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AN INTEGRATED MODEL FOR FORECASTING COTTON PRODUCTION,
DOMESTIC CONSUMPTION, GROWTH RATE, AREA HARVESTED,
EXPORTS AND IMPORTS IN PAKISTAN UP TO 2050
Farough Ali Shah*1, Farrukh Aziz2, Ali Ahmad Shah3, Ali Imran Hashmi4
*1,2Dept.
Engineering Management, Riphah International University Lahore, Pakistan.
3Institute
4Department
of Horticultural Sciences, University of Agriculture Faisalabad, Pakistan.
of Agribusiness and Applied Economics, Muhammad Nawaz Shareef Agriculture
University Multan, Pakistan.
ABSTRACT
Cotton is one of the most commonly consumed agricultural crop in the Indian subcontinent, especially in
Pakistan. It is difficult to find literature regarding cotton forecasting taking into account different
variables like production, consumption, growth rate, area harvested, exports and imports. Although some
researchers have already focused on cotton production forecasting. Time-series modelling has been
adopted with only historical data on cotton maximum up to 20 years, while several influential factors
have been neglected. This paper proposes that less historical data cannot provide complete information
and lacks the power to forecast future values precisely; thus, a simple but powerful model is derived that
includes economic and social factors using historical data from 1972 to 2020 which is the data of 48 years
and haven’t been used including all other variables as well. The values are forecasted up to 2050 using
Smooth exponential model of forecasting.
Keywords: Forecasting, Social Factors, Economic Factors, Damped Trend, Smooth Exponential.
I.
INTRODUCTION
Currently, Cotton is the world's top plant fibre crop and commercially grows in temperate and tropical
areas in over 50 countries (Smith 1999), covering 34 million hectares. The primary factors that limit crop
productivity include abiotic stresses, especially water deficit, salinity and extreme temperatures, which
account for a reduction in yields in worldwide excess of 50 percent (Boyer 1982). Increasing demand in
food, fibre, biological materials, and sustainable farming is being created by the quick growth in the
world's population combined with a general increase in global prosperity and decrease in arable land
(Ragauskas et al., 2006). Further enhanced use of pesticides was the development of some plagues. For
example, the bug in cotton mealy was the main pest, and in cotton growing areas of punjab, the pest in
dusky cotton bug. The pest break has also been amalgamated by insecticide resistance (Holt et al. 2007).
Agriculture in Pakistan is the main economic institution with a share of about 19% in the agricultural
sector, and the main container of the gross domestic product (GoP 2017). The performance of the
agricultural sector is, however, low in relation to possible deficiencies. On the other hand, agriculture's
share of GDP decreases over time. Cotton is a highly private cash crop and the source of raw materials is
often growing significantly in the textile industry. Cotton represents 5,2% of farm added value and 1% of
the gross domestic commodity (2017). Cotton is practised in the Punjab and Sindh provinces of Pakistan,
but it is the leading province of Punjab in the cultivated region and development. As far as Pakistan is
concerned, the main component of the GDP (GoP 2017) is the agriculture economy with an estimated
19% share. The performance of the agricultural sector is, however, low in relation to possible
deficiencies. The share of agriculture in GDP, on the other hand, is declining with time. Cotton is a widely
established cash crop and the source of raw materials is often growing in importance in the textile
industry. Cotton represents 5,2% of farm added value and 1% of the gross domestic commodity (2017).
Cotton is practised in the Punjab and Sindh provinces of Pakistan, but it is the leading province of Punjab
in the cultivated region and development. About 80 percent (Ali et al . 2013) of Punjab 's output come
from Sindh. In both provinces, productivity is below potential despite favourable soil characteristics and
viable environmental conditions. While other resources like pipeworks may be additional sources for
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dealing with irrigation needs, the irrigation system is still in place in both Provinces. The sector of textiles
makes the cotton sector a major contributor to the domestic economy. Pakistan is the world's fourth
largest producer of cotton. Pakistan has missed significantly in 2016-2017 (GOP, 2017), with a target of
10,671 million bales of cotton. This small production was ideal for pesticides, slow marketing and large
seed reductions. In 2014, Watoo and Mugera (19-28 per cent) have announced increases in tube well
owners and purchasers of water, but use of better quality seed is a major obstacle. The availability of
quality crops, fertilisers and irrigation is a small resource for farmers that has a major impact on
production (Chaudhry et al. 2009). Different studies were carried out to determine the factors which
increase production and ultimately benefit farmers. Khan et al. (1986) and Hassan (1991) noted the low
yield per hectare, and eventually the reduction in the farmer benefits, as a result of high costs of inputs, a
scarcity of resources, lack of access to the markets and untrained farmers. The production of cotton in
Pakistan shall be subject to favourable crop prices, fertilisers prices and crop area. Unfortunately,
marketing in Pakistan has collapsed (Carlos et al., 2002). Successful commercialisation can promote and
guarantee the quality at farm level of cotton production. More training and funding can also increase the
market involvement of farmers and improved transport facilities (Ali et al., 2017). The production of
cotton has been enhanced by the education of growers, plant conservation practises, fertilisers and input
availability (Nadeem et al . 2013). Techniques of forecasting are based on the abiotic factor, on simplicity
and precision of prevention models and complete quantitative biological knowledge, pest control ecology,
field studies, arthropod life history. Temperature is a major factor controlling the development of insects
and therefore population numbers or population outbreaks (Weisser et al., 1997). In Pakistan, the Bt
varieties of cotton are the most significant varieties. In Pakistan in 2010 the Bt cotton was officially
marketed. Bt variety was not officially cultivated already before 2010, but Bt cotton has grown rapidly
ever since, to 6.4 million acres in 2011, equivalent to 81% of Pakistan 's total acreage of cotton (James,
2011). BT cotton impacts in Pakistan were analysed in two recent studies, based on data from informal
cultivation in 2007 and 2009 (Ali and Abdulai, 2010). Krishna and Qaim (2012 ) have found that pesticide
decreases in Bt have increased in China and India as farmers gain new technology expertise. Second, only
bollworms but not pests are controlled by Bt. In Pakistan it may well or was especially high in 2010, so
pesticide use could not be reduced as much as anywhere else. Pesticide absorption pressure could be
increased. Thirdly, the best quality may not be all Bt seeds used by farmers. Because Bt seeds have grown
in 2010, inofficial Bt seeds are still available to some farmers before the official marketing process. In Bt,
the cotton yields are also significantly higher than in nonBt plots; 28 percent (Table 2) are the observed
yield differences. This is not because of higher Bt species genetic yield potentials, but because crop losses
are reduced. Bollworms cause significant damage to conventional cotton, despite chemical pesticide
application, that can be controlled more efficiently by Bt technology. The results of lower pesticide use
and higher Bt cotton outcomes are consistent with earlier studies for Pakistan (Ali and Abdulai, 2010). In
particular, the use of the production function and the harm control framework was shown more explicitly
in previous studies of Bt cotton in different countries (Calderón and Zilberman, 2003). Bt offers farmers
in Pakistan agronomic and financial advantages. However, farmers may suffer from selection prejudices
as farmers choose themselves among the group of adopters (Crost et al. 2007). Many research studies
have predicted the production of different crops in Pakistan. Provisions for rice production, planned
output of wheat, forecast wheat production , supply of wheat and projected demand, expected mango,
food crop planning actions in SAARC countries, wheat, rice crop planning and Ahmad et al. (2017). Cotton
plants, excluding Ali et al., are expected to be poor in Pakistan In comparison with sugarcane (2015),
expected. This forecast thus provides an overview of Pakistan's region and cotton production. In this
study, Punjab and Pakistan's field of cotton, production and output were analysed over time. This study
showed also how cotton is predicted and how future developments are visualised in Punjab and Pakistan.
Output forecasts are an important input for policymaking and production planning efficiency that is
believed to be useful in future years in the planning of cotton production policy.
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MATERIALS AND METHODS
Study was based on secondary data of cotton crop and data regarding area, production, consumption,
growth rate, import and export of cotton in entire Pakistan were taken from U.S department of
agriculture. The forecasting technique which was used is damped exponential forecasting using timeseries unseasonal data. To forecast the values, 2019 was set as base year while following formula was
applied. Using the naïve method, all forecasts for the future are equal to the last observed value of the
series,
̂
For h=1, 2…
̂
For h=1, 2…
(
̂
)
(
)
Where 0≤α≤1 is the smoothing parameter. The one-step-ahead forecast for time T+1 is a weighted
average of all of the observations in the series
. The rate at which the weights decrease is
controlled by the parameter α.
III.
FORECAST EQUATION
̂
(
)
Level equation
(
)(
)
Trend equation
(
)
(
)
Where ℓt denotes an estimate of the level of the series at time t, denotes an estimate of the trend
(slope) of the series at time t, α is the smoothing parameter for the level, 0≤α≤1, and β is the smoothing
parameter for the trend, 0≤ ≤1.
As with simple exponential smoothing, the level equation here shows that ℓt is a weighted average of
observation and the one-step-ahead training forecast for time t, here given by
+
. The trend
equation shows that is a weighted average of the estimated trend at time t based on − −1 and −1,
the previous estimate of the trend.
On the basis of previous data the productivity of next 31 years was forecasted by keeping 2019 as base
year. Various issues, problems and factors affecting the productivity were kept constant such as climatic
variability, unapproved varieties, and non-adoption of improved practices, market fluctuations,
production risks and adulterated inputs.
IV.
RESULTS
There were different test were performed during this research like regression analysis, correlation
analysis and then variables were forecasted by the method of trend exponential smoothing technique
using time-series data. This technique gave best results as the data was non-seasonal. In table 1 we
can the data has been given which was derived from U.S. Department of agriculture and the site which
was
used
is
index
mundi.
The
data
contains
on
production
per
1000 480 lb. Bales, area harvested per 1000(HA), Domestic consumption per 1000 480 lb , growth rate,
exports per 1000 480 lb and imports per 1000 480 lb.
Market
Year
Production
1972
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3100
Growth
Rate
-4.59%
Area
Harvested
2010
Domestic
Consumption
2382
Exports
Imports
822
4
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1973
2909
-6.16%
1845
2377
196
2
1974
2802
-3.68%
2031
1912
1060
1
1975
2269
-19.02%
1851
2146
418
0
1976
1921
-15.34%
1865
1846
65
2
1977
2539
32.17%
1843
1997
471
1
1978
2132
-16.03%
1902
1890
246
4
1979
3417
60.27%
2023
2000
1177
4
1980
3280
-4.01%
2109
2028
1489
5
1981
3434
4.70%
2214
2329
1096
5
1982
3782
10.13%
2263
2437
1272
4
1983
2271
-39.95%
2221
2174
377
240
1984
4630
103.87%
2242
2671
1260
9
1985
5587
20.67%
2364
2524
3146
6
1986
6062
8.50%
2505
3173
2870
3
1987
6744
11.25%
2568
3810
2358
4
1988
6551
-2.86%
2508
3726
3780
5
1989
6687
2.08%
2599
4880
1371
17
1990
7522
12.49%
2662
5748
1357
2
1991
10000
32.94%
2836
6582
2059
20
1992
7073
-29.27%
2836
6734
1175
24
1993
6282
-11.18%
2805
6784
318
350
1994
6250
-0.51%
2650
6800
148
696
1995
8272
32.35%
2998
7223
1433
122
1996
7319
-11.52%
3149
7023
119
279
1997
7175
-1.97%
2960
7212
380
120
1998
6863
-4.35%
2923
7025
10
925
1999
8776
27.87%
2983
7675
415
475
2000
8379
-4.52%
2928
8125
582
470
2001
8286
-1.11%
3116
8525
180
865
2002
7972
-3.79%
2794
9425
231
872
2003
7845
-1.59%
2989
9625
170
1805
2004
11138
41.98%
3192
10525
558
1756
2005
9850
-11.56%
3101
11525
288
1615
2006
9580
-2.74%
3250
12025
217
2305
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2007
8550
-10.75%
3000
12025
269
3907
2008
8540
-0.12%
2900
11125
357
1917
2009
9240
8.20%
3000
10425
725
1574
2010
8640
-6.49%
2800
9925
680
1443
2011
10600
22.69%
3000
10025
1160
900
2012
9300
-12.26%
3000
10775
450
1800
2013
9500
2.15%
2900
10425
510
1200
2014
10600
11.58%
2950
10625
510
950
2015
7000
-33.96%
2900
10325
250
3300
2016
7700
10.00%
2500
10325
125
2450
2017
8200
6.49%
2700
10925
160
3400
2018
7600
-7.32%
2300
10725
60
2850
2019
8000
5.26%
2500
10725
75
2900
V.
REGRESSION ANALYSIS
Regression analysis is carried out in order to analyse the relationship between the variables. The
regression analysis was performed to check the dependency of production variable with the other
variables i.e. growth rate, domestic consumption, imports and exports. As shown in table 2. All variables
had strong relationship with the production with highly significant value.
The Regression Analysis results showed that growth rate was positive and significant predictor of
Production (b = 13.243, t = 4.552, p =.000). Area harvested was positive and significant predictor of
Production (b = 1.132, t = 3.073, p =.004). Domestic consumption was positive and significant predictor of
Production (b = 0.811, t = 12.195, p =.000). Exports were positive and significant predictor of Production
(b = 0.765, t = 7.943, p =.000). Imports were positive and significant predictor of Production (b = -0.758, t
= -5.635, p =.000).
Standardized
Unstandardized Coefficients Coefficients
B
Growth Rate
Std. Error
Beta
T
Sig.
13.243
2.909
.120
4.552
.000
1.132
.368
.182
3.073
.004
Domestic Consumption
.811
.066
1.116
12.195
.000
Exports
.765
.096
.247
7.943
.000
Imports
-.758
.135
-.317
-5.635
.000
Area Harvested
a. Dependent Variable: Production
VI.
CORRELATION ANALYSIS
As shown in Table 3 Growth rate was positively associated and correlated Production (r = .102, p = 0.01).
Area harvested was positively associated and substantially correlated to Production (r=0.906** p = 0.01).
Domestic consumption was positively associated and substantially correlated to Production (r = .883*, p
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= 0.01). Exports were positively associated and substantially correlated to Production (r = -.082, p =
0.01). Imports were positively associated and substantially correlated to Production (r = -.532, p = 0.01).
Correlations
Growth
Area
Domestic
Rate Harvested Consumption Exports Imports
Production
Production
Pearson
Correlation
1
.102
.906**
.883**
-.082
.532**
Growth Rate
Pearson
Correlation
.102
1
-.045
-.128
.293*
-.192
Area
Harvested
Pearson
Correlation
.906**
-.045
1
.812**
-.150
.443**
Domestic
Pearson
Consumption Correlation
.883**
-.128
.812**
1
-.436**
.813**
Exports
Pearson
Correlation
-.082
.293*
-.150
-.436**
1
-.471**
Imports
Pearson
Correlation
.532**
-.192
.443**
.813**
-.471**
1
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
VII.
FORECASTING
In table 4 we can see the root mean square error and mean absolute percentage error of different
variables. As shown in Table 4 MAPE value of Production is 14.277% which indicates that the data we
forecasted is stable and accurate to use. Meanwhile the other variables like area harvested has MAPE
value of only 4% also, Domestic consumption has value of 6.78% which shows that good prediction of our
results. Same is the case with R-squared value which is giving the best results and showing the prediction
of our data was accurate.
R-squared
RMSE
MAPE
Production
.813
1159.438
14.277
Area Harvested
.882
148.213
4.226
Domestic Consumption
.985
445.035
6.782
Table 5 shows the future forecasted values of Production, Consumption, and Growth rate, Area Harvested,
Exports and Imports. Smooth exponential technique was used to forecast values of variables up to 2050
based on the values from 1972 to 2019. Different trends were used to forecast values like damped trend
and Holt’s linear trend to get accurate values of forecasting.
Forecasting Values 2020-2050
Year
Production
Domestic Consumption
Growth Rate
Area Harvested
Exports
Imports
2020
8107.4
10702.6
5.05%
2381.8
52.5
3008.3
2021
8196.9
10680.4
4.89%
2267.8
30.7
3071.6
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2022
8286
10666.7
4.74%
2154.2
8.9
3134.9
2023
8374.5
10658.3
4.59%
2041
-12.9
3198.2
2024
8462.6
10653
4.43%
1928.1
-34.8
3261.5
2025
8550.2
10649.8
4.28%
1815.6
-56.6
3324.8
2026
8637.2
10647.8
4.12%
1703.5
-78.4
3388.1
2027
8723.8
10646.6
3.97%
1591.7
-100.2
3451.4
2028
8809.9
10645.9
3.82%
1480.3
-122.1
3514.7
2029
8895.5
10645.4
3.66%
1369.3
-143.9
3577.9
2030
8980.6
10645.1
3.51%
1258.6
-165.7
3641.2
2031
9065.3
10644.9
3.35%
1148.3
-187.5
3704.4
2032
9149.4
10644.8
3.20%
1038.4
-209.4
3767.6
2033
9233.1
10644.7
3.05%
928.8
-231.2
3830.9
2034
9316.3
10644.7
2.89%
819.5
-253
3894.1
2035
9399.1
10644.7
2.74%
710.7
-274.8
3957.3
2036
9481.4
10644.7
2.58%
602.1
-296.6
4020.5
2037
9563.2
10644.7
2.43%
494
-318.5
4083.6
2038
9644.5
10644.6
2.28%
386.2
-340.3
4146.8
2039
9725.4
10644.6
2.12%
278.7
-362.1
4210
2040
9805.9
10644.6
1.97%
171.6
-383.9
4273.1
2041
9885.9
10644.6
1.81%
64.8
-405.8
4336.3
2042
9965.4
10644.6
1.66%
-41.6
-427.6
4399.4
2043
10044.5
10644.6
1.51%
-147.7
-449.4
4462.6
2044
10123.1
10644.6
1.35%
-253.4
-471.2
4525.7
2045
10201.3
10644.6
1.20%
-358.8
-493.1
10201.3
2046
10279.1
10644.6
1.04%
-463.8
-514.9
10279.1
2047
10356.4
10644.6
0.89%
-568.5
-536.7
10356.4
2048
10433.3
10644.6
0.74%
-672.8
-558.5
10433.3
2049
10509.7
10644.6
0.58%
-776.8
-580.4
10509.7
2050
10585.7
10644.6
0.43%
-880.5
-602.2
10585.7
Units
Production
1000 480 lb. Bales
Area Harvested
1000(HA)
Domestic Consumption
1000 480 lb. Bales
Exports
1000 480 lb. Bales
Imports
1000 480 lb. Bales
Table 6 showing us the units of the variables
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Different forecasting trends were observed of variables as shown in fig 1. The production of cotton in
future is going to be increased significantly from 8107.4 to 10585.7 (1000 480 lb. Bales). Domestic
consumption of cotton decreased slightly and then it remained constant. Area harvested was decreased
according to our prediction and that’s alarming situation and it shows the lack of farmers toward growing
cotton due to the lack of government support and lack of better technology and infrastructure. Exports of
cotton gradually increased but after that it decreased significantly which clearly shows that Pakistan
would not remain the exporter of cotton, meanwhile the trend of imports increased with the passage of
time. This shows that Pakistan would be dependent on others to fulfil its cotton needs in spite of being an
agricultural country.
FORECASTING CHART
35000
30000
25000
20000
15000
10000
5000
-5000
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
0
Production
Domestic Consumption
VIII.
Area Harvested
Exports
Imports
CONCLUSION
This study concluded the different variables connected to cotton which clearly showed us the future
picture of cotton position in Pakistan. A lot of results showed in this results tells the situation of cotton in
Pakistan in future up to 2050. Area harvested would be decreased significantly which shows that
Government of Pakistan should take steps to increase the harvesting area of cotton s cotton meets our
daily requirements and cotton industry plays a vital role of in any country’s economy. This research
depicted that the exports of cotton would be almost zero in near future so Government should initiate the
programs to increase the productivity of cotton by engaging farmers and giving them lucrative packages.
Research also emphasized that lack of technology and less profits also are the main cause of farmers
disinterest toward this crop which can be minimized by giving suitable policies. This research showed
that imports would be very high in future which will be ultimately a burden on economy so to minimize
that policies and actions should be taken to increase exports and decrease imports. Although the
production trend was increasing but when we see the predicted values of exports and area harvested in
future we can clearly get the idea that this would not be the case and eventually production would be
decreased so, it is the need of an hour to work on cotton sector more efficiently to save the future of this
crop in Pakistan. There are some limitations as well in this study like damped trend has exponential
smoothing technique has been used in this research, so one can also use artificial neural network to get
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better and efficient results than this. Moreover this study doesn’t explain social factors and governmental
policies in detail that area can be explored more. Overall this study gave us best over view of different
aspects of cotton which can be used to determine the future of cotton industry in Pakistan.
ACKNOWLEDGEMENT
The author wishes to express gratitude to Dr. Nokhaiz Tariq and for his support and advices.
IX.
[1]
[2]
[3]
[4]
[5]
[6]
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