.75° N and 38.35° to 38.95° E), and Hawassa Zuria (07° 01′ 54″ to 07° 50′ 36″ N and 38° 15′ 39″ to 38° 25′ 43″ E), were presented for this study. Adama Zuria in the Eastern Shewa zone, Arsi Negele in the West Arsi zone of Oromia regional states, and Hawassa Zuria in the Sidama regional states of Ethiopia were the study areas selected (Figure 1). Additional details about the stations are given in

Table 1. Locations of the study area’s altitude, rainfall, minimum temperature, and maximum temperature.

Location	Altitude	Location		Rainfall (mm)	Tmax (°C)	Tmin (°C)
		Lat	Long	Annual	Annual	Annual
Adama	1622	8.55	39.28	766	28	15
Arsi Negele	1913	7.35	38.66	2052	23	13
Hawassa	1694	7.06	38.48	1575	22	10

Note. Tmax—maximum temperature, and Tmin—minimum temperature.

.

2.2. Data and Sampling

Data for this study came from a survey of 384 smallholder farmer households in the GRV of Ethiopia conducted in 2020. The districts (Adama Zuria, Arsi Negele, and Hawassa Zuria) were purposefully chosen with considerations for their potential for maize crop production, high vulnerability to climate change, and representation of rural households (84, 81, and 100%, respectively). Kebeles, Ethiopia’s smallest administrative unit, and agricultural households were chosen using simple random and systematic sampling approaches, respectively. Based on Kothari (2004), the sample size for the survey study was calculated as follows:

$$\mathit{n}=\frac{\mathit{N}\ast {\mathit{z}}^{\mathbf{2}}\ast \mathit{p}\ast \mathit{q}}{\left(\mathit{N}-\mathbf{1}\right)\ast {\mathit{e}}^{\mathbf{2}}+{\mathit{z}}^{\mathbf{2}}\ast \mathit{p}\ast \mathit{q}}$$

(1)

where n is the sample size, Z is the standardized normal deviation set at a confidence level of 1.96 to 95%, p is the estimated fraction of a characteristic that is not present in the population (1–p) (0.5), and e is the degree of accuracy needed, generally set at 0.05 alpha level.

Structured questionnaires were administered face-to-face as a method of gathering data. Important data on the demographic background, the socioeconomic circumstances of the farmers, their means of subsistence, and institutional elements were gathered through surveys. The survey also included questions of utilization of CSA technologies at the smallholder farming level. Primary data on other pertinent variables were also collected.

2.3. Specification of Econometric Model

A multivariate probit regression model was employed to identify the factors that determine the smallholder farmers’ adoption decision for CSA technologies. The model aids in simulating the impact of the explanatory variables on the selection of each technology while allowing for the correlation of the unmeasured components (error terms) freely.

It is a generalization of the probit model used to estimate several correlated binary outcomes jointly. This model is a good fit for the study because farmers are more likely to use a variety of CSA technology.

The method is distinguished by a collection of n binary dependent variables ${\mathit{Y}}_{\mathit{h}\mathit{p}\mathit{j}}$ such that:

$${\mathit{Y}}_{\mathit{h}\mathit{p}\mathit{j}}^{\ast }={\mathit{X}}_{\mathit{h}\mathit{p}\mathit{j}}^{\prime }{\mathsf{\beta }}_{\mathit{j}}+{\mathsf{\upsilon }}_{\mathit{h}\mathit{p}\mathit{j}, } \mathit{j}=\mathbf{1},\mathbf{2},\dots .\mathit{m}$$

(2)

$${\mathit{Y}}_{hpj}=\{\begin{array}{c}1, if {\mathit{Y}}_{hpj}^{\ast }>0 or \left(if the farmer adopt\right)\\ \mathbf{0}, otherwise\end{array}$$

(3)

where j = 1, 2,…, m refers to the CSA technologies that are available; ${\mathit{X}}_{\mathit{hpj}}^{\prime }$ is a vector of explanatory variables; ${\mathsf{\beta }}_{\mathit{j}}$ refers to the vector of parameter to be estimated; and ${\mathsf{\upsilon }}_{\mathit{hpj} }$ refers to the random error terms, which are distributed as multivariate normal terms with zero means and unitary variance. The latent variable, ${\mathit{Y}}_{\mathit{hpj}}^{\ast }$, which reflects the unobserved preferences or demand associations with the jth option of CSA technologies, is assumed to exist in a rational h^th farmer.

2.4. Definition of Variables

The explanatory variables were chosen based on previous studies and preliminary survey before the actual household survey (Table 2).

Table 2. Variable types and their description.

Variables	Type of Variable	The Variable’s Description
Explanatory variables
Sex of HH	Dummy	If the household head is male, it will take the value 1; otherwise, it will take the value 0.
Age of HH	Continuous	Age of household head (in years)
Education of HH	Continuous	Education of household head (in years)
Family size	Discrete	Number of individuals in a household
Farmland size	Continuous	Total amount of land that households own and cultivate, expressed in hectares
Livestock ownership	Continuous	Using a common conversion factor, livestock holding is calculated using the *TLU.
Annual income	Continuous	A family farm’s annual gross agricultural income (ten thousand Ethiopian birr)
Access to credit	Dummy	If a family has access to credit services, it will take the value 1; otherwise, it will take the value 0.
Access to climate information	Dummy	Climate information accessibility is given the value 1 if a household has access to it and 0 otherwise.
Access to training	Dummy	If a household has access to training, it has a value of 1, otherwise it has none.
xtensionion contact	Continuous	Number of encounters with extension agents during a calendar year
Distance to marketplace	Continuous	The kilometers between home and the market
Dependent variable
Adoption of CSA technology	Dummy	If farmers have implemented CSA technology, it will take the value 1; otherwise, it will take the value 0.

*TLU refers to Tropical Livestock Unit, which is equivalent to 250 kg live animal weight. The dependent variables in this study were CSA technologies adopted. The technologies include crop diversification, agroforestry, integrated soil fertility management, small-scale irrigation, integrated pest management, conservation agriculture, and climate information services (

Table 3. CSA technologies adopted and their description.

CSA Technologies	Abr.	Definition and Description of the Technologies
Crop Diversification	CD	To increase productivity and stability of ecosystem services, CD is the deliberate addition of functional biodiversity at the temporal and/or geographical levels [20]. One method that households might utilize to lessen their susceptibility to external stresses such as climate change is CD. An important benefit of CD is improved ecosystem resilience and function [21]. A climate-smart agriculture technology called CD helps farmers to be more resilient in the face of unpredictable weather brought on by climate change [22]. It is climate-smart because it guarantees food security, boosts resistance to weather change, provides a way to find other livelihoods, and increases revenue [10].
Agroforestry	AF	A deliberate AF involves planting trees and shrubs alongside animals or crops. Reduced nutrient and pesticide runoff, carbon sequestration, enhanced soil quality, erosion management, improved wild life habitat, less fossil fuel consumption, and increased resilience in the face of an uncertain agricultural future are just a few of the positive effects of AF [23]. Adaptation and mitigation of the consequences of climate change are both possible with the use of AF. It can store a significant amount of carbon dioxide, boost resilience, and increase agricultural productivity [24].
Integrated Soil Fertility Management	ISFM	ISFM is a method for increasing agricultural output while protecting sustainable and long-term soil fertility. It involves the careful application of fertilizers, the utilization of recycled organic materials, the use of responsive crop types, and enhanced agronomic techniques [25]. It is a CSA because it can improve soil fertility and lower GHG emissions [26].
Small-Scale Irrigation	SSI	The utilization of basic technology to obtain water for irrigation is a defining characteristic of SSI [27]. Any technology that transports water from its sources to areas where it was previously unavailable is referred to as a “water access technology” [28]. SSI is vitally important as a cutting-edge method in African smallholder agriculture [27]. It enhances agricultural productivity, farm system climatic adaptation, household food security, and national development objectives [28]. The introduction of SSI has a very favorable effect on agricultural income [27].
Integrated Pest Management	IPM	IPM refers to the use of all available plant protection techniques along with the integration of appropriate countermeasures to prevent the spread of harmful organisms, limit the use of plant protection productions and other forms of intervention to what is economically and ecologically necessary, and minimize or eliminate risks to the environment and human health [29].
Conservation Agriculture	CA	CA is an agro-ecological technique that uses three interconnected practices to accomplish sustainable and economic intensification of agricultural systems: minimal soil disturbance, permanent soil cover, and crop rotations [30]. Improved water infiltration and reduced evaporation from the soil surface, along with corresponding reductions in runoff and soil erosion, are two of the primary advantages of CA in dryland agriculture [31]. It lessens soil runoff and erosion, traps carbon, boosts agricultural productivity, lowers the need for farm labor, and enhances soil quality [10].
Climate Information Services	CIS	CIS are defined as services that offer climate information in a way that helps people and organizations make decisions [32]. As a result, CIS entail the timely generation, translation, and dissemination of relevant climate data, information, and knowledge for societal decision-making as well as climate-smart policy and planning [33]. Thus, CIS have evolved into an all-inclusive method for utilizing scientific weather and climate data to lessen vulnerability and increase resilience. It is climate-smart because it raises income, strengthens agricultural resilience, and lowers emissions [34].

).

Table 2. Variable types and their description.

Variables	Type of Variable	The Variable’s Description
Explanatory variables
Sex of HH	Dummy	If the household head is male, it will take the value 1; otherwise, it will take the value 0.
Age of HH	Continuous	Age of household head (in years)
Education of HH	Continuous	Education of household head (in years)
Family size	Discrete	Number of individuals in a household
Farmland size	Continuous	Total amount of land that households own and cultivate, expressed in hectares
Livestock ownership	Continuous	Using a common conversion factor, livestock holding is calculated using the *TLU.
Annual income	Continuous	A family farm’s annual gross agricultural income (ten thousand Ethiopian birr)
Access to credit	Dummy	If a family has access to credit services, it will take the value 1; otherwise, it will take the value 0.
Access to climate information	Dummy	Climate information accessibility is given the value 1 if a household has access to it and 0 otherwise.
Access to training	Dummy	If a household has access to training, it has a value of 1, otherwise it has none.
xtensionion contact	Continuous	Number of encounters with extension agents during a calendar year
Distance to marketplace	Continuous	The kilometers between home and the market
Dependent variable
Adoption of CSA technology	Dummy	If farmers have implemented CSA technology, it will take the value 1; otherwise, it will take the value 0.

*TLU refers to Tropical Livestock Unit, which is equivalent to 250 kg live animal weight. The dependent variables in this study were CSA technologies adopted. The technologies include crop diversification, agroforestry, integrated soil fertility management, small-scale irrigation, integrated pest management, conservation agriculture, and climate information services (Table 3).

3. Results and Discussion

3.1. Characterization of Sampled Farmers

Analysis was performed on the responses of the 384 household heads (83 percent of whom were men) who relied mostly on rainfed agriculture. Given that the national productive age is between the ages of 15 and 64 and the average age of the respondents was 50.8 years, the majority of the respondents were in the productive age category. The average length of time spent in school was 1.2 years, which suggests that the majority of farmers had not finished primary level, which would have a detrimental impact on their ability to accept new technology. The typical family in the research area was made up of 5.8 people. The average household had 7.1 TLU in total land holdings. With regard to income, the typical household’s total income was 22,300 ETB. Of the respondents, 90%, 40%, and 50%, respectively, reported having access to loans, climate information, and training. The average distance to the neighborhood market was 3.0 km, and there were 6.1 average annual contacts with extension workers (

Table 4. Summary statistics of variables across the study sites. Source: (own survey, 2020).

Variables	Adama Zuria (n = 110)		Arsi Negele (n = 185)		Hawassa Zuria (n = 89)		Total for All 3 Study Sites (n = 384)
	Mean	Std. Dev.	Mean	Std. Dev.	Mean	Std. Dev.	Mean	Std. Dev.
Sex of HH	0.8	0.4	0.8	0.4	0.8	0.4	0.8	0.4
Age of HH	51.2	9.6	50.6	9.3	50.9	9.1	50.8	9.3
Education of HH	1.2	1.4	1.2	1.4	1.2	1.5	1.2	1.4
Family size	5.8	1.4	5.7	1.3	6.0	1.5	5.8	1.4
Farm size	0.9	0.5	0.8	0.5	0.8	0.5	0.8	0.5
Livestock possession	7.2	6.4	7.1	6.3	7.0	5.6	7.1	6.2
Annual income (‘000)	22.51	11.16	22.30	10.97	22.05	10.09	22.30	10.80
Wealth status	0.4	0.5	0.4	0.5	0.4	0.5	0.4	0.5
Access to credit	0.9	0.3	0.9	0.3	0.9	0.3	0.9	0.3
Access to climate information	0.4	0.5	0.5	0.5	0.5	0.5	0.4	0.5
Access to training	0.5	0.5	0.4	0.5	0.5	0.5	0.5	0.5
Extention contact	5.9	1.9	6.1	1.9	6.4	1.8	6.1	1.9
Distance to marketplace	2.9	0.5	3.0	0.5	3.0	0.6	3.0	0.5

).

3.2. Adoption Rates of the CSA Technologies

The findings showed that smallholder farmers in the GRV of Ethiopia had embraced a total of seven CSA technologies. The findings indicate that among the farmers in the research area, CD 208 (54%), AF 177 (46%), and ISFM 89 (23%) were comparatively the most extensively used technologies. Other CSA technologies with the lowest adoption rates were SSI 34 (9%), IPM 28 (7%), CA 20 (5%), and CIS 11. (3%). The 159 respondents who did not react, or 41%, continued with their regular activities (Figure 2).

The chi-square test results indicate a strong relationship between technology adoption and household sex, wealth position, loan availability, information of the climate, and training. Additionally, the outcome demonstrated that there is no connection between districts and the adoption of technology (

Table 5. Comparison of CSA technology adopters with non-adopters using the chi-square test. Source: (Own survey. 2020).

Variables	Adopters	Non-Adopters	χ2
District			5.16
Adama Zuria	66.4	33.6
Arsi Negele	53	47
Hawassa Zuria	59.6	40.4
Sex			31.63 ***
Male-headed	89.4	10.6
Female-headed	51.9	48.1
Wealth status			14.71 ***
Rich	69.8	30.2
Poor	50.2	49.8
Access to credit			39.97 ***
Yes	64.2	35.8
No	15.2	84.8
Access to climate information			55.76 ***
Yes	79.4	20.6
No	41.6	58.4
Access to training			211 ***
Yes	98.3	1.7
No	24.9	75.1

*** Statistically significant at 0.001 alpha level.

). Adopters were homes that had a male head of household, had greater incomes, and had access to loans, training, and information on the climate. The t test result showed that there is a significant difference between the CSA technology users and non-adopters in terms of mean age, education, farm size, animal ownership, income, and frequency of extension contacts. The outcome demonstrated the age of the adopters. Additionally, the adopters had higher levels of education, larger farms, higher incomes, and more frequent interactions with extension specialists (

Table 6. Comparison of adopters and non-adopters of CSA technologies using t test. Source: (Own survey. 2020).

Variables	Adopters	Non-Adopters	t-Test
Age of the HH	53.87 (9.5)	46.59 (7.3)	8.15 ***
Education	0.94 (1.5)	1.48 (1.2)	3.74 ***
Family size	5.86 (1.6)	5.69 (1.1)	1.21
Farm size	0.95 (0.6)	0.69 (0.3)	5.18 ***
Livestock	8.49 (7.2)	5.1 (3.4)	5.52 ***
Annual income	24.50 (12.7)	19.22 (6.25)	4.86 ***
Extension contacts	6.33 (1.9)	5.81 (1.8)	2.68 **
Marketplace	2.96 (0.6)	2.97 (0.4)	0.17

**, *** Statistically significant at 0.05 and 0.001 alpha level, respectively.

).

3.3. Determinants for Adoption of CSA Technologies

Results of a multivariate probit regression model are shown, and they highlight factors that influence the adoption of CSA technologies (

Table 7. Multivariate probit regression model parameter estimates on determinates of CSA technologies. Source: (Own survey, 2020).

Variables	CD	AF	ISFM	SSI	IPM	CA	CIS
Intercept	0.17 (0.10)	0.16 (0.10)	−1.19 (0.12)	2.33 (0.18)	−2.54 (0.196)	−2.9 (0.23)	−3.52 (0.31)
Sex	1.75 (0.69)	4.52 *** (0.99)	1.44 (0.90)	15.88 (3770.1)	15.41 (3328.60)	3.32 (2.634)	18.94 (4179.1)
Age	0.08 ** (0.04)	−0.04 (0.04)	0.01 (0.03)	−0.04 (0.06)	−0.09 (0.07)	−0.02 (0.06)	0.07 (0.08)
Education	0.28 (0.19)	0.15 (0.22)	0.48 ** (0.2)	−0.6 (0.69)	−1.03 (0.82)	0.93 * (0.52)	−0.10 (0.70)
Family size	−0.09 (0.24)	0.93 (0.3)	0.40 (0.23)	0.15 (0.37)	0.42 (0.41)	0.57 (0.41)	−0.95 (0.59)
Farm size	1.23 (2.88)	2.96 * (3.16)	1.80 * (2.96)	1.56 (4.59)	4.30 (4.88)	7.49 (4.81)	1.51 (5.38)
Livestock	0.30 (0.21)	1.14 *** (0.25)	0.42 ** (0.2)	122.4 * (113.14)	−162.8 (130.7)	−0.10 (0.51)	0.62 (1.06)
Income category	0.83 * (0.69)	1.02 * (0.93)	1.41 (0.80)	0.8 * (1.56)	−0.94 (1.72)	3.47 (2.62)	4.92 * (2.69)
Credit	0.61 * (0.54)	1.15 (0.92)	0.84 * (1.11)	15.87 (5390.4)	15.9 (5195.9)	15.17 (4315.2)	17.04 (5108.2)
Climate information	0.65 * (0.40)	1.58 *** (0.52)	−0.47 (0.48)	−0.54 (0.72)	−0.89 (0.76)	1.50 ** (0.76)	−0.94 (0.88)
Training	3.09 *** (0.42)	3.59 *** (0.45)	3.13 *** (0.6)	2.38 * (1.30)	2.48 * (1.48)	18.74 * (2328.8)	1.39 (1.17))
Extension contacts	0.06 (0.15)	0.06 (0.19)	0.16 * (0.15)	0.01 (0.25)	0.19 (0.27)	0.09 (0.30)	0.44 (0.33)
Distance to marketplace	−0.43 * (0.36)	−0.87 (0.42)	0.23 (0.26)	0.03 (0.45)	0.34 (0.50)	−0.12 (0.56)	−1.76 (0.66)

In brackets are the coefficients and standard errors. ***, **, * statistically significant at the alpha levels of 0.001, 0.05, and 0.1, respectively.

).The model’s findings showed that some of the explanatory factors had an impact on how small-scale farmers in the study area adopted particular CSA technology. According to the dependent variables, adoption of CD is positively and strongly correlated with farmers’ age, income, availability to financing, knowledge of the climate, and level of training. AF is significantly and positively correlated with the sex of farmers, ownership of livestock, income, and availability to training. Education of the farmers, the size of the farmland, the ownership of animals, access to training, and contacts with extension personnel are all favorably and significantly related to the adoption of ISFM. The ownership of livestock, money, and access to training are all positively and strongly correlated with the adoption of SSI. Age of farmers is inversely and significantly correlated with IPM adoption. The use of CIS has a good and considerable impact on farmers’ income (Table 4). Explanatory variables that significantly affect at least one dependent variable will be explored, especially in this section. The model’s findings revealed that the following factors strongly influenced the adoption of CSA technologies: farmers’ sex, age, education level, field size, animal ownership, access to credit, access to climatic information, access to training, and extension contact.

Sex of the household head: This variable has a positive and significant effect on AF. According to the findings, male-headed families are more likely than female-headed households to adopt agroforestry as CSA technology. This may be due to the fact that female-headed households have limited access to labor, information, land, and other resources, and they are also engaged in additional responsibility at home. This result is consistent with the findings of [35], who reported that more male-headed households are involved in agroforestry than female-headed households. According to [36], female-headed households are less likely to adopt the CSA technologies than male-headed households.

Age of the household head: This variable has a positive and significant effect on CD. The result implies older farmers are better than younger farmers at using CD.This tendency may be due to the fact that older farmers can acquire seeds of improved crop varieties, as they are in economically better situations and have larger farmland than the younger ones. The study result is in agreement with the findings of [35]. The age of farmers also has significant and negative effects on applying IPM as a CSA technology. This implies that younger farmers use IPM better than older farmers, which may be due to risk aversion of innovative technologies such as IPM by older farmers.

Education of the household head: This variable has a positive and significant effect on ISFM and CA. This implies that the probability of more educated farmers being aware of the use of ISFM and CA as a CSA technology is higher than that of less educated farmers. This may be because educated farmers would have easier access to information, be better able to interpret it, and be able to comprehend and assess the situation more readily than less educated farmers. The result is in line with the report of [29,30,31].

Farmland size: This variable has a positive and significant effect on AF and ISFM. This shows that farmers who have larger farmland size are better at applying AF and ISFM than the ones who have lesser farmland size. Evidence suggested that if farmers have more farmland, they tend to invest more in the use of CSA technologies [37]. This may be due to the fact that farmers with big farms have greater areas to plant trees for AF and greater financial capacity to purchase inputs to apply ISFM better than those having small-sized farms. This result is in line with the finding of [30,32].

Livestock ownership: This characteristic was predicted to increase the likelihood that households would decide to utilize CSA technologies. As expected, it has a positive and significant effect on AF, ISFM, and SSI. The result showed that farmers who have a large number of livestock are better at practicing AF, ISFM, and SSI than farmers who have a smaller number of livestock. The possible cause could be the fact that the traction and manure needed to maintain soil fertility are provided in large part by animals. Farmers with a larger herd size have a greater possibility of making more money to spend on the supplies and equipment needed for the CSA technologies (AF, ISFM, and SSI). A similar finding was reported in previous studies by [35].

Annual income: Annual income of the household has a positive and significant effect on the use of CD, AF, SSI, CIS and CA as a CSA technology. This could be apparent because farmers who have better income can purchase and rent inputs (improved crop varieties, fertilizer, land, labor, and equipment) and electronic equipment to use the mentioned technologies. The findings of this study are in line with those of other researchers who found that having more diversified income streams will encourage households to engage in irrigation activities by giving a start-up capital. Similar results are reported by other studies of [35].

Access to credit: The variable has a positive and significant effect on the use of CD and ISFM. This shows that farmers who have access to credit can better apply CD and ISFM CSA technologies. This is because having access to credit increases financial resources of farmers and their ability to meet transaction costs associated with the various CSA technologies they might want to take. The result of this study is consistent with the reports of [27,29,30].

Access to climate information: This factor significantly and favorably influences the use of CD, AF, and CA. This result revealed that farmers who have access to climate information are better at using CD, AF, and CA than the farmers who has no access to the service. This is possibly because farmers who have access to reliable information on current and future temperature and rainfall could have a chance to choose from among improved varieties (early maturing, drought resistance, and disease/pest resistance). As per the agro-advisory, the farmers obtain and plant trees and use crop residue. This result is in line with findings of related studies [29,33].

Access to training: The utilization of CD, AF, ISFM, SSI, IPM, and CA is significantly and favorably impacted by this variable. The result shows that farmers who have access to training were better users of CD, AF, ISFM, SSI, IPM, and CA than the ones who do not have access to training. This is because the more technical assistance and trainings farmers receive from the extension agents, the greater the likelihood that the farmers will use CSA technologies. A related finding is reported in certain research [27,29].

Extension contacts: The outcome supports the notion that extension workers’ frequent visits have a favorable and significant impact on ISFM. The results show that farmers who have more access to extension contacts were better at practicing ISFM than the farmers who have lesser access to the service. This could be due to the fact that extension services create access to information on agronomic practices, climate change, and CSA technologies. Farmers who have greater access to technology information and technical support are more aware of and capable of using the technology. This result is in line with the report of [38,39].

Distance to marketplace: The findings show that the use of CD as a CSA technology is significantly but adversely impacted by the distance to the nearest market. The results show that farmers who have a marketplace nearby were better at practicing CD than the farmers who do not have a marketplace nearby. This could be due to the fact that farmers can buy improved crop varieties and save time in purchasing other materials so that they have both the input and time to employ CD. This result is in line with the reports of previous studies [27,29,30].

4. Conclusions

Due to climate change and extreme weather occurrences, the research area—the GRV of Ethiopia—is an environmentally fragile and drought-prone region. For the local smallholder farmers, further current and future climate change and variability could be a significant problem. In order to find and execute workable CSA technologies at the microlevel, it is crucial to comprehend the forms and implementation of CSA technologies at the local level and evaluate associated adoption determinants. By using three districts in the GRV as case studies—districts that are drought-prone, are climate-change susceptible, are potential maize producers, and represent small-holder farmers—this study aimed to address these challenges. The CSA technologies being employed by smallholder farmers include CD, AF, ISFM, SSI, IPM, CA, and CIS according to their rate of adoption. CD, AF, and ISFM were the most widely practiced technologies. The results also reveal that households who were older and had higher incomes, greater credit access, more knowledge of the climate, better training, better education, larger farms, higher incomes, and more frequent interactions with extension specialists had positive and significant associations with CSA technology adopters. The model results showed that age, sex, and education of the head; farmland size; livestock ownership; income; access to credit; climate information; training; and extension contact influenced the selection of CSA technologies. This research implies that encouraging smallholder farmers with training in CSA technologies, specifically focusing on families headed by male and impoverished farmers, can significantly boost the adoption of CSA technology. In order to aid smallholder farmers in adapting to the changing climate and, as a side benefit, to reduce their emissions of greenhouse gases, there is also a need to encourage research, development, and spread of suitable and inexpensive CSA technology. To help smallholder farmers adapt to climate change while reducing emissions, effective action must be taken to reduce obstacles to the adoption of CSA technology. Policy and practice must also take these adoption factors into account. In addition, the district agricultural and rural development office and other relevant organizations should work to resolve the problems that hinder the use of CSA technology in the study area.