




































American International Journal of Agricultural Studies  

Vol. 2, No. 1; 2019 

ISSN 2641-4155   E-ISSN 2641-418X 

Published by American Center of Science and Education, USA 

 

39 

 

 

Vegetable Production Efficiency of Smallholders’ Farmer in West 

Shewa Zone of Oromia National Regional State, Ethiopia 

 

Aman Rikitu Dassa 

PhD Candidate 
Haramaya University, Ethiopia 

E-mail:aman.rikitu@gmail.com 

 

Bezabih Emana Lemu 

General Manager 

HEDBEZ Business & Consultancy  

PLC P.O.Box 15805, Addis Ababa, Ethiopia 

E-mail:emana_b@yahoo.com 
 

Jema Haji Mohammad 

Haramaya University 

 College of Agriculture and Environmental Science, Ethiopia 
E-mail:jemmahaji@gmail.com 

 

Ketema Bekele Dadi 

Haramaya University 

 College of Agriculture and Environmental Science, Ethiopia 

E-mail:ketmimi@yahoo.com 

Abstract 
This study tried to identify factors affecting vegetable production efficiency using cross-sectional data obtained from 

385 randomly and proportionally sampled households from three districts of West Shewa zone, Ethiopia. The data 

were analyzed using descriptive statistics such as mean, percentage, chi-square and mathematical approach data 

envelopment analysis DEA, econometrics model such as Tobit. Accordingly, DEA identify the average TE, AE and 

EE of farm households which encounter for 49.5%, 33.7% and 17.4% respectively. Factors affecting the inefficiency 

of vegetable production were identified using Tobit model. This model confirmed that age of households, education 

level, land size, access to irrigation, extension contact access to information and pesticide use were significantly 

affect TE, while age of the household, land size, access to irrigation, extension contact, access to information and 

pesticide use were factors affect AE of the farm households. Finally EE of the farm households was affected by age 

of the households, education level, land size, access to irrigation, access to information and pesticide use. The result 

suggested that improving the above problem can increase farmers’ economic efficiency in the study area. 

Keywords: TE, AE, EE, DEA, Tobit, West Shewa, Ethiopia. 

1. Introduction  

Horticulture is a part of agricultural sciences that employs scientific understanding to supply vegetables, fruits and 

flowers and enrich human diet (Christopher 2009; USID 2005). It’s used by each, individuals and industries to 

reinforce the organic process and economic standards (Fanos 2015; Welderufael 2016). This series of vegetation 

might support to fulfill basic requirements of human health and well-being. Farming crops are the first supply of 

poverty reduction in most agriculture-based economies (Fufa 2017). The enlargement of granger farming will cause 
a quicker rate of poverty alleviation, by raising the incomes of rural cultivators and reducing food expenditure, and 

so reduces financial gain difference. In Ethiopia, farming crops as well as fruits, vegetables and root crops contribute 

25 percent of the crop production (BV 2013). 

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Vegetable production is an important economic activity in Ethiopia, which ranging from gardening 

smallholder farming to commercial state and private farms (Rahiel Abraha  and Gebresilasie 2018; Bezabih Tesfaye 

and Milkessa 2015). It is an efficient way to address poverty reduction, take care of the health and well-being of the 

consumers, and offers new market opportunities for farmers, consumers, and agro-industry (Banjaw  2017; Chala 

and Chalchisa 2017). Vegetable production is integrated into mixed farming system where different types of crops 

are produced on the same plot of land or in sequence with other crops in rotation (Girma, n.d.; Asfaw, 2015). 
Depending on availability of land and crop suitability for intercropping, some vegetables are grown either 

as sole or intercropped with other vegetables or cereals (Hailu  2015). Vegetables such as tomato, potato, beetroot, 

carrot, cabbage, onion, sweet potato and hot pepper are dominantly grown in Ethiopia. From these vegetable 

production tomato, potato and onion are the major vegetables production of West Shewa zone (WSZIAO, 2017). 

Integrating vegetable production in a farming system has contributed substantially to food and nutrition security as 

the vegetables complement stable foods for a balanced diet by providing vitamins and minerals (Gani and Adeoti 

2011; Afari-sefa and Dinssa, 2015). 

Vegetables crops are sources of vitamins, minerals and dietary fiber, however their cultivation isn't wide 

practiced in developing countries, like Ethiopia because of small-scale farming systems and poor pre- and post-

harvest handling techniques (Rahiel Abraha  and Gebresilasie 2018). Vegetables are high value crops, which require 

intensive cultural practices, financial, and labor inputs involved are therefore greater than those required for most 

staple crops.  
Vegetable production is problem sensitive relative to other agricultural production. These problems cannot 

be easily solved if it is not managed at the right time and right way. Even if a number of research works were done 

in Ethiopia on problems of vegetable production as perceived by the farmers, however no significant impact of such 

research works had been observed. An understanding of problem perceived by the farmers in vegetable production 

efficiency will be helpful for planning and implementation of program. This study was undertaken to determine the 

problem faced by the farmers in vegetable production, compare the severity of the problems faced by the vegetable 

producers, determine some selected characteristics of the farmers and explore the relationship of the selected 

characteristics of the farmers with problem faced in vegetable production efficiency.  

From existing literature, research in this direction in West Shewa still remains out of the attention, even though 

vegetables occupy a few positions in both domestic and foreign food trade of Ethiopia. Therefore, it is very essential 

to analyze vegetables (tomato and potato) farmers’ production efficiency in West Shewa zone. Along these lines, the 
investigation might produce on the concurrent communication of family choices of vegetable production efficiency 

and the most affecting elements of the efficiency of smallholder farmers in West Shewa, Ethiopia. 

 

2. Materials and Methods 

Three districts namely, Abuna Gindeberet, Dire Inchini and Ejersa Lafo were ecologically stratified and randomly 

selected from West shew zon, oromia National regional state, Ethiopa for the purpose of this study. Finally, 385 

respondents were selected from nine kebekes which randomly and proportionally selected from three districts.  

Economic efficiency may be estimated principally by two approaches. They embrace econometric and 

mathematical approach. In econometrics approach, random frontier production perform is used to work out 

economic efficiency and its determinants. Alternative approaches involving econometric approaches are random 

profit frontier perform and value frontier function methods. Mathematical approach involves data envelopment 
analysis (DEA) approach. Advantages and drawbacks of econometrics model and mathematical approaches are 

discussed by Battese and Coelli (1995). Within the gift study, we've adopted random production frontier perform to 

seek out economic efficiency level in vegetable production.  

Therefore, to determine the problem of economic efficiency of vegetables production technical efficiency 

and allocative efficiency must consider. To analyze technical efficiency and allocative efficiency DEA was utilized 

as the following. 

 

2.1 Data Envelopment Analysis 

To analyze technical efficiency data envelopment analysis presented as the following. Suppose the number of DMU 

is n. Tj-th DMU is characterized as DMUj (j=l,2,…n). The input vector of DMUj is Xj=(X1j, X2j…Xmj)T, and the 

output vector is Yj= (Y1j, Y2j…Yrj) T. Here, m is the number of input, r is the number of output. The corresponding 
weight coefficient is V=(V1, V2…Vm) and U=(U1, U2…Ur) respectively. Also suppose Xij is the i-th input value 



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of the j-th DMU, Ykj is the k-th output value of the j-th DMU. Vi, Uk is the weight coefficient of the i-th and the k-

th index respectively. Then the corresponding appraisal efficiency index of the j-th DMU is: 

)1(

1

1








m

i
iji

s

k
kjk

j

T

j

T

i

xv

yu

xv

yu
H                                                        

Choosing the suitable weight coefficient V and U to let 1H i
. High hj indicates that DMU can 

use relative less input to obtain relative more outputs. 

Generally, to determine the relationship between socioeconomic and institutional factors and the computed 

indices of efficiencies, a Tobit regression model will be utilized again. This model will be adopted because the 

efficiency scores are double truncated at 0 and 1 as the scores lie within the range of 0 to 1. The functional form of 

this Tobit model is:  

 2
*  jjmxy mo

  

 

Where yi* representing the non-observed efficiency latent variable scores of farm j,  

β = a vector of unknown parameters,  

x jm
 = a vector of explanatory variables m (m = 1, 2, ..., k) for farm j and  


j
= an error term that is independently and normally distributed with mean zero and variance σ2  

Denoting yi as the observed variables, 

)3(

0

1

1

*

0

0

1




















 




y

yy

y

y

i

j

i

i

if

i
if

if

 In the Equation (21), the distribution of the dependent variable is not normally distributed rather its value 

varies between 0 and 1. Therefore, the maximum likelihood estimation which can yield the consistent estimates for 

unknown parameters vector for it has been used than the ordinary least square (OLS) estimation which gives biased 

estimates (Maddala, 1999). 

3. Result and Discussion  

This chapter presents the results of the study and discusses in comparison with the results of similar studies. It is 

organized under two sections; the first section deals with the description of demographic, socio-economic and 

resource allocation of the sample farmers using descriptive statistics. The second section identifies factors affecting 

vegetables technical, allocate and economic efficiency using Data envelopment analysis and Tobit model. 

 

3.1 Descriptive Statistics Results  

3.1.1 Demographic Characteristics of Households 

As showed in Table 1, out of the total sampled household 7.79 percent of them are female-headed households while 

92.21 percent are male-headed households. It indicates male-headed household’s dominance in vegetables farming 

in the study area. The average age of the sample households’ head is 46.89 with the standard deviation of 11.95. 

This indicates that more of the sampled households’ are in the range of productive age. The average family size of 
the farm household head was 7.15 persons, which is in comparable to West shewa zone average population of 5 

persons and larger than the Ethiopian average population which accounted to 4.6 persons per- household (CSA, 



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2016). In the study area most of the farm households used own family labor for specific activities. The study showed 

that the average family labor of the farm households is 4.48 person-day equivalents. This indicates that most of the 

farm households were used family labor than hired labor. 

As presented in Table 1, the average education level of household head of farm households head is 5.07 

(schooling years?) of formal education. This indicates that, on average the sample farm household attended the 

minimum (first cycle) education which was deemed enough to understand what development agents provide in 
agricultural production guide line for them in order to improve their productivity. 

 

Table 1: Demographic characteristics of sampled households 

Note: The number in the [ ] shows frequency and percent and *** show less than or equal to 1 percent significant 

level                                           Source: own surveyed data of 2018 computed by author 

 

3.1.2 Farm Characteristics and Land Allocation of Sample Households 

In the study area, most of the sampled farm, households produced potato depending on rainfall but all tomato 

producers used irrigation in vegetables production. This production seasonality and perishability of the vegetables 

farming caused farm households to earn less income from vegetable production. As indicated in Table 2 below, the 

average potato yield during the survey season is 981.09 kg while tomato yield is 841.12 kg. From the three-selected 

districts on average Ejersa lafo is the highest potato and tomato producer with the annual average production of 

1741.356 kg and 1743.875 kg respectively.  

Based on survey data as showed in Table 2 the average total cultivated land of the sampled farm household 

is 0.922ha covered by different crops. Out of this cultivated land, on average potato and tomato cover 0.115ha and 

0.087 ha respectively. From the selected districts, households in Ejersa Lafo allocative more of their lands for potato 

and tomato production than households in other districts. As survey, data in Table 2 indicate the average vegetable 

farm experience of the sampled farm households is 24.992 years, and Abuna Gindabarat has the highest vegetable 
farm experience than others districts. 

 

Table 2: Farm characteristics and land allocation of sample households 

Variable Mean A/G/beret D/Inchini E/lafo F-test 

Potato yield(kg) 981.09 886.462 739.583 1741.356 0.33 

Tomato yield (kg) 841.12 198.490 548.142 1743.875 0.00 

Cultivated land(ha) 0.922 1.413 1.630 2.205 7.46 *  

Land for potato(ha) 0.115 0.076 0.185 0.114 2.20* 

Land for tomato(ha) 0.087    0.061 0.019 0.092 0.64 

farming experience (year) 24.992 26.934 23.125 20.566 7.73* 

Not: * indicate the level of significance at less than or equal to 10 percent  

Source: own surveyed data of 2018 

 

Variables Total  

  or  

F-test Frequency    mean Percent        std. 

Female  headed HHs  [30] [7.79] -0.17 

Age of the household 46.89 11.94 1.19 

Family size 7.15   2.76 2.94*** 

Family labour PDe 4.48   3.10 0.80 

Education level of HHs 5.07   3.91 290.91*** 



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3.1.3 Institutional Service and Availability of Infrastructure 

Availability of infrastructure usually plays a great role in the economic development of any country while, 

institutional service improve the production and productivity of the farmers. As indicated in Table 3, only 84 (21.82 

percent) of farm households obtained credit for their vegetable production while the remaining 78.18 percent are 

non-credit users. 117 (32.96 percent) of the farm households had extension contact five times per year and 92 (25.92 

percent) of them gain only one time extension contact throughout a year. The average walking distance from farm 
household residence to development agent office is 0.69hr with standard deviation of 0.458 and the average distance 

of the farm households’ residence to that of nearest market is 0.74hr with standard deviation of 0.540. These show 

that extension service more close to the farmer than nearest market. Moreover, extension contact, distance from 

major town and distance from all-weather condition (road) were found to be significantly different among the 

districts at less than or equal to five percent significance levels respectively (Table 3). 

Table 3 also showed that about 28.57 percent of the sampled farm households are cooperative members. 

Being cooperative member helps the farm household to share more information about production as well as about 

market of the product. 

 

Table 3: Institutional service and availability of infrastructure 

Variable [Frequency]/ 

mean  

[Percent]/stnd. A/G/beret  D/Inchini E/lafo F-test 

Credit access [84] [21.82] 0.192 0.125 0.466 0.04   

Extension contact /year  [5]         [32.96]  2.873 3.593 2.500 5.98** 

Cooperative membership [110] [28.57] 0.436 0.093 0.016   0.06 

Distance to development agent 

office (hr)  

0.694 0.458 0.686 0.629 0.891 1.70 

Walking distance from the 

nearest market(hr) 

0.741 0.540 0.791 0.634 1.013 0.02 

Walking distance from major 

town(hr) 

1.872 0.987 1.524 2.793 1.724 4.24** 

Walking distance to all weather 

condition road(hr) 

0.609 0.602 0.602 0.494 0.879 5.92** 

Not: numbers in [ ] indicate frequency and percent and ** stands for 5 percent significances  

Source: own survey data of 2018 

 

3.1.4 Soil Management and Agronomic Practice of Vegetable Production 
Vegetable growers must specify the types of vegetable produced from their farms, identify the potential critical 

hazards and establish and monitor appropriate measures during all phases of farm production (GAP--VF, 2005). In 

addition to using adaptation, improved agricultural technology, appropriate soil management lead to enhance 

potential vegetable production. Sampled farm household in the study area were asked whether they used ‘good’ 

agricultural practice during the production season or not. These practices are farmyard manure, crop rotation, 

fallowing, using composite and inter-cropping system, weed and disease control mechanism. The results on farmers’ 

use of recommended soil management and agronomic practices of vegetable production in the study areas during 

2017/2018 cropping season presented in Table 4 below. 

 Farmyard Manure: Farmyard manure is one of the good practices of soil fertility management, which 

improve the productivity of land. Application of the farmyard manure contributes to sustainability of the 

farm (Ranogajec, et al., 2015). As shown in Table 4 above, 240 (67.04 percent) of sampled farm 

households applied farm yard manure while the remaining 110 (32.96 percent) of the respondents did not.  
 Crop Rotation: Crop rotation shows producing different crops in different cropping season on the same 

land. It is the old traditional system, which was used to reduce damage from insect pests, to limit the 

development of vegetable diseases, and to manage soil fertility. As indicated in the Table 4, 299 (86.17 

percent) of sampled farm households practiced crop rotation during 2017/2018 production season. 

 Fallowing: It is cultivating land that is not seeded for one or more growing season. It is the system in 

which, farmers can improve their soil fertility to increase the productivity of their land. As explained in 



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Table 4, out of total number of the respondents 159 (44.79perecent) of them used fallowing in 2017/2018 

cropping season.  

 Compost: Compost fertilizer is the main organic type of fertilizer which prepared from animal west (often 

from slaughterhouses) and from different plants. It is the means in which farmers can maintain their land 

fertility to increase the yield per hectare. As showed in Table 4, 143 (39.83 percent) of the sampled farm 

households used compost in 2017/2018 cropping season. 
 Intercropping: Intercropping is the mixed planting method of growing one crop alongside another 

(Mousavi and Eskandari, 2014; Smith and Liburd, 2012). The purpose behind intercropping is to 

extend yields by doubling up on available growing area. Intercropping creates multifariousness 

that attracts a range of useful and predatory insects that is uphill with monoculture horticulture. As showed 

in Table 4, about 63 (17.75 percent) of sampled farm households applied intercropping farming system in 

2017/2018 cropping season.  

 

Table 4: Soil management and agronomic practice of vegetable production 

Agricultural Practices   Frequency  Percent A/G/beret  D/Inchini E/lafo F-test 

Farmyard manure 240      67.61 66.04   13.96 20.00 3.20* 

Crop rotation 299        86.17 55.48 35.48 9.03 0.35 

fallowing 159        44.79 71.43 12.57 16.00 0.81 

 Compost  141        39.83 16.17 25.45 58.38 0.01 

Inter cropping 63        17.75 46.97 13.64 39.39 0.01 

Source: own survey data of 2018 
 

3.1.5. Weed Management System 

Appropriate weed control measure undertaken to improve quality and quantity of grain production. Weeds 

infestation reduces yields of the crop (yield), deteriorate the quality of farm produce, and trim down the market 

value of production. Effective weed control not achieved without appropriate control measure should be undertaken 

attain good grain harvest and quality grain production (Sareta, 2016). As showed in Table 5, 4.94 percent sampled 

farm households applied mulching, while 9.61 percent spread of wood ash and 100 percent of the respondents used 

hand weeding in 2017/2018 cropping season 

 

Table 5: weed management system 

Variable  Frequency  Percent A/G/beret  D/Inchini E/lafo F-test 

Mulching 19         4.94    27.03 54.05 18.92 0.21 

Spread of wood ash 37         9.61  100.00 0 0 0.02 

Hand weeding 385 100   59.48 24.94 15.58 - 

Source: own survey data of 2018 

 

3.1.6. Technology Adoption  

The contribution of technology to economic growth can only be realized when and if the technology is widely 

applied by the users (Samy, 2016). The increasing rate of technological advancement in the agricultural sector, has 
resulted in increased efficiency and productivity (Ugochukwu, 2018). As the result explained in Table 6, about 315 

(81.82 percent) of sampled farm households used chemical fertilizer in 2017/2018 cropping season. The result also 

showed that 96 (24.94 percent) of the sampled farm households applied improved seed of tomato while 243 (63.12 

percent) of them applied improved potato seed in 2017/2018 cropping season. Moreover, fertilizer application and 

improved seed use for tomato and potato production of the farm households were significantly different at less than 

or equal to one percent significance level among sampled districts in 2017/2018 cropping season. 



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The result in Table 6 showed that on average sampled household invest 571.12 Birr with standard deviation 

of 240.75 to purchase NPSb fertilizer and 600.08 Birr with standard deviation of 270.35 for tomato and potato 

production in 2017/2018 cropping season. This implies that, the sampled farm households use 0.5quintal of fertilizer 

in the study area. As shown in Table 6, sampled farm households invested on average 382 Birr and 377 Birr with 

standard deviation of 261.1 and 211.01 to purchase herbicide and pesticide for tomato and potato production in 

2017/2018 cropping season, respectively. 
 

Table 6: Technology adoption 

Variable  [yes]/ mean  [Percent]/stn

d. dev. 

A/G/beret  D/Inchini E/lafo F-test 

Fertilizer          [315]        [81.82] 60.95 22.86 16.19 2.09*** 

Improved seed (T) [96  ]      [24.94] 1.45 15.98 23.15 2.93*** 

Improved seed (P) [243]      [63.12] 53.74 16.76 24.94  2.11*** 

NPSb 571.12 240.75 59.38 25.00 15.63 1.42 

UREA 600.08 270.35 15.57 34.54 67.54 1.57 

Herbicide 377.48 211.01 59.48 24.94 15.58 0.01 

pesticide 382.8 261.10 15.00 1.78 25.89 2.35 

Source: own survey data of 2018 

3.1.7. Reasons for Not Adopting Agricultural Technology 
In addition to technology adoption, the study report identifies sampled farm household who did not use improved 

seed for vegetable production and the reason behind depicted in figure 5. As shown in figure 5, 25.8 percent of the 

sampled households did refuse improved seed because of high price and 23.09percent did refuse because of lack of 

transportation. The other section of sampled households, 10.55 percent did not use improved seed because of credit 

arrangement while 40.7percent of them did not use because of lack of supply in improved seed of tomato and potato 

for production in 2017/2018 cropping season. 

 

25.8%

10.55%

40.57%

23.09%

high price lack of credit

lack of supply lack of transportation

 

Figure 1: Main reason of not using improved seed 



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3.2. Efficiency Analysis Using Data Envelopment (DEA) Method 

This section assesses the efficiency of vegetables production by households in terms of TE, AE, and EE.  

Remembering the generally acknowledged method for doing DEA in efficiency analysis, it was settled on executing 

different input and, output kept away from the likelihood of experiencing issues related with useful structure 

misspecifications. The output factors were vegetable production characterized as amount of potato and tomato 

harvested in kilograms. The inputs used were land, labor, oxen seed, fertilizer and agrochemicals.   
The DEA technique used in the estimation of relative efficiency of DMUs was checked for potential 

problems such as sample size and outliers that can seriously affect the efficiency scores. Data Envelopment Analysis 

Program (DEAP) Version 2.1 was applied to compute the efficiency of vegetable (tomato and potato) production. 

Even though, data collected had zero value it is difficult to use zero in the DEAP therefore, In order to solve such 

hardle we employed the method of substituting small no recommended by (George, 1997; Battese, 1991).   

Since DEA is an intense factor technique, noise (even symmetrical noise with zero mean) such as 

measurement error can cause massive problems. The tests for the affectability of DEA effectiveness scores to enter 

yield anomalies are, hence, urgent to check the heartiness of the productivity results. Primarily, Z score and box plot 

strategy were utilized and confirmed the information is generally appropriated. Further, to confirm heartiness of 

effectiveness after effects of DEA model to input-output anomalies, we utilized, among others, the strategy utilized 

by (Yang et al., 2009). In the wake of tackling the DEA issues utilizing each of the perceptions creating for 

example, all homestead farms that were completely productive were excluded and DEA issues were addressed. 
Using DEAP 2.1 version result, there is the existence of difference in efficiency score level among the three 

efficiencies TE, AE and EE with score ranging the lowest from 3.8 %, 5.5% and 0.9% to 100%, respectively. From 

the total sampled households 20.5%, of them were technically efficient while, 0.78% of them were both allocative 

and economically efficient. This indicates that 79.5% of the respondents were technically Vs in efficient while, 

99.22% of the respondents were allocative and economically inefficient. Therefore, as the result implies that there is 

a room to households to improve their vegetable production with the current level of technology.  Data envelopment 

analysis package result revealed that, the average TE of the households’ were 49.5 percent indicating farm 

households are producing 50.5% much less of achievable output given their prevailing degree of technological 

know-how and input use. Under the assumption of CRS, the efficiency ratings continue to be identical in each input 

orientation (input minimization) and output orientation (output maximization). Thus, if we had chosen to maintain 

inputs constant and measure efficiency in output growing path the efficiency rating is additionally indicating that 
outputs improved by 50.5% to come to be efficient. 

The mean allocative efficiency and economic efficiency showed that, there was a significant difference in 

the level of inefficiency in production process. As DEA result indicated the mean AE of farm households was 33.7 

percent, this indicate that AE of the farm households’ shown 66.3% growth in output by improving AE, with current 

technology. As identified in DEA the average EE of farm households was 17.4 percent.  This quit end result 

indicated that if the commonplace farm household in the pattern end up to advantage the EE degree of his/her most 

efficient complement, then the not unusual farm household might also moreover need to experience a 82.6% 

increase in output by using way of improving each EE, with the winning era. Therefore, this stop surrender stop quit 

result suggests the life of big technical, allocative and economic inefficiency in vegetable production amongst 

smallholder farmers within the study area. 

As featured within the writing detail, the uses of DEA approach had some negative aspects. The primary 

issue is that the DEA approaches anticipate all DMUs are homogenous and indistinguishable of their 
responsibilities. Inside the event that the heterogeneous DMUs are surveyed by using DEA without an adjustment, 

the DEA yields a one-sided act rankings and wrong investigations. Therefore, to clear up such trouble we hired 

hierarchical cluster efficiency analysis, which is comprised of agglomerative techniques and divisive techniques that 

find clusters of observations within a data set. The divisive techniques begin with all of the observations in one 

cluster and then proceeds to split (partition) them into smaller clusters while, the agglomerative methods begin with 

every observation being considered as separate clusters and then proceeds to mix them till all observations belong to 

one cluster (Cimiano et al., 2001).  

Four of the highly acknowledged algorithms for hierarchical clustering are average linkage, whole linkage, 

single linkage and ward’s linkage. Average linkage clustering makes use of the common similarity of observations 

between two groups as the measure between the two groups (Yildirim and Birant, 2017). Complete linkage 

clustering makes use of the farthest pair of observations between two groups to decide the similarity of the two 
groups (Camiz, 2007). Single linkage clustering, on the other hand, computes the similarity between two groups as 



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the similarity of the closest pair of observations between the two groups (Mohbey, 2016; Stuetzle and Nugent, 

2007).Ward's linkage is distinct from all the other methods due to the fact it uses an analysis of 

variance approach to evaluate the distances between clusters. In short, this method attempts to minimize the Sum of 

Squares (SS) of any two (hypothetical) clusters that can be formed at each step (Murtagh, 2014;  Singh 2008). In 

general, this method was very efficient; however, it tends to create clusters of small size. At each clustering step, the 

cluster recognized for combination is the best that minimizes the sum of squared distance over all devices. The 
dendrogram is generally represented in squared distances as the following figure 2. 

 

 

0
20

40
60

80

L2
sq

ua
re

d 
dis

sim
ila

rit
y m

ea
su

re

G1
n=71

G2
n=34

G3
n=8

G4
n=31

G5
n=16

G6
n=22

G7
n=34

G8
n=123

G9
n=14

G10
n=32

Dendrogram for wards_linkage cluster analysis

 

Figure 2: Dendrograms for Ward's linkage hierarchical cluster analysis of efficiency scores 

Source: Own computation from efficiency scores of the survey data (2018) 

Reading the dendrogram from the bottom to the top, we see that clusters G5 to G8 are merged in quick 

succession while G2 to G3 and G9 to G10 were merged at about the same distance and G1 and G4 were initially 

separate. These five clusters remain stable until, at a much higher distance, G1 merges with the second cluster. This 

result clearly suggests a five-cluster solution. Increasing the number of clusters appears unreasonable, as many 

mergers take place at about the same distance. Looking at appendix (1), it is shown that the Duda/Hart (Je(2)/Je(1)) 

index yields the highest value for four clusters (0.6718), followed by a seven-cluster solution (0.5481). Conversely, 

the lowest pseudo T-squared value (53.64) occurs for ten clusters. 

Comparing the variable means across the five clusters, we find that respondents in the second cluster were 
very strong TE (0.97), but AE (0.59) and EE (0.58) moderate. Respondents in the fourth and fifth cluster have 

extremely high expectations regarding all five-performance features, as evidenced in average values below average. 

Finally, respondents in the first cluster do not express high expectations in general, except TE in terms of efficiency 

Table 7.  

           

Table 7: Mean value of efficiency scores 

                                Mean of efficiency level 

Cluster   Households   TE AE EE 

1 71(18.44%) 0.96        0.23      0.22   

2 42 (10.91%) 0.97      0.59     0.58   

3 31 (8.05%) 0 .58         0.54     0.32    

4 195(50.65%) 0.28         0.24      0.06    

5 46 (11.95%) 0.18        0.53      0.10    

Source: Own surveyed data (2018) 



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3.3 Assessment of Sources of Inefficiency 

After identifying each efficiency level, finding out the source of inefficiency of farm household is the primary 

objective of the study. To see this, the technical, allocative and economic inefficiency of the household was 

analyzed using Tobit model given in Table 8. To illustrate this, socio-economic, demographic and institutional 

factors that affect vegetable production efficiency of the households were observed. Before explaining the model, 

multicollinearity test was carried out and the mean VIF was 1.26 and a maximum VIF of 1.76. This shows that there 
is no problem of multicollinearity in the data set (Appendix Table 2).For interpretation purposes; the marginal 

effects of explanatory variables from Tobit regression model were used. In other words, the derived values for the 

significant explanatory variables indicated that the effects of a unit change in those variables on the expected 

unconditional value of TE, AE and EE conditional up on between 0 and 1, and probability of being between 0 and 1.  

Tobit model result showed that, age of household head, education level of the household, land size, access to 

irrigation, extension contact, access to information and pesticide use significantly affected TE of vegetable 

production, while age of the household, land size, access to irrigation, extension contact, access to information and 

pesticide use affect, allocative efficiency of the farm households. Finally economic efficiency of the farm 

households were affected by age of the households, education level of household head, land size, access to 

irrigation, access to information and pesticide use.  

As Tobit output in Table 8 shows age of household head affect significantly and negatively AE and EE of 

vegetable production. This implies that, as household head get older and older his/her AE and EE decreases. This is 
because of young people can easily get familiar with different technology and allocates their resources effectively 

and efficiently. In other words, younger people were more efficient in vegetable production than older people. As 

the study by Li and Sicular (2015) indicated technical efficiency of farm labour decreased after the age of 45. The 

study by Li and Sicular (2015) and Mutz et al.(2017) confirmed that as households get older their vegetable 

production efficiency level decreased. 

The Tobit regression result revealed that, education level of household head affect technical efficiency and 

economic efficiency negatively vegetables production. This implied that when education level of household head 

increases the opportunity of off farm income increase, which in contrast decreases farm management of the 

households. This is why increasing the level of education lead to decrease in technical efficiency and economic 

efficiency of vegetable production of farm households. The study by Thi and Dao (2013)  confirmed that the fact 

that an individual gets more education he/she perform less in vegetable production.  
Land size had significant and positive relationship with TE, AE and EE of vegetable production. This 

indicates that farmers with large land size had more opportunity to allocate his/her land for different production 

activities to improve the productivity of their land.  The results of this work indicate the importance of crop 

diversification. Farms favoring market-oriented products, such as vegetable production, have greater efficiency than 

farms focusing on staple crops such as teff, wheat and maize.  This result was agreed with the study of 

(Mokgalabone 2015). 

As the Tobit output in Table 8 details, access to irrigation affected AE and EE of vegetable production 

significantly and negatively. This variable hypothesized to influence vegetable production positively. Nevertheless, 

in this result it influences vegetable production efficiency negatively. This is for the reason why farmers in the study 

area were used traditional irrigation system, which needs more of labor force, and difficult to use mechanized 

agricultural input. Irrigated production regarded as either high input-low input or high input high output depending 

on the area and form of irrigation. Irrigation costs are used as a proxy for the quantity of water used for 
production because the irrigation water has been chargeon each unit of the cultivated area, making it difficult to 

quantify the quantity of water entering the farm. The study was agree with the studies by (Nwauwa et al., 2015 and  

Puozaa, 2015). 

As Table 8 identified extension contact affected TE and AE of vegetable production positively and 

significantly. This implies that as frequency of extension contact to vegetable producer increases farm households 

TE and AE were increased. The result revealed that, as frequency of extension contact increase farmers ability to 

apply input for their vegetable production as well as allocating cost of production during production process in good 

position because they gained necessary information from extension agents. This result is in line with the studies by 

(Awotide, 2018; Mokgalabone, 2015). The study also revealed that, access to information affected TE, AE and EE 

of farm households’ vegetable production positively and significantly. This implies that farmers with better access to 

information were in better position in terms of TE, AE and EE of vegetable production. This indicates that, farm 



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households having access to information were more efficient than others were. This result in lined with the study by 

(Abdul-salam and Phimister, 2015).  

Table showed that pesticide use affected TE, AE and EE of vegetable production of farm households 

positively and significantly. The positive marginal effect revealed that, as farmer increase the use of pesticide, 

vegetable production efficiency was also increased. This implies that farmers with pesticide use were more efficient 

in vegetable production than others were. This result is agreed with the studies by (Jha and Regmi, 2009;  Rahman, 
2018). 

 

Table 8: Tobit Model for Sources of Inefficiency Analysis  

variable Technical inefficiency Allocative inefficiency Economic inefficiency 

Marginal 

effect 

Std. Err Marginal 

effect 

Std. Err Marginal 

effect 

Std. Err 

Sex of HH   -0.111 0.074 0.016 0.028 -0.027 0.031 

Age of HH 0.002 0.001 0.001*** 0.000 0.001* 0.000 

Education level of 

household head  

0.019*** 0.006 -0.009 0.002 0.004* 0.002 

Land size -0.499*** 0.143 -0.457*** 0.053 -0.386*** 0.058 

Access to irrigation  -0.010 0.058 0.104*** 0.022 0.073*** 0.024 

Extension contact  -0.028** 0.012 -0.010** 0.004 0.003 0.005 

Fertilizer use  -0.009 0.049 -0.009 0.019 -0.016 0.020 

Access to information  -0.258*** 0.071 -0.075*** 0.027 -0.114*** 0.030 

Amount of credit used   0.005 0.009 0.006 0.003 0.003 0.004 

Off/non-farm income -0.003 0.004 0.001 0.001 -0.001 0.001 

Improved seed  -0.027 0.047 -0.024 0.018 -0.020 0.019 

Pesticide use -0.091* 0.049 -0.097*** 0.018 -0.065*** 0.020 

Note: ***, *** and * showed 1%, 5% and 10% significant level 

Source: survey data of 2018 

 

4. Conclusion and Policy Implications  
The study identifies the factors that determine vegetable production efficiency (tomato and potato) using DEA and 

Tobit model. DEA estimation employed to identify the overall TE, AE and EE of farm households’ vegetable 

production efficiency. Accordingly, the overall TE, AE and EE were 49.5%, 33.7% and 17.4%, respectively.  The 

result showed that, even though, it is below average TE is better than AE and EE. The result also indicated that if the 

households operate at full efficiency level, on average the farm household could reduce the cost of production by 

82.6% while producing the same amount of output.  

The study also revealed the sources of Technical, Allocative and Economic inefficiency using Tobit regression 

model. Accordingly, technical inefficiency of the farm households’ significantly affected by education level, land 

size, extension contact, access to information and pesticide use. Furthermore, allocative efficiency significantly 

affected by age of the household, access to irrigation, land size, extension contact, access to market information  and 

pesticide use. And economic efficiency significantly influenced by age of the household, education level, land size, 

access to irrigation, access to information and pesticide use.  

 

 



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Acknowledgements 

The authors gratefully acknowledge the Ethiopian ministry of science and higher education and Ambo University. 

 

References 

Abdul-salam, Yakubu, and Euan Phimister. 2015. “Efficiency Effects of Access to Information on Small Scale 

Agriculture : Empirical Evidence from Uganda.” 

Andrew W., Shepherd. 2007. “Approaches to Linking Producers to Markets,Food And Agriculture Organization Of 

The United Nations Rome, 2007.” 

Antoci, Angelo, Pier Luigi Sacco, and Paolo Vanin. 2009. “Social Capital in a Homogeneous Society,” no. 13661. 

Asfaw Zeleke. 2015. Production And Management Of Major Vegetable Crops In Ethiopia Volume I. Vol. I. 
Awotide, Diran Olawale. 2018. “Analysis Of Production Efficiency Of Food Crop Farmers In Ogun Analysis Of 

Production Efficiency Of Food Crop,” no. September 2012. 

Banjaw Td. 2017. “IMedPub Journals Review of Post-Harvest Loss of Horticultural Crops in Ethiopia , Its Causes 

and Mitigation Strategies of Post-Harvest Loss Horticultural Crops in Ethiopia In,” 1–4. 

Barrett, Christopher B, Maren E Bachke, F Marc, and Thomas F Walker. 2011. “Smallholder Participation in 

Agricultural Value Chains: Comparative Evidence from Three Continents,” no. 27829. 

Battese, George E. 1991. “Frontier Production Functions and Technical Efficiency: A Survey of Empirical 

Applications In Agricultural Economics,” no. 50. 

Brook, Keith. 2005. “Labour Market Participation : The Influence of Social Capital,” no. March: 113–23. 

BV, Triodos Facet. 2013. “Country Report Ethiopia.” 

Byron. 2014. “Determinants of Soybean Market Participation by Smallholder Farmers in Zimbabwe.” Journal of 

Development and Agricultural Economics 6 (2): 49–58. https://doi.org/10.5897/JDAE2013.0446. 
Camiz, Sergio. 2007. “Comparison of Single and Complete Linkage Clustering with the Hierarchical Factor 

Classification of Variables Comparison of Single and Complete Linkage Clustering with the Hierarchical 

Factor Classification of Variables,” no. June. https://doi.org/10.1556/ComEc.8.2007.1.4. 

Chala, Hailu, and Chalchisa Fana. 2017. “Determinants of Market Outlet Choice for Major Vegetables Crop : 

Evidence from Smallholder Farmers ’ of Ambo and Toke-Kutaye Districts , West Shewa ,” 4 (2): 161–69. 

Christopher, E P. 2009. Introductory Horticulture. 

Cimiano, Philipp, Andreas Hotho, and Steffen Staab. 2001. “Comparing Conceptual , Divisive and Agglomerative 

Clustering for Learning Taxonomies from Text.” 

Damalas, Christos A. 2017. “Farmers ’ Training on Pesticide Use Is Associated with Elevated Safety Behavior.” 

https://doi.org/10.3390/toxics5030019. 

Fanos, Tadele. 2015. “A Review on Production Status and Consumption Pattern of Vegetable in Ethiopia” 5 (21): 
82–93. 

Fufa, Nimona. 2017. “Opportunity , Problems and Production Status of Vegetables in Ethiopia : A Review” 4 (2): 1–

13. 

Gani, B S, and A I Adeoti. 2011. “Analysis of Market Participation and Rural Poverty among Farmers in Northern 

Part of Taraba State , Nigeria” 2 (1): 23–36. 

George E. Bathe. 1997. “A Note On The Estimation Of Cobb-Douglas Production Functions When Some 

Explanatory Variables Have Zero Values” 48 (2): 250–52. 

Girma Kebbede. n.d. “Farmers in the City : The Case of Addis Abeba,” 1–15. 

Hailu Gebru, Kebede W/Tsadik2, and Tamado Tana. 2015. “Evaluation of Tomato ( Lycopersicon Esculentum Mill 

) and Maize ( Zea Mays L .) Intercropping System for Profitability of the Crops in Wolaita Zone , Southern 

Ethiopia” 5 (1): 132–41. 

Jha, Ratna Kumar, and Adhrit Prasad Regmi. 2009. “Productivity of Pesticides in Vegetable Farming in Nepal,” no. 
43. 

Li, Min, and Terry Sicular. 2015. “Aging of the Labor Force and Technical Efficiency in Crop Production Evidence 

from Liaoning Province , China,” no. August 2013. https://doi.org/10.1108/CAER-01-2012-0001. 

Maponya P, Venter Sl, Du Plooy Cp, Modise Sd, and Van Den Heever. 2016. “An Evaluation of Market 

Participation Challenges of Small Holder Farmers in Zululand District, Kwazulu Natal, South Africa.” African 

Journal of Business and Economic Research 11 (1): 117–42. 

Mohamedl, Abdi. 2014. “Determinants Of Participation In Milk Marketing Of Small Holders In Jijiga Woreda, 



www.acseusa.org/journal/index.php/aijas          American International Journal of Agricultural Studies            Vol. 2, No. 1; 2019 
 

51 

 

 

Ethiopia.” 

Mohbey, Krishna K. 2016. “An Experimental Survey on Single Linkage Clustering An Experimental Survey on 

Single Linkage Clustering,” no. November. https://doi.org/10.5120/13337-0327. 

Mokgalabone, Maria Sylvia. 2015. “Analyzing The Technical And Allocative Efficiency Of Small-Scale Maize 

Farmers In Tzaneen Municipality Of Mopani District.” 

Murtagh, Fionn. 2014. “Ward’s Hierarchical Clustering Method: Clustering Criterion and Agglomerative 
Algorithm,” no. May. 

Musah, Abu Benjamin, Osei-asare Yaw Bonsu, and Wayo Seini. 2014. “Market Participation of Smallholder Maize 

Farmers in the Upper West Region of Ghana” 9 (31): 2427–35. https://doi.org/10.5897/AJAR2014.8545. 

Mutz, Rüdiger, Lutz Bornmann, and Hans-dieter Daniel. 2017. “Are There Any Frontiers of Research Performance ? 

Efficiency Measurement of Funded Research Projects with the Bayesian Stochastic Frontier Analysis for 

Count Data.” Journal of Informetrics 11 (3): 613–28. https://doi.org/10.1016/j.joi.2017.04.009. 

Nwauwa, Linus Onyeka Ezealaji and Omonona, Bola T . *. 2015. “Efficiency of Vegetable Production under 

Irrigation System in Ilorin Metropolis : A Case Study of Fluted Pumpkin ( Telferia Occidentalis ),” no. 

September. 

Omiti, John M, and Ellen Mccullough. 2009. “Factors Influencing the Intensity of Market Participation by 

Smallholder Farmers : A Case Study of Rural and Peri-Urban Areas of Kenya” 3 (1): 57–82. 

Osmani, Ataul Gani, and Elias Hossain. 2015. “Market Participation Decision Of Smallholder Farmers And Its 
Determinants In Bangladesh” 3 (62). 

Po, Rung-wei, Yuh-yuan Guh, and Miin-shen Yang. 2009. “A New Clustering Approach Using Data Envelopment 

Analysis.” European Journal of Operational Research 199 (1): 276–84. 

https://doi.org/10.1016/j.ejor.2008.10.022. 

Puozaa, Frederick Z. 2015. “Allocative Efficiency Of Irrigated Tomato Production In The Upper East Region , 

Ghana .” 

Rahiel, Hagos Abraha, Abraha Kahsay Zenebe, and Gebreslassie Woldegiorgis Leake. 2018. “Assessment of 

Production Potential and Post ‑ Harvest Losses of Fruits and Vegetables in Northern Region of Ethiopia.” 

Agriculture & Food Security, 1–13. https://doi.org/10.1186/s40066-018-0181-5. 

Rahman, Sanzidur. 2018. “Determinants of Pesticide Use in Food Crop Production in Southeastern Nigeria,” 1–14. 

https://doi.org/10.3390/agriculture8030035. 
Singh, Warsha. 2008. “Robustness of Three Hierarchical Agglomerative Clustering Techniques for Ecological 

Data,” no. October. 

Stuetzle, Werner, and Rebecca Nugent. 2007. “A Generalized Single Linkage Method for Estimating the Cluster 

Tree of a Density.” 

Thi, Giang, and Ngan Dao. 2013. “An Analysis of Technical Efficiency of Crop Farms In The Northern Region of 

Vietnam By,” No. September. 

Usid. 2005. “Global Horticulture Assessment.” 

Welderufael, Alemayehu Hailu. 2016. “Assessment of Horticultural Crops ( Vegetables , Tubers & Fruits ) 

Production Constraints and Opportunities in West and Southwest Shewa Zones of Oromia Region , Ethiopia” 

1 (3): 84–90. https://doi.org/10.11648/j.ijae.20160103.16. 

Yildirim, Pelin, and Derya Birant. 2017. “K-Linkage : A New Agglomerative Approach for Hierarchical Clustering” 

17 (4): 77–88. 

 

 

 

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