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American Journal of  Environmental
Economics (AJEE) 

Determinants of  Technical Inefficiency in Wheat Production:
 An Application of  the Stochastic Frontier Model - Nigeria

Bashir A. B1*, I.M Ali2, Togun O. M1, S.A Jibrin3

Volume 2 Issue 1, Year 2023
ISSN: 2833-7905 (Online)

DOI: https://doi.org/10.54536/ajee.v2i1.1155
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Article Information ABSTRACT

Received: January 03, 2023
Accepted: March 07, 2023
Published: May 09, 2023

This study examined wheat farming technical efficiency, its determinants and wheat 
production constraints among wheat farmers in the wheat producing states of  Nigeria. 
The study did not cover Borno and Yobe wheat producing states due to volatile insecurity 
situation in the areas. Primary data were obtained from 866 farmers using proportionate 
selection from the states list of  farmers. A structured questionnaire and interview schedule 
were administered to the farmers to collect data for the study. A Stochastic frontier model 
was used to capture wheat production efficiency and its determinants while, Likert scale was 
employed to reveal severity of  wheat production constraints among farmers in the study 
area. Land size, quantity of  seed planted, quantity of  NPK fertilizer applied, quantity of  
Urea fertilizer applied and labour used in man-day were found to be positive and statistically 
significant. The most severe constraints affecting wheat production in the study area were 
poor access to credit, lack of  dependable wheat market outlay, and access to improved and 
quality seed among others. There should be adequate incentives (timely supply of  improved 
wheat seed, fertilizer and irrigation facilities at affordable prices. 

Keywords

Determinants, Technical, 
Inefficiency, Wheat, 
Production, Nigeria

1 Department of  Agricultural Economics and Extension, Lake Chad Research Institute, Maiduguri, Nigeria
2 Department of  Agricultural Economics, University of  Maiduguri, Nigeria
3 Departmentof  Agricultural Economics and Extension Borno State University, Njimtilo, Nigeria
* Corresponding author’s e-mail: bashir5880@gmail.com

INTRODUCTION
Wheat is a global staple food usually process into 
wheat-based foods (flour noodles, semolina pasta, and 
biscuit wheat meal), beer, beverages and animal feed. 
Many developing countries including Nigeria are facing 
challenges of  low output, productivity and constrained 
input supply patterns. Wheat is one of  the most important 
agricultural commodities in Nigeria that needs accelerated 
local production as a result of  population growth. 
Nigeria produced approximately 60, 000 MT of  wheat 
in 2016 worth $12.66 Million (only 0.004% of  the global 
production) (Klynveld, Peat, Marwick and Goerdeler 
(KPMG, 2016; Proshare, 2018). In 2020, Nigeria 
imported $2.15B in Wheat, becoming the 4th largest 
importer of  Wheat in the world (National Bureau of  
Statistics (NBS, 2022 and the Observatory of  Economic 
Complexity (OEC, 2022). The surging wheat import 
could be attributed to the supply gap in the country and 
improved demand in the domestic market. This could 
be as a result of  technical inefficiency of  wheat farmers 
that need to be measured, since it provides information 
as to whether or not wheat farmers are producing a given 
quantity of  output using minimum quantity of  inputs 
(Oyekanmi, 2022)
Although, many studies have been conducted on 
technical efficiency of  wheat farmers in other parts of  
the world( Anatolia in Turkey, Bihar in Indian, South 
Wollo in North-Central Ethiopia by Alender & Oren 
(2006), Prafulla (2012) and Hassen (2016) respectively), 
in Nigeria, studies carried out on technical efficiency  
focused on crops other than wheat. For example, Mukhtar 
et al. (2018) employed Data Envelopment Analysis (DEA) 
to determine technical efficiency of  small holder pearl 
millet farmers in Kano state. However, no study has 

estimated technical efficiency of  wheat production, its 
determinants along with challenges domicile in wheat 
producing states of  the country. Hence the main focus 
of  this study is to determine the inefficiency status of  
Nigerian wheat farmers.

METHODOLOGY
The study was conducted in wheat growing zones of  
Nigeria that comprised northern states of  the country 
which lies between latitude 10026/N to 10055/ of  
the equator and longitudes 12026/E to 13045/ of  the 
Greenwich Meridian. The climate of  the area is tropical 
with distinct wet and dry seasons. The area experience 
4-6 months of  rainfall with a mean annual rainfall of  
about 1000mm-1500mm. The mean annual temperature 
is 26.30C. Majority   of  the people are full time farmers 
with a good number engaged in wheat farming. The 
major crops cultivated along with wheat are: sorghum, 
maize, groundnut and millet. Animal such as cattle, sheep, 
goat and poultry are also kept at both subsistence and 
commercial levels.

Sampling Technique
Samples were drawn from five out of  seven wheat 
producing states of  Nigeria which include: Kano, Bauchi, 
Jigawa, Kebbi and Sokoto.  Borno and Yobe states were 
excluded because of  the security challenges. The study 
considered 866 sample farmers from sample frame 
obtained from the states Association of  Wheat Farmers. 
These samples were proportionally selected from the 
above list.

Analytical Tool
The study employed the use of  stochastic frontiermodel 

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Am. J. Environ Econ. 2(1) 19-23, 2023

(SFM) to estimatetechnical efficiency of  wheat production 
and its determinants among wheat farmers in Nigeria.
Model specification:
InYi = β1xi + vi - ui
Yi = Wheat yield 
β1= Estimated Input parameters
vi = Random noise term (vi ~N(0,σ2 vi)
ui = Technical inefficiency

Cobb Douglas Production Form
InYi = β0 + Inβ1X1 + Inβ2X2  + Inβ3X3 + Inβ4X4+ Inβ5X5 
+ vi-ui
Y= Wheat yield (kg/farm)
X1= land size (hectare)
X2= seed planted (kg/ farm)
X3= NPK fertilizer used (kg/ farm)
X4= Urea fertilizer used (kg/ farm)
X5 = Labour (man-day/ farm)

RESULTS AND DISCUSSION
The result from table 1 revealed how land size (Lnlperhact), 
quantity of  seed planted (Lnseedq4planted), quantity 
of  NPK fertilizer applied (Lnfertilizerq3a), quantity 
of  Urea fertilizer applied (Lnfertilizerq3b) and labour 
used in man-day (Lnmanday2) influence wheat yield 
(Lnyield) across the study area. The parameter estimates 
showed positive signs all through which indicated direct 
relationship between wheat yield and its determinants. An 
increase in land size by one hectare will increase wheat 
yield by 0.2828kg which is statistically significant at 1%. 
Roersma (1997) findings was consistent with the result 
that farm size and labour price ratio positively correlated 
with high yield for the small farmers land and labour price 
ratio  and lower for the larger farms, resulting in more 

intensive cultivation (more family labour per unit of  land) 
and higher output per unit area than on larger farms. 
Moreover, most of  the respondents cultivates wheat 
on small scale, but devoted to cultivating other crops 
such as rice, groundnut, and cowpea among others on a 
large scale. A unit increase in labour use will result to an 
increase in wheat yield by 0.1845kg which is statistically 
significant at 1%. This suggests that labour surpluses on 
smaller farms permit more labour to be used per hectare 
to care for crops and generate higher yields. More so, 
additional one kg of  NPK or one kg of  Urea fertilizer 
use by respondent will increase wheat yield by 0.4008kg 
and 0.1726kg respectively both of  which are statistically 
significant at 1%. This result is also in line with Lachew et 
al. (2008) that fertilizer is positively correlated with value 
of  yield at 1% level of  significance. This implies that 
yield is likely to increase with increase in a kg of  fertilizer 
applied. In other words, wheat production performance 
could be said to be dependent on NPK and Urea fertilizer 
applied as recommended. This could be the reason why, 
NPK and Urea fertilizer are applied together alongside 
with wheat seed at planting before second application 
as at when due.  Furthermore, a unit increase in wheat 
seed planted will result to an increase in wheat yield by 
0.0640kg and it is statistically significant at 10%. This may 
suggests that wheat farmers across wheat producing areas 
might be planting wheat seed below the recommended 
rate and therefore need to increase the rate of  seed 
planting to be more efficient in production. 
More importantly, the statistically significant at 1% 
(Prob < = z = 0.000) from truncated normal result in 
testing the null hypothesis (H0=there is no inefficiency 
component) implies that we reject the null hypothesis 
(H0) and conclude that inefficiency component exist

Table 1: Stochastic Frontier Normal Truncated-Normal Model Table      
Lnyield Coef. Std. Err.         Z P>|z|     [95% Conf. Interval]
Lnlperhact 0.2828*** 0.0356   7.94          0.000 0.2130 0.3526
Lnseedq4planted  0.0640*           0.0368 1.74  0.082 -0.0081 0.1362
Lnfertilizerq3a 0.4008***              0.0482 8.32  0.000 0.3064 0.4952
Lnfertilizerq3b 0.1726***           0.0447 3.86 0.000     0.0850 0.2602
Lnmanday2 0.1845*** 0.0254     7.26   0.000 0.1347 0.2344
Const 3.6700*** 0.2266  16.19   0.000 3.2258 4.1142

Source: Field Survey, 2019. *** Significant at 1%, ** significant at 5%,*significant at 10%

The average predicted inefficiency (U) estimated was 
0.4969 or 49.69% with minimum and maximum technical 
inefficiency equals 0.0907 and 3.3114 respectively. This 
implies that close to half  of  the entire respondents are 
technically inefficient in wheat production in the study 

area.  Hence Tobit regression was used to determine 
factors that influence technical inefficiency among wheat 
farmers in the study area after validating regression 
assumptions. The post estimation result test table 3 
shows that there is no problem of   multicollinearity, no 

Table 2: Predicted Inefficiency 
Variable Observation Mean Std. Dev.       Min        Max
Predictedinefficiency(U) 830 0.4969 0.4106 0.0974 3.3114

Source: Field Survey, 2019

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specification problem, no influencial observations, but 
there is appropriate functional form and the residual are 
not normally distributed. The heterokedasticity problem 

from the table was corrected with robust standard error. 
It should be noted that the test pass majority of  the model 
assumption thereby confirming the reality of  the result. 

Table 3: Regression Assumption Test Table
Regression assumptions                   Test                                    We seek values
Hesterokedasticity problem                  Breusch-Pagan hettest  > 0.05

Chi2(1): 4.732       
p-value: 0.030       

No multicollinearity problem Variance inflation factor < 5.00
Lnfertilizerq3a : 2.44        Mean VIF |  1.81
Lnfertilizerq3b : 2.44
Lnseedq4planted : 1.62        
Lnlperhact : 1.44        
Lnmanday2 : 1.13        

Residuals are not normally 
distributed     

Shapiro-Wilk W normality test                 > 0.01
z: 8.001       
p-value: 0.000       

No specification problem                   Linktest                                      < 5.00
t: 0.320       
p-value: 0.749       

Appropriate functional form                Test for appropriate functional form          > 0.05
F(3,821):2.142       
p-value: 0.093       

No influential observations                Cook's distance                               < 1.00
no distance is above the cutoff

Source: Field Survey, 2019

The major constraints identified by respondents in the 
study area according to level of  severity in table 4 include: 
poor access to credit (40.07%), lack of  dependable 
market (39.49%), poor access to improved and quality 

seed (36.95%), high cost of  fertilizer (39.61%), lack 
of  proper germplasm wheat material (32.25%), poor 
utilization of  local wheat /preference of  imported 
wheat to local wheat (30.25%) which are the more severe 

Table 4: Constraints to Wheat Production Across Wheat Producing Area
S/N Constraints Not 

Severe 
(%)

Least 
Severe 
(%)

Moderately 
Severe (%)

Severe
(%)

More 
Severe 
(%)

Most 
Severe 
(%)

1 Lack of  dependable market system 0.35        13.51       11.09       10.05       25.52       39.49      
2 Poor utilization of  local wheat/preference 

of  imported wheat to local wheat
1.15  6.24        15.24 20.55       30.25       26.56      

3 High Energy cost for wheat irrigation 2.77        9.58       18.24       34.53  21.59       13.28      
4 Poor access to credit  0.92        4.39        7.62       8.43       38.57 40.07      
5 Pest and disease infestation 1.15           9.82       24.13       32.22       17.90       14.78      
6 Lack of  functional wheat-based farmers 

association
7.04        10.39       24.25 25.17       20.44       12.70      

7 Lack of  capacity building for extension staff  
and farmers

3.58        11.20       26.44       23.79       18.36       16.63      

8 Lack of  proper germ plasm wheat materials 4.97        10.74       15.94       15.24       32.22       20.90      
9 Poor access to improve and quality seed 3.12        9.12       11.89       23.21       15.70       36.95      
10 High fertilizer prices 1.27        4.04        9.82       9.24       39.61            36.03      
11 Non availability of  fertilizer at required time 7.62      12.70 17.55 25.98 16.97   19.17     
12 Non availability of  farm labour 7.74        34.76       21.25       16.17       12.47       7.62      

Source: Field Survey, 2019

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constraints in wheat production. Severe constraints are 
high energy cost for wheat irrigation (34.53%), pest and 
diseases infestation (32.22%), lack of  functional wheat-
based farmers association (25.17%) and non-availability 
of  fertilizer at required time (25.98%). Furthermore, lack 
of  capacity building for the extension staff  and farmers 
(26.44%) stands moderately severe in the study area, while 
non-availability of  farm labour as at when due (34.76%) 
is least severe. 

CONCLUSION AND RECOMMENDATION
The study concluded that about 50% technical 
inefficiencies existed among wheat farmers across the 
study area. Those inefficiencies determinants variables 
considered negatively correlated with wheat technical 
inefficiency. The major constraint affecting wheat 
production in the study area according to their level of  
severity includes; poor access to credit, lack of  dependable 
market, and poor access to improve and quality seed 
among others. Hence the study recommended the 
following:

1. There should be training and retraining of  more 
wheat farmers to reduce technical inefficiency among 
wheat farmers across the study area.

2. There should be adequate incentives (increased 
supply of  improved wheat seeds at affordable price, 
increase supply of  fertilizer at subsidized price, provision 
of  standard irrigation facilities) for wheat farmers.

3. Agro Seed companies should invest in wheat seed 
multiplication in order to make it available and accessible 
to wheat farmers.

4. To boost wheat production in the study area, the 
itemized constraints should be immediately addressed.

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