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Azawi et al.,, Tikrit Journal for Agricultural Sciences (2024) 24 (2):115-130
115
Sustainable Energy Use for Mechanized Wheat
Production Systems in Iraq
Abdulla Azawi , Thaer Turky , and Momtaz Isaak *
Agricultural Machinery and Equipment Department, College of Agriculture, Tikrit University, Iraq
*Correspondence email: momtaz.isaak@tu.edu.iq
ABSTRACT
The utilization of agricultural machinery was fully mechanized for
wheat production in Iraq. This study essentially aimed to inspect inputs and
outputs of energy for wheat production in Salah Al-Deen, Iraq. The data were
obtained from 45 wheat farms by using the face-to-face questionnaire method
in 2022. The findings from this study were determined for five basic
operations (i.e., tillage, sowing, fertilizing. spraying, and harvesting). Direct
energy sources (fuel and humans) accounted for about 51.39% of the total
energy used in cultivation. Energy exemplified in fuel recorded the highest rate
of the total expenditure of energy, with 51.09 % (6091.01 MJ/ha). Farmers
utilized nearly 879.11 MJ/ha of machinery energy, the highest rate of
expenditure of machinery energy was in harvesting, which recorded 38.38 %
(337.42 MJ/ha) of the total energy of machinery used in the study. Results of
analyzing the energy of fuel that farmers utilized indicated that operations of
tillage were about 25.98 % (1582.29 MJ/ha) of the total energy of fuel. This
rate denotes the highest operation of fuel consumption. Harvesting operations
followed it. These operations were implemented through the use of engines
powered with diesel, which accounted for about 23.43% (1427.05 MJ/ha). The
average energy input/output ratio was 4.40 for the wheat crop, while the
energy intensity was 1.79 MJ/kg for the wheat crop.
KEY WORDS:
renewable energy; machinery
energy; energy use efficiency
Received: 03/01/2024
Revision : 10/03/2024
Proofreading: 20/04/2024
Accepted: 30/05/2024
Available online: 30/06/2024
2024.This is an open access article
under the CC by licenses
http://creativecommons.org/licenses/by/4.0
ISSN:1813-1646 (Print); 2664-0597 (Online)
Tikrit Journal for Agricultural Sciences
Journal Homepage: http://www.tjas.org
E-mail: tjas@tu.edu.iq
TJAS
Tikrit Journal for
Agricultural
Sciences
Tikrit Journal for Agricultural Sciences (2024) 24 (2): 115-130
DOI: https://doi.org/10.25130/tjas.24.2.10
Azawi et al.,, Tikrit Journal for Agricultural Sciences (2024) 24 (2):115-130
116
879.11
337.42
1582.29
INTRODUCTION
Excessive energy usage in developed and developing nations has resulted in several
environmental, commercial, technological, and even societal issues that require further
research to offset the negative consequences. To minimize energy use and its environmental
consequences, all relevant information must be analyzed (Safa and Samarasinghe, 2011).
Agriculture, a high-energy input sector, yields vital energy for human survival. Wheat, a
cereal crop, serves as a staple food worldwide, meeting heightened demand due to people
growth through modern, energy-intensive farming practices (Ashraf et al.,, 2020; Imran et al.,
2021). Farmers have ramped up the use of input energy in wheat production to keep up with
rising demand. This heightened energy usage has far-reaching implications for both energy
security and environmental sustainability (Imran & Özçatalbaş, 2020). The Iraqi government
is working to enhance agriculture to increase food production. This is a crucial step in
stabilizing the country's economy and reducing food imports in favor of domestic production
and procurement to source the Public Distribution System's (PDS) food supply (CFSVA
2016). This is due to the importance of wheat production and its various uses through human
consumption of grains as well as animal consumption as fodder, adding to the entry of this
crop in industrial uses. These operations are managed using energy from various sources,
including human labor, machinery, fuel, fertilizer, chemical applications, and seeds.
Essentially, the production capacity of crops is directly consumed in the operation of
machinery and equipment and indirectly through the application of fertilizers and chemicals
used in agriculture. The timely availability of adequate energy is a prerequisite for the timely
Azawi et al.,, Tikrit Journal for Agricultural Sciences (2024) 24 (2):115-130
117
completion of wheat production, which is essential to ensuring maximum yield (Sami, 2014;
Muazu et al.,, 2015). Most of the researchers collected data on energy expenditures in fields
using the questionnaire method (Soltani et al., 2013; Ajabshirchi, 2013; Bilalis et al.,, 2013;
Yousefi et al., 2014; Khoshnevisan et al., 2014; Nabavi-Pelesaraei et al., 2014; Nabavi-
Pelesaraei et al., 2016; Barak et al., 2016; Singh et al.,, 2021; Alwan and Hassan, 2023). To
maximize benefits, farmers must have the right energy mix in time. Much of the energy input
indicates non-economic production and thus waste, which may lead to a reduction or loss of
utility, an increase in global warming, and some stress on the environment. Very little energy
is required to make it difficult to reach the maximum level of productivity to ensure the
required level of food sufficiency (Muazu et al., 2015; Al-jughaify and Alobaidy, 2023).
The energy analysis in crop production is executed to identify energy uses. The
obtained information is then used to improve performance by increasing yield, decreasing
production costs, and minimizing greenhouse gas emissions, which are accountable for an
alteration in the environment. Energy use is an important necessity for the sustainability of
agricultural production, as it reduces costs, restricts fossil fuels, and lowers air pollution
levels (Ghorbani et al., 2011; Shaaban and Omer, 2023). When analyzing energy, the farm's
inputs utilized in production and the outputs produced from it are recognized using a
boundary defined for the system. A clear definition of the study boundary is a cardinal issue
in assessing agricultural systems (Dixon et al., 2001). The inputs and outputs are evaluated
and then translated into energy values using proper energy conversion coefficients. Classical
mathematical equations are then used to provide an estimation of the energy flow into the
system. The number of inputs and outputs incorporated in the analysis, along with the
conversion coefficients adopted, have a tremendous effect on the estimated energy flow.
Most variations in the results of energy studies are hinged on these factors. Therefore, for
effective comparison among different energy studies, these variations need to be recognized
and addressed properly (Isaak et al.,, 2020).
An energy equivalent, otherwise called an energy conversion coefficient, is a value
that expresses the energy input expended in the production and distribution of a unit physical
material (e.g., pesticides, fuel, fertilizers, seeds, etc.) used as input in crop production. The
value is not fixed for any given material input but varies widely from place to place. It
reflects the level of technological development associated with the production or manufacture
of the given material input. In other words, using lower-energy coefficients denotes
improvements in the efficiency of production (Rathke et al.,, 2007; Tabatabaie et al.,, 2013).
Based on the literature, no study has been conducted or reported on energy use
analysis in wheat cultivation in Iraq. Thus, this study was conducted on energy input in wheat
cultivation to understand when, where, and how much energy inputs are consumed, and
finally to identify the opportunities for saving energy input for individual operations. The
objectives of the present study were to assess energy use analysis in mechanized wheat
cultivation in Iraq.
MATERIALS AND METHODS
In this study, 45 farms of wheat production were surveyed in Salah Al-Deen province,
Iraq, located at 34°27′N 43°35′E, by using the face-to-face questionnaire method in 2022.
The collection of data was conducted by randomly selected farms. The size of the sample was
Azawi et al.,, Tikrit Journal for Agricultural Sciences (2024) 24 (2):115-130
118
determined using the simple random sampling method. This method is expressed as
below: (Kizilaslan, 2009):
(1)
where n is the required sample size; s, is the standard deviation; t is the value at 95%
confidence limit (1.96); N, is the number of holding in the target population and d, is the
acceptable error (permissible error 5%). Consequently, the calculated sample size in this
study was 45. For the calculation of sample size, criteria of 5% deviation from the population
mean and 95% confidence level was used.
The recorded farm inputs from the six sources (namely human labor, machinery, fuel,
fertilizer application, chemical and seed) used by the wheat farmers and wheat yield from
each were converted into equivalent energy values using appropriate conversion coefficients
shown in Table 1.
Table 1 Energy conversion coefficients used to compute energy value for the farm inputs and
outputs
Material
conversion
coefficients
Unit
Source
Human labor
1.96
MJ/h
(Mohammadi et al., 2010)
Tractor
93.61
MJ/kg
(Canakci et al., 2005)
Machinery
62.7
MJ/kg
(Ozkan et al., 2004)
Diesel
56.31
MJ/l
(Mohammadi et al., 2010)
Nitrogen
78.1
MJ/kg
(Khoshnevisan et al., 2014)
Phosphorus
17.4
MJ/kg
(Khoshnevisan et al., 2014)
Potassium
13.7
MJ/kg
(Khoshnevisan et al., 2014)
Chemicals
120
MJ/kg
(Mohammadi et al., 2010)
Pesticides
315
MJ/kg
(Safa and Samarasinghe 2011)
Herbicides
310
MJ/kg
(Safa and Samarasinghe 2011)
Fungicides
210
MJ/kg
(Safa and Samarasinghe 2011)
wheat seed
13
MJ/kg
(Khoshnevisan et al., 2014)
The source-wise energy budget in megajoules per hectare (MJ/ha) is evaluated using the
classical equation for every one of the farm inputs used by farmers in the cultivation
operations of wheat. The evaluation utilized energy conversion factors as specified in Table
1, because of their popular applications in similar studies by previous researchers in the
Philippines (Flores et al., 2016), Iran (Lorzadeh, 2012; Taki et al, 2012), India (Yadav et al.,
2013 ; Mani et al., 2007), and Pakistan (Shafique et al., 2015) Machinery energy, fuel energy,
human energy, seed energy, chemical energy and fertilizer energy were calculated using the
following equation:
(2)
Azawi et al.,, Tikrit Journal for Agricultural Sciences (2024) 24 (2):115-130
119
Where ME refers to the energy of machinery (MJ/ha), CF refers to the conversion factor of
energy for the used machinery (MJ/kg), W refers to the machinery weight (kg), Fc refers to
the capacity of effective field (ha/h) and L refers to the economic life of the machinery (h).
The derivation of the economic life of farm machinery implemented by farmers was from the
management standard of farm machinery, as shown in Table 2.
Table 2 The economic life of farm machinery used by farmers in the study area
In computing the machinery energy for tillage operation which involves the use of a tractor
and chisel plow having different weight, energy conversion coefficient and economic life, the
total machinery energy was obtained as the summation of machinery energies due to the
tractor and chisel plow used in operating, as shown in Equation 3.
(3)
Where Cft is 93.61 MJ/kg the energy conversion factor for tractor, Wt is 2311 kg the weight
of the tractor, Lt is 12000 h the economic life of tractor, Cfr is 62.70 MJ/kg the energy
conversion factor for chisel plow, Wr is 325 kg the weight of chisel plow, Lr is 2000 h the
economic life of chisel plow. Fc is the effective field capacity for the tillage operation (ha/h).
The general formula for computing machinery energy expenditure per area basis (MJ/ha) is
expressed in Equation 4.
(4)
Where ME refers to the energy of machinery (MJ/ha), Cf refers to the conversion factor of
energy for the machinery used (MJ/kg), W refers to the machinery weight (kg), Fc refers to
the capacity of effective field (ha/h) and L refers to the machinery economic life (h).
(5)
Where FE is fuel energy (MJ/ha), fcon is the fuel consumed quantity (L), fc refers to the
conversion factor of fuel energy (MJ/L), and A refers to the covered area of the farm (ha).
The energy conversion coefficients adopted for diesel, as shown in Table 1, are 56.31 MJ/l .
Therefore, based on what fuel type is used the prime movers engaged in an operation,
appropriate fuel energy conversion factor (fc) is used in Equation 5.
Machine
Economic life/ h
Source
Tractor 2WD
12000
(ASABE standard D497. 2006)
Self-propelled combine harvester
3000
Chisel plow
2000
Sprayer
1500
Spreader
1200
Azawi et al.,, Tikrit Journal for Agricultural Sciences (2024) 24 (2):115-130
120
(6)
Where HE is human energy (MJ/ha), n is the number of workers engaged in an operation, H
is the time taken for the operation (h), lc refers to the conversion factor of energy for human
labor (1.96 MJ/h) and A is the farm area covered (ha). The average human energy
expenditure in the block was calculated as the total energy of human expended in the
cultivation in all farms.
(7)
Where SE refers to the energy of seed (MJ/ha), Sq refers to the weight of the seeds used in the
study (kg), sc refers to the conversion factor of seed energy (13 MJ/kg) and A refers to the
area of the farm under study (ha). The average seed energy in the block was computed as the
summation of seed energy in all farms under study.
(8)
Where CE is chemical energy (MJ/ha), Cq refers to the chemical weight used in the study
(kg), Cc refers to the conversion factor of chemical energy (MJ/kg) and A is the farm area
covered (ha). The average chemical energy in the block was computed as the total chemical
energy in the block.
(9)
Where FTE refers to the energy of fertilizer (MJ/ha), FTq refers to the fertilizer weight used
in the study (kg), FTi refers to the ith element percent composition (decimal), FTci refers to
the conversion factor of energy for the ith fertilizer element (MJ/kg) and A is the farm area
covered (ha). The average fertilizer energy in the block was computed as the summation of
fertilizer energy in all farms.
= + + + + + (10)
Where TEI refers to total input of energy (MJ/ha) and , , , , and are as
defined earlier.
The average overall energy input in the block was then obtained as the total energy inputs in
all farms.
TOE = Y * mc (11)
Where TOE is total energy output wheat (MJ/ha), Y is the harvested wheat yield (kg/ha) and
mc refers to the conversion factor of energy for wheat (MJ/kg).
Azawi et al.,, Tikrit Journal for Agricultural Sciences (2024) 24 (2):115-130
121
The block’s average energy output was determined as the ratio of energy output sum from all
farms. The energy ratios determined in this study were computed as follows:
(12)
Where EE is energy use efficiency (dimensionless), TEO is the total energy output (MJ/ha)
and TEI is the total energy input (MJ/ha). The average energy use efficiency in the block was
established as the total energy use efficiencies in all farms.
(13)
Where El = Energy intensity for wheat (MJ/kg), TEI Total energy input (MJ/ha) and Y =
Harvested wheat yield (kg/ha).
(14)
Where EP = Energy productivity for wheat (kg/MJ), Yc = Harvested wheat yield (kg/ha) and
TEI = Total energy input (MJ/ha).
NEG = TEO -TEI (15)
Where NEG = Net energy gain (MJ/ha), TEO = Total energy output (MJ/ha) and TEI = Total
energy input (MJ/ha).
(16)
Where PERi = Percent energy use for the ith source of energy input Eli = Energy input from
an ith source (MJ/ha) and TEI = Total energy input (MJ/ha).
RESULTS AND DISSCUSION
Farmers in the study area conduct tillage operation using two-wheel drive made of
medium size with ratings of engine power ranging between 65 and 120 hp as a prime mover.
The energy inputs used in performing the tillage operations are human labor, machinery and
fuel consumed by the prime movers, essentially during the data collection for tillage
operations. All tillage data were collected and analyzed. At first energy, analysis was made
regarding the share contribution of each or the three energy sources (human labor, machinery,
and fuel) used in conducting the tillage operations. The energy expenditures due to tillage
operations are presented in Table 3. Mean total energy of 1701.31 MJ/ha was expended in
performing the tillage operation. Fuel constitutes the bulk of the energy expenditure
accounting for 93 % (1582.29 MJ/ha) of the total energy expenditure (Turky et al., 2023).
The contributions of machinery and human labor were rather low, pegged at 6.38 and 0.62 %,
respectively.
Azawi et al.,, Tikrit Journal for Agricultural Sciences (2024) 24 (2):115-130
122
The energy data for sowing operations covers four farm inputs: humans, fuel,
machinery, and seeds. The energy expenditure data for the sowing operation is presented in
Table 3. A mean total energy of 3114.92 MJ/ha was used by the farmers in the study area.
The energy embodied in wheat seeds constitutes about 49.64% (1546.15 MJ/ha) of the total
budget of energy due to the planting operation. The large confidence interval was recorded in
seed energy, highlighting the wide variation in the seeding rate adopted by the farmers. The
highest and lowest seed energies were 1760.27 and 912.00 MJ/ha, representing a seeding rate
of about 115.81 and 60 kg/ha, respectively. The combined contributions of human labor, fuel,
and machinery energy to the total energy accruing to the planting operation were 0.20%,
41.55%, and 8.61%, respectively.
Farmers in the study area applied different types of fertilizers, both organic and
inorganic, at different rates and intervals. A total number of 1 to 3 fertilizer applications were
made by all farmers in the study area during the season in which the research was conducted.
The average fertilizer application frequency per farm was two. The fertilizer application was
done mechanically using the collected data. Essentially, the data collection exercise for
fertilizing operations covers four energy sources, including human labor, fuel, machinery,
and fertilizer applied. The detailed results for energy fertilizer application are given in Table
3. Summary for the average total expended in performing wheat fertilizing application as
indicated in Table 3 demonstrates that energy contained in fertilizer used by the farmers
accounted for 34.66% (or 4132.06 MJ/ha) of the average total energy expenditure of 4132.06
MJ/ha, which accrued to fertilizing operations. The operational energy due to human labor,
fuel, and machinery together recorded 22.34% of the total energy used in fertilizing
operations. Analysis of the result further shows that human labor energy is not only the least
contributor, with 5.24 MJ/ha, but also lags behind machinery energy expenditure by about
16.15 times. Thus, this indicates the high level of mechanization for the fertilizing operation,
which is fully mechanized in Iraq. The recorded large confidence interval in fertilizer energy
of 47.36 MJ/ha is indicative of huge variation in the use of fertilizer among the farmers.
Chemical application is intended to offer much-needed protection to wheat plants
against disease, insect pests, and weed infestations that could hamper yield. In the area under
study, farmers used about 15 types of assorted chemical pesticides on their wheat farms.
Generally, the farmers used tractor-mounted sprayers when applying pesticides to their farms.
The results for the distributed energy expenditure in pesticide applications are illustrated in
Table 3. Analysis of the results presented in Table 3 shows that about 13.50%, representing
162.11 MJ/ha of the total average energy expended (1201.04 MJ/ha) in conducting pesticide
application by the farmers, is from embodied energy in the pesticides used.
The harvesting operation of wheat is done mechanically in Iraq. The energy data for
the harvesting operation comprises three inputs, namely human labor, fuel, and machinery,
used in executing the operation. The results for the energy expenditures due to the three
energy sources used in harvesting operations are presented in Table 3. Analysis of the results
shown in Table 3 indicates that farmers utilized an average total expenditure of energy of
1773.87 MJ/ha in carrying out harvesting operations. The highest contribution of 1427.05
MJ/ha, representing 80.45% of the total average energy budget, came from fuel energy. The
share contributions were for human energy at 0.53% (9.40 MJ/ha), which is the least
significant contributor.
Azawi et al.,, Tikrit Journal for Agricultural Sciences (2024) 24 (2):115-130
123
Table 3 Operations – wise energy expenditure wheat
As outlined earlier, five farm inputs were used by the farmers in wheat cultivation.
The summary statistics for the inputs in energy equivalents are presented in Table 3. Direct
energy sources (fuel and humans) accounted for about 51.39% of the total energy used in the
cultivation, while indirect energy inputs (machinery, fertilizer, chemicals, and seeds)
accounted for 48.61% of the total energy input. One of the (5) operations represented by
fertilizer application comprised (4) inputs of energy, namely, fuel, machinery, human, and
fertilizer energy. This application has contributed the highest share of 34.66% (4132.06
±47.36 MJ/ha) of the total energy expenditure. The second highest energy expenditure is
followed by 26.12% (3114.92 ± 93.4 MJ / ha) for sowing operations. Harvesting operations
had a share contribution of 14.88% (1773.87 ± 59.7 MJ / ha). Tillage operations with three
energy inputs have a share contribution of 14.27% (1701.31± 42.75 MJ / ha). Moreover,
spraying operations were carried out with four inputs of energy, namely, the energy of
humans, fuel, machinery, and chemical application, which denoted the least consuming
operations of energy. The operation contributed to the overall expenditure of energy by
10.07% (1201.04 ± 27.98 MJ / ha), as revealed in Figure 1.
Figure 1 Operations-wise energy distribution
Operati
ons
HE
MJ/ha
FE MJ/ha
ME
MJ/ha
FE MJ/ha
CE
MJ/ha
SE MJ/ha
Total
MJ/ha
Tillage
10.5 ±
0.05
1582.29 ±
39.43
108.52±
3.27
Nil
nil
nil
1701.31 ±
42.75
Sowing
6.37 ±
0.12
1294.11 ±
33.72
268.29±
9.61
Nil
nil
1546.15 ±
49.95
3114.92 ±
93.4
Fertilizi
ng
5.24 ±
0.20
833.27 ±
27.25
84.61 ±
1.63
3208.94
± 18.28
nil
nil
4132.06 ±
47.36
Sprayin
g
4.37 ±
0.15
954.29 ±
22.56
80.27±
1.37
Nil
162.11 ±
3.90
nil
1201.04 ±
27.98
Harvest
ing
9.40 ±
0.13
1427.05 ±
35.68
337.42±2
3.89
Nil
nil
nil
1773.87 ±
59.7
Total
MJ/ha
35.88±
0.66
6091.01±
158.64
879.11±3
9.77
3208.94
± 18.28
162.11 ±
3.90
1546.15 ±
49.95
11923.2 ±
271.19
Tillage,
14.27 %
Sowing,
26.12 %
Fertilizing,
34.66 %
Spraying ,
10.07 %
Harvesting ,
14.88 %
Azawi et al.,, Tikrit Journal for Agricultural Sciences (2024) 24 (2):115-130
124
In terms of individual energy sources, the distribution of which is highlighted in
Figure 2, energy exemplified in fuel recorded the highest rate of the total expenditure of
energy, with 51.09 % (6091.01MJ/ha). The following rates were fertilizer energy, 26.91%
(3208.94 MJ/ha), seeds energy, 12.97% (1546.15 MJ/ha), machinery energy, 7.37 % (879.11
MJ/ha), chemical energy, 1.36 % (162.11 MJ/ha) and human energy, 0.30 % (35.88 MJ/ha) .
Figure 2 Energy distribution according to source
Results of analyzing expenditures of human energy demonstrated that in the area
under study, farmers used nearly 35.88± 0.66 MJ/ha of human energy. However, this value is
less than the recorded human energy that farmers used when working in farms of wheat in
Gorgan, Iran, of 142 ± 26 MJ/ha (Soltani et al., 2013); similar results (Safa and Samarasinghe
2011) got 6 % of machinery energy higher than human energy. The manual operations
recorded about in tillage operation 29.26 %, harvesting 26.20 %, sowing 17.75 %, fertilizing
14.60 %, and spraying 12.18%) of the total human energy used in the season, as shown in
Figure 3 Distribution of human energy based operations
Farmers utilized nearly 879.11±39.77 MJ/ha of machinery energy (see Table 3) in
carrying out the entire operations of cultivation. In the covered area, the highest rate of
expenditure of machinery energy was in harvesting, which recorded 38.38 % (337.42 ± 23.89
MJ/ha) of the total energy of machinery used in the study. The following rates were recorded
Human
energy, 0.30
%
Fuel energy,
51.09 %
Mechinery
energy, 7.37
%Fertilizer
energy,
26.91 %
Chemical
energy, 1.36
%
Seeds
energy,
12.97 %
Tillage,
29.26 %
Sowing,
17.75 %
Fertilizing,
14.60 % Spraying,
12.18 %
Harvesting,
26.20 %
Azawi et al.,, Tikrit Journal for Agricultural Sciences (2024) 24 (2):115-130
125
for operations of sowing, 30.52% (268.29± 9.61 MJ/ha), tillage operation, 12.34 % (108.52±
3.27 MJ/ha), fertilizing operation, 9.62 % (84.61 ± 1.63 MJ/ha), and spraying operation, 9.13
% (80.27± 1.37 MJ/ha) recorded less energy of machinery that farmers used in the study, as
shown in Figure 4.
Figure 4 Distribution of machinery energy-based operations
Results of analyzing the energy of fuel that farmers utilized indicated that operations
of tillage were about 25.98 % (1582.29 ± 39.43 MJ/ha) of the total energy of fuel. This rate
denoted the highest operation of fuel consumption. Harvesting operations followed it. These
operations were implemented through the use of engines powered with diesel were about
23.43% (1427.05 ± 35.68 MJ/ha). Sowing operation, 21.25 % (1294.11 ± 33.72 MJ/ha),
spraying operation, 15.67 % (954.29 ± 22.56 MJ/ha), and fertilizing operation, 13.68 %
(833.27 ± 27.25 MJ/ha) recorded less fuel energy that farmers used in the study, as shown in
Figure 5.
Figure 5 Distribution of fuel energy based operations
In the area under study, farmers were using an average of about 180.94 kg/ha of
assorted fertilizers. Figure 6 demonstrates the distribution percentage for the three main
mineral elements of fertilizer (Nitrogen, Potassium and Phosphorus), which were used by
farmers. Results analysis indicated that the highest share was for nitrogen use (NUR) by
Tillage,
25.98 %
Sowing,
21.25 %
Fertilizing,
13.68 %
Spraying,
15.67 %
Harvesting,
23.43 %
Tillage,
12.34 %
Sowing,
30.52 %
Fertilizing,
9.62 %
Spraying,
9.13 %
Harvesting,
38.38 %
Azawi et al.,, Tikrit Journal for Agricultural Sciences (2024) 24 (2):115-130
126
77.07 %, demonstrating an application rate of 24.73 kg/ha. Whereas, phosphorus (PUR) and
potassium (KUR), respectively accounted for 15.21 % and 7.73 % of the total rate of using
fertilizer. The rates of respective application for these two elements of fertilizer are 4.88 and
2.48kg/ha.
Figure 6 Distribution of NPK use rate by the farmers
Generally, nearly 2.19 kg/ha (162.11 MJ/ha) of assorted chemical applications were
used by farmers in the area under study. This application comprised pesticides, fungicides,
and herbicides. The energy content of herbicides takes the highest share, accounting for
60.65% (1.33 kg/ha) of the total chemical energy that the farmers used, as indicated in Figure
7. This is indicative of the high prevalence of weeds in comparison to infestation by fungal
diseases and insects on the wheat farms in the covered area. The share contributions for
pesticides and fungicides used in the study were about 30.66 % (0.67 kg/ha) and 8.69 % (0.19
kg/ha), respectively. In the study area, the least common chemical pesticides used by farmers
were fungicides.
Figure 7 Distribution of chemical use rate by type
Accordingly, the energy ratio analysis for the cultivation of one hectare of wheat in
the study area is summarized in Table 4. From the table, the average level of wheat yield in
the covered area was found to be 4033.96 kg/ha. The average energy productivity of wheat
NUR,
77.07 %
PUR,
15.21 %
KUR,
7.73 %
Herbicide,
60.65 %
Pesticide,
30.66 %
Fungicide,
8.69 %
Azawi et al.,, Tikrit Journal for Agricultural Sciences (2024) 24 (2):115-130
127
crops was 0.56 kg/MJ. This means that 0.56 units of wheat crop output were obtained per unit
of energy. The net energy gain and energy intensity of wheat crops were 45,238.30 MJ/ha
and 1.79 MJ/kg respectively. Net energy is positive. Therefore, it can be concluded that in
wheat crop production, energy is being acquired. The total mean input of energy in direct,
indirect, renewable, and non-renewable forms is shown in Table 4. The total consumed
energy of input could be categorized into direct energy (51.39 %), indirect energy (48.51%),
renewable energy (13.27 %), and non-renewable energy (86.73%). Based on Table 4, in the
area under study, farmers reaped nearly 7.28 times the energy they invested. Farmers
produced one kg of wheat by using 1.79 MJ of one of the five sources of energy input utilized
in the current study. In other words, farmers produce 560 g of wheat from 1 MJ of energy.
Table 4 Energy ratio analysis
Items
Unit
Value
Percentage (%)
Energy output
MJ/ha
52441.42
-
Energy input
MJ/ha
11923.20
100.00
Direct energy
MJ/ha
6126.89
51.39
Indirect energy
MJ/ha
5796.31
48.61
Renewable energy
MJ/ha
1582.03
13.27
Non-renewable energy
MJ/ha
10341.17
86.73
Energy use efficiency
-
4.40
-
Energy intensity
MJ/kg
1.79
-
Energy productivity
Kg/MJ
0.56
-
Net energy
MJ/ha
40518.22
-
CONCLUSION
The study aimed to assess the energy inputs and outputs of mechanized wheat
production systems in Salah Al-Deen, Iraq, by collecting data from 45 wheat farms through
face-to-face questionnaires in 2022. It analyzed five main operations: tillage, sowing,
fertilizing, spraying, and harvesting. Direct energy sources, primarily fuel and human labor,
accounted for 51.39% of the total energy input, with fuel being the largest contributor.
Machinery energy was predominantly utilized in harvesting, while fuel energy was highest
during tillage operations. The average yield for wheat cultivation in the study area was
4,033.96 kg/ha, with a total energy input of 12,539.57 MJ/ha. The energy use efficiency for
wheat crops was determined to be 7.28, while the energy intensity remained at 1.79 MJ/kg.
Approximately 37.61% of the total energy input for wheat cultivation originated from fossil-
based, non-renewable resources, with fuel, machinery, fertilizer, and chemicals contributing
about 22.15%, 3.20%, 11.67%, and 0.59%, respectively. Notably, fertilizer application
exhibited high mechanization, with machinery energy surpassing human labor significantly.
Overall, the study revealed a positive net energy gain, indicating the efficiency and
sustainability of mechanized wheat production in the region.
Azawi et al.,, Tikrit Journal for Agricultural Sciences (2024) 24 (2):115-130
128
CONFLICT OF INTEREST
The authors declare no conflicts of interest associated with this manuscript.
ACKNOWLEDGMENTS
The authors wish thank to the staff of the College of Agriculture, Tikrit University, for their
technical and general support.
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