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Journal of Cleaner Production 54 (2013) 89e100

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Journal of Cleaner Production

journal homepage: www.elsevier .com/locate/ jc lepro

Potential greenhouse gas emission reductions in soybean farming:a combined use of Life Cycle Assessment and Data EnvelopmentAnalysis

Ali Mohammadi a,b,*, Shahin Rafiee a, Ali Jafari a, Tommy Dalgaard b,Marie Trydeman Knudsen b, Alireza Keyhani a, Seyed H. Mousavi-Avval a,John E. Hermansen b

aDepartment of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, IranbAarhus University, Department of Agroecology, BlichersAllé 20, P.O. Box 50, DK-8830 Tjele, Denmark

a r t i c l e i n f o

Article history:Received 30 January 2013Received in revised form8 May 2013Accepted 11 May 2013Available online 27 May 2013

Keywords:Global warming potentialLCA þ DEA methodSoybeanIrrigationCrop residue

* Corresponding author. Department of AgricultuFaculty of Agricultural Engineering and Technology,Iran. Tel.: þ98 2612801011; fax: þ98 2612808138.

E-mail addresses: Mohammadia@ut.ac.ir, m(A. Mohammadi).

0959-6526/$ e see front matter � 2013 Elsevier Ltd.http://dx.doi.org/10.1016/j.jclepro.2013.05.019

a b s t r a c t

Joint implementation of Life Cycle Assessment (LCA) and Data Envelopment Analysis (DEA) has recentlyshowed to be a suitable tool for measuring efficiency in agri-food systems. In the present study,LCA þ DEA methodologies were applied for a total of 94 soybean farms in Iran to benchmark the level ofoperational input efficiency of each farmer. Likewise, potential reductions in the consumption levels ofthe physical inputs were determined, while estimating the environmental improvements linked to thesereduction targets. Our results indicate that 46% of the farms studied operated efficient. The estimatedGlobal Warming Potential (GWP) reduction for the whole sample was obtainedw11% according to DEAmodel results. Among the field operations, the contribution of irrigation to the total GWP reduction wasthe highest (63%) followed by fertilization (34%). The results also revealed that farms which burnt cropresidue in the field generate significantly more greenhouse gas emissions than other farms. The raising ofoperational input efficiency and limiting of crop residue burning in the field are recommended options toensure more environmental friendly soybean farming systems in the region.

� 2013 Elsevier Ltd. All rights reserved.

1. Introduction

Agriculture contributes significantly to atmospheric Green-house Gas (GHG) emissions, with w14% of the global net CO2emissions coming from this sector (IPCC, 2007). In Iran, agriculturalproduction has a smaller share of the total GHG emissions, esti-mated to be around 4% (Sekhavatjou et al., 2011). Emissions fromagriculture, however, shows an increasing trend during the last twodecades due to a high application of synthetic nitrogen, direct en-ergy inputs and intensive use of farm machinery in Iranian agri-culture (Beheshti Tabar et al., 2010). Production, formulation,storage, and distribution of these inputs and utilizationwith enginebased equipment result in combustion of fossil fuels, and alsoemissions of GHGs like CO2, N2O and CH4 into the atmosphere that

ral Machinery Engineering,University of Tehran, Karaj,

ohammadia2011@gmail.com

All rights reserved.

these emissions are responsible for global warming (Lal, 2004). Theconcept of a Global Warming Potential (GWP) for each greenhousegas was introduced over 12 years ago to help determine the relativeability which a particular gas may have toward forcing the world’sclimate (IPCC, 1990). With the growing concern regarding climatechange and global warming, it is increasingly important to under-stand what might happen when climate changes. Sanghi andMendelsohn (2008) reported one of the Earth’s biggest problemsis that warming will threat global agricultural and food productionchain. Thus, while the energy use and the related CO2 emissions areimportant in relation to GHG emissions, it is not the only factorinfluencing the GHG effect. With a so-called Life Cycle Assessment(LCA) the total GHG emissions are determined by aggregating theeffects of the different emissions taken place in all phases of theproduction chain.

LCA is an ISO-standardized methodology (ISO, 2006a, 2006b) toinventory the material, inputs and emissions associated with eachstage of a product life cycle and to express these in terms of theirquantitative contributions to a specified suite of environmentalimpact categories (Pérez Gil et al., 2013). Several LCA studies haveexamined life cycle impacts for food and field crops production

A. Mohammadi et al. / Journal of Cleaner Production 54 (2013) 89e10090

(Garnett, 2008; Romero-Gámez et al., 2012; Abeliotis et al., 2013),and it has also been used to analysis the soybean production invariety of countries such as China (Knudsen et al., 2010), Canada(Pelletier et al., 2008) and Brazil (Prudêncio da Silva et al., 2010). Inliterature there is no work on the environmental impacts of Iraniansoybeans using LCA or others approach, though few studies arefound about energy use in the soybean production (Mousavi-Avvalet al., 2011a, 2011b). North of Iran, especially the Golestan province,has the longest tradition of soybean cultivation in the country andabout 75% of total Iranian production comes from this province(Ministry of Jihad-e-Agriculture of Iran, 2010). Therefore, there arespecial needs to evaluate options that can reduce the environ-mental impacts of soybean crops in this region.

Data Envelopment Analysis (DEA) is a linear programmingbased frontier estimation technique for measuring the relative ef-ficiencies of a homogenous set of Decision Making Units (DMUs)having multiple inputs and outputs (Cooper et al., 2004). Based onthe ratio of the weighted sum of outputs to the weighted sum ofinputs, DEA distinguishes efficient DMUs from inefficient ones, andcalculates an efficiency score and target efficient values for thoseDMUs identified inefficient (Nassiri and Singh, 2009). In a scenario,where multiple input/output data are available for a wide range ofunits, the use of a methodological approach based on the jointimplementation of LCA and DEA has been suggested by Vázquez-Rowe et al. (2010). This method combines LCA and DEA in orderto establish a link between operational input efficiency and envi-ronmental impacts of multiple DMUs, and quantifies the environ-mental consequences of operational inefficiencies (Lozano et al.,2009; Samuel-Fitwiet al., 2012).

In recent years, several researchers have used LCA þ DEAmethodology to assess technical efficiency and environmentalperformance of DMUs. Vázquez-Rowe et al. (2012) computed theoperational efficiency scores for a sample of 40 grape producers inSpain, and concluded that 60% of the vineyards operated efficiently.For inefficient units, average reductions of up to 30% were esti-mated for inputs consumption, leading to impact reductionsranging from 28% for Eutrophication Potential (EP) up to 39% forGWP. In order to determine the level of operational input efficiencyof each farm, Iribarren et al. (2011) studied a total of 72 dairy farms

Fig. 1. Location of the studied

following a LCA þ DEA methodology. They benchmarked the po-tential reductions in the used inputs, while calculating the envi-ronmental gains linked to these reduction targets, and concludedthat a total of 31 farms were deemed efficient. In another study(Lozano et al., 2010), a whole sample of 83 mussel cultivation siteswas analyzed via this approach to discriminate the inefficientcultivation rafts and to estimate the corresponding potential im-provements. The results of this work showed that 59% of the siteswere operated inefficient. The large potential of the input use re-ductions led to considerable environmental gains which rangedfrom 11% to 67% depending on the chosen impact category.

The present study applies LCAþ DEAmethodology with the aimof performing a joint analysis of operational efficiency and globalwarming impact for soybean production in the north of Iran.Additionally, a technical efficiency study is undertaken in order todetect inefficient soybean farms and benchmark target input con-sumption levels for the inefficient producers, which with a reduc-tion in inputs consumption reduces GHG emissions.

2. Materials and methods

2.1. Site description and data collection

This work was carried out in 12 villages of the Golestan prov-ince, located in Northeast of Iran between latitudes of 36� 300 and38� 080 N and between longitudes of 53� 570 and 56� 220 E, with anelevation of 25 m above mean sea level, and less than 2% slope(Fig. 1). This province was identified as a representative of theIranian soybean production enterprises since it is the main regionof oilseed crops farming in the country, mainly due to the favorableecological conditions. The climate is Mediterranean. In the lastdecade, the mean annual rainfall and mean annual air temperaturewere 442 mm and 18 �C respectively.

The data used in the study were obtained from 94 farmers usinga face-to-face questionnaire method. The reliability of the ques-tionnaires was verified using Cronbach’s alpha (Cronbach, 1951).Cronbach’s alpha (a) is reported as an index of reliability whichtheoretically ranges from 0 to 1. If a is close to 0 then the quantifiedanswers are no reliable at all, and if it is near 1 the answers are very

area in the north of Iran.

A. Mohammadi et al. / Journal of Cleaner Production 54 (2013) 89e100 91

reliable. As a rule of thumb, a score is accepted by minimum of 0.7(Christ and Burritt, 2013). So, the Cronbach’s alpha level of 0.78demonstrated adequate construct reliability. Data collected wererelated to various inputs use (electricity, biocides, fertilizer, etc), thelands possessed by the farmers, their cropping pattern, crops yields,operations time, economical information, etc. The average size ofthe selected farms was 6.3 ha of which 100% was irrigated. Ingeneral, the farmers grow more than one crop in a growing season.Wheat, paddy, canola and barley were other crops grown besidessoybean. These data were also applied in a previous work to esti-mate energy use efficiency in soybean cultivation for this area(Mousavi-Avval et al., 2011a). The summary of results of energybalance is presented in Table 1.

2.2. DEA model and definition of DMU

The DEA is a non-parametric data analytic technique that allowsbuilding an envelopment surface or efficient production frontier toassess the efficiency of all production units under study, commonlycalled Decision Making Units (DMU’s), which receive multiple in-puts and produce multiple outputs (Mousavi-Avval et al., 2012).Given a sample of the DMUs, the purpose of the DEA is to establishthe relative efficiency of each DMU as long as they are comparablein the sense that they all consume the same inputs, albeit indifferent quantities, and produce the same set of outputs, also indifferent quantities (Galanopoulos et al., 2006). In this study DMUrefers to each soybean farm (1 farm ¼ 1 DMU). For assessment ofunits, an input-oriented slacks-based measure of efficiency CCR(Charnes, Cooper, Rhodes) model was employed (Nassiri and Singh,2009). Input-oriented model were assumed to be more suitablebecause there is only one output; while multiple inputs are used.Likewise, in the farming systems, a producer has more control overinputs rather than output levels, and input conservation for givenoutputs seems to be more reasonable (Galanopoulos et al., 2006).Hence, some works, linked mainly to eco-efficiency through thecombined application of LCA þ DEA, use the input-orientedmethod, assuming that this perspective focuses on reducing inputconsumption and its associated environmental burdens (whenLCA þ DEA is used) as much as possible (Vázquez-Rowe andTyedmers, 2013).

2.2.1. Technical efficiencyThe CCR model of DEA was initially proposed by Charnes et al.

(1978) and it is built on the assumption of constant returns to

Table 1Basic statistics of output, major inputs and energy equivalents used for soybean product

Item (unit) Quantity (unit ha�1)

A. Inputs1. Human labor (h) 1952. Machinery (h) 143. Diesel fuel (l) 1044. Chemicals (kg) 5.35. Chemical fertilizers (N, P, K) (kg) 1566. Farmyard manure (tone) 53457. Water for irrigation (m3) 33039. Electricity (kWh) 133610. Seed (kg) 69

Total energy inputRenewable energyNon-renewable energy

B. OutputSoybean (kg) 3233Total energy output (GJ ha�1)Energy use efficiency

a Indicates standard deviation of energy inputs and output (GJ ha�1).b Figures in parentheses indicate percentage of total energy input.

scale (CRS) of activities. The efficiency of this model is defined inform of technical efficiency (TE). TE is basically a measure by whichDMUs are evaluated for their performance relative to the perfor-mance of other DMUs in consideration. The TE can be defined asfollows (Cooper et al., 2004; Nassiri and Singh, 2009):

qc ¼Pn

r¼1 uryr; jPms¼1 vsxs; j

; (1)

where, qc is the TE score given to unit j; x and y represent input andoutput and v and u denote input and output weights, respectively; sis the number of inputs (s¼ 1, 2,.,m), r is number of outputs (r¼ 1,2,., n) and j represents jth DMUs (j ¼ 1, 2,., k).To solve Eq. (1),following Linear Programming (LP) was formulated: (Cooper et al.,2004;Mousavi-Avval et al., 2012):

Maximize : qc ¼Xs

r¼1

uryro (2)

Subject to :Ps

r¼1uryrj �Pm

i¼1vixij � 0 j ¼ 1;2; : : :;nPm

i¼1vixio ¼ 1

ur � 0; vi � 0;

The DEA software chosen to implement the selected model wasthe EMSei.e., Efficiency Measurement System (Barr, 2004).

2.3. LCA þ DEA framework

The present study focuses on the environmental impact andGHG emissions of soybean production in the surveyed region. Forthis purpose the input/output items of the model DMU shall beestablished within an LCA þ DEA framework. Fig. 2 shows the el-ements involved in the LCA þ DEA study of soybean farms. As canbe seen from the figure, DEA and LCA input/outputs items are notsame. For DEA, labor, machinery, diesel fuel, water for irrigation,electricity, chemical fertilizer, Farmyard Manure (FYM),biocidesand seed were considered as the inputs. Soybean seed harvestedwas included as the main output. For LCA analysis, crop residue(straw) and direct GHG emissions to air (CO2, N2O and CH4) alsoincluded in the system boundaries. All the selected items foranalysis were assumed to be independent from each other. Thefarm input and output data for DEA matrix and inventory data for

ion (Mousavi-Avval et al., 2011a).

Energy equivalent (GJ ha�1) SDa

0.38 0.100.96 0.254.95 1.370.90 0.337.02 2.431.60 1.863.37 1.21

15.94 9.710.25 0.04

35.37 11.965.60 (16%)b 2.4129.77 (84%) 10.23

80.83 12.312.29 1.06

Farm inputs

Fertilizer

Diesel fuel

Machinery

Electricity

Water

Biocides

Farm operations

Tilling

Planting

Applying biocides

Fertilizing

Weeding

Irrigation

Harvesting

Residue management

Output:

soybeans

Direct emissions to air:

CO2

N2O CH4

Labour

Output:straw

Seed

LCA elements

DEA elements

Farmyard manure

Indirect emissions from inputs production:

CO2

N2O

Fig. 2. LCA and DEA elements for each soybean farm (LCA elements are seen in the boxwith the dotted line and DEA elements shown in the box with the solid line).

A. Mohammadi et al. / Journal of Cleaner Production 54 (2013) 89e10092

the sample of 94 soybean farms are included in Tables A.1 and A.2,respectively, in the Appendix A. This data is related to 1 ha ofharvested soybean in the research area.

Operational and environmental patterns in soybean productionwere established according to the five-step LCA þ DEA method(Iribarren et al., 2010; Vázquez-Rowe et al., 2010). As shown inFig. 3, the first step of the methodology (step I) is based on datacollection for developing the Life Cycle Inventory (LCI) of everyDMU. The second stage includes the performance of the Life CycleImpact Assessment (LCIA) for each of the DMUs based on the LCIdeveloped in the first step. It constitutes the characterization of theenvironmental profile of the current DMUs for a given selection ofimpact categories (step II). The third stage involves DEA study forthe farms. In step III, the inputs and outputs from the LCIs are in-tegrated into a DEA matrix and implemented in a DEA model ofefficiency. This optimization model is aimed to minimize the inputconsumption levels while the output level is fixed (Eq. (2)). Theoptimal value of the objective function of themodel is the efficiencyscore assigned to the jth DMU. If the efficiency score would beobtained 1, the jth DMU deems to be efficient; otherwise, it isinefficient. The operational efficiency of each DMU is calculatedalong with the projection for the inefficient DMUs (target values).DEA targets define virtual DMUs that consume less input and or

I.LCI of each DMU

II. Environmental impacts of current DMUs

III.Current DMUs DEA: efficiency and super efficiencyanalysis

IV.Environmental impacts of target DMUs

V. Interpretation: Eco-efficiency verification

Fig. 3. Five-step LCA þ DEA methodology for soybean production in Iran.

produce more output. In order to identify the best performingsoybean farms a super-efficiency model was also used to estimatenew efficiency scores for ranking efficient DMUs. The target envi-ronmental impacts of DMUs from the previous step are subject to anew environmental characterization in the fourth step. Eventuallyin the final step, the potential environmental impacts for virtualDMUs are compared with the current ones to estimate a quanti-tative measure of the environmental risks of operational in-efficiencies in soybean farming. The SimaPro7 (Pré, 2009) softwareapplication was utilized to implement the LCA model and performthe assessment.

2.4. Field emissions and functional unit

As mentioned in the previous section, the Global WarmingImpact in the present study, in addition to emissions related toinput use, included three kinds of direct field emissions: carbondioxide (CO2), nitrous oxide (N2O) and methane (CH4) to air. Thesevalues were estimated using published references (Salvagiotti et al.,2008; Knudsen et al., 2010) and the IPCC 2006 guidelines (IPCC,2006) for the direct and indirect N2O emissions. The CO2 emis-sions related to inputs use such as fertilizers (urea, P2O5 and K2O),FYM, electricity, diesel and chemicals (herbicides and insecticides)were accounted. Regarding emissions from electricity, fertilizer andchemicals production, the standard values were taken from liter-ature data (Ecoinvent Centre, 2009; Williams et al., 2006). Thespecific NH3 emissions associated with urea and FYM application inthe context of N2O emissions calculations and the CH4 emissionscaused by burning crop residues in the soybean farms were alsoestimated according to (IPCC, 2006). It was also assumed that land-use changes related to soybean farming are not major contributorsof GHG emissions. Since land-use change due to the soybean areaexpansion does not often occur in the region. Thus, corrections byland-use impacts would cause negligible variations to ourcomputation of GWP and were ignored.

For the global warming analysis of the systems under study, thefunctional unit adopted was 1 kg of harvested soybean and deliv-ered to the oil extraction mills. Thus the unit of the GWP impact isexpressed in g CO2 eq. per kg of soybean.

3. Results and discussion

3.1. Current environmental characterization

In order to compute the LCA results, system boundaries weredefined for correct accounting of emissions associated with inputs,within field activities and the farm outputs. In the present study,average current global warming impact per kg of soybean wasfound to be 957 CO2 eq. This study is the first one that considersGHG emissions for this crop in Iran, thus no work exists which wecan compare our results with. But in comparison to other countries,our findings are much higher than some results for GWP of 1 kg ofsoybean production such as 248 g CO2 eq. in Canada (Pelletier et al.,2008), 263 g CO2 eq. in China (Knudsen et al., 2010) and757 g CO2 eq. in Turkey (Özilgen and Sorgüven, 2011). This highdifference may be caused by large consumption of agricultural in-puts such as chemical fertilizers (mainly N) and electricity energyfor irrigation which enhances GHG emissions into the atmosphere.Therefore, these consequences can indicate the importance ofdeveloping better habits and behavior of producers to use thesources efficiently and decrease the emissions.

A contribution analysis has also been performed with referenceto soybean for the average GWP of the whole sample and farmswith the highest and lowest impacts (Fig. 4). As illustrated in Fig. 4,the major contribution for the mean global warming impact came

0% 20% 40% 60% 80% 100%

Farm with the lowest GWP

Farm with the highest GWP

Average GWP of farms

Chemicals Fertilizer Farmyard manure Diesel Electricity Field emissions

Fig. 4. Contribution of different stages to the global warming impact produced from1 kg soybean (average of the whole sample, farm with the highest impact and farmwith the lowest impact).

A. Mohammadi et al. / Journal of Cleaner Production 54 (2013) 89e100 93

from the field emissions (40%) and secondly from the electricity(31%). For farm with the largest GWP (1640 g CO2 eq. kg�1), elec-tricity had the most contribution (516 g CO2 eq. kg�1), whereas thefield emissions with 356 g CO2 eq. per 1 kg soybeans are the maincontributor for farm with the smallest GWP (530 g CO2 eq. kg�1).Our data on soybean confirmed the results reported by other au-thors (Pelletier et al., 2008; Soni et al., 2013) showing the impor-tance of the field emissions as the main contributor to the globalwarming assessment.

In previous work (Mousavi-Avval et al., 2011a) we estimated theenergy intensity for the soybean farms under consideration. Ourstudy revealed that soybean farms in the north of Iran depend onsignificant inputs in the form of non-renewable energy associatedwith fertilization, irrigation and machinery practices, and that theshare of these inputs accounted to more than 80% of total energyinput (Table 1). With the aim of establishing a simple relationshipbetween energy intensity and GWP of soybean farms in area, Fig. 5plots GWP impact versus energy intensity for the whole sample. Ascan be seen from Fig. 4, there is a direct relationship between thetwo variables and obviously the GWP is increased with the increasein energy intensity. Interestingly, a similar trend between the en-ergy consumption (MJ ha�1) and the GWP was also observed (datanot shown). The energy intensity for the farms with the lowest andhighest GWP ranged from w4 MJ to w20 MJ for 1 kg of soybeanproduction. The big difference among the farms for the energyintensity and GWP is mainly due to inputs management in fieldoperations and the burning of crop residue in some farms, alsoimplying that there is a great scope for improving operational inputefficiency and GHG performance of soybean production.

400

600

800

1000

1200

1400

1600

1800

0 5 10 15 20 25

GW

P (

g C

O2

eq. k

g-1)

Energy intensity (MJ kg-1)

Fig. 5. Relationship between GWP (g CO2 eq. kg�1) and energy intensity (MJ kg�1) forthe 94 soybean farms.

3.2. DEA þ LCA results

The technical efficiency of all DMUs with a TE less than 1 ispresented in Table 2. From the total of 94 farms considered, 43(46%) were identified as efficient farms and the remaining 51were inefficient; i.e., their efficiency scores were less than onewith mean value of 0.81. This shows that the same level of outputcould be produced with 81% of the resources if these units wereperforming on the frontier. Another interpretation of this result isthat 19% of overall resources could be saved by raising the per-formance of these DMUs to the highest level. Likewise, targetoperating points were estimated (operational benchmarking).These points that convert inefficient units into efficient are alsopresented in Table 2 as reduction percentages of the currentoperational values.

After identifying inefficient farms and reduction levels of usedinputs, a new environmental characterization was obtained foreach of these inefficient farms, in order to estimate their potentialenvironmental impacts. The results in Table 3 indicate that if inef-ficient farms are managed under efficient operational conditions,considerably reduction in GHG emissions is possible without anychange in output level. Reduction percentages of GWP of inefficientfarms varied from 3% to 34%. This wide range that associated withhigh range in TE (61%e99%) of inefficient units implies that all ofthe farmers were not fully aware of the right production techniquesor do not apply them at the proper time in the optimum quantity,especially for C-intensive inputs. The relative contributions of theseparate GHGs to the reduction potential of the total CO2 equiva-lents emissions are given in Table 3. The major contributor to thepotential emission reductions was CO2 that varied from 64% to 98%in the soybean farms. The contribution of N2O emission to theenvironmental impact reduction ranged from 2% to 36%, that thisvariation in the N2O emission reductions were affected from theamount of applied synthetic fertilizers (mainly nitrogen) in thefarms. Table 4 depicts the GWP for farm operations in current andtarget conditions, and possible GWP reduction of farm operationsin the total GWP reduction. It is noted from Table 4 that thefertilizing generated the highest CO2 eq. emissions among fieldpractices with 524 g CO2 eq. kg�1. In target condition this valuecould be reduced to 487 g CO2 eq. kg�1 according to DEA modelresults, that indicating around 34% contribution to the total GWPreduction. Nitrogenous fertilizer is a main source of CO2 and N2Oemissions, therefore improving fertilizer use efficiency and findingalternatives is important to reducing GHG emissions (Lal, 2004).Soybean is an annual legume and legumes due to their ability tobiologically fix N2 in soil, can decrease N fertilizer requirementscompared to cereal crop. On the other hand, the farmers who arenot informed about this information apply high nitrogen doses tomaximize yield, while this practice especially before floweringseriously reduces symbiotic N2 fixation. The consequences of theseoperations are inefficient use of nitrogen fertilizer, high costs ofsoybean production and GHG emissions. Our data is in agreementwith Özilgen and Sorgüven (2011) that showed fertilizing with634 g CO2 kg�1 has the highest contribution in CO2 emission forsoybean production in Turkey.

The irrigation was found second main operation in GHG emis-sions (301 g CO2 eq. kg�1) and the largest contribution to the totalGWP reduction with 63%; implying that there is a great scope forGWP reduction by improving the irrigation systems efficiency. Inamong inefficient producers, the electricity input also showed awide range of optimization with potential improvements of up to62% (Table 4). In the area research the most of producers (86%) useelectricity to power pumps, which distribute irrigation wateramong their fields. Some of them apply the traditional methods likeflood irrigation that use water wastefully. Therefore, raising

Table 2Technical efficiency (TE) and operational reduction percentage of physical inputs for the inefficient soybean farms.

DMU TE Operational reduction (%)

Labor Machinery Diesel Water Electricity Chemicals Fertilizer FYM Seed

3 0.79 0.0 2.8 0.0 0.0 45.3 0.0 11.4 24.5 0.44 0.84 27.9 3.8 0.0 0.0 13.8 0.0 10.4 12.1 0.05 0.91 0.0 34.6 24.5 0.0 60.7 0.0 16.9 0.0 38.36 0.92 0.0 34.9 24.8 5.0 0.0 7.6 13.7 78.6 0.08 0.84 0.0 0.0 0.1 9.5 0.0 0.0 63.1 0.0 18.910 0.75 9.4 31.3 23.4 13.5 0.0 35.7 0.0 0.0 18.312 0.84 6.2 0.0 7.4 0.0 39.9 12.0 48.9 8.7 0.013 0.90 42.4 21.8 16.4 0.0 7.7 23.6 17.5 0.0 27.514 0.72 30.0 22.9 20.8 0.0 0.0 0.4 29.1 0.0 32.116 0.71 13.4 6.8 0.0 0.0 31.0 0.0 35.4 37.7 0.017 0.86 38.7 10.5 9.3 15.4 47.2 0.0 42.9 56.6 0.018 0.90 28.9 3.4 0.0 0.0 23.5 0.0 45.2 0.0 26.019 0.66 18.4 9.3 8.0 29.2 39.1 21.3 65.6 28.1 0.023 0.67 3.6 10.5 1.1 0.0 4.5 0.0 34.9 0.0 0.024 0.95 38.5 1.8 0.0 0.0 20.0 0.0 0.0 0.0 22.126 0.78 27.7 42.0 36.8 27.3 41.0 0.0 77.5 38.7 0.027 0.75 25.6 26.8 22.0 29.4 20.9 0.0 26.2 25.8 0.033 0.80 0.0 5.4 1.1 20.6 40.2 0.0 7.5 15.2 0.036 0.88 0.0 24.6 24.0 0.0 32.4 0.0 5.1 0.0 11.937 0.87 0.0 15.4 10.9 11.5 29.1 0.0 14.0 0.0 0.038 0.93 0.0 0.0 0.5 44.2 57.6 54.5 0.0 0.0 7.441 0.64 0.0 7.0 0.0 18.9 45.8 21.1 29.8 0.0 0.043 0.88 32.1 0.0 25.9 21.0 38.5 37.7 21.1 15.6 0.044 0.99 0.0 36.2 17.7 7.0 59.8 55.9 8.2 0.0 15.646 0.64 0.0 9.6 0.0 0.9 10.4 0.0 14.3 5.4 0.047 0.87 25.7 9.2 0.0 0.0 40.0 0.0 28.8 71.4 0.048 0.97 0.0 0.0 9.8 18.5 54.6 8.2 84.7 0.0 0.049 0.66 6.9 15.5 0.0 0.0 20.7 0.0 26.3 40.6 0.050 0.99 18.1 0.0 13.3 44.9 70.3 7.1 15.3 0.0 0.053 0.83 0.0 27.5 27.2 54.8 47.7 19.7 31.6 28.4 7.158 0.92 25.2 1.0 0.0 26.6 14.3 1.2 86.2 90.1 0.059 0.88 0.0 0.0 10.0 29.9 39.2 0.0 0.0 45.2 0.060 0.80 0.0 6.6 10.7 34.8 44.6 0.0 0.0 45.6 0.061 0.66 16.5 6.7 0.0 8.4 19.9 0.0 37.9 45.4 0.063 0.72 0.0 0.0 0.0 3.9 65.5 0.0 48.2 0.0 0.464 0.88 0.0 0.0 3.7 0.0 47.4 0.0 9.5 45.7 0.066 0.67 0.0 0.0 10.8 1.4 52.7 13.1 46.2 0.0 0.067 0.96 16.9 29.1 22.1 0.0 0.0 0.0 12.1 0.0 0.368 0.76 17.0 0.0 3.1 0.0 22.2 0.0 0.0 31.4 0.069 0.95 0.0 9.9 0.0 0.0 57.4 0.0 15.1 39.8 2.070 0.64 11.7 0.5 0.0 0.0 32.4 0.0 46.8 53.1 0.071 0.93 0.0 4.8 0.0 9.3 30.2 0.0 24.3 25.4 0.072 0.65 13.3 0.9 0.0 7.3 30.4 0.0 40.5 27.7 0.073 0.85 12.4 0.3 0.0 0.0 34.9 0.0 37.7 0.0 0.074 0.78 0.0 18.8 20.3 0.0 25.2 0.0 30.9 0.0 4.077 0.81 0.0 9.1 0.0 0.0 35.1 0.0 23.3 29.1 0.078 0.67 0.0 5.0 12.5 37.8 58.0 4.4 0.0 0.0 11.279 0.79 0.0 0.0 4.0 0.0 61.9 15.3 2.9 52.6 0.082 0.61 8.1 0.0 0.5 0.0 47.0 0.6 23.1 56.7 0.087 0.93 0.0 5.6 12.5 0.0 0.0 47.7 79.7 91.2 12.091 0.84 8.4 0.0 8.1 46.6 0.0 0.0 33.5 70.8 4.4Meana 0.81 10.3 10.0 8.7 11.3 32.5 7.6 27.9 24.3 5.1Meanb 0.89 5.6 5.4 4.7 6.1 17.6 4.1 15.1 13.2 2.8

DMU: decision making unit.a Mean of the inefficient farms.b Mean of the farms of the whole sample.

A. Mohammadi et al. / Journal of Cleaner Production 54 (2013) 89e10094

efficiency of water pumping equipment and applying new irriga-tion methods are important to decreasing emissions.

On the other hand, natural gas is a primary energy source toprovide electricity in Iran, until 2010, all fuels for electricity gen-eration and also price of electricity were subsidized by the energyand power ministry. Over this period, this price has been lowerthan its international level. Lower price of electricity would in-crease the rate of electricity consumption, and accordingly cancause an increase of the GHG emissions (Moslem Mousavi et al.,2012). Since 2010, all subsides allocated to energy production hasbeen removed gradually, that it means the fuels and electricitywould be priced based on the produced cost in the near future. Inthis condition, further investment and development in renewable

energy sources, especially biomass in agriculture, would be ex-pected. Thus the use of bioenergy from crop residues for electricitygeneration is among the options to reduce the energy required andconsequently CO2 emissions from irrigation. Similar to fertilizer,irrigation is a very C-intensive and energy consuming practice (Lal,2004), many studies have been carried out to find share of irriga-tion in emissions. Schlesinger (1999) obtained emissions fromirrigation at 220e830 kg CO2 eq. ha�1. Follett (2001) calculatedGHG emissions by pump irrigation at 150e200 kg CO2 eq. ha�1

depending on the energy source. Harvesting, tilling, applying bio-cides and residue management are other sources of emissions inthe soybean cultivation which their values has been given inTable 4.

Table 3GWP in target conditions for inefficient soybean farms and contribution (%) of theseparate GHGs to the total GWP reduction.

DMU GWP (g CO2 eq. kg�1) Reduction (%) CO2 (%) N2O (%)

3 883 21.0 95.1 4.94 849 7.7 78.1 21.95 700 26.4 94.6 5.46 587 9.9 66.7 33.38 863 6.2 64.8 35.210 739 3.3 71.3 28.712 764 15.1 94.3 5.713 902 3.8 80.2 19.814 775 2.6 65.1 34.916 984 17.3 90.1 9.917 760 22.5 91.8 8.218 1015 15.3 83.6 16.419 1091 28.5 87.6 12.423 859 3.1 74.1 25.924 769 6.8 87.3 12.726 951 30.7 84.9 15.127 1370 16.7 81.3 18.733 905 18.4 92.5 7.536 1067 16.5 96.6 3.437 699 10.6 91.4 8.638 705 24.5 96.9 3.141 1044 26.9 95.0 5.043 853 18.1 87.6 12.444 608 22.5 94.1 5.946 910 6.5 74.4 25.647 739 29.1 79.0 21.048 596 19.7 88.9 11.149 961 13.2 81.5 18.550 666 37.7 96.7 3.353 1108 26.9 89.9 10.158 676 33.7 69.1 30.959 836 21.2 84.8 15.260 946 25.1 88.4 11.661 988 13.3 80.1 19.963 841 30.8 95.3 4.764 695 20.2 90.7 9.366 973 25.9 95.3 4.767 638 5.6 64.4 35.668 882 10.2 82.7 17.369 657 24.5 88.2 11.870 1104 22.7 78.6 21.471 905 17.6 83.8 16.272 1161 16.2 90.3 9.773 828 14.1 94.8 5.274 1207 14.3 88.7 11.377 724 17.6 81.7 18.378 998 27.9 98.2 1.879 803 29.4 90.2 9.882 879 20.2 79.2 20.887 575 25.6 67.5 32.591 716 22.7 64.8 35.2Meana 858 18.6 84.6 15.4Meanb 847 11.5 45.9 8.4

a Mean of the inefficient farms.b Mean of the farms of the whole sample.

Table 4Estimates of GHG emissions for farm operations in current and target conditions forthe DEA model in soybean farming (g CO2 eq. kg�1).

Field operation Current GWP Target GWP Reduction emissions

Tilling 35 34 0.6Planting 9 9 0.2Applying biocides 26 26 0.7Fertilizing 524 487 37.8Weeding 0 0 0.0Irrigation 301 231 70.2Harvesting 41 41 0.8Residue management 19 19 0.0Total 957 847 110

A. Mohammadi et al. / Journal of Cleaner Production 54 (2013) 89e100 95

3.3. GHG emissions from crop residue

One of the main factors that influences GHG emissions and leadto high CO2 emissions even in efficient farms, could be the residuemanagement. Around 58% of the surveyed farmers burnt the part ofthe crop residues in the field; 27% of them left the crop residues inthe field and the rest of the farmers (15%) removed it from the fieldfor the livestock fodder. Since the residue management practice wasfound key environmental issue, a relationship between GWP(g CO2 eq. kg�1) and crop residue fatewas also evaluated for soybeanfarms which its results has been shown in Fig. 6. As it is seen fromFig. 6 (I), the highest GWP was estimated in the first group (burntresidue in field) of farms withw1030 g CO2 eq. kg�1 followed by thesecond group (left residue in field) and third group (removed residuefor fodder) with w900 and w 860 g CO2 eq. kg�1, that was found asignificant difference at P � 0.05 by Duncan’s multiple range testbetween the first group with other groups. The largest mean value ofN2O emission (381g CO2 eq. kg�1) was observed in the second group.The left residue in the field contributed about 7% of total N2Oemission. Fig. 6 (II) shows the amount of GWP in the target condition,and evidence that if inefficient producers utilize their inputs effi-ciently, the emissions would be decreased 18%, 11% and 17%,respectively in grouped farms based on crop residue fate. In thetarget condition, the GWP of the first group is significantly greaterthan the third group of farms, which implies the importance ofresidue management in the soybean fields. Biomass burning hasbeen identified as an important source of emissions in agriculturalenvironment especially for the developing countries like Iran;however, it continues to be associated with large information gapsand uncertainties (Yuttitham et al., 2011). Biomass burning is alsoconsidered as a serious human health hazardmainly for children andyoung who are more sensitive to air pollution (Agarwal et al., 2012).Low price of straw could be one of the principle reasons that farmersdon’t havemotivation to remove residue from the field. An economicallocation was also applied between soybean and straw, and around2% of impact was allocated for by-product. Kramer et al. (1999) re-ported that CO2 equivalent emissions of crops depend on the pricesof seeds and straw. Lower prices of straw will result in higher GHGemissions of these crops. However the higher price of straw wouldencourage the farmers not to lose crop residue by burning or leavingit on the field. Considerable efforts have been carried out to obtainthe different utilizations for by-products such as electricity genera-tion (Scarlat et al., 2011) and bioenergy production (Jiang et al., 2012).Wang et al. (2010) evaluated the distribution in utilization of cropstraw as industrial raw material, feed for livestock, and direct fuel orbiomass energy resources in China.

3.4. Super-efficiency analysis

The results of standard DEA model separate the DMUs into twosets of efficient and inefficient ones. The inefficient DMUs can beranked on the basis of their efficiency scores; while, DEA lacks torank the efficient DMUs. In this study, a super-efficiency analysiswas carried out in order to determine which farmers attained thebest performing patterns from an operational perspective. Super-efficiency analysis is formulated on a DEA model so that effi-ciency scores can be greater than or equal to 1, then drawing adistinction among the efficient DMUs (Vázquez-Rowe et al., 2012).Table 5 shows the results of the super efficiency computing for 43efficient DMUs. This super-efficiency analysis discriminated thosefarms that are likely to entail the best practice management. Forexample, if a cut-off criterion of SE >1.30 is followed, only 12soybean farms would be included. Interestingly, in the most ofthese super-efficient farms (9 out of 12), the crop residue wereremoved or left from/in the field, while it was burnt in the field just

0

200

400

600

800

1000

1200

Burned in field Left in field Removed forfodder

GW

P (

g C

O2

eq. k

g-1)

CH4 N2O CO2

0

200

400

600

800

1000

1200

Burned in field Left in field Removed forfodder

GW

P (

g C

O2

eq. k

g-1)

CH4 N2O CO2

(I) (II)

Fig. 6. Average of GWP (g CO2 eq. kg�1) for soybean farms versus crop residue fate in the current (I) and target (II) conditions.

A. Mohammadi et al. / Journal of Cleaner Production 54 (2013) 89e10096

in three of them. However, these efficient farms could be proposedfor environmental policy makers as reference units in terms ofbenchmarking (Iribarren et al., 2010).

To sum it up, the application of a combined LCA þ DEA revealeduseful results to benchmark operational inputs and global warmingimpact in soybean cultivation in the north of Iran. Although thispaper has focused on the GWP, but LCA þ DEA methodology canactually be applied to a number of environmental impactsaddressing climate change, resource depletion, and human andecological health (Lozano et al., 2010; Vázquez-Rowe et al., 2012).However, the results of the current work show that a great po-tential for GHG emission reductions exists in the farms studied.There is a common belief among Iranian farmers that furtherincreased input use, especially in the form of chemical fertilizersand water for irrigation will increase the crop yield, and eventuallyfarm profit. The widespread use of fossil fuels within the currentenergy infrastructure is considered as the largest source ofanthropogenic emissions of CO2, which is largely blamed for globalwarming and climate change (Demirbas and Balat, 2009).Achieving solutions to the environmental problems that we facetoday requires new long-term actions for sustainable development.In the case of N fertilizer for soybeans, although its consumption isless than for cereal and oilseed crops in Iran (Beheshti Tabar et al.,2010), the consequences of this study showed that N fertilizerconsumption has a great potential for reduction. The emissions ofN2O are principally caused by the production and application ofsynthetic N fertilizer. Therefore, if its consumption is reduced and/or to a higher degree substituted by the other nitrogen sources(green fertilizer and better utilization of N fixed from the

Table 5Superior efficient scores (SE) for the farms that were identified as efficient.

DMU SE DMU SE DMU SE DMU SE

1 1.30 29 1.66 52 1.19 83 1.412 1.54 30 1.73 54 1.62 84 1.367 1.17 31 1.16 55 1.14 85 1.049 1.26 32 1.12 56 1.09 86 1.3011 1.04 34 1.49 57 1.11 88 1.2515 1.70 35 1.17 62 1.45 89 1.0520 1.05 39 1.14 65 1.03 90 1.0721 1.21 40 1.04 75 1.34 92 1.0022 1.17 42 1.01 76 1.05 93 1.0325 1.15 45 1.53 80 1.01 94 1.0128 1.32 51 1.08 81 1.30 e e

atmosphere or N in manures compost), the environmental profileof soybean production could be improved. Overall the efficient useof operational inputs and stopping burning crop residue appear tobe the more effective solutions that accordingly new policiesshould be developed to achieve sustainable production of this crop.Here, the local agricultural institutions have significant roles toundertake strategies for enhancing inputs-use efficiency andensure more environmental friendly farming systems in Iranianagriculture. These pathways can for example include the provisionof educational opportunities for farmers related to field manage-ment, training them to use various input sources in a more properpattern, the planning of new governmental policies on research,and the introduction of new agricultural methods like integratedfarming, organic farming systems, and the develop more efficientirrigation practices.

4. Conclusions

The aim of this study was to examine the operational andenvironmental performance of soybean production in north Iran.A sample of 94 soybean farms was analyzed using the definedLCA þ DEA methodology. Based on the outcome of DEA model atotal of 43 farms were distinguished efficient. Large differences inTechnical Efficiency and CO2 equivalent emissions were observedamong the inefficient farms. This shows that the farmers werenot fully aware of the optimal production techniques, or do notapply them at the proper time in the optimum quantity, espe-cially for Carbon intensive inputs. The current fertilizationemitted the highest CO2 equivalents among the field practices;and consequently the change to other fertilizer sources can be anoption to reduce emissions. The irrigation dramatically contrib-utes in total GWP reduction. It is strongly suggested that thepumping systems efficiency is enhanced or replaced with newpumping facilities for irrigation. Stopping of crop residue burningin the field could also play a considerable role in improving GHGperformance of soybean. In conclusion, if farms under consider-ation in this study operated with the recommended pattern,about 11% reduction in the GWP for the whole sample could beachieved.

The joint application of LCA and DEA has proven to be a suit-able method for quantifying operational and environmental tar-gets. Hence this technique can be utilized by agriculturalorganizations as a useful management tool to support decisionmaking. It is also expected from policy makers to use LCA þ DEA

A. Mohammadi et al. / Journal of Cleaner Production 54 (2013) 89e100 97

methodology in order to establish the reference values for envi-ronmental regulations in production systems. Finally, the com-bined implementation of LCA and DEA can be extended for otherstudies via further research on the unexplored potentials of thisapproach.

Table A.1The farm input and output data (quantity per ha) for DEA matrix for 94 soybean farms.

DMU Input

Labor (h) Machinery (h) Diesel (L) Water (m3) Electricity (kWh

1 169 16 70 2016 02 142 15 65 2150 03 197 22 88 3360 19534 254 35 122 2722 12865 138 32 111 2464 14326 152 28 98 2419 07 148 28 109 2419 7038 213 27 109 4838 14069 159 18 76 2822 010 137 28 96 2822 011 272 26 105 2016 012 185 31 126 2419 140613 228 35 119 1344 78114 264 22 91 1890 015 200 45 150 4032 175816 289 32 115 3528 217917 282 35 130 3024 175818 209 24 83 2621 152419 268 33 119 7258 290120 210 55 168 3024 153821 139 27 108 1260 73222 179 23 109 4536 115423 200 40 131 2822 82024 245 29 106 2016 117225 222 31 93 3226 128926 263 54 175 5443 217527 285 64 203 5443 217528 124 17 69 2419 029 215 15 88 5645 328230 134 14 76 4838 140631 137 17 64 2016 131832 201 17 68 3024 87933 159 38 128 3528 205134 269 50 160 2016 73235 223 10 65 4838 281336 145 24 101 3360 207537 176 29 108 4032 117238 183 29 100 3629 145039 167 23 93 6048 307640 238 26 95 4032 87941 290 34 117 6048 351642 206 24 93 3528 205143 350 21 120 4032 153844 133 31 100 3024 115445 169 25 92 3528 102546 157 34 108 2822 82047 239 35 120 2822 164148 170 21 89 3360 85549 220 50 146 3110 158250 277 29 117 4838 281351 186 48 165 2952 179152 189 20 81 2688 78153 104 35 124 4838 211054 170 19 75 2520 146555 112 20 101 3226 211056 144 25 106 2112 107457 179 15 86 3024 197858 215 30 109 3024 76959 146 34 134 3629 184660 162 40 147 4657 236961 245 34 119 3528 141062 196 9 61 2016 51363 187 19 79 2957 1934

Appendix A

Tables A.1 and A.2 present the DEA matrices (input andoutput data) and inventory data used for the assessment soybeanfarms.

Output

) Chemicals (kg) Fertilizer (kg) FYM (kg) Seed (kg) Soybean (kg)

5.5 156 0 60 35003.5 78 2500 60 30003.0 165 2500 70 30005.0 156 7500 70 36004.5 101 2222 100 30005.0 156 2000 60 31506.0 156 563 60 41503.0 252 1250 100 35002.0 122 0 80 23006.5 122 0 70 23005.0 218 750 63 35007.5 174 1500 60 34008.0 174 7500 80 30005.0 172 0 100 23151.5 92 16,667 80 37504.5 229 7500 75 32505.5 197 4500 60 35004.0 92 6000 80 2600

11.0 92 12,500 80 30001.0 110 10,000 60 35007.5 229 9375 60 35005.5 69 4000 80 40006.5 229 1111 70 31154.5 133 3750 80 3500

11.0 0 11,000 80 42006.5 92 12,500 70 31005.5 92 25,000 70 30003.5 126 0 55 32001.0 133 0 70 35001.5 69 0 70 20006.0 101 833 60 33003.5 156 0 60 36005.5 156 7500 70 35008.5 110 0 60 42007.5 110 1500 60 30003.0 110 0 60 25005.0 110 2083 70 3500

13.6 83 208 60 28005.5 119 0 60 30005.5 110 0 55 33309.0 289 0 90 35008.0 220 3000 60 40009.0 250 3000 60 34009.5 165 0 75 35006.5 0 6250 70 40006.0 156 3750 70 28006.6 202 15,000 70 40004.5 265 0 60 35007.0 228 10,000 80 35005.8 156 0 60 38004.5 92 21,429 65 37001.5 110 0 60 26666.0 106 12,000 60 26005.0 92 0 60 27005.0 217 0 70 34003.8 156 1500 60 35701.5 192 0 60 30006.0 261 22,500 55 35005.5 133 15,000 70 38005.5 133 15,000 70 35006.0 192 10,000 70 30005.5 55 417 100 25004.5 252 0 70 2800

(continued on next page)

Table A.1 (continued )

DMU Input Output

Labor (h) Machinery (h) Diesel (L) Water (m3) Electricity (kWh) Chemicals (kg) Fertilizer (kg) FYM (kg) Seed (kg) Soybean (kg)

64 163 21 88 2880 1465 6.0 165 2500 70 357065 243 21 86 3024 824 6.0 261 5000 55 370066 196 22 103 3326 2175 5.5 252 0 70 280067 178 26 100 2150 0 3.8 156 1500 60 300068 214 33 132 2688 1367 6.5 156 7500 70 350069 169 27 92 2520 1282 4.0 142 7500 70 360070 208 28 104 2464 1432 6.0 261 20,000 70 280071 165 37 124 4032 1758 5.0 77 15,000 70 350072 261 32 124 3528 2179 4.5 229 7500 70 290073 283 38 138 3024 1758 4.5 169 5000 70 355074 167 37 136 3276 1904 4.0 92 7500 70 260075 211 31 122 2903 1477 1.0 96 10,000 60 340076 155 36 129 3780 1030 5.5 69 10,000 70 380077 154 30 96 2520 1007 5.0 156 7500 70 330078 176 28 119 6451 2344 5.5 142 0 80 260079 195 18 89 3629 1846 9.0 188 10,000 80 330080 144 21 93 3226 1641 5.0 252 0 70 390081 108 29 98 1613 820 5.0 110 7500 60 370082 279 27 116 3360 916 8.5 275 15,000 90 330083 309 12 55 2822 820 2.0 220 0 80 360084 95 12 66 4704 0 6.0 183 10,000 60 340085 141 25 105 2268 0 3.5 266 0 60 320086 152 27 103 2100 0 2.5 206 12,500 60 315087 127 19 82 2520 0 7.5 229 9375 65 305088 121 21 84 2016 1172 5.0 46 10,000 60 310089 213 6 47 2688 1074 3.5 197 0 70 200090 171 17 69 4032 2344 4.0 92 0 70 250091 192 18 87 6451 328 4.0 110 18,667 75 300092 217 6 48 2688 1074 4.5 197 0 70 200093 199 19 72 3629 2110 5.0 110 0 60 300094 211 16 58 2520 1007 4.5 156 0 60 3200

DEA: data envelopment analysis.DMU: decision making unit.

Table A.2Selected inventory data (quantity per ha) for 94 soybean farms.

Farm Energy Chemicals and fertilizers Direct emissions Outputs

Diesel (L) Electricity (kWh)a Herbicides (kg) Insecticides (kg) Urea (kg) P2O5 (kg) K2O (kg) FYM (kg) CH4 (kg)b N2O (kg) Soybean (kg) Straw (kg)c

1-B 70 0 4 1.5 110 46 0 0 9.3 3.2 3500 43122-B 65 0 3 0.5 55 23 0 2500 8.4 3.1 3000 38893-B 88 1953 2 1 96 69 0 2500 8.4 3.2 3000 38894-B 122 1286 3 2 110 46 0 7500 9.5 3.4 3600 43975-R 111 1432 2 2.5 78 23 0 2222 0.0 3.2 3000 38896-L 98 0 3 2 110 46 0 2000 0.0 3.7 3150 40167-R 109 703 3 3 110 46 0 563 0.0 3.2 4150 48628-B 109 1406 1 2 137 115 0 1250 9.3 3.3 3500 43129-B 76 0 0 2 76 46 0 0 7.1 3.1 2300 329610-R 96 0 3 3.5 76 46 0 0 0.0 3.4 2300 329611-B 105 0 3 2 103 115 0 750 9.3 3.2 3500 431212-R 126 1406 3 4.5 82 92 0 1500 0.0 3.6 3400 422713-B 119 781 3 5 82 92 0 7500 8.4 3.4 3000 388914-B 91 0 3 2 100 72 0 0 7.1 3.2 2315 330915-B 150 1758 0 1.5 92 0 0 16,667 9.8 3.6 3750 452416-R 115 2179 3 1.5 114 115 0 7500 0.0 3.4 3250 410017-R 130 1758 3 2.5 105 92 0 4500 0.0 3.3 3500 431218-B 83 1524 3 1 92 0 0 6000 7.7 3.3 2600 355019-B 119 2901 3 8 92 0 0 12,500 8.4 3.5 3000 388920-B 168 1538 0 1 64 46 0 10,000 9.3 3.4 3500 431221-L 108 732 3 4.5 114 115 0 9375 0.0 3.9 3500 431222-L 109 1154 5 0.5 69 0 0 4000 0.0 3.8 4000 473523-B 131 820 3 3.5 114 115 0 1111 8.6 3.2 3115 398624-B 106 1172 3 1.5 87 46 0 3750 9.3 3.3 3500 431225-B 93 1289 3 8 0 0 0 11,000 10.6 3.3 4200 490426-B 175 2175 3 3.5 92 0 0 12,500 8.6 3.5 3100 397427-B 203 2175 3 2.5 92 0 0 25,000 8.4 3.9 3000 388928-L 69 0 3 0.5 78 23 25 0 0.0 3.5 3200 405829-B 88 3282 0 1 87 46 0 0 9.3 3.1 3500 431230-B 76 1406 0 1.5 69 0 0 0 6.6 3.1 2000 304331-R 64 1318 3 3 78 23 0 833 0.0 3.1 3300 414332-R 68 879 3 0.5 110 46 0 0 0.0 3.2 3600 439733-B 128 2051 3 2.5 110 46 0 7500 9.3 3.4 3500 4312

A. Mohammadi et al. / Journal of Cleaner Production 54 (2013) 89e10098

Table A.2 (continued )

Farm Energy Chemicals and fertilizers Direct emissions Outputs

Diesel (L) Electricity (kWh)a Herbicides (kg) Insecticides (kg) Urea (kg) P2O5 (kg) K2O (kg) FYM (kg) CH4 (kg)b N2O (kg) Soybean (kg) Straw (kg)c

34-B 160 732 3 5.5 64 46 0 0 10.6 3.1 4200 490435-B 65 2813 3 4.5 64 46 0 1500 8.4 3.1 3000 388936-B 101 2075 0 3 64 46 0 0 7.5 3.1 2500 346637-L 108 1172 3 2 64 46 0 2083 0.0 3.6 3500 431238-R 100 1450 3 10.6 48 35 0 208 0.0 3.1 2800 372039-L 93 3076 3 2.5 50 69 0 0 0.0 3.5 3000 388940-B 95 879 3 2.5 64 46 0 0 9.0 3.1 3330 416841-B 117 3516 3 6 197 92 0 0 9.3 3.4 3500 431242-B 93 2051 3 5 128 92 0 3000 10.2 3.3 4000 473543-B 120 1538 4 5 159 92 0 3000 9.1 3.4 3400 422744-B 100 1154 3 6.5 96 69 0 0 9.3 3.2 3500 431245-L 92 1025 3 3.5 0 0 0 6250 0.0 3.7 4000 473546-B 108 820 3 3 110 46 0 3750 8.0 3.3 2800 372047-L 120 1641 3 3.6 156 46 0 15,000 0.0 4.3 4000 473548-B 89 855 3 1.5 115 0 150 0 9.3 3.2 3500 431249-B 146 1582 3 4 135 81 13 10,000 9.3 3.5 3500 431250-B 117 2813 1.25 4.5 110 46 0 0 9.9 3.2 3800 456651-L 165 1791 1.5 3 92 0 0 21,429 0.0 4.3 3700 448152-B 81 781 0 1.5 64 46 0 0 7.8 3.1 2666 360653-B 124 2110 3 3 83 23 0 12,000 7.7 3.5 2600 355054-B 75 1465 3 2 92 0 0 0 7.9 3.2 2700 363555-B 101 2110 2 3 92 0 125 0 9.1 3.2 3400 422756-L 106 1074 3.5 0.3 110 46 0 1500 0.0 3.7 3570 437157-B 86 1978 0 1.5 92 0 100 0 8.4 3.2 3000 388958-R 109 769 3 3 123 138 0 22,500 0.0 3.9 3500 431259-L 134 1846 3 2.5 87 46 0 15,000 0.0 4.1 3800 456660-B 147 2369 3 2.5 87 46 0 15,000 9.3 3.6 3500 431261-B 119 1410 3 3 92 0 100 10,000 8.4 3.4 3000 388962-L 61 513 3 2.5 32 23 0 417 0.0 3.4 2500 346663-B 79 1934 2 2.5 137 115 0 0 8.0 3.3 2800 372064-B 88 1465 3 3 115 0 50 2500 9.4 3.3 3570 437165-B 86 824 3 3 123 138 0 5000 9.7 3.4 3700 448166-B 103 2175 3 2.5 137 115 0 0 8.0 3.3 2800 372067-L 100 0 3.5 0.3 110 46 0 1500 0.0 3.6 3000 388968-L 132 1367 3.5 3 110 46 0 7500 0.0 3.9 3500 431269-L 92 1282 3.5 0.5 92 0 50 7500 0.0 3.9 3600 439770-B 104 1432 3 3 123 138 0 20,000 8.0 3.8 2800 372071-L 124 1758 3 2 77 0 0 15,000 0.0 4.0 3500 431272-L 124 2179 3 1.5 114 115 0 7500 0.0 3.8 2900 380473-B 138 1758 3 1.5 69 0 100 5000 9.4 3.2 3550 435474-B 136 1904 3 1 92 0 0 7500 7.7 3.4 2600 355075-B 122 1477 0 1 46 0 50 10,000 9.1 3.3 3400 422776-R 129 1030 3 2.5 69 0 0 10,000 0.0 3.4 3800 456677-R 96 1007 3 2 110 46 0 7500 0.0 3.4 3300 414378-B 119 2344 3 2.5 92 0 50 0 7.7 3.2 2600 355079-B 89 1846 3 6 119 69 0 10,000 8.9 3.5 3300 414380-L 93 1641 2 3 137 115 0 0 0.0 3.8 3900 465181-L 98 820 3.5 1.5 64 46 0 7500 0.0 3.8 3700 448182-B 116 916 4 4.5 160 115 0 15,000 8.9 3.7 3300 414383-B 55 820 1 1 128 92 0 0 9.5 3.2 3600 439784-R 66 0 3.5 2.5 87 46 50 10,000 0.0 3.4 3400 422785-R 105 0 2 1.5 174 92 0 0 0.0 3.3 3200 405886-L 103 0 0 2.5 91 115 0 12,500 0.0 3.9 3150 401687-B 82 0 3 4.5 114 115 0 9375 8.5 3.5 3050 393188-L 84 1172 3 2 46 0 0 10,000 0.0 3.8 3100 397489-R 47 1074 2 1.5 105 92 0 0 0.0 3.2 2000 304390-L 69 2344 3 1 92 0 0 0 0.0 3.5 2500 346691-L 87 328 3 1 64 46 0 18,667 0.0 4.0 3000 388992-B 48 1074 3 1.5 105 92 0 0 6.6 3.2 2000 304393-B 72 2110 3 2 64 46 0 0 8.4 3.1 3000 388994-R 58 1007 3 1.5 110 46 0 0 0.0 3.2 3200 4058

Note: The letter following the number in the farm column represents the crop residue (straw) fate, B ¼ Burned in field (58% of farms); L ¼ Left in field (27% of farms);R ¼ Removed for fodder (15% of farms).

a Electricity generation for the grid was assumed to be from natural gas source (Mazandarani et al., 2010).b CH4 emitted from crop residue burning.c Estimated from (IPCC, 2006).

A. Mohammadi et al. / Journal of Cleaner Production 54 (2013) 89e100 99

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