RESEARCH ARTICLE

Energy Consumption Flow and Econometric Models of SugarcaneProduction in Khouzestan Province of Iran

Javad Taghinezhad • Reza Alimardani •

Ali Jafari

Received: 11 February 2013 / Accepted: 6 October 2013

� Society for Sugar Research & Promotion 2013

Abstract The aims of this study were to determine the

energy consumption and evaluate the inputs sensitivity for

sugarcane production in Khouzestan province of Iran. The

data were collected from sugarcane & by-Products Devel-

opment Company during 2010–2011. The results showed

that sugarcane production consumed a total energy of

80,986 MJ ha-1, in which the energy for electricity was

38.89 % followed by fertilizers (14.01 %), diesel (13.46 %)

and seed (12.2 %), respectively. The share of direct, indi-

rect, renewable and non-renewable energies was 61, 39, 21

and 79 %, respectively. The energy use efficiency and

energy productivity were found as 1.38, 1.15 kg MJ-1,

respectively. Cobb–Douglas production function was used

to determine a relation between input energies and yield in

sugarcane production. Results indicated that by using Cobb–

Douglas production function to determine mathematical

relationship between energy input and yield, human labor

energy had the highest impact on yield. The sensitivity

analysis indicated the marginal physical productivity value

of 1.61 for human labor, which indicates that 1 MJ addi-

tional use of seed energy would lead to an increase in yield

by 1.61. The impact of direct, indirect and non-renewable

energies on yield was significant at 1 % level. The benefit-

cost ratio for sugarcane production was calculated at 2.58.

Keywords Sugarcane � Input energy �Sensitivity analysis � Cobb–Douglas � Economic analysis

Introduction

Sugarcane (Saccharum species hybrid) is an important

economic crop in the tropics and sub-tropics due to its high

sucrose content and bioenergy potential (Sampietro et al.

2006). Sugarcane is an important raw material for sugar

industries and provides about 65 % of the sugar produced in

the world (Zambrano et al. 2003). Sugarcane is the feedstock

used in the ethanol industry (Murali and Hari 2011) and has

the potential as a renewable energy source and the highest

rate of energy per hectare (0.5–2 GJ ha-1) (Chen and Chou

1993) having rich typologies of high energetic content by-

products (leaves and tops, bagasse, and molasses). Biomass

of sugarcane is one of the main energy sources that modern

technologies could widely develop. Sugarcane production is

highly labour intensive, requiring about 3,300 man-hours

for doing different operations (Murali and Balakrishnan

2012). Today, sugar is produced in 121 countries and global

production exceeds 120 million tons per annum. Production

of sugarcane occurs in warm, humid climates throughout the

world (Salassi et al. 2002). In Iran, sugarcane is cultivated in

the south of Khouzestan province on an area of about

68,352 ha with an annual production of about 5,685,090

tons (FAO 2010).

The relation between agriculture and energy is very

close. Agriculture is one of the most important sectors

which consumes and supplies energy in the form of bio-

energy (Pahlavan et al. 2011). Effective use of energy in

agriculture is one of the conditions for sustainable agri-

cultural production, since it provides financial savings,

fossil resources preservation and air pollution reduction

(Pahlavan et al. 2011). Energy consumption per unit area in

agriculture is directly related to the development of tech-

nology in farming and the level of production (Hamedani

et al. 2011a). Shortages of energy are a serious constraint

J. Taghinezhad (&) � R. Alimardani � A. Jafari

Department of Agricultural Machinery Engineering, Faculty of

Agricultural Engineering & Technology, University of Tehran,

P.O. Box 4111, 13679-47193 Tehran, Iran

e-mail: j.taghinezhad@ut.ac.ir

123

Sugar Tech

DOI 10.1007/s12355-013-0280-3

on the development of low income countries (IEA 2001).

Shortage of energy is caused or aggravated by widespread

technical inefficiencies, capital constraints and a pattern of

subsidies that undercut incentives for conservation.

Energy use in agriculture has developed considerably

with the introduction of high-yielding varieties, mechanized

crop production practices, increasing populations, limited

supply of arable land and a desire for an increasing standard

of living (Tabatabaie et al. 2012). In all societies, these

factors have supported an increase in energy use to maximize

yields, minimize labor intensive practices or both. Therefore,

it was thought necessary to analyze the energy input and

output in crop production (Pishgar-Komleh et al. 2012).

There is no standard method for computing the energy bal-

ance. Therefore, research efforts have emphasized energy

and economic analysis of various agricultural productions

for planning resources in the ecosystem (Singh et al. 2002).

Several researches attempted to determine energy indi-

ces for various agricultural products and estimated a rela-

tionship between energy input and yield such as sweet

cherry, cherries, citrus, apricot, cotton, sugar beet, green-

house vegetable and some field crops.

The primary goal in agriculture production is to decrease

costs and to increase yield. Also, it is realized that crop

yields and food supplies are directly linked to energy

consumption. Thus the aim of the present study was to

investigate the energy input and output per hectare for the

production of sugarcane in Khouzestan province, Iran and

to evaluate different forms of energy indices (direct, indi-

rect, renewable, and non-renewable) and to make a cost

and economic analysis for sugarcane production, to com-

pare energy sources in sugarcane production and to analyze

the sensitivity of energy inputs on yield. It also identifies

operations where energy savings could be realized by

changing current practices in order to increase the energy

ratio, and proposes improvements to reduce energy con-

sumption for sugarcane production.

Materials and Methods

This study was conducted at Sugarcane & By-Products

Development Company in Khozestan province, Iran, which

includes 7 Agro-Industries situated in southern region of

Iran and there are above 107,000 ha of sugarcane fields.

In order to specify the inputs and output energy of

sugarcane production in different systems, the amounts of

fuel, human labor, machinery, seed, fertilizer and chemical

as inputs and sugarcane yield as output were determined.

In order to calculate the inputs and output energy of

sugarcane production, the amounts of diesel, human labor,

machinery, seed, fertilizer, chemicals and irrigation inputs

and sugarcane yield as output were determined. The

amounts of input were determined per hectare and then,

these input data were multiplied by the coefficient of

energy equivalent. To estimate the energy equivalents’

coefficients previous researches were used (Table 1). The

energy equivalences of unit inputs are given in Mega Joule

(MJ) unit. The total input equivalent can be calculated by

adding up the energy equivalences of all inputs in MJ

(Zangeneh et al. 2010). For example, the amount of energy

consumption (MJ ha-1) for diesel fuel was calculated by

multiplying the quantity of diesel fuel per unit area

(L ha-1) by its energy equivalent (56.31 MJ L-1).

Machinery energy was calculated by the following formula

(Canakci and Akinci 2006):

ME ¼ W� Eð Þ = T � EFCð Þ ð1Þ

where ME is the machine energy input in MJ per hectare; W

is the weight of the implement in kg; E is the production

energy of machine or implements in MJ per kg; T is the

economic life of the implement in h; and EFC is the effective

field capacity in ha per hour. The effective field capacity of

each implement was determined in the operation conditions.

The production energy of a machine (‘E’ in Eq. 1) is

composed of the energy quantity of materials, energy

required in the manufacturing process, the transportation of

the machine to the consumer and the energy sequestered in

repairs (Kitani 1999).

Table 1 Energy equivalents of inputs and outputs in the sugarcane

production system

Unit Energy

equivalent

(MJ unit-1)

Reference

Input

Machinery h 62.7 (Hamedani et al. 2011a;

Pishgar-Komleh et al.

2011a; Bonnie 1987)

Human labor h 1.96 (Singh et al. 2002;

Canakci and Akinci 2006)

Diesel L 47.7 (Ozkan et al. 2004)

Fertilizer

Nitrogen kg 75.4 (Bonnie 1987)

Phosphorus kg 10.9 (Bonnie 1987)

Chemical

Herbicide kg 238 (Ozkan et al. 2004)

Insecticide kg 101.2 (Yaldiz et al. 1993)

Fungicide kg 216 (Pathak and Binning 1985)

Seed kg 1.2 (Ricaud 1980)

Electricity kWh 12.7 (Bonnie 1987)

Water m3 1.02 (Rafiee et al. 2010;

Tabatabaie et al. 2012)

Output

Sugarcane stalks kg 1.2 (Ricaud 1980)

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Based on the energy equivalents of the inputs and out-

put, the energy indices such as energy ratio (energy use

efficiency), energy productivity, specific energy and net

energy were calculated by Eqs. 8–11, respectively. For

growth and development, energy demand in agriculture can

be divided into direct and indirect, renewable and non-

renewable energies. Indirect energy (IE) included the

energy embodied in sugarcane stem cuttings, fertilizers,

chemicals, machinery, while direct energy (DE) covered

human labor, diesel fuel and electricity used in the sugar-

cane production. Non-renewable energy (NRE) includes

diesel fuel, chemical, electricity, fertilizers and machinery

and renewable energy (RE) consists of human labor, sug-

arcane stem cuttings (Mohammadi and Omid 2010). For

economic analysis, total production value, gross return, net

return, benefit to cost ratio and productivity were also

computed by Eqs. 12–16, respectively.

In order to find and analyze the relationship between

energy inputs and yield several mathematical functions such

as linear, linear-logarithmic, logarithmic-linear and second

degree polynomial were tested. Cobb–Douglas function

yielded better estimates in terms of statistical significance

and expected signs of parameters compared to other func-

tions. Cobb–Douglas production function is expressed as:

Y ¼ f xð Þ exp uð Þ ð2Þ

Cobb–Douglas function has been used by several authors

to examine the relationship between energy inputs and yield

(Hamedani et al. 2011b; Ramedani et al. 2011; Pishgar-

komleh et al. 2011a, 2012; Tabatabaie et al. 2012) and can be

linearized and expressed in the following form:

ln Yi ¼ a0 þXn

j¼1

aj ln Xij

� �þ ei . . . i ¼ 1; 2; . . .; n ð3Þ

where Yi denotes the yield of the ith farmer, Xij the vector

of inputs used in the production process, a0 is the constant

term, aj represent coefficients of inputs which are estimated

from the model and ei is the error term. In this study,

with assumption that the yield is a function of energy

inputs, Eq. (3) can be expressed as:

lnYi ¼ a0 þ a1lnX1 þ a2lnX2 þ a3lnX3 þ a4lnX4

þ a5lnX5 þ a6lnX6 þ a7lnX7 þ a8lnX8 þ ei ð4Þ

where Yi denotes the yield level of the ith farmer, and X1,

X2, X3, X4, X5, X6, X7 and X8 are machinery, human labor,

diesel, chemicals, fertilizers, seed, electricity and water for

irrigation, respectively.

The effect of direct, indirect, renewable and non-

renewable energies on production was modeled by using

the following equations (Rafiee et al. 2010):

lnYi ¼ b0 þ b1ln DEi þ b2lnIDEi þ ei ð5Þ

lnYi ¼ c0 þ c1lnREi þ c2lnNREi þ ei ð6Þ

where Yi is the ith farmer’s yield, bi and ci are coefficients

of exogenous variables. DE and IDE are direct and indirect

energies, respectively, RE is renewable energy and NRE is

non-renewable energy. Eqs. (4)–(6)were estimated using

ordinary least square (OLS) technique.

In production, returns to scale (RTS) refer to changes in

output subsequent to a proportional change in all inputs

(where all inputs increase by a constant factor). In the

Cobb–Douglas production function, it is indicated by the

sum of the elasticities derived in the form of regression

coefficients. If the sum of the coefficients is greater than

unityPn

j¼1

aj [ 1, it indicates increasing IRS. That means an

increase in inputs may result in an increase in output in

greater proportion than the input increase.

If the function becomes less than unityPn

j¼1

aj\1, it

indicates decreasing returns to scale (DRS). That means an

increase in inputs may result in an increase in output in less

proportion than the input increase; and if the result is unityPn

j¼1

aj ¼ 1, it shows constant returns to scale, which implies

that despite changing inputs, the output is constant (Singh

et al. 2004).

Marginal physical productivity (MPP) technique based

on response coefficient of inputs was used to determine the

sensitivity of a particular energy input on production. The

MPP of an input indicates the change in the output with a

unit change in that input, keeping all other factors constant

at geometric mean level (Hamedani et al. 2011b).

The MPP of the various inputs was computed using the aj

of the various energy inputs as given by Singh et al. (2004):

MPPxj ¼change in total physical product

change in variable input

¼ GMðYÞGMðXjÞ

� aj ð7Þ

where, MPPxj is the marginal physical productivity of jth

input; aj is the regression coefficient of jth input; GM (Y) is

the geometric mean of yield; GM (Xj) is the geometric

mean of jth input on a per-hectare basis.

Basic information on energy inputs, costs and economic

indices of sugarcane production were entered into Excel

2010 spread-sheets and SPSS 20 software program.

Results

The energy inputs used for sugarcane production, the

quantity of each input per unit area (ha), their energy

equivalents (MJ ha-1), and output energy equivalent are

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shown in Table 2. The last column in Table 2 represents the

respective percentage energy input of the total inputs for

each variable. The results showed that the machinery and

human labor inputs were 149.87 and 254.6 h ha-1, respec-

tively. Diesel fuel (193.6 L ha-1) and total fertilizers

(240.05 kg ha-1) (nitrogen, 135.34 kg ha-1; phosphate,

104.71 kg ha-1) use are provided for the survey area. Total

mean energy used by different farm operations for sugar-

cane production system was 80985.97 MJ ha-1. Mrini et al.

(2001) calculated the energy input for sugarcane production

in Morocco at 104,123 MJ ha-1.They reported that human

labor, fertilizer, and diesel fuel inputs were 1,025 h ha-1

223.6 kg ha-1, and 211.4 L ha-1, respectively.

In most of the reports of other investigations, the energy

input of chemical fertilizers represented the highest pro-

portion of the total energy input in producing sugar beet

(Erdal et al. 2007), potatoes (Zangeneh et al. 2010), and

kiwi fruit (Mohammadi and Omid 2010), while the highest

energy use in sugarcane production belonged to electricity

energy input, which accounted for about 38.89 % of total

energy consumption, followed by fertilizer (14.01 %) and

diesel (13.46 %).The electricity energy was consumed for

irrigation, while the diesel was mainly used for tractors and

various machinery operations. Table 2 also shows that the

consumption of seed, machinery, water, chemicals, and

human labor represented 12.2, 11.6, 8.4, 0.83, and 0.62 %

of the total energy input during sugarcane production,

respectively. The average yield of sugarcane was estimated

at 93,200 kg ha-1,with a calculated total energy output of

111,840 MJ ha-1. Mrini et al. (2001) reported that the total

energy output in sugarcane production was 89,600MJ ha-1

in Morocco. In Queensland, South Africa, Louisiana and

India, the energy output of sugarcane was reported to be

141480, 106800, 73077 and 122400 MJ ha-1 (Mrini et al.

2001; Gifford 1978; Austin et al. 1978; Ricaud 1980;

Sundara and Subramanian 1987). The present study showed

that efforts to increase overall energy efficiency should be

focused primarily on reducing the electricity used for irri-

gation, fertilizers, and fuel consumption. However, signif-

icant reductions in electricity use were not considered to be

practicable because it was used for irrigation and a decrease

in irrigation would cause irrecoverable damage to the

production yield, unless new methods are used for irrigat-

ing sugarcane fields.

Reducing the amount of fertilizer could be possible by

training the farmers and laborers that do not know the

required amount of fertilizer for crop as it has become a

common belief among them that excessive use of fertilizer

will increase the yield (Chauhan et al. 2006). Besides,

reducing diesel fuel use by improving tractor operation per-

formance is both feasible and recommended (Mohammadi

and Omid 2010).The share of seed energy consumption was

calculated about 12.2 % of all energy inputs in sugarcane

production, which can be reduced by applying fewer amounts

of seed per hectare and using high quality seed. Moreover,

qualified seed will help to reduce the chances of pest and weed

infestation, reduce the energy needed in weeding and chem-

ical application and increase the yield. The new planters can

also reduce the amount of seed used for planting. Pahlavan

et al. (2011) reported that the diesel, electricity and chemical

fertilizers are the major energy-consuming inputs for tomato

production in Iran. Ramedani et al. (2011) indicted that the

share of diesel fuel in the energy equivalents was 66.67 % and

was followed by chemical fertilizers and water for irrigation

with 14.32 and 6.18 %, respectively. They also showed that

the total input and output energy use of soybean production

farms was to be 18,026.50 and 71,228.86 MJ ha-1, respec-

tively. Asgharipour et al. (2012) reported fertilizer (28.5 %)

followed by irrigation water (22.1 %) and electricity

(15.6 %)are the most energy-consuming inputs with

42,231.7 MJ ha-1 total energy input and 56,645.4 MJ ha-1

total energy output for sugar beet production. Hamedani et al.

(2011b) indicated the fertilizers, electricity and farmyard

manure contained the highest energy shares with 37.25, 19,

and 17.84 %, respectively. Pahlavan et al. (2011) reported

that the diesel, electricity and chemical fertilizers are the

major energy-consuming inputs for tomato production in

Iran. In sugarcane production in Morocco, the electricity, fuel

and seed contained the highest energy shares with 40, 23 and

12 %, respectively (Mrini et al. 2001).

Table 3 indicated energy indices of sugarcane produc-

tion and the forms of energy inputs as DE and IE and

renewable and non-renewable energy. Energy ratio is one

Table 2 Energy consumption and relationship between energy

input–output in the sugarcane production system

Quantity per

unit area (ha)

Unit Energy

(MJ ha-1)

Percentage

Inputs

Machinery 149.87 h 9,396.655 11.60

Human labor 254.6 h 499.016 0.62

Diesel 193.6 L 10,901.62 13.46

Chemical 668.492 0.83

Herbicides 0.96 kg 228.48 0.28

Insecticides 3.11 kg 314.732 0.39

Fungicides 0.58 kg 125.28 0.16

Fertilizer 11,345.98 14.01

Nitrogen 135.34 kg 10,204.64 12.6

Phosphor 104.71 kg 1,141.339 1.41

Seed 8,231 kg 9,877.2 12.20

Electricity 2,480 kWh 31,496 38.89

Water 6667.66 m3 6,801.013 8.40

Total energy input 80,985.97 100.00

Output

Sugarcane 93,200 kg 111,840 111,840a

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of the best energy indices that shows the efficient use of

energy in sugarcane production. The results indicated an

average energy ratio of 1.38, which shows 1.38 times

energy was produced per unit of energy used in the sug-

arcane production system. In other studies, the energy ratio

of sugarcane was 1.3 in Morocco, 2.8 in Brazil, 2.6 in

Australia, 1.6 in South Africa, 1.8 in Taiwan, 1.9 in Nepal

and 1.6 in Louisiana (Mrini et al. 2001; Gifford 1978;

Austin et al. 1978; Ricaud 1980); and for other crops, it

was 4.1 for soybean (Ramedani et al. 2011), 1.53 for rice

(Pishgar-Komleh et al. 2011b), 13.4 sugar beet (Asghari-

pour et al. 2012), 2.59 for corn (Banaeian and Zangeneh

2011), and 1.1 for potato (Hamedani et al. 2011a). Energy

productivity, specific energy and net energy of sugarcane

production were calculated at 1.15 kg MJ-1, 0.87 MJ kg-1

and 30,854.03 MJ ha-1, respectively. Better management

and using less energy input and producing more energy

output (more yield) are two methods to reach higher energy

ratio value. Some researchers such as Pishgar-Komleh

et al. (2011b), Zanganeh et al. (2010), and Hamedani et al.

(2011a) also determined the energy productivity of agri-

cultural crop production.

Table 3 shows the distribution of total energy input as

direct or indirect (DE vs. IDE), and renewable or non-

renewable (RE vs. NRE) and the percentages of these

energy. As it can be seen from the table, the total energy

input consumed could be classified as DE (61 %), IDE

(39 %), RE (21 %) and NRE (79 %) for sugarcane

production.

It is clear from Table 3 that in comparison with DE, the

portion of IE is higher, and also, the value of NRE was

greater than that of renewable energy (RE) consumption in

sugarcane production. This result was in agreement with

the findings of Erdal et al. (2007) for sugar beet, Mousavi-

Avval et al. (2011) for canola, Pishgar-Komleh et al.

(2011a) for corn silage, and Ghorbani et al. (2011) for

irrigated and dry land wheat production, Pishgar-Komleh

et al. (2011b) for rice, Asgharipour et al. (2012) for sugar

beet, Unakitan et al. (2010) for canola and Mohammadi

et al. (2008) for potato. It is important to better utilize the

RE sources for making up for the increasing energy deficit,

as they represent an effective alternative to fossil fuels for

preventing resources depletion and for reducing air pollu-

tion (Zangeneh et al. 2010). Agriculture sector should

move in ways to substitute NRE sources by renewable

forms of energy. This can be achieved by incorporating

solar and wind energies instead of fossil energies (Taba-

tabaie et al. 2012).

In this study for estimating a relationship between energy

inputs and yield, Cobb–Douglas production function was

adopted by using Ordinal Least Square (OLS) estimation

technique. Therefore, the yield of sugarcane was assumed to

be a function of machinery, human labor, diesel fuel,

chemicals, fertilizers, seed electricity and irrigation water

energies. Regression results for model 1 showed the sig-

nificant impact of machinery, human labor and seed energy

on sugarcane yield at the level of 1 % (Table 4). Also, diesel

and water had a significant impact at 5 % and fertilizers had

a significant impact at 10 % probability level. Other inputs

such as chemicals and electricity had no significant impact

on sugarcane yield. Among all inputs, human labor had the

highest impact (0.859), followed by machinery (0.233) and

seed (0.193) energy inputs. The regression results (model 1)

expressed with a 10 % increase in fuel, machinery and

biocide energy, sugarcane yield increases by 8.59, 2.33 and

1.93 %, respectively. The results of MPP values indicated

1 MJ increase in human labor and machinery energy led to

1.607 and 0.296 kg ha-1 increase in the yield of sugarcane

production, respectively. For the data used in this study,

autocorrelation was tested using Durbin–Watson test. To

validate Model 1, Durbin–Watson test was performed.

Analysis of model 1 resulted 1.75 for Durbin–Watson value,

i.e. there was no autocorrelation in the estimated model

(significant level of 5 %). The model’s coefficient of

determination was calculated at 0.99 (R2 = 0.99). The

return to scale index (RTS) for sugarcane production is

greater than unit, which shows an increasing return to scale.

It is concluded that a proportionate increase in all inputs

results in a greater than proportionate increase in output for

sugarcane production.

To realize the relationship between sugarcane yield and

the forms of energy (direct and indirect), a regression

analysis (Model 2) was performed (Table 5). It became

evident that the impact of direct and IE on sugarcane yield

was significant at 1 % level with coefficient values of 0.537

and 0.191 for direct and indirect energies, respectively. The

impact of DE is more than that of the IDE on yields of

Table 3 Energy forms and indices in sugarcane production

Item Unit Value %

Energy ratio – 1.38

Energy productivity kg MJ-1 1.15

Specific energy MJ kg-1 0.87

Net energy MJ ha-1 30,854.03

Direct energya MJ ha-1 49,697.65 61

Indirect energyb MJ ha-1 31,288.32 39

Renewable energyc MJ ha-1 17,177.23 21

Non-renewable energyd MJ ha-1 63,808.74 79

Total energy MJ ha-1 80,985.97

a Included human labor and diesel fuel electricity and water for

irrigationb Included machinery, seed, fertilizer and chemicalc Included water for irrigation, seed and human labord Included machinery, fertilizer, diesel fuel, chemical and electricity

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sugarcane production. Similar results can be seen in the

study of Pishgar-Komleh et al. (2011b) for rice production,

Hamedani et al. (2011b) for grape production and Pishgar-

Komleh et al. (2011b) for corn silage.

The regression coefficients of renewable and non-

renewable energies on yield were investigated (Model 3).

As it can be seen in Table 5, the impact of renewable and

non-renewable was 0.351 and 0.272 at a probability level

of 1 %, respectively.

The MPP values of direct, indirect, renewable and non-

renewable were 0.348, 0.215, 0.419 and 0.286, respec-

tively. As the MPP values specified, consuming more

(1 MJ) direct, indirect, renewable and non-renewable

energy leads to more (0.348, 0.215, 419 and

0.286 kg ha-1) sugarcane yield. The RTS for model 2 and

model 3 is less than unit, which shows a decreasing return

to scale. It is concluded that a proportionate increase in all

inputs results in a less than proportionate increase in sug-

arcane yield. Durbin–Watson values for model 2 and 3

were 1.003 and 2.1, respectively (significant at the 5 %

probability level). In addition, the model’s coefficient of

determination was the same (0.99) for models 2 and 3.

The costs of each input used and total production costs

calculated in sugarcane production are given in Table 6.

The fixed and variable expenditures included in the cost of

production are calculated separately. The highest share of

variable costs goes to fertilizers, while purchasing imple-

ments has the largest share of fixed costs.

The total cost of sugarcane production and the gross

value of production are calculated using Eqs. 12–16 and

are shown in Table 7. By multiplying sale price by sug-

arcane yield, the production value was calculated. Total

cost of production was 0.016 $ kg-1. The gross return was

obtained by subtracting the variable cost of production per

hectare in sugarcane production from the value of pro-

duction and was 4,485.251 $ ha-1. This economic index

was calculated for grape, garlic and potato production

(Hamedani et al. 2011b; Samavatean et al. 2011; Mo-

hammadi et al. 2008). Net return was 3,829.111 $ ha-1.

The benefit-cost ratio of sugarcane planting in different

planting systems was calculated at 2.58. The economic

Table 4 Econometric estimation and sensitivity analysis results of

inputs for sugarcane production

Coefficient t-ratio MPP

Model 1: lnYi = a0 ? a1 lnX1 ? a2 lnX2 ? a3 lnX3 ? a4

lnX4 ? a5 lnX5 ? a6 lnX6 ? a7 lnX7 ? a8 lnX8 ? ei

Constant 2.879 1.608ns

Machinery 0.233 5.823a 0.296

Human labor 0.859 4.697a 1.607

Diesel 0.055 2.586b 0.069

Chemicals 0.092 1.638ns 0.165

Fertilizers -0.039 -1.69c -0.049

Seed 0.193 10.084a 0.244

Electricity 0.029 0.187ns 0.033

Water -0.175 -2.398b -0.231

Durbin-Watson 1.75

R2 0.99

RTS 1.247

a Indicates significance at 1 % probability levelb Indicates significance at 5 % probability levelc Indicates significance at 10 % probability levelns Indicates no-significance level

Table 5 Econometric estimation results of direct, indirect, renewable

and non-renewable energies

Coefficient t-ratio MPP

Model 2: lnYi = b0 ? b1 lnDEi ? b2 lnIDEi ? ei

Constant(b0) 3.839 18.877a

DE(b1) 0.537 10.631a 0.348

IDE(b2) 0.191 5.674a 0.215

R2 0.99

Durbin–Watson 1.003

RTS 0.728

Model 3: lnYi = c0 ? c1 lnREi ? c2 lnNREi ? ei

Constant(c0) 5.193 46.787a

RE(c1) 0.351 16.94a 0.419

NRE(c2) 0.272 9.79a 0.286

R2 0.99

Durbin–Watson 2.1

RTS 0.623

a Indicates significance at 1 % probability level

Table 6 Costs of each input in sugarcane production farms

Item Quantity

Variable costs ($ ha-1) 1,760.556

Seed 536

Fertilizers 489.35

Chemicals 72.63

Human labor 188.38

Fuel 193.6

Electricity 165.416

Water 88.5

Repairs and maintenance 26.68

Fixed costs ($ ha-1) 656.14

Purchasing implements 410.8

Depreciation 175.24

Interest 58.42

Insurance and housing 11.68

Total production costs ($ ha-1) 2,416.696

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research in other crops production revealed a benefit to cost

ratio value of 1.57 for corn silage (Pishgar-Komleh et al.

2011a), 1.3 for sugar beet in Iran (Asgharipour et al. 2012),

1.1 for soybean (Ramedani et al. 2011), 1.43 for wheat

(Shahan et al. 2008), 0.86 for cotton (Yilmaz et al. 2005),

2.08 for grape production (Hamedani et al. 2011b), 1.17 for

sugar beet (Erdal et al. 2007) and 1.57 for greenhouse

tomato (Canakci and Akinci 2006).

At the end of the economic analysis of sugarcane pro-

duction, the economic productivity was calculated

38.57 kg $-1 (kg $-1 indicates the amount of sugarcane

produced per dollar spent on production).

Conclusion

The relationship between energy inputs and the yield using

Cobb–Douglas function and energy use patterns for sug-

arcane production in Khouzestan province of Iran were

investigated. Based on the results, energy inputs and output

of sugarcane production were calculated to be 80,985.97

and 111,840 MJ ha-1. Electricity for irrigation was the

biggest energy consumer (38.89 % of total energy usage),

followed by fertilizers (14.01 %), diesel fuel (13.46 %),

seed (12.2 %) and machinery (11.6 %). Machinery was

discovered as the least demanding energy input among all

inputs (0.62 %). It is essential to use modern technologies

and new machinery for different operations, especially for

irrigation, to decrease the high amount of energy use in

sugarcane production. Optimal consumptions of fertilizers,

diesel fuel and other major inputs would be useful not only

in reducing the negative effects to the environment

and human health, but maintaining sustainability and

decreasing production costs and it is possible through

training the farmers and laborers. Energy ratio, energy

productivity, specific energy and net energy were 1.38,

1.15 kg MJ-1, 0.87 MJ kg-1 and 30854.03 MJ ha-1,

respectively. The share of NRE for sugarcane production

was 78.79 %. Therefore, a reduction in the total NRE ratio,

specifically in electricity and fertilizer usage would have

positive effects on the sustainability of sugarcane produc-

tion and bring about other positive environmental effects.

Regression coefficient values for machinery, fuel, labor,

diesel, chemicals, fertilizer, seed, electricity and water

were 0.233, 0.859, 0.055, 0.092, -0.039, 0.193, 0.029 and

-0.175, respectively. The RTS results revealed that energy

consumption for sugarcane production was IRS. That

means an increase in the total inputs may result in an

increase in output in greater proportion than the input

increase. The impact of direct (0.537), indirect (0.191),

renewable (0.351) and non-renewable (0.272) energy

was significant at 1 % level on sugarcane yield. The

average value of total cost of production, gross return, net

return, benefit-cost ratio and productivity of sugarcane

production were calculated to be 1520.675, 4485.251,

3829.111 $ ha-1, 2.58 and 38.57 kg $-1, respectively.

Appendix

Energy ratio =Energy output ðMJ ha�1ÞEnergy input ðMJ ha�1Þ

ð8Þ

Energy productivity ¼ Sugarcane output ðkg ha�1ÞEnergy input ðMJ ha�1Þ

ð9Þ

Specific energy ¼ Energy input ðMJ ha�1ÞSugarcane output ðkg ha�1Þ

ð10Þ

Net energy ¼ Energy output ðMJ ha�1Þ� Energy input ðMJ ha�1Þ

ð11Þ

Total production value ¼ Sugarcane yield kg ha�1� �

� Sugarcane price ð$ kg�1Þð12Þ

Gross return ¼ Total production value ð$ ha�1Þ� Variable planting cost ð$ ha�1Þ ð13Þ

Net return ¼ Total production value ð$ ha�1Þ� Total planting cost ð$ ha�1Þ ð14Þ

Table 7 Economic analysis of sugarcane planting in different

planting systems

Cost and return components Unit Quantity

Yield kg ha-1 93,221

Sale price $ kg-1 0.067

Production $ ha-1 6,245.807

Total production costs $ ha-1 1,520.675

Total production costs $ kg-1 0.016

Gross return $ ha-1 4,485.251

Net return $ ha-1 3,829.111

Benefit to cost ratio – 2.58

Productivity kg $-1 38.57

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Benefit cost ratio ¼ Total production value ð$ ha�1Þ=Total planting cost ð$ ha�1Þ ð15Þ

Productivity ¼ Sugarcane yield ðkg ha�1Þ=Total planting cost ð$ ha�1Þ ð16Þ

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