Wheat Consumption Determinants and Food Security Challenges:
Evidence from Pakistan
SANIA SHAHEEN1, LAL K. ALMAS2, MUHAMMAD USMAN3
1 Paul Engler College of Agriculture and Natural Sciences, West Texas A&M University (WTAMU),
and Ph.D. Student, International Institute of Islamic Economics (IIIE),
International Islamic University, Islamabad, PAKISTAN
2Agriculture Business & Economics, Paul Engler College of Agriculture & Natural Sciences,
WTAMU, Canyon, Texas, USA
3Department of Economics, PMAS Arid Agriculture University, Rawalpindi, PAKISTAN.
Abstract: This study aims to explore the wheat consumption determinants in Pakistan as well as to analyze the
own price, cross price, and income elasticity of demand for wheat. For estimation purpose, time series data
were used based on annual observations covering the period from 1972-2020. Autoregressive Distributed Lag
approach (ARDL) econometric technique was applied to analyze the existence of a long-term connection
among wheat demand and wheat consumption determinants. Based on empirical analysis, the results of wheat
prices, real GDP, and population show that wheat is a necessity staple food in Pakistan. Futher, results of rice
prices and corn consumption reveal that rice and corn commodities are substitutes to wheat with less elastic
demand in Pakistan. The estimated result of wheat imports exhibits a direct and significant impact on wheat
consumption. Overall, the results suggest that domestic efforts required to reduce the wheat demand and supply
gap such as, through advanced innovative production techniques, latest wheat varieties, land expansion, and
exploring the additional water resources for irrigated agriculture. Additionally, this study recommends policy
makers, Pakistan government and stakeholders to pay attention on increasing domestic wheat production in
order to lessen the wheat imports, saving useful foreign exchange, and to resolve the food security issues in
Pakistan.
Key-Words: Wheat Consumption Determinants, Own Price Elasticity, Cross Price Elasticity, Income Elasticity,
Wheat Supply and Demand Gap, Food Security, Autoregressive Distribute Lag (ARDL).
Received: March 8, 2022. Revised: March 25, 2022. Accepted: March 30, 2022. Published: April 8, 2022.
1 Introduction
The agriculture sector is the backbone of Pakistan
economy. It is one of the major sector of economic
activity of a country. Its contribution in the country
GDP is 19.2% and creates employment
opportunities approximately 38.5% of the working
force. More than 65-70% of the country population
depend on agriculture for their livelihood [1]. Major
sub-sector of agriculture are crops. From all
important food crops, wheat is one of the major
staple crop of Pakistan and therefore, it is essential
for the country food security. It contributed 9.2% to
agriculture value added and 1.8 percent to GDP.
Pakistan is one of the main wheat producers in the
globe. In Pakistan, the biggest cropped area is under
wheat cultivation. Current year wheat is cultivated
on an area of 9178 thousand hectares [1]. Though,
sometimes domestic wheat production not sufficient
according to the population needs, which is
currently 229 million and population growth is
estimated as 1.9% percent. Therefore, to ensure food
security and to meet the domestic demand for wheat
the government has dependent on wheat imports. In
the fiscal year 21, wheat imports were estimated at
983.3 US million$ [2] [1]. It is estimated that
imports cover 10 to 20 percent of country
consumption needs [3]. Poor families in Pakistan
spend major share of income (23% of total income)
on wheat Furthermore, it has been reported that
country current demand for wheat is more than its
output and if per year population grow at an
expected rate of 1.9%, then it anticipated that
country will endure a wheat net importer [4].
Due to the strategic importance of wheat as it is
the main food ingredient, the government intervenes
by providing not only the guarantee of supplying the
consumers with wheat at affordable prices, but also
by supporting the producers in the market.
Therefore, through subsidy, government provides
wheat at a lower price to the consumer and a support
WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT
DOI: 10.37394/232015.2022.18.42
Sania Shaheen, Lal K. Almas,
Muhammad Usman
E-ISSN: 2224-3496
427
Volume 18, 2022
price of wheat to market producers [3]. In Pakistan,
the demand for wheat is highly price-inelastic [5],
and sometime production is not sufficient to meet
demand for wheat due to higher population growth
[3]. Rapid population growth will create a serious
food security challenges in upcoming years in
Pakistan. Currently, it is calculated that in Pakistan
approximately 53 million peoples are living below
the poverty line and unemployment rate in current
year has been reached at 16 percent [2].
Additionally, the pandemic COVID-19 has
revealed a severe effect on the worldwide economic
situation. The food security situation in the
developed and developing economies is getting
worse with the evolution of the COVID-19
pandemic. [6] and [7] report highlight that around
271.8 million peoples were intensively insecure
from food because of disparaging effect of COVID-
19 globally. Likewise, 20 to 30 percent population,
which is, 40-62 million peoples of Pakistan have
been suffering severe food insecurity because of
Pandemic, and other socio-economic,
environmental, and climatic problems [8].
Moreover, during this pandemic era, Pakistan
inflation rate was increased by 8.2 percent from
May 2019 -May 2020 and food inflation increased
in the rural (13.73%), and urban areas 10.94%
[6] [7] [8] [9].
Overall, these socio-economic indicators may even
lead to worsen the food security challanges in
Pakistan.
[10] in Pakistan a huge gap exist among actual
and acquired output, that suffers because of
inadequate technology, improper usage of inputs,
unavailability of water and land utilization, lack of
knowledge about control of insects and pests. As a
result, these factors adversely affect the production.
The water scarcity issue in agriculture have a
detrimental effect on the Pakistan economy because
this sector directly subsidizes its gross domestic
product (GDP), and greater than 40% of labor force
directly or indirectly involved in it [11]. Wheat crop
in Pakistan are planted on a float basin that is
directly flooded with water for irrigation. Water
losses are huge under this form of irrigation.
Evaporation and deep percolation losses also a
reason of severe shortage to crops linked to
overexploitation of ground water, motivating the
research for another methods of water application to
crops, e.g., Raised Bed (RB) technology, to fulfil
the water demand [10].
[12] report that in the middle of the 21st century,
one of the serious challenges for farmers will be to
fulfill the food needs of nine billion peoples.
Production of more food with less quantity of water
in Arid and Semi-Arid lands is a challenge for
today's agriculture [13]. Water scarcity issue leads
to land degradation because of rain-fed agriculture
[14], and a reduction in food production, especially
in agriculture and semi-agriculture African areas
[15]. Almost 80% of global agriculture consist of
rain-fed land, which produces 80% of the food
globally [16] [17]. In North Africa and West Asia
95% of land is rain-fed, and almost 40% land has
been used in Uzbekistan due to water scarcity,
causing despoiled fields [18] [19].
Wheat being a widely consumable staple food
and major Rabi crop of Pakistan, carries a
significant tag to ensure the country food security.
Wheat is harvested in all of the four provinces of
Pakistan. Specially Punjab and Sindh. Only Punjab
contributed 70-75% to the total annual wheat
production of Pakistan. Since 1975, 27% increase in
total area and 52% increase in yield per hectare for
wheat are reported. While, 33% increase in wheat
availability per capita was deemed insufficient. In
this situation, imports of wheat were the most
apparent result due to higher growth of population.
To fulfill the dream of food self-sufficiency,
government facilitated farmers by providing high
yielding varieties, fertilizers at a subsidized rate,
irrigation water at a lower rate than tube well water
etc. Though, these facilities have not been able to
reach the desire level of output mainly due to (i)
poor economic conditions of the farmers, lack of
knowledge on the latest useful techniques and
advancement. (ii) low price of production at
harvesting time made the farmers insecure about
investments they have done for inputs. (iii)
inappropriate land levelling along with late sowing
resulted in lower production. (iv) Insufficiency,
inequity, and unreliability in water distribution are
mutually affect the farmers irrigation calendars for
the wheat crop. Water stress to wheat at sensitive
stages, hinders the entire effort of production [20].
During 2020-21, the total water availability for
crops in Kharif 2020 reached to 65.1 Million Acre
Feet (MAF) viewing a minor decline of 0.1 percent
relative to 65.2 MAF of Kharif 2019-20. Rabi
season 2020-21 got 31.2 MAF, displaying a slight
rise of 2% compare to 29.2 Rabi 2019-20 [1]. The
water consumption for wheat is 4372 M3 per hectare
and 4639 M3 per hectare in Punjab and Pakistan,
respectively [21] but on farm availability is only 50
percent of the agronomic needs [22]. The water
irrigation inequity, and scarcity is one of the major
factors in exploiting the yield potential of wheat in
Pakistan. The inequity and scarcity are much more
pronounced at the end of distributaries and
WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT
DOI: 10.37394/232015.2022.18.42
Sania Shaheen, Lal K. Almas,
Muhammad Usman
E-ISSN: 2224-3496
428
Volume 18, 2022
watercourses. The efficiency and equity of irrigation
water at the farm level is imperative to ensure
country wheat self-sufficiency [23].
Limited water leads to vulnerability to water
scarcity settings, causing wheat biomass to lessen
wheat crops [24] [25] [26]. Poor and meagrely
distributed rainfall in arid regions of Pakistan
further worsens this situation. Losses ranging from
very low yields to complete losses under severe
water stress in wheat crops have been well
documented [25]. Additionally, the Pakistan
government still needs an improvement for the
production of wheat in various varities. As prior
literature reported that the slow growth rate of crop
variety replacement by farmers encouraging a new
variety of wheat in Pakistan [27] [28].
[4] projected the demand and supply of wheat
in Pakistan from 2008 to 2030. Their results indicate
that if population grow at an assumed rate 1.8
percent per year, then wheat demand will rise from
19 million tonnes to 30 million tonnes by 2030. The
forecasted estimates of wheat supply determined
from the production function technique highlight
that by 2030, wheat production will reach 28 million
tons. The wheat demand is projected to be more
than its supply. In other words, the country is likely
to suffer a deficit in wheat. The findings postulate
that if production technology remains constant and
production growth will be slower, the wheat deficit
will become wider. Hence, suitable policy measures
required to overcome the likely deficit in wheat.
Similarly, [23] agriculture sustainability and wheat
self-sufficiency is dependent on timely sowing of
wheat, balance usage of fertilizers, Judicious usage
of water irrigation, increase the usage of certified
and pure seeds, expand the knowledge and
technologies frontier, and improve farm
management practices to increase productivity in
Pakistan.
Different economic theories provide the
background for analyzing the demand of any
commodity. The demand of any commodity
depends on its own price, income, price of other
related commodities, taste and preferences of
consumer, and seasonal effects. Similarly, the
demand of wheat dependent on many direct and
indirect factors. Internal factors include wheat price,
per capita income, and wheat supply. External
factors include price of substitute and
complementary goods, population of a country,
inflation, preferences and taste of a consumer [29]
[30] [31] [32]. Positive elasticity of income shows
that at higher income level, demand for wheat
increases and vice versa. On the contrary, at low
level of per capita income wheat demand also
increases due to poverty. Increasing poverty and
hunger index are the fundamental challenges to
make sure food security in the world [29].
Wheat is more income elastic relative to rice
and other related products. [33] estimated own and
cross price elasticities for hard red winter and hard
spring wheat, soft red and white wheat, and durum
wheat. Their findings indicate that soft wheat
varities respond less to their own price relative to
hard wheat varieties. However, the results show that
the cross-price elasticities of hard red winter, hard
red spring, and soft wheat varieties are economic
substitutes. [34] estimated the short-term and long-
term wheat elasticities of demand for Pakistan. The
study findings suggest that income is the
fundamental determinant of wheat consumption in
the long run. Whereas, in the short run wheat prices
is the key factor that affect the wheat consumption.
Low domestic prices of wheat put a burden on
wheat imports [35] [36] [37]. [38] analyzed the
speculative wheat demand determinants and their
influence on consumer loss for district Mandi
Bahuddin, Pakistan. Their findings show that the
lower-income consumers speculate on wheat price
and drive prices up around 8.92% above the normal
prices, costing them to drop consumer welfare and
surplus. The results determined that if consumer
avoid speculation they may enjoy wheat price lower
than 8.92% price and benefit from increased
consumer welfare and surplus. Similarly, [39]
analyzed the behaviour of prices in two significant
exporting markets of Russia. The results reveal that
Russia behaves competitively in wheat exports to
Egypt and turkey. Though, it gets market power in
Turkey with 13.5 % estimated mark-up. Later on,
[40] investigated factors that expand the domestic
wheat process product consumption in Korea. Their
results indicate that high quality, safety and size of
processing companies are the important factors that
expand the domestic wheat processed product
consumption.
Additionally, [41] analyzed the influence of
domestic political instability on trade of wheat in
Egypt. Their findings show that the severe inverse
demand shock of 2011, caused by the political
instability of country that resulted from the Arab
spring. Further, results reveal that in Egypt urban
demand persist longer relative to rural, which is
fulfilled through wheat imports. Any delay in wheat
imports increased food unsustainability and
vulnerability in Egypt.
Also, [42] reported that in the urban areas wheat
demand is significantly higher than rural areas in
Bangladesh. Moreover, [43] investigated the wheat
demand determinants in Sub-Saharan Africa. Their
WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT
DOI: 10.37394/232015.2022.18.42
Sania Shaheen, Lal K. Almas,
Muhammad Usman
E-ISSN: 2224-3496
429
Volume 18, 2022
findings indicate that rising income level,
population growth and increasing female labor force
participation are the main drivers. Further, results
show that share of expenditure on wheat in urban
area is generally larger than in rural areas and Africa
has satisfied a large part of wheat demand through
imports and partly through domestic production.
Further, [44] reported that production,
consumption and imports are the key drivers of food
security in Egypt. [45] analyzed the determinants of
wheat consumption in Egypt. Their findings suggest
that own price, GDP per capita and population
growth are the determinants of wheat consumption.
Moreover, rice prices, corn and barely consumption
are substitutes for wheat in Egypt.
Overall, from the literature review it can be
noted that the existing literature has focused on the
wheat production, supply and availability in
Pakistan
[23] [4] [28] [34] [5] [8]. Whereas, demand of
wheat with alternative consumption shift in the
context of Pakistan is missing in the literature.
There is a significant need to uncover the challenges
related to wheat demand in Pakistan. Motivated by
the growing body of literature, this study will cover
this gap in the context of Pakistan. This study will
attempt to answer all of the following questions that
are not appropriately addressed in the existing
literature. What are the alternative food staple crops
available for wheat consumption in Pakistan? What
are the economic and demographic factors
increasing the demand of wheat in Pakistan? What
are the significant food security problems are facing
the population of Pakistan? How government may
address these issues through sustainable policy
practices? To address all these questions this study
carried out with following research objectives:
1. To investigate the wheat consumption
determinants with substitute shift of wheat
consumption in Pakistan.
2. To analyze the short-term and long-term impact
of demand determinants on wheat consumption.
3. To examines the own, cross price elasticity, and
income elasticity of demand for wheat in
Pakistan.
4. To decrease wheat imports through alternative
food consumption and stabilize food security.
The study findings show that the wheat production
and consumption gap are anticipated to increase
further in the upcoming years due to higher
population growth that will exert a continuous
pressure on wheat consumption, and that would be a
reason for food insecurity issues in Pakistan in the
upcoming years. A sustainable food security policy
is required to overcome the upcoming food security
issues in Pakistan.
This study will contribute to the empirical
literature by investigating the wheat demand
determinant and substitute shifts of wheat demand
in the context of Pakistan. As previous literature
was only focused on the wheat production, wheat
supply and its availability in the markets. The
contribution of this study is significant in the
empirical literature by suggesting the alternative
wheat substitute commodities in order to shift the
wheat demand with alternative consumption shift.
Further, this study highlights the upcoming food
security challenges in Pakistan, and suggests some
useful policy recommendations for policy makers in
order to overcome the food security issues in
Pakistan.
The other parts of the study are arranged as
follows. Section 2. describes the material and
methods. Section 3. explains the empirical results of
the study. The last section concludes the study
results and suggests some policy recommendations.
2 Material and Methods
2.1 Theoretical Framework
According to Keynesian school of thought, country
aggregate domestic consumption depends on the
aggregate level of per Capita income [46]. The
aggregate demand function shows its association
among own price, per capita income, and other
related commodities (Substitutes and
Complementary).On the basis of aggregate demand
theory, wheat demand of an economy depends on
own price of wheat, income of consumer, and
substitute prices [29]. Any commodity that has more
substitutes, the quantity demanded of that
commodity will be less elastic [45]. Hence, the
demand theory functional relationship can be
explained as:
𝑄𝑑𝑥= 𝑓(𝑃
𝑥, 𝑌
𝑡, 𝑃
𝑠, 𝑃
𝑐) (1)
Where 𝑄𝑑𝑥 is the quantity demand of commodity X,
𝑃
𝑥 price of commodity X, 𝑌
𝑡 level of per capita
income at time period t. 𝑃
𝑠 is the price of
substitution commodity, 𝑃
𝑐 price of complementary
commodity.
2.2 Empirical Model
On the basis of different macroeconomic theories,
wheat demand dependent on its own price, income
of the consumer, price of related staple food crop,
and exogenous factors such as population size, and
inflation rate [43]. Hence, the country demand for
WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT
DOI: 10.37394/232015.2022.18.42
Sania Shaheen, Lal K. Almas,
Muhammad Usman
E-ISSN: 2224-3496
430
Volume 18, 2022
wheat dependents on wheat price, price of related
staple food crops such as (Rice, Corn, Barely, and
Sorghum etc), population per capita, gross domestic
product and rate of inflation [45]. Therefore, to
analyze the determinants of wheat demand and food
security challenges in the context of Pakistan. This
study used the double log econometric model to
measures the elasticities of each variable in the
model.
𝑄𝑑𝑥= 𝛽𝑜+ 𝛽1𝑃
𝑥+ 𝛽2𝑌
𝑡+ 𝛽3𝑃
𝑠+ 𝛽4𝑃
𝑐+ 𝜀
(2)
This study transformed the generalized
aggregate demand (AD) according to the theoretical
framework and data accessibility connected to the
important determinants of wheat demand. The
dependent association of wheat consumption (WC)
was specified with substitutable primary foods like,
Rice prices (RP), Corn consumption (CC). Due to
unavailability of corn prices data, this study used the
corn consumption data as a substitute variable.
Further key variables which effect the wheat
demand are Real Gross Domestic Product (RGDP),
Population (POP), Wheat Imports (WI), and
Inflation. This research included wheat import in the
model in order to fulfill the production and
consumption gap as Pakistan government relied
heavily on imports. The modified form of wheat
aggregate demand equation as follows:
𝑊𝐶𝑡= 𝛽𝑜+ 𝛽1𝑊𝑃𝑡+ 𝛽2𝑅𝑃𝑡+ 𝛽3𝐶𝐶𝑡+
𝛽4𝑅𝐺𝐷𝑃
𝑡+ 𝛽5𝑃𝑂𝑃𝑡+ 𝛽6𝑊𝐼𝑡+ 𝛽7𝐼𝑁𝐹𝑡+ 𝜀𝑡 (3)
In order to calculate the elasticities of wheat
consumption for both short-term and long-term
dynamics, this study taken the natural log of all
variables given in equation 3. The linear double log
model has fundamental features, though we can
calculate the elasticities coefficient that shows the
percentage changes in dependent in dependent
variable due to percentage changes in the
explanatory variables [47]. By taking the log of
equation (3), following given below model has been
estimated:
𝐿𝑛𝑊𝐶𝑡= 𝛽𝑜+ 𝛽1𝐿𝑛𝑊𝑃𝑡+ 𝛽2𝐿𝑛𝑅𝑃𝑡+
𝛽3𝐿𝑛𝐶𝐶𝑡+ 𝛽4𝐿𝑛𝑅𝐺𝐷𝑃
𝑡+ 𝛽5𝐿𝑛𝑃𝑂𝑃𝑡+
𝛽6𝐿𝑛𝑊𝐼𝑡+ 𝛽7𝐿𝐼𝑁𝐹𝑡+ 𝜀𝑡 (4)
Equation (4) shows that all variables in natural
logarithmic form. The dependent variable in this
equation is 𝑊𝐶𝑡 and the independent variables are
𝑊𝑃𝑡, 𝑅𝑃𝑡, 𝐶𝐶𝑡, 𝑅𝐺𝐷𝑃𝑡, 𝑃𝑂𝑃𝑡, 𝑊𝐼𝑡, 𝐼𝑁𝐹𝑡. 𝛼𝑜 is the
intercept parameter of the model and 𝛽1 to 𝛽7 are
slope parameters, “t” shows the time duration, 𝜀𝑡 is
the error term. All selected variables brief detail is
given in table 1.
2.3 Data Sources
To examine the main objectives, this study collected
the time series data based on annual observations
from 1972 to 2020. The data is collected from
various published secondary sources such as WDI,
IFS, Bureau of Statistics, various issues of Pakistan
economic survey, Index Mundi, and Punjab Food
Department.
2.4 Estimation Techniques
To investigate the wheat demand determinants, this
study used the linear double-log model. The linear
double log has fundamental characteristics through
which we can examine the elasticities of each
candidate variable in order to explain the short and
long run relationship [45] [47]. Therefore, to
examines the short-term and long-term elasticities of
wheat consumption determinants this study
employed an Autoregressive Distributive Lag
approach (ARDL). Different influential variables of
wheat consumption has been used in the estimated
model in order to analyze own price, cross price and
income elasticities along with control variables e.g.,
wheat imports, inflation, and country population.
The bound test of ARDL was proposed by Pesaran
et al. (2001). The ARDL model has an ability, it
simultaneously works with the coefficients of short
and long term and suitable for both I(0) level, and
I(1) first order stationary variables [45] [48].
This study we investigated the determinants of
wheat consumption by complementary analysis.
Additionally, we projected the wheat consumption,
country population, and wheat production from
2021 to 2100 along with this study measured the
projected gap among wheat consumption and wheat
production on the basis of projected population.
WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT
DOI: 10.37394/232015.2022.18.42
Sania Shaheen, Lal K. Almas,
Muhammad Usman
E-ISSN: 2224-3496
431
Volume 18, 2022
Table1. Variables Description
3 Results and Discussion
3.1 Stationarity Results
This study applied an ARDL model to investigate
the wheat consumption determinants and food
security challenges in Pakistan. Before applying
ARDL estimation technique, in the first step to
check the stationarity of each candidate variable unit
root test was applied. The findings of unit root test
given in below table 2.
Table2. Unit Root-ADF Test Results
Source: Authors own calculations
Notes: **, *** indicates significance at 5% and 1%
Table 2. highlight the Augmented Dicky-Fuller
(ADF) test results. The results of ADF test shows
that the variables wheat consumption, wheat prices,
Rice prices, Corn consumption, and real GDP are
integrated in order one I(1). However, wheat
imports, population and inflation are integrated at
level I(0). Hence, the variables used in the model
are a combination of both variables integrated at I(0)
and I(1) that is essential to adopt the ARDL
estimation technique for short and long-term
dynamics.
3.2 Lag Length Criteria
The time series data usually shows the behaviour of
time trend and effects of current values relatives to
earlier period (lag) value. Any ups and down in the
time series data instigate the issue of
autocorrelation. The autocorrelation problem can be
eliminated through suitable lag selection. In time
series data the process of lag selection is applied to
change the given variable in to growth form, that is
helpful for removing the issue of autocorrelation in
the data set.
[35] claimed that for autocorrelation removal in the
data set lag difference and Cochrane-Orcutt methods
are applicable. Therefore, this study applied an
optimal lag length and various estimation techniques
like Schwarz Bayesian Criterion (SC), Hannan
Quinn Criterion (HQ), Akaike Information Criterion
Variables
Description
Units
𝑊𝐶𝑡
Wheat consumption
Metric Tonnes
𝑊𝑃𝑡
Wheat Prices
PKR per 40 kg
𝑅𝑃𝑡
Rice Prices
PKR per 40 kg
𝐶𝐶𝑡
Corn Consumption
Metric Tonnes
𝑅𝐺𝐷𝑃𝑡
Real Gross Domestic
Product
Millions, Domestic
Currency (base year
2005)
𝑃𝑂𝑃𝑡
Total Population
Millions
𝑊𝐼𝑡
Wheat Imports
Metric Tonnes
𝐼𝑁𝐹𝑡
Inflation, consumer
prices
Annual percentage
Source: Authors own’s collections
Variables
ADF Test
I(0)
I(1)
T-Stat
T-Stat
WCt
-1.2131
(0.8954)
-3.7357**
(0.0302)
WPt
-0.7308
(0.8290)
-6.8934***
(0.000)
RPt
-0.9215
(0.7729)
-6.6432***
(0.000)
CCt
1.0558
(0.9966)
-7.2673***
(0.000)
WIt
-3.1661**
(0.0283)
-5.4801***
(0.0003)
RGDPt
-0.9586
(0.9400)
-5.0924***
(0.0008)
POPt
-3.5725**
(0.011)
-0.4978
(0.8819)
INFt
-3.2392**
(0.0237)
-8.0281***
(0.000)
WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT
DOI: 10.37394/232015.2022.18.42
Sania Shaheen, Lal K. Almas,
Muhammad Usman
E-ISSN: 2224-3496
432
Volume 18, 2022
(AIC), etc. The method of AIC is more effective
relative to HQ and SC. However, HQ and SC results
are consistent relative to AIC [45] [48]. The
estimated findings of lag selection criteria are given
in table 3.
Table 3. Lag length Criteria
Sour
ce:
Auth
ors
own
Calc
ulati
ons
Note
s: * indicates lag order selected by the criterion
AIC: Akaike Information Criteria
SC: Schwarz information Criteria
HQ: Hannan-Quinn Information Criteria
Table 3. shows the results of lag length criteria. This
study selected AIC approach for optimal lag length
followed by [45]. After selection of optimal lag
length, ARDL model is estimated by using E-Views
09 computer package.
3.3 Co-integration Test
In order to investigate the long-term relationship
among candidate variables applied the ARDL-
Bound Test. Table A1. report the results of ARDL-
bound test (See Appendix A1). The results of bound
test reveal that the calculated value of F-stat is
higher than upper bound at each level of
significance that highlight the rejection of null
hypothesis (No long-term connection exist among
candidate variables) and acceptance of alternative
hypothesis (long run relationship exist among
variables). Overall, the results of bound test report
the existence of long-term association among wheat
consumption and its determinants (WP, RP, CC,
RGDP, WI, POP, INF). In other words, the results
show that co-integration relationship exist between
estimated variables.
3.4 ARDL-Based Long-Term Results
After analyzing the cointegration between variables
estimated the long-term and short-term coefficients.
ARDL-based model results of long-term is given in
table 4.
Table 4. report the estimated results of long-
term coefficients. The dependent variable of this
study is wheat consumption and independent
variables are envisioned to determine the key
demographic and economic drivers that raise the
demand for wheat in Pakistan. The intercept
parameter value 3.0459 is positively significant at
5% significance level reveal that by keeping the
explanatory variables fixed, the exogenous factors
Table 4. ARDL-based long Run estimations
Source: Author’s own contribution
Notes: *,**, *** indicates significance at 10%, 5%, and 1%
level
like meat, fruit, vegetables etc., affect the wheat
consumption in Pakistan positively. Further, the
coefficient associated with WPt is -0.0580 and
statistically significant at five percent significance
level. The negative sign associated with coefficient
of wheat prices indicate that the famous law of
demand holds for Pakistan wheat consumption.
According to law of demand, when prices goes up
peoples willing to demand less quantity and vice
versa. Hence, as the prices of wheat goes up peoples
willing to demand less quantity of wheat, and when
prices go down they willing to spend more on wheat
quantity [29]. The coefficient of wheat prices shows
that as 1% decrease in wheat prices tend to increase
the wheat consumption by 5.8% in Pakistan. Wheat
demand is less elastic in Pakistan because wheat is
the necessity good of Pakistan. The findings are
consistent with [30] claimed that global prices of
wheat are not necessary to determine the domestic
consumption of wheat. Whereas, domestic prices of
wheat affect the wheat consumption. [49] view that
wheat prices significantly affect the wheat supply
but donot significantly affect the wheat demand.
[45] concluded that law of demand holds in the
context of Egypt. Conversely, [50] explained that
law of demand does not hold in case of Iran because
they considered wheat as a giffen good.
Rice is a complementary food in various
countries of the world, and others countries
consume rice as a substitute of wheat. This study
Lag
AIC
SC
HQ
1
-22.51
-19.91
-21.55
2
-25.24
-20.05
-23.32
3
-27.49
-19.70*
-24.60
4
-32.96*
-22.58
-29.11*
Dependent Variable LWC
Variables
Coefficient
T-stat
P-value
LWPt
-0.0580
-2.5126
0.0121**
LRPt
0.3143
2.2963
0.0291**
LCCt
-0.2401
-3.0454
0.0049***
INFt
-0.0025
-0.1350
0.8935
LWIt
0.0302
3.8609
0.0006***
LRGDPt
0.3982
4.8223
0.0000***
LPOPt
0.3818
1.9225
0.0644*
C
3.0459
2.4575
0.0202**
WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT
DOI: 10.37394/232015.2022.18.42
Sania Shaheen, Lal K. Almas,
Muhammad Usman
E-ISSN: 2224-3496
433
Volume 18, 2022
used the rice prices (RPt) variable as an explanatory
variable to investigate the association among rice
and wheat food in Pakistan. The coefficient
associated with rice prices is positive (0.3143) at 5%
significance level indicate that rice prices have a
negative effect on wheat consumption in Pakistan.
In other words, it shows substitute relationship
among rice and wheat food in Pakistan. As prices of
rice decline, peoples of Pakistan are willing to
demand more rice by substituting the consumption
of wheat. Overall, the findings show that in the
long-term rice consumption as a substitute for wheat
consumption in Pakistan. The coefficient of rice
prices exhibits that as one percent decrease in rice
prices wheat consumption decreases by 31 percent.
Rice is less-elastic substitution for wheat in
Pakistan. Our results are consistent with [45] [50]
who claimed that rice is substitutable food for wheat
and it has the capacity to decrease the imports of
wheat. Overall, on the basis of results Pakistan
government should develop an inclusive policy for
food security, specifically the consumer demand for
wheat and rice food.
The coefficient of corn consumption is
negatively significant at 1% level. The results
display that corn consumption has an inverse
significant effect on wheat consumption in Pakistan.
The estimated coefficient of corn consumption is -
0.2401. Hence, the elasticity of corn consumption is
-0.24 reveal that in the long run both corn and wheat
consumed as a substitute in Pakistan. The estimated
coefficient of corn displays that corn is less-elastic
substitution with consumption of wheat in Pakistan.
As 1% percent increase in corn consumption tends
to decrease the wheat consumption by 24 percent
and vice versa. Our findings are in line with [51]
who claimed wheat flour can be substituted with
corn flour but adding up of fruits enhance the bread
contents and quality. Additionally, our results are
also consistent with [45]. Overall, results show that
the cross-price elasticity of rice is higher than corn
in Pakistan.
The inflation estimated coefficient is negative (-
0.0025) but not significant in case of Pakistan. It
may be because wheat is the necessity commodity
of Pakistan. Hence, an increase in the inflation rate
donot affect the wheat demand in Pakistan.
The wheat imports estimated coefficient is
positive (0.0302) and significant at 1% level exhibit
a wheat imports direct and significant impact on
wheat consumption in Pakistan. The results show
that a 1% increase in wheat imports tends to raise
the wheat consumption by 3.2% in the long-term.
The coefficient associated with wheat imports
explain the domestic wheat production deficiency
and reliance on wheat imports. This result is similar
with the [41] found that temporary barriers in wheat
imports increased the food insecurity and
vulnerability in Egypt. On the contrary, [50]
highlight that wheat imports donot affect the
domestic prices, and wheat imports can be
decreased through domestic production and
consumption shifts.
The real GDP estimated value is positive and
statistically significant at one percent level. The
estimated coefficient of real GDP is 0.3982, exhibit
that as 1% increase in country GDP leads to
increase the wheat consumption by 39.82 percent.
Hence, the estimated value of real GDP show that
wheat demand is income elastic. Country wheat
demands increases at higher real GDP level, because
at higher income level people demand more wheat
by considering wheat as a necessity commodity
[43]. The poverty rate is high in Pakistan which is
the cause of higher wheat demand in Pakistan. The
poor people first priority is to spend on necessary
commodities to survive. Our results are consistent
with [45] who found for Egypt and contrast with
[50] whose findings claimed that peoples in Iran
considered wheat as an inferior commodity with the
increasing level of income.
The coefficient associated with total population
is positive (0.3818) and significant at ten percent
level reveal that total population has both
economically and statistically significant impact on
consumption of wheat in Pakistan. The estimated
results show that as 1% increase in population tends
to increase the wheat demand by 38%. Population
has elastic wheat demand that positively affect the
wheat demand at national level [45] [52]. [49] and
[53] found that population is a key driver of wheat
demand. Increasing population size put upward
burden on wheat demand, as peoples are ready to
buy more wheat. It is expected that increasing level
of population growth and wheat demand will
generate food insecurity issues in Pakistan. To
ensure food security and to meet the consumer
demands an extensive wheat production, substitute
consumption and wheat import policy is needed for
Pakistan.
3.5 ARDL- Short Run Analysis
The estimated results of short-run elasticities are
given in below table 5 with speed of adjustment
coefficient.
Table 5. results report that the estimated short
run coefficient of wheat consumption is
insignificant. Similarly, the short run estimated
value rice prices are positive but not significant.
However, the lag coefficient of rice prices is
WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT
DOI: 10.37394/232015.2022.18.42
Sania Shaheen, Lal K. Almas,
Muhammad Usman
E-ISSN: 2224-3496
434
Volume 18, 2022
inversely significant at one percent level reveal that
lower rice prices in previous period increases the
wheat consumption in current period. Our results are
consistent with [54] who claimed that rice is
substitutable food for wheat and has the potency to
Table 5. ARDL-based Short Run Estimations
Source: Authors Own Contribution
Notes: *,**, *** at 10%, 5%, and 1% significance level
decrease the country wheat imports. Peoples used
rice as a complementary food for wheat in the short-
term. The estimated coefficient of corn consumption
is negative and statistically significant at 1% level
show that in the short run peoples used corn as a
substitutable food for wheat. Corn consumption
behave same as in the long run. Our findings are in
line with [51] who claimed that wheat flour has
substitution with corn flour.
In the short run, the estimated value of inflation
is insignificant. The inflation results are same as the
long run which indicate that due to the effect of
inflation people donot change the wheat demand
because wheat is the necessary food of Pakistan.
The estimated short run coefficient of wheat imports
and fist lag period of wheat imports are negative and
insignificant. However, the estimated coefficient of
second lag period of wheat imports is also negative
but statistically significant at 1% level show that
previous period wheat imports reduced the current
consumption of wheat. This result is in line with
[45]. The short run coefficient of RGDP is
insignificant but the first lag period of RGDP
coefficient is positively significant at 1% level
reveal that previous period real GDP affect
positively on current wheat consumption. If peoples
have more income in previous period they spend
higher on current consumption of wheat by
considering wheat as a necessity commodity. Our
findings are in contrast with [50] who explains the
inverse income elasticity of demand for wheat by
claiming that consumer in Iran heed wheat as an
inferior good with the increasing level of income. In
our analysis the positive sign of income elasticity of
demand display that peoples change attitude
positively for wheat with the increasing level of
income. In other words, when consumer income
increases peoples increase the wheat demand.
Hence, people get ready to demand more wheat at
increasing income level show that in Pakistan
people have low level of income and usually face
food security challenges. Our results are in line with
[45].
The estimated short run coefficient of
population is directly significant at one percent
level. The finding is same as in the long run.
Steadily [49] [53], and [45] established that
population size is the key determinant of wheat
consumption.
The estimated coefficients of co-integration
calculate the speed of adjustment. If variability
occur in consumption of wheat, the coefficient of
error correction model (ECM) highlight the
convergence period. The negative and significant
ECM coefficient exhibits the existence of long- term
co-integration association among the given
variables, and the estimated model shows a
convergence behaviour. The estimated coefficient of
ECM(-1) is -1.16 and significant at 1% level reveal
that the estimated model has higher adjustment
speed [48]. The estimated coefficient of ECM (-1)
indicate that if any disequilibrium occurs in wheat
consumption, the coefficient of adjustment speed is
high (1.16) remarkably.
3.6 Diagnostic Testing
The estimated results of residual diagnostic testing
given in table 6. In order to test, the
heteroskedasticity exists in the model or not. This
study applies the Breusch Pegan (BP) test. The test
result shows that the test statistics value of BP test is
very low and insignificant indicates the acceptance
of hypothesis that “no heteroskedasticity” exist in
the estimated model.
Similarly, in order to check our sample data
series is normally distributed or not we employ the
Jarque Berra Test (JB). The test results indicate that
the test-statistics value of JB test is low and
insignificant reveal that data is normally distributed.
Likewise, in order to test whether there is a
problem of autocorrelation in the data set or not. We
use the LM test and the test-statistic of LM
calculated value is low and insignificant shows the
acceptance of null hypothesis “no autocorrelation”
exist in the estimated data set, and the results are
valid for policy implications.
Dependent Variable LWCt
Variables
Coefficient
T-Stat
P-Value
D(LWCt)
-0.0672
-0.5220
0.6065
D(LRPt)
0.2055
1.6902
0.1017
D(LRP(-1))
-0.3434
-4.1869
0.0002***
D(LCC)
-0.2784
-3.3631
0.0022***
D(INF)
-0.0030
-0.1341
0.8942
D(LWI)
-0.0020
-0.2694
0.7895
D(LWI(-1))
-0.0031
-0.3371
0.7385
D(LWI (-2))
-0.0212
-2.7824
0.0094***
D(LRGDP)
0.5865
1.0926
0.2835
D(LRGDP(-1))
2.1176
3.5561
0.0013***
D(LPOP)
0.4429
1.9711
0.0583*
ECM(-1)
-1.1599
-6.933
0.0000***
WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT
DOI: 10.37394/232015.2022.18.42
Sania Shaheen, Lal K. Almas,
Muhammad Usman
E-ISSN: 2224-3496
435
Volume 18, 2022
Additionally, to check the structural stability of
the model this study estimated the structural
stability of model through Cummulative Sum
(CUSUM) and Cummulative sum of square
(CUSUMQ), see appendix (Figure 1&2). The
estimated results of CUSM and CUSUMQ are lying
within 5% bound reveal that there is no structural
instability exist in the estimated model, and also
slope coefficients of the estimated model identify
the presence of long-run relationship [45] [55] [56].
Additionally, the estimated results of diagnostic
tests (BP, JB and LM) report that the model is good
fitted and has the capacity to suggest some valuable
policy recommendations.
Table 6. Residual Diagnostic and Stability Analysis
Source: Authors Own Contributions
3.7 Forecasted Consumption and Production
Gap analysis based on Population
This study make a graphical analysis of wheat
demand with the growing population rate in
Pakistan. Figure 3, given in appendix explained the
actual and projected value of wheat consumption,
wheat production, total population, wheat
consumption, and production gap in Pakistan. The
forecasted estimates show that in the beginning of
1975 to 1986 Pakistan achieved near self-
sufficiency in wheat production. The period from
1987 to 2001 showing a small wheat consumption
and production gap which was fulfilled through
wheat imports. In 2003, the graph showing negative
two million MT gap among wheat consumption and
production. In that period, Pakistan exported wheat
approximately 0.193 million MT. From 2004 to
2020 the graph showing self-sufficiency in wheat
production and in that duration, Pakistan exported
approximately 16 million MT of wheat (Data
Source: Index Mundi). Overall, from 1975 to 2020
trend shows that Pakistan was somewhere a net
importer and somewhere a net exporter. This trend
is showing irregularity in food policy of Pakistan.
However, the forecasted years trends from 2021 to
2100 showing an increasing production and
consumption gap which may be a reason of food
insecurity issues in Pakistan. The projected
population trend shows that higher population
growth will exert a continuous pressure on wheat
production. The increasing consumption and
production gap will put upward pressure on imports
of wheat which creates food insecurity. In 2020, the
consumption of wheat in Pakistan was 26 million
MT and increasing at an increasing rate. The
forecasting analysis shows that in 2050 the
consumption of wheat would be 38 million MT
because of the pressure of population. The
forecasting analysis highlight that Pakistan need a
57 million MT of wheat to feed the 491million
population of Pakistan in 2100. The production of
wheat in 2020 was 26 million MT that would be
around 32 million MT in 2050 and 43 million MT in
2100.In contrast, in 2020 consumption of wheat in
Pakistan was 26 million MT and will be 38 million
MT in 2050, and by 2100 the consumption would
reach around 57 million MT. Likewise, population
growth rate is increasing at an increasing rate, in
2020 the Pakistan population was 221 million, that
would be double in 2086, and in 2100 it would
reach around 491million. Overall, the forecasting
analysis highlight that in the future Pakistan will
face a severe food security problem as the demand
and supply gap of wheat is going up over the
projected period, in addition to population
would be double in upcoming 65 years.
4 Conclusion and Policy
Recommendations
Wheat is one of the major food crop and play a vital
role in Pakistan national diet. It is widely
consumable staple food in Pakistan. Worldwide,
wheat is extensively consumable commodity and
play a vital role in food security. This study
conducted to explore the wheat consumption
determinants in Pakistan. Additionally, we
calculated the own price, cross price elasticity of
demand, and income elasticity of demand for wheat.
For estimation purpose, utilized the annual time
series data covering the period from 1972-2020. To
estimate the short-term and long-term co-integration
relationship among candidate variable ARDL
econometric technique is applied. Wheat prices
shows an inverse relationship between wheat
consumption indicate that law of demand holds for
Pakistan. The possible reason is that consumers of
Pakistan are ready to buy more wheat as prices of
wheat declines, because wheat is an essential food
Diagnostic Test
Test Statistics
P-value
Breusch-Pegan
Goldfrey (BP)-
Heteroskedasticity
Test
1.1915
0.3313
Jarque Bera Test (JB)
(Normality Test)
1.1146
0.5727
Autocorrelation Test
(LM Test)
0.7987
0.4602
WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT
DOI: 10.37394/232015.2022.18.42
Sania Shaheen, Lal K. Almas,
Muhammad Usman
E-ISSN: 2224-3496
436
Volume 18, 2022
commodity in Pakistan. The findings of rice prices
and corn consumption displays a significant effect
on consumption of wheat. Hence, wheat, rice, and
corn commodities are consumed as substitutes in
Pakistan. This result reveal that demand of wheat
fluctuates relatively when prices of rice and corn
declines in Pakistan. The estimated coefficient of
RGDP shows a direct significant impact on wheat
consumption in Pakistan indicate that wheat is a
necessity commodity in Pakistan. The result of
RGDP highlight that peoples of Pakistan willing to
buy more wheat as their income is increasing. The
income behaviour of wheat meets the conditions of
economic theories related to the necessity
commodity. In Pakistan, population growth rate is
high. The estimated results indicate that increasing
rate of population put an upward pressure on wheat
consumption in Pakistan. In Pakistan, the
government fulfilled the wheat production and
consumption fulfilled through wheat imports. The
coefficient of wheat imports report a significant
direct influence on wheat consumption in Pakistan.
Further, the estimated result of inflation is
insignificant indicate that inflation does not affect
the wheat consumption in Pakistan. It may be
because wheat is a necessity commodity in Pakistan
and if inflation rate goes high, people donot
decrease the demand for wheat. Further, the findings
of projected analysis highlight that Pakistan from
1975 to 2020 was near self-sufficient in wheat. This
duration sometime Pakistan was somewhere a net
exporter and somewhere a net importer shows
irregularity in food policy of Pakistan. However, our
projected findings highlight that food insecurity
issues may arise in Pakistan in the upcoming years
due to the increasing growth rate of population
which put an upward pressure on wheat demand.
There is possibility that wheat consumption and
production gap is increasing over the coming years.
This study’s contribution is very important in
the empirical literature especially in the context of
Pakistan. This is the first study that investigates the
determinants of wheat consumption as well as
highlights the alternative substitute shift of wheat
like corn and rice in Pakistan. Further, this study
projected wheat demand and supply gap based on
the growing population rate of Pakistan for the
upcoming 79 years, and highlights the food security
challenges that Pakistan will face in the upcoming
years as previous literature had focused on the
wheat production, supply and its availability.
Therefore, this study’s findings will not only
contribute to the empirical literature but also
suggests policies to policy makers in order to
explore ways to increase the wheat production and
hence decrease the wheat demand and supply gap.
On the basis of empirical analysis, this study
suggests some useful policy recommendation that
should need to be addressed in order to fulfill the
demand for wheat in Pakistan.
1. An inclusive policy required for substitutable
wheat consumption regarding rice and corn.
2. Government should pay attention on alternative
wheat production policies to meet the domestic
demand for wheat consumption.
3. Pakistan government should focus on
identifying further water resources for irrigated
agriculture and alternative water production
technology.
4. Government should manage the domestic
production of wheat more than the population
growth in order to decrease the country wheat
imports. Wheat imports can be decreased by
imposing tariff on imports and by shifting the
population consumption pattern to substitutable
commodities.
5. A comprehensive policy is needed for wheat
imports and wheat output in order to fulfill the
gap among wheat demand and supply.
Government should pay attention on pro-poor
agriculture growth in order to fulfill the wheat
demand and substitutable staple commodities.
6. Mapping needed to meet the wheat
consumption. Hence, ministry of food and
agriculture should manage the increasing level
of wheat demand.
7. Latest production techniques and advanced
wheat varities would be beneficial to increase
the production of wheat.
4.1 Future Directions
Research is needed for wheat demand,
production gap and substitutable shifts of staple
food crops at micro level. Micro level research
is required to analyze the deficiencies in wheat
production and apply advanced techniques to
raise the wheat production. Further, research
required to investigate the wheat production
pattern and lacking problems, through Pakistan
economy may achieve wheat production self-
sufficiency.
References:
[1] Economic Survey of Pakistan 2020-21.
Economic Advisor’s Wing, Finance
Division, Islamabad. Government of
Pakistan (GOP).
WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT
DOI: 10.37394/232015.2022.18.42
Sania Shaheen, Lal K. Almas,
Muhammad Usman
E-ISSN: 2224-3496
437
Volume 18, 2022
[2] Pakistan Statistical Year Book 2019-2020.
Bureau of Statistics, Statistical Division,
Islamabad, Pakistan. Available online
http://www.finance.gov.pk/survey/chapt
ers_19/2-Agriculture.
[3] Mukhtar, T., Javed, M. T., & Ilyas, M.
(2010). Cointegration and error correction
modelling of wheat consumption in
Pakistan. Humanomics,26(3),212-223.
[4] Haider, S. H., Tariq, A., & Nazli, H. (2019).
Supply and demand for cereals in Pakistan,
2010- 2030. IFPRI Discussion Paper 01222.
[5] Rani, S., Vanzetti,D., Petersen,V., & Qasim,
M., (2020). Estimation of Supply and
Demand Elasticities for Major Crops
Produced in Pakistan. Lahore Journal of
Economics, 25(1), 139-152.
[6] Food and Agriculture Organization of the
United Nations (FAO). (2020a). National
agri-food systems and COVID-19 in
Pakistan: Effects, policy responses, and
long-term implications.
http://www.fao.org/3/cb1343en/CB1343
EN.pdf. Accessed on 01 Oct 2020.
[7] Food and Agriculture Organization of the
United Nations (FAO). (2020b). COVID-19
emergency in Pakistan: Evolution of prices
of major food items since COVID-19
outbreak (February 28–June 11, 2020).
http://Pakistan | Humanitarian response
(May–December 2020) (fao.org). Accessed
on 09 Oct 2020.
[8] Ubaid‐ur‐Rehman, H., Asghar, W., &
Khalid, N. (2021). Food security
challenges for Pakistan during COVID‐
19 pandemics: An overview of the
response plan. World Food Policy, 7(1),
82-89.
[9] Noreen, N., Dil, S., Niazi, S., Naveed, I.,
Khan, N., Khan, F., & Kumar, D.(2020).
COVID- 19 pandemic & Pakistan;
limitations and gaps. Global
Biosecurity, 2(1),1-11.
[10] Rehman, A., Jingdong, L., Shahzad, B.,
Chandio, A. A., Hussain, I., Nabi, G., &
Iqbal, M. S. (2015). Economic
perspectives of major field crops of
Pakistan: An empirical study. Pacific
Science Review B: Humanities and Social
Sciences, 1(3), 145-158.
[11] Economic Survey of Pakistan 2019-20.
Economic Advisor’s Wing, Finance
Division, Islamabad. Government of
Pakistan (GOP).
[12] FAO, 2009. World Summit on Food
Security. FAO, Rome.
[13] Shideed, K. H. (2011). Informing policy
development for sustainable and
productive food production systems in dry
areas. International centre for agricultural
research in the dry areas (ICARDA),
Aleppo, Syria.
[14] Suleimenov, M., Saparov, A., Akshalov,
K., & Kaskarbayev, Z. (2012). Land
degradation issues in Kazakhstan and
measures to address them: research and
adoption. Pedologist, 55(3), 373-381.
[15] De Fraiture, C., Molden, D., & Wichelns,
D. (2010). Investing in water for food,
ecosystems, and livelihoods: An
overview of the comprehensive assessment
of water management in agriculture.
Agricultural Water Management,
97(4), 495-501.
[16] Falkenmark, M., Fox, P., Persson, G., &
Rockström, J. (2001). Water harvesting for
upgrading of rainfed agriculture.
Problem analysis and research needs,
Stockholm International Water
Institute report. Foundation for Strategic
Environmental Research, MISTRA.1-94
[17] Valipour, M. (2013). Increasing irrigation
efficiency by management strategies:
cutback and surge irrigation.
Journal of Agricultural and Biological
Science, 8(1), 35-43.
[18] Shaumarov, M., & Birner, R. (2013).
Dryland pastoral systems in transition:
what are the options for institutional
change in Uzbekistan?. conference
proceeding German Society of Economic
and Social Sciences in Agriculture
(GEWISOLA).
[19] Zakaria, S., Al-Ansari, N., Ezz-Aldeen, M.,
& Knutsson, S. (2012). Rain water
harvesting at eastern Sinjar
Mountain, Iraq. Geoscience Research,
3(2), 100-108.
[20] Mudasser, M., Hussain, I., & Aslam, M.
(2001). Constraints of land and water
productivity of wheat in Pakistan and
India:A comparative Analysis. International
Water Management Institute, (Research
Report No 65-IWMI), Colombo,Sri Lanka
https://hdl.handle.net/10535/4462.
[21] PARC, 1982. Consumptive Use of Water
for Crops in Pakistan. Report of Pakistan
of Agric. Res. Council, Islamabad.
WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT
DOI: 10.37394/232015.2022.18.42
Sania Shaheen, Lal K. Almas,
Muhammad Usman
E-ISSN: 2224-3496
438
Volume 18, 2022
[22] Government of Pakistan, 2005. Year
Book of Agricultural Statistics of
Pakistan, Ministry of Food, Agric. and
Livestock, Islamabad.
[23] Abbas, M., Sheikh, A., Shahbaz, M., &
Afzaal, A. (2007). Food security through
wheat productivity in Pakistan. Sarhad
Journal of Agriculture, 23(4), 1239-1249.
[24] Oweis, T., & Hachum, A. (2006). Water
harvesting and supplemental irrigation for
improved water productivity of dry farming
systems in West Asia and North Africa.
Agricultural Water Management, 80(1-3),
57-73.
[25] Oweis, T. (1997). Supplemental irrigation:
A highly efficient water-use practice:
International Center of Agricultural
Research in the Dry Areas (ICARDA),
Aleppo, Syria.
[26] Xie, Z.-k., Wang, Y.-j., & Li, F.-m. (2005).
Effect of plastic mulching on soil water use
and spring wheat yield in arid region of
northwest China. Agricultural Water
Management, 75(1), 71- 83.
[27] Heisey, P. W., & Ahmad, M. (1990).
Accelerating the transfer of wheat
breeding gains to farmers: A study of the
dynamics of varietal replacement in
Pakistan. Research Report (Vol. 1):
CIMMYT, Mexico.
[28] Iqbal, M., Khan, M. A., & Ahmad, M.
(2002). Adoption of recommended
varieties: a farm-level analysis of wheat
growers in irrigated Punjab. The Pakistan
Development Review,41(1), 29-48.
[29] Barnes, J. N., & Shields, D. A. (1998). The
growth in US wheat food demand. Wheat
Yearbook. Economic Research
Service/USDA, 21-29.
[30] Mason, N. M., Jayne, T. S., & Shiferaw, B.
(2015). Africa's rising demand for wheat:
trends, drivers, and policy implications.
Development Policy Review, 33(5), 581-13.
[31] Mottaleb, K. A., Rahut, D. B., Kruseman,
G., & Erenstein, O. (2018). Wheat
production and consumption dynamics in an
Asian rice economy: The Bangladesh case.
The European Journal of Development
Research, 30(2), 252-275.
[32] Wolf, J., & Timsina, J. (2018). Can
Bangladesh produce enough cereals to meet
future demand? Agriculture system,
163(2018),36-44.
[33] Terry, J. J., & Marsh, T. L. (2000). Derived
demand for wheat by class. Conference
proceeding of Western Agriculture
Economic Association Annual Meetings,
Vancouver, British, Columbia.
[34] Mukhtar, T., Javed, M. T., & Ilyas, M.
(2010). Cointegration and error correction
modelling of wheat consumption in akistan.
Humanomics, 26(3),212-223.
[35] Abdalla, M., & Al-Shawarby, S. (2017). he
Tamween food subsidy system in Egypt. he
1.5 Billion People Question-Chapter 3,
World Bank Group,107-149.
[36] Fabiosa, J. F. (2006). Westernization of
the Asian diet: the case of rising wheat
consumption in Indonesia. Center for
Agricultural and Rural Development Iowa
State University, Working paper No.(6)-
WP-422,1-36.
[37] Tanaka, T. (2018). Agricultural self–
sufficiency and market stability: a
revenue neutral approach to wheat sector in
Egypt. Journal of Food Security, 6(1), 31-
[38] Haral, M. A., & Yasin, M. (2019).
Determinants of Speculative Demand of
Wheat and Its Impact on Consumer
Welfare Loss. Journal of Economic
Impact, 1(3), 87-91.
[39] Uhl, K. M., Perekhozhuk, O., & Glauben,
T. (2019). Russian market power in
international wheat exports: Evidence
from a residual demand elasticity
analysis. Journal of Agricultural &
Food Industrial Organization, 17(2), 1- 13.
[40] Han, J., & Jeong, H. (2010). Factors
expanding consumption of domestic
wheat processed products. Korean
Journal of Agricultural Management and
Policy,37(3),573-589.
[41] Veninga, W., & Ihle, R. (2018). Import
vulnerability in the Middle East: effects of
the Arab spring on Egyptian wheat trade.
Food security, 10(1), 183-194.
[42] Matin, M., & Alam, S. (2004). Demand,
supply estimation and projection of wheat
situation in Bangladesh. Bangladesh Journal
of Agricultural Economics, 27(454-2016-
36662), 25-44.
[43] Mason, N. M., Jayne, T. S., & Shiferaw, B.
(2015). Africa's rising demand for wheat:
trends, drivers, and policy implications.
Development Policy Review, 33(5), 581-
613.
WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT
DOI: 10.37394/232015.2022.18.42
Sania Shaheen, Lal K. Almas,
Muhammad Usman
E-ISSN: 2224-3496
439
Volume 18, 2022
[44] Alamri, Y., & Al-Duwais, A. (2019). Food
security in Saudi Arabia (case study: Wheat,
barley, and poultry). Yosef Alamri, and
Abdulaziz Al-Duwais,Food Security in
Saudi Arabia (Case Study: Wheat, Barley,
and Poultry). Journal of Food Security, (2),
36-39.
[45] Almas, L. K., & Usman, M. (2021).
Determinants of Wheat Consumption,
Irrigated Agriculture, and Food Security
Challenges in Egypt. WSEAS
Transactions on Environment and
Development, 17(67), 696-712.
[46] Keynes, J. M., (1936). The General
Theory of Employment, Interest and
Money. London: Macmillan.
[47] Uzunoz, M., & Akcay, Y. (2009). Factors
affecting the import demand of wheat in
Turkey. Bulgarian Journal of
Agricultural Science, 15(1), 60-66.
[48] Usman, M., Hameed, G., Saboor, A., &
Almas, L. K. (2021). Research and
Development Spillover, Irrigation Water
Use and Agricultural Production in
Pakistan. WSEAS Trans. Environ.
Dev, 17(2021), 840-858
[49] Zulfiqar, M., Chishti, A. F., Khan, M.,
Arifullah,S.A.,Khan,D.,Sarfaraz,N.,&Amin,
H.(2011). Development of Supply and
Demand functions of Pakistani's Sugarcane
crop. Sarhad Journal of Agriculture.
27(2),285-290.
[50] Yazdanshenas, L., Moghadasi, R., &
Yazdani, S. (2012). A Model for the
Wheat Market in Iran. International
Journal of Agricultural Science and
Research, 2(2), 49-55.
[51] Oladunmoye, O., Akinoso, R., &
Olapade, A. (2010). Evaluation of some
physical–chemical properties of wheat,
cassava, maize and cowpea flours for
bread making. Journal of Food Quality,
33(6), 693-708.
[52] Mottaleb, K. A., Rahut, D. B., Kruseman,
G., & Erenstein, O. (2018). Wheat
production and consumption dynamics in
an Asian rice economy: The Bangladesh
case. The European Journal of Development
Research, 30(2), 252-275.
[53] Dibaba, R. (2019). Wheat Production,
Marketing and Consumption in Ethiopia.
[54] Aslam, M. (2009). Food Autarky through
substitution between Rice and Wheat: An
econometric analysis. Pakistan Journal of
Agriculture Research, 22(1-2),86-92.
[55] Chandio, A. A., Jiang, Y., & Rehman, A.
(2019).Using the ARDL-ECM approach to
investigate the nexus between support price
and wheat production: An empirical
evidence from Pakistan. Journal of Asian
Business and Economic Studies (JBES),
26(1),139-152.
[56] Koondhar, M. A., Li, H., Wang, H., Bold, .,
& Kong, R. (2020). Looking back over the
past two decades on the nexus between air
pollution, energy consumption, and
agricultural productivity in China: a
qualitative analysis based on the ARDL
bounds testing model. Environmental
Science and Pollution Research, 27(12),
13575-13589.
Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
All authors equally contributed in this research
regarding the data collection, empirical analysis, and
writing of the manuscript. Sania Shaheen conceived
the study idea, reviewed the literature, collected data
and completed the writeup of this research.
Lal K. Almas provided the technical support, model
development, abstract, and suggested the policy
recommendations. Muhammad Usman did the
empirical analysis of this study.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
The research in this manuscript has partially been
supported by the Ogallala Aquifer Program, a
consortium of the USDA Agricultural Research
Service, Kansas State University, Texas A & M
AgriLife Research, Texas A & M AgriLife
Extension Service, Texas Tech University, and West
Texas A & M University.
Visiting Research Fellow, Sania Shaheen, from
Pakistan has been funded by Higher Education
Commission (HEC), Pakistan through her academic
institution, International Islamic University
Islamabad (IIUI), Pakistan.
Creative Commons Attribution License 4.0
(Attribution 4.0 International, CC BY 4.0)
This article is published under the terms of the
Creative Commons Attribution License 4.0
https://creativecommons.org/licenses/by/4.0/deed.en
_US
WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT
DOI: 10.37394/232015.2022.18.42
Sania Shaheen, Lal K. Almas,
Muhammad Usman
E-ISSN: 2224-3496
440
Volume 18, 2022
Appendix
A1. Bound Testing-Cointegration
Fig. 1: Cummulative sum of recursive residuals
(CUSUM)
-16
-12
-8
-4
0
4
8
12
16
92 94 96 98 00 02 04 06 08 10 12 14 16 18
CUSUM 5% Significance
Fig. 2: Cummulative sum of square recursive
residuals (CUSUMQ)
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
92 94 96 98 00 02 04 06 08 10 12 14 16 18
CUSUM of Squares 5% Significance
Fig. 3: Wheat Demand with growing Population Rate in Pakistan
-10000
0
10000
20000
30000
40000
50000
60000
70000
1960 1980 2000 2020 2040 2060 2080 2100 2120
Forcecasted Wheat Domestic Consumptions (1000 MT)
Forecasetd Wheat Domestic Production (1000 MT)
Forcasted Population(10000)
Consumption and Production Gap(1000 MT)
Test -Stat
Value
Significance level
I(0)
I(1)
F-Stat
8.1186
10%
2.03
3.13
5%
2.32
3.5
2.5%
2.6
3.84
1%
2.96
4.26
WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT
DOI: 10.37394/232015.2022.18.42
Sania Shaheen, Lal K. Almas,
Muhammad Usman
E-ISSN: 2224-3496
441
Volume 18, 2022