IJERD – International Journal of Environmental and Rural Development (2013) 4-2

Research article
erd

Determinants of Sugarcane Productivity in Pakistan(1981-2011)
ANWAR HUSSAIN*
Pakistan Institute of Development Economics (PIDE), Islamabad, Pakistan
Email: anwar@pide.org.pk
Received 16 December 2012

Accepted 10 June 2013(*Corresponding Author)

Abstract The paper evaluates the impact of water availability, fertilizer consumption andcredit disbursement on sugarcane productivity in Pakistan using time series data over theperiod 1981-2011. The data has been taken from Economic Survey of Pakistan,
Agricultural Statistics of Pakistan and National Fertilizer Development Corporation(NFDC). The study estimates log-log model using ordinary least square method. For
checking the stationarity of the time series data, Augmented Dickey Fuller (ADF) test isused. The empirical findings reveal that the yield elasticities of water availability, fertilizer
consumption and credit disbursement are 0.16, 0.14 and 0.04 respectively. It is
recommended that water availability should be ensured by the government at the grassroot level for higher sugarcane productivity. The farmers should be given support inconsumption of fertilizer. Micro Agriculture credit should be given for sugarcane farmers
for purchasing the inputs, technology and high yielding varieties.
Keywords sugarcane, productivity, water availability, agriculture credit

INTRODUCTION
Sugarcane is one of the important cash crops of Pakistan and is the important source of thelivelihood of the poor community in rural areas. It is used for industries like sugar, chipboard, andpaper. Looking over the statistical picture of sugarcane crop, its share in value added of agricultureand GDP are 3.4 percent and 0.7 percent, respectively. In the year 2008-09, the area undersugarcane crop was 1029 thousand hectares which was lower by 17.1% as compared to the year
2007-08. The production of the sugarcane was 50.00 million tons as compared to production of
63.9 million tons in the year 2007-08, showing 21.7% decrease against the year 2007-08. The
deteriorating situation was mainly due to water shortages and shifting of the area for the cultivationof rice crop. The lack of in time payments to the farmers by the sugar mills was also the major
factor (Government of Pakistan, 2009).
Sugarcane production showed significant fluctuations in the past. In 1960-61, the total area
under sugarcane crop was 388 thousands hectares which has been increased to 1029 thousands
hectares in 2006-07. On the other hand, in 1960-61, the total sugarcane production was 11641
thousands tonnes which has been increased to 54742 thousands tonnes in 2006-07. The total credit
disbursement in the agriculture sector in Pakistan was Rs.103.78 million in 1960-61 which has
been increased to Rs. 168830.46 million in 2006-07. The total fertilizer off-take in 1960-61 was
31.40 thousand nutrient tonnes, which has been increased to 3672 thousand nutrient tonnes in
2006-07 (Government of Pakistan, 2007).
The recent statistics reveals that the area under sugarcane crop is 988 thousands hectares in theyear 2010-11 which is higher against the year 2009-10 of 943 thousands hectares. Similarly thesugarcane production also improved in the year 2010-11 against the year 2009-10. The productionof the sugarcane in the year 2009-10 and 2010-11 was 49,373 thousand tones and 55,309 thousand
tonnes respectively. It is also interesting to note that sugarcane yield in kilograms per hectare has
also been increased to 55,981 kilograms per hectare in the year 2010-11 as compared to 52,357 in© ISERD
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the year 2009-10. Agriculture production in general and particularly sugarcane production can beincreased through applying appropriate agricultural input policy. The major agriculture inputs
mainly credit disbursement, area under cultivation, water availability and fertilizer which mattermuch.
A very limited researchers conducted studies about the econometric analysis of different
aspects of sugarcane crop. Ulveling and Fletcher (1970) estimated Cobb-Douglas production
function using input elasticities as a quadratic function of a capital intensity index and determined
the rate of returns to scale through incorporating the cost function. Alcantara and Prato (1973)
conducted study in Brazil in which they estimated the returns to scale for sugar mills farms. The
findings revealed that the elasticities for labor, land, fertilizer, pesticides and machinery/equipment
were 0.340, 0.285, 0.351, 0.113 and -0.089 respectively. They recommended for government toincrease the efficiency in sugarcane industry and focusing on expansion of sugarcane farms. Bhatti
and Yanagida (1990) developed a supply response model and empirically estimated standard
regression procedures. The principal factors affecting sugarcane supply response were the official
procurement sugarcane for sugarcane paid at the sugar mill gate, the scale of operation and therelative returns to alternative uses of sugarcane.
Farooq et al. (1999) outlined the possible causes of wide spread cultivation of a nonrecommended, high yielding but low in sucrose contents sugarcane variety i.e. Co-1148. It hascreated different problems for both the farmers and sugar mills. Ali et al. (2000) conducted study
about fertilizer-use efficiency and cane yield under different nitrogen levels and weed management
practices in spring planted sugarcane. He pointed out the average fertilizer use efficiency (FUE) to
be 36.10 in weed-free crops compared with 21.94 in weedy crops, with corresponding yields of
99.87 and 75.94 t/ha. FUE ranged between 150 and 225 kg N/ha. Yadav and Yaduvanshi (2001)
studied that the yield of millable cane from the planted sugarcane was affected by fertilizer N ratherintercropped green manuring or plant arrangement. He observed that whether it is the plant cane or
the ratoon crop, the quality of cane juice is not affected in either case. The organic carbon content
and available N in the soils have been increased by residues from the green manures and N
fertilizer treatments. Chattha et al. (2001) evaluated the effect of different production factors on theyield of 15 sugarcane cultivars in Punjab, India. The factors were mainly planting methods and date,soil amendments, irrigation and plant protection. Results revealed that sugarcane yield improved by
21.96% through trench planting, by 43.75% through effective weed control, by 34.50% through the
integrated use of press mud and fertilizers, by 26% through skip furrow irrigation and by 50% withurea application by drilling. Through proper weed management he also observed 32%
improvement in ratoon crop.
Muhammad, et al. (2001) observed that non-adoption of recommended agricultural
technologies was the responsible factor for the low per hectare yield of crops. The data werecollected from 191 sugarcane growers selected through stratified random sampling technique from
16 villages selected by using multistage sampling method. The data suggested that awareness andadoption of sugarcane production practices were very poor. Lack of awareness of recommendations
appeared to be the major cause of non-adoption. Nixon and Simmonds (2004) investigated theimpact of fallowing and green manuring on soil conditions and the growth of sugarcane in
Swaziland. He observed that yields were improved from 129 tonnes per hectare to 141–144 tonnes
per hectare after fallowing and green manuring. He also assessed positive relation between root
length and air-filled porosity. Hussain et al. (2006) made economic analysis of sugarcane
production in Pakistan using time series data ranging from 1990 to 2002. Their findings revealed
that Pakistan has no comparative advantage in producing sugar at export parity prices although
plays role as import substitution. Gana (2008) evaluated the sustainability of organic fertilizer onsugarcane production in Nigeria. He conducted experiments using plots with and without fertilizer
application. The findings revealed that application of organic and inorganic fertilizers matters insugarcane production.
Fernandez1 and Nuthall (2009) conducted a study in the Central Negros area, Philippines onsources of input use inefficiency through data envelope analysis. They have taken individual farms
in which same type of input-output were used for which the mean relative technical, scale andoverall technical efficiencies were determined and these indices were found as 0.7580, 0.9884 and© ISERD
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0.7298, respectively. The findings further revealed that technical inefficiency was the major sourceof overall inefficiencies as compared to scale effect. It was also found that farmer's age andexperience, access to credit, nitrogen fertilizer application, soil type and farm size were positively
associated with overall technical efficiency. Niamatullah et al (2010) assessed the impact of priceof fertilizer and agriculture credit on sugarcane acreage in Khyber Pakhtunkhwa over the period of
1991-92 to 2007-08. Their findings revealed that price of fertilizer and agriculture credit were theinfluencing factors for sugarcane acreage.
OBJECTIVE
The significance of the sugarcane crop in the economy of Pakistan cannot be ignored.
Unfortunately, there is dearth of studies over the issue under consideration. The present study willbridge this gap and attempts to estimate the impact of water availability, fertilizer consumption andcredit disbursement on sugarcane productivity in Pakistan using time series data over the period
1981-2011.
METHODOLOGY
According to the classical economist, output (Q) is a function of labor (L) and the relationship isexpressed as: Q = f(L)The neoclassical economist added the capital (K) component in this production function,
taking the form: Q = f(L,K)
In non-linear form, the relationship is expressed as:
Q = ALαKβeµ(1)This is the conventional Cobb-Douglas (1928) production function. α and β are the output
elasticities of labor and capital respectively. ‘A’ is the rate of technology and µ is the random term,
satisfying all the usual assumptions. Taking natural logarithm to both sides, the equation becomes:
lnQ = lnA + αlnL + βlnK + µ(2)This model can be estimated by applying the ordinary least square method.
The model not only shows the input output relationship but also gives information about therate of returns to scale.
The concern of this study is to evaluate the impact of major determinants of sugarcane
productivity in Pakistan, so the following model is estimated using the ordinary least square
method:ln(SYt) = bo + b1ln(WAt) + b2 ln(FCt) + b3 ln(CDt) + µt

(3)

Where
SY = Sugarcane yield (Kgs/Hectare) in Pakistan
WA = Water availability1 (million acre feet) in Pakistan
FC = Fertilizer consumption for sugarcane crop (000 N/T) in Pakistan
CD = Credit disbursement2 (Rs. in million) in Pakistan‘t’ is the time period and µ is the random term satisfying all the usual assumptions.
The data on these variables has been taken from Economic Survey of Pakistan, Agricultural
Statistics of Pakistan and National Fertilizer Development Corporation (NFDC).
1

The crop specific water consumption data is also not available; therefore, it has been obtained bymultiplying the water availability for Kharif (season) crop by its corresponding weight. The weights usedwere obtained dividing the area under sugarcane crop by total cropped area.
2
The data on crop wise credit disbursement is not available. This variable is constructed by multiplying thetotal credit disbursement by weights and the weights have been obtained by dividing the area undersugarcane crop by total cropped area.

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For checking the reliability of the regression results, equation-3 has been checked for
econometric properties. Augmented Dickey-Fuller (1981) test has been used for checking thestationarity of the data3. Variables which were non-stationary at level have been made stationary
after taking first difference and second difference. To detect the long-term relationship among theseries, the Cointegrating Regression Durbin Watson (CRDW) test4 of Sargan and Bhargawa (1983)
has been used. Furthermore, to check the stability of the coefficients, the cumulative sum andcumulative sum of square residuals have been plotted. A statistical package Eviews is used forderiving the results.
RESULTS AND DISCUSSION
The results given in table 1 showed that the water availability for sugarcane crop has the highest
elasticity (highest contribution towards sugarcane yield) as compared to other determinants. A one
percent increase in the water availability for sugarcane crop increases sugarcane yield by 0.16
percent. The coefficient of WA is statistically significant at 5% and 10% level of significance.
Water availability is the crucial policy variable for policy makers in Pakistan. The worsening
situation of water availability may be a serious threat for sugarcane yields in future. Because, thewater availability has declined from 100 million acre feet in the year 2007-08 to 80 million acre
feet in year 2009-10 (Economic Survey of Pakistan, 2010).
Table 1 Regression results of the determinants of sugarcane productivity
Variable
Constantln (WA)ln(FC)ln(CD)
R-squared
F-statistic
Durbin-Watson stat

Coefficient
9.45
0.16
0.14
0.04
0.87
61.62
1.57

Std. Error
t-Statistic
0.13
73.52
0.07
2.290
0.04
3.819
0.01
3.072
Adjusted R-squared
Prob(F-statistic)

Prob.
0.00
0.03
0.00
0.01
0.86
0.00

The yield elasticity of FC and CD are 0.14 and 0.04 respectively. These coefficients are also
statistically significant and show positive impact on sugarcane yield. Fertilizer also place
importance because its consumption reduced from 340.77 thousands, NT in the year 2001-02 to
314.64 thousands NT in the year 2010-11 (Economic Survey of Pakistan, 2011). Among the otherfactors, fertilizer supply is important which is continuously affected by ongoing power crisis. The
availability of the compressed natural gas, shortage of electricity, destructive rains in Sindh
Province and increasing prices of fertilizer also supplemented the issue.
Credit disbursement issue if tackled well, can enhance sugarcane productivity. The small
farmers not only are deprived from receiving the loans but also these are not given at the time when
it is required for purchasing farm inputs. Further the total supply of agriculture credit reduced by
6.5% and 6% in the years 2009-10 and 2010-11 respectively (Economic Survey of Pakistan, 2011).
In addition, agriculture research and development (R&D) expenditures 5 can also increasesugarcane productivity through introducing high yielding varieties. In Pakistan, it is 0.59% of thetotal GDP in the year 2008 as compared to 2-3% in developed countries (Pakistan Council for
Science and Technology, 2009). According to World Development Report (2008), developing
countries invest only one-ninth of what industrial countries put into agriculture R&D as a share of
agriculture GDP.

3

The Akaike Information Criterion (AIC) has been used to select the optimum ADF lag.
It is used to test the null hypothesis that the value of d is equal to 0.
5
Due to non availability of data, the variable R&D expenditures is not included in the model. Similarly,
having too many complications with using climatic variables, these have not been incorporate in the model.
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The overall test of significance, depicted by the value of F-statistics, favors the model. The fit
is also good shown by the high value of R-square (0.87). The Durbin Watson statistics shows that
there is no serious problem of auto correlation.
The ADF test results shows that ln(SY) and ln(WA) are stationary at level while ln(CD) andln(FC) have been made stationary after taking the first difference (Table 2). This may provide somedoubts to have spurious regression. But the Durbin Watson value 1.57 (Table 1) evidences that thevariables included in the model are cointegrated. Because this value exceeds the critical values
provided by Sargan and Bhargawa6.
Table 2 ADF test results for stationarity (including both intercept and trend)
VariableLn(SY)ln(WA)
Ln(FC)ln(CD)

Test Statistic-4.05[0]-4.04[0]-1.91[0]-1.98[1]I(0)Critical value 5%
-3.50
-3.51
-3.53
-3.50

Test Statistic-6.72[0]-5.69[0]I(1)Critical value 5%
-3.53
-3.50

Results
I(0)
I(0)
I(1)
I(1)

The cumulative sum (CUSUM) and cumulative sum of square (CUSUMSQ) plots show
stability in the coefficients of the model estimated. Both the plots (Fig. 1 and Fig. 2) fall within thecritical bounds of 5 percent representing that the model is stable structurally.
20

10

0

- 10

- 20
86

88

90

92

94

96

98

CU SU M

00

02

04

06

08

10

5% Signif ic a nc e

Fig 1 Plot of cumulative sum of recursive residuals
1.6

1.2

0.8

0.4

0.0

-0.4
86

88

90

92

94

96

98

CUSUM of Squares

00

02

04

06

08

10

5% SignificanceFig 2 Plot of cumulative sum of square residuals

6

According to Sargan and Bhargawa (1983), the critical values at 1%, 5% and 10% level of significance are
0.511, 0.386 and 0.322 respectively, for testing the null hypothesis that the value of d is equal to 0.

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CONCLUSION
It is concluded that the policy variables, water availability, fertilizer consumption and credit
disbursement have positive and significant impact on sugarcane productivity. It is recommended
that the government should make efforts to increase water availability which is becoming
worsening in Pakistan. This may create problem for food security in general and particularly forsugarcane productivity in future. The policy options for this can be increasing investment in thewater and irrigation system. Agriculture micro credit programs should be improved to assist
sugarcane farmers in purchasing the inputs and high yielding varieties. Agriculture R&D
expenditures must be increased for ensuring higher productivity. It is also worth mentioning thatsugarcane productivity can also be increased through appropriate sugarcane input-output policy,
farm subsidies, technology etc but due to data availability constraint, these have not been includedin the analysis.
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