J. Agric. Environ. Sci. Vol. 5 No. 2 (2020) ISSN: 2616-3721 (Online); 2616-3713 (Print)
Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 27
A Paradigm Shift from Area-Led to Productivity-Led Production of Maize in Nigeria
Sadiq, M.S.
1*
, Singh, I.P.
2
and Ahmad, M.M.
3
1
Department of Agricultural Economics and Extension, FUD, Dutse, Nigeria
2
Department of Agricultural Economics, SKRAU, Bikaner, India
3
Department of Agricultural Economics, BUK, Kano, Nigeria
*Corresponding author: sadiqsanusi30@gmail.com
Received: May 14, 2020 Acceped: September 11, 2020
Abstract: Unlike in the past when production performance was judged from the area, in recent time's growth
performance is hinged on productivity. In Nigeria, increasing maize production through area expansion is no
longer feasible owing to pressure on demand for arable limited land for allied sectors, urbanization,
industrialization etc. thus threatening sustainable maize production which is the precursor for self-sufficiency in
maize production. It is in lieu of this that the present research empirically examined the ex-post and ex-ante
production trend of maize production in Nigeria. Time series data that spanned for 58 years (1961-2018) and
covered production, area, yield and crop prices were used. The data were sourced from the FAO database and
the collected data were analyzed using both descriptive and inferential statistics. The empirical evidence
showed that maize production is not sustainable as area growth rate predominates in increasing the production
growth rate of maize in the studied area. In addition, it was observed that variability in the production of maize
owes majorly to uncertainty viz. weather vagaries. Furthermore, weather vagaries viz. drought and flood; and
non-remunerative price of maize affected the supply response of maize. In a decade ahead, a deficit in the
supply of maize is very imminent which will be owed to poor productivity, thus affecting the food security of
maize in the studied area. Thus, the onus lies on the policymakers to invest adequately on technology and
infrastructure in order to achieve a sustainable production of maize that will guarantee maize food security in
the country.
Keywords: Acreage response, Area expansion, Maize, Nigeria, Sustainable production
This work is licensed under a Creative Commons Attribution 4.0 International License
1. Introduction
Maize is an important staple food for more than 1.2
billion people in sub-Saharan Africa (SSA) and
Latin America; and is the most important cereal
crop in SSA (IITA, 2020). In SSA, it is a staple
food for those living in SSA (Anonymous, 2020) as
it is food for approximately 50 percent of its
population (Agricdemy, 2020). The worldwide
production of maize is 785 million tons, with the
largest producer, the United States, producing 42
percent. Africa produces 6.5 percent and imports 28
percent from countries outside the continent. In
addition, the worldwide consumption of maize is
more than 116 million tons, with Africa consuming
30 percent and SSA 21 percent. East and South
Africa use 85 percent of its production as food,
while Africa as a whole uses 95 percent, compared
to other world regions that use most of its maize as
animal feed (IITA, 2020; Anonymous, 2020).
In 2007, the largest producer of maize in Africa
was Nigeria with nearly 8 million tons, followed by
South Africa (IITA, 2020). But currently, Nigeria is
the 2
nd
largest producer of maize in Africa after
South Africa and the 11
th
largest maize producing
nation in the world (Agricdemy, 2020). This
showed that South Africa has swapped its position
with Nigeria; taken the lead rank in Africa.
As a versatile crop that is not just consumed
domestically, the crop is used industrially by
confectionery and animal feed manufacturers, flour
mills, breweries and bakeries. Despite Nigeria‟s
high production volumes, the country‟s average
yield of 1.8 tons/ha is one of the lowest among the
top ten producers in Africa (Agricdemy, 2020;
IITA, 2020). It lags behind countries such as Egypt
and South Africa where the yields are 7.7 tons/ha
and 5.3 tons/ha respectively, making it difficult for
the country to meets its total domestic and
industrial demand. Generally, the average yield of
maize in Nigeria and other sub-Sahara Africa
countries is low i.e. 1.68 tons/hectare, which is very
J. Agric. Environ. Sci. Vol. 5 No. 2 (2020) ISSN: 2616-3721 (Online); 2616-3713 (Print)
Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 28
low when compared to the average yield in the
United States: 9.3 tons/hectare over the same
period (Anonymous, 2020).
In Nigeria, an increase in maize production has
been achieved greatly by area expansion rather than
an increase in productivity. The cultivated area
increased from 2.8 million hectares in 1986 to over
3 million hectares in 2000 and over 6 million
hectares by 2011. Of the total world production
(1,133,540 million tons) in 2018, Nigeria, the
largest producer in sub-Sahara Africa produced 11
million tons, representing 0.009% of the world
production. Anonymous (2020) reported that
Nigerian‟s maize production increased has grown
at an average annual rate of 6.89.
Nigeria‟s surging population is expected to reach
200 million by 2025. Thus, this growth will lead to
an increasing demand for maize to serve both
domestic and industrial consumption; and this
represents a golden opportunity for farmers and
entrepreneurs to explore. It is because of this that
this research was conceptualized to devise a
roadmap that will help the country to achieve a
sustainable maize production which can guarantee
maize food security in the country. Therefore, the
broad objective of this study is to determine a
sustainable maize production that can guarantee
maize food security in the country. The specific
objectives were to examine the production trend
and growth pattern of maize production, extent and
magnitude of production instability, determine
factors influencing farmers‟ acreage allocation
decision and, forecast the production trend of maize
in Nigeria.
2. Materials and Methods
Time series data sourced from FAO data bank that
covered production, area, yield, producers‟ prices
of maize, rice, sorghum and millet, and spanned
from 1961 to 2018 were used. For proper
examination, the data were divided according to the
reform periods which marked the economy of the
country. The reform periods were pre-Structural
Adjustment Period (SAP) (1961-1984), SAP (1985-
1999) and post-SAP (2000-2018). The collected
data were analyzed using both descriptive and
inferential statistics. The first objective was
achieved using descriptive statistics and compound
growth model. The second objective was achieved
using the instability index and Hazell‟s
decomposition model while the third and last
objectives were achieved using Nerlove‟s
distributed lag model and ARIMA model,
respectively.
2.1 Model specification
2.1.1 Growth rate
The compound growth rate is used to study growth.
Thus, the compound annual growth rate was
calculated using the exponential model indicated
below:
[1]
[2]
[3]
Where,
CAGR = Compound growth rate;
t = Time period in a year
y= Area/Yield/Production
= Intercept
= Estimated parameter coefficient
2.1.2. Instability index
Coefficient of variation (CV), Cuddy-Della Valle
Index (CDII) and Coppock‟s index were used to
measure the variability in the production, area and
yield (Boyalet al., 2015; Sandeepet al., 2016).
[4]
Where
= standard deviation
CDII = CV*(1-R
2
)
0.5
[5]
Where
CDII = Cuddy-Della instability index;
CV = Coefficient of variation;
R2 = Coefficient of multiple determination
(Cuddy-Della Valle, 1978).
Note: The instability index classification is low
instability (20%), moderate instability (21-40%)
and high instability (>40%) (Shimla, 2014, Umar et
al., 2019).
Unlike a CV, Coppock‟s instability index gives a
close approximation of the average year-to-year
percentage variation adjusted for trend (Coppock,
1962; Ahmed and Joshi, 2013, Kumar et al., 2017,;
Umar et al., 2019).
[6]
[7]
J. Agric. Environ. Sci. Vol. 5 No. 2 (2020) ISSN: 2616-3721 (Online); 2616-3713 (Print)
Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 29
Where
,
CII
instability index
;
2.1.3 Source of change in production
Instantaneous change: It measures the relative
contribution of the area and yield to the total output
change of a crop and it has been used to study the
growth performances of crops by several kinds of
research. The instantaneous decomposition model
as used by Sandeepet al. (2016) is given below:
[8]
[9]
Where,
P, A and Y = production, area and yield,
respectively. The subscript
0 and n = base and the n
th
years, respectively.
[10]
[11]
[12]
From equation (5) and (9) we can write
[13]
Therefore,
[14]
[15]
Hazell’s decomposition model
Hazell's (1982) decomposition model was used to
estimate the change in average production and
change in the variance of production with respect to
between regimes and the overall period. Hazell
decomposed the sources of change in the average
of production and change in production variance
into four (4) and ten (10) components.
Decomposition analysis of change in production
assesses the quantum of increase or otherwise of
production in year „n‟ over the base year that
results from the change in the area, productivity or
their interaction. Following Hazell‟s (1982) as
adopted by Umar et al. (2017; 2019), the model is
presented below:
I. Changes in average production are affected by
changes in area-to-yield covariance and also
changes in the mean area and mean yield.
[16]
[17]
Table 1: Components of change in the average
production
Sources of
change
Symbols
Components of
change
Change in
mean area
Change in
mean yield
Interaction
effect
Changes in
area-yield
covariance
II. Change in variance decomposition: In this, the
production variance was decomposed into its
sources, i.e., area variance, yield variance, area-
yield covariance, and interaction of higher-order
between area and yield. A change in each of these
components can result in a shift in output variance.
[18]
J. Agric. Environ. Sci. Vol. 5 No. 2 (2020) ISSN: 2616-3721 (Online); 2616-3713 (Print)
Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 30
Table 2: Components of Change in Variance production
Sources of change
Symbols
Components of change
Change in mean area
Change in mean yield
Change in area variance
Change in yield variance
Interaction effect I (changes in the
mean area and mean yield)
Changes in area-yield covariance
Interaction effect II (changes in
the mean area and yield variance)
Interaction effect II (changes in
mean yield and area variance)
Interaction effect IV (changes in
the mean area and mean yield and
changes in area-yield covariance)
Residual
2.1.4. Nerlovian’s model
Directly, the supply response was calculated by
including partial adjustment and minimal adaptive
expectations (Nerlove, 1958). The Nerlovian model
describes the supply dynamics by incorporating
price expectations and partial adjustment of the
area. Since the desired output is a function of price
expectation in this model, the supply function as
the Nerlove‟s response model as adopted is
presented below (Sadiq et al., 2017).
[19]
The first equation is behavioural, stating that
desired acreage
depend upon the following
independent variables.
Where,
;
;
;
;
;
;
;
;
;
;
.
Price and yield risks were measured by the
standard deviation of the three preceding years. For
the weather index, the impact of weather on yield
variability was measured with a Stalling‟s index
(Stalling, 1960; Ayalew, 2015). To get the
predicted yield the actual yield was regressed on
time. The actual yield ratio to the predicted yield is
defined as the weather variable. In the acreage
response model, the weather effects such as
rainfall, temperature etc. can be captured by this
index (Ayalew, 2015).
The number of years required for 95 percent of the
effect of the price to materialize is given below
(Sadiqet al. 2017).
[20]
Where;
r = coefficient of adjustment (1-coefficient of
lagged area)
n = number of year
J. Agric. Environ. Sci. Vol. 5 No. 2 (2020) ISSN: 2616-3721 (Online); 2616-3713 (Print)
Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 31
Marginal effect and price elasticities for semi-
logarithm functional form are given below
[21]
[22]
[23]
2.1.5. ARIMA
Box and Jenkins (1976) submitted that ARIMA (p,
d, q), which is a combination of Auto-regressive
(AR) and Moving Average (MA) with an
integration or differentiation order (d), denotes a
non-seasonal ARIMA model. The p and q are
respectively the order of autocorrelation and the
moving average (Gujarati et al., 2012). ARIMA in
its general form is as follows:
[24]
Where
[25]
[26]
Here,
are values of past series
with lag 1… p, respectively.
Forecasting accuracy
For measuring the accuracy in fitted time series
model, mean absolute prediction error (MAPE),
relative mean square prediction error (RMSPE),
relative mean absolute prediction error (RMAPE)
(Paul, 2014), Theil‟s U statistic and R
2
were
computed using the following formula:
[27]
[28]
[29]
[30]
[31]
Where,
= coefficient of multiple determination
= Actual value
= Future value
T = time period
3. Results and Discussion
3.1 Trend and growth pattern of maize
production
The production trend of maize exhibited fluctuating
trend during the pre-SAP era with the output being
characterized by slight rise and decrease.
Thereafter, the production trend was marked by a
steep rise which persisted and peaked in the year
1995 and afterwards, the production trend declined
steeply till the end of the SAP period i.e. 1999.
Furthermore, during the post-SAP period, a
cyclical trend marked the production of maize: a
steep increase in the production that exhibited a
cyclical trend viz. ebb-recovery-prosperity and
peaked in the year 2016. Afterwards, a declined
cyclical trend set in during the end of the post-SAP
transition (Figure 1-4). It was observed that the
production trend was majorly driven by area
expansion from the pre-SAP era through to the
SAP era with yield effect been marginal. The yield
was marked by a marginal cyclical trend that
persisted through pre-SAP and SAP transitional
periods. However, during the post-SAP period,
both area expansion and yield simultaneously were
the driving force which caused steeped increase in
the production trend of maize till the end of the
transitional era. Therefore, it can be inferred that
the area effect predominates in the supply
expansion of maize production in the studied area.
J. Agric. Environ. Sci. Vol. 5 No. 2 (2020) ISSN: 2616-3721 (Online); 2616-3713 (Print)
Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 32
0
5000
10000
15000
20000
25000
0
2000000
4000000
6000000
8000000
10000000
12000000
14000000
1961
1964
1967
1970
1973
1976
1979
1982
1985
1988
1991
1994
1997
2000
2003
2006
2009
2012
2015
2018
Yield (hg/ha)
Production (ton) & Area (ha)
Figure 1: Production trend of Maize (1961-2018)
Production Area Yield
0
2000
4000
6000
8000
10000
12000
14000
16000
18000
0
200000
400000
600000
800000
1000000
1200000
1400000
1600000
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
Yield (hg/ha)
Production (ton) & Area (ha)
Figure 2: Pre-SAP production trend of Maize (1961-1984)
Production Area Yield
J. Agric. Environ. Sci. Vol. 5 No. 2 (2020) ISSN: 2616-3721 (Online); 2616-3713 (Print)
Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 33
.
Furthermore, it was observed that both the average
annual production and area increased
hyperbolically from the pre-SAP transition to SAP
transition: both increased by five-folds and
thereafter inclined gently from SAP era to post-
SAP era (Table 3). However, the average annual
yield increased in an arithmetic pattern from the
pre-SAP period to SAP period and in turn from
SAP regime to post-SAP regime. Thus, this
reinforced the evidence of area expansion which
was concluded to be the major factor that
determined the increasing trend that marked maize
production in the studied area. Generally, it can be
inferred that this production trend is not favourable
for maize food security in the country.
The results of the growth rate showed that during
the pre-SAP era, the maize production witnessed a
declined growth rate annually i.e. negative growth
rate (-1.9%) and thereafter, during the SAP
transition, the production of maize was marked by
an inclined growth rate annually i.e. positive
growth rate (4.5%). The inclined growth rate
persisted through to the post-SAP period with the
annual growth rate being 5.5% (Table 3). The
annual area growth rate during the pre-SAP period
exhibited similar growth pattern with that of the
production i.e. negative growth rate; and thereafter,
from SAP to post-SAP transitions, the growth rate
increased steeply. For the yield, it recorded a
positive annual growth rate during the pre-SAP
(2.1%) and subsequently became stagnant during
the SAP regime; and thereafter, witnessed a gentle
increase during the post-SAP regime (1.0%).
During the SAP period, there was no growth in the
yield. Generally, for the overall period, the growth
rate of maize production inclined owing to the
pronounced growth rate in the area in spite of the
marginal increased growth rate in yield, thus
implying food insecurity in the supply of maize.
0
2000
4000
6000
8000
10000
12000
14000
16000
18000
0
1000000
2000000
3000000
4000000
5000000
6000000
7000000
8000000
1985198619871988198919901991199219931994199519 96199719981999
Yield (hg/ha)
Production (ton) & Area (ha)
Figure 3: SAP production trend of Maize (1985-1999)
Production Area Yield
0
5000
10000
15000
20000
25000
0
2000000
4000000
6000000
8000000
10000000
12000000
14000000
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Yield (hg/ha)
Production (ton) & Area (ha)
Figure 4: Post-SAP production trend of Maize (2000-2018)
Production Area Yield
J. Agric. Environ. Sci. Vol. 5 No. 2 (2020) ISSN: 2616-3721 (Online); 2616-3713 (Print)
Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 34
Therefore, it can be inferred that there is a deficit in the supply of maize in the studied area.
Table 3: Growth pattern of maize production
Variables
Pre-SAP
SAP
Post-SAP
Overall
Area (ha)
CGAR %
96.1***
104.0**
104.5***
103.9***
AGR %
-4.0***
4.0**
4.5***
3.9***
AA
994791.7
4134827
4606115
2989890
Status
277.29***(A)
-55525.47***(D)
2345.97***(A)
759.49***(A)
Yield (hg/ha)
CGAR %
102.1***
100.4
NS
101.0*
101.4***
AGR%
2.1***
0.4
NS
1.0*
1.4***
AA
10243.88
12952.13
16664.47
13047.59
Status
6.813***(A)
35.97
NS
(S)
-27.78
NS
(S)
0.100***(A)
Production
(ton)
CGAR %
98.1**
104.5***
105.5***
105.4***
AGR%
-1.9**
4.5**
5.5***
5.4***
AA
960750
5288600
7654956
4272951
Status
587.23**(A)
-53037.41**(D)
-2475.61***(D)
2539.23***(A)
Source: Authors‟ computation, 2020
Note: CGR- Compound growth rate; AGR- Annual growth rate; AA- Annual Average; A- Acceleration; D-
Deceleration; S- Stagnation; ton = tone; hg = hectogram; and, ha = hectare
*** ** *
& NS
means significant at 1, 5, 10% and Non-significant respectively
3.2 Magnitude and extent of instability
The coefficient of variation (CV) results showed
that production of maize was marked by moderate
instability throughout the transitional phases viz.
pre-SAP, SAP and post-SAP; and it owed to
moderate instability in the area given that yield
variability was low (Table 4). Though, the
moderate instability which marked production
during the pre-SAP regime owed both to area and
yield who exhibited moderate instability
simultaneously. However, the precipitated high
instability which marked the production of maize
for the overall period owed majorly to the high
shock in the area cultivated under maize production
in the studied area.
Furthermore, in determining the exact direction of
the production instability (CDII), it was observed
that fluctuation in the production of maize was
marked by moderate instability during the overall
and pre-SAP periods; and it owed to high
fluctuation in the area alongside low yield
instability for the former and moderate instability
in the area during the latter period. However,
production of maize witnessed moderate instability
during the SAP and it owed to moderate instability
which marked area as yield instability was low
(Table 4). Surprisingly, the fluctuation in the
production of maize during the post-SAP transition
was low and it might be due to the policy effect as
both area and yield witnessed moderate instability.
It was observed that the effect of price volatility
(CII) on production across the transitional periods
was high and it owed to the simultaneous effect of
both area and yield which were high across the
reform phases (Table 4). Therefore, it can be
inferred that the deficit of maize supply affected the
price of maize given that the commodity has
multiple demand purposes.
The empirical evidence showed that variability in
the production of maize between pre-SAP and SAP
periods was majorly due to “interaction between
changes in mean area” alongside “change in area
variance” and “change in the mean area”.
Furthermore, between SAP and post-SAP periods,
the production variability was due to “change in
area variance” (Table 5). However, examining
production variability vis-à-vis the entire
transitional periods, evidence showed “residual
effect” viz. uncertainty which owed to weather
vagaries to be the prime factor which caused
variability in the level of maize production. Thus, it
can be inferred that weather vagaries viz. erratic
rainfall: flood and dry-spell are the majors affecting
maize production in the studied area.
J. Agric. Environ. Sci. Vol. 5 No. 2 (2020) ISSN: 2616-3721 (Online); 2616-3713 (Print)
Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 35
Table 4: Magnitude of area, yield and production instability in maize production (%)
Regimes
Variables
CV
CDII
CII
Pre-SAP
Area (ha)
34.98
25.5378
58.54538
Yield (hg/ha)
24.569
19.7624
46.81379
Production (ton)
28.518
25.61863
71.86593
SAP
Area (ha)
27.931
23.78239
55.07161
Yield (hg/ha)
12.096
11.93772
42.66285
Production (ton)
24.164
19.3463
49.97626
Post-SAP
Area (ha)
28.617
13.14514
44.40454
Yield (hg/ha)
13.944
12.78749
43.68011
Production (ton)
29.304
6.68234
50.43801
Overall
Area (ha)
65.342
40.80607
105.8339
Yield (hg/ha)
27.137
15.20641
45.38572
Production (ton)
76.638
36.67431
116.6881
Source: Authors‟ computation, 2020
Table 5: Sources of instability in maize production
Source of variance
Pre-SAP to SAP
SAP to Post-SAP
Overall
Change in mean yield
-6.22
14.74
-4.44
Change in mean area
26.63
0.55
0.70
Change in yield variance
-3.97
-0.31
-0.85
Change in area variance
38.64
56.90
33.56
Interaction between changes in mean yield and
mean area
-10.40
0.14
-0.86
Change in area yield covariance
16.50
-4.16
1.82
Interaction between changes in the mean area
and yield variance
-64.55
-0.07
0.66
Interaction between changes in mean yield and
area variance
23.13
37.29
-29.16
Interaction between changes in the mean area
and yield and change in area-yield covariance
69.42
-1.80
-1.47
Change in residual
10.81
-3.28
100.04
Total change in variance of production
100.00
100.00
100.00
Source: Authors‟ computation, 2020
Table 6: Instantaneous source(s) of change in maize production (Intra-wise %)
Source of change
Pre-SAP
SAP
Post-SAP
Overall
Area effect
100.6434
68.65467
53.38427
66.07131
Yield effect
335.822
47.30645
71.24788
59.07376
Interaction effect
-336.536
-15.957
-24.6304
-25.1439
Total change
100
100
100
100
Source: Authors‟ own computation, 2020
Table 7: Sources of change in maize production (Inter-regime wise %)
Source of change
Pre-SAP to SAP
SAP to Post-SAP
Area effect
6.15
66.45
Yield effect
73.42
26.43
Interaction effect
19.41
7.57
Covariance effect
1.02
-0.46
Total change
100
100
Source: Authors‟ computation, 2020
J. Agric. Environ. Sci. Vol. 5 No. 2 (2020) ISSN: 2616-3721 (Online); 2616-3713 (Print)
Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 36
3.3 Source(s) of change in the production level
For the instantaneous sources of change in the
average annual production status of maize, the
empirical evidence showed “yield effect” to be the
major source of growth in maize production during
the post-SAP period. However, both “yield effect”
and “interaction effect” affected the production
growth of maize during the pre-SAP transition with
the former increasing the production growth while
the latter plummeting the production growth of
maize in the studied area. For the SAP and overall
periods, the average annual production growth was
majorly due to “yield effect” (Table 6). Therefore,
it can be concluded that area expansion
predominates in driving growth in the average
annual production level of maize in Nigeria.
Furthermore, it was observed that “change in the
mean area” was responsible for the production
growth of maize during the SAP period to be
higher than that of the preceding period while
production growth of SAP been lower than that of
the post-SAP owed majorly to “change in mean
yield” (Table 7). This showed that the effect of
innovation viz. improved varieties in the production
of maize during the post-SAP period.
3.4 Farmers’ acreage response
The OLS estimation showed the semi-logarithm
functional form to be the best fit for the specified
equation among all the estimated functional forms
given that it satisfied the economic theory,
statistical criterion and econometric criterion. The
diagnostic tests showed the residual to be devoid of
heteroscedasticity, serial correlation, and Arch
effect and are normally distributed as indicated by
their respective test statistics which were different
from zero at the plausible margin of 10%
probability level. In addition, the specified equation
is adequate, the data has no structural break and
there is no change in the parameter(s) estimates as
indicated by their respective test statistics which
were not different from zero at 10% degree of
freedom. Thus, the parameter estimates of the best
fit functional form are reliable for future prediction
(Table 8).
The coefficient of multiple determination been
0.9394, means that 93.94% of the variation in the
current acreage under maize production is been
determined by explanatory variables included in
the model while disturbed economic reality
accounted for 6.06%. The parameter estimates that
influenced the current acreage under maize
production are weather index, lagged maize
producer price, lagged yield risk of maize, lagged
price risk of maize, time index and lagged area of
maize as indicated by their respective t-statistics
which were different from zero at the acceptable
margin of 10% degree of freedom.
The negative significant of the weather index
implied that poor weather condition i.e. weather
vagaries viz. flood and drought decreased the
current acreage allocated to maize production. In
addition, non-remunerative of the producer price of
the studied crop discouraged maize producers as
indicated by the negative significant of the
estimated parameter, thus, this made the farmers
shift to the production of the alternative crop(s) that
fetched remunerative price. This price disincentive
is due to the importation of maize into the country,
thus dampening the price of the locally produced
maize. This price disincentive made the farmers
decrease the current acreage cultivated under maize
production. Thus, government price support
measures were not in the right direction to attain
the desired goal of higher maize production in the
studied area. The short-run elasticity showed the
acreage responsiveness of the current area to price
change to be -0.66. A negative acreage response is
not an uncommon feature as previous studies viz.
Sadiqet al. (2017) observed negative price
coefficients for maize and bajra in Rajasthan,
India. In addition, in a related study, Sadiqet al.
(2019) reported a negative price coefficient for
cowpea in Nigeria. Furthermore, if given a
sufficient time for adjustment, the acreage
responsiveness of maize to a price change in the
long-run will be -1.18, as indicated by the long-run
elasticity (LRE) index. Thus, it can be inferred the
impact of price policy on this crop would be high
in the long-run given that the crop showed a high
elasticity. It was observed that maize required a
moderate time viz. 3.65 years for the price effect to
materialize. The moderate is the time for an
adjustment; the less effective would be the price
policy instrument in bringing desired change in the
supply of maize in the studied area.
It was observed that the farmers were risk-averse to
yield fluctuation while they had risk preference for
variability in maize price as indicated by negative
and positive significances of the former and latter
respectively. Thus, risk aversive attitude of the
farmers towards yield variability affected farmers‟
J. Agric. Environ. Sci. Vol. 5 No. 2 (2020) ISSN: 2616-3721 (Online); 2616-3713 (Print)
Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 37
current acreage allocation decision while farmers‟
risk preference for price fluctuation encouraged
them to increase the current acreage cultivated
under maize in the studied area. The downward
fluctuation in the price of maize led to an increase
in the production of maize.
Furthermore, the empirical evidence revealed that
economic policies in the country viz. innovations,
subsidies, credit policies etc. had a positive impact
on the farmers‟ current acreage allocation decision,
thus encouraged them to increase the current area
cultivated under maize production as evidenced by
the significance of the time index parameter
estimate. It was observed that the rate of
adjustment of the area under maize cultivation was
moderate as indicated by the estimated adjustment
coefficient of 0.44. In addition, it can be inferred
that there is less rigidity in the adjustment of area
cultivated under maize as indicated by the positive
significant of the lagged acreage parameter
estimate.
J. Agric. Environ. Sci. Vol. 5 No. 2 (2020) ISSN: 2616-3721 (Online); 2616-3713 (Print)
Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 38
Table 8a: Farmers’ acreage response
Items
Linear
t-stat
Exponential
t-stat
Semi-log (+)
t-stat
Double-log
t-stat
Intercept
293697(530606)
0.553
NS
13.711(0.42285)
32.43***
−2.65e+7(8.65e+6)
3.070***
−1.6237(3.4182)
0.475
NS
MP
t-1
11.900(48.348)
0.246
NS
7.283e-6(2.43e-5)
0.299
NS
−2.09e+6(1.07e+6)
1.956*
−0.6887(0.4215)
1.634
NS
RP
t-1
−38.017(33.718)
1.127
NS
−3.06e-5(1.89e-5)
1.614
NS
1.11e+6(702400)
1.580
NS
0.04918(0.2777)
0.177
NS
SP
t-1
−50.761(49.27)
1.030
NS
−4.68e-5(2.69e-5)
1.739*
499560(851103)
0.587
NS
0.2635(0.3365)
0.783
NS
MLP
t-1
69.618(78.91)
0.882
NS
6.47e-5(4.21e-5)
1.536
NS
312584(1.05e+6)
0.298
NS
0.28005(0.4139)
0.676
NS
MPR
t-1
−40.580(54.487)
0.744
NS
−4.85e-5(2.95e-5)
1.647
NS
517862(210834)
2.456**
0.2057(0.0833)
2.469**
RPR
t-1
82.889(47.254)
1.754*
3.06e-5(2.02e-5)
1.519
NS
18862.4(187954)
0.100
NS
0.01638(0.0743)
0.220
NS
SPR
t-1
−8.599(53.914)
0.159
NS
2.21e-5(3.13e-5)
0.705
NS
−315233(266883)
1.181
NS
−0.1267(0.1055)
1.201
NS
MLPR
t-1
−15.453(70.521)
0.219
NS
2.19e-5(4.46e-5)
0.493
NS
−340934(217434)
1.568
NS
−0.0535(0.0859)
0.622
NS
Y
t-1
71.061(36.031)
1.972*
1.889e-5(2.36e-5)
0.801
NS
951775(793005)
1.200
NS
0.3627(0.3135)
1.157
NS
YR
t-1
18.250(86.94)
0.209
NS
2.46e-5(5.97e-5)
0.412
NS
−149964(84660.3)
1.771*
−0.00668(0.0334)
0.199
NS
T
t
7954.74(16022.2)
0.496
NS
0.0031(0.0145)
0.210
NS
104752(33323.4)
3.143**
0.0129(0.0132)
0.980
NS
WI
t
−1.01e+6(604636)
1.668
NS
−0.60292(0.30384)
1.984*
−1.16e+6(663972)
1.740*
−0.0628(0.2625)
0.239
NS
A
t-1
0.9375(0.0739)
12.67***
4.30e-7(8.35e-8)
5.151***
1.39e+6(295203)
4.697**
0.8937(0.1167)
7.658**
R
2
0.9546
0.8852
0.9394
0.9434
F-stat
234.69***
71.73***
34.59***
37.24***
Autocorrelation
1.44{0.245}
NS
Arch effect
4.66{ 0.19}
NS
Heteroscedasticity
12.69{ 0.47)
NS
Normality
2.71{ 0.25}
NS
RESET test
2.51{ 0.12}
NS
Chow test
3.71{0.827}
NS
CUSUM test
-0.136{0.892}
NS
Source: Authors‟ own computation, 2020
Note: *** ** *
NS
means significant at 1%, 5%, 10% probabilities and Non-significant respectively.
Values in ( ) and { } are standard error and probability level respectively
J. Agric. Environ. Sci. Vol. 5 No. 2 (2020) ISSN: 2616-3721 (Online); 2616-3713 (Print)
Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 39
Table 8b: Short and long-run elasticity estimates
Variables
Mean
Marginal Effect
SRE
LRE
MP
t-1
20809.2
-100.213
-0.6622
-1.18307
RP
t-1
22954.42
48.35845
0.352492
0.629755
SP
t-1
18356.96
27.21366
0.158635
0.283413
MLP
t-1
18331.15
17.05207
0.099261
0.177337
MPR
t-1
3156.401
164.0673
0.164446
0.293797
RPR
t-1
3283.017
5.745447
0.00599
0.010701
SPR
t-1
3547.603
-88.858
-0.1001
-0.17884
MLPR
t-1
3141.25
-108.535
-0.10826
-0.19342
Y
t-1
13467.21
70.67352
0.302235
0.539967
YR
t-1
1144.526
-131.027
-0.04762
-0.08508
T
t
27
3879.704
0.033264
0.059429
WI
t
0.997916
-1157702
-0.36686
-0.65543
A
t-1
3057552
0.453458
0.440272
0.786581
Source: Authors‟ own computation, 2020
Table 9: ARIMA model
Items
Production (ton)
Area (ha)
Yield (hg/ha)
ADF
Level
-2.005
nst
-2.171
nst
-1.890
nst
1
st
Diff
-6.846
st
-4.775
st
-6.913
st
KPSS
Level
2.581
nst
2.176
nst
2.276
nst
1
st
Diff
0.1712
st
0.0916
st
0.0293
st
ADF-GLS
Level
-1.651
nst
-1.696
nst
-1.241(0.197)
nst
1
st
Diff
-4.411
st
-4.646
st
-1.026(2.57e-5)
st
ARIMA (1,1,1)(AIC)
1684.31
1660.15
1022.75
+
ARIMA (1,1,0)(AIC)
1682.64
+
1659.20
1033.49
ARIMA (0,1,1)(AIC)
1682.68
1658.61
+
1031.52
Autocorrelation test
0.462(0.793)
NS
1.219(0.543)
NS
1.298(0.254)
NS
Arch LM test
1.838(0.606)
NS
0.893(0.826)
NS
1.983(0.575)
NS
Normality test
6.037(0.048)*
22.19(1.51e-5)***
2.303(0.316)
NS
Source: Authors‟ computation, 2020
Note: ADF-GLS and KPSS tau critical levels at 5% probability are -3.03 and 0.462 respectively.
*** ** *
NS, nst&st
means significant at 1, 5, 10%, Non-significant, non-stationary and stationary respectively
Table 10: One step ahead forecast of maize production
Period
Production (ton)
Area (ha)
Yield (hg/ha)
Actual
Forecast
Actual
Forecast
Actual
Forecast
2014
10058968
8549160
6346551
5806963
15850
16150.48
2015
10562050
10324339
6771189
6571066
15599
16848.14
2016
11547980
10744958
6579692
6885711
17551
16792.65
2017
10420000
11766024
6540000
6530214
15933
17864.9
2018
10155027
10484217
4853349
6592849
20924
17120.53
Source: Authors‟ computation, 2020
Table 11: Validation of models
Variable
R
2
RMSE
RMSPE
MAPE
RMAPE (%)
Theil‟s U
Production(ton)
0.987971
724085.5
49147.4
-126896
-1.39099
0.999683
Area (ha)
0.94096
794940.6
128724.1
-367122
-7.47741
1.014577
Yield (hg/ha)
0.983918
2016.634
211.6839
276.156
0.473083
0.800314
Source: Authors‟ computation, 2020
J. Agric. Environ. Sci. Vol. 5 No. 2 (2020) ISSN: 2616-3721 (Online); 2616-3713 (Print)
Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 40
3.5 Production forecast of maize (2019-2030)
The conventional unit root tests viz. ADF and
KPSS showed that at a level all the variables were
non-stationary as indicated by their respective tau-
statistics which were not different from zero at 5%
t-critical level. But after the first difference, they
became stationary as their respective tau-statistics
were different from zero at 5% t-critical level. In
addition, in order to validate the results of the
classical unit root tests, the neo-classical unit root
test viz. ADF-GLS was applied to the variables and
it showed a similar result, thus indicating the
reliability of the variables for future prediction.
Furthermore, for the forecasts, results of ARIMAs
at different levels showed ARIMA (1, 1, 0),
ARIMA (0, 1, 1) and ARIMA (1, 1, 1) to be the
best fit to forecast production, area and yield. In
addition, residuals of the chosen ARIMAs had no
problem of serial correlation and Arch effect; and,
were normally distributed as indicated by their
respective t-statistics which were not different from
zero at the plausible margin of 10% (Table 9).
Furthermore, through the one-step-ahead forecast,
the validity of the predictive power of the chosen
ARIMAs and how closely they could track the path
of the actual observations were verified (Table 10).
In addition, it was observed that the chosen
ARIMAs were reliable for prediction as indicated
by their respective Theil‟s inequality coefficient
(U) and the relative mean absolute prediction error
(RMAPE) which were less than 1 and 5%
respectively (Table 11). Thus, the selected
ARIMAs can be used for ex-ante projection with
high projection validity and consistency as the
predictive error associated with the estimated
equations in tracking the actual data (ex-post
prediction) are insignificant and low.
The results of the one-step-ahead-out of the sample
forecast for the period 2019 to 2030 showed that
gentle increase i.e. arithmetic rate increase would
permeate the future production trend of maize
(Table 12 and Figure 5). Also, area and yield
forecasts would be marked by the same trend that
marked production, thus, the simultaneous effect of
area and yield would drive the production trend of
maize in the country (Table 12 and Figure 6 & 7).
Furthermore, even the optimistic production level
is not good enough to balance the supply and
demand for maize in the country given that it serves
both domestic and industrial purposes. Therefore, it
can be inferred that a deficit in the supply of maize
looms ahead and will owed to poor productivity,
thereby affecting the food security of maize in the
studied area. Thus, onus lies on the policymakers to
invest adequately in the area of technology and
infrastructure so as to contain the supply deficit
affecting domestic maize consumption in the
country.
J. Agric. Environ. Sci. Vol. 5 No. 2 (2020) ISSN: 2616-3721 (Online); 2616-3713 (Print)
Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 41
Table 12: Out of sample forecast of the variables
Year
Production (ton)
Area (ha)
Forecast
Pessimistic
Optimistic
Forecast
Pessimistic
Optimistic
2019
10282044.58
9125030.68
11439058.48
4339397.05
3403175.74
5275618.36
2020
10437586.91
8740742.80
12134431.02
4389075.22
2835674.17
5942476.27
2021
10595204.95
8489231.21
12701178.70
4438753.39
2451393.50
6426113.27
2022
10752974.04
8305100.82
13200847.26
4488431.56
2146179.69
6830683.43
2023
10910754.12
8163187.53
13658320.72
4538109.73
1888074.20
7188145.26
2024
11068535.01
8050892.39
14086177.62
4587787.90
1662171.25
7513404.55
2025
11226315.95
7960858.53
14491773.36
4637466.07
1460080.78
7814851.36
2026
11384096.89
7888348.42
14879845.36
4687144.24
1276525.27
8097763.21
2027
11541877.84
7830098.94
15253656.73
4736822.41
1107929.16
8365715.66
2028
11699658.78
7783749.21
15615568.36
4786500.58
951737.13
8621264.03
2029
11857439.73
7747525.74
15967353.72
4836178.75
806047.83
8866309.67
2030
12015220.67
7720056.15
16310385.19
4885856.92
669401.10
9102312.75
Year
Yield (hg/ha)
Forecast
Pessimistic
Optimistic
2019
19687.08
16295.60
23078.55
2020
19164.49
15378.73
22950.25
2021
18996.22
15119.59
22872.84
2022
19003.69
15105.04
22902.35
2023
19098.34
15194.29
23002.40
2024
19236.23
15330.85
23141.61
2025
19395.57
15489.86
23301.28
2026
19565.54
15659.75
23471.33
2027
19740.79
15834.98
23646.60
2028
19918.66
16012.85
23824.48
2029
20097.83
16192.01
24003.65
2030
20277.64
16371.83
24183.46
Source: Authors‟ computation, 2020
Figure 5: Production forecast of maize (2019-2030)
4e+006
6e+006
8e+006
1e+007
1.2e+007
1.4e+007
1.6e+007
1.8e+007
1995 2000 2005 2010 2015 2020 2025 2030
Production (Tons)
Years
95 percent interval
Production
forecast
J. Agric. Environ. Sci. Vol. 5 No. 2 (2020) ISSN: 2616-3721 (Online); 2616-3713 (Print)
Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 42
Figure 6: Area forecast of maize (2019-2030)
Figure 7: Yield forecast of maize (2019-2030)
4. Conclusions and Recommendation
The empirical evidence showed area expansion to
be the major factor which drives the increasing
trend exhibited by maize production. Generally, the
growth rate of maize production was not
sustainable as the growth rate of area expansion,
which was more pronounced and the yield growth
rate was marginal. It was observed that “area
effect” was the major factor which made
production growth of SAP to exceed that of the
pre-SAP period, while “yield effect” was the prime
factor which made the production growth of maize
during the post-SAP period to be higher than the
production level of SAP transition. Furthermore,
uncertainty viz. weather vagaries were the major
factors which caused fluctuation in the production
of maize in the study area. In addition, the
allocation decision of the farmers was affected by
weather vagaries and poor remunerative producer
price of maize. The future supply of maize cannot
guarantee maize food security in the country as
production growth will be premised on area
expansion at the expense of productivity.
Therefore, this study calls on policymakers to
adopt area-risk and uncertainty- smart agriculture
minimizing policies to boost maize production in
order to achieve sustainable production in the
studied area.
Conflict of interest
The authors declared that there is no conflict of
interest for publication of the manuscript in this
journal.
References
Ahmed, S.I., and Joshi, M.B. (2013). Analysis of
instability and growth rate of cotton in three
0
1e+006
2e+006
3e+006
4e+006
5e+006
6e+006
7e+006
8e+006
9e+006
1e+007
1995 2000 2005 2010 2015 2020 2025 2030
Area (Hectares)
Years
95 percent interval
Area
forecast
10000
12000
14000
16000
18000
20000
22000
24000
26000
1995 2000 2005 2010 2015 2020 2025 2030
Yield (Hectogram)
Years
95 percent interval
Yield
forecast
J. Agric. Environ. Sci. Vol. 5 No. 2(2020) ISSN: 2616-3721 (Online); 2616-3713 (Print)
Journal of the College of Agriculture & Environmental Sciences, Bahir Dar University 43
districts of Marathwada. International Journal
of Statistika and Mathematika. 6(3):121-124
Agricdemy (2020).Farming in Nigeria. Accessed
on 11/05/2020 fromhttps://agricdemy.com
/post/maize-farming-nigeria
Anonymous (2020).Culled from https://www.
agriculturenigeria.com/production/crop-
production/general-crops/maize/
Ayalew, B. (2015). Supply response of maize in
Ethiopia: co-integration and vector error
correction approach. Trends in Agricultural
Economics. 8 (1):13-20
Boyal, V.K., Pant, D.C., and Mehra, J. (2015).
Growth, instability and acreage response
function in the production of cumin in
Rajasthan. The Bioscan. 10(1):359-362
Coppock, J.D. (1962). International Economic
Instability. McGraw-Hill, New York, pp 523-
525.
Cuddy, J.D.A., and Valle, P.A.D. (1978).
Measuring the instability of time series data.
Oxford Bulletin and Economic Statistics.
40:53-78.
Gujarati, D., Porter, D., and Gunasekar, S. (2012).
Basic Econometrics. McGraw Hill, New Delhi.
Hazell, P.B.R. (1982). Instability in Indian food
grain production.Research Report 30,
Washington, D.C., USA: International Food
Policy Research Institute.
IITA (2020). Maize production. Accessed on
11/05/2020 from https://www.iita.org/crops
new/maize/
Kumar, N.S, Joseph, B., and Muhammed, J.P.K.
(2017).Growth and Instability in Area,
Production, and Productivity of Cassava
(Manihotesculenta) in Kerala.International
Journal of Advanced Research, Ideas and
Innovations in Technology. 4(1): 446-448.
Nerlove, M. (1958). The Dynamics of Supply:
.
Baltimore, USA: John Hopkins
Paul, R.K. (2014). Forecasting wholesale price of
pigeon pea using long memory time-series
models. Agricultural Economics Research
Review. 27(2): 167-176.
Sadiq, M.S., Singh, I.P., and Karunakaran, N.
(2017). Supply response of cereal crop farmers
to price and non-price factors in Rajasthan
state of Nigeria. Journal of Agricultural
Economics and Rural Development. 3(2): 203-
210
Sadiq, M.S., Singh, I.P., Ahmad, M.M., and
Hafizu, M.S. (2019).Roadmap to self-
sufficiency ofcowpea production in Nigeria. Ife
Journal of Agriculture. 31(3):60-76
Sandeep, M.V., Thakare, S.S. and Ulemale, D.H.
(2016).Decomposition analysis and acreage
response of pigeon-pea in western Vidarbha.
Indian Journal of Agricultural Research,
50(5): 461-465
Sihmla, R. (2014). Growth and instability in
agricultural production in Haryana: A District
level analysis. International Journal of
Scientific and Research Publications, 4:1-12.
Stalling, J.L. (1960). Weather indexes. Journal of
Farm Economics. 42: 180-186.
Umar, S.M., Suhasini, K., Jainuddin, S.M. and
Makama, S.A. (2019). Sources of growth and
instability in cassava production in Nigeria:
evidence from Hazell‟s Decomposition
Model. SKUAST Journal of Research,
21(1): 86-95
Umar, S.M., Suhasini, K., Sadiq, M.S. and Aminu,
A. (2017). Growth and Instability in Yam
Production in Nigeria: An Inter Zone and State
Level Analysis. Dutse Journal of Agriculture
and Food Security. 4(1):10-24
