Impact of row planting teff technology adoption on the income of smallholder farmers: The case of Hidabu Abote District, North Shoa Zone of Oromia Region, Ethiopia

In Ethiopia, improved agricultural technologies, like row planting are promoted in the recent times in order to address low agricultural productivity. However, despite such production enhancing technologies, utilization of such technologies remained low in Ethiopia. This study is focusedon the impact of row planting teff technology adoption on the income of smallholder farmer in the context of Hidabu Abote District, North Shoa zone. The study uses cross-sectional data that were collected from 181 randomly selected smallholder farmers. The data were analyzed using descriptive, inferential statistics,and econometrics models. From the descriptive statistics, it was found that out of thirteen explanatory variables seven of them were a signifi cant difference among the groups. Results of the probit model revealed that, distance to central market, extension contact,off/non-farm income,farm size,household size, access to mass media,and access to credit affected the farmer’s adoption decision of the row planting of teff signifi cantly. The fi nding of the propensity score matching analysis revealed that the average teff income per hectare of adopter is greater than that of non-adopter. Therefore, the fi ndings of the study safely recommended that those signifi cant factors adoption decision of the row planting of teff by any development intervention and policy makers, should be considered in setting their policies and strategies to speed up the use of row planting of teff. Research Article Impact of row planting teff technology adoption on the income of smallholder farmers: The case of Hidabu Abote District, North Shoa Zone of Oromia Region, Ethiopia Fekadu Adamu Negussie* Department of Agricultural Economics, College of Agriculture, Oda Bultum University, Chiro, Ethiopia Received: 23 July, 2020 Accepted: 29 October, 2020 Published: 31 October, 2020 *Corresponding author: Fekadu Adamu Negussie, Department of Agricultural Economics, College of Agriculture, Oda Bultum University, Chiro, Ethiopia, E-mail: fi keadamu79@gmail.com


Introduction
The Sub Sahara African countries and Ethiopia specifi cally, is highly infl uenced by sustenance security issues because of the absence of receiving improved agricultural technologies. In Ethiopia, smallholder farmer's production and profi tability are tremendously low and the development of rural productivity has just barely kept rate with the development of the population.
Still, the lack of agricultural technology practicing discourages farmers to improve productivity [1].
In Ethiopia, teff is one of the major crops widely produced much better than any other cereals by small-scale farmers. National Academy Science (1996) reported that, nutritionally, Teff has equal, or even more food quality than the other major grains: wheat, barley and maize. Teff grains contain 72.1-75.2% carbohydrate, 14-15% proteins, 11-33 mg iron, 100-150 mg calcium and rich in potassium and phosphorous. As indicated in the same report, the low level of anemia in Ethiopia seems to be associated with the level of Teff consumption as the grains contain high iron. Teff has got high lysine content compared to all cereals with the exception of rice and oats.
It is highly adaptable to a wide range of soil types. It has the ability to perform well in black soils and, in some cases, in low soil acidities. In addition, teff has the capacity to withstand waterlogged, rainy conditions, often better than other cereal grains (other than rice) (ATA, 2013b).
Moreover, teff is the main crop of the country and stands fi rst both in area coverage and production among cereals. For instance, the land covered by teff in 2016/2017 meher season is 24.00%; maize covered 16.98 %, sorghum 14.97 %, and wheat covered 13.49% of the total area covered by grain [2].
Teff has also the largest value in terms of both production and consumption in Ethiopia and the value of commercial surplus of teff is second only to coffee [3].
However, in spite of its economic importance and well adapted to growing environments in Ethiopia, the productivity of teff remains low [4]. In Ethiopia, the broadcasting method of teff planting used by the farmers is one of the main reasons why teff productivity is low [5]. It was also argued that the broadcasting method of teff planting reduces the productivity due to uneven distribution of seed increase competition between teff plant for nutrient, water, and light [6].
On the other hand, to alleviate this low production row planting technology with proper distance between crop rows was recommended in the country [7]. As past study result showed that the productivity of teff difference between the broadcasting and row planting of teff was 14.8 quintals per hectare and 20.1quintals per hectare respectively [8]. But, currently only a few farmers were using the row planting technology on their farming activity even though, teff research has received limited national and international attention, the latter presumably because of its localized importance in Ethiopia [6]. Moreover, teff productivity is low because of agronomic constraints that include lodging, low modern input use, and high post-harvest losses [6,9].
Actually, there are some fi ndings focusing on impact and adoption of row planting technology of teff in Ethiopia.
For instance; Behailu [10] studied on factors affecting farmers' adoption level of row planting technology and yield improvement on the production of teff; Tadele studied on adoption and intensity of adoption of row planting of teff.
The former studies focus only on adoption while the latter incorporates intensity of adoption and fi lls the gap of the former two fi ndings. Moreover, only Amare Fantie (no date) and Yonas, B. [11] studied on the impact of row planting teff on the welfare of households at differet location. According to their study teff row planting technology had acceptable more teff crop yield and income than the broadcast planting method. Though, there is lack of more empirical knowledge on the impact of manual teff row planting on on teff crop income per hectare in the country. Following the above gap, before studying the farmer's intensity of adoption and continued application of farmers the impact of row planting technology of teff by smallholder farmers in the study area is necessarily investigated.
Hidabu Abote District is one of the areas in North Shoa zone of Oromia region,Ethiopia. Most of the farmers in the area are rural and highly produce teff for their consumption purpose and as sources of income. But, the teff productivity could not reach its required level. The method of teff planting which is practiced in the area is teff broadcasting. This is one of the major problem farmers to increase their teff productivity.
Moreover, as far as the knowledge of the researcher concerned there was gap of study particularly in the study area. Some earlier fi ndings were studied at national, regional and/or zonal level. While an investigation on location-specifi c regarding appropriate agricultural technology is essential to improve the adoption system and to support the assumption on adoption decision. Consequently, this investigation was initiated to fi ll the gap of the farmer's adoption decision and the impact of row planting of teff on smallholder farmer's teff income per hectare in the case of study area.

Objectives of the study
The general objective of this study was to assess the impact of row planting technology adoption on teff crop income of smallholder farmers in the case of Hidabu Abote District.
To identify factors affecting the adoption decision of row planting technology of teff.
To analyze the impact of row planting technology adoption on teff crop income per hectare.

Description of thestudy area
Hidabu Abote District is one of the 13 district in North Showa Zone known for predominantly growing teff. It is The average annual rainfall of the district is 800 mm-1200mm with low variability. It is bimodality distributed in which the small rains are from March to April and the main rainy season from July to September. Hence, crop and livestock production is not constrained by the distribution of rainfall.
Altitude in district ranges from 1160m to 3000m above sea level (masl). The temperature of the district is minimum13 0 c and maximum 20 0 c. The soil types of the area is sandy soil 14%, clay soil 51%, and silt 35%.The agro-climate/ecological zone of the area is, highland 6%, mid-altitude 50%, and lowland 44%.
In the study area agriculture contributes much to meet major objectives of farmers such as food supplies and cash needs. The sector is characterized by it is rain-fed and subsistence nature. It is the mixed farming type where crop and livestock productions are undertaken side by side.Hidabu Abote is one of the potential teff producing district in Oromia region and ranked the 5 th from top 25 teff producing district at the national level, and it ranked to the 4 th in Oromia region and the 1 st in North Shoa administrative zone. Furthermore, teff is the major crop produced in mid-altitude area in the district and which is the major source of income for households Figure 1.

Sampling methods and sample size determination
The data used in this study consists of household sample survey data collected in the rural area of Hidabu Abote District in North Shoa zone. The multi-stage sampling technique was employed to select the sample respondent. In fi rst stage, Hidabu Abote District has three agro-ecological zones: lowland, mid-altitude, highland. The dominant teff producing agro-ecological zone is mostly mid-altitude area. Hence, the target farming households are from this area. Out of the total kebeles found in mid-altitude agro ecology of the district the potential teff producing kebeles were identifi ed. Hence, these kebeles have both households practicing the row planting with improved teff seed and those practices broadcasting planting method with improved teff seed.
In the second stage,based on time, accessibility, and considering how well the sample size is representative, three kebeles were selected by using a random sampling technique.
Moreover, selection of the three kebeles is also possible because of the total distributions of the farm households of the area are socioeconomically, culturally and institutionally similar for the potential teff producer kebeles in the district.
Moreover, the administration, technology diffusion procedures and plans of development by the leaders are almost the same for these selected kebeles and so any household from any kebeles can be representative of each other.Then, the farmers in each randomly selected kebeles were stratifi ed into adopter and non-adopter categories giving the relative homogeneity of sample respondents' adoption status. Due to heterogeneity of the population the sample size was determined using the formula developed by Cochran's [12]. Where n is the sample size for the study, z is the selected critical value of desired confi dence level which is 1.96; p is the estimated proportion of an adopters of row planting teff attribute that is present in the population of teff potential producers in the district which is 0.36, q=1-p =0.64 and due to heterogeneous characteristics of the farmers the precision level e value of 0.07 was used. In the fi nal stage, 181 180.63   n farm households consisting of 72 row planting adopters and 109 non-adopters were selected from the identifi ed list using simple random sampling technique taking into account probability proportional to size of the identifi ed households in each of the three selected kebeles.

Method of data collections and methods of data analysis
The research design that was used in this study is the cross-sectional design. Both primary and secondary data were used for this study. Primary data was collected with the help of the survey by means of the structured interview schedule for the quantitative data. After coding and feeding the collected primary data into the computer, STATA version 13.0mp software packagewas employed for the analysis. The data were analyzed using descriptive statistics, inferential statistics and econometric models.

Econometric models
Determinants of the farmer's adoption decision of the row planting of teff crop technology: Binarydependent variable models have been widely used in technology adoption studies.
Logit and probit models are the convenient functional forms for models with binary dependent variables [13]. These two models are commonly used in studies involving qualitative choices.
The logit model uses the cumulative logistic function. But this is not the only cumulative distribution function that one can use. In some applications the normal cumulative distribution function has been found useful. The estimating model that emerges fromnormal cumulative distribution function is popularly known as the probit model [14]. For this study a probit model selected over the logit, because the dependent variable has a latent observable value.

Specifi cation of econometric model
By following Feleke and Zegeye [15], Janvry, et al. [16], and Kohansal andFiroozzare [17], Ghimire, et al. [18], the adoption decision can bemodeled in a random utility framework as follows: i U  is the latent variable which represents the probability of household's decision to adopt the row planting of teff, takes the value '1' and '0' otherwise. In the analysis a probit equation was specifi ed for weather or not the household participating in the row planting of teff technology. The term X i represents explanatory variables explaining the adoption decision, y is a vector parameters to be estimated, and u i is the error term assumed to be independent and normally distributes as u i~ N (0, 1).
a latent variable that takes the value 1 if the farmer adopt the row planting of teff technology (U i =1) and zero otherwise, U i =1 is the observed variable which represent farmers adoption of the row planting technology of teff, X-is a vector of explanatory variables hypothesized to infl uence the decision to use, y-is a vector of parameters and μi-is error term.
The probit model for the analysis of U i is (1, 0) where the information on the latent variable is only observed through the index function. The probability that a farmer will adopt the modern row planting is a function of the vector of explanatory variables and the unobserved error term. As the form of  is not known, we assume  to have a cumulative normal distribution on the assumption that μ i has a normal distribution.
In this study a probit model was employed todetermine the probability of adoption decision the row planting of teff using a cross sectional survey data. Therefore, in present study the estimated themarginal effect of independent variables in the probitmodel which can be obtained by differentiating thefi rst and second order conditions as follows [19]: Prior to running the probit model, an assessment for an existence of multicollinearity was checked. Accordingly, a separate test for continuous and dummy variables included in the model was undertaken using VIF and contingency coeffi cient (CC) procedures respectively. VIF test was used to detect the presence of multicollinearity problem among continuous dependent variables. Accordinglly, VIF can be computed by using the formula: is the multiple correlations between Xi and others explanatory variables. As a rule of thumb a VIF value of more than 10 indicates high correlation among explanatory variables, while a VIF value less than 10 indicates weak association among explanatory variables [14]. Similarly, the existence of association among discrete explanatory variables was tested using contingency coeffi cient method by using the formula. ; Where, C.C contingency of coefficient, n sample size, n A value of 0.75 or more indicates stronger associations while a value less than 0.75 indicates weak association among explanatory variables. Additionally, an assessment for an existence of Heteroskedasticity was checked. Heteroskedasticity occurs when the variance of the error term is not consistent, Leading to the ineffi cient and invalid test of hypothesis [5].
If present in the data the estimates will not be the best linear unbiased estimates (BLUE). In this study, the Breusch-Pagan/ Cook Weisbergi test was used to test for heteroskedasticity under the null hypothesis of a constant variance. In this study, A goodness of fi t value was estimated. A goodness of fi t measure is a statistic showing the accuracy with which a model approximates the observed data. To measure the goodness of fi t in the qualitative model Greene (2003) suggests the use of the LR. The LR is also called McFadden R 2 or pseudo R 2 and is analogous to R 2 in a regression (ibid). of the treatment impact. In a study by Michalopoulos, et al. [21] to assess which non-experimental method provides the most accurate estimates in the absence of random assignment, they conclude that propensity score methods provided a specifi cation check that tended to eliminate biases that were larger than average. On the other hand, the fi xed effects model did not consistently improve the results. Therefore, in this study propensity score matching model was used.
Based on Rosenbaum and Rubin [22], propensity score can be defi ned as the conditional probability of receiving a treatment given pretreatment characteristics. Therefore, Let Yi T and Yi C are the outcome variable for participant (row planting) and non-participant (broadcast), respectively. The difference in outcome between treated and control groups can be seen from the following mathematical equation: The average effect of treatment on the treated (ATT) for the sample households is given by: misleading, yet taking the mean outcome of non-participants as an approximation is not advisable, since participants and non-participants usually differ even in the absence of treatment [23]. A solution to this problem is to construct the unobserved outcome which is called the counterfactual outcome that households would have experienced, on average, had they not participated (Rosenbaum and Rubin, 1983) [22], and this is the central idea of matching. According to Rosenbaum and Rubin [22], the effectiveness of matching estimators as a feasible estimator for impact evaluation depends on two fundamental assumptions, namely: Assumption 1: Conditional Independence Assumption (CIA): It states that treatment assignment (Di) conditional on attributes, X is independent of the post program outcome (Yi T ,Yi C ). In formal notation, this assumption corresponds to: Assumption 2: Assumption of common support: 0<P(X)<1 The assumption is that P(x) lies between 0 and 1. This restriction implies that the test of the balancing property is performed only on the observations whose propensity score belongs to the common support region of the propensity score of treated and control groups. ndividuals that fall outside the common support region would be excluded in the treatment effect estimation. This is an important condition to guarantee improving the quality of the matching used to estimate the ATT Marginal effects in probit coeffi cients: The probit coeffi cients give the predicted probability. The coeffi cients cannot be interpreted directly without further calculation as suggested by Greene [19]. Therefore, in order to know the amount of change in probability due to a unit change in the explanatory variable, marginal effects were used. Marginal effects were calculated by taking commands for adoption probability. Off/non-farm income: According to the fi nding of the study revealed in Table 2 off/non-farm income is statistically and positively signifi cant which affect farmers' adoption decision towards row planting technology of teff at P< .05 level  to know which observables affect both participation and the outcomes of interest [24]. Hence, implementing matching requires choosing a set of variables X that credibly satisfy this condition. After estimating the propensity score, the next step is to verify the quality of the match by controlling the region of common support between the treatment and control group. In practice, all those treatment observations were deleted whose propensity score is smaller than the minimum and higher than the maximum propensity score of the control group Table 3. The main purpose of the propensity score estimation was to balance the observed distributions of covariates across two farmer groups.

Choice of matching algorithm and matching
Alternative matching estimators can be employed in matching the treatment and comparison groups in the common support region. The fi nal choice of a matching estimator can be done by taking selection criterion either of balancing test, pseudo-R 2 , and matched sample size. Accordingly, a matching estimator which balances all explanatory variables, a model which bears a low pseudo R 2 value and results in large matched sample size is a preferable matching algorism [20]. Therefore, for this study the kernel matching that matches a treated unit to all control units weighted in proportion to the closeness between the treated unit and the control unitwas employed.

Testing the balancing properties and covariates
Balancing test conducted to know whether there is a statistical signifi cant difference in the mean values of covariates adopters and non-adopters of technology. The higher the covariates with minimum mean difference after matching is the more balanced covariates. Keeping other selection criterion, the balancing test indicates the quality of the matching algorithm implemented. While evaluating treatment effect, the major econometric problem is selection bias as stated in Maddala [25], percentage of bias before matching is in the range of 9.1% and 87.1% while after matching, percentage bias lies between 1.9% and 16.9%, which is below the critical level of 20%.
In all cases, it is evident that sample differences in the unmatched data signifi cantly exceed those in the samples of matched cases. The process of matching thus creates a high degree of covariate balance between the treatment and control samples that are ready to use in the estimation procedure. In a similar approach, t-tests could have been conducted to verify the equality of means for both the treatment and the control group. Differences in means before matching are natural, but after matching there should not be signifi cant differences in the means as the covariates should be balanced satisfyingly. Similarly, t-values show that before matching seven of chosen variables exhibited statistically signifi cant differences while after matching all of the covariates are balanced. As data result also revealed in the balancing test, the covariate balancing after matching indicates the low pseudo R 2 and insignifi cant p-value bias and are a pure effect of income due to technology adoption [26][27][28][29][30][31][32][33].

Conclusions and recommendations
Based on the study fi ndings, the following recommendations are drawn that should be taken in to consideration by respective government bodies in the study area. The result of the study revealed that distance to central market was The implication of the fi ndings is straight forward; though the adoption of improved teff technologies is quite low in the study area. Hence, scaling up the best practices of the adopters toother farmers can be considered as one option to enhance teff productivity in the area while introducing new practices and technologies which save time and labor cost is another option.
As a result, scaling up and diffusion of the technology in the study area should be highly recommended.

Sensitivity analysis
There may be hidden biases against the result of matching estimators and hence testing robustness the result is recommended. As it is not possible to estimate the magnitude of the selection bias with non-experimental data, the problem can be addressed through using sensitivity test. The basic issue in testing sensitivity is to check whether the treatment effect is due to unobserved factor or not. Rosenbaum [22] proposes using Rosenbaum