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Journal of Cleaner Production 93 (2015) 65e74

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Journal of Cleaner Production

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

Life Cycle Inventory for the agricultural stages of soybean productionin the state of Rio Grande do Sul, Brazil

Vinícius Gonçalves Maciel a, b, Rafael Batista Zortea c, Wagner Menezes da Silva b,Luiz Fernando de Abreu Cybis c, Sandra Einloft a, b, Marcus Seferin a, b, *

a PUCRS e Pontifical Catholic University of Rio Grande do Sul, Post-Graduation Program in Materials Engineering and Technology, Av. Ipiranga, 6681,Porto Alegre, Brazilb PUCRS e School of Chemistry, Brazilc UFRGS e Institute of Hydraulic Research, 9500, Bento Gonçalves Av., Porto Alegre, Brazil

a r t i c l e i n f o

Article history:Received 11 August 2014Received in revised form6 January 2015Accepted 8 January 2015Available online 16 January 2015

Keywords:Life Cycle InventorySoybeanAgricultureLand use changeGlobal warmingBiodiesel

Abbreviations: Consumption, machinery-specific cdensity (kg L�1); DC, distance covered (km ha�1); Egsued of a substance from a particular machinery or iFC, fuel consumption specific to each intervention (Lfrom the combustion of Diesel (kg gas kg�1 Diesel);number of harvests under the influence of the applicatLUC, land use change; MOM, Machinery Operation MRio Grande do Sul; TCi, total consumption of fuel to aWF, Work Factor; WS, Working Speed (km h�1); wsa,same application.* Corresponding author. PUCRS e Pontifical Catholi

Sul, Post-Graduation Program in Materials Engineerianga, 6681, Porto Alegre, Brazil. Tel.: þ55 51 3320 35

E-mail address: seferin@pucrs.br (M. Seferin).

http://dx.doi.org/10.1016/j.jclepro.2015.01.0160959-6526/© 2015 Elsevier Ltd. All rights reserved.

a b s t r a c t

Soybean is an important feedstock and its oil represents 70% of the raw material used for the productionof Brazilian biodiesel. The main purposes of this work was to present a Life Cycle Inventory of soybeanproduced in Rio Grande do Sul, state in Brazil, based on primary data of agricultural soybean productionin this region. Inventory data was collected in 23 municipalities that account for 32% of total soybeanproduction. A Machinery Operation Modeling was suggested to adjust agricultural machinery inputs/outputs for harvesting under study. The soybean cultivation Life Cycle Inventory was divided into fourstages and not as a black box. Based on questionnaire responses, it was possible to characterize someregional peculiarities of soybean production. For the estimation of Greenhouse Gas emissions from directland use change and nitrous oxide emissions from soil, an assessment of soybean advancement overdistinguished areas was performed. The results showed that for 15.4% of cultivated area from 1992/93 to2012/13 transition from pasture to farming has occurred, mainly over rice and corn crops. It should beunderscore that no evidence of soybean advances from forest to farming was found for the region.Moreover, this work considered nitrous oxide emissions from soil and a complete inventory was pre-sented. Lastly, this works aims to offer soybean inventory data specifically to Rio Grande do Sul state andpresents a new approach to perform environmental results related to agricultural life cycle assessment.

© 2015 Elsevier Ltd. All rights reserved.

1. Introduction

Soybean is a major source of protein and vegetable oil in theworld (FAO, 2013). Besides these main products a wide range of co-

onsumption (L h�1); d, is fueli, (kg gas soybean kg�1) is is-ntervention eq (equivalent).;ha�1); Fgi, factors emissionsGHG, Greenhouse Gas; hia,ion; LCI, Life Cycle Inventory;odeling; p, soybean yield; RS,specific equipment (kg ha�1);number of works done in the

c University of Rio Grande dong and Technology, Av. Ipir-00.

products can be obtained form soy, especially glycerin, lecithin,carboxylic acids and its derivatives, lubricants and biodiesel. InBrazil soybean represents 70% of the feedstock for biodiesel pro-duction (ANP, 2013), and the increasing Brazilian biodiesel pro-duction depends strongly on soybean oil since its production chainis the only one ready to supply enough oilseed for the current de-mand (Castro, 2011). In 2012/2013 crop, the state of Rio Grande doSul state (RS) harvested 12.5 million tons of soy (CONAB, 2013a),amount higher than Paraguay soybeanproduction, the sixth biggestworld producer (FAO, 2013; IBGE, 2013b).

The exponential growth of soybean's biodiesel production hasbeen the target of discussion in the scientific community, the focusof which has been mainly its environmental performance(Castanheira and Freire, 2013; Pousa et al., 2007; Raucci et al.,2014). When life cycle assessment (LCA) studies were applied toevaluate the environmental impact of soy based biodiesel thesoybean production appeared as the primary contributing stage forthe environmental impacts of this product system due to several

Fig. 1. System boundaries.

V.G. Maciel et al. / Journal of Cleaner Production 93 (2015) 65e7466

inputs and agricultural practices, specially for Greenhouse Gas(GHG) emissions (Cavalett and Ortega, 2010; Panichelli et al., 2008).Other important sources of GHG are the land use change (LUC) andemissions of nitrous oxide from soil (Borjesson and Tufvesson,2010). Studies point out that impacts due to GHG emissions differgreatly if the changes in the land used were accounted for(Gnansounou et al., 2009; Cherubini, 2010; Humpen€oder et al.,2013). According to the Lapola et al. (2014) 80% of CO2 eq emis-sions in Brazil in 2005 arised from LUC and agriculture. Nitrousoxide, a GHG 298 times more important than CO2, is naturallyproduced in the soil, and is related to the use of nitrogen fertilizers,crop residue and LUC.

Based on this information, it is necessary to evaluate whichenvironmental impacts result exclusively from the agriculturalstage in soybean production of RS and other Brazilian states.Following ISO standards 14040:2006 and 14044:2006, Life CycleInventory (LCI) is one of the LCA phases in which the productionsystem is defined and its modeling might be the hardest and mostdifficult effort due to the lack of high quality data, and historicallyestimation has sometimes been necessary (Ruviaro et al., 2012).

Several authors has pointed out that there is a high discrepancyamong the LCA results related to soybean production (Malça andFreire, 2011; Panichelli et al., 2008; Raucci et al., 2014) sinceregional specificities are key factors for assessing environmentalimpacts and discrepancies can emerge from the consideration ofdifferent energy sources, modes of transportation, agriculturalpractices and whether or not the LUC will be accounted for(Piekarski et al., 2012). Most of the published studies haveconsidered Brazilian soybean production taking in account na-tional averages, disregarding that there are several very distinctregions where this grain is cultivated. It is important to remindthat traditionally several LCA studies approach the agriculturalphase as a black box, disregarding an important source of infor-mation for assessing the environmental performance of productsystems based on agriculture feedstock. In fact at our bestknowledge there is only one important LCA approach for GHGemission related to Brazilian soybean production that hascollected primary data directly from producers in Mato Grossostate (Raucci et al., 2014).

This work aims to construct an LCI for soybean production avoidusing data sources that diverge from the actual practices in theregion under study, making an inventory that resembles as closelyas possible the existing conditions for soybean production in RS.Data were collected from the local offices for technical support foragricultural activities and the material flows modeling was struc-tured by describing four distinct phases for soybean production,taking in account the machinery operation. For the first time a LUCrelated GHG emissions were also evaluated after the assessment ofsoybean advancement over other cultivations and grassland areasin RS in the last 20 years.

2. Methods

The LCI elaboration was conducted accordingly to the ISO14040 and 14044 standards. The system boundaries and thereference flow with energy and material inputs/outputs are pre-sented in Fig. 1. The functional unit (FU) is one kg of soybeanproduced.

Primary data were collected aiming to model the steps thatcompose the main links of soybean production using question-naires and interviews. It was also adopted calculation methodolo-gies to estimate GHGeLUC emission flows, nitrous oxides from thesoil and limestone use. The GHG emissions from fossil fuels com-bustion by agricultural machinery were calculated based on emis-sion factors available in the bibliography.

A Machinery Operation Modeling (MOM) is suggested to adjustinputs/outputs flows related to agricultural machinery to harvestthe season under study.

2.1. Study considerations

Only direct LUC related GHG emissions. The indirect land usechange (iLUC) was not addressed, because according to Broch et al.(2013) there is uncertainty and variability related to iLUCmodeling,given the lack of available data on the indirect conversion of soils(Castanheira and Freire, 2013; Milazzo et al., 2013; Kendall e Chang,2009).

The soybean cultivated in RS does not use irrigation, i.e., de-pends on only rain season. Infrastructure, electrical energy andmaintenance of machinery were not taken in account.

This study considers that the whole mass of pesticides andfertilizers are issued to the soil, the worst-case scenario. However,for nitrogen fertilizer was necessary to consider the loss to the air,associated with the emission of N2O from soil.

2.2. Data collection

The data collected by the questionnaire refers the 2012/2013soybean crop cultivated in RS. The main goal of this questionnairewas to quantify inputs and outputs, productivity, agriculturalpractices and the kind of machinery used.

The information was collected from 2013 July to November. Thequestionnaire was drawn up based on five field visits made tofarms, consultation of LCA experts and agronomy subjects, as wellas technical literature (EMBRAPA, 2011; SBCS, 2004) in order toseek for reliability in the data collected.

The target audience was local offices of the department of ruraltechnical supports of RS. The collected data prioritizedmunicipalitiesthat presented the biggest recent soybean harvests. From the data ofthe municipal agricultural production (IBGE, 2013b), it is observedthat 405 of the 497municipalities in RS have some record of soybeanproduction in the period between the years of 2007e2011. Consid-ering the average production in this period, we can see that thefifteen largest producers accounted for 25% of production and theforty-six largest producers are responsible for 50% of production.

Each local office answered a questionnaire, where answers frommunicipalities whose representativeness were less than 0.01%werenot considered. The average value for each input and output wascalculated by arithmetical average of the answers in eachmunicipality.

2.3. Machinery Operation Modeling (MOM)

This modeling was used to determine the amount of fuelconsumed for machinery in each farming phase. The main idea is toobtain an index for actual covered distances and the number of

V.G. Maciel et al. / Journal of Cleaner Production 93 (2015) 65e74 67

times each agricultural input (including pesticides, herbicides, lime,etc.) was applied in every harvest. Based on this idea, the followingEquation (1) is suggested:

DC ¼ 10=mw (1)

Where DC (km ha�1) is distance covered by the machinery perhectare; mw (m) is machinery's width or input's reach. This isimportant because some agricultural practices have effects thataffect more than one crop, for example, lime application. Further-more, some agricultural supplies are applied simultaneously;therefore the quantity of fuel used per each agricultural input hasbeen adjusted for.

In these cases, other agricultural equipment is engaged to thetractor to perform functions including: spraying, liming, sowingand fertilization. In order to seek for accuracy on fuel consumption,the equipment's width, or its reach must be known, once thenumber of times said equipment would be required to cover a givenquantity of land depends on it.

The Work Factor (WF) on equation (2) evaluates one applicationof the machinery to the land, otherwise known as intervention:number of works done in the same application (wsa) and numberof harvests under the influence of the application (hia).

WFi ¼1

wsa$hia(2)

Work Factor was defined as the number of inputs done per yearper hectare for one intervention. In the case of liming, the WF willbe 0.2857, since the lime is applied once each 3.5 years (wsa ¼ 1,hia¼ 3.5). In the case of seed and fertilizing application, theWFwillbe 0.5, because they are both applied at the same time (wsa ¼ 2,hia ¼ 1).

Therefore, fuel consumption (FC) specific to each interventionmay be calculated using equation (3):

FCi ¼DC$consumption

WS(3)

Where FCi (L ha�1) is fuel consumption specific to each interven-tion; Consumption (L h�1) is machinery-specific consumption andWorking Speed (WS) (km h�1) is the speed of the machinery atwork (5 km h�1; EMBRAPA, 2011).

In this way, the Equation (4) estimates the total consumption offuel of specific equipment in “i” number of interventions.

TCi ¼ FCi$d$Xn1

WFi (4)

WhereTCi (kgha�1) is total fuel consumptionof specific equipment in“i” interventions; d (kg L�1) is fuel density (0.84 kg L�1; BRAZIL, 2013).

2.4. Pesticides

The quantity of pesticides was calculated from the concentra-tions of the active principles present in a liter of chemical products.The formulas and active ingredients were obtained from thedatabase of Phytosanitary Agro Toxic System (AGROFIT) by theMinistry of Agriculture, Livestock and Food Supply (MAPA, 2014).Other compounds likewater and additiveswere ignored to quantifypesticides inputs and outputs.

2.5. Diesel emissions

GHG emissions from the combustion of fossil fuel for farmtractors, limestone applicator trucks and harvester were estimated

from the factors emissions from the combustion of Diesel (Fgi)presented in Table 1 and applied to Equation (5), where Egi(kg gas soybean kg�1) is issued for a substance from a particularmachinery or intervention and “p” soybean yield, included to es-timate the values in terms of UF.

Egi ¼ Xn

1

TCi$Fgi

!$1p

(5)

2.6. Land use change emissions

GHG emissions from carbon-stock changes caused by LUC werecalculated using the Equation (6), following the IPCC tier 1 (IPCC,2006a) and Renewable Energy Directive (EC, 2009, 2010; IPCC,2006b). The adopted standard factors suggested by the IPCC(2006b) are for warm temperate regions, moisture and type ofclay soil, no-tillage system, as recommended by the (EC, 2010) andadopted by other studies (Castanheira and Freire, 2013; Grisoliet al., 2012) to geographic sites like RS.

E ¼ ðCSRi � CSAiÞ$ð44=12Þ$ð1=20Þ (6)

Where E (t CO2eq ha�1 ano�1) is the annual GHG emissions fromcarbon-stock change due to LUC; CSR (tC ha�1) is the carbon stockassociatedwith the reference (previous) land use; CSA (tC ha�1) is thecarbon-stock associatedwithpresent landuse (soybean). The fraction44/12was used to convert the results in CO2 eq. The fraction 1/20wasused forobtainingannual values, accordingly theestimatedperiod forcarbon reservoir equilibrium reaching as suggested by IPCC.

CSi ¼ ðSOCi þ CVEGÞ$A (7)

The CSR and CSA were calculated using the equation (7), whereSOCi (tC ha�1) is organic carbon-stock of soil in reference to (SOCR)and effective use of soil (SOCA), Cvegi (tC ha�1) is the carbon-stockin, above and underground the vegetation in reference to (CvegR)and current use (CvegA). The study area (A) is the ratio between LUCarea and crop soybean area in the 2012/2013 harvest. It is importantto mention that the value 1 is assigned to this variable on themajority of studies and LUC emissions are estimated from non-representative scenarios. For this study an investigation of soy-bean advancement in 20 years at RS was performed to estimate thetransition scenarios and the actual value for (A).

Besides that, the equation (8) was used to calculate organiccarbon-stock at mineral soil SOCi, where SOCST (tCha�1) is a stan-dard organic carbon and standard values FLU, FMG and FI are factorsthat reflect the difference in the SOCST as it relates to the kind ofland use (FLU). The quantity of organic carbon soil is derived frommanagement practices in relation to current organic carbon in soil(FMG), and others kinds of carbon inputs into soil (FI). Based onpresent land use, CvegA is equal to zero, because the soybean crop isannual. In these cases, carbon-stock is assumed to be the biomasslost during the harvest and mortality of the crop in the same year.Therefore, there was not a net storage of carbon from biomass(IPCC, 2006a).

SOCi ¼ SOCST$FLU$FMG$FI (8)

2.6.1. Advancement of the soybean in the last 20 years in RSThe analysis of soybean advance in the last 20 years was based

on official data and statistics from Land Use in RS (IBGE, 2010), anAgricultural Census (IBGE, 2013a), historical data of corn, rice andsoybean from National Food Supply Company (CONAB, 2013b,2013c, 2013d), an estimated crop-year report of rice 2013/2014from IRGA (2013) and the annual crop report of soybean and rice

Table 1Combustion emission factors of fossil fuels used in this work.

Machinery Factor CO2 (kg CO2 kg�1 diesel) Factor CO (kg CO kg�1 diesel) Factor NOX (kg NOX kg�1 diesel) Source

Tractor 4.697 0.017 0.004 (Janulevi�cius et al., 2013)Truck and Harvester 3.179 0.008 0.048 (MMA, 2011).

V.G. Maciel et al. / Journal of Cleaner Production 93 (2015) 65e7468

(Kist et al., 2003; Santos et al., 2013). The aim of this analysis is toestimate changeovers (pasture-farming), as well as the advance-ment of the soybean over other crops. Based on collected data, totalemissions from carbon-stock change were calculated from LUC.Fig. 2 shows a flow diagram utilized to quantify soybean advanceand the development of these scenarios.

Due to lack of information about conservation conditions inpasture areas before its conversion into soybean plantations, threescenarios were developed using different conservation conditions(R1, R2 and R3).

The scenario (S1) represents the best conservation condition(R1). In this case, all soybean area taken from pasture areas wasconsidered to have high conservation conditions and a hugeamount of carbon-stock. The scenario (S2) is based on intermediaryconservation conditions (R2). Scenario (S3) represents an opposi-tion to S1 wherein soybean area taken from pasture areas has se-vere degradation that results in low carbon-stock. Lastly, forcomparison with other works purpose, a fourth scenario (S4) issuggested. In this case, all area was taken from forests (R4).

2.7. N2O emissions from soil

The direct and indirect emissions are calculated from Equations(9) and (10), respectively, according to tier 1 methodology by IPCC(2006b).

N2ODIRECT ¼ ðFSN þ FCR þ FSOMÞ$EF1$44=28$1=p (9)

N2OINDIRECT ¼hðFSN$FracGASFÞ$EF4Þ þ ðFSN þ FCR

þ FSOMÞ$FracLEACH�ðHÞ$EF5�i

44=28$1=p (10)

Where, FSN is the amount of synthetic nitrogen fertilizers applied(kg N ha�1); FCR is the annual amount of N in crop residues (aboveand underground) that returned to the soil (kg N ha�1). FSOM(kg N ha�1) is the annual amount of N in mineral soils that ismineralized, in association with a loss of C from soil organic matteras a result of changes to land use or management, kg N yr�1.Standard values suggested by IPCC (2006b) to EF1, EF4, EF5, FracGASFand FracLEACH were adopted, taking into account regions of warmtemperate climate, moist and Low Activity Clay Soil and no-tillage.The amount of FSN was estimated from the questionnaire and theamount of N in crop residues (FCR) was estimated on the basis of the

Fig. 2. Flow diagram to es

soybean yield (p) and default factors for above-/below-groundresidue as given by the IPCC (2006b). As FSOM is directly related tothe loss of C from soil (SOCi), its contribution was estimated ac-cording to Equation (11), where R is the ratio between C:N (IPCCdefault: 15); DSOCi is the carbon changing in the soil based onreference to past data and current use. Table 2 presents the resultsof the FSOM for the scenarios described above to LUC (S1, S2, S4 eS4).

FSOM ¼ ðDSOCi$1=R$1000Þ$ð1=20Þ$A: (11)

The amounts of N added/released (FSN, FCR and FSOM), defaultemission factors (EF1, EF4 and EF5), fractions that volatilize (Frac-GASF) and are lost through leaching and runoff (FracLEACH) are pre-sented in Table 2.

3. Results and discussion

The data collection and results obtained are presented thebelow. It is worth pointing out that all final inventory can be seen inthe Section 3.5, in this work.

3.1. Data collection

Accordingly to the described methodology, questionnaires weresent to municipalities that accounted for the greatest amount ofsoybean production in RS. Twenty-three answers were collected,encompassing 32% of total soybean production in RS. Fig. 3 presentsa distribution map of soybean average production from 2007 to2011 at RS highlighting municipalities where data were collected.As can be observed the collect data was performed for areas withhigher production.

3.2. Characterization of specificities of soybean production in RS

Based on responses provided to the questionnaires distributed,it was possible to characterize the peculiarities of soybean pro-duction, especially the kind of machinery used and the mainadopted agricultural management practices. Based on these data, aflow chart of soybean cultivation was created (Fig. 4). This flowchart presents interventions, stages, and the agricultural practicesthat occur during the production of a soybean crop in RS.

timate LUC emissions.

Table 2Parameters and emission factors for N2O emission calculation to harvest soybean2012/2013 of state Rio Grande do Sul, Brazil (IPCC, 2006a).

Parameters and emission factors Amount

FSNa 5.58 (4.53e15)FCRb 40.89 (18.79e68.72)FSOMc

No-LUC 0S1 15.26S2 5.06S3 e

S4 43.37FracGASFd 0.1 (0.03e0.3)FracLEACHe 0.3 (0.1e0.8)EF1f

00.01 (0.003e0.03)

EF4f00

0.01 (0.002e0.05)EF5f

0000.0075 (0.0005e0.025)

S1: LUC Scenario 1; S2: LUC Scenario 2; S3: LUC Scenario 3; S4: LUC Scenario 4.a N input from synthetic fertilizer (kg N ha�1).b N in crop residues (kg N ha�1).c N mineralized (kg N ha�1).d Fraction of FSN that volatilizes as NH3 and NOx (kg NH3eN þ NOxeN kg N�1

applied).e Fraction of all N added/mineralized that is lost through leaching and runoff

(kg N kg�1 N additions).f Emission factors adopted for N2O emissions from N additions, from atmospheric

deposition of N on soils and water surfaces and from N leaching and runoff,respectively; f0 (kg N2OeN kg�1 N), f00 (kg N2OeN (kg NH3eN þ kg NOxeN vola-tilized)�1), f

000(kg N2OeN kg�1 N leaching/runoff).

V.G. Maciel et al. / Journal of Cleaner Production 93 (2015) 65e74 69

In the soil preparation stage, all producers adopted a no-tillagesystem. 30% used tractors to mirror limestone and 70% used trucks.The average time between applications is 3.5 years.

When asked about the main practices used in the seed treat-ment and sowing stage, 100% of respondents stated that fungicideseed treatment is a common practice. 96% also used insecticides,57% molybdenum and 65% used an inoculation technique. Jointfertilization seeding was reported by 86% of respondents and themain formulation used is NPK 02e20e20.

In the growing period, tractors were the main agricultural ma-chinery used, coupled to a field sprayer. Harvesting is carried outwith a harvester and average productivity (p) is 2969.65 kg ha�1 ofsoybean harvested, with a minimum value of 2,300 kg ha�1, and a

Fig. 3. Distribution map of soybean average productio

maximum value of 3500 kg ha�1. Table 3 presents the work factors(WF) and the distances covered per equipment per hectare (DC)used in this study.

3.3. GHG emissions due to land use change

The soybean crop area increased 49% from 1992 to 2013(CONAB, 2013d). In this way, the Agricultural Census 1920/2006(IBGE, 2013a) was used to associate the increase in soybean cropwith reductions of occupied area for other uses. As provided inTable 4, it is possible to note a reduction in grasslands and anincrease in croplands and forested lands. Therefore, it is possible toconclude that only a fraction of soybean crops were planted ingrasslands. The remaining area for soybean advance occurredpartially over rice crops area following data of RS Rice Institute(IRGA, 2013). Also in Rice and Soybean Yearbooks (Kist et al., 2003)it is possible to verify that in 2012/2013, 6% of soybean crops usedrice lands. This value reaches 8% when the average area in the last20 years is considered. According to data from Rice Yearbook 2013(Santos et al., 2013), the soybean plantation that used rice landsincreased fourfold in the last three years. However, the area usedby rice planting remained the same during the last years, i.e.,around 1 million hectares (CONAB, 2013c). This can be explainedtaking in account that 5.5 million hectares of wetlands are avail-able in RS for rice cultivation but only 3 million hectares have beenused for rice crop. Of these 3 million hectares, only 1 million areused for each harvest period being the other two thirds fallowland that may be used to plant corn and soybeans withoutdecreasing the area for rice plantation (IBGE, 2010; Santos et al.,2013).

According to IBGE data, a decrease in corn plantation has alsogiven space to the soybean crop. The increase or decrease of corncrop area has been directly linked with soybean plantation, whichis totally dependent on international market (IBGE, 2010). In orderto compare expanded soybean plantation area and lost cornplantation area, the plantation area analysis based on CONAB'sdata for the 1992e2003 period was used (CONAB, 2013b, 2013d).In the last 20 years, corn area plantation decreased by 534.5thousand hectares, where 426.7 ha were lost after 2000/2001harvest season. As can be seen in Fig. 5, there is a direct

n from 2007 to 2011 at RS and data collect areas.

Fig. 4. Flow chart of soybean cultivation in Rio Grande do Sul.

Table 3Work factor and distance covered per machinery.

Work WF Machinery and agriculturalequipment

DC(km/ha)

Liming 0.28 Tractor þ lime applicator 1.13lime applicator truck 0.88

Field Spraying (SP) e herbicide 1.5 Tractor þ Field sprayer 0.68Field Spraying (SP) e insecticide 0.5 Tractor þ Field sprayer 0.68Sowing 0.5 Tractor þ planter and

fertilizer applicator1.71

Fertilization 0.5 Tractor þ planter andfertilizer applicator

1.71

Field Spraying (GP) e herbicide 1.5 Tractor þ Field sprayer 0.68Field Spraying (GP) e insecticide 1.5 Tractor þ Field sprayer 0.68Field Spraying (GP) e fungicide 1.0 Tractor þ Field sprayer 0.68Harvest 1 Harvester 1.32

SP: soil preparation. GP: growing period. DC: distances covered per hectare.

Table 4Land use in the Rio Grande do Sul (hectare).

Land use 1985 1996 2006 Variation2006e1996

Variation2006e1985

PerennialCroplands

183,784 208,993 294,187 85,194 110,403

TemporaryCroplands

6,408,301 5,426,369 6,611,395 1,185,026 203,094

Natural Grasslands

11,939,994 10,523,566 8,252,504 �2,271,062 �3,687,490

Grass LandsPlantation

1,023,466 1,156,762 954,160 �202,602 �133,296

Natural Forest 1,664,612 1,881,493 2,269,334 387,841 604,722Forests

Plantation567,848 630,138 778,524 148,386 210,676

Source: Adapted Agriculture Census 1920/2006 (IBGE, 2013b).

V.G. Maciel et al. / Journal of Cleaner Production 93 (2015) 65e7470

relationship between changes in areas of corn and soybeans invirtually all crops. The main factors that contribute to farmers'discouragement are low prices, a lack of organization in the pro-duction chain, and competition from the soybean crop (Kist et al.,2003).

Fig. 5. Comparison between the variation of the area planted in corn and soybeanharvest 1992/93 the 2012/13.

Table 5Soybean plantation expanded on different land uses.

Land use Quantity expanded(Hectare)

% in relation to 1992

Rice e Temporary cropland 272,000 8.8Corn e Temporary cropland 534,500 17.2Natural Grassland 712,100 23.0Total e Soybean 1,518,600 49.0

Table 6Result of SOCi and factors used to current use and to other four kinds of reference useof soil.

# SOCi CVEG(t C ha�1)

A

SOCst(t C ha�1)

Flu FMG Fi SOCi(t C ha�1)

Current 63 0.69 1.15 1 49.99 0 (1 and 0.154)a

R1 63 1 1.14 1.11 79.72 6.8 0.154R2 63 1 0.95 1 59.85 6.8 0.154R3 63 1 0.7 1 44.1 6.8 0.154R4 63 1 1 1 63 31 1

R1: Pasture improved with high conservation; R2: Pasture moderate degradation;R3: Pasture with severe degradation.

a Factor with value equal one was used to estimate S4 and the value 0.154 wasused for other scenarios (S1, S2 and S3).

V.G. Maciel et al. / Journal of Cleaner Production 93 (2015) 65e74 71

For the purposes of this study all corn planting area wasconsidered to be lost and replaced by soybean. It is important tohighlight that planting area data (CONAB, 2013b) refers to the firstcorn harvest, and the interim-harvest of corn was not consideredbecause it is not a competitor of the soybean crop.

In order to calculate the soybean area that expanded overgrasslands, a data analysis was done based on the AgriculturalCensus 1920/2006 (IBGE, 2013a). Table 4 presents the variationbetween 1985e2006 and 1996e2006. In both periods, a reductionof natural and planted grassland areas can be noted, as well as ameaningful increase in perennial and temporary croplands. Anincrease in natural and planted forests can also be seen. For the1996e2006 period planted grassland areas accounted for 9% ofnatural grasslands and between 1985 and 2006 this factor felldown to 2%. Based on this information, it was deduced that thesoybean crop spread out into natural grasslands. The data relatedto soybean plantation area considered in this work is presented atTable 5.

Despite of the expansions that have taken place in differentland uses, only pasture areas are considered for the purposes ofcalculation due to LUC, i.e., the soybean advance over corn andrice cultivation was not considered because these cultivations arenot a LUC (IPCC, 2006b). Fig. 6 presents comparative soybeanarea between 1992 and 2013 and the source of occupation inhectares. It is possible to note that area that is related with LUCconstitutes 15.4% of the current area cultivated with soybean and,therefore, the value of 0.154 to the factor A of Equation (4) wasdetermined.

Table 6 presents the results of SOCi calculated for current useand sources of occupation (R1, R2, R3 and R4) and the factors Cvegi,FLUi, FMGi, and FIi used in this study and suggested by Europeancommission (2010), related to the region, weather, kind of soil andmanagement system. The results of net emissions for each scenarioare presented in Fig. 7.

Taking into account the soybean advance in RS, it becomes clearthat, based on Table 4, there was no soybean advance over forestedland. Baldi and Paruelo (2008) in their study of land use in pilotareas in South America, in the periods 1985e1989 and 2002e2004,

Fig. 6. Expansion of soybean in the last 20 y

also argue that the advancement of crops predominantly occurredon pastures.

Therefore, this result is very important because it has great in-fluence on the results of studies. For example, Guerci et al. (2013) inhis paper, believes that all soybeans produced in Brazil is defores-tation area, which may have overestimated GHG emissions in theirstudy.

In this contest, the results of this study are vastly differentcompared with other studies. According to Castanheira and Freire(2013), forests are replaced by no-tillage cultivation in the southof Brazil which results in GHG emission in the order of8.06 t CO2 eq ha�1 year�1, same value found in this study for thistype of transition (S4). Grisoli et al. (2012) presents LUC emissionsin RS in the order of 2.05 t CO2 eq ha�1 year�1. Moreover, accordingto Castanheira and Freire (2013) pasture-farming transition (soy-bean in no-tillage system) is responsible for emissions in the orderof 0.18, 3.11 and 6.78 t CO2 eq ha�1 year�1, as it relates to conser-vation references of pastures R3, R2 and R1, respectively. This dif-ference occurs because these studies assume that soybean cultureadvance over forested areas, cultivations (perennial crops) andpasture, and does not consider expansion over other cultures(seasonal crops, i.e. corn and rice). In this context, the soybeanadvancement over rice and corn cultivation areas is highly relevantand highlights the influence of economic and social factors onsoybean production. Besides that, rice and corn culture areastogether correspond to 53% of soybean expansion area. Moreover,the pasture converted into soybean area and considered respon-sible for LUC, corresponds to 46.9% of total expansion area duringthe last 20 years. Therefore, this means that for each soybeanhectare cultivated in 2012/2013 harvest, 15.4% of these areas orig-inated as pasture, which contributes greatly to GHG emissions dueto the change in stock carbon by LUC.

ears and source of occupation (103 ha).

Fig. 7. Net emissions and stock-carbon variation from in the origin and current usein each scenario.

Fig. 8. Direct and indirect N2O emissions from agricultural soils under different scnarios of LUC.

V.G. Maciel et al. / Journal of Cleaner Production 93 (2015) 65e7472

e-

Table 7Inputs and outputs in step 1.

Substance Averagemass

Amountminimum

Amountmaximum

(kg of substance kg soybean�1)

InputsGlyphosate (CAS: 1071-83-6) 3.30E-04 1.62E-04 4.85E-042.4-D-dimethylammonium(CAS: 94-75-7)

2.98E-04 1.46E-04 4.38E-04

Diflubenzuron (CAS: 35367-38-5) 2.78E-05 0.00 1.11E-04Fipronil (CAS: 120068-37-3) 4.44E-05 0.00 1.78E-04Lime (Dolomite) 2.40E-01 1.42E-01 3.78E-01Diesel fieldsprayer e Herbicide 1.63E-03 8.49E-04 4.24E-03Diesel fieldsprayer e Insecticide 5.45E-04 2.83E-04 1.41E-03Diesel Liming Tractor 1.97E-04 7.14E-05 2.62E-04Diesel Liming Truck 2.86E-04 1.11E-04 5.55E-04Diesel Liming Total 4.83E-04 1.82E-04 8.17E-04Diesel Tractor Total 2.38E-03 1.20E-03 5.92E-03Diesel Truck Total 2.86E-04 1.11E-04 5.55E-04Diesel Total 2.66E-03 1.31E-03 6.47E-03

OutputsGlyphosate 3.30E-04 1.62E-04 4.85E-042.4-D 2.98E-04 1.46E-04 4.38E-04Diflubenzuron 2.78E-05 0.00Eþ00 1.11E-04Fipronil 4.44E-05 0.00Eþ00 1.78E-04CO2 Lime (Dolomite) 1.15E-01 6.75E-02 1.80E-01CO2 Diesel e Total 1.19E-02 5.95E-03 2.93E-02NOX Diesel e Total 5.50E-05 2.11E-05 1.30E-04CO Diesel e Total 1.20E-05 5.81E-06 2.87E-05CO2 Diesel Tractor Total 1.12E-02 5.65E-03 2.78E-02NOX Diesel Tractor Total 4.14E-05 2.10E-05 1.03E-04CO Diesel Tractor Total 9.65E-06 4.89E-06 2.41E-05CO2 Diesel Liming Total 9.26E-04 3.35E-04 1.23E-03NOX Diesel Liming Total 3.44E-06 1.24E-06 4.56E-06CO Diesel Liming Total 8.02E-07 2.90E-07 1.06E-06CO2 Diesel Liming Truck and Truck Total 7.65E-04 2.97E-04 1.48E-03NOX Diesel Liming Truck and Truck Total 1.36E-05 1.10E-07 2.64E-05CO Diesel Liming Truck and Truck Total 2.37E-06 9.19E-07 4.59E-06CO2 Diesel Liming Tractor 9.26E-04 3.35E-04 1.23E-03NOX Diesel Liming Tractor 3.44E-06 1.24E-06 4.56E-06CO Diesel Liming Tractor 8.02E-07 2.90E-07 1.06E-06CO2 Diesel field sprayer e Herbicide 7.67E-03 3.99E-03 1.99E-02NOX Diesel field sprayer e Herbicide 2,85E-05 1.48E-05 7.40E-05CO Diesel field sprayer e Herbicide 6.64E-06 3.45E-06 1.72E-05CO2 Diesel field sprayer e Insecticide 2.56E-03 1.33E-03 6.64E-03NOX Diesel field sprayer e Insecticide 9.49E-06 4.93E-06 2.47E-05CO Diesel field sprayer e Insecticide 2.21E-06 1.15E-06 5.75E-06

3.4. Emissions N2O from soil

Fig. 8 shows the values of direct emissions (D) indirect emissions(InD) and sum of both (Total) N2O emissions from agricultural soilsin five scenarios. The first (no-LUC) relates no contribution of LUCemissions.

The amount of N2O issued due to the use of synthetic nitrogenfertilizer equals 3.91 E�05 kg N2O kg soybean�1, with minimumvalue of zero and a maximum of 5.95 E�04 kg N2O kg soybean�1 forall scenarios. Table 9 shows this value as output from the use ofnitrogenous fertilizers in step also subtracted the equivalentamount of flow of nitrogen (N) issued. Table 11 presents the totalemissions of N2O with this contribution.

3.5. Inventory results (inputs and outputs)

All the calculated inputs/outputs for the four agricultural stages,as well as the calculated GHG emissions due to LUC for RS soybeanproduction are showed in Tables 7e11.

4. Conclusions

Despite the importance of Brazil as soybean producer, only afew works has analyzed the environmental performance of itsagricultural stage as a feedstock for food or industrial productsystems. Since Brazil has a very large area and many different soil,climate, crop management practices and other cultural conditionsfor soybean production, these peculiarities should be regarded and

there is a lack of regionalized LCI soybean studies. The currentstudy aimed to present a regional LCI of soybean crop at RS, Brazil.Primary data collection allowed stating four steps for soybeancultivation as well as its respective input/output flows assessment.The study also covered direct LUC for the soybean advancement inthe studied region in the last 20 years that lead to the conclusionthat no forest to farming transition has occurred. The results ofsoybean advancement were used to calculate LUC and pointed outan important discussion about methods employed and consider-ations chosen to quantify GHG emissions from this source, sincemost published work has considered Brazilian averages over-estimating the emissions. It is important to highlight that thesoybean advancement over rice cultures requires further attention,especially because this expansion was made in areas that use aplanting system called “micro-camale~ao”, not encompassed withinIPCC methodology for GHG emission modeling for agriculturalactivities.

The methodology employed for data collection and MOM hereadopted contributed to inputs determination and GHG emissionsinvolved in each intervention, offering a new perspective about LCIconstruction for agricultural products. Therefore, this studycreated a Work Factor that considers variables such as kind of

Table 8Inputs and outputs in step 2.

Substance Average mass Amountminimum

Amountmaximum

(kg of substance.kg soybean�1)

InputsSeeds 1.74E-02 1.35E-02 2.19E-02Fludioxonil (CAS: 131341-86-1) 8.49E-07 4.21E-07 1.68E-06Metalaxyl-M (CAS: 1370630-17-0) 3.40E-07 1.68E-07 6.73E-07Fipronil (CAS: 120068-37-3) 8.06E-06 4.21E-06 1.26E-05Nitrogen (N) 1.88E-03 0.00Eþ00 5.05E-03Potassium (K2O) 2.08E-02 6.73E-03 2.53E-02Phosphorus (P2O5) 2.05E-02 1.01E-02 2.56E-02Diesel Fertilization 1.37E-03 5.66E-04 3.11E-03Diesel Seeding 1.37E-03 5.66E-04 3.11E-03Diesel Total 2.73E-03 1.13E-03 6.22E-03

OutputsFludioxonil 8.49E-07 4.21E-07 1.68E-06Metalaxyl-M 3.40E-07 1.68E-07 6.73E-07Fipronil 8.06E-06 4.21E-06 1.26E-05N 1.85E-03 0.00Eþ00 4.67E-03K2O 2.08E-02 6.73E-03 2.53E-02P2O5 2.05E-02 1.01E-02 2.56E-02N2Oa 3.91E-05 0 5.95E-04CO2 Diesel Total 1.28E-02 5.31E-03 2.92E-02NOX Diesel Total 4.77E-05 1.97E-05 1.08E-04CO Diesel Total1 1.11E-05 4.60E-06 2.53E-05CO2 Diesel Seeding 6.42E-03 2.66E-03 1.46E-02NOX Diesel Seeding 2.38E-05 9.86E-06 5.42E-05CO Diesel Seeding 5.56E-06 2.30E-06 1.26E-05CO2 Diesel Fertilization 6.42E-03 2.66E-03 1.46E-02NOX Diesel Fertilization 2.38E-05 9.86E-06 5.42E-05CO Diesel Fertilization 5.56E-06 2.30E-06 1.26E-05

a Emission N2O soil from synthetic nitrogen fertilizers.

Table 9Inputs and outputs in step 3.

Substance Averagemass

Amountminimum

Amountmaximum

(kg of substance kg soybean�1)

InputsGlyphosate (CAS: 1071-83-6) 3.76E-04 1.62E-04 8.08E-04Diflubenzuron (CAS: 35367-38-5) 2.16E-05 1.60E-05 4.00E-05Imidacloprid (CAS: 138261-41-3) 3.68E-05 2.73E-05 1.36E-04Cyfluthrin (CAS: 68359-37-5) 4.60E-06 3.41E-06 1.70E-05Acephate (CAS: 30560-19-1) 5.67E-05 4.21E-05 2.10E-04Epoxiconazol (CAS: 135319-73-2) 1.84E-05 1.26E-05 3.16E-05Pyraclostrobine (CAS: 175013-18-0) 4.89E-05 3.36E-05 8.40E-05Azoxystrobin (CAS: 131860-33-8) 3.07E-05 2.10E-05 5.26E-05Diesel Total 4.36E-03 2.26E-03 1.13E-02Diesel field sprayer e Herbicide 1.63E-03 8.49E-04 4.24E-03Diesel field sprayer -Insecticide 1.63E-03 8.49E-04 4.24E-03Diesel field sprayer e Fungicide 1.09E-03 5.66E-04 2.83E-03

OutputsGlyphosate 3.76E-04 1.62E-04 8.08E-04Diflubenzuron 2.16E-05 1.60E-05 4.00E-05Imidacloprid 3.68E-05 2.73E-05 1.36E-04Beta-ciflutrina 4.60E-06 3.41E-06 1.70E-05Acephato 5.67E-05 4.21E-05 2.10E-04Epoxiconazol 1.84E-05 1.26E-05 3.16E-05Pyraclostrobine 4.89E-05 3.36E-05 8.40E-05Azoxystrobin 3.07E-05 2.10E-05 5.26E-05CO2 Diesel Total 2.05E-02 1.06E-02 5.31E-02NOX Diesel Total 7.59E-05 3.94E-05 1.97E-04CO Diesel Total 1.77E-05 9.20E-06 4.60E-05CO2 Diesel field sprayer e Herbicide 7.67E-03 3.99E-03 1.99E-02NOX Diesel field sprayer e Herbicide 2.85E-05 1.48E-05 7.40E-05CO Diesel field sprayer e Herbicide 6.64E-06 3.45E-06 1.72E-05CO2 Diesel field sprayer e Insecticide 7.67E-03 3.99E-03 1.99E-02NOX Diesel field sprayer e Insecticide 2.85E-05 1.48E-05 7.40E-05CO Diesel field sprayer e Insecticide 6.64E-06 3.45E-06 1.72E-05CO2 Diesel field sprayer e Fungicide 5.12E-03 2.66E-03 1.33E-02NOX Diesel field sprayer e Fungicide 1.90E-05 9.86E-06 4.93E-05CO Diesel field sprayer e Fungicide 4.43E-06 2.30E-06 1.15E-05

Table 10Inputs and outputs in step 4.

Substance Averagemass

Amountminimum

Amountmaximum

(kg of substance kg soybean�1)

InputsDiesel 4.48E-03 2.02E-03 1.01E-02

OutputsCO2 Diesel Total 1.00E-02 4.53E-03 2.27E-02NOX Diesel Total 1.79E-04 8.07E-05 4.04E-04CO Diesel Total 3.11E-05 1.40E-05 7.02E-05Soybean 2969 e e

Table 11Other outputs belonging to all stages.

Substance Averagemass

Amountminimum

Amountmaximum

(kg of substance kg soybean�1)

OutputsCO2 eq. LUC S1 3.47E-1 e e

CO2 eq. LUC S2 1.58E-1 e e

CO2 eq. LUC S3 8.76E-2 e e

CO2 eq. LUC S4 2.72 e e

N2O no-LUC Total 2.83E-04 3.01E-05 2.02E-03N2O LUC S1 Total 3.75E-04 5.46E-05 2.36E-03N2O LUC S2 Total 3.14E-04 3.83E-05 2.13E-03N2O LUC S3 Total 2.83E-04 3.01E-05 2.02E-03N2O LUC S4 Total 5.45E-04 9.97E-05 3.00E-03

V.G. Maciel et al. / Journal of Cleaner Production 93 (2015) 65e74 73

machinery, distance covered, number of interventions and thetemporal influence of each intervention. This factor helps oncalculating GHG emissions during the soybean harvest aiming torepresent as near as possible all components of cultureproduction.

Acknowledgments

The authors thank CNPq e National Council of Scientific andTechnological (Conselho Nacional de Desenvolvimento CientíficoTecnol�ogico) and CAPES e Coordination of Improvement of HigherEducation Personnel (Coordenaç~ao de Aperfeiçoamento de Pessoalde Nível Superior) for the support provided throughout this study.

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