# Determinants of Small-Scale Irrigation Use for Poverty Reduction: The Case of Offa Woreda, Wolaita Zone, Southern Ethiopia

Authors: Zekarias Zemarku; Mulumels Abrham; Elias Bojago; Tsegeye Bojago Dado
Journal: Advances in Agriculture
 (2022)

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## Abstract

Small-scale irrigation is one of the agricultural activities used by rural farmers to improve the overall livelihood of the rural community by increasing income, securing food, meeting social requirements, and reducing poverty. The main objective of this study was to look into the factors that influence small-scale irrigation for poverty reduction among small-holder farmers in the Offa Woreda, Wolaita Zone. Government and nongovernmental organizations supplied small-scale irrigation and training to small-holder farmers in the study area at various times; however, the key determinants impacting small-scale irrigation use were not fully identified in the study area. The study location was chosen for this study purpose because no prior in-depth research had been conducted. Simple random sampling was used to select the three kebeles for the study. A total of 134 people were included in the study sample frame. The sample was chosen using simple random sampling and the proportional probability-to-size (pps) method, which was proportional to the size and used multistage sampling procedure. The primary data were collected using the interview schedule. Both quantitative and qualitative data were analyzed using descriptive and econometric analyzes in SPSS version 21. Qualitative data were also collected through focus groups and key informant interviews. A factor that influences small-scale irrigation use was investigated using a binary logistic regression model. The binary logistic regression model revealed that both the irrigation participants (37.3%) and the nonparticipant (62.7%) families had a combination of personal and demographic, economic, institutional, and social influencing factors that hinder their likelihood of using irrigation. HH age, annual income of the household, distance from the closest farm sight, farm experience, participation in training, access to irrigation, and sex of HH were positively and significantly influencing factors in the model, while total ownership of livestock of households and health status of households were negatively significant variables. As a result, the Agricultural and Natural Resources Development Office, as well as the Water Mining and Energy Offices, should devote scientific attention to the significant factors that influence irrigation use to boost farm family production and productivity.


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## Body

## 1. Introduction

### 1.1. Background of the Study

Ethiopia’s agricultural activity is dominated by small farmers, who produce more than 95% of the country’s agricultural output [1–3]. Agriculture, which accounts for 41.4% of Ethiopia’s GDP and 80% of total employment, is the country’s economic backbone [4]. Ethiopian agriculture and the national economy as a whole are characterized by the country’s inability to produce enough food to feed its population [5, 6]. Famines have occurred in Ethiopia in the past due to high population pressure, resource depletion, and drought, all of which have a significant impact on rainfed agriculture [7]. The primary causes of rural poverty and food insecurity have been proven to be low farm production and productivity as a result of the use of outdated technologies and other modern inputs that improve productivity [8, 9].By growing crops of higher value for the market and harvesting more than once a year, irrigation in Ethiopia helps farmers improve their income and household’s resilience and buffer their livelihoods against shocks and stressors [10, 11]. As a result, they were able to increase their assets, buy more food and nonfood household items, educate their children, and reinvest in farm input or livestock to improve their productivity. On the other hand, benefits are distributed fairly evenly among households. Furthermore, Ethiopia has abundant water resources that could be used for small-scale irrigation, among other things, to decouple economic performance from rainfall variability [12–14]. The Ethiopian government has launched an agriculture-led development program, with small-scale irrigation development as a key component, due to the importance of agriculture for the Ethiopian economy. Only about 5% of the 3.5 million acres of irrigated land have been developed, according to estimates [15, 16]. Despite potentially irrigable land and ample surface runoff, 52% of Ethiopia’s population is considered food insecure, according to [17, 18]. Agriculture has contributed more to reducing poverty and reducing food security than any other intervention [19].Africa’s population is expected to reach 2.1 billion people by 2050, putting enormous pressure on agriculture to feed people and provide jobs [20].Given the goals for small-scale irrigation development, it is critical to know how small-scale irrigation is now helping to alleviate poverty. As a result, small-scale irrigation significantly helps farmers’ productive and livelihood activities to meet the issues of drought and rainfall variability. SSI also increases income and ensures food self-sufficiency (the ability to meet consumption needs, particularly for staple food crops, through own production rather than buying or importing) [21, 22]. This primarily relates to the production function (the production function is a functional relationship between input and output). It also reduces risk and uncertainty in the agriculture industry, which has the potential to greatly improve productivity and reduce poverty. As a result, increasing income and decreasing poverty have a direct and indirect impact on household living standards [23]. Expanding small-scale irrigation is a policy goal in Ethiopia, and particularly in the Oromia area, to improve rural lives, reduce and grow poverty, and adapt to climate change [24]. Irrigation improves people’s lives by increasing income, food security, job opportunities, social needs, and poverty reduction [25]. Irrigated agriculture decreases crop failure and external shocks while also increasing production, resulting in higher income and food security [26]. As a result, one of the ways to increase production levels, particularly for small farmers, is to invest in small-scale irrigation. In Ethiopia, farmers’ participation in small-scale irrigation has been minimal [19, 27].Small-scale irrigation development receives special attention due to its low capital demand. Despite this, the amount of attention paid to irrigation development in this sector has not improved [28]. Additionally, current irrigation farms are operating at suboptimal levels, and many small-scale irrigation (SSI) projects are not operating at the required economic efficiency [29, 30]. Several studies have demonstrated the advantages and disadvantages of irrigation in terms of poverty reduction [31]. Irrigation, according to most, can increase production and productivity. This, in turn, offers new job opportunities on and off the farm, potentially increasing rural income, livelihoods, and quality of life [29, 31–33]. However, no research has been done on the effect of poverty on SSIs in the study area. Despite this, the sector has been unable to provide enough food for the people. It is also incapable of increasing the revenue for the individual involved and eradicating poverty. As a result, several researchers have advanced various causes for the sector’s failure to reduce poverty, improve income, and achieve self-sufficiency. It is primarily concerned with the production function. The functional relationship that exists between input and output is called the production function. To raise agricultural output or productivity, we must either increase inputs (the number of factors of production) or apply better technologies. Increasing input involves employing more labor, land, human capital, physical capital, etc., whereas improved technology involves implementing production-enhancing techniques such as irrigation, genetic modeling, certain chemicals, extension programs, etc. However, increasing input is not an effective way to increase output because growing land and other limiting resources over an endless time horizon are problematic. The adoption of better technologies, such as irrigation, will undoubtedly reduce risk and uncertainty in the agriculture industry, perhaps increasing output and reducing poverty. Irrigation is particularly important in raising income and alleviating poverty due to its direct and indirect impact on household living conditions [23].In addition, no research has been done to determine the relevant socioeconomic characteristics and the main factors that influence small-scale irrigation use in the study area are unknown. Although irrigation is available to many households in the study area, the impact it has had on poverty reduction and household influencing factors has not yet been well studied in the area.The purpose of this research was to identify the primary factors that influence the use of small-scale irrigation to reduce poverty in the Offa Woreda. Because poverty reduction is the ultimate measure of development effectiveness, this study investigated the impact of small-scale irrigation on the reduction of rural poverty.


## 1.1. Background of the Study

Ethiopia’s agricultural activity is dominated by small farmers, who produce more than 95% of the country’s agricultural output [1–3]. Agriculture, which accounts for 41.4% of Ethiopia’s GDP and 80% of total employment, is the country’s economic backbone [4]. Ethiopian agriculture and the national economy as a whole are characterized by the country’s inability to produce enough food to feed its population [5, 6]. Famines have occurred in Ethiopia in the past due to high population pressure, resource depletion, and drought, all of which have a significant impact on rainfed agriculture [7]. The primary causes of rural poverty and food insecurity have been proven to be low farm production and productivity as a result of the use of outdated technologies and other modern inputs that improve productivity [8, 9].By growing crops of higher value for the market and harvesting more than once a year, irrigation in Ethiopia helps farmers improve their income and household’s resilience and buffer their livelihoods against shocks and stressors [10, 11]. As a result, they were able to increase their assets, buy more food and nonfood household items, educate their children, and reinvest in farm input or livestock to improve their productivity. On the other hand, benefits are distributed fairly evenly among households. Furthermore, Ethiopia has abundant water resources that could be used for small-scale irrigation, among other things, to decouple economic performance from rainfall variability [12–14]. The Ethiopian government has launched an agriculture-led development program, with small-scale irrigation development as a key component, due to the importance of agriculture for the Ethiopian economy. Only about 5% of the 3.5 million acres of irrigated land have been developed, according to estimates [15, 16]. Despite potentially irrigable land and ample surface runoff, 52% of Ethiopia’s population is considered food insecure, according to [17, 18]. Agriculture has contributed more to reducing poverty and reducing food security than any other intervention [19].Africa’s population is expected to reach 2.1 billion people by 2050, putting enormous pressure on agriculture to feed people and provide jobs [20].Given the goals for small-scale irrigation development, it is critical to know how small-scale irrigation is now helping to alleviate poverty. As a result, small-scale irrigation significantly helps farmers’ productive and livelihood activities to meet the issues of drought and rainfall variability. SSI also increases income and ensures food self-sufficiency (the ability to meet consumption needs, particularly for staple food crops, through own production rather than buying or importing) [21, 22]. This primarily relates to the production function (the production function is a functional relationship between input and output). It also reduces risk and uncertainty in the agriculture industry, which has the potential to greatly improve productivity and reduce poverty. As a result, increasing income and decreasing poverty have a direct and indirect impact on household living standards [23]. Expanding small-scale irrigation is a policy goal in Ethiopia, and particularly in the Oromia area, to improve rural lives, reduce and grow poverty, and adapt to climate change [24]. Irrigation improves people’s lives by increasing income, food security, job opportunities, social needs, and poverty reduction [25]. Irrigated agriculture decreases crop failure and external shocks while also increasing production, resulting in higher income and food security [26]. As a result, one of the ways to increase production levels, particularly for small farmers, is to invest in small-scale irrigation. In Ethiopia, farmers’ participation in small-scale irrigation has been minimal [19, 27].Small-scale irrigation development receives special attention due to its low capital demand. Despite this, the amount of attention paid to irrigation development in this sector has not improved [28]. Additionally, current irrigation farms are operating at suboptimal levels, and many small-scale irrigation (SSI) projects are not operating at the required economic efficiency [29, 30]. Several studies have demonstrated the advantages and disadvantages of irrigation in terms of poverty reduction [31]. Irrigation, according to most, can increase production and productivity. This, in turn, offers new job opportunities on and off the farm, potentially increasing rural income, livelihoods, and quality of life [29, 31–33]. However, no research has been done on the effect of poverty on SSIs in the study area. Despite this, the sector has been unable to provide enough food for the people. It is also incapable of increasing the revenue for the individual involved and eradicating poverty. As a result, several researchers have advanced various causes for the sector’s failure to reduce poverty, improve income, and achieve self-sufficiency. It is primarily concerned with the production function. The functional relationship that exists between input and output is called the production function. To raise agricultural output or productivity, we must either increase inputs (the number of factors of production) or apply better technologies. Increasing input involves employing more labor, land, human capital, physical capital, etc., whereas improved technology involves implementing production-enhancing techniques such as irrigation, genetic modeling, certain chemicals, extension programs, etc. However, increasing input is not an effective way to increase output because growing land and other limiting resources over an endless time horizon are problematic. The adoption of better technologies, such as irrigation, will undoubtedly reduce risk and uncertainty in the agriculture industry, perhaps increasing output and reducing poverty. Irrigation is particularly important in raising income and alleviating poverty due to its direct and indirect impact on household living conditions [23].In addition, no research has been done to determine the relevant socioeconomic characteristics and the main factors that influence small-scale irrigation use in the study area are unknown. Although irrigation is available to many households in the study area, the impact it has had on poverty reduction and household influencing factors has not yet been well studied in the area.The purpose of this research was to identify the primary factors that influence the use of small-scale irrigation to reduce poverty in the Offa Woreda. Because poverty reduction is the ultimate measure of development effectiveness, this study investigated the impact of small-scale irrigation on the reduction of rural poverty.


## 2. Research Methodology

### 2.1. Description of the Study Area

This research was carried out in Offa Woreda, which is located in the Southern Nations, Nationalities and Peoples’ Region of Ethiopia. Offa Woreda is a Woreda in the Wolaita Zone and is bordered to the south by the Gamo Gofa Zone, to the west by Kindo Didaye, to the north by Kindo Koysha, to the northeast by Sodo Zuria, and to the east by Humbo. Offa’s administrative center is Gesuba. It is situated at a latitude of 6°44′ 59.99″ N and a longitude of 37°29′ 59.99″ E. The Woreda is divided into 21 rural kebeles and four municipal administrative districts, with a total land area of 38,537 ha. The current study area, Offa Woreda, is one of the Woredas insecure in the Southern region due to the large number of people in chronic food insecurity who have benefited from the productive safety net program (PSNP) in recent years [34]. (Figure 1). Kolla (lowland), Weyna Dega (middle), and Wet Dega (highland) are the three main agro-ecological zones in the Woreda, covering 31%, 48%, and 21% of the total area, respectively. Maximum and minimum temperatures range from 34°C to 14°C, respectively [35]. The rainy season is bimodal, with the short rainy season (Belg) lasting from mid-February to May and the long rainy season (Kiremt) beginning in June and lasting until October. Annual rainfall ranges from 850 to 1450 mm, with June to September being the wettest month (OWARDO, 2015).Figure 1
Map of the study area.Woreda’s population is 134,259 people, with 65,733 men and 68,526 women. There are 37,273 households in Woreda, with an average of 6.3 family members per household [36]. Agriculture is the main source of income for the people of the study region, as is in many other parts of Ethiopia. It involves rainfed agriculture for crop production and livestock rearing at the subsistence level. Agriculture is a traditional ox-ploughing activity that is supplemented by occupations like pottery, carpentry, and the planting of root crops and some fruit crops in the vicinity of homesteads.


### 2.2. Research Design

The study used a mixed research methodology that combined an explanatory research design and a qualitative approach to collect relevant data. Using both qualitative and quantitative methodologies together yielded a better understanding of the research problem than using either method alone.


#### 2.2.1. Sample and Sampling Techniques

The Offa Woreda was purposively selected because of its potential for irrigation. The kebeles were also purposively chosen in the second stage based on irrigation use. That is, three kebeles were chosen at random from a total of 21 in Woreda. Again, the sampling frame/list of households that practice small-scale irrigation was used to select the households for each sample from each kebele sample (Table1). Using a multistage sampling technique, the sample households were chosen from a list/sample frame using the proportional probability to sample size.Table 1
Number of sample households from each kebele.
Name of sample kebeleTotal householdsSample householdsUserNonuserTotalUserNonuserTotalDekeya74337411203454Busha72327399142640Mancha58312370162440Total20497611805084134Source: Own summary, 2020.


#### 2.2.2. The Sample Size Determination

Population refers to all study participants; a target population is the larger group to which one wishes to generalize or apply research findings [37]. There are numerous techniques to determine the sample size and imitate a sample size from previous research, including using public tables and algorithms to establish a sample size. Kothari [38] simplified formula was employed in this study. As a result, the 95% confidence level and ±5% precision level were used as a criterion to define and calculate the sample size.Thus,(1)n=z2∗p∗qe2N−1+Z2∗p∗q,where n is the sample size; N is the population size, which is 1180 in this case; e is the accepted error or degree confidence desired, usually at 0.05; z is the standard variation, which is 1.96; p is the standard deviation, which is 0.11 (11%); and q = 1-p = 0.89; therefore,(2)n=z2∗p∗qe2N−1+Z2∗p∗q,n=1.962∗0.11∗0.89∗11800.052∗1179+1.962∗0.11∗0.89=381.734032.9475+0.37609264,n=443.78931523.32359264=134.The proportion of sampling to the population was utilized to determine the appropriate samples of the three kebeles for each stratum. Finally, a representative sample was chosen for each stratum using systematic random sampling approaches. The sample size for each stratum in the three kebeles will be determined using(3)i=n⋅pi,pi=N1/N,thenn=n=n⋅pi,where the total population and the sample population of irrigation users are 399 and 67, respectively. Pi = 152/399 = 0.38, then n = n.pi, 67∗0.38 = 25; N represents the total population number of population, n represents the total sample size, i reflects the number of stratum items chosen, and pi represents the proportion of population included in the stratum.


### 2.3. Sampling Procedure for Qualitative Data

#### 2.3.1. Selection of Key Informant Interviews

Include 6 individuals from each sample of kebeles that were selected using the objective method. In addition, the Director of the Agriculture and Natural Resources Development Office and two experts from the same office were among the participants.


#### 2.3.2. Selection of Focus Group Discussion (FGD)

Nine people from various backgrounds were chosen from each kebele sample to form the focus group. Furthermore, each focus group contained two women and seven men.


### 2.4. Types of Data

Primary and secondary sources were used to gather qualitative and quantitative data for this study.


### 2.5. Method of Data Analysis

The researchers used descriptive statistics and an econometric analysis approach. Percentages, means, and standard deviations are used in the descriptive data analysis method. The chi-square test was used to test the relationship of the dummy variables between the two groups, and thet-test was used to see if there was a significant mean difference between the two groups in the method of comparing the socioeconomic, demographic, and institutional characteristics of households in the study area. In the economic analysis, binary logistic models were used.


#### 2.5.1. Binary Logit Econometric Model

In a regression study, a qualitative response (dependent) variable of the “Yes” or “No” type is typically encountered. Binary choice models are discrete choice models that deal with such binary responses. The binary logit econometric model was used to examine the factors that influence the use of small-scale irrigation. When dependent variables are not represented using ordinary least squares, the use of linear regression is limited. The dependent variable is represented as a linear function in the linear model; therefore, ordinary least squares works. The underlying assumption of binary choice is that individuals express their preference between two choices; that is, there is a chance of selecting one alternative over the other. As a result, inconsistency and bias in the estimate using the ordinary least squares (OLS) parameter will occur. The linear probability model, logistic model, and probit regression model were suggested as the best solutions to overcome the constraint in this case (Wooldridge, 2005). The error term in the equation is normally distributed in the probit model, with a mean of zero and a standard deviation of one. However, in logistic regression, the error term was supposed to have a normal distribution, while the standard deviation was more than one. Despite the fact that the logit and probit models are nearly identical and that the model choice is arbitrary, the logit model offers several advantages (simplicity and ease of interpretation).


#### 2.5.2. Specification of the Logit Model

The logistic function is utilized because it is easier to work with and provides a close approximation to the cumulative normal distribution. Furthermore, as Train (1986) pointed out, a logistic distribution (logit) has an advantage over others in the analysis of dichotomous outcome variables in that it is a mathematically extremely flexible and easily used function (model) that lends itself to a meaningful interpretation and is relatively inexpensive to estimate. Additionally, the logit model was applied in a binary choice of outcomes (use versus nonuse of irrigation technology). The exogenous factors in the model are those currently provided by households. The model provides empirical estimates of how changes in these exogenous variables influence the likelihood of irrigation use, and it is used to assess the extent to which people use technology [39].As a result, a logistic function with odds ratios was employed to calculate the coefficients of explanatory variables most likely to influence farmers’ views about irrigation use. The level of use of irrigation technology was the dependent variable in this analysis, while the independent variables were the 16 variables chosen (Table2).Table 2
Description of dependent and independent variables.
NoVariable codeDescriptionMeasurementVariable typeExpected sign1AgeAge of household headMeasured in years and put in to rangesContinuous—2EDUCLEVEducation level of household headMeasured in gradeContinuous+3ACCSAccess to creditMeasured in availabilityDummy+4HealthHealth status of HHHMeasured in health conditionDummy+5FertlyzrUse of fertilizer1 = user, 0 = notDummy+6DSTDistance from farmMeasured in kilometersContinuous—7NONFRMEngagement in nonfarm activities1 = participate, 0 = notDummy+8FoodFood aid1 = get, 0 = notDummy+9ACCESIRRGAccess to irrigation1 = have access, 0 = notDummy+10TrainParticipation in training1 = participate, 0 = notDummy+11HHSIZEHousehold sizeMeasured in numberContinuous+12FRSZFarm size1 = available, 0 = notDummy+13TLULivestock ownershipMeasured in numberContinuous+14SexSex of HHH1 = male, 0 = femaleDummy+15IncomeAnnual incomeMeasured in birrContinuous+16Farm experienceFarm experienceMeasured in birrContinuous−/+Because the dependent variable in this study was dichotomous, the econometric model assigned a value of 0 to nonusers and a value of 1 to users.Gujarati [40] specifies the functional form of the logit model as follows:(4)Pi=EY=1xi=11+e−β0+β1x1.For the case of exposition, we write (1) as(5)Pi=11+e−zi.The probability of the farmer being a nonuser is expressed as (3), while the probability of the user is(6)1−Pi=11+ezi.Therefore, we can write(7)Pi1−Pi=1+ezi1+e−zi.Now (Pi/1-Pi) is simply the odds ratio in favor of using irrigation, that is the ratio of the likelihood that a farmer would use to the likelihood that this would not use.Finally, if we take the natural log of equation (5), we get(8)Li=lnPi1−Pi=Zi=β0+β1X1+β2X2+⋯+βnXn,where Pi is the probability of being a user, it ranges from 0 to 1; Zi is a function of n explanatory variables (x), which is also expressed as(9)Zi=βo+β1X1+β2X2+⋯+βnXn,where βo is an intercept; β1,β2, -----------------------βn are the slopes of the equation in the model; Li is the log of the odds ratio, which is linear in Xi, but also linear in the parameters; and Xi is the vector of relevant farmers’ characteristics.If the disturbance term (Ui) is introduced, the logit model becomes(10)Zi=βo+β1X1+β2X2+⋯+βnXn+Ui.


### 2.6. Summary of the Variables

The description of the dependent and independent variables is given in Table 2.


## 2.1. Description of the Study Area

This research was carried out in Offa Woreda, which is located in the Southern Nations, Nationalities and Peoples’ Region of Ethiopia. Offa Woreda is a Woreda in the Wolaita Zone and is bordered to the south by the Gamo Gofa Zone, to the west by Kindo Didaye, to the north by Kindo Koysha, to the northeast by Sodo Zuria, and to the east by Humbo. Offa’s administrative center is Gesuba. It is situated at a latitude of 6°44′ 59.99″ N and a longitude of 37°29′ 59.99″ E. The Woreda is divided into 21 rural kebeles and four municipal administrative districts, with a total land area of 38,537 ha. The current study area, Offa Woreda, is one of the Woredas insecure in the Southern region due to the large number of people in chronic food insecurity who have benefited from the productive safety net program (PSNP) in recent years [34]. (Figure 1). Kolla (lowland), Weyna Dega (middle), and Wet Dega (highland) are the three main agro-ecological zones in the Woreda, covering 31%, 48%, and 21% of the total area, respectively. Maximum and minimum temperatures range from 34°C to 14°C, respectively [35]. The rainy season is bimodal, with the short rainy season (Belg) lasting from mid-February to May and the long rainy season (Kiremt) beginning in June and lasting until October. Annual rainfall ranges from 850 to 1450 mm, with June to September being the wettest month (OWARDO, 2015).Figure 1
Map of the study area.Woreda’s population is 134,259 people, with 65,733 men and 68,526 women. There are 37,273 households in Woreda, with an average of 6.3 family members per household [36]. Agriculture is the main source of income for the people of the study region, as is in many other parts of Ethiopia. It involves rainfed agriculture for crop production and livestock rearing at the subsistence level. Agriculture is a traditional ox-ploughing activity that is supplemented by occupations like pottery, carpentry, and the planting of root crops and some fruit crops in the vicinity of homesteads.


## 2.2. Research Design

The study used a mixed research methodology that combined an explanatory research design and a qualitative approach to collect relevant data. Using both qualitative and quantitative methodologies together yielded a better understanding of the research problem than using either method alone.


### 2.2.1. Sample and Sampling Techniques

The Offa Woreda was purposively selected because of its potential for irrigation. The kebeles were also purposively chosen in the second stage based on irrigation use. That is, three kebeles were chosen at random from a total of 21 in Woreda. Again, the sampling frame/list of households that practice small-scale irrigation was used to select the households for each sample from each kebele sample (Table1). Using a multistage sampling technique, the sample households were chosen from a list/sample frame using the proportional probability to sample size.Table 1
Number of sample households from each kebele.
Name of sample kebeleTotal householdsSample householdsUserNonuserTotalUserNonuserTotalDekeya74337411203454Busha72327399142640Mancha58312370162440Total20497611805084134Source: Own summary, 2020.


### 2.2.2. The Sample Size Determination

Population refers to all study participants; a target population is the larger group to which one wishes to generalize or apply research findings [37]. There are numerous techniques to determine the sample size and imitate a sample size from previous research, including using public tables and algorithms to establish a sample size. Kothari [38] simplified formula was employed in this study. As a result, the 95% confidence level and ±5% precision level were used as a criterion to define and calculate the sample size.Thus,(1)n=z2∗p∗qe2N−1+Z2∗p∗q,where n is the sample size; N is the population size, which is 1180 in this case; e is the accepted error or degree confidence desired, usually at 0.05; z is the standard variation, which is 1.96; p is the standard deviation, which is 0.11 (11%); and q = 1-p = 0.89; therefore,(2)n=z2∗p∗qe2N−1+Z2∗p∗q,n=1.962∗0.11∗0.89∗11800.052∗1179+1.962∗0.11∗0.89=381.734032.9475+0.37609264,n=443.78931523.32359264=134.The proportion of sampling to the population was utilized to determine the appropriate samples of the three kebeles for each stratum. Finally, a representative sample was chosen for each stratum using systematic random sampling approaches. The sample size for each stratum in the three kebeles will be determined using(3)i=n⋅pi,pi=N1/N,thenn=n=n⋅pi,where the total population and the sample population of irrigation users are 399 and 67, respectively. Pi = 152/399 = 0.38, then n = n.pi, 67∗0.38 = 25; N represents the total population number of population, n represents the total sample size, i reflects the number of stratum items chosen, and pi represents the proportion of population included in the stratum.


## 2.2.1. Sample and Sampling Techniques

The Offa Woreda was purposively selected because of its potential for irrigation. The kebeles were also purposively chosen in the second stage based on irrigation use. That is, three kebeles were chosen at random from a total of 21 in Woreda. Again, the sampling frame/list of households that practice small-scale irrigation was used to select the households for each sample from each kebele sample (Table1). Using a multistage sampling technique, the sample households were chosen from a list/sample frame using the proportional probability to sample size.Table 1
Number of sample households from each kebele.
Name of sample kebeleTotal householdsSample householdsUserNonuserTotalUserNonuserTotalDekeya74337411203454Busha72327399142640Mancha58312370162440Total20497611805084134Source: Own summary, 2020.


## 2.2.2. The Sample Size Determination

Population refers to all study participants; a target population is the larger group to which one wishes to generalize or apply research findings [37]. There are numerous techniques to determine the sample size and imitate a sample size from previous research, including using public tables and algorithms to establish a sample size. Kothari [38] simplified formula was employed in this study. As a result, the 95% confidence level and ±5% precision level were used as a criterion to define and calculate the sample size.Thus,(1)n=z2∗p∗qe2N−1+Z2∗p∗q,where n is the sample size; N is the population size, which is 1180 in this case; e is the accepted error or degree confidence desired, usually at 0.05; z is the standard variation, which is 1.96; p is the standard deviation, which is 0.11 (11%); and q = 1-p = 0.89; therefore,(2)n=z2∗p∗qe2N−1+Z2∗p∗q,n=1.962∗0.11∗0.89∗11800.052∗1179+1.962∗0.11∗0.89=381.734032.9475+0.37609264,n=443.78931523.32359264=134.The proportion of sampling to the population was utilized to determine the appropriate samples of the three kebeles for each stratum. Finally, a representative sample was chosen for each stratum using systematic random sampling approaches. The sample size for each stratum in the three kebeles will be determined using(3)i=n⋅pi,pi=N1/N,thenn=n=n⋅pi,where the total population and the sample population of irrigation users are 399 and 67, respectively. Pi = 152/399 = 0.38, then n = n.pi, 67∗0.38 = 25; N represents the total population number of population, n represents the total sample size, i reflects the number of stratum items chosen, and pi represents the proportion of population included in the stratum.


## 2.3. Sampling Procedure for Qualitative Data

### 2.3.1. Selection of Key Informant Interviews

Include 6 individuals from each sample of kebeles that were selected using the objective method. In addition, the Director of the Agriculture and Natural Resources Development Office and two experts from the same office were among the participants.


### 2.3.2. Selection of Focus Group Discussion (FGD)

Nine people from various backgrounds were chosen from each kebele sample to form the focus group. Furthermore, each focus group contained two women and seven men.


## 2.3.1. Selection of Key Informant Interviews

Include 6 individuals from each sample of kebeles that were selected using the objective method. In addition, the Director of the Agriculture and Natural Resources Development Office and two experts from the same office were among the participants.


## 2.3.2. Selection of Focus Group Discussion (FGD)

Nine people from various backgrounds were chosen from each kebele sample to form the focus group. Furthermore, each focus group contained two women and seven men.


## 2.4. Types of Data

Primary and secondary sources were used to gather qualitative and quantitative data for this study.


## 2.5. Method of Data Analysis

The researchers used descriptive statistics and an econometric analysis approach. Percentages, means, and standard deviations are used in the descriptive data analysis method. The chi-square test was used to test the relationship of the dummy variables between the two groups, and thet-test was used to see if there was a significant mean difference between the two groups in the method of comparing the socioeconomic, demographic, and institutional characteristics of households in the study area. In the economic analysis, binary logistic models were used.


### 2.5.1. Binary Logit Econometric Model

In a regression study, a qualitative response (dependent) variable of the “Yes” or “No” type is typically encountered. Binary choice models are discrete choice models that deal with such binary responses. The binary logit econometric model was used to examine the factors that influence the use of small-scale irrigation. When dependent variables are not represented using ordinary least squares, the use of linear regression is limited. The dependent variable is represented as a linear function in the linear model; therefore, ordinary least squares works. The underlying assumption of binary choice is that individuals express their preference between two choices; that is, there is a chance of selecting one alternative over the other. As a result, inconsistency and bias in the estimate using the ordinary least squares (OLS) parameter will occur. The linear probability model, logistic model, and probit regression model were suggested as the best solutions to overcome the constraint in this case (Wooldridge, 2005). The error term in the equation is normally distributed in the probit model, with a mean of zero and a standard deviation of one. However, in logistic regression, the error term was supposed to have a normal distribution, while the standard deviation was more than one. Despite the fact that the logit and probit models are nearly identical and that the model choice is arbitrary, the logit model offers several advantages (simplicity and ease of interpretation).


### 2.5.2. Specification of the Logit Model

The logistic function is utilized because it is easier to work with and provides a close approximation to the cumulative normal distribution. Furthermore, as Train (1986) pointed out, a logistic distribution (logit) has an advantage over others in the analysis of dichotomous outcome variables in that it is a mathematically extremely flexible and easily used function (model) that lends itself to a meaningful interpretation and is relatively inexpensive to estimate. Additionally, the logit model was applied in a binary choice of outcomes (use versus nonuse of irrigation technology). The exogenous factors in the model are those currently provided by households. The model provides empirical estimates of how changes in these exogenous variables influence the likelihood of irrigation use, and it is used to assess the extent to which people use technology [39].As a result, a logistic function with odds ratios was employed to calculate the coefficients of explanatory variables most likely to influence farmers’ views about irrigation use. The level of use of irrigation technology was the dependent variable in this analysis, while the independent variables were the 16 variables chosen (Table2).Table 2
Description of dependent and independent variables.
NoVariable codeDescriptionMeasurementVariable typeExpected sign1AgeAge of household headMeasured in years and put in to rangesContinuous—2EDUCLEVEducation level of household headMeasured in gradeContinuous+3ACCSAccess to creditMeasured in availabilityDummy+4HealthHealth status of HHHMeasured in health conditionDummy+5FertlyzrUse of fertilizer1 = user, 0 = notDummy+6DSTDistance from farmMeasured in kilometersContinuous—7NONFRMEngagement in nonfarm activities1 = participate, 0 = notDummy+8FoodFood aid1 = get, 0 = notDummy+9ACCESIRRGAccess to irrigation1 = have access, 0 = notDummy+10TrainParticipation in training1 = participate, 0 = notDummy+11HHSIZEHousehold sizeMeasured in numberContinuous+12FRSZFarm size1 = available, 0 = notDummy+13TLULivestock ownershipMeasured in numberContinuous+14SexSex of HHH1 = male, 0 = femaleDummy+15IncomeAnnual incomeMeasured in birrContinuous+16Farm experienceFarm experienceMeasured in birrContinuous−/+Because the dependent variable in this study was dichotomous, the econometric model assigned a value of 0 to nonusers and a value of 1 to users.Gujarati [40] specifies the functional form of the logit model as follows:(4)Pi=EY=1xi=11+e−β0+β1x1.For the case of exposition, we write (1) as(5)Pi=11+e−zi.The probability of the farmer being a nonuser is expressed as (3), while the probability of the user is(6)1−Pi=11+ezi.Therefore, we can write(7)Pi1−Pi=1+ezi1+e−zi.Now (Pi/1-Pi) is simply the odds ratio in favor of using irrigation, that is the ratio of the likelihood that a farmer would use to the likelihood that this would not use.Finally, if we take the natural log of equation (5), we get(8)Li=lnPi1−Pi=Zi=β0+β1X1+β2X2+⋯+βnXn,where Pi is the probability of being a user, it ranges from 0 to 1; Zi is a function of n explanatory variables (x), which is also expressed as(9)Zi=βo+β1X1+β2X2+⋯+βnXn,where βo is an intercept; β1,β2, -----------------------βn are the slopes of the equation in the model; Li is the log of the odds ratio, which is linear in Xi, but also linear in the parameters; and Xi is the vector of relevant farmers’ characteristics.If the disturbance term (Ui) is introduced, the logit model becomes(10)Zi=βo+β1X1+β2X2+⋯+βnXn+Ui.


## 2.5.1. Binary Logit Econometric Model

In a regression study, a qualitative response (dependent) variable of the “Yes” or “No” type is typically encountered. Binary choice models are discrete choice models that deal with such binary responses. The binary logit econometric model was used to examine the factors that influence the use of small-scale irrigation. When dependent variables are not represented using ordinary least squares, the use of linear regression is limited. The dependent variable is represented as a linear function in the linear model; therefore, ordinary least squares works. The underlying assumption of binary choice is that individuals express their preference between two choices; that is, there is a chance of selecting one alternative over the other. As a result, inconsistency and bias in the estimate using the ordinary least squares (OLS) parameter will occur. The linear probability model, logistic model, and probit regression model were suggested as the best solutions to overcome the constraint in this case (Wooldridge, 2005). The error term in the equation is normally distributed in the probit model, with a mean of zero and a standard deviation of one. However, in logistic regression, the error term was supposed to have a normal distribution, while the standard deviation was more than one. Despite the fact that the logit and probit models are nearly identical and that the model choice is arbitrary, the logit model offers several advantages (simplicity and ease of interpretation).


## 2.5.2. Specification of the Logit Model

The logistic function is utilized because it is easier to work with and provides a close approximation to the cumulative normal distribution. Furthermore, as Train (1986) pointed out, a logistic distribution (logit) has an advantage over others in the analysis of dichotomous outcome variables in that it is a mathematically extremely flexible and easily used function (model) that lends itself to a meaningful interpretation and is relatively inexpensive to estimate. Additionally, the logit model was applied in a binary choice of outcomes (use versus nonuse of irrigation technology). The exogenous factors in the model are those currently provided by households. The model provides empirical estimates of how changes in these exogenous variables influence the likelihood of irrigation use, and it is used to assess the extent to which people use technology [39].As a result, a logistic function with odds ratios was employed to calculate the coefficients of explanatory variables most likely to influence farmers’ views about irrigation use. The level of use of irrigation technology was the dependent variable in this analysis, while the independent variables were the 16 variables chosen (Table2).Table 2
Description of dependent and independent variables.
NoVariable codeDescriptionMeasurementVariable typeExpected sign1AgeAge of household headMeasured in years and put in to rangesContinuous—2EDUCLEVEducation level of household headMeasured in gradeContinuous+3ACCSAccess to creditMeasured in availabilityDummy+4HealthHealth status of HHHMeasured in health conditionDummy+5FertlyzrUse of fertilizer1 = user, 0 = notDummy+6DSTDistance from farmMeasured in kilometersContinuous—7NONFRMEngagement in nonfarm activities1 = participate, 0 = notDummy+8FoodFood aid1 = get, 0 = notDummy+9ACCESIRRGAccess to irrigation1 = have access, 0 = notDummy+10TrainParticipation in training1 = participate, 0 = notDummy+11HHSIZEHousehold sizeMeasured in numberContinuous+12FRSZFarm size1 = available, 0 = notDummy+13TLULivestock ownershipMeasured in numberContinuous+14SexSex of HHH1 = male, 0 = femaleDummy+15IncomeAnnual incomeMeasured in birrContinuous+16Farm experienceFarm experienceMeasured in birrContinuous−/+Because the dependent variable in this study was dichotomous, the econometric model assigned a value of 0 to nonusers and a value of 1 to users.Gujarati [40] specifies the functional form of the logit model as follows:(4)Pi=EY=1xi=11+e−β0+β1x1.For the case of exposition, we write (1) as(5)Pi=11+e−zi.The probability of the farmer being a nonuser is expressed as (3), while the probability of the user is(6)1−Pi=11+ezi.Therefore, we can write(7)Pi1−Pi=1+ezi1+e−zi.Now (Pi/1-Pi) is simply the odds ratio in favor of using irrigation, that is the ratio of the likelihood that a farmer would use to the likelihood that this would not use.Finally, if we take the natural log of equation (5), we get(8)Li=lnPi1−Pi=Zi=β0+β1X1+β2X2+⋯+βnXn,where Pi is the probability of being a user, it ranges from 0 to 1; Zi is a function of n explanatory variables (x), which is also expressed as(9)Zi=βo+β1X1+β2X2+⋯+βnXn,where βo is an intercept; β1,β2, -----------------------βn are the slopes of the equation in the model; Li is the log of the odds ratio, which is linear in Xi, but also linear in the parameters; and Xi is the vector of relevant farmers’ characteristics.If the disturbance term (Ui) is introduced, the logit model becomes(10)Zi=βo+β1X1+β2X2+⋯+βnXn+Ui.


## 2.6. Summary of the Variables

The description of the dependent and independent variables is given in Table 2.


## 3. Results and Discussion

### 3.1. Descriptive Analysis

In 2021, 37.3% interviewed were users (small-scale irrigation participants), while 62.7% were nonusers (nonparticipants in small-scale irrigation). The current status of small-scale irrigation use was found to be very low when the number of users in the study area was compared to the number of nonusers. In addition to the survey results, the respondents in Figure2 stated that small-scale irrigation is insufficient, especially in Dekeya compared to the other two kebeles.Figure 2
Adoption status of SSI in the study area.


#### 3.1.1. Age of Household Head

The age of the household leader has a significant influence on agricultural productivity. Table3 shows that the average age of the household heads of the sample was 27.17, with a standard deviation of 6.67. The average age of the household heads of the sample was 28.48 for users and 26.41 for nonusers, with standard deviations of 7.16 and 6.29, respectively. The user households had a different average age than the nonuser households. With a t-test value of 47.12 and a p-value of 0.027, the statistical study discovered a significant difference in the mean age of the heads of the households between the user and the nonuser households. As a result, age was the deciding factor in whether the household used small-scale irrigation. Another finding suggested that age had a positive and negative relationship with irrigation water access due to its nonlinearity [41, 42]. As a result, the demand for irrigation technology is expected to initially increase due to increased working capacity and then decrease as people age. It also has a positive and negative impact on household income. As a result, it would have an inverted U-shaped relationship in both cases. These findings are in line with those of [43], who found that respondents’ age is positively significant in some areas and negatively significant in others, resulting in a U-shaped relationship in this case.Table 3
Statistical summary of users and nonusers sampled on continuous variables.
VariablesUsersNonuserst-valueMean(Std. dev)Mean(Std. dev)Age28.487.1626.41676.2947.122∗∗∗Farming experience29.0411.222.58.9230.122∗∗∗Farmland size.4130.264.3970.2781.229Family size4.941.854.791.771.53Distance from farm4.971.965.532.3427.42∗∗∗Annual income9123.804811.236689.5467516.9∗∗∗TLU2.760.93.470.5631.212∗


#### 3.1.2. Sex of Household Head

In most of the rural parts of Ethiopia, men are expected to invest their labor in practical work than women. From the point of view of Das, this was also true in the study area that male household heads were more effective than female participation in agricultural activities, especially in irrigation activities. According to the statistics in Table4, 67.16% of the 134 respondents were men, while 22.84% were women. Males led 77.4% of nonuser households, while females led 22.6%.Table 4
Statistical summary of users and nonusers sampled on dichotomous variables.
VariablesDescriptionUsersNonusersX2-valueNo.%No.%Credit accessNo2346.041490.306Yes2754.04351Sex of the sample respondentsMale25506577.410.655Female25501922.6Off-farm activity participation (OFAP)No2448.04047.60.258Yes2652.04452.4Food aidNo2958.041491.805Yes2142.04351Access to irrigationNo1632.06577.426.9∗∗∗Yes3468.01922.6Participation in trainingNo2142.047564.82∗Yes2958.03744Health status of the HHHsNo1122.02934.52.348∗∗Yes3978.05565.5Use of fertilizerNo2550.04452.40.237Yes2550.04047.6Educational levelDescriptionUserNonuser1.49No%No%Illiterate0000Grade 1–440806172.6Grade 5–810202327.4Grade 9–100000Above 100000Total5010084100Source: own data model output (2021).The chi-square test of 10.655 revealed that the sex of the households was strongly related to the use of irrigation. As a result, the use of small-scale irrigation is related to the gender of the heads of the households. Female-headed families, according to the literature cited in Astetkei [44], have less access to improved technologies, land, and extension than male-headed households. This finding is similar to those of Akudugu et al. [45] and Abbasi and Nawab [46], who both discovered significant gender differences in technology adoption in Ghana.


#### 3.1.3. Educational Level of the Respondents

According to the findings in Table4, 100% of the respondents in the area have a formal education and can read and write. In terms of upgraded households, 100% had formal education, while the remaining 0% had no formal education. This is not always the case, though older farmers are with no formal education dominate SISs, yet they are more knowledgeable of scheme governance. This indicates that households with a higher level of education were not more likely to engage in irrigation farming. The statistical analysis of the chi-square test revealed that the level of education of the head of the household did not have significant differences between the households of users and nonusers (Table 4). This contradicts the findings of [43, 47], who found that if the head of the household is literate, he is more likely to accept extension services, irrigation use, and other income-generating activities. Education has also been shown to have a positive impact on adoption in previous studies.


#### 3.1.4. Access to Credit

Of the total of households sampled, 51% have access to credit services, while 49% do not. Similarly, 54% of the user families have access to a credit service, while 46% do not. To see if there was a significant difference in the use of credit services between the two groups, the chi-square test (x2 = 0.36) was used. Table 4 shows that there was no statistically significant difference in credit availability between users and nonuser households. As a result, there is no correlation between household credit availability and irrigation usage.


#### 3.1.5. Engagement in Nonfarm Activities

Table4 shows a dummy variable for participation in off-farm activities. The availability of job opportunities outside the farm expands the possibility of using irrigation to increase household income. Off-farm employment opportunities in Offa Woreda included vegetable trading, cattle trading, grain trading, petty trade, and others, according to the study’s findings. According to the chart, approximately 52% and 48% of user households have participated in off-farm employment and have no experience, respectively. However, about 52.4% and 47.6% of nonuser households, respectively, had participated and had not participated in off-farm employment to earn more revenue to improve their income status. In addition to that, when its relation with irrigation use was observed independently, the chi-square (X2 = 0.258) result was also found not to be significant.


#### 3.1.6. Food Aid

One of the explanatory variables is the amount of food aid in kilograms. Existing productive safety and other emergency programs were providing vulnerable households with access to food. As a result, households receiving food items would be able to meet their food gap demands. It provides an opportunity for creative approaches with the potential to close the food gap. According to Table4, the percent of SSI users who receive food aid is 21 (42%), while the percent of who do not use SSIs is 43 (51%). Furthermore, the chi-square test shows that there is no link between food aid availability and irrigation use.


#### 3.1.7. Access to Irrigation

Irrigation is one of many technical options available to small farmers who want to grow directly edible food grains or diversify their crops while also addressing agriculture’s moisture deficit. As a result, it helps to increase production. It is a dummy variable in the model because it is thought to have a direct relationship with the availability of food in the home. As a result, it is expected that homes with access to irrigation will have a positive impact on the use of irrigation in the home. According to Table4, 60.4% of the 134 total sampled households do not have access to irrigation, whereas 39.6% do. Sixty-eight percent of users’ households have access, while 32% do not. Also, the chi-square test indicates that there is a link between irrigation access and irrigation use.


#### 3.1.8. Participation in Training

Extension services play a vital role in providing guidance and information to rural farmers. Training is one of these services that are beneficial to introducing and developing current technology techniques (proper types and rates of fertilizer, improved varieties of seeds, agrochemicals, etc.). As a result, households that participated in FTC training or farm demonstrations are expected to implement their expertise to increase farm production. As a result, homes would be in a better position to use irrigation. Table4 further demonstrates that of the total sample homes, 49.3% have participated in training, while 50.7% have not. Of the households of irrigation users, 58% participated, while 42% did not. Also, the chi-square test indicates that there is a link between training and irrigation use. As a result, the study’s determinant variable was training participation.


#### 3.1.9. Farming Experience

In this study, the total average farm experience of the selected households dating from the beginning of farming was 24.94 years. The average farm experience of the user and nonuser households was 29.04 years and 22.5 years, respectively, with standard deviations of 11.2 and 8.92, respectively. As seen in Table3, there was a statistically significant mean difference between the two groups. The t-value (30.122) also provides sufficient evidence that there was a significant mean difference between the two groups. As a result, the association between agricultural experience and irrigation use is statistically significant. This finding is in line with Chekol et al. [48], who found that there is a statistically significant relationship between agricultural experience and irrigation use.


#### 3.1.10. Health Status of the Household Head

The farmer’s physical well-being was required to participate in agricultural activities. If the farmer is healthy, he or she can be involved in farm operations and farm management. As a result, the health of the family had influenced irrigation use. The number of days the head of the household was sick each year was calculated (out of farming work). Beneficiary irrigation use was expected to be positively influenced by its health status. According to Table4, out of a total of 134 respondents, 70.1% stated that they were healthy throughout the year, while the remaining 29.9% stated that they were not in full health. Only 22% of irrigation users were determined to be ill, while 78% were judged healthy. Also, the chi-square test of 2.348 indicates that there is some relationship between health status and irrigation use. Therefore, being healthy was the determinant variable of the study.


#### 3.1.11. Use of Fertilizer

The application of fertilizers to crops has a significant impact on yield and productivity. Irrigation requires the use of improved agricultural inputs, such as fertilizers, to grow high-value crops, such as vegetables, by definition. The availability of irrigation encourages farmers to use fertilizer in the cultivation of high-value crops most of the time. As a result, fertilizer-using households were more likely to use irrigation. According to Table4, 48% of the respondents and 51.5% of the nonrespondents were identified as users and nonusers, respectively. Furthermore, the same proportions (50%) of irrigation user families were found to be fertilizer users and nonusers. This suggests that there is not much of a difference between those who use irrigation and those who do not.


#### 3.1.12. Distance from the Farm

It refers to the distance between the residence of the family and the nearest cultivated ground. The closer a farmer is to farmland, the greater the chance of increasing productivity, lowering harvesting costs, and managing land and cropland [49]. As a result, residents closer to their farmland are projected to have cheaper harvesting and transportation costs and will be able to produce more easily thanks to irrigation alternatives. According to Table 3, the average distance from the farm to all houses was 5.138 miles, with a standard deviation of 2.16 miles. The average distance for irrigation users was 4.97 miles, while it was 5.53 miles for nonirrigating households. This shows the statistically significant mean difference between irrigators and nonirrigators (Stringer et al. [50]).


#### 3.1.13. Farmland Size

The smallest and largest land holdings, according to Table3, are 0.06 and 2.5 hectares, respectively. To see whether there was a significant mean difference in land holding between the two groups of respondents, the statistical t-test was used. Between the two groups, there was no significant difference in land ownership. There was no statistically significant difference. The irrigation users in the study area were found to have roughly the same land holdings as the nonusers. As a result, the size of the land holding was not a factor in this investigation.


#### 3.1.14. Livestock Ownership (TLU)

The ownership of livestock by rural farm households is critical for revenue production, food, tractor power, social security, organic fertilizer, and asset holding. This study was closely related to the identification of restrictions on farm household’s production and productivity with the use of small-scale irrigation, by Sohoulande Djebou et al. [51] and Muhoyi and Mbonigaba [52]. For the sake of simplification, the number of cattle owned by respondent households was translated into tropical livestock units. According to Table 3, both sample respondents in the study area have (3.21) tropical livestock units on average. Irrigation users had an average tropical cattle unit of (2.76) while nonusers had (2.76) (3.47). In terms of the statistically significant level of significance of livestock, the statistical t-test (31.220) revealed a statistically significant mean difference between the two categories (irrigation users and nonusers).


#### 3.1.15. Annual Income of the Household

It is the annual income of the household. Higher-income households were expected to use small-scale irrigation more frequently than lower-income households. Salazar and Rand [53] found a significant mean difference between households that used irrigation and those that did not. Furthermore, the t-value of 16.9 was statistically significant. As a result, farm income was determined to be significant at a level of significance less than 5% in Table 3.


#### 3.1.16. Family Size

Converting the number of people at home to equivalent adult reveals the number of adults. The family size is required for the production of agricultural activities in irrigation. A household with a larger number of family members can share the labor load and contribute significantly to the security position of the unique household. As a result, it is expected that it will benefit household irrigation. According to Table3, the average family size for all families was 5.134. The average family size among users was 4.94, while it was 4.79 among nonusers. The t-value was 1.53. This shows that the difference between irrigation users and nonusers is not statistically significant.


### 3.2. The Role of Irrigation in Production, Employment, and Poverty

Irrigation can help alleviate poverty by increasing crop yields, cropping areas, and higher value crops, all of which create jobs (directly from farm workers and indirectly from other workers if wages are bid up). Increased mean yields can result in more food supply, more calories consumed, and better nutrition. This study investigated whether there were significant differences in the levels of production, employment, asset endowment, consumption, and income between irrigation users and nonusers and discovered the following findings.


#### 3.2.1. Irrigation Increased Production

Due to inconsistency in input use, a comparative yield analysis by crop type could not be performed. Figure3 shows the gross yield for the main crops that have access to irrigation. Irrigation use has significantly contributed to households’ goal of higher production, as predicted, and this finding is consistent with other data [29]. Data analysis of the major cereals and horticultural crops revealed that irrigation users produce more maize, green pepper, potato, tomato, red onion, cabbage, and barley per household than nonusers. This evidence shows that irrigation use increases food supply and security. Tomatoes, onions, peppers, and cabbage are all grown exclusively in irrigation homes. This also shows that irrigation encourages crop diversification and intensity.Figure 3
Average crop yields per quintal and per household (1 quintal = 100 kg).


#### 3.2.2. Irrigation Enhanced Employment Opportunities

In theory, one of the most significant advantages of irrigation is the creation of jobs. Irrigation has increased the beneficiaries’ harvests from once a year (rainy season) to two or three times a year, and labor efficiency has improved. Table5 shows that the average hour spent on an irrigated farm for all activities, from plowing to disposal, is much higher than on a rainfed farm. Similarly, the average labor cost for irrigation users (including hired labor) is more than twice that of nonuser families. This implies that irrigation creates more job opportunities. Most small-holder activities rely on family labor, with neighbor help and casual labor supplementing specific operations. The development of irrigation schemes has created employment opportunities for nearby farmers and irrigation users during traditionally dry periods.Table 5
Labor hour and cost of irrigation use.
Average labor hourIrrigation useMeanStandard deviationt/pPlowingUser76.9084.388.464/0.001∗∗Nonuser21.7125.61WeedingUser90.79116.187.085/0.001∗∗∗Nonuser26.5138.11HarvestingUser87.3197.747.445/0.001∗∗∗Nonuser28.3339.66TrashingUser70.9876.127.113/0.001∗∗∗Nonuser24.2338.81Labor cost per ha in Ethiopian birr (ETB)User535.94800.952.988/0.003∗∗Nonuser305.92495.36Source: own survey, 2021.


#### 3.2.3. Irrigation Increased Income

Irrigation is expected to increase cash earnings and has been shown to do so in the past [29, 31, 54]. Similarly, irrigation recipients earned 10161.5 birr per home per year, 33.6% more than nonusers. Irrigation use benefits households that generate crop and livestock revenue, but nonusers earn more off-farm income. A closer look at the data reveals that irrigated agriculture generated remunerative off-farm revenue streams like transportation and commerce, whereas nonirrigators dominated inferior livelihood activities like fire wood and charcoal sales, and causal work. This finding is similar to Getaneh’s [29] discovery that small-scale irrigation reduces nonfarm income.Crop income accounts for 76 and 70.5% income for users and nonusers, respectively, with livestock and off-farm activities accounting for the remainder. Irrigators earned 47.4% more from crops than nonirrigators, a statistically significant difference (Table6).Table 6
Income earned by households with and without irrigation.
Irrigation useIncome source (ETB)UserNonuserTMeanStandard deviation% ShareMeanStandard deviation% ShareLivestock1451.62826.613.51070.22150.313.71.324Crop8138.56012.176.05520.93879.370.54.635∗∗∗Off farm1125.22549.610.51234.72239.915.8−0.0.4Total10161.55612.71007606.04280.61004.562∗∗∗Source: own survey, 2021.


#### 3.2.4. Irrigation-Improved Asset Endowment

Irrigation expands the amount of land available for agricultural use, and access to irrigation improves asset ownership [31]. This research looked at basic production resources, such as land and animals, as well as the overall value of household items (agricultural tools and equipment). Furniture costs are estimated at the time of purchase. As a result, irrigator assets are three times more valuable than nonirrigator assets. Irrigation increases livestock ownership by 0.91 tropical livestock units (TLU) and increases land ownership by 0.38 ha (Table 7).Table 7
Endowment of assets of households with and without irrigation.
Assets ownedIrrigation useMeanStandard deviationTTotal value of asset (ETB)User2060.166510.742.500∗∗Nonuser597.583450.67Total size of plots (ha)User1.513.84Nonuser1.120.76Total livestock (TLU)User5.453.802.008Nonuser4.553.88Source: own survey, 2021.


#### 3.2.5. Irrigation-Improved Household Consumption

To assess the impact of irrigation on household consumption, the expenditure pattern was used as a proxy indicator of standard of living. This typically refers to the household’s ability to produce/purchase a basket of low-calorie and nonfood commodities. As a result, irrigators’ average annual consumption expenditure per adult equivalent (AE) is more than double that of nonirrigators.Similarly, nonusers’ home consumption, food, and nonfood expenses are significantly higher than those of users. Nonirrigators, for example, consume only about 51% of the food that irrigation recipients consume. This implies that having access to irrigation improves food security by increasing the frequency of production. It also increases food availability by 50.7%. As a result, there is a link between nutritional status and access to irrigation. It also has a positive impact on nonfood intake. Nonfood consumption of nonusers was 60.8% that of irrigators (Table8). As a result, this study could show that irrigation availability improves the overall welfare of rural households by improving food access, nonfood consumption, and asset accumulation. This finding is consistent with the findings of [55–57], who discovered that irrigation availability improves the commune’s resilience to drought and improves user assistance.Table 8
Expenditure pattern of households with and without irrigation.
Expenditure (ETB)UserNonuserFPMeanStandard devMeanStandard devFood3467.82965.21715.61813.640.934P≤0.01∗∗∗Nonfood2540.64725.51546.52052.26.0730.014∗∗VOC5968.919828.13047.12660.73.570.060∗Source: own survey, 2021.


#### 3.2.6. Irrigation Contributed to the Reduction of Poverty

Poverty is increasingly understood to be a multidimensional concept that includes everything from low income and expenditure to a lack of education and poor health, as well as other social dimensions such as powerlessness, insecurity, vulnerability, isolation, social exclusion, and gender disparities. In this study, the cost of necessities was used to establish poverty lines. The first step in this approach is to identify a package of food and nonfood products typically consumed by the 20% lowest income quartile and estimate the cost of meeting this need [58–60]. As a result, the food poverty level (FPL) for this study is 1016.49 ETB per AE per year, while total nonfood expenditure (including clothes, medication, taxes and social obligations) is 310.64 birr per AE per year. When all expenditures from the lowest income group are added together, the total poverty line is reached, above which an individual is considered nonpoor. As a result, the annual poverty line was established at 1016.49 birr per AE.


#### 3.2.7. Poverty Status and Indices by Access to Irrigation

Table9 shows that 30.4% of the 313 sample families are poor, accounting for 47.6% of nonusers and 10.3% of users, implying that the incidence of poverty in rainfed farms is 37.3% higher than in irrigation farms. The remaining 89.7% of users and 52.3% of nonusers are not poor. This demonstrates the importance of irrigation development in alleviating poverty. The fact that 10.3% of irrigation beneficiaries are poor implies that, on the one hand, access to irrigation is a necessary but not sufficient condition for alleviation of poverty and, on the other hand, poverty can be negatively impacted if irrigation is mismanaged, and it could be the root cause of poverty; this finding is consistent with the findings of other studies [59, 61].Table 9
Poverty status and indices by access to irrigation
Irrigation usagePoverty statusX2PPoorNonpoorN%N%User13089.71510.351.152P≤0.01∗∗∗Nonuser8852.38047.6Total21869.69530.4Irrigation usageHead count index (α = 0)Poverty gap (α = 1)Squared poverty gap (α = 2)User0.10.0420.02Nonuser0.480.170.09Source: own survey, 2021.Furthermore, poverty must be recognized as a multifaceted phenomenon. According to the survey, per head count, 48% of nonuser households and 10% of user households lived below the locally designated poverty threshold. The associated poverty gap by irrigation use was 0.042 and 0.17 for users and nonusers, respectively, while the poverty severity index was 0.02 and 0.09 for users and nonusers, respectively (Table9). As a result, poverty is worse and more widespread among nonirrigators than among irrigators. This finding is consistent with [59, 62], who discovered that poverty is worse and more widespread among nonirrigators than among irrigators.


### 3.3. Determinants of Small-Scale Irrigation Use by Farm Households

#### 3.3.1. The Age of Farm Household Heads

The age of the household heads has a positive effect on their decision to use small-scale irrigation to improve income and products at a significant level of 5%; with increased age, the probability of using irrigation increases by 1.14 compared to those in their youth hood level to use small-scale irrigation to improve income and products (Table10). As a result, the decision on age and irrigation use was positively associated at a significance level of less than 5%. The findings contradict those of Yenealem (2013) and Tale et al. [63], who discovered that age and small-scale irrigation have no relationship.Table 10
Parameter estimation of binary logistic regression on the use of small-scale irrigation by farmers in Offa Woreda 2020.
Dependent variableY = 0, Y = 1Explanatory variablesBWaldSig.Exp (B)Age of HH0.1314.8920.027∗∗1.140Sex of HH2.5677.5110.006∗∗∗13.027Education level of HH0.1520.0170.8971.164Credit access−0.6080.5030.4780.545Engagement in off-farm activity0.9721.496.2212.643Food aid0.4110.2930.5881.508Access to irrigation4.83415.3740.000∗∗∗125.712Participation in training1.8163.1380.076∗6.147Farm experience0.18511.9020.001∗∗∗1.203Health status−3.3547.9250.005∗∗0.035Use of fertilizer0.2910.1500.6991.338Distance to nearest farm sight1.5322.3780.0827∗1.040Farmland size−1.1721.1120.2920.310TLU−0.5983.1790.075∗0.550Income2.0014.3790.0538∗1.000Family size−0.1310.3370.5610.878∗∗∗Significant at less than 1% probability level; ∗∗significant at less than 5% probability level; ∗significant at less than 10% probability level. Observation: N = 134 −2log likelihood 67.1; model chi-square = 109.941; sensitivity/correct prediction of user = 90; specificity/correct prediction of nonuser = 96.4; and overall cases correctly predicted = 94 (source: model output (2021)).


#### 3.3.2. Sex of Household Head

Sex was significant at the 5% significance level, with a beneficial effect on household irrigation use decisions. The probability of using irrigation increases in the unit of 13 as the probability that the household is male increases. As a result, at a level of significance of less than 5%, the gender of farm households and the decision about irrigation use were positively connected. The results are similar to those of the results of the binary logit model of Yenealem (2013) and Tale et al. [63]. They suggest that gender influences the adoption of improved maize varieties, and adoption of improved agricultural technology is less intense in female-headed households where decisions are made jointly by men and women than in male-headed households where decisions are made jointly (Table 10).


#### 3.3.3. TLU (Livestock Holdings of Agriculture Households)

The Tropical Livestock Unit (TLU) in rural areas was used to count the number of animals, which indicates an accumulation of wealth, security against emergencies, gift, and cultural privilege. They can also be swiftly converted into cash if the need arises. It was expected to have a positive relationship with the dependent variable because the household would be more inclined to use small-scale irrigation to supplement their income as the total number of animals at home increased. This can be attributed to a growth in agricultural households’ wealth and income base, which results in more money accessible to the households. When all other factors are kept constant, it is strongly associated with a probability of less than 10%, and the odds ratio in favor of being an irrigation user decreases by a factor of 0.55. The inverse relationship implies that households with a large herd size are more likely to be nonirrigators because they can earn more money from livestock production and have other options for survival (Table10).


#### 3.3.4. Distance to Nearest Farm Sight

Physical infrastructure access indicators are good predictors of institutional conditions that influence irrigation access and other concerns. Farmers in rural locations, isolated from agriculture, and with inadequate transportation infrastructure were projected to have fewer opportunities. The long distance to the agricultural center was also considered to reduce the need for irrigation. The distance was significant at the 10% level and had a positive influence on the irrigation decision-making of farmers’ households. When the farm distance increases by 1 km in comparison to the nearby farm, the likelihood of using small-scale irrigation also increases by 1 km. As shown in Table10, the distance from the farm and the decision to use irrigation were positively related at a level of significance less than 10%. The results of FGD and KII support the preceding premise. Households near the farm field had easier access to and from the farm. Furthermore, because all activities in the research area are performed by hand, increasing agricultural distance from an irrigation water source exposed households to significant costs due to the difficulty of transporting water to one’s farmland. This finding is consistent with the findings of [54, 64–66].


#### 3.3.5. Farming Experience

The number of years the small farmer engaged in farming activities after the irrigation system was installed in the area was used to determine the farming experience. It was projected that there would be a direct relationship between farm experience and the use of tiny amounts of irrigation (Table10). Farmers with substantial farming experience should be willing to experiment/use small-scale irrigation after learning about the benefits of different irrigation technologies. The farming experience was significant at the 10% significance level, with a favorable effect on farm household irrigation use decisions; the chance of using small-scale irrigation improves by 1.2 compared to nonusers. As a result, at a level of significance less than 10%, farm experience and irrigation use decisions were positively associated. The results of FGD and KII also confirm the previous conclusion. Farmers who have been farming for a long time are more knowledgeable about the benefits of improved irrigation techniques. They were shown to benefit from better types of seeds from irrigable crops. The finding of [67–70] all came to the same conclusion.


#### 3.3.6. Annual Income of the Household

Small-scale irrigation is more common in higher-income households than in lower-income households. As a result, the expected sign of this variable was positive. This indicates that increased agricultural income may increase the likelihood of employing small-scale irrigation. This finding can be explained by the fact that higher affluence provides a better opportunity to finance key supplies for small-scale irrigation. As a result, at a level of significance less than 10%, the total annual income and the decision on irrigation use were positively connected. That is, for every Ethiopian birr increase, the likelihood of agricultural households becoming irrigation users increases by one. Sufdar et al. [71] found that households with higher incomes are more likely to use biogas technology than those with lower incomes (Table 10).


#### 3.3.7. Access to Irrigation

It is included in the model as a dummy variable because it is thought to have a direct relationship with the availability of household food. As a result, it was expected that homes with access to irrigation would have a positive impact on household irrigation use. At the 1% level, it was significant and had a positive impact on the use of household irrigation. When access to irrigation sources increases by one unit, the likelihood that families will become irrigation users increases by 126 units. Furthermore, the FGD and KII findings show that farmers with access to irrigation sources outperformed those without [72] (Table 10).


#### 3.3.8. Participation in Training

Extension programs are critical in providing rural farmers with assistance and information. Training is an important service for introducing and developing new technology (proper types and rate of fertilizer, improved varieties of seeds, agrochemicals, etc.). As a result, households that participated in FTC training or farm demos are expected to put their knowledge to use to boost farm production. As a result, homes would be better able to use irrigation [73, 74]. Training had a positive effect on modest irrigation use at a level of likelihood less than 10%. The probability of a farm home becoming an irrigation user increased six times after training access/chance increased one time (Table 10).


#### 3.3.9. Health Status of the Household Head

To engage in agricultural activity, the farmer’s physical well-being was essential [75]. The health problem was statistically significant at less than 1% and had a detrimental influence on irrigation use. As a consequence, it negatively related to irrigation usage when all other factors were kept constant. This implies that farmers can spend their off-season doing anything other than irrigation (Table 10).


## 3.1. Descriptive Analysis

In 2021, 37.3% interviewed were users (small-scale irrigation participants), while 62.7% were nonusers (nonparticipants in small-scale irrigation). The current status of small-scale irrigation use was found to be very low when the number of users in the study area was compared to the number of nonusers. In addition to the survey results, the respondents in Figure2 stated that small-scale irrigation is insufficient, especially in Dekeya compared to the other two kebeles.Figure 2
Adoption status of SSI in the study area.


### 3.1.1. Age of Household Head

The age of the household leader has a significant influence on agricultural productivity. Table3 shows that the average age of the household heads of the sample was 27.17, with a standard deviation of 6.67. The average age of the household heads of the sample was 28.48 for users and 26.41 for nonusers, with standard deviations of 7.16 and 6.29, respectively. The user households had a different average age than the nonuser households. With a t-test value of 47.12 and a p-value of 0.027, the statistical study discovered a significant difference in the mean age of the heads of the households between the user and the nonuser households. As a result, age was the deciding factor in whether the household used small-scale irrigation. Another finding suggested that age had a positive and negative relationship with irrigation water access due to its nonlinearity [41, 42]. As a result, the demand for irrigation technology is expected to initially increase due to increased working capacity and then decrease as people age. It also has a positive and negative impact on household income. As a result, it would have an inverted U-shaped relationship in both cases. These findings are in line with those of [43], who found that respondents’ age is positively significant in some areas and negatively significant in others, resulting in a U-shaped relationship in this case.Table 3
Statistical summary of users and nonusers sampled on continuous variables.
VariablesUsersNonuserst-valueMean(Std. dev)Mean(Std. dev)Age28.487.1626.41676.2947.122∗∗∗Farming experience29.0411.222.58.9230.122∗∗∗Farmland size.4130.264.3970.2781.229Family size4.941.854.791.771.53Distance from farm4.971.965.532.3427.42∗∗∗Annual income9123.804811.236689.5467516.9∗∗∗TLU2.760.93.470.5631.212∗


### 3.1.2. Sex of Household Head

In most of the rural parts of Ethiopia, men are expected to invest their labor in practical work than women. From the point of view of Das, this was also true in the study area that male household heads were more effective than female participation in agricultural activities, especially in irrigation activities. According to the statistics in Table4, 67.16% of the 134 respondents were men, while 22.84% were women. Males led 77.4% of nonuser households, while females led 22.6%.Table 4
Statistical summary of users and nonusers sampled on dichotomous variables.
VariablesDescriptionUsersNonusersX2-valueNo.%No.%Credit accessNo2346.041490.306Yes2754.04351Sex of the sample respondentsMale25506577.410.655Female25501922.6Off-farm activity participation (OFAP)No2448.04047.60.258Yes2652.04452.4Food aidNo2958.041491.805Yes2142.04351Access to irrigationNo1632.06577.426.9∗∗∗Yes3468.01922.6Participation in trainingNo2142.047564.82∗Yes2958.03744Health status of the HHHsNo1122.02934.52.348∗∗Yes3978.05565.5Use of fertilizerNo2550.04452.40.237Yes2550.04047.6Educational levelDescriptionUserNonuser1.49No%No%Illiterate0000Grade 1–440806172.6Grade 5–810202327.4Grade 9–100000Above 100000Total5010084100Source: own data model output (2021).The chi-square test of 10.655 revealed that the sex of the households was strongly related to the use of irrigation. As a result, the use of small-scale irrigation is related to the gender of the heads of the households. Female-headed families, according to the literature cited in Astetkei [44], have less access to improved technologies, land, and extension than male-headed households. This finding is similar to those of Akudugu et al. [45] and Abbasi and Nawab [46], who both discovered significant gender differences in technology adoption in Ghana.


### 3.1.3. Educational Level of the Respondents

According to the findings in Table4, 100% of the respondents in the area have a formal education and can read and write. In terms of upgraded households, 100% had formal education, while the remaining 0% had no formal education. This is not always the case, though older farmers are with no formal education dominate SISs, yet they are more knowledgeable of scheme governance. This indicates that households with a higher level of education were not more likely to engage in irrigation farming. The statistical analysis of the chi-square test revealed that the level of education of the head of the household did not have significant differences between the households of users and nonusers (Table 4). This contradicts the findings of [43, 47], who found that if the head of the household is literate, he is more likely to accept extension services, irrigation use, and other income-generating activities. Education has also been shown to have a positive impact on adoption in previous studies.


### 3.1.4. Access to Credit

Of the total of households sampled, 51% have access to credit services, while 49% do not. Similarly, 54% of the user families have access to a credit service, while 46% do not. To see if there was a significant difference in the use of credit services between the two groups, the chi-square test (x2 = 0.36) was used. Table 4 shows that there was no statistically significant difference in credit availability between users and nonuser households. As a result, there is no correlation between household credit availability and irrigation usage.


### 3.1.5. Engagement in Nonfarm Activities

Table4 shows a dummy variable for participation in off-farm activities. The availability of job opportunities outside the farm expands the possibility of using irrigation to increase household income. Off-farm employment opportunities in Offa Woreda included vegetable trading, cattle trading, grain trading, petty trade, and others, according to the study’s findings. According to the chart, approximately 52% and 48% of user households have participated in off-farm employment and have no experience, respectively. However, about 52.4% and 47.6% of nonuser households, respectively, had participated and had not participated in off-farm employment to earn more revenue to improve their income status. In addition to that, when its relation with irrigation use was observed independently, the chi-square (X2 = 0.258) result was also found not to be significant.


### 3.1.6. Food Aid

One of the explanatory variables is the amount of food aid in kilograms. Existing productive safety and other emergency programs were providing vulnerable households with access to food. As a result, households receiving food items would be able to meet their food gap demands. It provides an opportunity for creative approaches with the potential to close the food gap. According to Table4, the percent of SSI users who receive food aid is 21 (42%), while the percent of who do not use SSIs is 43 (51%). Furthermore, the chi-square test shows that there is no link between food aid availability and irrigation use.


### 3.1.7. Access to Irrigation

Irrigation is one of many technical options available to small farmers who want to grow directly edible food grains or diversify their crops while also addressing agriculture’s moisture deficit. As a result, it helps to increase production. It is a dummy variable in the model because it is thought to have a direct relationship with the availability of food in the home. As a result, it is expected that homes with access to irrigation will have a positive impact on the use of irrigation in the home. According to Table4, 60.4% of the 134 total sampled households do not have access to irrigation, whereas 39.6% do. Sixty-eight percent of users’ households have access, while 32% do not. Also, the chi-square test indicates that there is a link between irrigation access and irrigation use.


### 3.1.8. Participation in Training

Extension services play a vital role in providing guidance and information to rural farmers. Training is one of these services that are beneficial to introducing and developing current technology techniques (proper types and rates of fertilizer, improved varieties of seeds, agrochemicals, etc.). As a result, households that participated in FTC training or farm demonstrations are expected to implement their expertise to increase farm production. As a result, homes would be in a better position to use irrigation. Table4 further demonstrates that of the total sample homes, 49.3% have participated in training, while 50.7% have not. Of the households of irrigation users, 58% participated, while 42% did not. Also, the chi-square test indicates that there is a link between training and irrigation use. As a result, the study’s determinant variable was training participation.


### 3.1.9. Farming Experience

In this study, the total average farm experience of the selected households dating from the beginning of farming was 24.94 years. The average farm experience of the user and nonuser households was 29.04 years and 22.5 years, respectively, with standard deviations of 11.2 and 8.92, respectively. As seen in Table3, there was a statistically significant mean difference between the two groups. The t-value (30.122) also provides sufficient evidence that there was a significant mean difference between the two groups. As a result, the association between agricultural experience and irrigation use is statistically significant. This finding is in line with Chekol et al. [48], who found that there is a statistically significant relationship between agricultural experience and irrigation use.


### 3.1.10. Health Status of the Household Head

The farmer’s physical well-being was required to participate in agricultural activities. If the farmer is healthy, he or she can be involved in farm operations and farm management. As a result, the health of the family had influenced irrigation use. The number of days the head of the household was sick each year was calculated (out of farming work). Beneficiary irrigation use was expected to be positively influenced by its health status. According to Table4, out of a total of 134 respondents, 70.1% stated that they were healthy throughout the year, while the remaining 29.9% stated that they were not in full health. Only 22% of irrigation users were determined to be ill, while 78% were judged healthy. Also, the chi-square test of 2.348 indicates that there is some relationship between health status and irrigation use. Therefore, being healthy was the determinant variable of the study.


### 3.1.11. Use of Fertilizer

The application of fertilizers to crops has a significant impact on yield and productivity. Irrigation requires the use of improved agricultural inputs, such as fertilizers, to grow high-value crops, such as vegetables, by definition. The availability of irrigation encourages farmers to use fertilizer in the cultivation of high-value crops most of the time. As a result, fertilizer-using households were more likely to use irrigation. According to Table4, 48% of the respondents and 51.5% of the nonrespondents were identified as users and nonusers, respectively. Furthermore, the same proportions (50%) of irrigation user families were found to be fertilizer users and nonusers. This suggests that there is not much of a difference between those who use irrigation and those who do not.


### 3.1.12. Distance from the Farm

It refers to the distance between the residence of the family and the nearest cultivated ground. The closer a farmer is to farmland, the greater the chance of increasing productivity, lowering harvesting costs, and managing land and cropland [49]. As a result, residents closer to their farmland are projected to have cheaper harvesting and transportation costs and will be able to produce more easily thanks to irrigation alternatives. According to Table 3, the average distance from the farm to all houses was 5.138 miles, with a standard deviation of 2.16 miles. The average distance for irrigation users was 4.97 miles, while it was 5.53 miles for nonirrigating households. This shows the statistically significant mean difference between irrigators and nonirrigators (Stringer et al. [50]).


### 3.1.13. Farmland Size

The smallest and largest land holdings, according to Table3, are 0.06 and 2.5 hectares, respectively. To see whether there was a significant mean difference in land holding between the two groups of respondents, the statistical t-test was used. Between the two groups, there was no significant difference in land ownership. There was no statistically significant difference. The irrigation users in the study area were found to have roughly the same land holdings as the nonusers. As a result, the size of the land holding was not a factor in this investigation.


### 3.1.14. Livestock Ownership (TLU)

The ownership of livestock by rural farm households is critical for revenue production, food, tractor power, social security, organic fertilizer, and asset holding. This study was closely related to the identification of restrictions on farm household’s production and productivity with the use of small-scale irrigation, by Sohoulande Djebou et al. [51] and Muhoyi and Mbonigaba [52]. For the sake of simplification, the number of cattle owned by respondent households was translated into tropical livestock units. According to Table 3, both sample respondents in the study area have (3.21) tropical livestock units on average. Irrigation users had an average tropical cattle unit of (2.76) while nonusers had (2.76) (3.47). In terms of the statistically significant level of significance of livestock, the statistical t-test (31.220) revealed a statistically significant mean difference between the two categories (irrigation users and nonusers).


### 3.1.15. Annual Income of the Household

It is the annual income of the household. Higher-income households were expected to use small-scale irrigation more frequently than lower-income households. Salazar and Rand [53] found a significant mean difference between households that used irrigation and those that did not. Furthermore, the t-value of 16.9 was statistically significant. As a result, farm income was determined to be significant at a level of significance less than 5% in Table 3.


### 3.1.16. Family Size

Converting the number of people at home to equivalent adult reveals the number of adults. The family size is required for the production of agricultural activities in irrigation. A household with a larger number of family members can share the labor load and contribute significantly to the security position of the unique household. As a result, it is expected that it will benefit household irrigation. According to Table3, the average family size for all families was 5.134. The average family size among users was 4.94, while it was 4.79 among nonusers. The t-value was 1.53. This shows that the difference between irrigation users and nonusers is not statistically significant.


## 3.1.1. Age of Household Head

The age of the household leader has a significant influence on agricultural productivity. Table3 shows that the average age of the household heads of the sample was 27.17, with a standard deviation of 6.67. The average age of the household heads of the sample was 28.48 for users and 26.41 for nonusers, with standard deviations of 7.16 and 6.29, respectively. The user households had a different average age than the nonuser households. With a t-test value of 47.12 and a p-value of 0.027, the statistical study discovered a significant difference in the mean age of the heads of the households between the user and the nonuser households. As a result, age was the deciding factor in whether the household used small-scale irrigation. Another finding suggested that age had a positive and negative relationship with irrigation water access due to its nonlinearity [41, 42]. As a result, the demand for irrigation technology is expected to initially increase due to increased working capacity and then decrease as people age. It also has a positive and negative impact on household income. As a result, it would have an inverted U-shaped relationship in both cases. These findings are in line with those of [43], who found that respondents’ age is positively significant in some areas and negatively significant in others, resulting in a U-shaped relationship in this case.Table 3
Statistical summary of users and nonusers sampled on continuous variables.
VariablesUsersNonuserst-valueMean(Std. dev)Mean(Std. dev)Age28.487.1626.41676.2947.122∗∗∗Farming experience29.0411.222.58.9230.122∗∗∗Farmland size.4130.264.3970.2781.229Family size4.941.854.791.771.53Distance from farm4.971.965.532.3427.42∗∗∗Annual income9123.804811.236689.5467516.9∗∗∗TLU2.760.93.470.5631.212∗


## 3.1.2. Sex of Household Head

In most of the rural parts of Ethiopia, men are expected to invest their labor in practical work than women. From the point of view of Das, this was also true in the study area that male household heads were more effective than female participation in agricultural activities, especially in irrigation activities. According to the statistics in Table4, 67.16% of the 134 respondents were men, while 22.84% were women. Males led 77.4% of nonuser households, while females led 22.6%.Table 4
Statistical summary of users and nonusers sampled on dichotomous variables.
VariablesDescriptionUsersNonusersX2-valueNo.%No.%Credit accessNo2346.041490.306Yes2754.04351Sex of the sample respondentsMale25506577.410.655Female25501922.6Off-farm activity participation (OFAP)No2448.04047.60.258Yes2652.04452.4Food aidNo2958.041491.805Yes2142.04351Access to irrigationNo1632.06577.426.9∗∗∗Yes3468.01922.6Participation in trainingNo2142.047564.82∗Yes2958.03744Health status of the HHHsNo1122.02934.52.348∗∗Yes3978.05565.5Use of fertilizerNo2550.04452.40.237Yes2550.04047.6Educational levelDescriptionUserNonuser1.49No%No%Illiterate0000Grade 1–440806172.6Grade 5–810202327.4Grade 9–100000Above 100000Total5010084100Source: own data model output (2021).The chi-square test of 10.655 revealed that the sex of the households was strongly related to the use of irrigation. As a result, the use of small-scale irrigation is related to the gender of the heads of the households. Female-headed families, according to the literature cited in Astetkei [44], have less access to improved technologies, land, and extension than male-headed households. This finding is similar to those of Akudugu et al. [45] and Abbasi and Nawab [46], who both discovered significant gender differences in technology adoption in Ghana.


## 3.1.3. Educational Level of the Respondents

According to the findings in Table4, 100% of the respondents in the area have a formal education and can read and write. In terms of upgraded households, 100% had formal education, while the remaining 0% had no formal education. This is not always the case, though older farmers are with no formal education dominate SISs, yet they are more knowledgeable of scheme governance. This indicates that households with a higher level of education were not more likely to engage in irrigation farming. The statistical analysis of the chi-square test revealed that the level of education of the head of the household did not have significant differences between the households of users and nonusers (Table 4). This contradicts the findings of [43, 47], who found that if the head of the household is literate, he is more likely to accept extension services, irrigation use, and other income-generating activities. Education has also been shown to have a positive impact on adoption in previous studies.


## 3.1.4. Access to Credit

Of the total of households sampled, 51% have access to credit services, while 49% do not. Similarly, 54% of the user families have access to a credit service, while 46% do not. To see if there was a significant difference in the use of credit services between the two groups, the chi-square test (x2 = 0.36) was used. Table 4 shows that there was no statistically significant difference in credit availability between users and nonuser households. As a result, there is no correlation between household credit availability and irrigation usage.


## 3.1.5. Engagement in Nonfarm Activities

Table4 shows a dummy variable for participation in off-farm activities. The availability of job opportunities outside the farm expands the possibility of using irrigation to increase household income. Off-farm employment opportunities in Offa Woreda included vegetable trading, cattle trading, grain trading, petty trade, and others, according to the study’s findings. According to the chart, approximately 52% and 48% of user households have participated in off-farm employment and have no experience, respectively. However, about 52.4% and 47.6% of nonuser households, respectively, had participated and had not participated in off-farm employment to earn more revenue to improve their income status. In addition to that, when its relation with irrigation use was observed independently, the chi-square (X2 = 0.258) result was also found not to be significant.


## 3.1.6. Food Aid

One of the explanatory variables is the amount of food aid in kilograms. Existing productive safety and other emergency programs were providing vulnerable households with access to food. As a result, households receiving food items would be able to meet their food gap demands. It provides an opportunity for creative approaches with the potential to close the food gap. According to Table4, the percent of SSI users who receive food aid is 21 (42%), while the percent of who do not use SSIs is 43 (51%). Furthermore, the chi-square test shows that there is no link between food aid availability and irrigation use.


## 3.1.7. Access to Irrigation

Irrigation is one of many technical options available to small farmers who want to grow directly edible food grains or diversify their crops while also addressing agriculture’s moisture deficit. As a result, it helps to increase production. It is a dummy variable in the model because it is thought to have a direct relationship with the availability of food in the home. As a result, it is expected that homes with access to irrigation will have a positive impact on the use of irrigation in the home. According to Table4, 60.4% of the 134 total sampled households do not have access to irrigation, whereas 39.6% do. Sixty-eight percent of users’ households have access, while 32% do not. Also, the chi-square test indicates that there is a link between irrigation access and irrigation use.


## 3.1.8. Participation in Training

Extension services play a vital role in providing guidance and information to rural farmers. Training is one of these services that are beneficial to introducing and developing current technology techniques (proper types and rates of fertilizer, improved varieties of seeds, agrochemicals, etc.). As a result, households that participated in FTC training or farm demonstrations are expected to implement their expertise to increase farm production. As a result, homes would be in a better position to use irrigation. Table4 further demonstrates that of the total sample homes, 49.3% have participated in training, while 50.7% have not. Of the households of irrigation users, 58% participated, while 42% did not. Also, the chi-square test indicates that there is a link between training and irrigation use. As a result, the study’s determinant variable was training participation.


## 3.1.9. Farming Experience

In this study, the total average farm experience of the selected households dating from the beginning of farming was 24.94 years. The average farm experience of the user and nonuser households was 29.04 years and 22.5 years, respectively, with standard deviations of 11.2 and 8.92, respectively. As seen in Table3, there was a statistically significant mean difference between the two groups. The t-value (30.122) also provides sufficient evidence that there was a significant mean difference between the two groups. As a result, the association between agricultural experience and irrigation use is statistically significant. This finding is in line with Chekol et al. [48], who found that there is a statistically significant relationship between agricultural experience and irrigation use.


## 3.1.10. Health Status of the Household Head

The farmer’s physical well-being was required to participate in agricultural activities. If the farmer is healthy, he or she can be involved in farm operations and farm management. As a result, the health of the family had influenced irrigation use. The number of days the head of the household was sick each year was calculated (out of farming work). Beneficiary irrigation use was expected to be positively influenced by its health status. According to Table4, out of a total of 134 respondents, 70.1% stated that they were healthy throughout the year, while the remaining 29.9% stated that they were not in full health. Only 22% of irrigation users were determined to be ill, while 78% were judged healthy. Also, the chi-square test of 2.348 indicates that there is some relationship between health status and irrigation use. Therefore, being healthy was the determinant variable of the study.


## 3.1.11. Use of Fertilizer

The application of fertilizers to crops has a significant impact on yield and productivity. Irrigation requires the use of improved agricultural inputs, such as fertilizers, to grow high-value crops, such as vegetables, by definition. The availability of irrigation encourages farmers to use fertilizer in the cultivation of high-value crops most of the time. As a result, fertilizer-using households were more likely to use irrigation. According to Table4, 48% of the respondents and 51.5% of the nonrespondents were identified as users and nonusers, respectively. Furthermore, the same proportions (50%) of irrigation user families were found to be fertilizer users and nonusers. This suggests that there is not much of a difference between those who use irrigation and those who do not.


## 3.1.12. Distance from the Farm

It refers to the distance between the residence of the family and the nearest cultivated ground. The closer a farmer is to farmland, the greater the chance of increasing productivity, lowering harvesting costs, and managing land and cropland [49]. As a result, residents closer to their farmland are projected to have cheaper harvesting and transportation costs and will be able to produce more easily thanks to irrigation alternatives. According to Table 3, the average distance from the farm to all houses was 5.138 miles, with a standard deviation of 2.16 miles. The average distance for irrigation users was 4.97 miles, while it was 5.53 miles for nonirrigating households. This shows the statistically significant mean difference between irrigators and nonirrigators (Stringer et al. [50]).


## 3.1.13. Farmland Size

The smallest and largest land holdings, according to Table3, are 0.06 and 2.5 hectares, respectively. To see whether there was a significant mean difference in land holding between the two groups of respondents, the statistical t-test was used. Between the two groups, there was no significant difference in land ownership. There was no statistically significant difference. The irrigation users in the study area were found to have roughly the same land holdings as the nonusers. As a result, the size of the land holding was not a factor in this investigation.


## 3.1.14. Livestock Ownership (TLU)

The ownership of livestock by rural farm households is critical for revenue production, food, tractor power, social security, organic fertilizer, and asset holding. This study was closely related to the identification of restrictions on farm household’s production and productivity with the use of small-scale irrigation, by Sohoulande Djebou et al. [51] and Muhoyi and Mbonigaba [52]. For the sake of simplification, the number of cattle owned by respondent households was translated into tropical livestock units. According to Table 3, both sample respondents in the study area have (3.21) tropical livestock units on average. Irrigation users had an average tropical cattle unit of (2.76) while nonusers had (2.76) (3.47). In terms of the statistically significant level of significance of livestock, the statistical t-test (31.220) revealed a statistically significant mean difference between the two categories (irrigation users and nonusers).


## 3.1.15. Annual Income of the Household

It is the annual income of the household. Higher-income households were expected to use small-scale irrigation more frequently than lower-income households. Salazar and Rand [53] found a significant mean difference between households that used irrigation and those that did not. Furthermore, the t-value of 16.9 was statistically significant. As a result, farm income was determined to be significant at a level of significance less than 5% in Table 3.


## 3.1.16. Family Size

Converting the number of people at home to equivalent adult reveals the number of adults. The family size is required for the production of agricultural activities in irrigation. A household with a larger number of family members can share the labor load and contribute significantly to the security position of the unique household. As a result, it is expected that it will benefit household irrigation. According to Table3, the average family size for all families was 5.134. The average family size among users was 4.94, while it was 4.79 among nonusers. The t-value was 1.53. This shows that the difference between irrigation users and nonusers is not statistically significant.


## 3.2. The Role of Irrigation in Production, Employment, and Poverty

Irrigation can help alleviate poverty by increasing crop yields, cropping areas, and higher value crops, all of which create jobs (directly from farm workers and indirectly from other workers if wages are bid up). Increased mean yields can result in more food supply, more calories consumed, and better nutrition. This study investigated whether there were significant differences in the levels of production, employment, asset endowment, consumption, and income between irrigation users and nonusers and discovered the following findings.


### 3.2.1. Irrigation Increased Production

Due to inconsistency in input use, a comparative yield analysis by crop type could not be performed. Figure3 shows the gross yield for the main crops that have access to irrigation. Irrigation use has significantly contributed to households’ goal of higher production, as predicted, and this finding is consistent with other data [29]. Data analysis of the major cereals and horticultural crops revealed that irrigation users produce more maize, green pepper, potato, tomato, red onion, cabbage, and barley per household than nonusers. This evidence shows that irrigation use increases food supply and security. Tomatoes, onions, peppers, and cabbage are all grown exclusively in irrigation homes. This also shows that irrigation encourages crop diversification and intensity.Figure 3
Average crop yields per quintal and per household (1 quintal = 100 kg).


### 3.2.2. Irrigation Enhanced Employment Opportunities

In theory, one of the most significant advantages of irrigation is the creation of jobs. Irrigation has increased the beneficiaries’ harvests from once a year (rainy season) to two or three times a year, and labor efficiency has improved. Table5 shows that the average hour spent on an irrigated farm for all activities, from plowing to disposal, is much higher than on a rainfed farm. Similarly, the average labor cost for irrigation users (including hired labor) is more than twice that of nonuser families. This implies that irrigation creates more job opportunities. Most small-holder activities rely on family labor, with neighbor help and casual labor supplementing specific operations. The development of irrigation schemes has created employment opportunities for nearby farmers and irrigation users during traditionally dry periods.Table 5
Labor hour and cost of irrigation use.
Average labor hourIrrigation useMeanStandard deviationt/pPlowingUser76.9084.388.464/0.001∗∗Nonuser21.7125.61WeedingUser90.79116.187.085/0.001∗∗∗Nonuser26.5138.11HarvestingUser87.3197.747.445/0.001∗∗∗Nonuser28.3339.66TrashingUser70.9876.127.113/0.001∗∗∗Nonuser24.2338.81Labor cost per ha in Ethiopian birr (ETB)User535.94800.952.988/0.003∗∗Nonuser305.92495.36Source: own survey, 2021.


### 3.2.3. Irrigation Increased Income

Irrigation is expected to increase cash earnings and has been shown to do so in the past [29, 31, 54]. Similarly, irrigation recipients earned 10161.5 birr per home per year, 33.6% more than nonusers. Irrigation use benefits households that generate crop and livestock revenue, but nonusers earn more off-farm income. A closer look at the data reveals that irrigated agriculture generated remunerative off-farm revenue streams like transportation and commerce, whereas nonirrigators dominated inferior livelihood activities like fire wood and charcoal sales, and causal work. This finding is similar to Getaneh’s [29] discovery that small-scale irrigation reduces nonfarm income.Crop income accounts for 76 and 70.5% income for users and nonusers, respectively, with livestock and off-farm activities accounting for the remainder. Irrigators earned 47.4% more from crops than nonirrigators, a statistically significant difference (Table6).Table 6
Income earned by households with and without irrigation.
Irrigation useIncome source (ETB)UserNonuserTMeanStandard deviation% ShareMeanStandard deviation% ShareLivestock1451.62826.613.51070.22150.313.71.324Crop8138.56012.176.05520.93879.370.54.635∗∗∗Off farm1125.22549.610.51234.72239.915.8−0.0.4Total10161.55612.71007606.04280.61004.562∗∗∗Source: own survey, 2021.


### 3.2.4. Irrigation-Improved Asset Endowment

Irrigation expands the amount of land available for agricultural use, and access to irrigation improves asset ownership [31]. This research looked at basic production resources, such as land and animals, as well as the overall value of household items (agricultural tools and equipment). Furniture costs are estimated at the time of purchase. As a result, irrigator assets are three times more valuable than nonirrigator assets. Irrigation increases livestock ownership by 0.91 tropical livestock units (TLU) and increases land ownership by 0.38 ha (Table 7).Table 7
Endowment of assets of households with and without irrigation.
Assets ownedIrrigation useMeanStandard deviationTTotal value of asset (ETB)User2060.166510.742.500∗∗Nonuser597.583450.67Total size of plots (ha)User1.513.84Nonuser1.120.76Total livestock (TLU)User5.453.802.008Nonuser4.553.88Source: own survey, 2021.


### 3.2.5. Irrigation-Improved Household Consumption

To assess the impact of irrigation on household consumption, the expenditure pattern was used as a proxy indicator of standard of living. This typically refers to the household’s ability to produce/purchase a basket of low-calorie and nonfood commodities. As a result, irrigators’ average annual consumption expenditure per adult equivalent (AE) is more than double that of nonirrigators.Similarly, nonusers’ home consumption, food, and nonfood expenses are significantly higher than those of users. Nonirrigators, for example, consume only about 51% of the food that irrigation recipients consume. This implies that having access to irrigation improves food security by increasing the frequency of production. It also increases food availability by 50.7%. As a result, there is a link between nutritional status and access to irrigation. It also has a positive impact on nonfood intake. Nonfood consumption of nonusers was 60.8% that of irrigators (Table8). As a result, this study could show that irrigation availability improves the overall welfare of rural households by improving food access, nonfood consumption, and asset accumulation. This finding is consistent with the findings of [55–57], who discovered that irrigation availability improves the commune’s resilience to drought and improves user assistance.Table 8
Expenditure pattern of households with and without irrigation.
Expenditure (ETB)UserNonuserFPMeanStandard devMeanStandard devFood3467.82965.21715.61813.640.934P≤0.01∗∗∗Nonfood2540.64725.51546.52052.26.0730.014∗∗VOC5968.919828.13047.12660.73.570.060∗Source: own survey, 2021.


### 3.2.6. Irrigation Contributed to the Reduction of Poverty

Poverty is increasingly understood to be a multidimensional concept that includes everything from low income and expenditure to a lack of education and poor health, as well as other social dimensions such as powerlessness, insecurity, vulnerability, isolation, social exclusion, and gender disparities. In this study, the cost of necessities was used to establish poverty lines. The first step in this approach is to identify a package of food and nonfood products typically consumed by the 20% lowest income quartile and estimate the cost of meeting this need [58–60]. As a result, the food poverty level (FPL) for this study is 1016.49 ETB per AE per year, while total nonfood expenditure (including clothes, medication, taxes and social obligations) is 310.64 birr per AE per year. When all expenditures from the lowest income group are added together, the total poverty line is reached, above which an individual is considered nonpoor. As a result, the annual poverty line was established at 1016.49 birr per AE.


### 3.2.7. Poverty Status and Indices by Access to Irrigation

Table9 shows that 30.4% of the 313 sample families are poor, accounting for 47.6% of nonusers and 10.3% of users, implying that the incidence of poverty in rainfed farms is 37.3% higher than in irrigation farms. The remaining 89.7% of users and 52.3% of nonusers are not poor. This demonstrates the importance of irrigation development in alleviating poverty. The fact that 10.3% of irrigation beneficiaries are poor implies that, on the one hand, access to irrigation is a necessary but not sufficient condition for alleviation of poverty and, on the other hand, poverty can be negatively impacted if irrigation is mismanaged, and it could be the root cause of poverty; this finding is consistent with the findings of other studies [59, 61].Table 9
Poverty status and indices by access to irrigation
Irrigation usagePoverty statusX2PPoorNonpoorN%N%User13089.71510.351.152P≤0.01∗∗∗Nonuser8852.38047.6Total21869.69530.4Irrigation usageHead count index (α = 0)Poverty gap (α = 1)Squared poverty gap (α = 2)User0.10.0420.02Nonuser0.480.170.09Source: own survey, 2021.Furthermore, poverty must be recognized as a multifaceted phenomenon. According to the survey, per head count, 48% of nonuser households and 10% of user households lived below the locally designated poverty threshold. The associated poverty gap by irrigation use was 0.042 and 0.17 for users and nonusers, respectively, while the poverty severity index was 0.02 and 0.09 for users and nonusers, respectively (Table9). As a result, poverty is worse and more widespread among nonirrigators than among irrigators. This finding is consistent with [59, 62], who discovered that poverty is worse and more widespread among nonirrigators than among irrigators.


## 3.2.1. Irrigation Increased Production

Due to inconsistency in input use, a comparative yield analysis by crop type could not be performed. Figure3 shows the gross yield for the main crops that have access to irrigation. Irrigation use has significantly contributed to households’ goal of higher production, as predicted, and this finding is consistent with other data [29]. Data analysis of the major cereals and horticultural crops revealed that irrigation users produce more maize, green pepper, potato, tomato, red onion, cabbage, and barley per household than nonusers. This evidence shows that irrigation use increases food supply and security. Tomatoes, onions, peppers, and cabbage are all grown exclusively in irrigation homes. This also shows that irrigation encourages crop diversification and intensity.Figure 3
Average crop yields per quintal and per household (1 quintal = 100 kg).


## 3.2.2. Irrigation Enhanced Employment Opportunities

In theory, one of the most significant advantages of irrigation is the creation of jobs. Irrigation has increased the beneficiaries’ harvests from once a year (rainy season) to two or three times a year, and labor efficiency has improved. Table5 shows that the average hour spent on an irrigated farm for all activities, from plowing to disposal, is much higher than on a rainfed farm. Similarly, the average labor cost for irrigation users (including hired labor) is more than twice that of nonuser families. This implies that irrigation creates more job opportunities. Most small-holder activities rely on family labor, with neighbor help and casual labor supplementing specific operations. The development of irrigation schemes has created employment opportunities for nearby farmers and irrigation users during traditionally dry periods.Table 5
Labor hour and cost of irrigation use.
Average labor hourIrrigation useMeanStandard deviationt/pPlowingUser76.9084.388.464/0.001∗∗Nonuser21.7125.61WeedingUser90.79116.187.085/0.001∗∗∗Nonuser26.5138.11HarvestingUser87.3197.747.445/0.001∗∗∗Nonuser28.3339.66TrashingUser70.9876.127.113/0.001∗∗∗Nonuser24.2338.81Labor cost per ha in Ethiopian birr (ETB)User535.94800.952.988/0.003∗∗Nonuser305.92495.36Source: own survey, 2021.


## 3.2.3. Irrigation Increased Income

Irrigation is expected to increase cash earnings and has been shown to do so in the past [29, 31, 54]. Similarly, irrigation recipients earned 10161.5 birr per home per year, 33.6% more than nonusers. Irrigation use benefits households that generate crop and livestock revenue, but nonusers earn more off-farm income. A closer look at the data reveals that irrigated agriculture generated remunerative off-farm revenue streams like transportation and commerce, whereas nonirrigators dominated inferior livelihood activities like fire wood and charcoal sales, and causal work. This finding is similar to Getaneh’s [29] discovery that small-scale irrigation reduces nonfarm income.Crop income accounts for 76 and 70.5% income for users and nonusers, respectively, with livestock and off-farm activities accounting for the remainder. Irrigators earned 47.4% more from crops than nonirrigators, a statistically significant difference (Table6).Table 6
Income earned by households with and without irrigation.
Irrigation useIncome source (ETB)UserNonuserTMeanStandard deviation% ShareMeanStandard deviation% ShareLivestock1451.62826.613.51070.22150.313.71.324Crop8138.56012.176.05520.93879.370.54.635∗∗∗Off farm1125.22549.610.51234.72239.915.8−0.0.4Total10161.55612.71007606.04280.61004.562∗∗∗Source: own survey, 2021.


## 3.2.4. Irrigation-Improved Asset Endowment

Irrigation expands the amount of land available for agricultural use, and access to irrigation improves asset ownership [31]. This research looked at basic production resources, such as land and animals, as well as the overall value of household items (agricultural tools and equipment). Furniture costs are estimated at the time of purchase. As a result, irrigator assets are three times more valuable than nonirrigator assets. Irrigation increases livestock ownership by 0.91 tropical livestock units (TLU) and increases land ownership by 0.38 ha (Table 7).Table 7
Endowment of assets of households with and without irrigation.
Assets ownedIrrigation useMeanStandard deviationTTotal value of asset (ETB)User2060.166510.742.500∗∗Nonuser597.583450.67Total size of plots (ha)User1.513.84Nonuser1.120.76Total livestock (TLU)User5.453.802.008Nonuser4.553.88Source: own survey, 2021.


## 3.2.5. Irrigation-Improved Household Consumption

To assess the impact of irrigation on household consumption, the expenditure pattern was used as a proxy indicator of standard of living. This typically refers to the household’s ability to produce/purchase a basket of low-calorie and nonfood commodities. As a result, irrigators’ average annual consumption expenditure per adult equivalent (AE) is more than double that of nonirrigators.Similarly, nonusers’ home consumption, food, and nonfood expenses are significantly higher than those of users. Nonirrigators, for example, consume only about 51% of the food that irrigation recipients consume. This implies that having access to irrigation improves food security by increasing the frequency of production. It also increases food availability by 50.7%. As a result, there is a link between nutritional status and access to irrigation. It also has a positive impact on nonfood intake. Nonfood consumption of nonusers was 60.8% that of irrigators (Table8). As a result, this study could show that irrigation availability improves the overall welfare of rural households by improving food access, nonfood consumption, and asset accumulation. This finding is consistent with the findings of [55–57], who discovered that irrigation availability improves the commune’s resilience to drought and improves user assistance.Table 8
Expenditure pattern of households with and without irrigation.
Expenditure (ETB)UserNonuserFPMeanStandard devMeanStandard devFood3467.82965.21715.61813.640.934P≤0.01∗∗∗Nonfood2540.64725.51546.52052.26.0730.014∗∗VOC5968.919828.13047.12660.73.570.060∗Source: own survey, 2021.


## 3.2.6. Irrigation Contributed to the Reduction of Poverty

Poverty is increasingly understood to be a multidimensional concept that includes everything from low income and expenditure to a lack of education and poor health, as well as other social dimensions such as powerlessness, insecurity, vulnerability, isolation, social exclusion, and gender disparities. In this study, the cost of necessities was used to establish poverty lines. The first step in this approach is to identify a package of food and nonfood products typically consumed by the 20% lowest income quartile and estimate the cost of meeting this need [58–60]. As a result, the food poverty level (FPL) for this study is 1016.49 ETB per AE per year, while total nonfood expenditure (including clothes, medication, taxes and social obligations) is 310.64 birr per AE per year. When all expenditures from the lowest income group are added together, the total poverty line is reached, above which an individual is considered nonpoor. As a result, the annual poverty line was established at 1016.49 birr per AE.


## 3.2.7. Poverty Status and Indices by Access to Irrigation

Table9 shows that 30.4% of the 313 sample families are poor, accounting for 47.6% of nonusers and 10.3% of users, implying that the incidence of poverty in rainfed farms is 37.3% higher than in irrigation farms. The remaining 89.7% of users and 52.3% of nonusers are not poor. This demonstrates the importance of irrigation development in alleviating poverty. The fact that 10.3% of irrigation beneficiaries are poor implies that, on the one hand, access to irrigation is a necessary but not sufficient condition for alleviation of poverty and, on the other hand, poverty can be negatively impacted if irrigation is mismanaged, and it could be the root cause of poverty; this finding is consistent with the findings of other studies [59, 61].Table 9
Poverty status and indices by access to irrigation
Irrigation usagePoverty statusX2PPoorNonpoorN%N%User13089.71510.351.152P≤0.01∗∗∗Nonuser8852.38047.6Total21869.69530.4Irrigation usageHead count index (α = 0)Poverty gap (α = 1)Squared poverty gap (α = 2)User0.10.0420.02Nonuser0.480.170.09Source: own survey, 2021.Furthermore, poverty must be recognized as a multifaceted phenomenon. According to the survey, per head count, 48% of nonuser households and 10% of user households lived below the locally designated poverty threshold. The associated poverty gap by irrigation use was 0.042 and 0.17 for users and nonusers, respectively, while the poverty severity index was 0.02 and 0.09 for users and nonusers, respectively (Table9). As a result, poverty is worse and more widespread among nonirrigators than among irrigators. This finding is consistent with [59, 62], who discovered that poverty is worse and more widespread among nonirrigators than among irrigators.


## 3.3. Determinants of Small-Scale Irrigation Use by Farm Households

### 3.3.1. The Age of Farm Household Heads

The age of the household heads has a positive effect on their decision to use small-scale irrigation to improve income and products at a significant level of 5%; with increased age, the probability of using irrigation increases by 1.14 compared to those in their youth hood level to use small-scale irrigation to improve income and products (Table10). As a result, the decision on age and irrigation use was positively associated at a significance level of less than 5%. The findings contradict those of Yenealem (2013) and Tale et al. [63], who discovered that age and small-scale irrigation have no relationship.Table 10
Parameter estimation of binary logistic regression on the use of small-scale irrigation by farmers in Offa Woreda 2020.
Dependent variableY = 0, Y = 1Explanatory variablesBWaldSig.Exp (B)Age of HH0.1314.8920.027∗∗1.140Sex of HH2.5677.5110.006∗∗∗13.027Education level of HH0.1520.0170.8971.164Credit access−0.6080.5030.4780.545Engagement in off-farm activity0.9721.496.2212.643Food aid0.4110.2930.5881.508Access to irrigation4.83415.3740.000∗∗∗125.712Participation in training1.8163.1380.076∗6.147Farm experience0.18511.9020.001∗∗∗1.203Health status−3.3547.9250.005∗∗0.035Use of fertilizer0.2910.1500.6991.338Distance to nearest farm sight1.5322.3780.0827∗1.040Farmland size−1.1721.1120.2920.310TLU−0.5983.1790.075∗0.550Income2.0014.3790.0538∗1.000Family size−0.1310.3370.5610.878∗∗∗Significant at less than 1% probability level; ∗∗significant at less than 5% probability level; ∗significant at less than 10% probability level. Observation: N = 134 −2log likelihood 67.1; model chi-square = 109.941; sensitivity/correct prediction of user = 90; specificity/correct prediction of nonuser = 96.4; and overall cases correctly predicted = 94 (source: model output (2021)).


### 3.3.2. Sex of Household Head

Sex was significant at the 5% significance level, with a beneficial effect on household irrigation use decisions. The probability of using irrigation increases in the unit of 13 as the probability that the household is male increases. As a result, at a level of significance of less than 5%, the gender of farm households and the decision about irrigation use were positively connected. The results are similar to those of the results of the binary logit model of Yenealem (2013) and Tale et al. [63]. They suggest that gender influences the adoption of improved maize varieties, and adoption of improved agricultural technology is less intense in female-headed households where decisions are made jointly by men and women than in male-headed households where decisions are made jointly (Table 10).


### 3.3.3. TLU (Livestock Holdings of Agriculture Households)

The Tropical Livestock Unit (TLU) in rural areas was used to count the number of animals, which indicates an accumulation of wealth, security against emergencies, gift, and cultural privilege. They can also be swiftly converted into cash if the need arises. It was expected to have a positive relationship with the dependent variable because the household would be more inclined to use small-scale irrigation to supplement their income as the total number of animals at home increased. This can be attributed to a growth in agricultural households’ wealth and income base, which results in more money accessible to the households. When all other factors are kept constant, it is strongly associated with a probability of less than 10%, and the odds ratio in favor of being an irrigation user decreases by a factor of 0.55. The inverse relationship implies that households with a large herd size are more likely to be nonirrigators because they can earn more money from livestock production and have other options for survival (Table10).


### 3.3.4. Distance to Nearest Farm Sight

Physical infrastructure access indicators are good predictors of institutional conditions that influence irrigation access and other concerns. Farmers in rural locations, isolated from agriculture, and with inadequate transportation infrastructure were projected to have fewer opportunities. The long distance to the agricultural center was also considered to reduce the need for irrigation. The distance was significant at the 10% level and had a positive influence on the irrigation decision-making of farmers’ households. When the farm distance increases by 1 km in comparison to the nearby farm, the likelihood of using small-scale irrigation also increases by 1 km. As shown in Table10, the distance from the farm and the decision to use irrigation were positively related at a level of significance less than 10%. The results of FGD and KII support the preceding premise. Households near the farm field had easier access to and from the farm. Furthermore, because all activities in the research area are performed by hand, increasing agricultural distance from an irrigation water source exposed households to significant costs due to the difficulty of transporting water to one’s farmland. This finding is consistent with the findings of [54, 64–66].


### 3.3.5. Farming Experience

The number of years the small farmer engaged in farming activities after the irrigation system was installed in the area was used to determine the farming experience. It was projected that there would be a direct relationship between farm experience and the use of tiny amounts of irrigation (Table10). Farmers with substantial farming experience should be willing to experiment/use small-scale irrigation after learning about the benefits of different irrigation technologies. The farming experience was significant at the 10% significance level, with a favorable effect on farm household irrigation use decisions; the chance of using small-scale irrigation improves by 1.2 compared to nonusers. As a result, at a level of significance less than 10%, farm experience and irrigation use decisions were positively associated. The results of FGD and KII also confirm the previous conclusion. Farmers who have been farming for a long time are more knowledgeable about the benefits of improved irrigation techniques. They were shown to benefit from better types of seeds from irrigable crops. The finding of [67–70] all came to the same conclusion.


### 3.3.6. Annual Income of the Household

Small-scale irrigation is more common in higher-income households than in lower-income households. As a result, the expected sign of this variable was positive. This indicates that increased agricultural income may increase the likelihood of employing small-scale irrigation. This finding can be explained by the fact that higher affluence provides a better opportunity to finance key supplies for small-scale irrigation. As a result, at a level of significance less than 10%, the total annual income and the decision on irrigation use were positively connected. That is, for every Ethiopian birr increase, the likelihood of agricultural households becoming irrigation users increases by one. Sufdar et al. [71] found that households with higher incomes are more likely to use biogas technology than those with lower incomes (Table 10).


### 3.3.7. Access to Irrigation

It is included in the model as a dummy variable because it is thought to have a direct relationship with the availability of household food. As a result, it was expected that homes with access to irrigation would have a positive impact on household irrigation use. At the 1% level, it was significant and had a positive impact on the use of household irrigation. When access to irrigation sources increases by one unit, the likelihood that families will become irrigation users increases by 126 units. Furthermore, the FGD and KII findings show that farmers with access to irrigation sources outperformed those without [72] (Table 10).


### 3.3.8. Participation in Training

Extension programs are critical in providing rural farmers with assistance and information. Training is an important service for introducing and developing new technology (proper types and rate of fertilizer, improved varieties of seeds, agrochemicals, etc.). As a result, households that participated in FTC training or farm demos are expected to put their knowledge to use to boost farm production. As a result, homes would be better able to use irrigation [73, 74]. Training had a positive effect on modest irrigation use at a level of likelihood less than 10%. The probability of a farm home becoming an irrigation user increased six times after training access/chance increased one time (Table 10).


### 3.3.9. Health Status of the Household Head

To engage in agricultural activity, the farmer’s physical well-being was essential [75]. The health problem was statistically significant at less than 1% and had a detrimental influence on irrigation use. As a consequence, it negatively related to irrigation usage when all other factors were kept constant. This implies that farmers can spend their off-season doing anything other than irrigation (Table 10).


## 3.3.1. The Age of Farm Household Heads

The age of the household heads has a positive effect on their decision to use small-scale irrigation to improve income and products at a significant level of 5%; with increased age, the probability of using irrigation increases by 1.14 compared to those in their youth hood level to use small-scale irrigation to improve income and products (Table10). As a result, the decision on age and irrigation use was positively associated at a significance level of less than 5%. The findings contradict those of Yenealem (2013) and Tale et al. [63], who discovered that age and small-scale irrigation have no relationship.Table 10
Parameter estimation of binary logistic regression on the use of small-scale irrigation by farmers in Offa Woreda 2020.
Dependent variableY = 0, Y = 1Explanatory variablesBWaldSig.Exp (B)Age of HH0.1314.8920.027∗∗1.140Sex of HH2.5677.5110.006∗∗∗13.027Education level of HH0.1520.0170.8971.164Credit access−0.6080.5030.4780.545Engagement in off-farm activity0.9721.496.2212.643Food aid0.4110.2930.5881.508Access to irrigation4.83415.3740.000∗∗∗125.712Participation in training1.8163.1380.076∗6.147Farm experience0.18511.9020.001∗∗∗1.203Health status−3.3547.9250.005∗∗0.035Use of fertilizer0.2910.1500.6991.338Distance to nearest farm sight1.5322.3780.0827∗1.040Farmland size−1.1721.1120.2920.310TLU−0.5983.1790.075∗0.550Income2.0014.3790.0538∗1.000Family size−0.1310.3370.5610.878∗∗∗Significant at less than 1% probability level; ∗∗significant at less than 5% probability level; ∗significant at less than 10% probability level. Observation: N = 134 −2log likelihood 67.1; model chi-square = 109.941; sensitivity/correct prediction of user = 90; specificity/correct prediction of nonuser = 96.4; and overall cases correctly predicted = 94 (source: model output (2021)).


## 3.3.2. Sex of Household Head

Sex was significant at the 5% significance level, with a beneficial effect on household irrigation use decisions. The probability of using irrigation increases in the unit of 13 as the probability that the household is male increases. As a result, at a level of significance of less than 5%, the gender of farm households and the decision about irrigation use were positively connected. The results are similar to those of the results of the binary logit model of Yenealem (2013) and Tale et al. [63]. They suggest that gender influences the adoption of improved maize varieties, and adoption of improved agricultural technology is less intense in female-headed households where decisions are made jointly by men and women than in male-headed households where decisions are made jointly (Table 10).


## 3.3.3. TLU (Livestock Holdings of Agriculture Households)

The Tropical Livestock Unit (TLU) in rural areas was used to count the number of animals, which indicates an accumulation of wealth, security against emergencies, gift, and cultural privilege. They can also be swiftly converted into cash if the need arises. It was expected to have a positive relationship with the dependent variable because the household would be more inclined to use small-scale irrigation to supplement their income as the total number of animals at home increased. This can be attributed to a growth in agricultural households’ wealth and income base, which results in more money accessible to the households. When all other factors are kept constant, it is strongly associated with a probability of less than 10%, and the odds ratio in favor of being an irrigation user decreases by a factor of 0.55. The inverse relationship implies that households with a large herd size are more likely to be nonirrigators because they can earn more money from livestock production and have other options for survival (Table10).


## 3.3.4. Distance to Nearest Farm Sight

Physical infrastructure access indicators are good predictors of institutional conditions that influence irrigation access and other concerns. Farmers in rural locations, isolated from agriculture, and with inadequate transportation infrastructure were projected to have fewer opportunities. The long distance to the agricultural center was also considered to reduce the need for irrigation. The distance was significant at the 10% level and had a positive influence on the irrigation decision-making of farmers’ households. When the farm distance increases by 1 km in comparison to the nearby farm, the likelihood of using small-scale irrigation also increases by 1 km. As shown in Table10, the distance from the farm and the decision to use irrigation were positively related at a level of significance less than 10%. The results of FGD and KII support the preceding premise. Households near the farm field had easier access to and from the farm. Furthermore, because all activities in the research area are performed by hand, increasing agricultural distance from an irrigation water source exposed households to significant costs due to the difficulty of transporting water to one’s farmland. This finding is consistent with the findings of [54, 64–66].


## 3.3.5. Farming Experience

The number of years the small farmer engaged in farming activities after the irrigation system was installed in the area was used to determine the farming experience. It was projected that there would be a direct relationship between farm experience and the use of tiny amounts of irrigation (Table10). Farmers with substantial farming experience should be willing to experiment/use small-scale irrigation after learning about the benefits of different irrigation technologies. The farming experience was significant at the 10% significance level, with a favorable effect on farm household irrigation use decisions; the chance of using small-scale irrigation improves by 1.2 compared to nonusers. As a result, at a level of significance less than 10%, farm experience and irrigation use decisions were positively associated. The results of FGD and KII also confirm the previous conclusion. Farmers who have been farming for a long time are more knowledgeable about the benefits of improved irrigation techniques. They were shown to benefit from better types of seeds from irrigable crops. The finding of [67–70] all came to the same conclusion.


## 3.3.6. Annual Income of the Household

Small-scale irrigation is more common in higher-income households than in lower-income households. As a result, the expected sign of this variable was positive. This indicates that increased agricultural income may increase the likelihood of employing small-scale irrigation. This finding can be explained by the fact that higher affluence provides a better opportunity to finance key supplies for small-scale irrigation. As a result, at a level of significance less than 10%, the total annual income and the decision on irrigation use were positively connected. That is, for every Ethiopian birr increase, the likelihood of agricultural households becoming irrigation users increases by one. Sufdar et al. [71] found that households with higher incomes are more likely to use biogas technology than those with lower incomes (Table 10).


## 3.3.7. Access to Irrigation

It is included in the model as a dummy variable because it is thought to have a direct relationship with the availability of household food. As a result, it was expected that homes with access to irrigation would have a positive impact on household irrigation use. At the 1% level, it was significant and had a positive impact on the use of household irrigation. When access to irrigation sources increases by one unit, the likelihood that families will become irrigation users increases by 126 units. Furthermore, the FGD and KII findings show that farmers with access to irrigation sources outperformed those without [72] (Table 10).


## 3.3.8. Participation in Training

Extension programs are critical in providing rural farmers with assistance and information. Training is an important service for introducing and developing new technology (proper types and rate of fertilizer, improved varieties of seeds, agrochemicals, etc.). As a result, households that participated in FTC training or farm demos are expected to put their knowledge to use to boost farm production. As a result, homes would be better able to use irrigation [73, 74]. Training had a positive effect on modest irrigation use at a level of likelihood less than 10%. The probability of a farm home becoming an irrigation user increased six times after training access/chance increased one time (Table 10).


## 3.3.9. Health Status of the Household Head

To engage in agricultural activity, the farmer’s physical well-being was essential [75]. The health problem was statistically significant at less than 1% and had a detrimental influence on irrigation use. As a consequence, it negatively related to irrigation usage when all other factors were kept constant. This implies that farmers can spend their off-season doing anything other than irrigation (Table 10).


## 4. Conclusions and Recommendations

The objectives of this article were to investigate the factors that influence small-holder farmers’ use of small-scale irrigation for poverty reduction among small-holder farmers in the Offa Woreda, Southern Ethiopia. To collect data, a multistage sampling technique was used, and 134 families were interviewed. The study indicates that the current status of small-scale irrigation use was very low when the number of users was compared with the number of nonusers in the study area. That is, the number of irrigation farmers who used was only about 37.3%, while the number of nonusers was more than half present in the study area. Even those who used irrigation technologies of different kinds did not fully utilize small-scale irrigation technologies of all types, but tried to use at least some of the improved irrigation technologies. The results of the descriptive analysis suggest that nine explanatory variables, including yearly income, TLU (livestock ownership), farm distance, health status, farming experience, training, irrigation access, sex, and age, had a significant impact on the adoption of small-scale irrigation by small-holder farmers. The parameters that influence the use of small-scale irrigation by small-holder farmers were discovered using a binary logistic regression model. The model’s findings revealed that of the 16 explanatory variables considered, in the binary logit regression, nine variables had a significant influence on small-holder farmers’ use of small-scale irrigation. This is based on economic research and the results of this investigation. These findings have substantial policy implications that should be considered:(I)
By incorporating income-generating technologies, the Woreda Office of Agriculture and Rural Development should provide technical assistance to increase farm productivity and improve household farm income. Furthermore, the Woreda Office of Water, Minerals, and Energy should pay special attention to households with very low income.(II)
The Woreda Office of Agriculture and Natural Resource Development in collaboration with different NGOs should strengthen modern and better training for farm housekeepers who have little farm experience in the study area.(III)
In addition, experience sharing and field visit programs in the fields of the first experienced irrigator/user/farmers should be prepared in the area.(IV)
The office development of water, mining, and energy should try to invite different NGOs and government bodies to construct different irrigation choices for farmers who do not have access. Additionally, the same office should raise awareness of farmers who do not use existing sources in the far area by using other techniques. Moreover, capital investment/budgeting/by local/Woreda/government for the use of small-scale irrigation is needed.(V)
As a result, special training should be provided for households with children under the age of 18. In addition, the Woreda Office of Agriculture and Natural Resource Development should plan field trips and share experience to teach nonuser youth about farm households.(VI)
By providing various training and related activities, the Woreda Office of Agriculture and Natural Resource Development should pay special attention to the use of small-scale irrigation.(VII)
Woreda’s government should pay special attention to the use of small-scale irrigation, particularly during the off-season, so that farmers do not waste time.(VIII)
The Woreda Office of the Woreda Office of Agriculture and Natural Resource Development should focus on special annual training on the use of small-scale irrigation. This includes training farmers, agricultural experts, and other interested parties to pay more attention to the use of small-scale irrigation.(IX)
As a result, the Office of Agriculture and Natural Resource Development as well as other concerned bodies, such as the Woreda government and NGOs in the study area, were required to introduce and use more improved small-scale irrigation technologies by farm households to improve agricultural production and productivity.(X)
Not only is the utilization of improved irrigation technologies essential, but also the improvement in favorable supporting systems of institutions, especially educational and training institutions. That is to say, the provision of appropriate and modernized training and extension services is needed to improve the use of small-scale irrigation.



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*Source: 4049868-2022-06-29.xml*