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© 2019 Conscientia Beam. All Rights Reserved. 

ASSESSMENT OF TECHNICAL EFFICIENCY OF LAYER PRODUCTION IN MAMPONG 
MUNICIPALITY: STOCHASTIC FRONTIER APPROACH   

 

 

 Yenibehit, N.1 

 Murshed, M.2+ 

 Islam, M. J.3 

 

1Teaching and Research Assistant, Department of Agricultural Economics and Extension, University of 
Education, Winneba, Mampong Campus, Ghana 
2Master of Science in Economics, North South University, Dhaka, Bangladesh 

 
3Master of Science in Agricultural Economics, BSMRAU, Dhaka, Bangladesh 
(+ Corresponding author) 

 

 
 

  ABSTRACT 
 
Article History 
Received: 10 January 2019 
Revised: 8 February 2019 
Accepted: 14 March 2019 
Published: 3 May 2019  
 
 
 

Keywords 
Stochastic frontier analysis 
Layer production 
Agriculture and livestock 
Cobb-Douglas function 
Mampong municipality 
Ghana. 

  
This study used a Cobb-Douglas functional form of the stochastic production model to 
assess the technical efficiency of layer producers under commercial production system in 
the Mampong Municipality of Ghana. The study captures thirty (30) randomly selected 
layer poultry farms within the Asante Mampong enclave for the study. This study 
utilized the Maximum Likelihood Estimation (MLE) criteria to obtain farm specific 
technical efficiencies as well as their determinants of output and inefficiency. The study 
revealed that output was positively correlated to the number of birds, medication and 
quantity of water which all have significant coefficients at 5% and 10% levels of 
significance. The significant estimated gamma value of 0.99 implied that 99% of the total 
variation in layer output in the study area is due to the technical inefficiency. The study 
also showed that age, education, experience, extension contact, credit and type of poultry 
housing were related to socioeconomic characteristics influencing technical inefficiency. 
It is recommended for egg producers to observe the proper routine medication and add 
other supplementary drugs to boost the laying performance of their birds while 
intensifying stock size and increasing water usage to maximize production and close up 
the production gap. Poultry policies should consider motivating and increasing extension 
contact between extension agents and poultry farmers in the study area.  
 

Contribution/Originality: The paper's primary contribution is finding that it captures thirty randomly selected 

layer poultry farms within the Asante Mampong enclave for the study by utilizing the Maximum Likelihood 

Estimation (MLE) criteria to obtain farm specific technical efficiencies as well as their determinants of output and 

inefficiency in the context of this municipality of Ghana. 

 

1. INTRODUCTION 

Sustainable agricultural practice reduces food loss and can play a key role in overall development of the 

economy [1, 2]. Thus, emphasis has to be given to this sector, particularly in the context of developing economies 

that are predominantly characterized as large agriculturally-intensified economies. The agricultural sector in Ghana 

has long been considered as a firm foothold of the country's economy and contributes 23% of Gross Domestic 

Products (GDP) while holding a share of 43% of national employment, with small scale farmers contributing 80% of 

all agricultural production [3].  The rapid population growth and urbanization is a driving force for increasing 

demand for livestock products including poultry and these have resulted in the expansion of the sector in recent 

times  [4].  

Current Research in Agricultural Sciences 
2019 Vol. 6, No. 1, pp. 20-28 
ISSN(e): 2312-6418 
ISSN(p): 2313-3716 
DOI: 10.18488/journal.68.2019.61.20.28 
© 2019 Conscientia Beam. All Rights Reserved. 

 
 
 

 
 

 

 
 
 
 

https://orcid.org/0000-0002-9006-4803
https://orcid.org/0000-0001-9872-8742
https://orcid.org/0000-0002-0574-1508
https://www.doi.org/10.18488/journal.68.2019.61.20.28


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Poultry farming, especially on a commercial basis, is a major component of the agriculture sector of the 

country. Although the crops sector holds a major share of activity in the agricultural sector with about 14.2%, the 

livestock sub-sector of which the poultry sector is cardinal has maintained a study economic growth of 5.3% 

between 2014 to 2016 and has contributed a value of GHC 2119 as real GDP in 2017. Poultry production plays an 

important role in leading commercial activity and possesses enormous potential for quick economic growth 

especially when considering livestock – based vocations [5]. Poultry production according to Assa [6] is 

considered as a leader in the global meat production and is, therefore, recording faster growth in developing and 

transitional countries of the world. Among the subsidiary occupations of farmers, poultry farming is the foremost, 

supplementing farm’s incomes with its quick returns and more importantly with minimum requirements for space, 

investment and expertise [7].  For instance in Ghana, there has been an increase in annual growth from 2000 to 

2007 mostly in the southern part of the country with an 80% growth during this period [8]. 

Commercial poultry production in Ghana takes the form of raising birds for eggs (layer production system) or 

raising birds for meat (broiler production system). However, this commercial poultry industry is more tilted 

towards layer production with only some seasonal broiler production is undertaken to meet seasonal demands in 

festive seasons. Unlike broiler production, which faces stiffer competition with cheaper imported chicken parts form 

the EU, USA and Brazil, there is minimal competition in layer or egg production. It is, therefore, profitable to raise 

layers especially for the fact that about 80% of feed produced by commercial feed millers in the country is layer feed. 

According to FAO [9] the annual growth in consumption of eggs in Ghana from 1995 to 2005 was 4.0%. Poultry 

meat and eggs only hold an average share of 0.60% of the total daily calories consumed in Ghana. Killebrew and 

Plotnick [4] pointed out that, an estimated 1.2 kg of meat and 12 eggs is the annual per capita consumption of meat 

and eggs in Ghana as compared to the recommended world average of 9.7 kg of meat and 154 eggs per person per 

year. Meanwhile, in terms of productivity, a fowl have the ability to produce 300 eggs in its first year of laying life 

but under tropical conditions, potential fecundity ranges between 180-200 eggs although higher levels have been 

reported [10]. This further makes the sector a reliable sector for a fast and efficient source of animal protein and for 

closing the country’s huge unemployment gap.  

With these positive indicators supporting the prospects of the egg production sector of the poultry industry, 

one would have expected the sector to continue to grow but in recent times most farms have folded up and other 

large farms that were operating at full capacity are now operating below capacity. The recent crisis in the sector has 

been cited has been caused by the very high cost of production (feed, inputs and energy) and lack of credit [11]. 

Apart from the feed which constitutes a major cost of poultry production, prices of other inputs are increasing and 

therefore requires that the production process be devoid of wastages and ensure efficient output production. This 

requires empirical data on the technical efficiency of egg production in order to advice farmers. It, however, saddens 

to know that there is lean literature on technical efficiency of layer production. Most of the economically related 

researches in the poultry industry only focus on profitability [12] challenges and prospects Adei and Asante [13] 

and Etuah [14] who also focused on cost efficiency and economies of scale leaving an information lag in the area of 

technical efficiency of egg production. This study, therefore, seeks to assess the technical efficiency of layer 

production in the Asante Mampong Municipality of Ghana. The study will provide production efficiency data that 

can be used in policy direction and also consolidated the database of the poultry sector especially with regards to 

technical efficiency. 

 

2. THEORETICAL FRAMEWORK OF TECHNICAL EFFICIENCY 

The stochastic production frontier approach developed by Aigner, et al. [15] and Meeusen and Van Den 

Broeck [16] was adopted for this study by using the two-stage analysis method. In the first stage, an index of the 

vector of individual technical efficiency is generated for the sample layer producers in the study area. Whereas in 

the second stage, an econometric method is used to analyze and explain the intra-farms inefficiency. 



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The stochastic frontier production frontier is made up of a production function that has a composite error term 

that is equal to the summation of two error terms [17]. The random effects caused by the statistical or white noise 

constitute the first error term while the second component constitutes the socioeconomic factors or effects that are 

not explained by the production function but are associated with technical inefficiency. However, this cannot be 

captured by a conventional production function [17]. The frontier production function for this study is specified in 

Equation 1 as: 

                                                          (1) 

where Yi is the output produced by farm i; Xi is an (n+1) row vector of inputs where the first element ‘1’ 

represents the intercept and the other elements represent inputs quantities used to produce Y;   (n+1) represents a 

column vector of technological parameters to be estimated;  stands for a random error that is independently and 

identically distributed  as ; and Ui denotes a one-sided error ) that represents the 

technical inefficiency of the ith farm [17]. 

As defined in equation 1 above, the structure of technology of production, deterministic production frontier or 

external factors to the production process determine the stochastic production function. They are combined to 

produce the production frontier. From the above representation, the deterministic production function is  

and the stochastic production function is represented as . 

From the output orientation method, the technical efficiency of the ith farmer can be calculated as the ratio of 

the observed output relative to the potential output as shown in Equation 2: 

 TEi   =                                        (2) 

Efficiency assumes values between 0 and 1 with smaller ratios indicating higher inefficiency. TE is a measure of 

the actual output to the potential output as measured on the frontier and is produced from the same set of inputs 

from a fully efficient firm (u = 0), with values implying that actual output is equal to frontier output. The frontier 

output is gotten by obtaining estimates of technology parameter vector through econometric methods or linear 

programming techniques. 

The Cobb-Douglas functional form is often used to estimate the production function parameters due to its 

ability to provide more efficient parameters [18]. Additionally, the output elasticities obtained from the Cobb-

Douglas may be equivalent to elasticities from the log transformation specification at the sample mean. Following 

this tradition and for the purpose of this study, a Cobb-Douglas function for egg production in the Mampong 

Municipality is specified in Equation 3: 

                                           (3) 

where εi = vi – ui with the subscript i indexing farmers and j indexing inputs use in production [17]. The log-

linear transformation of the above equation, shown in Equation 4 gives the equation for estimating the parameters 

using frontier regression analysis:    



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                                             (4) 

where Yi = Ln (yi); βo= Ln (bo);  βij = Ln (bij); Xij = Ln (xij); εi =  Ln (℮i); Ln = natural logarithm. By this 

logarithmic expression of the stochastic production function, the technical efficiency according to Battese and Coelli 

[19] is specified in Equation 5 as: 

                                                                         (5) 

In estimating stochastic frontier production effects of the socio-economic characteristics, the variation of the 

dependent variable (Yi) is often lumped together with the error term, which takes account for the component of 

variation due to random, unsystematic and unexplained noise.  

The error term (εi) is made up of two components, ui and vi , such that, εi = vi - ui, where vi is a random error due 

to random factors out of control of the farmer with a zero mean and a variance equal to δ2
v distributed as N (0,δ2

v). 

The ui component is inefficiency component of the error term assumed to have a non-negative half normal 

distribution truncated at zero and distributed as N (0, δ2
u). It is associated with farm-specific factors and may also 

have other distributions such as gamma exponential.  The mean values of ui  are determined by the equation 

   where Zi represents inefficiency variable for the ith respondent; i =1, 2......N;  = vector of 

parameters  to be estimated,  represents statistical random noise introduced to capture factors beyond farmers 

control and is independently and identically distributed (iid) as  The inefficiency variables include farm 

characteristics such as gender, age, experience, extension contact, access to credit and poultry housing type. These 

factors can influence the level of technical efficiency among poultry farmers. 

 

3. EMPIRICAL ESTIMATION OF TECHNICAL EFFICIENCY OF LAYER PRODUCERS 

The stochastic frontier was estimated following the two-step estimation process. This was done by firstly using 

the linearly transformed Cobb-Douglass production function. The total number of crates of eggs produced per 

farmer over the production period (Yi) was regressed against independent input variables including the number of 

birds, medication, the quantity of water, the quantity of feed and number of employees. The function is as specified 

in Equation 6 as: 

           (6) 

where Yi = quantity of eggs produced over the production period; X1 = Number of birds; X2 = medication; X3 = 

quantity of water (l); X4 = feeding; X5 = Number of employees;  = the vector of input parameters to be estimated; 

vi = random errors and ui = represents technical inefficiency effects. In the second step, the maximum Likelihood 

method was used to assess the effect of various socio-economic variables on the variation of TE. The inefficiency 

component of the error term (ui) was regressed against factors that are hypothesized to have an influence on the 

deviation of the observed output (Yi) from the potential output level (Y*). This is shown in Equation 7:  

          (7) 



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© 2019 Conscientia Beam. All Rights Reserved. 

where; Z1= gender of the farmers (dummy, male =1, female= 0); Z2 = age of the farmers; Z3 = experience of the 

farmers, Z4 = Interactive term between age and experience of the farmers; Z5 = extension contact;    Z6 =credit 

access (dummy, Access = 1, 0 = otherwise); and Z7 =housing type and , ,  , ..................  = parameter 

estimates of the linear regression model. 

 

4. VARIABLES AND DATA  

The Mampong Municipality which was chosen for this study is partly situated on the Mampong Scarp which 

runs eastwards. The highest point of the Municipality is 2,400m above sea level, while the lowest is about 135m. 

Thus, the Municipality is generally low lying and rise gradually through rolling hills stretching southwards 

towards Mampong. The Municipality is fairly drained by several streams and rivers. The municipality has several 

species of livestock with notable poultry farmers who are basically into egg production for supply to the urban area. 

Broilers are only raised during festive seasons in the short run. 

Cross-sectional primary data was collected from poultry producers that are raising layers for egg production in 

the municipality. Simple random sampling was used to draw participants for the study using a list of poultry 

farmers listed with the help of MOFA staff in the municipality. Data was on socio-economic characteristics of the 

farmers, inputs used and outputs obtained. Output was measured as the number of crates of eggs produced within 

the production cycle of layers until taken like two years before they are deposed off. The number of birds was 

measured as the quantity of matured birds that were laying in the farm, medication was measured as the number of 

bottles of vaccines and other drugs from day-old to laying and at laying. Total water intake was recorded in liters of 

water used in the lifespan of the birds, the feed was recorded in kilograms of feed provided throughout the 

production, employees were considered as the number of laborers both family and hired labor working on the farm.  

Gender was a dummy variable with male = 1, female = 0; age was the age of the farmers in years; experience was 

the number of years in egg production; extension contact was considered as the frequency of interaction between a 

farmer and extension personnel in the two years period; credit is important and can influence production through 

expansion and was therefore dummied with farmers that have access assigned 1 and those without access given a 

value of 0; and lastly, housing was a dummy variable with wire mesh and concrete walls assigned 1, 0 = otherwise. 

The dataset was analyzed with the FRONT 4.1 software [20] and Microsoft Excel spreadsheet. 

 

5. RESULTS AND DISCUSSIONS 

5.1. Descriptive Statistics of Variables 

The descriptive statistics of the poultry farmers as presented in Table 1 indicates that the mean age of the 

farmers was about 47.77 years and ranges from 32 and 61 years showing that the farmers were within the working 

population. The farmers have an average of almost 8 years of experience in a range of 3 and 15 years in the poultry 

business. This means almost all the farmers have rich experience in the layer production business. 

There was a mean extension contact of 1.4 for two years with some farmers reported never having contact with 

any extension service although some have managed to get 3 extension contacts. There were about 3 employers per 

poultry farm in the study area. In terms of farm inputs, the reported mean feed intake was 38,275.47kg with a mean 

water intake in a production season which was defined as two years, as 7,622.67 liters. These were required to 

produce or cater for a mean number of 4669 birds that required a mean of about 8 bottles of all medication in the 

production cycle of the birds. The mean number of crates of eggs produced in the study area was recorded as 

28,949.70 crates by the time the layers will be disposed off in two years’ time. 

 

 



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Table-1. Descriptive statistics of Poultry farmers in Mampong Municipality. 

VARIABLES Units MIN MAX MEAN STD DEV SKEWNESS 

Output Egg Crates 597.00 247,296.00 28,949.70 14.28 3.46 
Number of Birds Number 450.00 11,500.00 4,668.67 4,301.32 0.55 

Drugs Bottles 4.00 16.00 7.50 3.09 1.39 
Water intake Litres 600.00 30,000.00 7,622.67 8,251.92 1.74 
Feed intake Kilograms 15,392.00 60,736.00 38,275.47 38,275.47 0.31 
Employees Number 1.00 5.00 2.73 1.39 0.27 

Age Years 32.00 61.00 45.77 6.81 0.08 
Experience Years 3.00 15.00 7.90 3.09 0.56 

Extension contact Number 0 3 1.4 0.89 0.64 

CATEGORICAL VARIABLE 
Variable Label Frequency Percentage 

Gender    
 Male = 1 22 26.7 
 Female=0 8 73.3 

Credit Access    
 Access= 1 7 23.3 
 No access= 0 23 76.7 

Housing Type    
 Concrete = 1 24 80.0 

 Mudhouse=0 6 20.0 
       Source: Authors’ computation, 2018. 

 

In terms of the categorical variables included in the study, 22 (73.7%) were males with only about 8 (23.3%) of 

the farmers being females. The dominance of the males could be due to the tedious nature of the business and high 

risk involved in the venture. Very few farmers (7) representing 23.3% of the respondents claim they have access to 

credit whereas about 76.7% reported not having access to credit. The gross high reportage in lack of access to credit 

could be as a result of collateral requirements of most of the financial institutions and the lack of interest in most of 

them to advance credit to people involved in agriculture. Most of the farmers (80%) were able to build their poultry 

houses with concrete whereas only 20% of them use mud houses to house their birds.   

 

5.2. Stochastic Production Frontier Function 

The Maximum Likelihood Estimates presented in Table 2 indicates that three independent variables were 

positive and significant at 10% and 5% levels, implying that a percentage increase in any of these variables (Number 

of birds, medication, and Quantity of water) will result in an increase in output. As seen in the table, if the number 

of birds is increased by 1%, the output will increase by 11.4%. Medication also has a significant effect on output such 

that when increased by 1%, the output will increase by 79.7%. Increasing the quantity of water (ml) by 1%, 

resultantly increases output by 80.6%. A positive significant influence of stock size was also reported in the studies 

of Ezeh, et al. [21] and Adepoju [22]. 

 
 Table-2. Maximum Likelihood Estimates. 

Variables Parameter Coefficient Standard error t-ratio 

Constant β0 0.172 0.078 2.205 

Number of birds β1 0.114** 0.038 3.000 

Medication β2 0.797** 0.261 3.054 

Quantity of water β3 0.806* 0.087 9.264 

Feeding β4 -0.303 0.042 7.214 

Number of employees β5 -0.634 0.076 8.342 

sigma-squared  0.821 0.259 3.170 
Gamma  0.999 0.135. 7.400 

Note: Significant Codes ‘**’0.05 ‘*’0.01  
                             Source: Authors’ Computation, 2018. 

 



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The sigma-squared (0.821) is significantly different from zero, indicating good fit and correct specification of 

the model. The value of Gamma measures the relationship between random variations in the use of inputs. Gamma 

in this study is computed as 99% and therefore indicates that the random variation in egg production in the study 

area is explained by inefficiency in resource utilization. 

 

5.3. Determinants of Technical Inefficiency   

A negative sign on inefficiency variables means that the variables increase T.E, while a positive sign reduces 

T.E. All the independent variables were expected to have a negative sign, which implies a positive sign for any 

quadratic term. From Table 3, producers Age (-0.0039), Gender (-0.0828), Interactive term, thus age and experience 

(-0.0003), and extension contact (-0.4354), all have negative coefficients, implying that, these variables influence the 

level of technical efficiency of the farmers. While Experience (+0.0171), Credit (+0.0138), and Housing type 

(+0.1065) were positive implying that, these variables reduce T.E and rather tend to increase inefficiency among 

egg producers in the study area. The inefficiency input variables in Table 3 were all statistically significant at 10%, 

5% and 1% significant levels respectively. These variables have been reported in several studies including Ezeh, et 

al. [21]. 

 
Table-3. Determinants of technical inefficiency. 

Variables Parameters Coefficient Standard Error t- ratio 

Constant Z0 0.8579* 0.0619 13.839 
Gender  Z1 -0.0828** 0.0326 -2.543 
Age Z2 -0.0039** 0.0023 -1.752 
Experience Z3 0.0171** 0.0099 1.713 
Age*Experience Z4 -0.0003** 0.0002 -1.820 
Ext. Contact Z5 -0.4354*** 0.0439 -9.919 
Credit Access Z6 0.0138** 0.0133 1.038 
Housing Type Z7 0.1067** 0.0719 1.482 

Significant Codes ‘***’ 0.001 ‘**’0.05 ‘*’0.01  

        Source: Authors’ Computation, 2018. 

 

6. CONCLUSIONS 

This study used Cobb-Douglas log transformed function to assess the technical efficiency of 30 randomly 

selected layer producers in Mampong municipality. A FRONT 4.1 software that incorporates inefficiency factors 

were used to estimate the Maximum Likelihood Estimation (MLE), provided farm specific technical efficiencies as 

well as their determinants of output. The results revealed a positive and significant influence of the number of birds, 

medication and water intake to the number of crates of eggs produced in the study area. Farm-specific and 

socioeconomic factors that were chosen for the study also showed that all the selected factors had varying 

significance in influencing technical inefficiency positively or negatively. Age, gender and extension contact 

increase Technical Efficiency while experience, credit access and housing type rather decrease Technical Efficiency 

and increases technical inefficiency.  

It is recommended for egg producers to observe the proper routine medication and add other supplementary 

drugs to boost the laying performance of their birds while intensifying stock size and increasing water usage to 

maximize production and close up the production gap.  Proper housing structures should be provided and extension 

contact increased to reduce inefficiency among farmers. 

 
Funding: This study received no specific financial support.    
Competing Interests: The authors declare that they have no competing interests.  
Contributors/Acknowledgement: All authors contributed equally to the conception and design of the 
study. 

 

 



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REFERENCES 

[1] M. J. Islam, M. Kamruzzaman, M. J. Uddin, and S. J. Rayhan, "Growth rate and marketing channel identification of 

seasonal fruits: An empirical study based on Chittagong Hill Districts of Bangladesh," International Journal of 

Economics, Commerce and Management, vol. 5, pp. 186-200, 2017. 

[2] M. J. Islam, "Contextual estimation of marketable and marketed surplus of selected seasonal fruits: A study based on 

Chittagong Hill tracts (CHT) of Bangladesh," International Journal of Sustainable Agricultural Research, vol. 6, pp. 21-32, 

2019. Available at: https://doi.org/10.18488/journal.70.2019.61.21.32. 

[3] A. S. P. R. Monitoring and Evaluation Directorate, "Monitoring and evaluation directorate agricultural sector 

progress report, 2016. Ministry of Food and Agriculture, Accra, Ghana." Available: http://www.mofa.gov.gh. 

[Accessed 25th August, 2018], 2017. 

[4] K. Killebrew and R. Plotnick, "Poultry markets in West Africa Ghana. Evans school of policy analysis and research," in 

Prepared for the Market Access Team of the Bill & Melinda Gates Foundation. EPAR Brief 83, 2010. 

[5] P. Ekunwe, O. Soniregun, and J. Oyedeji, "Economics of small scale deep litter system of egg production in Oredo local 

government area of Edo State, Nigeria," International Journal of Poultry Science, vol. 5, pp. 81-83, 2006. Available at: 

https://doi.org/10.3923/ijps.2006.81.83. 

[6] M. Assa, "Poultry production and rural poverty among small-scale farmers in Mzimba District of Malawi," 2012. 

[7] K. Rajendran and S. Mohanty, "Comparative economic analysis and constraints in egg production under cage vs. deep 

litter systems of rearing in India," International Journal of Poultry Science, vol. 2, pp. 153-158, 2003. Available at: 

https://doi.org/10.3923/ijps.2003.153.158. 

[8] Food and Agricultural Organization, "The structure and importance of the commercial village based poultry in 

Ghana," Poultry Review Report, 2014. 

[9] FAO, Food and agricultural organisation statistics book on national crop production. Rome, Italy: FAQ, 2005. 

[10] A. Kumar and U. Pandey, "Growth performance of livestock sector in India." Sources of Growth in Livestock Sector, 

Policy Paper No. 9, 1999. 

[11] L. Flake and E. Ashitey, "Ghana’s poultry and products annual Report," USDA Foreign Agriculture Services GAIN 

Report2008. 

[12] B. T. Anang, C. Yeboah, and A. A. Agbolosu, "Profitability of broiler and layer production in the Brong Ahafo region 

of Ghana," Journal of Agriculture and Biological Science, vol. 8, pp. 423-430, 2013. 

[13] D. Adei and B. Asante, "The challenges and prospects of the poultry industry in Dormaa District," Journal of Science 

and Technology (Ghana), vol. 32, pp. 104-116, 2012. Available at: https://doi.org/10.4314/just.v32i1.11. 

[14] S. Etuah, "Cost efficiency and economies of scale in broiler production in Ghana. A case study of the Ashanti Region," 

(Doctoral Dissertation), 2014. 

[15] D. Aigner, C. K. Lovell, and P. Schmidt, "Formulation and estimation of stochastic frontier production function 

models," Journal of Econometrics, vol. 6, pp. 21-37, 1977. Available at: https://doi.org/10.1016/0304-4076(77)90052-5. 

[16] W. Meeusen and J. Van Den Broeck, "Efficiency estimation from Cobb-Douglas production functions with composed 

error," International Economic Review, vol. 18, pp. 435-444, 1977. Available at: https://doi.org/10.2307/2525757. 

[17] B. Ben-Belhassen and A. W. Womack, Measurement and explanation of technical efficiency in Missouri hog production. 

Tampa FL: In American Agricultural Economics Association Annual Meeting, 2000. 

[18] S. Yao and Z. Liu, "Determinants of grain production and technical efficiency in China," Journal of Agricultural 

Economics, vol. 49, pp. 171-184, 1998. Available at: https://doi.org/10.1111/j.1477-9552.1998.tb01262.x. 

[19] G. E. Battese and T. J. Coelli, "Frontier production functions, technical efficiency and panel data: With application to 

paddy farmers in India," Journal of Productivity Analysis, vol. 3, pp. 153-169, 1992. Available at: 

https://doi.org/10.1007/bf00158774. 

[20] T. J. Coelli, "A guide to FRONTIER version 4.1: A computer program for stochastic frontier production and cost 

function estimation. CEPA Working Papers," vol. 7, pp. 1-33, 1996. 

http://www.mofa.gov.gh/


Current Research in Agricultural Sciences, 2019, 6(1): 20-28 

 

 
28 

© 2019 Conscientia Beam. All Rights Reserved. 

[21] C. Ezeh, C. Anyiro, and J. Chukwu, "Technical efficiency in poultry broiler production in Umuahia capital territory of 

Abia State, Nigeria," Greener Journal of Agricultural Sciences, vol. 2, pp. 001-007, 2012. Available at: 

https://doi.org/10.15580/gjas.2013.3.1206. 

[22] A. Adepoju, "Technical efficiency of egg production in Osun State," International Journal of Agricultural Economics and 

Rural Development, vol. 1, pp. 7-14, 2008. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

  

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