Medicine, Science & Technology

Transition to second generation cellulosic biofuel production systems reveals limited negative impacts on the soil microbial community structure

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Currently we lack knowledge on how the soil microbial community responds to the transition from traditional grain production to cellulosic feedstock production systems with residue removal for biofuel production. Implementation of second-generation
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  Transitionto   second   generation   cellulosic   biofuel   production   systemsreveals   limited   negative   impacts   on   the   soil   microbial   communitystructure. Mary-JaneOrr a ,MarianneBischoffGray a ,BruceApplegate b,c ,JeffreyJ.Volenec a ,SylvieM.Brouder a ,RonaldF.Turco a, * a PurdueUniversity,DepartmentofAgronomy,LillyHallofLifeSciences,915   WestStateStreet,WestLafayette,IN,USA b PurdueUniversity,DepartmentofBiologicalSciences,915   WestStateStreet,WestLafayette,IN,USA c PurdueUniversity,DepartmentofFoodScience,745AgriculturalMallDrive,WestLafayette,IN,USA A   R    T   I   C   L    E   I   N   F   O  Article   history: Received   14   November   2014Received   in   revised   form   30   May2015Accepted   2    June   2015Available   online   xxx Keywords: Cellulosic   biofuelSoil   microbial   community   and   biomassFungal   diversityPLFAITS-DGGEResidue   removalCorn Miscanthus   giganteus PrairieSorghumSwitchgrass A   B   S   T   R    A   C   T Currentlywelackknowledgeonhowthesoilmicrobialcommunityrespondstothetransitionfromtraditionalgrainproductiontocellulosicfeedstockproductionsystemswithresidueremovalforbiofuelproduction.Implementationofsecond-generationbiofuelproductionsystemscouldcreateachallengeinmeetingthegoalofprovidingcellulosebiomassiftheprocessesimpactsoil ’ s   functionaspartofecosystemservices.Ourgoalwastoassessthetransitionperiodaswemovefromagrainharvestsystemwithresiduereturnintobiomassproductionsystemswithbothresidueandgrainremoval.Theexistingtraditionalsystemswerecontinuouscorn(CC)andcorn – soybeanrotations(corn – soybean – corn(R1)orsoybean – corn – soybean(R2)).Thebiomassproductionsystemsincludeapreviouslyestablishedmixedtallgrassprairiedominatedbybigbluestem(  Andropogongerardii ;   PR)wheretheabovegroundbiomassisnowharvestedwhenithadbeenpreviouslyburned,corn – soybeanrotationstransitionedtoresiduerecoverybasedon: Miscanthus   giganteus (MS),dual-purposesorghum( Sorghumbicolor  ;SG),uplandswitchgrass( Panicumvirgatum ;c.v. Shawnee ;   SW)andatilledcontinuouscornchangedtocontinuousno-tillcorn(  Zeamays ;CR)withtheresidueremoved.Overathree-yeartransitionperiod,biologicalindicatorsofsoilhealthincludedmicrobialbiomassandcommunitystructurepro 󿬁 leusingphospholipidfattyacid(PLFA)biochemicalmarkers,andanassessmentofthesaprotrophicfungaldiversityusingdenaturinggradientgelelectrophoresis(DGGE)ofpolymerasechainreactionampli 󿬁 edITSrRNA   geneticmarker.Overthecourseofthetransition,bacterialbiomassdominatedsoilsintheannualsystemsofCC,R1,R2,CRandSG,andtheperennialsystemofMS.WhilefungalbiomassdominatedsoilsunderPR.FungalPLFAsignaturesandITS-DGGEpro 󿬁 lesdemonstratedsimilaritybetweenPRandthetransitionedSWsystem,andtoalesserextentSG.Incontrast,MSdidnotstatisticallyalterthemicrobialbiomass,communitystructureorfungaldiversityawayfrompatternsobservedinthecorn – soybeansystem.Our 󿬁 ndingssupportmicrobialcommunitystabilityandresilienceaswefoundnonegativeimpactsinanyofthebiomassrecoverysystems(PR,MS,SG,SWandCR)relativetothetraditionalsystems(CC,R1andR2),asindicatedbymeasuresofsoilhealth.Wedid 󿬁 ndthattheselectionoftheplanttypesusedinthebiomasssystemcouldhaveasigni 󿬁 cantimpactonthestructureofbothbacterialandfungalcommunities.Withrespecttosoilhealthindicatorsincludingthefungalcommunity,our 󿬁 ndingssuggestthedual-purposeSGsystemprovidesanencouragingalternativetocontinuousmaizewhenconsideringplantbiomassproduction.Theselectionofthisdedicatedperennialbiomasssystemoffersanavenuetoenhanceindicatorsofsoilhealthandassociatedecosystemserviceswhilesupportingthebioenergyeconomy. ã 2015PublishedbyElsevierB.V. 1.   Introduction The   possible   widespread   introduction   of    second-generationcellulosic   ethanol   production   within   the   Midwestern   Corn   Beltoffers   a   unique   opportunity   for   landscape   diversi 󿬁 cation,   but   theimpacts   on   soil   health   are   unclear.   In   2012,   fermentation   of    corn *   Corresponding   author.   Fax:   +1   765   496   2926. E-mail   address:   rturco@purdue.edu   (R.F.   Turco).http://dx.doi.org/10.1016/j.apsoil.2015.06.0020929-1393/ ã   2015   Published   by   Elsevier   B.V.Applied   Soil   Ecology   95   (2015)   62 – 72 Contents   lists   available   at   ScienceDirect Applied   Soil   Ecology journal   homepage:   www.else   vie   r.com/locate    /apsoil  grain   was   the   predominant   agricultural   source   for   ethanol   ( 󿬁 rst-generation   biofuel),   with   the   United   States   producing   13.3   billiongallons   of    ethanol   using   this   process   (Renewable   Fuels   Association,2013).   However,   the   use   of    grain   has   raised   concerns   related   to   foodmarket   competition   and   hidden   environmental   costs;   consequent-ly   the   development   of    bioethanol   utilizing   pre-existing   cropresidues   and/or   dedicated   plant   biomass   have   been   in   develop-ment   (Hill   et   al.,   2006;   Hill,   2007).   The   cellulosic   ethanol   platformis   now   an   emerging   technology   with   some   20   facilities   and   projectsunder   development   across   the   U.S.   (Advanced   Ethanol   Council,2013).Cellulosic   ethanol   offers   󿬂 exibility   as   materials   can   srcinateeither   from   pre-existing   grasslands   or   annual   cropland,   or   from   theestablishment   of    dedicated   perennial   biomass   systems.   Inclusive   of the   production   system   chosen,   it   is   important   to   identify   andunderstand   ecosystem   impacts   on   the   soil   quality   and   health   as   thecrop   residues,   a   major   nutrient   input   to   soil,   are   removed.The   quality   of    the   soil   resource   is   a   fundamental   component   of agroecosystem   sustainability,   driving   system   stability   and   thelong-term   capacity   to   produce   plants.   In   turn,   soil   microbialdynamics   drive   soil   quality,   and   changes   in   soil   biology   can   serve   aspredictive   indicator   of    negative   impacts   of    a   purposed   change(Kennedy   and   Smith,   1995).In   the   discussion   of    agroecosystemsustainability,   the   term   soil   health   is   used   interchangeably   withsoil   quality.   However,   we   use   soil   health   to   emphasize   the   livingnature   of    soil   that   is   dynamic   in   both   functional   capacity   andresponse   to   management   changes   (Doran   and   Zeiss,   2000).   Soilhealth   is   a   function   of    the   soil   microbial   community ’ s   ability   torecover   (i.e.,   resilience),   and   the   soils   buffering   capacity   (i.e.,resistance)   (Holling,   1987).Therefore,   the   expression   of    bothresilience   and   resistance   is   unique   to   soil   type,   environmentalconditions,   and   the   legacy   effects   of    past   management   (Wertzet   al.,   2007).With   regard   to   best   management   practices   for   cellulose   biofuelproduction,   changes   in   the   type   and   abundance   of    above   andbelowground   organic   inputs   may   in 󿬂 uence   soil   biology   by   shiftingmetabolic   niche   diversity   with   C   input   changes   (Blagodatsky   et   al.,2010;    Jangid   et   al.,   2011;   Coleman   and   Whitman,   2005).Transitioning   to   perennial   plant   species   or   no-till   practices   maychange   microbial   abundance   and   community   structure   bychanging   soil   structure   through   increased   aggregate   formation(Drijber   et   al.,   2000;   Frey   et   al.,   1999).   Though   some   reports   havedemonstrated   strong   plant   species   effects   (Aira   et   al.,   2010;   Kuskeet   al.,   2002;   Grayston   et   al.,   2001;   Hartmann   et   al.,   2009;   Hed  enecet   al.,   2014;   Smalla   et   al.,   2001),   others   have   reported   that   changesin   vegetation   had   no   effect   on   soil   biology   (Girvan   et   al.,   2003;Hedlund   et   al.,   2003;   Kennedy   et   al.,   2004.)A   clear   case   of    the   environmental   detriment   is   the   decline   in   soilorganic   carbon   (SOC)   under   increased   stover   harvest   in   continuousmaize   production   systems   has   been   shown   in   multiple   studies(Blanco-Canqui   and   Lal,   2007;Benjamin   et   al.,   2010;   Lal,   2005;Lemus   and   Lal,   2005;   Orr,   2012).   In   light   of    the   identi 󿬁 ed   risksassociated   with   the   possible   loss   of    SOC,   distinct   knowledge   gapsconcerning   the   impacts   on   soil   biology   and   more   broadly   soilhealth   associated   with   proposed   cellulosic   feedstock   productionsystems   exist.In   this   paper,   we   report   on   a   side-by-side   production   systemcomparison   of    how   biological   measures   of    soil   health   shifts   withthe   management   changes   (e.g.,   system   changes   away   from   corn – soybean   and   the   use   of    residue   removal).   The   application   of    bothphospholipid   fatty   acid   (PLFA)   biochemical   pro 󿬁 lingand   molecular 󿬁 ngerprinting   of    the   saprotrophic   fungal   community   by   polymer-ase   chain   reaction   denaturing   gradient   gelelectrophoresis   (PCR-DGGE)   can   provide   a   thorough   evaluation   on   the   effect   of environmental   drivers   such   as   plant   species   and   disturbance   onthe   soil   microbial   community   composition   (Agnelli   et   al.,   2004;Acosta-Martinez   et   al.,   2010;   Anderson   and   Cairney,   2004;   Blumeet   al.,   2002;   Brodie   et   al.,   2003;   Coleman   and   Whitman,   2005;García-Orenes   et   al.,   2013;   Gil   et   al.,   2011;   Hed  enec   et   al.,   2014;Liang   et   al.,   2012).   Combining   biochemical   and   genetic   pro 󿬁 lingprovides   complementary   surveys,   with   PLFA   signatures   distin-guishing   abundance   of    broad   microbial   groups   (e.g.,   Gm  /+bacteria,   fungi),   while   resolution   of    PCR-DGGE   can   be   re 󿬁 ned   totarget   populations   of    interest   (e.g.,   soil   fungi)   to   the   genus   level(Anderson   and   Cairney,   2004)   as   saprotrophic   fungi   are   animportant   functional   group   in   litter   decomposition   and   soilformation.   Recent   advances   in   molecular   techniques   have   expand-ed   evaluation   of    shifts   in   diversity   as   additional   early   indicators   of changes   in   soil   health   (Duchicela   et   al.,   2013).Notwithstandingcon 󿬂 icting   󿬁 ndings,   consideration   of    management   practices   on   soilmicrobiology   is   a   valuable   component   in   assessing   soil   healthrelated   to   long-term   ecosystem   sustainability.Our   assessments   of    cellulosic   biofuel   production   systems   arerelative   to   traditional   corn – soybean   systems   typical   of    theMidwest,   USA.   We   anticipated   shifts   in   the   soil   communitystructure   related   to   the   start   of    residue   removal   and   choice   of cellulosic   biofuel   feedstock   compared   to   traditional   grain   corn – soybean   production   systems.   Therefore,   the   objectives   were   toexamine:   (i)   if    a   conversion   from   corn-soybean   to   cellulosic   biofuelsystems   affected   microbial   group   abundance   and   communitystructure   and   (ii)   compare   annual   and   perennial   biofuel   cropproduction   system   impacts   on   soil   biology.   This   effort   was   part   of lager   study   investigating   production   system   changes   and   possibleimpacts   on   nutrient   losses   to   drainage   water   and   conducted   on   thelong-term   plots   at   Purdue ’ sWater   Quality   Field   Station. 2.   Materials   and   methods  2.1.   Site   description Candidate   cellulosic   feedstock   production   systems   wereestablished   on   a   subset   of    󿬁 eld   plots   at   the   Purdue   UniversityWater   Quality   Field   Station   (WQFS;   40.498667,    86.998111)located   at   the   Agronomy   Center   for   Research   and   Education(ACRE).   A   detailed   description   of    WQFS   can   be   found   in   Hofmannet   al.   (2004)   and   Hernandez-Ramirez   et   al.   (2009).The   WQFS   wasinitially   established   in   1992   with   four   󿬁 eld   replicates   representingtwelve   agronomic   management   treatments   in   a   randomizedcomplete-block   design.   Field   plots   are   located   on   top   of    drainagelysimeters   (10   m      48.5   m)   which   were   constructed   by   interlockingbentonite   clay   walls   into   the   subsurface   glacial   till.   Each   plotcontains   a   drainage   collection   tile   drain   at   a   depth   of    0.9   m.   Toquantify   the   impacts   of    the   cropping   strategies   linked   to   residueremoval   in   2007   󿬁 vecorn   or   soybean   plots   were   converted   tobiofueld   production   systems   (Table   1)   and   were   grown   adjacent   toconventional   corn – soybean   systems   (with   residue   return).  2.2.   Transition   to   cellulosic    biofuel   systems Pre-existing   but   residue   recovery   modi 󿬁 ed   systems   includedcontinuous   corn   and   tall   grass   big   bluestem   dominated   prairie.   Theprairie   system   (PR)   was   seeded   with   big   bluestem   (  Andropogon gerardii   Vitman),   indiangrass   ( Sorghastrum   nutans   (L.)   Nash),   andnative   forbs   in   1992.   The   PR    system   was   burned   annually   until2006,   and   transitioned   to   biomass   harvest   in   2008.   A   continuouscorn   treatment   was   converted   to   no-till   in   the   fall   of    2007   to   offsetthe   impact   of    residue   removal   (CR).   Prior   to   the   planting   of    dual-purpose   sorghum   ( Sorghum   bicolor  ;   SG),   the   treatment   plots   hadbeen   in   continuous   corn   production.   The   most   substantial   land   usechange   was   associated   with   establishing   perennial   monoculturesbiomass   feedstocks   of    upland   ecotype   Shawnee   switchgrass( Panicum   virgatum ;SW)   and   Miscanthus       giganteus   (MS)   intoplots   previously   in   soybean   ( Glycine   max .(L.)   Merr.)   rotation. M.-J.   Orr    et    al.    /     Applied   Soil   Ecology   95   (2015)   62 – 72   63   Table   1 Agronomic   management   practices   for   󿬁 ve   biofuel   and   three   conventional   maize-soybean   production   systems.   Field   operations   not   recorded   are   indicated   as   not   reported(NR),   and   actions   not   applied   to   cropping   system   treatments   are   indicated   as   not   applicable   (NA).   Preplant   fertilization   consists   of    28%   urea – ammonium – nitrate   knifed   intothe   soil,   starter   application   of    19 – 17 – 0fertilizer   was   applied   at   maize   seeding,   and   Urea   +   Agrotain 1 was   broadcast   applied   using   a   Gandy   Air 󿬂 ow   spreader.   Operation   timingisindicated   as   month/day   within   each   year.Cropping   system   Ab.   (#)   Crop   variety   Year   Fertilization:   type   and   Timing   N   rate   (kg   N   ha  1 )   Tillage:   type   and   timing   Harvest a Grain   BiomassPrairie(Est.   1992)PR    1    Andropogon    gerardii (dominant   grass), Sorghastrum   nutans, and   native   forbs2006   NA   NA   NA   NABurned2007   None2008   08/272009   10/292010   10/27 Miscanthus       giganteus   MS2   Maize(715RR)2006   Preplant;   04/21   180   Disk;   05/08   11/02   NAStarter;   05/09   24   Field   cultivate;   05/09Soybean(Becks   321)2007   NA   0   Field   cultivate;   05/14   11/06   NA Miscanthus   2008   Urea   +   Agrotain 1 ;   08/04   90   NA   NANA Miscanthus   2009   Urea   +   Agrotain 1 ;   05/11   57   NA   NA10/29 Miscanthus   2010   Urea   +   Agrotain 1 ;   05/04   50   NA   NA10/27Switchgrass, P.   virgatum (uplandShawneecultivar)SW4   Soybean   (NR)   2006   NA   0   Disk;   05/08   11/02   NAField   cultivate;   05/09Switchgrass   2007   NA   0   Field   cultivate;   05/14   NANASwitchgrass   2008   Urea   +   Agrotain 1 ;   05/01   75   NA   NA08/27Switchgrass   2009   Urea   +   Agrotain 1 ;   05/11   57   NA   NA10/29Switchgrass   2010   Urea   +   Agrotain 1 ;   05/04   50   NA   NA10/27Sorghum   SG   5   Maize(715RR)2006   Preplant;   04/21   157Disk;   05/08   11/02   NAStarter;   05/09   24   Field   Cultivate;   05/09Maize(P34A20RRLLWCB)2007   Preplant;   05/14   180   NA   11/05   NAStarter;   05/15   24Sorghum(PU216A      P90344)2008   Preplant;   05/27   180   Disk;   04/24   NASee   2009Sorghum(PU216A      P90344)2009   Preplant;   05/21   180   Disk;   05/22   10/21   01/07-08Field   cultivate;   07/13   11/13Sorghum(PU216A      P90344)2010   Preplant;   05/25   180   Disk;   04/02   10/07   10/11Tilled;   11/02ContinuousmaizeCR3   Maize   (715RR)   2006   Preplant;   04/21   202   Disk;   05/08   11/02   NAStarter;   05/09   24   Field   cultivate;   05/09Maize   (P34A20RRLLWCB)   2007   Preplant;   05/14   180   NA   11/05   NAStarter;   05/15   24Maize   (P34P87RR)   2008   Preplant;   05/27   180   NA   11/03   See   2009Starter;   05/28   24Maize   (B6733)   2009   Preplant;   05/21   180   NA   10/01   01/07-08Starter;   05/28   17   11/13Maize   (B5435HXR)   2010   Preplant;   05/25   180   NA   09/2310/27Starter;   05/26   24Continuous   maize   CC   12   Maize   (715RR)   2006   Preplant;   04/21   180   Disk;   05/08   11/02   NAStarter;   05/09   24   Field   cultivate;   05/09Maize   (P34A20RRLLWCB)   2007   Preplant;   05/14   180   NA   11/05   NAStarter;   05/15   24Maize   (P34P87RR)   2008   Preplant;   05/27   180   Disk;   04/24   11/03   NAStarter;   05/28   24Maize(B6733)2009   Preplant;   05/21   180   Disk;   05/22   10/01   NAStarter;   05/28   17Maize   (B5435HXR)   2010   Preplant;   05/25   180   Disk;   04/02   09/23NAStarter;   05/26   24Maize – soybeanrotationR1   6   Soybean   (NR)   2006   NA   0   Disk;   05/08   11/02   NAField   cultivate;   05/09Maize   (P34A20RRLLWCB)   2007   Preplant;   05/14   157Field   Cultivate;   11/05   NAStarter;   05/15   24   05/14Soybean   (Becks   354)   2008   NA   0   Disk;   04/24   10/23   NAMaize   (B6733)   2009   Preplant;   05/21   157Disk;   05/22   10/01   NAStarter;   05/28   17Soybean   (BR325)   2010   NA   0   Disk;   04/02   10/19   NAMaize – soybeanrotationR2   7   Maize   (715RR)   2006   Preplant;   04/21   157Disk;   05/08   11/02   NAStarter;   05/09   24   Field   cultivate05/09Soybean   (Becks   321)   2007   NA   0   NA   11/06   NAMaize   (P34P87RR)   2008   Preplant;   05/27   157Disk;   04/24   11/03   NA64   M.-J.   Orr    et    al.    /     Applied   Soil   Ecology   95   (2015)   62 – 72  Production   system   transition   and   󿬁 eld   operations   are   outlined   inTable   1.  2.3.   Soil   sampling  Soils   at   the   WQFS   are   predominantly   Drummer   silty   clay   loam( 󿬁 ne-silty,mixed,   superactive,   mesic   Typic   Endoaquoll)withsome   ( < 2%)of    Raub   siltyclay   loam( 󿬁 ne-silty,mixed,   superactive,mesic   Aquic   Argiudoll);slopes   range   from   0   to   2%.   A   total   of 96   composite   surface   soils(0 – 5cm)   was   collected   over   the   threeyear   study   (5    June   2008,   23   April2009,and21   April2010).   Thesampling   strategy   was   chosen   to   capture   changesrelated   tosystem   establishment   and   inter-annualvariationsas   part   of    theexisting   drainage   study.   Surfacelitter   was   removed   prior   tosampling.   A   samplewas   collected   by   taking   a   composite   of 30   handprobe   cores   (2.5   cm   diam.,5   cm   deep)acrosseachreplicate   plot.   Between   󿬁 eldplots,the   soil   probe   waswashed   andsurface   sterilized   with   70   %   ethanol.Duringcollection   andtransport,   soils   were   placedin   a   cooler   with   ice   packs,   andtransferred   to   4  C   storage   prior   to   sieving.Each   compositesample   was   homogenized   by   sieving(4   mm)   within   48h   of sampling,   and   care   was   taken   to   remove   allvisible   plant   tissuesand   fauna.From   each   composite,   sub-sampleswere   taken   andstored   according   to   optimal   conditions   forsubsequent   analysis.Soil   wasfrozen   at  20  C   forcommunityDNA   extraction   andfurther   freeze-dried   forassessingmicrobialbiomass.   Remainingsoil   was   air-dried,   andcrushed   to   pass   through   a   2   mm   sieve   forchemical   characterization   reported   in   Orr   (2012).  2.4.   Soil   microbial   community   structure   diversity Total   lipid   extracts   were   obtained   from   2   g   lyophilized   soilsfollowing   the   method   described   by   Acosta-Martínez   et   al.   (1999),modi 󿬁 edfrom   Findlay   et   al.   (1989)   and   Bobbie   and   White   (1980).Brie 󿬂 y,   soil   lipids   were   extracted   with   a   one   phase   cholorform:methanol:phosphate   (1:2:0.8)   buffer,   and   phospholipid   fatty   acids(PLFA)   were   isolated   by   silicic   acid   columns   were   washed   withchloroform,   acetone,   and   eluted   in   methanol.   PLFA   extracts   werepartitioned   to   characterize   community   structure   by   quantifyingbacterial   fatty   acid   methyl   ester   (FAME)   signatures.   The   FAMEswere   prepared   by   alkaline   methanolysis   of    PLFA   extract,   and   theorganic   phase   was   puri 󿬁 ed   by   liquid – liquid   hexane   extraction.Prior   to   FAME   analysis,   methyl   nonadecanoate   (19:0)   was   added   toall   samples   as   an   internal   standard.   The   FAMEs   were   separatedaccording   to   retention   time   along   a   capillary   column   (J&WScienti 󿬁 c   DB-5,   60   m,   0.25   mm   ID,   0.25   m m   󿬁 lm   capillary)   in   agas   chromatograph   with   a   󿬂 ame   ionization   detector   (GC-FIDAgilent   Technologies   7890A).The   PLFA – FAME   pro 󿬁 les   for   each   soil   sample   were   assessed   by   asubset   of    15   individual   FAMEs   identi 󿬁 ed   and   quanti 󿬁 ed   byreferencing   retention   time   and   peak   area   to   a   quantitative   standardmix   (#1114,   Matreya   LLC,   Pleasant   Gap,   PA).   The   FAME   signaturesare   expressed   as   the   absolute   abundance   (nmol   g   soil  1 ),   and   bythe   proportional   abundance   (mol%).   Nomenclature   of    FAMEsfollows   the   convention   of    “  X  : Y  v  Z  ” ,where    X    represents   the   numberof    carbon   atoms   in   the   chain,   Y    is   number   of    double   bonds,   and    Z    isdouble   bond   position   from   the   distal   end   of    the   molecule.   TheFAME   isomers   are   indicated   by   a   suf  󿬁 x   c    ( cis )   or   t    ( trans ),   the   chainbranching   pattern   is   represented   by   pre 󿬁 xes i   ( iso -)   or   a   ( antiiso -),and   the   pre 󿬁 x cy   notes   the   presence   of    a   cyclopropyl   group.   Foreach   soil   sample,   individual   PLFA – FAME   signatures   were   pooledinto   previously   documented   taxonomic   guilds   (Frostegård   et   al.,1993;   Zak   et   al.,   1996;   Zelles,   1999),   representing   universal   (16:0,18:0),   total   bacteria   (14:0,   15:0,   17:0,   i 15:0,   a 15:0,   i 16:0,   i 17:0,16:1 v 7 c  , cy 17:0,   cy 19:0;   TB),   Gram   positive   ( i 15:0,   a 15:0,   i 16:0, i 17:0;Gm+),   Gram   negative   (16:1 v 7 c  ,   cy 17:0,   cy 19:0;   Gm  ),   andsaprotrophic   fungi   (18:2 v 6,9 c  ,18:1 v 9 c  / t  ;   SF).Fungal   community   structure   was   evaluated   by   direct   ampli 󿬁 -cation   of    the   internal   transcribed   spacer   (ITS)   fungal   speci 󿬁 cgenomic   marker   using   polymerase   chain   reaction   and   denaturinggradient   gel   electrophoresis   (PCR-DGGE)   according   to   Andersonet   al.   (2003).Soil   fungi   were   targeted   from   total   environmentalgenomic   DNA   extracted   following   MoBio   Ultra   Soil   DNA   kitspeci 󿬁 cations   (MoBio   Laboratories   Inc.,   Carlsbad,   CA).   Total   soilDNA   was   visualized   by   electrophoresis   in   a   0.75%   (w/v)   agarose   gelstained   with   GelRed TM (Biotium,   Hayward,   CA),   and   quanti 󿬁 ed   byspectrophotometry   at   260   nm   l .   Two   environmental   DNA   extrac-tions   were   pooled   for   each   system   󿬁 eld   block.   PCR    ampli 󿬁 cationswere   run   on   a   MJ   mini   thermal   cycler   (BioRad,   Hercules,   CA)   withan   initial   denaturation   at   95  C   for   5   min.,   followed   by   35   cycles   of 94  C   for   30   s,   55  C   for   30   s,   and   72  C   for   45   s,   with   a   󿬁 nal   step   of extension   at   72  C   for   15   min.   The   ITS   region   was   ampli 󿬁 ed   fromapproximately   50   ng   soil   DNA   using   ITS1-F   (Gardes   and   Bruns,1993)   and   ITS2   (White   et   al.,   1990)primers   synthesized   by   IDT(Integrated   DNA   Technologies,   Inc.,   Coralville,   IA).   To   enabledownstream   DGGE   amplicon   separation,   a   GC-clamp   (Muyzeret   al.,   1993)was   added   to   the   5 0 end   of    the   ITS1-F   primer.   50   m l   PCR reactions   were   prepared   in   nuclease-free   distilled   water   with   the 󿬁 nal   concentration   of    reagents   as   follows:   1X   standard   taq   reactionbuffer   [50   mM   KCl,   10   mM   Tris-HCl,   1.5mM   MgCl 2 ,   pH   8.3   at   roomtemperature]   (10X   buffer,   New   England   BioLabs,   Ipswich,   MA),   anadditional   1.0   mM   MgCl 2 ,   1000   ng   m l  1 bovine   serum   albumin(Roche   Applied   Science,   Mannheim,   Germany),   20   pmol   of    eachprimer,   200   m M   of    each   deoxynucleotide   triphosphate   (Promega,Madison,   WA),   and   1.5   units   of    polymerase   (New   England   BioLabs,Ipswich,   MA).   A   no-DNA   negative   control   was   included   in   all   PCR ampli 󿬁 cations   and   gel   analysis.   All   ampli 󿬁 cations   were   veri 󿬁 ed   byelectrophoresis   in   1.2%   (w/v)   agarose   gels   for   target   ampliconproducts   approximately   300   bp   in   size.   The   ITS   region   wasampli 󿬁 ed   for   each   composite   soil   sample,   and   the   󿬁 ngerprintpro 󿬁 lewas   tested   by   DGGE.   Field   replicate   pro 󿬁 leswere   found   tobe   consistent   for   each   system   within   a   sampling   time.   Therefore,PCR    amplicon   products   were   pooled   in   equal   molar   concentrationfor   each   respective   system   to   facilitate   comparisons   betweenmanagement   approaches   across   the   study   period.  Table   1   ( Continued )Cropping   system   Ab.   (#)   Crop   variety   Year   Fertilization:   type   and   Timing   N   rate   (kg   N   ha  1 )   Tillage:   type   and   timing   Harvest a Grain   BiomassStarter;   05/28   24Soybean   (B364)   2009   NA   0   Disk;   05/22   10/20   NAMaize   (B5435HXR)   2010   Preplant;   05/25   157   Disk;   04/02   09/23NAStarter;   05/26   24 a Biomass   removal   rate   estimated   at   75%.   In   2008   maize   and   sorghum   stover   removed   by   1-row   forage   chopper,   and   prairie   and   switchgrass   harvested   by   a   Carter   harvester;in   2009 – 2010   a   biomass   harvester   was   used   for   maize,   Miscanthus ,   prairie,   sorghum,   and   switchgrass   systems. M.-J.   Orr    et    al.    /     Applied   Soil   Ecology   95   (2015)   62 – 72   65  The   ITS   amplicon   diversity   was   visualized   in   DGGE   󿬁 ngerprintsgenerated   using   the   DCode   universal   mutation   detection   system(BioRad,   Hercules,   CA).   Sample   amplicons   were   separated   bypolyacrylamide   gels   (8%   v/v   in   1X   TAE;   BioRad,   Hercules,   CA)   alonga   25% – 55%   denaturant   gradient,   where   100%   denaturant   iscomposed   of    7   M   urea   (Invitrogen,   Grand   Island,   NY),   and   40%de-ionized   formamide   (Amresco,   Solon,   OH).   Electrophoresis   wasperformed   at   60   V   in   1X   TAE   buffer   at   a   constant   temperature   of 60  C   for   15   h.   Gels   were   stained   for   15   min   with   Syber   Green(Sigma – Aldrich,   St.   Louis,   MO)   and   documented   by   BioRad   geldockimaging   station   and   QuantOne   imaging   software   (BioRad,   Hercu-les,   CA).   Fungal   diversity   was   visualized   by   ITS-DGGE   󿬁 ngerprintanalysis   in   GelComparII   (V.6.5,   Applied   Maths   2010,   Austin,   TX).Each   gelwas   normalized   against   three   in-gel   standard   DNA   lanes,and   background   subtraction   and   least   square   󿬁 ltering   values   weredetermined   according   to   spectral   analysis.   Similarity   matrix   wasgenerated   based   on   the   ranked   Pearson   correlation   of    thedensitometric   curve   between   sample   lanes   with   an   optimizationof    0.5%.   Similarity   between   production   system   fungal   diversity   wasillustrated   by   a   neighbor-joining   cluster   dendrogram   based   on   thesimilarity   matrix,   and    jackknife   analysis   of    averaged   similaritieswas   carried   out   within   the   GelComparII   software   to   evaluatefrequency   of    replicates   clustering   within   respective   systems.  2.5.   Statistical   analysis All   PLFA   signatures   were   expressed   relative   to   total   PLFA   nmolg   soil  1 quanti 󿬁 edfor   each   composite   soil   sample   and   the   valuesaveraged   over   two   pseudo-replicates.   System   mean   values   ( n   =   4)were   analyzed   for   taxonomic   guild   relative   abundance   of    summedPLFAs,   including   universal,   total   bacteria   (TB),   Gram   positive   (Gm+),   Gram   negative   (Gm  ),   saprotrophic   fungi   (SF),   and   fungal   tobacterial   biomass   ratio   (F/B).   Guild   groupings   were   screened   toassess   compliance   with   assumptions   of    homogeneity,   normality,equal   variance,   and   presence   of    outliers   to   meet   requirements   forparametric   statistical   analysis.   Differences   in   total   PLFA   abun-dance,   guild   group   abundances,   and   F/B   ratios   were   evaluatedusing   “ proc   mixed ” ;   applying   󿬁 eld   blocks   as   a   random   variable   and Julian   day   of    sampling   as   a   repeated   measure   (Littell   et   al.,   1998).The   interaction   between   main   effects   of    crop   type   and   samplingtime   were   evaluated   for   each   PLFA   guild   group.   In   the   absence   of    aninteraction,   the   abundances   were   averaged   over   sample   time,   andin   the   case   of    a   signi 󿬁 cant   interaction,   the   main   effects   were   testedseparately.   Main   effect   differences   were   identi 󿬁 ed   by   Tukeypairwise   multiple   comparisons   at   P    <   0.05.   Statistical   analysiswas   performed   in   SAS   9.2   (SAS   Institute   Inc.,   Cary,   NC),   andgraphics   reproduced   in   SigmaPlot   11.0(Systat   Software   Inc.,   San Jose,   CA).Multivariate   data   reduction   and   visualization   tools   wereapplied   to   both   the   PLFA – FAME   and   ITS-DGGE   assessments   of community   structure.   Soil   microbial   community   structure   wasanalyzed   by   ordination   of    normalized   PLFA – FAME   signatures   usingprincipal   component   analysis   (PCA)   based   on   a   covariance   matrixof    FAMEs   across   sampling   times.   For   each   soil   sample,   quanti 󿬁 edFAME   signatures   were   normalized   by   converting   to   mol%.   RareFAMEs   and   those   with   ratios   on   average   less   than   2   %   were   notincluded   in   subsequent   multivariate   analysis.   The   relationshipbetween   FAME   signatures   and   soil   chemical   properties   reported   inan   earlier   study   (Orr,   2012)was   evaluated   by   correlation   analysis. 3.   Results  3.1.Patterns   in   microbial   biomass Over   the   three-year   study,   total   microbial   biomass   wasstatistically   similar   among   all   production   systems,   with   theexception   of    microbial   biomass   in   prairie   (PR)   which   wassigni 󿬁 cantly   higher   (  p   <   0.05)   than   the   lowest   total   biomass   valuesobserved   in   continuous   corn   (CR)   (Fig.   1).   Within   each   of    thesystems,   respective   total   microbial   biomass   and   universal   PLFAsignatures   remained   constant   throughout   the   study   period   (datanot   shown).   In   differentiated   PLFA   microbial   guilds,   no   interactionsbetween   system   type   and   sampling   time   in   the   relative   abundanceof    total   bacteria,   Gm+   and   Gm  were   observed.   The   highest   totalbacterial   biomass   PLFA   signatures   were   observed   in   the   continuouscorn   systems   (CR    and   CC),   with   both   showing   signi 󿬁 cantly(  p   <   0.05)   higher   bacterial   abundance   than   the   PR    and   SW   systems(Fig.   2).   The   relative   Gm+   abundance   was   comparable   across   theCR,   CC,   R2,   MS,   SG,   SW,   and   PR    systems,   and   signi 󿬁 cantly   (  p   <   0.05)greater   in   CR    than   the   R1   system.   The   greatest   Gm    abundancewas   observed   in   the   CR    and   SG   systems,   and   signi 󿬁 cantly   (  p   <   0.05)lower   in   PR    than   the   other   systems.The   annual   cropping   systems   presented   higher   bacterial   groupabundances,   while   the   PR    system   was   dominated   by   fungalbiomass   as   indicated   by   the   F/B   ratios   across   time   (Fig.   3a   and   b).   Incontrast,   CR    had   the   lowest   fungal   abundance   and   F/B   ratios   duringthe   study   period.   The   fungal   biomass   abundance   ranged   from   17.2%(CR,   2008)   to   44.9%   (PR,   2008)   of    the   total   PLFA   signature   mol%abundance.   Differences   in   the   abundance   of    the   fungal   biomasswere   observed   in   both   main   system   effects   and   the   system    sample   time   interaction.   In   2008,   the   fungal   biomass   under   MS   wassimilar   to   PR,   but   PR    was   signi 󿬁 cantly   higher   than   in   CC,   CR,   R2,   SG,and   SW   (Fig.   3a).   In   2009,   fungal   biomass   was   similar   across   PR,   SGand   R1.   The   PR    and   R1   systems   presented   signi 󿬁 cantly   higher   fungiabundance   compared   to   the   CC,   CR,   MS   and   R2   systems;   and   SGfungal   biomass   improved   relative   to   the   CR    and   MSsystems.   In   thelast   sampling   year   (2010),   the   fungal   biomass   in   the   PR    systembecame   signi 󿬁 cantly   higher   than   all   other   production   systems.   Thefungal   abundance   in   CR    was   also   signi 󿬁 cantly   lower   compared   tothe   CC,   MS,   R1,   R2,   SG   and   SW   systems.   The   relative   fungalabundance   and   F/B   ratio   increased   (  p   <   0.05)   in   2010   from   previousyears   in   PR    and   SW,whereas   in   the   annual   systems   of    SG   and   R1,the   fungal   biomass   was   greatest   in   the   2009   sample   year. Fig.   1.   Microbial   community   structure   according   to   mean   total   PLFA – FAMEabundance   (  SE)   for   󿬁 ve   biofuel   and   three   conventional   production   systems   overa3   year   study   period.   Lower   case   letters   refer   to   signi 󿬁 cant   differences   tested   byTukey ’ s   mean   separation   (  p   <   0.05).   Biofuel   systems:   CR,   no-till   continuous   corn;MS,   Miscanthus       giganteus ;PR,   prairie;   SG,   dual-purpose   sorghum;   and   SW,switchgrass.   Conventional   systems:   CC,   continuous   corn;   R1(C/S/C),   and   R2(S/C/S),corn   soybean   rotation.66   M.-J.   Orr    et    al.    /     Applied   Soil   Ecology   95   (2015)   62 – 72
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