Renewable Energy 74 (2015) 208e213
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Energy consumption analysis of wheat production in West Azarbayjan utilizing life cycle assessment (LCA) Hamid Taghavifar*, Aref Mardani Department of Mechanical Engineering of Agricultural Machinery, Faculty of Agriculture, Urmia University, Urmia, Iran
a r t i c l e i n f o
a b s t r a c t
Article history: Received 5 February 2014 Accepted 9 August 2014 Available online 23 August 2014
Wheat is a very strategic production in West Azarbayjan which is cultivated in extensive scale and can have strong role economically for the sustainable development. The aim of the present study was to analyze the energy consumption of wheat production in West Azarbayjan utilizing life cycle assessment (LCA). The assessment was carried out in terms of energy input and output, yield, energy use efficiency, specific energy, energy productivity, and net energy gain. As well, the allocations of different direct, indirect, renewable and nonrenewable energy sources were encompassed. The energy input and output were obtained as 30626.4 and 53480.4 MJ ha1, respectively. It was disclosed that the greatest shares of input energy with 11984 MJ ha1 and 6824.2 MJ ha1 corresponded to the diesel fuel and Nitrogen, respectively. The total consumed energy input could be classified as direct energy (53.81%), and indirect energy (38.39%) or renewable energy (30.23%) and nonrenewable energy (88.81%). Finally, some economic indices were incorporated in the present investigation such as the total value of production, productivity and beneﬁt. © 2014 Elsevier Ltd. All rights reserved.
Keywords: Energy productivity Nonrenewable energy Renewable energy Speciﬁc energy West Azarbayjan
1. Introduction Wheat (Triticum spp.) is a principal source of vegetable protein in human food, having a higher protein content than other major cereals, maize (corn) or rice . It is cultivated under a wide range of climatic conditions and is a key element of food chain system of the human. Moreover, this strategic grain is planted extensively on more land area than any other commercial food. Iran is ranked 12th in the globe among the world's greatest wheat producers with 13.8 million tons in 2012 (FAO) . It also holds second ranking in Iran's economy among the other agricultural products after tomato and before potato . Wheat not only is grown in extensive scale of Iran, but also it is a critical element from the energy consumption and costs standpoint during the agricultural season. Furthermore, it features a dramatically fundamental component for the sustainable development of Iran should the energy consumption and the crop yielding factors are optimized. This is mainly attributed to the fact that sustainable agricultural development is closely connected with effective energy use due to financial savings, crude oil resources as
* Corresponding author. Dept. of Mechanical Engineering in Farm Machinery, Faculty of Agriculture, Urmia University, P.O. Box 165, Urmia 0098, Iran. Tel.: þ98 914 388 2707; fax: þ98 441 277 19 26. E-mail addresses: [email protected]
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(H. Taghavifar). http://dx.doi.org/10.1016/j.renene.2014.08.026 0960-1481/© 2014 Elsevier Ltd. All rights reserved.
well as preservation and air pollution reduction . Although a great deal of energy sources, pesticides, and fertilizers are consumed to increment the wheat production, a remarkable portion of either unharvested or decayed crop, mainly as a result of mismanagement, reduces the wheat productivity in farmlands. This is mainly attributed to the fact that the total locally demand for the crop is drastically less than the production by orchardists. This leads to the drop of proﬁtability and hence, the cultivation of the objective crop is not economically justiﬁed. Therefore, a meticulous prognostication of the wheat yield, costs and energy resource consumption prior to agricultural practices would be beneﬁcial to manage the surplus of products and handle the prices. Additionally, crop production requires considerable amount of non-commercial energy sources such as seed, manure and animate energy, as well as commercially available energies such as diesel fuel, electricity, fertilizer, plant protection, chemical, irrigation water, machinery . The overuse of inputs results in environmentally detrimental phenomena such as fossil energy resources consumption, fertilizers, increased global warming, erosion, compaction, and pollution of water, soil and air . The ever-increasing demand for food production as well as augmented energy crisis owing to the global overpopulation and geopolitical turmoil of crude oil providers leads to the much attention being paid to optimize the crop yielding. It is well documented that assessment of environmental factors is the chief element in the assessment of ecological sustainability
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. Energy inputeoutput analyses are usually made to measure the energy efﬁciency and environmental aspects that determine how efﬁciently the energy is used . Life cycle assessment is a term referred as to the technique of dealing with the holistic environmental impacts concerned with a product while all the consumed resources are recognized and quantiﬁed [8e11]. It is noteworthy that energy inputs such as fuel, electricity, and fertilizer are remarkable inﬂuential in the production system of modern agriculture . The International Organization for Standardization (ISO) deﬁnes life cycle assessment (LCA) as the compilation and evaluation of the inputs, outputs and the potential environmental impacts of a product system throughout its life cycle. LCA considers the potential environmental impacts throughout a product's life cycle (i.e. cradle-to-grave) from raw material acquisition through production, use and disposal. In the scope of an LCA the items such as the functional unit, the system boundaries, and allocation procedures are considered. System boundaries, functional units, allocation procedures and several other aspects contribute to there being substantial differentiation in the structure of LCA applications in fruit production systems, leading to signiﬁcantly different results. The functional unit is a key element of LCA which has to be clearly deﬁned. The functional unit is a measure of the function of the studied system and it provides a reference to which the inputs and outputs can be related. This enables comparison of two essential different systems. The system boundaries determine which unit processes to be included in the LCA study. Deﬁning system boundaries is partly based on a subjective choice, made during the scope phase when the boundaries are initially set. Additionally, life cycle impact assessment aims to evaluate the signiﬁcance of potential environmental impacts using the results coming out from the life cycle inventory phase. West Azarbayjan province with 640,000 tons of wheat plays fundamental role in production of wheat in Iran. This province with an approximately 37,437 km2 of area is also an important
agricultural hub of Iran due to its appropriate climatic condition of this region. Hence, it is essential to have a detailed analysis to the wheat production in West Azarbayjan considering its contributing portion in Iran's developing economy. There are several studies associated with the energy consumption assessment of different crops and wheat in particular, however, as far as our literature review is concerned, there is no study dedicated to the assessment of energy assessment of wheat production in West Azarbayjan as an importantly strategic region with extensive portion in the production of wheat. It is further intended to classifying processes where energy savings could be comprehended by modiﬁcation of the existing practices to augment the energy ratio, and minimize the energy consumption for wheat production. 2. Materials and method 2.1. Data acquisitioning Data for this study were acquired from wheat farmers by use of face-to-face questionnaires in Urmia; a city located in the vicinity of the prominent Lake Urmia and is trade center for a fertile agricultural region. West Azarbayjan is a central province located within 37 330 10.0800 N, 45 40 33.2400 E (Fig. 1). The objective farmhouses were randomly selected from the rural communities in the investigation zone. The sample size of each stratification was calculated based on the derivation of Neyman technique as presented by Eq. 1 .
P ð Nh Sh Þ P N2 D2 þ Nh S2h
where n is the required sample size; N is the number of farmers in the target population; Nh is the number of the farmers in the h stratification; S2h is the variance of the h stratification; d permitted
Fig. 1. Geographical map of the studied region.
H. Taghavifar, A. Mardani / Renewable Energy 74 (2015) 208e213
error ratio deviated from average of population x X, z is the reliability coefficient (1.96 which represents 95% confidence); D2 ¼ d2/z2; the permissible error in the sample population was defined to be 5% within 95% confidence interval . Accordingly, it was obtained to be 18 farms using face-to-face questioner method. However, data were collected from 20 farmers to ameliorate the accuracy of inquiry step. Energy in agricultural processes is classiﬁed within direct, indirect, renewable and nonrenewable sources . Direct energy includes labor work, fossil, and electricity while indirect energy sources are incorporated of fertilizers, manures, spray pesticide and machinery. For instance, human labor and manure typify the renewable energy sources where diesel fuel, chemical fertilizers and machinery are representatives of nonrenewable energy supplies. Furthermore, input energy resources were incorporated of human labor, chemical fertilizers, manure, biocides, machinery, diesel fuel, electricity, natural gas and seeds while wheat and straw were considered as output energy parameters. The required irrigation and pumping energy in this study is encompassed as the electricity input. Energy efﬁciency has close relation with the outputs to inputs ratio . In order to convert inputs and output into the energy equivalents, energy equivalent coefficients is detailed in Table 1. The corresponding energy coefﬁcients were extracted from the mentioned references in order to calculate the consumed energy at different operations or energy content of various inputs. Aiming at obtaining each energy equivalent discretely, the input materials are manifested a following. Agricultural practices for the ﬁeld being studied included field preparation, seeding, post-seeding, fertilization, irrigation, plant protection and harvesting Field preparation includes plowing and disk-harrowing while the herbicides are often used after plowing using disk-harrowing The water requirements are met with stream irrigation, the frequency of which depends on soil characteristics, season, and ambient temperature . In this paper, a cradle-to-farm-gate approach was utilized in the LCA procedure. The system boundary encompassed the total inputs from the cradle (e.g. fertilizer and pesticide production from raw materials) to the farm gate (harvested crops) as well as operational inputs including, fertilizer, pesticide, water, machinery, fuel, and electricity. Mass-based and land-based functional units are dominant in LCA studies of agricultural systems . Hence, the land function measured by cultivated hectare per year, and physical units such as kg was used.
Table 1 Energy equivalents of inputs and output in wheat production. Inputs A. Inputs Human labor Machinery Seeds Chemical fertilizers Nitrogen (N) Phosphate (P2O5) Potassium (K2O) Diesel fuel Gasoil Gasoline Electricity Chemicals Herbicides Insecticides B. Output Wheat
Energy equivalent (MJ ha1)
h h kg
1.96 142.7 15.7
  
kg kg kg
60.6 11.1 6.7
  
L L kWh
38 37 12.1
kg kg kg
278 237 14.7
   
2.2. Energy analysis 2.2.1. Fuel consumption Time duration was counted from initiation of each agricultural practice and also using the information from the experienced orchardists the fuel consumption was calculated using the following equation.
Fc ¼ Fhr t
where Fc (MJ/ha), Fhr and t are fuel consumption required for agricultural practice at 1 ha (Liter per hectare), fuel required for 1 h operation (Liter per hour) and working time of tractor (Hour per hectare), respectively. 2.2.2. Machinery, implements and tools Machinery effective lifetime, machine weight and the average area that the machine is engaged with are of signiﬁcant factors that are required to determine the share of machinery, implements and tools in the energy inputs using as following .
ME ¼ E
W Qh T
where ME is the machinery energy (MJ/ha), E is a coefﬁcient equal to 62.7 MJ/kg which is associated with the energy provided by a machine, W is the weight of machine, T is the effective lifetime of tractor and Qh is the representative of the total machine working hours during an agricultural season (hr/ha). 2.2.3. Electricity Electricity is mainly consumed in wheat cultivation for irrigation and pumping purposes. Water for irrigation was pumped from local agricultural well by electric pumps. To this aim, the power required for pumping a hectare in 1 h was quantiﬁed and then multiplied with the energy equivalent, the total amount of electric energy was yielded. Energy for pumping water was calculated as following .
HQ ET LG 3600 εr
where Ee is electricity energy (MJ/ha), g is gravitational acceleration (m/s2), Q is water ﬂow rate (m3/ha), H is the dynamic well head, T is irrigation time (hr), L is the numer of irrigation in one agricultural season, and E is the energy equivalent (MJ/kWh) and εr is the pumping efﬁciency varying between 0.7 and 0.9. 2.2.4. Spraying After inquiring the required data from the expert orchardists, the total consumed amount of spray was obtained and then multiplied with the corresponding equivalent to deﬁne the sprayer as an input (Table 1). Alternatively, it is possible to determine the amount of effective material percentage per liter of spraying. The amount speciﬁc consumed spraying (kg/L). The multiplication of speciﬁc consumed spray with the effective material percentage of spray yields the net spray consumption. This index can be multiplied with the energy equivalent to determine the spraying energy. 2.2.5. Fertilizer The chemical composition of chemical fertilizers and the nutritional content values of NitrogenePotassium-Phosphate (NKP) are different; however, the ingredients of the chemical fertilizers were obtained from the producers. Accordingly, the calculations were carried out based on N, P2O5 and K2O to determine the energy in the utilized fertilizers.
H. Taghavifar, A. Mardani / Renewable Energy 74 (2015) 208e213 Table 2 Quantity and energy equivalent of inputs and output in wheat production. Inputs (unit) A. Inputs Human labor (h) Machinery (h) Seeds (kg) Chemical fertilizers Nitrogen (kg) Phosphate (kg) Potassium (kg) Diesel fuel Gasoil (L) Gasoline (L) Electricity (kWh) Chemicals Herbicides (kg) Insecticides (kg) Total energy input (MJ/ha) B. Output Wheat (kg)
Ef ¼ Wf Ek
Quantity per unit area (ha)
Total energy equivalent (MJ ha1)
94.89796 15.04555 152.0382
186 2147 2387
112.6106 123.7838 130.7463
6824.2 1374 876
315.3684 93.4594 70.4958
11984 3458 853
0.607319 7.010292 7.793929 22.28208 4.486326 2.860277 39.12964 11.29091 2.785179
Energy output MJ ha1 Energy use efficiency ¼ Energy input MJ ha1
Energy productivity ¼
Specific Energy ¼
Wheat output kg ha1 Energy input MJ ha1
Energy input MJ ha1 Wheat output kg ha1
Net Energy ¼ Energy output MJ ha1 Energy input MJ ha1 (9)
321.5 215.7 30626.4
where Ef is the fertilizer energy per hectare (MJ/ha), Wf is the weight of the consumed fertilizer (kg/ha), and Ei is the existing energy in fertilizer (MJ/kg). 2.2.6. Human labor The energy equivalent of human labor is the muscle power used in field operations of crop production where the muscle power is the ability to exert an average energy in 1 h of agricultural activities . To aim this, the number of required workers for any particular practice was inquired as well as the working duration for an individual worker. Accordingly, the corresponding energy equivalent was extracted to determine the human labor energy. It is essential to assess the energetic efficiencies of the agricultural system by the energy ratio between the outputs and the inputs. Based on the energy equivalents (Table 1), the energy use efficiency, the energy productivity, the net energy gain, the energy intensiveness and the specific energy were computed as :
Furthermore, the economic analysis of wheat production such as net profit, gross profit and benefit to cost ratio has to be investigated by the following terms .
Total production value ¼ Wheat Yield kg ha1 wheat price $kg1
Gross return ¼ Total production value $ha1 Variable cost of production $ha1
Net return ¼ Total production value $ha1 Total production cost $ha1
Total production value $ha1 Variable cost of production $ha1
Wheat yield kg ha1 Total production cost $ha1
Fig. 2. The share of total mean energy inputs in wheat production.
H. Taghavifar, A. Mardani / Renewable Energy 74 (2015) 208e213
3. Results and discussion Table 2 summarizes quantity per unit area and total energy equivalents of wheat production encompassing different inputs. As it can be seen the total input energy was calculated as 30626.4 MJ ha1. The greatest shares of input energy with 11984 MJ ha1 and 6824.2 MJ ha1 corresponded to the diesel fuel and Nitrogen, respectively. It should be pointed out that a great deal of fuel in the tractors was mainly consumed for tillage process along with land preparation, cultural practices and transportation. Table 2 also demonstrates that human labor has a negligible portion in the total energy input with 186 MJ ha1 which could be attributed the adopted mechanization trend in the West Azarbayjan province. Despite the fact that among the chemical fertilizers Potassium and Phosphate are more consumed than Nitrogen, the latter item has signiﬁcantly greater inﬂuence on input energy. This emanates from higher energy equivalent which corresponds to the Nitrogen when compared to the aforementioned elements. This matter is crucial from the standpoint of ecology, owing to the increasing use of chemicals and machinery and as a result increasing the share of nonrenewable energies that can lessen the sustainability of agricultural systems. It is known that Nitrogen fertilizer has an effective role in the growth and yield of agricultural plants since it has been always raised as a serious challenge in relation to energy consumption in agriculture . It was also discovered that the maximum yield of wheat is 3638.12 tons ha1. Finally, the output energy for wheat equal to 53480.4 MJ ha1 is deducible as appreciated form Table 2. 94.89796 h and 15.04555 h of the human labor and machine power are required per hectare of wheat production. Similar trends have been reported in literature for other surveyed locations [26e28]. For an instance, in Ref. , the total consumer chemicals included 30.21% of the total energy inputs (9821.87 MJ ha1), prior to seed (5937.54 MJ ha1), irrigation water (4345.3 MJ ha1) and human labor (431.98 MJ ha1) with 18.27%, 13.37%, and 1.33% allocations, respectively. Among the consumer chemicals nitrogen fertilizer, devoted the highest energy consumption (23.52% of the total energy input) and among all inputs was on the second place after the diesel fuel (7643.8 MJ ha1) . These ﬁndings are in compliance with the achieved results for the West Azarbayjan province. Fig. 2 demonstrates the share of total mean energy inputs and the energy use pattern in wheat production of West Azarbayjan. As illustrated, the summation of the consumed fuel and chemical fertilizer form approximately the 80% of the energy inputs. It is also worth to mention that adoption of machinery and implements has an inﬂuential role in this diagram. There is typically a reverse relationship between human labor and machinery adoption. This means that when mechanization on farmyards decrease, in contrast, human labor would increase inevitable to compensate the deﬁciency. Additionally, the incomparably greater share of machinery than that of human labor is an indication of the improved agricultural practices in the in the area of survey. However, when labor share in production is restricted, the social problems caused by rural migration to cities have been rising .
Fig. 3. The share of total energy input in the form of direct, indirect, renewable and nonrenewable in wheat production.
The energy input and output, yield, energy use efficiency, specific energy, energy productivity, and net energy gain of wheat production in the West Azarbayjan province were calculated using Eqs. 6e9 and the results are tabulated in Table 3. It is concluded that the energy use efficiency increases by augmentation the crop yield and or by decrement of the energy input consumption. Wheat farming is energy efﬁcient with a net energy balance of 20,596 MJ per ha and energy ratio of 2.34 . These results are further in compliance with the results achieved in the present investigation. Fig. 3 shows the distribution of total energy input as direct, indirect, renewable and nonrenewable. Percentages of these energy forms clarify the share of each group and provide beneﬁcial guidelines for the orchardists and policy makers to follow the sustainable development in the agronomy sector. The total consumed energy input could be classified as direct energy (53.81%), and indirect energy (38.39%) or renewable energy (30.23%) and nonrenewable energy (88.81%). High consumption of nonrenewable energies decrease the energy use efﬁciency in production systems, since production of chemicals and using of machinery as the major index of common systems need greater of energy consumption . The obtained ﬁndings indicate that the portion of renewable energies in the production of wheat is very little. This subject is crucially signiﬁcant from the ecological viewpoint, because the source of nonrenewable energies is limited. For sure, this drawback is not concerned with the surveyed region and the results of long-term studies in Iran show that agriculture in Iran is very much dependent on nonrenewable energies . The economic analysis indices of wheat production in the West Azarbayjan province were calculated using Eqs. 10e14 and the results are tabulated in Table 4. Furthermore, the allocation of some economic indices are illustrated and compared in Fig. 4. As it can be inferred, the total value of production equal to 967.73 $ ha1 was achieved for the surveyed region while the productivity was
Table 4 Economic analysis of wheat production. Cost and return components
Table 3 The energetic efficiency of the agricultural system evaluated by the energy ratio between the outputs and the inputs. Item
Energy use Efﬁciency () Energy Productivity (kg/MJ) Speciﬁc Energy (MJ/kg) Net Energy (MJ ha1)
1.746219 8.418194 0.1187 22854
Yield Sale price Total value of production Total cost of production Fixed cost of production Variable cost of production Total return Beneﬁt Productivity
kg ha $ kg1 $ ha1 $ ha1 $ ha1 $ ha1 $ ha1 e kg $1
3638.12 0.266 967.73 1127.68 675.47 452.21 515.52 0.85 3.22
H. Taghavifar, A. Mardani / Renewable Energy 74 (2015) 208e213
Fig. 4. The share of different economic indices in wheat production.
obtained to be 3.22 kg ha1. Other economic indices are detailed in Table 4. 4. Conclusion The present investigation was aimed at analyzing the energy consumption of wheat production in West Azarbayjan utilizing life cycle assessment (LCA). The assessment was carried out in terms of energy input and output, yield, energy use efficiency, specific energy, energy productivity, and net energy gain. As well, the allocations of different direct, indirect, renewable and nonrenewable energy sources were encompassed. It was disclosed that the greatest shares of input energy with 11984 MJ ha1 and 6824.2 MJ ha1 corresponded to the diesel fuel and Nitrogen, respectively. The total consumed energy input could be classified as direct energy (53.81%), and indirect energy (38.39%) or renewable energy (30.23%) and nonrenewable energy (88.81%). Finally, some economic indices were incorporated in the present investigation such as the total value of production, productivity and beneﬁt. The total value of production equal to 967.73 $ ha1 was achieved for the surveyed region while the productivity was obtained to be 3.22 kg ha1. References  www.ars.usda.gov/main/site_main.htm?modecode¼12-35-45-00.  Food and Agriculture Organization (FAO), www.fao.org; 2014.  Pahlavan R, Omid M, Rafiee S, Mousavi-Avval SH. Optimization of energy consumption for rose production in Iran. Energy Sustain Dev 2012;16(2): 236e41.  Khoshnevisan B, Raﬁee Sh, Omid M, Youseﬁ M. Prediction of environmental indices of Iran wheat production using artiﬁcial neural networks. Int J Energy Environ 2013;4(2):339e48.  Nemecek T, Huguenin-Elie O, Dubois D, Gaillard G, Schaller B, Chervet A. Life cycle assessment of Swiss farming systems: II. Extensive and intensive production. Agric Syst 2011;104(3):233e45.
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