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Sustainable Production and Consumption
journalhomepage:www.elsevier.com/locate/spc
Research article
Sustainability assessment of farms using SALCAsustain methodology
Andreas Roesch
a,∗, Aurelia Nyfeler-Brunner
b, Gérard Gaillard
aaAgroscope, Reckenholzstrasse 191, Zurich 8046, Switzerland
bDepartement für Bau und Umwelt (DBU), Amt für Umwelt, Frauenfeld 8510, Switzerland
a rt i c l e i nf o
Article history:
Received 16 October 2020 Revised 15 February 2021 Accepted 15 February 2021 Available online 21 February 2021 Editor: Adisa Azapagic
Keywords:
Sustainability Indicators Correlation analysis Semi-structured interviews
a b s t r a c t
Inrecentdecades,manysustainabilityindicatorsandmethodshavebeendevelopedatfarmlevel,buta validatedsetofquantitativeandscientifically-soundindicatorscoveringallthreedimensionsofsustain- ability isstillneeded.Forthisreason, thesustainabilitymethodSALCAsustainwasdevelopedinorder toestimatetheenvironmentalimpactand economicand socialsituationoffarmsusingamanageable number ofindicators.The primaryaim ofthisstudy wasto assess thefeasibility,explanatory power, andacceptabilitytofarmersoftheSALCAsustainmethodicalframework.Toachievethisgoal,SALCAsus- tainwasappliedforthefirsttimetoselectedSwissfarms.In-depthpersonalfeedbackinterviewswere conductedtogainmoreinsightsintothefeasibilityand farmers’acceptanceofthemethod.Theresults showedthatSALCAsustainisafeasible, acceptableandrobustmethodforassessingfarm sustainability basedonasetofindicators.Correlationanalysisdemonstratedthatthenumberofenvironmentalindi- catorscanbereducedduetohighcorrelation,butthatthecorrelationbetweenenvironmentalimpact and socioeconomic indicators was generallylow. Evaluation ofresponses to questionnairesand semi- structuredinterviewswithfarmersrevealedthatthemajoritywouldadjusttheirmediumandlong-term planningtoachievehighersustainabilityscores.Additionaleffortsareneededtospeedupdatacollection and torefineplausibility checks,throughexploitingtheincreasingdigitalisationinagriculture.Recom- mendationsandinstructionsonactionsformoresustainablefarmmanagementarealsoneeded.
© 2021 The Author(s). Published by Elsevier B.V. on behalf of Institution of Chemical Engineers.
ThisisanopenaccessarticleundertheCCBY-NC-NDlicense (http://creativecommons.org/licenses/by-nc-nd/4.0/)
1. Introduction
Agricultural production significantly impacts the environment throughthereleaseofgreenhousegases,nitrateleaching,residues from application of pesticides and manure, and use of natural resources such as land, water, non-renewable energy (fossil fu- els)andminerals (phosphorus, potassium)(Nemeceketal., 2011; IPCC, 2013). Intensive agriculture isalso responsible fora crucial loss of biodiversity, leading to profound negative changes in the functioningofagroecosystems(Emmersonetal.,2016).Thesignifi- cantpressureofagricultureonthenaturalenvironmentcanbeas- sessed usingtheconcept ofplanetaryboundaries,the boundaries of a safeoperating space for Earthsystem processes (Rockström etal.,2009;Campbelletal.,2017).
In the past few decades, it has become generally accepted that economic and social sustainability must also be included when consideringthelong-termsustainabilityoffarmingsystems (Riley, 2001; Sadoket al., 2009; Purvis etal., 2019). Thisimplies
∗Corresponding author.
E-mail address: andreas.roesch@agroscope.admin.ch (A. Roesch).
thatsustainablefarmsshouldbeenvironmentallysound,economi- callyfeasibleandsociallyacceptable(RasulandThapa,2004).The explicitlyequalstatusofthethreesustainabilitydimensions(envi- ronmental,economic andsocial)wasfirstsuggestedinthe‘Triple Bottom Line’concept formulated byElkington(1999),which pos- tulates that sufficient sustainability can only be achievedin one dimension when a minimum levelof sustainability is reachedin the other two dimensions (McKenzie, 2004). Today, the three- pillarmodelof sustainabilityiswidely appliedin theagricultural sphere(KrishnaveniandNandagopal,2018).Nevertheless,thema- jority oftools andmethods focus on the environmental impacts, largely ignoring economic and social sustainability, which results in an imbalance between the three dimensions of sustainability (Finkbeiner et al., 2010). Looking more closely at existing sus- tainability approaches revealsa lack of indicator sets that are as quantitativeaspossibleandtargetedtonational conditions.Inor- dertofillthisgap,wedevelopedanindicator-basedsustainability method calledSALCAsustain (Roesch et al., 2017). The method is especially adapted for holistic sustainability assessment of Swiss farms using indicators that are reproducible, scientifically-based andasquantitativeaspossible.
https://doi.org/10.1016/j.spc.2021.02.022
2352-5509/© 2021 The Author(s). Published by Elsevier B.V. on behalf of Institution of Chemical Engineers. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )
In this study, we apply SALCAsustain for the first time to a smallsampleofSwissfarmsbelongingtotheIP-SUISSEfederation offarmers, theaimofwhichisto produceinan environmentally sound manner according to integrated production (IP) standards.
TheprimaryaimwastoassesstheentireSALCAsustainmethodical framework foritsfeasibility,acceptabilitytofarmersandinforma- tivevalue.Theevaluationconsideredallstepsnecessaryforholistic farm sustainabilityassessment,such asdataacquisition, selection of calculationmethods, statisticalanalysesandcommunicationof the resultsto the farmer. Data acquisition usingExceldata entry formsandcalculationofallsustainabilityindicatorsweretestedin thisstudy.This allowedusto evaluatethe entireprocess,includ- ing subjects such asthetime needed fordatacollection onboth thefarmers’sideandtheanalysts’side.
There weretwomainobjectivesofthestudy:i)toanalysethe informative value of sustainability indicators,including synergies andtrade-offsbetweenindicators,andii)toevaluatethefeasibility andacceptabilitytofarmersoftheSALCAsustainmethod,basedon acomprehensivequestionnaireandpersonalinterviewswithfarm managers.SelectedSwissfarms wereusedtoillustrateapplication ofthemethodandthetool.Theresultswerethenusedtoformu- late recommendations for improvement in order to simplify and acceleratetheentireprocessfromdata-gatheringtographicalrep- resentationoftheresults.
The remainder of this paper is structured as follows:
Section 2 reviewsthe relevant literature, Section 3 describesthe sustainability indicatorsandthe procedure usedfor dataacquisi- tion, thesemi-structuredinterviews withfarmmanagersandthe structureofthepilotfarms.Section4summarisesthemainresults, whicharediscussed inSection 5.Some conclusionsare presented inSection6.
2. Literaturereview
During the pasttwo decades, manydifferentapproaches have been developed for assessing overall sustainability (Singh et al., 2009; Schader et al., 2014; De Olde et al., 2016). Most sustain- ability methods are structured across the three sustainability di- mensions (environmental, economic, social). The challenge is to select an appropriate set of indicators based on existing assess- ment methods (Lebacq et al., 2013). Therefore, several authors havedevelopedguidelinesforthispurpose(Marchandetal.,2014; Bockstaller et al., 2008; Sala et al., 2015). Essential steps for developing a suitable sustainability framework include identify- ing the end-users (scientists, advisors, farmers, decision makers, consumers) and determining the practical objectives. The latter can be, forexample, acquiring knowledge abouta systemor se- lecting the ‘best’ system or communicating complex information in a simple and easily understandable way (Sadok et al., 2008; Bockstalleretal.,2015).
Several classification schemes for comparing existing sustain- ability methods are suggested in the literature. Gasparatos and Scolobig (2012) classify sustainability tools into monetary tools, biophysicaltoolsandindicator-basedtools.Monetarytoolsrelyon thesubjectivepreferencesofindividuals,oftenexpressedbyone’s willingness to pay (Gasparatos and Scolobig, 2012). They suffer from thefact that they are preference-based andrelyon models ofhumanbehaviour.
Use of indicators is a broadly accepted concept for assess- ing thesustainability of farms based on a conceptual framework (Bockstaller et al., 2015). An indicator is defined as “a variable which supplies information on other variables which are diffi- cult to access andcan be used as a benchmark to make a deci- sion” (Lebacq et al., 2013). The use of indicators is required be- cause environmentalimpacts cannot be directly measured orthe system’s complexity, such as biodiversity or soil quality, is too
high(Bockstalleretal., 2015). Indicatorssimplifyandquantifyin- formation so that it can be easily communicated andintuitively understood, allowing policy-makers and decision-makers to base their decisions on evidence (Layke, 2009). Numerous indicator- based sustainability approacheshave beendeveloped in the past few decades.However,only alimitednumberassessall threedi- mensionsofsustainabilityatthesinglefarmlevel(Schaderetal., 2014; De Olde et al., 2016). Quite a few of the existing ap- proaches can only be used for a specific branch, such as dairy (DairySAT)(EnglandandWhite,2009)orcoffeeandcocoa (COSA) (Giovannucci et al., 2008). Some of the methods that deal with overall sustainability at farm level use a system of rather sim- plistic indicators. The French IDEA method (Indicateurs de Dura- bilité des Exploitations Agricoles or Farm Sustainability Indica- tors)is basedon 41 indicatorsofa multi-criterion character that have to be adapted to local farming before use (Zahm et al., 2008,2018).Meuletal.(2008)developedthemultilevelindicator- based Monitoring Tool for Integrated Farm Sustainability (MO- TIFS),whichprovidesavisual overviewoffarmsustainability,but also allows zoom-in to learn more about specific themes. Three methods,Response-InducingSustainabiltyEvaluation(RISE)(Grenz et al., 2012), Sustainability Monitoring and Assessment RouTine (SMART)(Schaderetal.,2014)andSALCAsustain,coversustainabil- itycomprehensively,andconcretemeasures forimprovementand decision-makingcan be derived forrelevantinterest groupsfrom theresults.ThestrengthofRISEisitsflexibleapplicabilitythatal- lowsitsusein advisoryworkandteaching.SMART enablesrapid screeningof farmsustainability andprovides resultsthat alsoal- low forinter-farm comparisonsthat can easily be communicated tothirdparties.SALCAsustainismorecomplexandisparticularly suitableforansweringresearch queriesandforanalysingdifferent farmmanagement strategies.In thepresentstudy, theSALCAsus- tainmethodwasverifiedbyapplyingitforthefirsttime tosome typicalSwissfarms.
Thescope ofcurrentsustainabilityassessmentmethods differs widely.Schaderetal.(2014) developedatypologyforcharacteris- ing sustainability methods by defining a set ofcriteria, including thelevel ofassessment,geographical scopeandthe primarypur- pose. Those authors claim that the goal of the studylargely de- termines the appropriate tool, but that the workload indata ac- quisition and the required precision of the indicator values also have to be carefully evaluated when choosing the most suitable tool.However, GasparatosandScolobig(2012) pointout thattool selectionisgenerallydonebytheanalystandusuallydependson time,dataandfinancialconstraints,whereasthequalityofthein- dicatorsandthecontext ofthe studyareoftennottakenintoac- count in decision-making.Sophisticated sustainability assessment toolsoftenrequirealargeamountofinput data,leadingto apo- tentiallysignificanttimecommitmentfromfarmers.Itistherefore importantto critically assess participatingfarmers’ acceptance of the tool. Nevertheless, Whitehead et al. (2020) claim that most studies on sustainability tools focus on the development phase, while less attention is paid to how the tool might be success- fully implemented. Triste et al. (2014) show that adoption of a sustainabilitytool by farmersandfarm advisorscan be challeng- ingforvariousreasons,butsuggestthattooladoptioncanbesub- stantiallyimprovedthroughearlyinvolvementofstakeholdersand end-users and a well-prepared introduction to appropriate tool use. Van Messel et al. (2011) found that participatory processes positively influence success in practical use of a tool. De Mey etal.(2011)concludedthat individual discussionsbetweenfarm- ers, advisorsandmodeldevelopers are crucialforsuccessfultool implementation.Thepresentstudygivesafirstinsightintoaccep- tanceoftheindicator-basedSALCAsustainmethod,basedoncom- prehensive questionnairescomplemented with individual face-to- faceinterviews.
Some existingmethodsprovideanexplicitaggregationofindi- cators, aimingatareductioninthecomplexityandthusfacilitat- inginterpretationforinterestedstakeholdersanddecision-makers.
Building composite indicatorsrequiresnormalisation andweight- ing ofindividual indicators.Normalisationinvolvescalculatingthe magnitudeoftheindicatorresultsrelativetosomereferenceinfor- mation (ISO, 2006b).Weightingsareoftenbasedonvalue choices (Pizzoletal.,2017; Grubert,2017). Toreduce thesubjectivecom- ponent in theweighting process, itis crucialto examine thede- pendency and structure of the individual indicators. Multivariate dataanalysisallows reducingthesubjectivevaluejudgementthat isnecessaryinmostweighting schemes(Ahlrothetal.,2011).Ac- cording to EC-JRC-IES(2008),multivariate data analysisis one of the keystepsforreducing thenumberofindicatorsby determin- ingappropriate weights.Theweightsof(possiblycorrelated)indi- cators canbedeterminedusingdifferentmethods.Duetothead- vantages of objectivity, principal component analysis(PCA) is of- ten used to determine the weights of individual indicators and to integratethemintoone sustainabilityscore(Jiang etal., 2018).
PCAtransformscorrelatedoriginalvariables intoanewsetofun- correlated variables using a covariance matrix, or its standard- ised form – a correlation matrix. Correlation analysis, which is methodologicallycloselyrelatedtothePCAmethod,isalsoasuit- able method for estimating weights for individual indicators. It helps to provide insights into the synergies and trade-offs be- tween sustainabilityindicators,withpositivecorrelationspointing tosynergiesandnegativecorrelationstotrade-offs.Thisisimpor- tantinidentifyingmanagementsolutionstoimprovesustainability (German et al., 2017). Reducing the numberof sustainability in- dicators and avoiding redundancy is also crucial for the sake of parsimony. This helps to reduce double-counting or overweight- ing of some processes when constructing an aggregatedsustain- ability indicator. According to (Dormann et al., 2013), regression models that includepredictor variables witha correlation coeffi- cient above a thresholdof|0.70| leadto degraded predictions,so specialattentionshouldbepaidtovariableswithcorrelationcoef- ficient >|0.70|. High correlationbetween indicatorsindicates that theyarestronglycoupledtoasimilarunderlyingmechanism.Sev- eral studies have analysed the correlation betweensustainability indicators.Forexample,inaprevious PCAof14selectedenviron- mentalindicators,Yuetal.(1998)foundgreatredundancyamong theindicators.Basedonmorethan14,000accountanciesofSwiss dairy farms, Zorn etal.(2018) found great potential forreducing thenumberofeconomicindicatorsbasedonacorrelationanalyses of17indicators.Usingacorrelationanalysis,Janetal.(2012)found a positive relationship betweenfarm economic performance and environmental performance. In the present study, we examined thecorrelationpatternbetweensustainabilityindicatorsestimated by theSALCAsustain methodfor a smallsample oftypical Swiss farms.
3. Methods
The study was based on the indicator-based sustainability method SALCAsustain, which is summarised in Table 1 and de- scribedindetailinRoeschetal.(2017).
Inthefollowing,informationisprovidedonmethodicalaspects, toolsappliedandthedataflowinSALCAsustain.Duetofundamen- tal differences inthe methodology, toolsapplied anddata acqui- sition, the information is given separately for the environmental dimensionofsustainabilityandthesocioeconomicindicators.
3.1. Environmentalimpacts
The environmentalimpactswerecomputedusinglifecycleas- sessment (LCA) according to ISO 14040 and 14044 (ISO 2006a,
2006b).Thismethodologyallowscomputationoftheenvironmen- talimpactsassociatedwithallstagesofthelifecycle(‘fromcradle tograve’)ofaprocess,serviceorproduct.
Direct emissions from field and farm were calculated based on the Swiss Agriculture Life Cycle Analysis (SALCA) method (GaillardandNemecek,2009).LifeCycleImpactAssessment(LCIA) was conducted using SimaPro software (PRéConsultants, 2019), supplemented with data from the ecoinvent v3.5 database (ecoinventCentre,2018)andAGRYBALYSE(KochandSalou,2015).
LifeCycleInventories(LCI)forthepilot farmsweretakenfrom an ongoing long-termproject at Agroscope, that aims at climate change mitigation by implementing differentmeasures to reduce greenhouse gasemissions (Alig etal., 2015). The LCIconsists of:
1) a comprehensive dataset containing information on agricul- tural activities (e.g. fertiliser and manure application), the type andamountofproductionmeans(seeds,plantprotection,fertiliser, feedstuffs,machines,buildings)andenergyuse(i.e.fuel,gas,elec- tricity)and2)theresourcesused(inputs)and3)theemissionsre- leasedinrelationtooneunitofinfrastructureorproductinorder toincludeprocessesinthebackgroundsystem.TheExceltemplate fordataacquisitioninGermanisprovidedasSupplementary Ma- terialS1.
In contrast to the other environmental impacts, the system boundary for soil quality and biodiversity was the farm, ignor- ingupstreamprocesses.Soilqualitywasassessedusingthestand- aloneExcel-based tool SALCA-SQ,whichshowstheimpact ofon- farmagriculturalactivitiesonsoilquality(Oberholzeretal.,2012).
Thistoolrequiresdetailedinformationonallfieldoperations(ma- chineryweight,wheelload,operatingwidth),whichwascollected bythefarmerusingExceldataentryformswithdrop-downmenus andawell-developedhelptooltominimiseerroneousentries(the Exceltemplate inGerman isprovided asSupplementary Material S2).
For biodiversity, the IP-SUISSE credit point system was pre- ferred over SALCA-BD (SALCA-biodiversity) dueto time, cost and dataconstraints.Thiscreditpointsystemactsatthefarmscaleand coversacatalogueof32optionswithwhichfarmerscanpositively influencebiodiversityontheirfarms.Farmerscan‘scorepoints’by applyingthesemeasuresontheirfarms(Jennyetal.,2013).
3.2. Economicindicators
In SALCAsustain, the financial situation of a farm is charac- terisedbytwo indicatorsfromeachofthreethemes:profitability, liquidityandstability(Roeschetal.,2017).Theeconomicindicators aredepictedbyfinancialratiosthatfacilitatescomparisonofdiffer- entlystructuredfarms(Zornetal.,2018).Greatvalueisplacedon selectingindicatorsthathavepracticalrelevanceforfarmmanage- ment,whichalsoenablesfarmadviceandself-assessmentatfarm level.
Profitabilityratios relate theprofitduringa periodto thefac- tors ofproduction,such ascapitalandlabour.The twoindicators proposed are income per family work unit and returnon assets.
Theincomeper familyworkunitisderivedfromthefarmnetin- come,whilethereturnonassetsrelatestothereturnontotalfarm investment.
Forliquidity,thatis,afarm’sliabilitytomeetitsfinancialobli- gations, the two indicators cash flow ratio and dynamic gearing ratioarerecommended.Thecash flowratiodividesthecash flow bytheturnover.Thedynamicgearingratioisobtainedbydividing farmliabilities,includingshort-andlong-termdebts, bythecash flow.
The stability of a farm determines risk with respect to prof- itabilityandliquidity,thereby underscoringthelong-termcompo- nentofeconomicsustainability.Thetwoeconomicratiosfixedas- setstototalassetsandequitytofixedassetsratiorepresentplau-
Table 1
Sustainability dimensions and subjects evaluated in the SALCAsustain method and the indicators used. The practical implementation is provided for each indicator.
Dimension Subject Indicator Implementation
Social Well-being Workload in terms of time Ratio of need to available labour units ( Roesch et al., 2017 , Chapter 3)
Landscape quality Landscape diversity and aesthetics Shannon Index, calculated from annual farm census data ( Schüpbach et al., 2020 )
Economic Profitability Income per family work unit
Calculation of financial ratios based on accounting data; equations are presented in Roesch et al. (2017 , Chapter 7)
Return on capital
Liquidity Cash flow ratio
Dynamic gearing ratio Stability Fixed assets to total assets
Equity to fixed assets ratio
Environment Resource use Non-renewable energy resources Cumulative energy demand ( ecoinvent Centre, 2010 ) Phosphorus and potassium CML 2001 method ( Guinée et al., 2001 )
Water requirement (fresh water) Method of Pfister et al. (2009)
Land occupation CML 2001 method ( Guinée et al., 2001 ).
Climate change Greenhouse gases (CO 2, CH 4and N 2O) Global warming potential according to IPCC (2013) (100-year time horizon)
Nutrient-related environmental impacts
Eutrophication (aquatic and terrestrial) Eutrophication potential (EDIP2003 method) ( Hauschild and Potting, 2005 )
Acidification (aquatic and terrestrial) Acidification potential: ‘accumulated exceedance’
method for terrestrial acidification, see Seppälä et al. (2006) and Posch et al. (2008)
Ecotoxicity Ecotoxicity (terrestrial) CML2001 method ( Guinée et al., 2001 )
Biodiversity Genetic and species diversity
IP-SUISSE credit point system ( Birrer et al., 2014 ) Habitat diversity and linkage
Diversity of agricultural crops Potentially natural habitat Plant-protection products Fertiliser use
Irrigation
Use intensity, management technique Functional aspects
Soil quality Physical indicators: rooting depth, macropore volume, aggregate stability
SALCA-SQ ( Oberholzer et al., 2012 ) Chemical indicators: organic carbon, heavy metal content,
organic pollutants
Biological indicators: microbial activity, microbial biomass, earthworm biomass
sibleandpractical indicatorsforassessing thestabilityofafarm.
Forthefixedassetstototalassetsratio,fixedassets(withoutlive- stock) are related to total assets.The equity to fixed assets ratio represents therelationship betweenown capital or(farm)equity andthefixedassets(Zornetal.,2018).
Thedatausedforcomputationofeconomicindicatorswereac- countingdatacollectedonExceldataentryformsprovidedtothe farmers(seeSupplementaryMaterialS2).
3.3. Socialindicators
The landscape quality indicator wascalculated asthe equally weightedmeanoftwosub-indicators(Schüpbachetal.,2020).The first sub-indicatorcovered naturalness,or visual quality,andwas computedasan area-weightedmeanofthe‘preference values’of the landscape elements of a farm. The preference values reflect the preference of the general public for various land-use types.
Thesecondsub-indicatorcoveredtheaspectofcomplexityandthe
‘ephemera’ ofthelandscape,andwasapproximated bytheShan- nondiversityindex.
The indicator for landscape quality (LCI) was computed with thestatisticalsoftwareR(RCoreTeam,2017),usingthefarmstruc- turecensusresultsthatarecompiledannuallybytheSwissFederal Statistical Office(FSO).Thecensusinvolvesanexhaustivefarmin- ventory in terms ofcrop andgrassland areas, livestock data and thelabourforce.
The indicator for temporal workload is expressed as the ra- tio of need for available labour units. The number of labour units required was estimated by the ART Work Budget System (Schick etal., 2007), while the labouravailable on the farm was
computedfrominformationon labourinformationavailable from thefarmstructurecensus.
The input data required for computing social indicators were collected on Excel data entry forms, enhanced by additional in- formation.Simpleplausibilitycheckswereperformedonallinput data,inordertoconfirmtheirvalidity.Inthefirststage,verysim- pleautomatedqualitycontrolprocedureswerecarriedout,mostly checkingwhetherthevalueiswithintheexpectedrange(e.g.per- centagesbetween0%and100%).Inthesecondstage,thedatawere verifiedbyvisualinspection,atime-consumingprocesswherethe qualityandsuccessareheavily dependentontheskillandexper- tiseoftheanalyst.
3.4. Correlationanalysis
Spearman’srankcorrelationanalysiswasperformedonthecal- culated sustainability indicators. The generally skewed distribu- tionofsustainabilityindicatorswasconsideredby usingthenon- parametric Spearman approach, which does not require a linear relationship between the variables (Hauke and Kossowski, 2011).
ComparedwithPearson’scorrelationcoefficient,Spearman’scorre- lationcoefficientislesssensitivetooutliersandmoreappropriate forasmallsamplesize (ShevlyakovandOja, 2016; Schoberetal., 2018).
3.5. Questionnaires
Tolearnmoreaboutfarmers’perceptionsoftheentireSALCA- sustain process fromdata acquisition to presentationof thefinal results,bothtestphasesforthetwooperatingyears2016and2018
Table 2
Mean key structural parameters of the pilot farms evaluated in 2016: utilised agricultural area (UAA), ecological focus area (EFA) and livestock units (LU).
Figures in parentheses refer to the percentage of UAA. Note that the sum of arable land, grassland and EFA is not equal to UAA, as extensive grasslands belong to the EFA.
Number UAA [ha] Arable land [ha] Grassland [ha] EFA [ha] Total livestock [LU]
Mountain farms (MOUNT) 5 34.2 5.2 (15.2%) 28.5 (83.3%) 7.9 (23.1%) 51.5
Arable farms (ARAB) 3 35.7 30.3 (84.9%) 5.0 (14.0%) 6.7 (18.8%) 5.4
Lowland fattening farms (FAT) 4 22.0 5.9 (26.8%) 16.0 (72.7%) 2.2 (10.0%) 83.3
wereevaluatedusingaquestionnaire.Fortheoperatingyear2016, thefarmers’perceptionsonacceptance,feasibilityandinformative value werecollectedusinga26-itemquestionnaire(Questionnaire S3inSupplementarymaterial).Theseitemsweregroupedintothe followingfivecategories:(i)generalquestionsonsustainability,(ii) information/feedback duringthe entirecourse ofthe project,(iii) data amountanddataacquisition, (iv)thefarmer’s personal sup- portduringtheprojectand(v)theexpectedimpactoftheproject onbehaviourandbusinessmanagement.Thequestionnaireforthe second test phase (Questionnaire S4 in Supplementary material) was revisedand its structure wasadapted in orderto group the answersintermsofthethreethematicareas(acceptance,feasibil- ityandinformativevalue(benefits)).Thewordingwasonlyslightly adapted, but moredetailed informationwas requested aboutthe timerequiredfordatacollectionandpriorknowledgeonthetopic of sustainability. The questionnaire included various types of re- sponse options: yes/no, five-point answer scale (‘strongly agree’,
‘agree’,‘neutral’,‘disagree’,‘stronglydisagree’),andplaintext.
The questionnaire was sent to the farmers by e-mail before- hand andwascompleted inface-to-face meetings. Theseface-to- face meetingsallowed usto clarifythefarmers’ responsesandto obtainnewinsightsintopossibleweaknessesoftheindicators.
3.6. Sample
Thestudyinvolvedasmallsampleofpilotfarmsfortheoperat- ingyears2016and2018.Duetochangesinpersonalcircumstances overtime, thefarmsamplewasnotidenticalinthefirstandsec- ondtestphases. Thesampleconsistedof12farmsin2016and13 in 2018, 10 of which were identical. In the following, some key parameters ofthe 12 pilot farms that were analysed in 2016 are summarised.
ThesamplecoveredthreefarmtypesrepresentingtypicalSwiss productionsystems:mountaindairy farms(MOUNT),arablefarms (ARAB)andlowlandfatteningfarms(FAT).Themeankeystructural parameters forthe sampleusedinthe firsttest phase (2016)are showninTable2.
The mountainfarms(MOUNT)studiedwere comprisedmainly of grassland witha relatively highpercentageof ecologicalfocus areas (EFA), such as low-input meadows andpastures andmoist meadows (Table 2). The principal productionanimals were dairy cows and suckler cows. The three sampled arable farms (ARAB) were characterised by ahighpercentage ofarable landandlittle livestock. They primarily grow winter wheat, grain maize, pota- toes, sugarbeet andrapeseed. The lowland fatteningfarms (FAT) typically hadsmallutilised agriculturalarea (UAA)andecological focusareas(EFA),andahighnumberoflivestock(mainlyfattening pigs)(Table2).
4. Results
4.1. Sustainabilityindicators:descriptivestatistics
4.1.1. Environmentalindicators
Ashortsummary ofsomekeystatisticalmeasuresforenviron- mentalimpactsperhectare(ha)onthe12pilotfarms analysedin 2016 isprovidedinTable3.Theindicatorvaluesatfarmlevelare
giveninTableS5 (SupplementaryMaterial)for2016 andinTable S6(SupplementaryMaterial)for2018.
Themeanenergydemand amountedto54.1GJ-eq per haand year.Detailed analysisatfarm levelrevealed that theenergy de- mandforFATfarms wasmarkedly higherthanthat fortheother farmtypes,primarilyduetopurchasedconcentrates(Table3).The lowest energy demand per ha was found for the ARAB farms.
Global warming potential (GWP) with a 100-year time horizon showeda similar patternto energy demand.On average,slightly morethan11.3tonsofCO2-eqwereemittedperunitareain2016.
Asfoundforenergydemand,thetwoFATfarmsanalysedalsohad thehighestGWP values(Table 3).The aquaticeutrophication po- tential (AEP) was equal to about 78 kg N per unit area, with a rangeof 24.4–179.3kg N per ha UAA. The acidificationpotential (AP) was highest for the FAT farms, as acidification was largely relatedto ammonia (NH3) emissions, causedprimarily by animal husbandryandproductionofpurchasedanimals.Themedianvalue ofterrestrialecotoxicitypotential(TEP),i.e.theimpactoftoxicpol- lutants such aspesticidesonsoil ecosystems,was7.02 kg 1,4-DB eq per unit area and year butwith highvariability, as indicated by coefficient ofvariation (COV)of 1.07.Thisis clearlyabove the valuesfoundfortheotherenvironmentalimpacts(Table3).Closer verification at the farm level revealed that the high TEP values wereprimarilycausedbypurchasedconcentratefeedonFATfarms andpesticideuseonARABfarms.
Thebiodiversity scorefollowingBirreretal.(2014)ranged be- tween19.5and30.3.The MOUNTfarmsprovidedthemostbene- ficiallandscapestructure intermsof promotingbiodiversity. This was primarily due to their high percentage of high-quality eco- logical compensation areas. The FAT farms generally ranked low in potential contribution to biodiversity, due to modest fractions ofEFAandfewenhancement measuresonarableland.Evaluation ofsoil quality based on nine soil quality indicatorscomputed by SALCA-SQrevealedhighvariationamongthepilot farmsanalysed.
Somefarmssufferedfromnegativesoilcompactioneffectscaused byheavymachinery,leadingtoreducedmacroporevolumeandag- gregatestability.Analysesof themodelresultssuggestedthat in- sufficientsupplyoforganicmattertosoilsalsocontributedtore- ducedsoilquality,expressedbynegativeeffectsonthesimulated biologicalsoilqualityindicatorsearthwormbiomassandmicrobial biomass/activity.
Theenvironmentalimpactsscaledbythesamplemeandiffered stronglyforthetwocommonlyusedfunctionalunitshaUAAand MJdigestibleenergy(DE)(Fig.1).Thiswasdirectlyrelatedtothe fact that MJ DE produced per ha varied significantly among the farmsanalysed(byafactorof28).Thereweremarkedlylowerval- uesfortheMOUNTfarms,withgenerallyconservinglandmanage- ment, compared with the ARAB and FAT farms, characterised by highproductiveoutput(Baumgartneretal.,2011).TheTEPresults clearlyshowedthehighestvariabilityofallenvironmentalimpacts amongtheindividualfarms,duetohighlyvariableheavymetalin- put via fertilisers andvery different amounts ofpurchased feed- stuffs.Thus theenvironmentalimpacts ofthesampledfarmsvar- iedsignificantly(Fig.1),astheydifferedwidelyregardingtype,ac- tivitiesandmanagementpractices.
Table 3
Selected statistical variables for environmental impacts of the pilot farms evaluated within this study (year 2016, sample size 12). COV: coefficient of variation, ED: energy demand, GWP: global warming potential (100-year time horizon), LO: land occupation, AEP: aquatic eutrophication potential, AP: acidification potential, TEP: terrestrial ecotoxicity potential. Functional unit: ha utilised agricultural area (UAA).
ED [GJ-eq/ha]
GWP [t CO 2-eq/ha]
LO [ha/ha]
AEP (N) [kg N/ha]
AP [m 2/ha]
TEP
[kg 1,4-DB eq/ha]
Mean 54.1 11.32 1.91 78.25 2022 17.79
Median 42.2 10.83 1.78 69.65 1768 7.02
Stdev 38.3 7.35 0.80 40.35 1470 19.0
COV 0.71 0.65 0.42 0.52 0.73 1.07
Minima 14.9 1.87 1.09 24.4 200 1.14
Maxima 134.6 27.05 3.56 179.5 5319 63.03
Fig. 1. Boxplots for selected environmental impacts, scaled by the mean of the 12 pilot farms analysed, 2016. fu: functional unit; UAA: utilised agricultural area; DE: digestible energy.
Table 4
Economic indicators for the 12 pilot farms analysed in 2016. IWU: income per fam- ily work unit, ROC: return on capital, CFR: cash flow ratio, DGR: dynamic gearing ratio, FATA: fixed assets to total assets, EFAR: equity to fixed assets ratio. COV: co- efficient of variation.
Profitability Liquidity Stability
IWU[CHF] ROC[%] CFR[%] DGR[ ] FATA[ ] EFAR[ ]
Mean 47940 -11.9 48.2 10.85 0.76 1.00
Median 46910 -4.9 36.0 11.59 0.85 0.93
Stdev 22350 17.0 35.8 10.38 0.19 0.83
COV 0.466 -1.4 0.7 0.96 0.25 0.82
Minima 16770 -54.3 -7.0 0.44 0.30 0.16
Maxima 88850 1.0 119.0 29.06 0.91 3.12
4.1.2. Economicindicators
Economic sustainability was assessed by six commonly used economic ratios, two each for profitability, liquidity and stability (Table4).
The economic performanceof thefarms analyseddiffered sig- nificantly regarding profitability, liquidity and stability (Table 4).
Annualincome(IWU)variedbetween16,770and88,850CHF,with
a mean of 47,940 CHF, which was close to the value of 47,200 CHF forthe entireSwiss agriculturalsector in2016 (Hoopet al., 2017). The variability measure COV was clearly lowest for FATA, definedas the ratioof fixed assets (machinery andbuildings) to totalassets.Anumberofthepilotfarmssufferedfromlowincome and/or critical liquidity andstability. The mean returnon capital (ROC)of-11.9%meansthatfarmprofitafterremunerationoffam- ilymemberswasnegative. Onlytwo farms showedaprofit, with a slightly positive ROC. Generally, the sampled farms seemed to havesufficient financial resources, asmean cash flow ratio(CFR) amounted to 48.2%, indicating that cash flow wasapproximately half of turnover (Table 4). Inspecting the liquidity measure dy- namicgearingratio(DGR)revealedthatthepilotfarmsneededan averageofalmost11yearstopayalltheirdebtswiththecashflow generatedin2016,withamassivedifferencebetweentheleastand mostliquidfarms(Table4).Theaverageequitytofixedassetsratio (EFAR)of1.0providesevidencethatthefarmsweregenerallyeco- nomicallystablebecausetheycouldlargelycovertheirfixedassets (machinery andbuildings) withtheir own capital.A criticalsitu- ation intermsofinsufficientcapital wasfoundforsome MOUNT farms.ArablefarmingseemedtohaveafavourableeffectonEVAR.
Fig. 2. Social indicators for the 12 pilot farms analysed, 2016. Temporal workload (WL): panels (a)–(c), and panels (d)–(f): landscape quality (LQ). Panels (c) and (f) show the composite indicators for WL und LQ. ART WBS: ARTWork Budget System. Colour codes: red: mountain (MOUNT) farms, green: arable (ARAB) farms, blue: animal fattening (FAT) farms. Manpower (MP) in panels (a) and (b) is given in standard labour units (SLU), with 1 SLU = 2800 h. LQ indicators are normalised with the mean in the respective reference group (‘homogenous agricultural zones’). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
4.1.3. Socialindicators
The results for the two social indicators, temporal workload (TW) andlandscape quality (LQ), forthe 12 sampled pilot farms in2016areillustratedinFig.2.
Fig. 2a and b show the distribution of the theoretically de- rived work time requirements and the manpower available on the farm.Theworkload(WL) differedgreatlyamongthe sampled farms, withthe mean value of 0.86 pointingto slight underem- ployment on the pilot farms (Fig. 2c). Three farms withWL>1.2 showed a clear tendency toward a potential overload, while one farm with WL = 0.29 seemed to suffer from distinct underem- ployment. Further evaluation clarified that no farming type was particularly prone to strong under- or overemployment. Detailed analysesandtheface-to-faceinterviewswithfarmersshowedthat the manpowervaluescalculatedby theART WorkBudget System hastwopotentialdeficiencies:ameandegreeofmechanisationfor allfarmsanalysedwasassumed,andsomenicheproduction(such asownproductionofmarmaladeforafarm shoporkeepingrare farmanimals)andsomelandtypeswerenotcaptured.
Fig.2dandeshowthetwo normalisedsub-indicators(PVand S), normalised with the mean value in the respective reference group.Mostofthefarmsanalysedhadabove-averageLQ,depicted by valuesabove 1(Fig.2f).Thegraphicrepresentationshowsthat boththeaestheticvalue ofthelandscapeelementsasrepresented in the area-averaged preference value (Fig.2d) and the diversity (shownby theShannonindexinFig.2e)contributetothisresult.
RegardingWL,nopatternforthedifferentfarmtypeswasseen.
4.2. Sustainabilityindicators:correlationanalysis
This section provides some insights into the relationship be- tween thesustainabilityindicators(cf.Table1),asidentifiedfrom correlationanalysis.
As can be seen from Fig. 3, several environmental indicators, such as energy demand, GWP, land occupation and acidification,
werehighlycorrelated,withcorrelationcoefficientsabove0.9.The relationshipwiththetwoenvironmentalimpacts,aquaticeutroph- ication N (AEN) and terrestrial ecotoxicity potential (TEP), was clearlyweaker. The correlation coefficientsbetween TEP and the other environmental impacts were generallylow andnot signifi- cantlydifferentfromzero.TEPandbiodiversitycanbeexpectedto behighlynegativelycorrelated(R=-0.78),aspesticideuseisone of themajor factors affecting biological diversity. The correlation ofbiodiversityandsoilqualityscoreswiththeotherenvironmen- talimpactswasgenerallylowandnotstatisticallysignificant.
Thestrongandconsistentrelationshipobservedbetweenmany oftheenvironmentalimpactindicatorsreflectedthefactthatthey aredrivenbysimilarphysicalprocesses.Fertilisermanagementon- farm has strong impacts on both the energy demand and GWP, through purchased mineral fertilisers. The fertilisers applied to thefieldsstronglyaffectammoniaemissionsandthus nitrousox- ide emissions, leading to increased GWP. The statistically signif- icant correlation between AP and AEN (R=0.67) is due to the factthat thesetwoenvironmentalimpacts arebothlargely deter- mined by the ammonia emissions. The high correlation between land occupation and energy demand is primarily related to the fact that purchased feed and livestock are associated with high emissions and land use. Land is used for grazing livestock and cultivation of crops, production of concentrated feed is energy- intensive and cattle produce methane through their digestive processes.
The evaluationbased on the datafrom thesecond test phase in2018(notshown)generallyconfirmedthefindingsobtainedfor thefarmsanalysedin2016,althoughthestrengthoftherelation- shipbetweentheimpactsanalyseddifferedslightly.Thisisnotsur- prising,giventhesmallsamplesize andthe highcomplexityand diversityoftheprocessesinvolvedindescribingthevarious envi- ronmentalimpactsdiscussedabove.
Therelationship betweensocioeconomic indicatorswasgener- allyweakandnot significant atthe95% confidencelevel(Fig.4).
Fig. 3. Correlation matrix of environmental indicators. Results are based on the analysis of 12 pilot farms for 2016. EDha: energy demand per ha; GWPha: global warming potential per ha; LOha: land occupation per ha; APha: acidification potential per ha; AENha: aquatic eutrophication N per ha; TEPha: terrestrial ecotoxicity potential per ha;
BD: biodiversity score; BQ: soil quality indicator. All environmental impacts except BD and BQ are per ha utilised agricultural area (UAA). Positive correlations are displayed in blue and negative correlations in red. Colour intensity and circle size are proportional to the correlation coefficients (see colour key on the right). Crosses indicate non- significant correlation coefficient at 95% confidence level. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Analysis revealed that the environmental impacts (represented here by GWP) were generallyweakly correlated withthe socioe- conomic indicators analysed. Interestingly, a higher workload in terms oftimedid notnecessarilylead tobetter economicperfor- mance,withthepossibleexceptionofthetwoindicatorsIWUand ROCcharacterisingfarmprofitability(Fig.4).Thepronouncedneg- ativecorrelation(R= -0.96)betweenEFAR andDGRisreasonable fromaneconomicpointofview,ashighfarmliabilities,including short-andlong-termdebts,aregenerallyassociatedwithlowcap- ital.This indicates that thesustainability assessmentcan besim- plifiedbyusingareducednumberoffinancialratios,asconfirmed inarecentstudyonconstructingasimplifiedcompositeindicator for economic sustainability based on more than 14,000 accounts forSwissdairyfarms(Zornetal.,2018).
4.3. Evaluationofquestionnaires
The main information gathered from the questionnaires sent to the farmers for the first test phase in 2016 and the second test phase in2018 isdescribedbelow.As theknowledge andex- perience of the participating farmers differed between the first andsecondtestphases,themainfindingsfromtheevaluationare treatedseparately.
4.3.1. Questionnaire:firsttestphase(2016)
The 12 farmersanalysed in 2016 were all familiar (7 strongly agreed, 5 agreed) with the concept of sustainability; all farmers believedthat sustainabilityassessmentis(very)importantforthe agriculturalsector ingeneral. To thequestionofwhetheraspects ofsustainability aremissingin theSALCAsustain method(indica- torslistedinTable1),two participantsmentionedanimalwelfare andtwo mentionedagroforestry.Somefarmersstressedthatlocal conditionsarenotsufficientlyconsideredinthecollecteddata;for example,thecomputationofmanpowerrequirementsonthefarm ignores variousworkingproceduresrelatedto handworkorniche products, such as productionof marmalade or keeping rare ani- mals.
Regardingtheinformation/feedbackduringtheentirecourseof the project, the participants were mostly satisfied; the informa- tion provided as part of an information event and the possibil- ity for telephone enquiries were highly appreciated. Further, the farmersappreciatedthepersonal feedback, althoughit wastime- consumingforbothparties.
Onlyfourofthe12participantsweresatisfiedwiththedataac- quisitionprocess.Themainshortcomingsreportedwereintheap- plicationofdifferenttoolsandthegraphicaluserinterface,which didnot allowa reasonablegroupingofinputvariables. Threeout
Fig. 4. Correlation matrix of socioeconomic indicators. Results are based on the analysis of 12 pilot farms for the year 2016. All codes as in Table 4 , global warming potential (GWP) per ha is included for illustration. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
of12farmersdemandedmoreon-sitesupport,e.g.forfeedingde- tails ofownmachineryintothesystem, theprovisionofaccount- ing dataortheentryofplantprotectionproducts.The evaluation showedthat theaverage timeexpenditureforthefarmer wasal- mostninehours, witharangebetweenfourand16hours;young farmers tended to be faster than elderly farmers. Note that this time expendituredidnot includethetime usedforcompilingthe inventory data used inLCA. The farmersdid not agree regarding the questionof whetherthe system design and data acquisition were appropriate fora large group offarms. Their answers indi- cated that the main stumbling blocks were the large amount of inputdatarequiredandtheuser-unfriendlysoftware.
Theparticipantsreportedthattheywouldprofitfrommorein- teractionswithotherfarmersinvolved(Fig.5).Further,theyagreed thatmorecoursesontopicsrelatedtosustainabilityshouldbepro- videdbyagriculturalconsultants,whichtheybelievedwouldtrig- gergeneral acceptanceofindicator-basedassessments ofsustain- abilityatthefarmlevel.
Thegreatmajorityoftheparticipantsagreedthat participating inthestudyhadinfluencedtheirmediumandlong-termplanning of operational management. Eight of the 12 farmers agreed the projectwillaffectthekindoffeedstuffstheypurchase.
4.3.2. Questionnaire:secondtestphase(2018)
The median time required for the participating farmers to gatherallthedata(excludingdatanecessaryforcompilingtheLCI) was threehours, clearlyshorter thanin thefirst test phase. This was dueto learning effects anduse of certain data (e.g.on ma- chinery,sizeandnameoftheplots)takenfromthefirsttestphase.
Mostofthetime wasneededforprovidingthe accountancydata and the single machine passages across plots for estimating soil compaction. Data plausibility checks and further data processing required 10-15hours per farm; this work wasdone by scientific technical staff at Agroscope and an external office. The time re- quiredfor the actual computationofthe sustainability indicators for all pilot farms is given in Table 5. The very time-consuming computationofsoil qualitywasremarkable,butcanbe explained by a very tedious procedure due to lack of automation and the requirement of several input files to feed the Excel data entry form.
Most of the participants mentioned that a single tool would considerably ease the data acquisition process. The farmers’ re- sponses led to the conclusion that they would accept differ- ent technical implementations,such asa simple Excel tool (9/13 agreed or strongly agreed), a web interface (10/13) or data en- try via an app on a smartphone or tablet (9/13). It is interest- ing to note that the farmers thought that reducing the input data catalogue would lower the accuracy and expressive power of the sustainability assessment. A structured pulldown menu for selection of machinery and animal houses would ease data acquisition.
The second part ofthe questionnaire dealtwiththe expected benefits fromthe sustainabilityassessment. Eleven of13 farmers agreed orstronglyagreed that comparing thefarm’s ownindica- torsagainstthoseofasimilarreferencegroupmighthelpidentify strengthsandweaknessesintheirfarmmanagement.Thefarmers confirmedthattheywouldbenefitfromanin-depthunderstanding oftheindicators.However,theybelievedthattheevaluationwould
Fig. 5. Evaluation of the questionnaire used in the first test phase (2016). Q1: Are you interested in participating in a working group? Q2: Would you like to exchange information on sustainability topics with other farmers? Q3: Should courses on sustainability for farmers be offered by trained agricultural advisers?
Table 5
Time required for computation of sustainability indicators for all 13 farms analysed in the second test phase (2018). Biodiversity is not listed, as the biodiversity scores were provided by IP-SUISSE.
Indicator(s) Model/ Tools Time used [h]
Environmental impacts and resources (listed in Table 1 ), except for soil quality and biodiversity
SALCA model, SimaPro, ecoinvent database 4
Soil quality SALCA-SQ (stand-alone Excel tool), see Oberholzer et al. (2012) and Table 1 8
Economic indicators (six financial ratios, see Table 1 ) Stand-alone Excel tool 4
Workload in terms of time ART Work Budget System ( Schick et al., 2007 ) 4
Landscape quality R-Programme (developed at Agroscope, Zurich) 1
primarily affecttheirlong-termplanning,whileintheshort-term (andpartlymid-term)they wouldtake noactionsto improvethe farm’soverallsustainability(Fig.6).
The participants’ responses provided strong evidence that ac- ceptanceofthesustainabilityassessmentcanbeincreasedbyon- site feedback providing deeper insights into the results. Further- more, allbutone participantagreed orstronglyagreed that they would profitfroma comparisonof their own farm’sresults with thoseofareferencegroup.Thefarmersindicatedafterthesecond test phase thatthey were equallyinterestedinthethreesustain- abilitydimensions,withnoclearpreferencefortheenvironmental, economicorsocialdimension.Theacceptableexpenditureoftime for collecting all data (including the data forcompiling the LCI)
variedsignificantlyandwasbetween3and30hours.Asmostpar- ticipantswereinterestedinthesustainabilityassessment,itisnot surprisingthat 12 ofthe 13 farmerswere ready to providetheir dataeverysecondyear.
5. Discussion
Thefocusinthissectionisonthecorrelationsbetweenthesus- tainability indicatorsandfarmers’ perception ofthe process used forthesustainabilityassessment.Themainfindingsfromtheques- tionnaires,reflectingtheviewsofthefarmers,were usedtoanal- yse the feasibility and the expected benefits gained during the project.The difference betweenthe average sustainabilityindica-
Fig. 6. Evaluation of the questionnaire used in the second test phase (2018). Q1: Will the results of the sustainability assessment influence your short-term planning? Q2:
Will the results of the sustainability assessment influence your mid-term planning (following year)? Q3: Will the results of the sustainability assessment influence your long-term planning?
torsin2016and2018isnotdiscussed,asthesamplesizewastoo smalltoverifystepstowardschanged (orimproved)management practicesattheindividualfarmleveloreventoderivereliableand robusttrendsintheSwissagriculturalsector.
5.1. Regressionanalysis
Regression analysis revealed that the environmental impacts GWP,AP, EP andtheuseofenergyandlandresources were gen- erally highly positively correlated, in line with previous findings (BergerandFinkbeiner,2011;Laurentetal.,2012;Röösetal.,2013; Mu etal., 2017). Correlationcoefficient valueswere clearlyabove 0.7forGWP,APandlanduse,suggestingthatareducedsetofindi- catorsmaybesufficientforadequatedescriptionofafarm’simpact on the environment (Dormann et al., 2013). This is in line with Mu etal.(2017),whodefinedareducedset ofenvironmentalin- dicatorstobenchmarkdairysystemsinanefficientway.Thehigh correlation coefficient valuescanbe attributedtosimilar physical processes driving these impacts and resource uses. For example, landisusedforgrazingcattle, productionofconcentratefeedand production ofroughage. Ruminants (andmonogastrics toa much lesserextent)producethehighlyeffectivegreenhousegasmethane from digestionandnitrous oxide frommanurestorage andman- agement (Broucek, 2017). Further, the production of concentrate feed is a very energy-intensive process, but also requires much land. Manure from cattle is responsible for significant ammonia emissions, leadingtoaciddepositionandeutrophication,withad- verseeffectsontheaquaticecosystemsofriversandlakes.There- fore,itisevidentthatmostoftheenvironmentalimpactsandre- source useon farms will show apositive linear relationship.The highnegativecorrelationbetweenterrestrialecotoxicityandbiodi- versity wasalsoexpected, aspesticide use hasa strong negative impactonbiodiversity(Relyea,2005).
Incontrasttotheenvironmentaldimension,thecorrelationma- trixforsocioeconomicindicatorsshowedtheyweregenerallyonly slightlycorrelated. Analysisrevealede.g. that above-averagework input did not necessarily lead to better economic performance.
However, theanalysisalsoshowedthat someofthesixsuggested financialratios(seeTable1)canprobablybeexcludedwhencom- puting an aggregated composite indicator for economic sustain- ability.Theresultsofthepresentcorrelationanalysisandthoseof Zornetal.(2018)suggestthatoneofthetwoselectedprofitability indicators,IWUandROC,couldpossiblybeignoredwhenassessing economicsustainability.
There was no evidence of statistically significant correlations between environmental and socioeconomic indicators. Previous studies have reportedconflicting findings on therelationship be- tween environmentaland socioeconomic indicators.For example, Janetal.(2012)found nomutualconflictsbetweenafarm’senvi- ronmental andeconomic objectives. whereas Salvati andCarlucci (2011) showed that e.g. soil-improving crops with positive envi- ronmentaleffectscontributeverylittletofarmprofitability.These conflicting results are probably mainly related to very different conceptual frameworks,methods andobjectives applied inprevi- ous studies. The results obtained in the present study must be viewedwithcautionduetothesmallsamplesize.Inaddition,the overallvalidityofthemethodsuffersfromincompletecoverage of thesocialdimension,sincee.g.humanwell-beingandanimalwel- fare were not included in theevaluation dueto limitedfinancial resources and lackof methodologicaladjustment toSwiss condi- tions.Further,correlatingtheenvironmentalandsocioeconomicin- dicators usedintheSALCAsustainmethodmaybe critical,dueto the useofdifferentsystem boundariesforLCAandthesocioeco- nomicindicators.WhileLCA,bydefinition,includesupstreampro- cesses, the economicindicators arebased onthe farm’s accounts anddonot followtherules forlifecyclecosting(LCC).The same
applies to thetwo social indicators analysed,which consider the on-farmtemporalworkloadandtheon-farmlandscapequality,ig- noring background processes such asthe workingconditions for feedorfertiliserproducersorthelanduseforproductionofcon- centratedfeedorfertilisers.
Despite the limited size of the farm sample, the results ob- tained from analysing the farms participating in the second test phase(2018) were similarto thefindings retrievedfromthe first testphase(2016). Thustheresultscanstill beconsideredreason- ablyrobust.
5.2. Evaluationofquestionnaires
The questionnaireresponses providedinteresting insights into thefarmers’ perceptionsofthe entirestudyfromlaunchtocom- pletion.Evaluationofthetwotestphasesbasedoncomprehensive questionnairesandindividual semi-structuredinterviewswiththe farmersrevealedthatthestudydesignallowsthesustainabilityin- dicatorslistedinTable1tobe computedwithsufficientaccuracy.
Asevaluationofthefeasibility,acceptanceandexpectedbenefitsof theSALCAsustain methodwasaprimary goalofthisstudy,these aspectsarediscussedindetailbelow.
5.2.1. Feasibility
Therequiredinputdatacanbecollectedbythefarmerbutthe time requiredis considerable,reducing thefeasibility ofthe sus- tainabilityassessmentonalargersample.Datacollectionandpro- cessing, including plausibility checks, format conversion and ad- ditional computations for deriving further input parameters, re- quireconsiderabletimeonthepartofthedatacollectionagency.
Anumberofcomponentscontributetothistime-intensiveprocess chain: (i) the data are collected using various tools,such asdif- ferentlystructuredExcelformsandmobileappsforsmartphones, (ii)differentsourcesoforiginaldata(LCI, fertiliserbalance)(Suis- seBilanz, see Agridea, 2015) or accounting data,(iii) rather user- unfriendlyExcel data entryforms withno option forstructuring data according to one’s own wishes, (iv) insufficient written as- sistance fordata entryand(v) use ofunusual units that are not employedinpractice.Asthesignificanttimerequirementsfordata acquisitionhampersustainabilityassessmentforalargefarmsam- pleatreasonablecost andpersonal resources,themajorinforma- tiontechnology(IT)projectSALCAFuturehasbeenlaunchedatour institute(Lanscheetal.,2017). Thisprojectincludesan optimised data processingtool, user-friendly dataentryvia a web interface and a flexible and efficient calculation procedure using modular programming. Furthermore,accessto external users via a central websitewillbeprovided.
Theresponsesofparticipantsinthepresentstudyshowedthat availability by phone and email was considered good by most farmers. Personal feedback was appreciated, although this might not be offered when extending the sample size due to financial considerationsandlimitedresources.
5.2.2. Acceptance
Thefarmers’feedbacksuggestedthattheyweregenerallyinter- estedinthetopicofsustainability.Acceptancecanbeincreasedby periodicfeedback,goodreachabilityandapersonalfeedbackinter- view abouttheir ownfarm’sresults.We providedeachindividual farmerwithavisualsummary oftheindicators,includingacom- parisonwiththeother participatingfarms, butno advicewasof- fered on translationinto practice aimed at improving the farm’s overall sustainability. In the literature, this discrepancy between knowledge and practice, also known as the knowledge-to-action gap (Siebrecht, 2020), is widely recognised (Pretty et al., 2010; Vellema,2011). Some farmersstressed thatacceptance wouldin- creaseifthey wereprovidedwithadviceonhowtoimprovesus-
Table 6
Four different strategies (A-D) for practical implementation of the SALCAsustain method and their advantages ( + ) and disadvantages (-). For details of the strategies, see the text.
Use of specific farm data Use of default values
Complete indicator set A
+ Comprehensive, all aspects of sustainability are covered + Farm-specific
−High computational effort
−Extensive set of input data
B
+ Comprehensive, all aspects of sustainability are covered + Less farm-specific data
−High computational effort
−Less farm-specific statement Reduced set of indicators C
+ Reduced set of input data + Reduced computational effort + Allows farm-specific evaluations
−limited informative value
−Enhancement may be costly
D
+ Reduced set of input data + Reduced computational effort
−Very limited informative value
−Enhancement may be costly
tainability at little additional cost and reasonabletime expendi- ture. From the farmers’ responses, it is evident that exchanges with a group offarmers engaged in similar activities would fur- ther increase acceptance of the assessment method. The partici- pantspointedoutthatthetimerequiredfordatacollectionshould be decreased considerably,to makethe SALCAsustainmethodac- ceptable to a wide range of farmers. In addition, they indicated a needforfinancial compensation(for thetime usedandforthe data).
Existing findings on how well farmers adopt sustainabil- ity assessment tools are contradictory and depend on both the underlying set of criteria and the tool(s) examined.
Triste et al. (2014) found for the indicator-based sustainability assessment tool MOTIFS (MOnitoring Tool for Integrated Farm Sustainability) that, despite the participatory tool development process, adoption of the tool by farmers was disappointing.
In-depth interviews revealed that the main reason for this un- satisfactory result was differences in expectations on the tool’s objectives between thetool developers andstakeholders. In con- trast, De Olde et al. (2016) found that farmers regarded RISE (Response Inducing Sustainability Evaluation; Häni et al., 2013) as an appropriate tool forgaining insights intothe sustainability performance of their farm. However, based on an evaluation of four sustainability assessment tools, De Olde et al. (2016) also found that farmersgenerallyhesitate to apply the resultsgained fromsustainabilitytoolsintheirdecisionmaking.
Onereasonforthefarmers’generallypositiveperceptionsabout the SALCAsustain method could be that itwas testedon a small groupofhighlymotivatedfarmersintheIP-Suisseassociation,par- ticularly since all farmersvoluntarilyagreed to participate. Farm- ers’acceptancemighthavebeenlowerifthestudyhadbeenbased on a randomly selected sampleof Swiss farms. The feedback in- terviewsalsorevealedthatitmightbecriticaltogeneralisestate- ments, butthey should still be adapted to site-specific contexts, includingthesocialenvironment(Slätmoetal.,2017).
5.2.3. Benefits
A large majority of the farmers agreed that participation in the studywouldlead tomoresustainable useofresourcesinthe long-term, andthey planned to adapt their agricultural activities andmanagementtowardsincreasedsustainability.Workinggroups consistingof farmerswithsimilar agriculturalactivities andchal- lengeswithregardtomoresustainablefoodproductioncouldcom- municatethebenefitsofparticipatinginthesustainabilityassess- ment.
5.2.4. Recommendations
Comprehensive sustainability assessmentbasedon theSALCA- sustainmethodcouldbeimprovedbyreducingthetemporalwork-
load for data acquisition to increase acceptance and benefits for farmersandotherstakeholders.Otherrecommendationsareto:
(i) Provideadiscussionplatformforfarmersengagedinsimilar agriculturalactivitiesandwithsimilarinterests.
(ii) Provide recommendations for actions and practical advice toachievemoresustainableproduction.Presentingonlythe valueoftheindicatorsisnotsufficient.
(iii) Provideuser-friendly data entry forms and easily available help.Datashouldalwaysbeinunitswithwhichthefarmer isfamiliar.
(iv) Implementcomprehensiveplausibilitycheckstoavoidtime- consumingdataworklaterintheproject.
(v) Providesufficientsupportduringtheentireprocess.
Basedontheabove,weformulatedfourstrategies(A-D)forre- ducingthecomplexityoftheSALCAsustainmodelwhenputtingit into practice. Table 6 summarises these strategies, including the main advantagesanddisadvantages. Inorder to reduce thecom- plexityandthetimerequiredforthecalculations,eitherthenum- ber ofindicatorsor theamount ofdatacan be reduced.The lat- teroptioncanbeachievedbyusingappropriatedefaultvaluesfor some input variables.Strategy Aisthe implementationdescribed inthisstudy;Bkeepsthecompleteset ofindicators,butreduces the time for data acquisition by using default values; C reduces the numberofsustainability indicators butrequires farm-specific inputdata;andDisthemostsimplevariantbysimultaneouslyre- ducing the number of indicators andusing default values. There isnocleardividinglinebetweenthefourstrategies,butthereare somemaindifferences(Table6).Theinformativepowerdecreases whenomittingcertain indicators(strategies CandD)orallowing theuseofdefaultvalues(strategiesBandD).Whenacquisitionof an input variable isvery challengingor error-prone,specification ofa defaultvalue may even increase the accuracy. However, use oftoomanydefaultvaluescarriestherisk ofmissingsite-specific properties(e.g.equipmentused,informationaboutanimalhousing orthesizeofthebiogasplant)orfarmmanagementpractices(e.g.
fertiliserapplied,pesticidesusedorcultivationofarablecrops).
5.2.5. AdaptionofSALCAsustaintoothercountries
The SALCAsustain method is especially designed for use in Switzerland. However, the conceptual framework allows the tool to be adapted forusein other countries, particularly CentralEu- ropean countrieswithpedoclimatic conditionssimilarto thosein Switzerland.AdditionalworkisneededtoadapttheSALCAmodel e.g. for soil typesthat are not known in Switzerland. Additional efforts in dataharmonisation are alsorequired,as dataavailabil- itygenerallydiffersbetweencountries.Regardingtheeconomicdi- mension, itshould benotedthat accountingandcommonlyused indicatorsmaydifferbetweencountries. Insummary,thegeneral
