Facing up to the paradigm of ecological intensification in agronomy

14 Pages·2011·0.45 MB·English

by  Thierry Doré

Europ.J.Agronomy34 (2011) 197–210
ContentslistsavailableatScienceDirect
European Journal of Agronomy
journal homepage: www.elsevier.com/locate/eja
Review
Facing up to the paradigm of ecological intensification in agronomy:
Revisiting methods, concepts and knowledge
ThierryDoréa,∗,DavidMakowskib,EricMalézieuxc,NathalieMunier-Jolaind,
MarcTchamitchiane,PabloTittonellf
aAgroParisTech,INRA,UMR211,F-78850Thiverval-Grignon,France
bINRA,AgroParisTech,UMR211,F-78850Thiverval-Grignon,France
cCIRAD,URHortSys,BoulevarddelaLironde,TAB-103/PS4,34398MontpellierCedex,France
dINRA,UMR102GénétiqueetEcophysiologiedesLégumineuses,17rueSully,BP86510,F-21065DijonCedex,France
eINRA,UnitéEcodéveloppement,CentrePACA,F-84914AvignonCedex9,France
fCIRAD-Persyst,SystèmesdeCultureAnnuels,TAB-102/02AvenueAgropolis,34898MontpellierCedex5,France
a r t i c l e i n f o a b s t r a c t
Articlehistory: Agricultureisfacinguptoanincreasingnumberofchallenges,includingtheneedtoensurevarious
Received18October2010 ecosystemservicesandtoresolveapparentconflictsbetweenthem.Oneofthewaysforwardforagricul-
Receivedinrevisedform5February2011 turecurrentlybeingdebatedisasetofprinciplesgroupedtogetherundertheumbrellaterm“ecological
Accepted16February2011
intensification”.Inpublishedstudies,ecologicalintensificationhasgenerallybeenconsideredtobebased
essentiallyontheuseofbiologicalregulationtomanageagroecosystems,atfield,farmandlandscape
Keywords: scales.Weproposeherefiveadditionalavenuesthatagronomicresearchcouldfollowtostrengthen
Agroecology
theecologicalintensificationofcurrentfarmingsystems.Webeginbyassumingthatprogressinplant
Agroecosystem
sciencesoverthelasttwodecadesprovidesnewinsightofpotentialusetoagronomists.Potentiallyuse-
Plantscience
Farmers’knowledge fulnewdevelopmentsinplantscienceincludeadvancesinthefieldsofenergyconversionbyplants,
Meta-analysis nitrogenuseefficiencyanddefencemechanismsagainstpests.Wethensuggestthatnaturalecosys-
Comparativeanalysis temsmayalsoprovidesourcesofinspirationforcroppingsystemdesign,intermsoftheirstructureand
functionontheonehand,andfarmers’knowledgeontheother.Naturalecosystemsdisplayanum-
berofinterestingpropertiesthatcouldbeincorporatedintoagroecosystems.Wediscussthevalueand
limitationsofattemptingto‘mimic’theirstructureandfunction,whileconsideringthedifferencesin
objectivesandconstraintsbetweenthesetwotypesofsystem.Farmersdevelopextensiveknowledge
ofthesystemstheymanage.Wediscusswaysinwhichthisknowledgecouldbecombinedwith,orfed
intoscientificknowledgeandinnovation,andtheextenttowhichthisislikelytobepossible.Thetwo
remainingavenuesconcernmethods.Wesuggestthatagronomistsmakemoreuseofmeta-analysisand
comparativesystemstudies,thesetwotypesofmethodsbeingcommonlyusedinotherdisciplinesbut
barelyusedinagronomy.Meta-analysiswouldmakeitpossibletoquantifyvariationsofcroppingsystem
performancesininteractionwithsoilandclimateconditionsmoreaccuratelyacrossenvironmentsand
socio-economiccontexts.Comparativeanalysiswouldhelptoidentifythestructuralcharacteristicsof
croppingandfarmingsystemsunderlyingpropertiesofinterest.Suchanalysiscanbeperformedwith
setsofperformanceindicatorsandmethodsborrowedfromecologyforanalysesofthestructureand
organisationofthesesystems.Thesefiveapproachesshouldmakeitpossibletodeepenourknowledge
ofagroecosystemsforaction.
© 2011 Elsevier B.V. All rights reserved.
Contents
1. Introduction.......................................................................................................................................... 198
2. Diversifyingsourcesofknowledgetoguideecologicalintensification............................................................................. 199
2.1. Mobilizingadvancesinplantsciences....................................................................................................... 199
∗
Correspondingauthor.Tel.:[phone];fax:[phone].
E-mailaddress:[email protected](T.Doré).
1161-0301/$–seefrontmatter© 2011 Elsevier B.V. All rights reserved.
doi:10.1016/j.eja.2011.02.006
198 T.Doréetal./Europ.J.Agronomy34 (2011) 197–210
2.1.1. Anewlookatthebasics............................................................................................................ 199
2.1.2. Thecultivatedplantanditsbiologicalenvironment............................................................................... 199
2.1.3. Waystoimprovetheuseofplantsciencesforecologicalintensification......................................................... 200
2.2. Learninglessonsfromthefunctioningofnaturalecosystems............................................................................... 201
2.2.1. Whatdoes“Mimickingnaturalecosystems”mean?............................................................................... 201
2.2.2. Agroecosystemsascomplexsocio-ecologicalsystems............................................................................ 202
2.3. Farmers’knowledgeandlayexpertisevalorisationandintegrationintoscientificknowledge............................................ 203
2.3.1. Valueoffarmers’knowledgeforagronomy........................................................................................ 203
2.3.2. Qualificationandvalidationoflayexpertiseandknowledgeexpression......................................................... 204
3. Methodsforsynthesizinginformation.............................................................................................................. 204
3.1. Meta-analysisandagronomy................................................................................................................ 205
3.2. Comparativeanalysisofagroecosystems.................................................................................................... 205
3.2.1. Comparativeanalysisbasedonmultipleindicators............................................................................... 206
3.2.2. Analysingthestructureandfunctioningofagroecosystems...................................................................... 206
4. Overalldiscussionandconclusion................................................................................................................... 207
Acknowledgement................................................................................................................................... 208
References........................................................................................................................................... 208
1. Introduction age.Thisapproachfocusesprincipallyonthefateoffertilisersand
their use by crops. Witt et al. (2006) applied a similar approach
Newagriculturalsystemsarerequiredtoallowagricultureto to oil palm plantations. According to Chevassus au Louis and
satisfytheincreasinglydiverseexpectationsofsociety.Fordecades, Griffon (2008) and a number of other authors (Affholder et al.,
agronomyhasproducedknowledgeanddesignedagroecosystems 2008; Mikolasek et al., 2009; Hubert et al., 2010; Bommel et al.,
formaximisingtheproductionofprimaryfoodandfibre,eitherfor 2010), ecological intensification is a pathway towards the pro-
directconsumptionorforindustrialuse.Agriculturalproduction duction of more agricultural product, the production of “new”
issues have recently been expanded to include other ecosystem things (ecosystem services) and different means of production
services(Zhangetal.,2007).Likeothernaturalandsemi-artificial (environmentally friendly). According to Chevassus au Louis and
ecosystems,agroecosystemscanprovideservices,suchascarbon Griffon (2008), ecological intensification is based on “intensifi-
sequestration,pollination,orwaterfiltration.Thecapacityofagri- cation in the use of the natural functionalities that ecosystems
culturetoprovidesuchservicesis,ofcourse,notalwaysguaranteed, offer”. Though relatively vague, this definition remains a possi-
and there are many examples of adverse effects of agricultural ble starting point for the consideration of alternative pathways
practicesontheenvironment,leadingtoecologicaldisservicesof of development for agriculture. This definition is much broader
agriculture(Matsonetal.,1997;Swintonetal.,2007).Disservices thanthatofCassman(Cassman,1999),andprovidesaninteresting
mayincludedecreasesinwaterandairqualityoracontributionto havenforscientistspromotingtheuseofbiologicalregulationin
biodiversityloss.Asagroecosystemsareecosystemscontrolledby agroecosystems.
humans,adoptingthecorrectapproachtoawiderangeofproduc- Manyarticleshavebeenpublishedonbiologicalregulationin
tionissuesrequiresanunderstandingofthewayinwhichnatural agroecosystems,mostlyundertheheading“agroecology”,andnew
andhuman-drivenorforcedprocessesinteractwithintheecosys- papers are continuing to appear. Research on this topic remains
tem. highlynecessary,andisprobablyachallengeformostagronomists
Agronomistshavearguedthatthemissionsofmulti-objective familiarwithindividualphysicaland/orchemicalaspectsofagroe-
agriculture could best be achieved by making better use of cosystems. However, ecological intensification calls for both a
biologicalregulationmechanismsatdifferentlevels:cropmanage- widerdiversificationofsourcesofknowledgeandthedevelopment
ment,croppingsystemdesign,landscapelayoutandmanagement of new data analysis methods. Agronomists have, until recently,
(Matsonetal.,1997;Médièneetal.,2011).Thisassumesthatbiolog- reliedessentiallyontheirownscientificoutput.Prototyping(e.g.,
icalmechanismsareabletoreplacechemicalorphysicalinputs,or Vereijken,1997;Lanc¸onetal.,2007;Debaekeetal.,2009)andthe
tointeractfavourablywiththem,playingthesameagronomicrole model-based design of agricultural systems (e.g., Rossing et al.,
withoutexternalcosts,includingenvironmentalcostsinparticu- 1997; Bergez et al., 2010) are fed by results processed through
lar.Theuseofbiologicalregulationinagroecosystemstoachieve simulationstudies,statisticalhypothesistestingandgroupanaly-
bothahighleveloffoodproductionandtoprovideecosystemser- sis,fromresearchgroupsworkingmostlyatexperimentalstations
vices, apparently opposite aims, has been placed at the core of (Fig. 1). We argue here that agronomists would be placed in a
what is increasingly called “ecological intensification”. The Food betterpositiontotackleecologicalintensificationiftheydiversi-
and Agriculture Organisation (FAO, 2009) recently defined “eco- fiedtheirsourcesofknowledgeandthemethodsusedtocompile,
logicalintensification”(or“sustainableintensification”)withinthe organiseandanalysesuchknowledge.Thediversificationofknowl-
frameworkoforganicagricultureas“Maximizationofprimarypro- edge sources may include (i) making use of recent advances in
duction per unit area without compromising the ability of the plantsciences,(ii)learninglessonsfromthefunctioningofnatural
systemtosustainitsproductivecapacity”.Theexpression“ecolog- ecosystems, guiding the design and management of acroecosys-
icalintensification”wasalreadyinusemorethantwodecadesago tems and (iii) embracing local farmers’ knowledge. Methods for
(Egger,1986),whenitreferredtoakindofecologicalengineering assessingthesesourcesofknowledgearenecessarilydiverse,and
inagropastoralsystemsinAfrica,replacingsomeperennialspecies couldbeextendedtodataminingandthemeta-analysisoflarge
toimprovesoilorganicmattercontent. datasets containing heterogeneous information and comparative
AmorerecentuseoftheexpressionbyCassman(1999)focused analyses of agroecosystems at different scales. We present here
on cereal production and highlighted the need for progress in theargumentsforfurtheragronomicresearchinthesetworelated
plant and soil science to achieve a continuous increase in cereal domains:sourcesofknowledgeforagronomistsanddataprocess-
yields (intensification) without environmental (ecological) dam- ingmethods.
T.Doréetal./Europ.J.Agronomy34 (2011) 197–210 199
Data Analysis &
Sources of
generation synthesis
knowledge
Existing
Modelling
Simulation studies
agronomic
knowledge
On station
Statistical s
Plant sciences experiments hypothesis testing on em
e st
(genetics, g sy
ecophysiology) Oexnp-efarrimments Glorcoaul pv aalunaatlyiosnis & nowled ms for sign
Natural ve k ste de
ecosystem Surveys, Meta-analysis & data- vati osy
functioning participatory mining nno oec
research I gr
a
Farmers’ Comparative studies of
knowledge agroecosystems
Fig.1. Summaryofnewavenuesofagronomicresearchforecologicalintensification.
2. Diversifyingsourcesofknowledgetoguideecological by agronomists. Nevertheless, results recently obtained in plant
intensification sciencessuggestthatthissimpleparadigmcouldbeimproved,as
shownforexamplebyZhuetal.(2010),whoanalysedthewaysin
2.1. Mobilizingadvancesinplantsciences whichimprovementsinphotosynthesisefficiencycouldcontribute
totherequiredincreaseinyields.
Therehasbeentremendousprogressinplantsciencesinrecent Nutrient use efficiency is also clearly a key point in ecologi-
decades,withdetailedelucidationofthegeneticandenvironmen- calintensification.Oneofthemostimportantissuesisdecreasing
taldeterminismofplantdevelopment,growthandreproduction. the use of nitrogen fertilisers, to decrease greenhouse gas emis-
This progress was made possible, in particular, by increases in sions,toreducethedependenceofagricultureonfossilfuelsand
ourabilitytodissectcellularandmolecularprocesses,supported topreventhealthandenvironmentaldisorders,withoutdecreasing
byexponentialprogressinlaboratorytechniquesandthecapac- productivity(Gallowayetal.,2008;Spiertz,2010).Plantscientists
itytoanalysemassesofgenomicdata(e.g.,TardieuandTuberosa, haveinvestigatedindetailtheexchangesofnitrogenbetweenroots
2010).Thisknowledgeaboutthehighlycomplexlifeofplantshas and their environment (Jackson et al., 2008). Glass (2003) sum-
often been developed in a simplified environment, far removed marisedthefactorsdecreasingnitrogenabsorptionefficiency,on
from the reality of farmers’ fields. This has led to a widening of the basis of molecular knowledge and empirical data. Decreases
the gap between the research objectives of plant scientists and innitrogentransporteractivityandratesofnitrateabsorptionfol-
agronomists.Wehighlightbriefly,withafewexamples,waysin lowincreasesinsoilammoniumconcentration,lowtemperature
whichagronomistscouldmakeuseofadvancesinplantsciences andincidentradiation.Thesemechanismsmayaccount,atleastin
todesignecologicallyintensivecroppingsystems. part,forthehighvariabilityoffertiliserefficiencyobservedinfield
experiments.Theyalsoprovideuswithopportunitiestoimprove
nitrogenmanagementinthesoil.Moregenerally,thewaysinwhich
2.1.1. Anewlookatthebasics
plantsmakeuseofadaptationmechanismstodealwithmineral
Agronomistsinvolvedinthedesignandevaluationofcropping
depletionhavebeenextensivelystudiedonaphysiologicalbasis
systemsoftenmakeuseofasimplifiedcropdescription(Monteith,
(GrossmanandTakahashi,2001).Agronomistscouldmakeuseof
1977),despitetheavailabilityofmoremechanisticmodelssimu-
this work to define the limits within which plant environments
latingcanopyphotosynthesis(Spittersetal.,1986;Spitters,1986;
mustbecontainedtoavoidunfavourableplantreactions.
dePury and Farquhar, 1997). In this simplified description, the
canopy,representedasa“bigleaf”,interceptsphotosynthetically
activeradiationandconvertsittobiomass.Branchingisgenerally 2.1.2. Thecultivatedplantanditsbiologicalenvironment
consideredtobetheoutcomeofinterplantcompetition.Mineral Sincethemiddleofthelastcentury,thegradual“artificialisa-
nutritionisrepresentedasasimplefluxfromsoiltoplantroots, tion”ofagriculturehasledtoagronomistspayinglessattentionto
depending on soil mineral and water contents. Such simplified thebiologicalcomponentsoffields.Agroecologyhasemergedasa
representations have proved sufficient and highly successful for reactionagainstthisexcessivesimplificationofthesystem,placing
cropping system design. Moreover, the more sophisticated rep- thebiologicalcomponentbackattheheartofthesystem(Altieri,
resentations of the basic processes of plant life implemented in 1989), and resulting in the development of an “agroecosystem”
more complex models do not necessarily improve the ability of view(Conway,1987).Nevertheless,commonagronomicpractices
cropmodelstopredictbehaviourinarangeoffluctuatingcondi- stilllargelyignorebiologicalinteractionsincultivatedfields,and
tions.Suchrepresentationshavethereforebeenusedonlyrarely agroecologists often emphasise the need for an empirical and
200 T.Doréetal./Europ.J.Agronomy34 (2011) 197–210
holisticapproachtoagroecosystems.Newfindingsinplantsciences ofplantmicro-organismsinplant×plantinteractions(Sanonetal.,
concerningtherelationshipsbetweentheplantanditssurrounding 2009;Lietal.,2008)andthecompetitionofmicrobialcommunities
bioticenvironmenthaverecentlyemergedandareofgreatinterest. promotingbothplantgrowthandhealth(Lemanceauetal.,2009)
Studiesofinteractionsbetweenrootsandsoilmicro-andmacro- illustratethebenefitsthatagronomistsmayobtainfromadvances
organismshaverevealedtheexistenceofprocessesofparamount inresearchonplant–micro-organisminteractionsforrhizosphere
importanceforagronomists.Someoftheseinteractionsarevery engineeringandmanagement(Ryanetal.,2009).Beyondtheques-
familiar to agronomists, including nitrogen fixation by symbio- tion of production, Jackson et al. (2008), focusing on nitrogen,
sis between Rhizobium sp. and leguminous or non-leguminous derived from current knowledge on root/micro-organism inter-
(Mehboob et al., 2009) plants. Other associations, such as that actions the trends in ecosystem services supplied by cropping
between other endophytic di-azotrophic bacteria and grasses or systems in different agricultural situations. Thanks to the deep
cereals,alsoexistandmaybeofinterest,aspointedoutbyReis insightnowavailable,thecontributionofagronomistsatsystem
etal.(2000).Plantsmaybeinjuredbysoilpathogenicorganisms, level can be built on mechanistic rather than empirical knowl-
but they may also benefit from organisms present in the rhizo- edge,asdemonstratedbycertainexamplesinprecisionagriculture
sphere, through improvements in growth and mineral nutrition, (Welbaumetal.,2004).
an increase in resistance to unfavourable abiotic conditions, and Interactions between aerial parts of the plant and the sur-
protectionagainstoranincreaseinresistancetopathogens(Sturz rounding biotic environment have also been described in detail
andNowak,2000;KiersandDenison,2008). in recent years. The metabolic pathways by which plants react
Whatever the types of organisms considered, the species bothlocallyandsystemicallytoinfectionorwoundingareincreas-
or plant genotype drives selection of the bacterial community ingly well known (de Bruxelles and Roberts, 2001; Kessler and
and determines the benefits of plant–rhizosphere mutualism. Baldwin, 2002). Some result in the production of volatile sub-
Improvements in the genomic characterisation of rhizobacterial stances,whichplayaroleinherbivorerepulsionorplant-to-plant
communities have made it possible to demonstrate that plant signalling. These findings are promising for genetic engineer-
genotype influences bacterial assemblages by modifying exuda- ing approaches, provided that the genetic basis of the metabolic
tionpatterns(Micallefetal.,2009).Anunderstandingoftheplant pathwayscanbeidentified(DudarevaandPichersky,2008).How-
genomewouldmakeitpossibletodeterminethegeneticbasisof ever, cropping system may also play a role, as the expression
themechanismandtomakeuseofgeneticvariantsforthemanage- ofthemetabolicpathwaysinvolvedindirectorindirectdefence
mentandmanipulationoftherhizospherecommunity(Ryanetal., probably depends on interactions between genotype and envi-
2009; Wissuwa et al., 2009). These rhizosphere associations and ronment (Le Bot et al., 2009). Moreover, it may be possible
theirbenefitstothecropalsodependstronglyoncroppingsystem, to elicit some of these pathways deliberately, with appropriate
so it would seem reasonable to conclude that adapted cropping techniques.
systems(includingcroprotationandcropmanagementmeasures)
couldalsoincreaseefficiency.TheefficacyoftheRhizobium/legume 2.1.3. Waystoimprovetheuseofplantsciencesforecological
associationisalsohighlydependentoncroppingsystem,through intensification
theeffectsofpracticesonthephysicalandchemicalpropertiesof Theprecedingtwosectionsdonotprovideadetailedreviewof
soilsandtheirwaterstatus(Sprentetal.,1987).Theseeffectsare theextensiveliteratureinplantsciences.Instead,theydealwith
well known, but should be considered in the light of the recent afewexamplesofrecentprogressandthepossiblebenefitsthat
developmentoflegumenodulationgenomics(Staceyetal.,2006). agronomistscouldderivefromtheseadvances(seeTable1).These
SturzandNowak(2000)haveenlargedtheirvisiontotheoverall examplesdemonstratethatcloserconsiderationoftheresultsof
communities of endophytic rhizobacteria with potentially bene- plant sciences could help agronomists to reach their objectives,
ficialeffectsoncropgrowththroughanincreaseinresistanceto pavingthewayforhigherlevelsofproduction,betterqualityprod-
unfavourableabioticconditionsandtopathogenaggression,and ucts, and less harmful consequences for the environment. Other
throughimprovementsingrowthandmineralnutrition.Theagro- advancesinplantsciences,concerningplantarchitecture,leafand
nomicbenefitsoftheseassociationswithendophyticrhizobacteria rootmorphogenesis(McSteenandLeyser,2005;WangandLi,2008;
depend on the survival of bacterial communities, which in turn Walteretal.,2009),floralbiology(e.g.,Bossetal.,2004),therole
dependsonsoilandcropmanagement(BowenandRovira,1999; ofaquaporins(e.g.,Maureletal.,2008),cellseparationprocesses
Acosta-Martinezetal.,2008).Oneofthewaysbywhichcropman- (Robertsetal.,2002)andlongdistancesignalswithinplants(Lough
agementcanmodulatetheevolutionofmicrobialcommunities,is and Lucas, 2006), for example, are also of great potential inter-
itseffectonrootexudates.Inadditiontoalteringthephysicaland esttoagronomistsworkingonecologicalintensification,asthey
chemicalpropertiesofthesoil,rootexudateshavebeenshownto mighthelpcropstoavoidortoresistdeleteriousstresses.How-
affectbothsoilmicro-organismcommunitiesandothereukaryotes ever,majoreffortsarestillrequiredtoscale-uptheresultsfrom
(Bertinetal.,2003).Baisetal.(2004,2006)reviewedthenature individualgenes,cellsororganstothecanopy,andtotestthesta-
ofthechemicalsinvolvedandthecorrespondinginteractionpro- bilityofbiologicalresultsinawiderangeofagriculturalconditions.
cesses for various ecological roles. However, one of the aspects Itisalsoimportanttocheckthatadvancesinoneareaarenotasso-
of crop/soil community interactions most frequently ignored by ciatedwithseveredrawbacksinothers.However,thesefindings
agronomistsisprobablytheroleofthecommonmycorrhizalnet- arenonethelessprecioustoagronomists,whowillneedtouseall
works (CMNs), which may be affected directly or indirectly by themeansavailabletoconstructnovel,moreresource-useefficient
soiltillage,fertilisers,pesticideuseandaerialplantmanagement and/orproductivecroppingsystems.
(Pietikäinen and Kytöviita, 2007). The networks that these fungi Finally, there are many different drivers of change in ecolog-
establish between plants may provide a major route for mineral ical intensification (see introduction and subsequent sections).
transferfromplanttoplant(Heetal.,2003).vanderHeijdenand Innovative systems that have already been developed in the
Horton(2009)recentlyreviewedthepossibilitiesforCMNforma- domain of ecological intensification, such as the use of mix-
tionbetweendifferentplantspecies,theirecologicalsignificance turesofcultivarsorspecies,agroforestryandno-tillagesystems,
andthebenefitsgenerated.Theyfoundthatthereweremanypos- would certainly benefit from the knowledge provided by plant
sibilities for CMN development, but that there were also large sciences.However,thesesystemswillthemselvesraisenewques-
differences in the benefits accrued, particularly in terms of pro- tions and issue new challenges to plant science. For example,
motionofthegrowthofinterconnectedplants.Similarly,therole although progress has been made in this area, plant sciences
T.Doréetal./Europ.J.Agronomy34 (2011) 197–210 201
Table1
Examplesofrecentresultsfromplantsciencesusefulinagronomy.
Topicsinplantsciences Keyreferences Potentialagronomicbenefits
Plantarchitecture Zhuetal.(2010) Increasedradiationinterception
Walteretal.(2009)
dePuryandFarquhar(1997)
Photosynthesisefficiency WangandLi(2008) Canopypatterntargetforcropmanagement
Increaseinyield
Identificationofgenotypesadaptedforcropmixture
Exchangesofnitrogenbetween Jacksonetal.,2008 Improvedfertiliseruseefficiency
rootsandenvironment
Roleoforganicanionexudation Glass(2003) Improvednitrogenmanagement
Ryanetal.(2001)
Interactionbetweenrootsand Mehboobetal.(2009) Improvedmineralnutrition
soilorganisms
Brussaardetal.(2007)
Roleofcommonmycorrhizal Micallefetal.(2009) Improvedcropgrowth
networks
Ryanetal.(2009) Adaptationofcropmanagement
SturzandNowak(2000)
vanderHeijdenandHorton(2009)
Interactionbetweenaerial deBruxellesandRoberts(2001) Managementofnaturaldefencesforimprovedresistancetopests
partsoftheplantand
environment
results are still often obtained in highly simplified systems and 2.2.1. Whatdoes“Mimickingnaturalecosystems”mean?
therefore cannot easily be translated to multispecies systems. Therehavebeenonlyafewpracticalattemptstodesignagroe-
Above-ground competition for light and below-ground competi- cosystems from nature. Jackson and Jackson (1999) aimed to
tion for water are major processes in ecological intensification developsustainablecroppingsystemsbymimickingthemid-grass
thatrequirestudyinsystemsincludingfacilitationbetweenplants American prairie, creating crop mixtures analogous to the vege-
(Long and Nair, 1999; Zhang et al., 2008; Malézieux et al., tation structure of the prairie. Traditional agroecosystems in the
2009). tropics, long unknown or disparaged by some agronomists, are
frequently based on the integrated management of local natural
2.2. Learninglessonsfromthefunctioningofnaturalecosystems resourcesand,inmanycases,onthemanagementoflocalbiodi-
versity.Thesesystemsmayalsobeconsideredtoresultfromthe
Strategies for agroecosystem design and management may observationofnearbynaturalecosystemsbygenerationsoffarm-
be derived from the observation of natural ecosystems, guid- ers, who have aimed to mimic the functioning and structure of
ing alternative agronomic practices. Several authors (e.g., Ewel, thesenaturalsystems.Forexample,slashandburnsystemscanbe
1999;Altieri,2002;Jackson,2002;Vandemeer,2003;Malézieux, consideredtomimicnaturebehaviourafterfire.Agroforestrysys-
2011)havealreadysuggestedthatnaturalecosystemsmayprovide temsinthehumidtropicsmimicthestructureandfunctioningof
appropriatemodelsforagroecosystemdesigntoachievebothenvi- rainforests.AccordingtoEwel(1999),humidtropicalecosystems
ronmentalandsocialgoalswhileensuringlong-termsustainability. appeartobeparticularlysuitableforapplicationofthe“mimicry
This idea is based on the assumption that natural ecosystems ofNature”concept.Agroforestrysystemsinthehumidtropicsare
are adapted to local constraints, due to a long process of natu- basedonthetropicalrainforestmodel.Theycombineseveralstrata,
ral selection (Dawson and Fry, 1998; Ewel, 1999). It is therefore haveahighlevelofspeciesdiversityandareverywidespreadin
assumedthattheincorporationofcertaincharacteristicsofnatu- Asia,Oceania,AfricaandLatinAmerica.Suchsystemsprovideboth
ral ecosystems into agroecosystems would improve some of the subsistence for local populations and major environmental and
propertiesofagroecosystems,suchasproductivity(Fukai,1993), socio-economicservices(Sanchez,1995;Nair,2001).Lyinghalfway
stability (Aerts, 1999; Schulte et al., 2003) and resilience (Lefroy betweenagro-andforestecosystems,agroforestrysystemscom-
etal.,1999).Thesefeaturesareparticularlyusefulfordealingwith bine annual and perennial, herbaceous and woody species, in a
pestoutbreaks(Trenbath,1993)andincreasingenergyefficiency moreorlesscomplexwholeintermsofthenumberofplantspecies
inacontextofthedepletionoffossilfuels(Hatfield,1997).Asim- andpractices(Torquebiau,2007).ThedamaragroforestsofSuma-
ilar reasoning was followed in the framework of Ecoagriculture, tra,orthecocoa-basedagroforestsofCameroonorCostaRica,are
proposed by McNeely and Scherr (2003), which places biodiver- originalwaysinwhichfarmingcommunitiesusenaturalresources
sity at the heart of strategies to conserve and restore ecosystem inhumanreconstructionsofboth“natural”andproductiveecosys-
services,increasewildpopulationsinagroecosystems,andsustain temsfromnaturalecosystems(Michonetal.,1995,2007;Schroth
agriculturalproduction.Anillustrationofthismimicryisprovided etal.,2001,2004).
forcroppingsystemsinFig.2withanemphasisoncropprotection. Thescientificfoundationsofthemimicryparadigm,however,
Innaturalecosystems,thevariousanimalandplantspeciesinteract remaintobestudiedthoroughly(Malézieux,2011).Thepotential
throughpopulationdynamicsandtrophicnetworks,providingthe of this approach to generate innovative agroecosystems in prac-
finalecosystemwithservices,suchaspollination.Instandardcrop- ticealsoremainslargelyunknown.Ewel(1999)andvanNoordwijk
pingsystems,theseinteractionsmayleadtopestdamageoncrops, andOng(1999)proposedtwoprinciplesforthedesignofagroe-
whichmaybemanagedwithvariouscontrolmethodstolimityield cosystemsbasedonnaturalecosystemmimicry.Accordingtothe
loss.Anincreaseinplantspeciesdiversityinsystemsmimicking firstoftheseprinciples,agroecosystemsshouldmimicthestructure
naturalecosystemscouldallownaturalenemiestocontrolpests andfunctionofnaturalecosystemsexistinginagivenpedoclimatic
andgenerateecosystemservices. zone.Accordingtothesecond,agroecosystemsshouldalsomimic
202 T.Doréetal./Europ.J.Agronomy34 (2011) 197–210
Cropping system Agroecosytem "inspired by
Natural ecosystem
natural ecosystems"
Cultivated biota Biota
Crop(cultivar)
(plant species/cultivars) (plant species)
Crop rotation
Spatial arrangement over time Spatial arrangement
CCrrooppmmaannaaggeemmeenntt
Management over time
Associated biota
Pests Pests Fauna
Natural enemies
Chemical, genetic
and cultural Population dynamics Population dynamics
Trophic networks Trophic networks
control
Yield(s)
Yield Ecosystem services
EEccoossyysstteemmsseerrvviicceess
Fig.2. Acomparisonofnaturalecosystems,conventionalcroppingsystemsandagroecosystemsinspiredfromnaturalecosystems,withanemphasisoncropprotection.
the diversity of species existing in natural ecosystems, thereby foroptimisingnutrientmanagementinareasworkedbyhumans,
maintainingthediversityofnaturalecosystemsinthegivenzone. communityecologyinnaturalecosystemsmayfacilitatethedesign
Thefirstoftheseprinciplesisclearenough,butmustbeextended ofnewcropprotectionstrategiesandanunderstandingoffacilita-
tobeeffective.Indeed,therearemanyfunctions,andstructurecan tionwithinnaturalecosystemsshouldmakeiteasiertomakeuseof
beassessedatdifferentscales.Furthermore,basingagroecosytem thisprocessinagroecosystems.Finally,approachesbasedonmim-
designsolelyonnaturalecosystemspresentinthesameareamay ickingnaturalecosystemswillinevitablybeconfrontedwiththe
betoolimiting:somegoodideasmightemergefromthestudyof “aimproblem”.Naturalecosystemsprovidemanyservicesbutare
verydistantsystems. not targeted. Agroecosystems, by contrast, are designed to opti-
Accordingtothesecondprinciple,theredesignofagroecosys- misedifferentaspectsandtoachievedifferentgoals.Consequently
tems in more ecologically intensive configurations implies their approachesmimickingnaturalecosystemsarelimitedbycertain
diversification.Thishasbeenthecase,forexample,inCuba,where agriculturalobligations,suchastheremovalofthemineralscon-
small- and medium-scale farmers have tended to diversify their tainedinagriculturalproducts.Someinsightmaybegainedfrom
productionsystemsinresponsetotheirlimitedaccesstoortotal regardingagroecosystemsascomplexsystemswithmanysimul-
lackofagriculturalinputstosustainproductivity(Funes-Monzote taneousfeedbackloopsincludingadimensionabsentfromnatural
et al., 2009). The resulting diversified systems are energetically ecosystems:humanagency.
more efficient, less dependent on external inputs, more produc-
tive,adaptableandresilient.Thediversificationofagroecosystems 2.2.2. Agroecosystemsascomplexsocio-ecologicalsystems
withinthemimicryparadigmmaybeachievedbyincreasingthe Agroecosystemsaresystemsthatcombinesociologicalandeco-
numberofmicroorganisms,plantandanimalspeciesrelevantto logicaldynamics,ininteraction.Incomplex,dynamicandspatially
agriculture over space and time, or through agrobiodiversity, a heterogeneoussystems,interactionstakeplaceoverscalesgenerat-
subset of general biodiversity (Brookfield et al., 2003). However, ingemergentpropertiesandself-regulatorymechanisms(Holling,
natural ecosystem mimicry cannot mean reproducing the diver- 1973).Thesemechanismsoftenmanifestascross-scalefeedback,
sity observed in natural ecosystems, for at least three reasons. or panarchy (Gunderson and Holling, 2002), and societies con-
First,recentreviewsofexistingknowledgeinecologyhavedemon- tribute to system regulation through adaptive management. For
stratedthatfunctionalcompositioncontrolsecosystemfunctioning example,insmallholderagriculturalsystemsmakinguseofcom-
morefrequentlythanspeciesdiversity(Hooperetal.,2005).Asour munally shared resources, buffering and regulatory mechanisms
purposeistoimproveagroecosystemfunctioningthroughecologi- often emerge from collective action (Meinzen-Dick et al., 2004).
calintensification,andnottoconservenaturalspeciesbiodiversity This is why agroecosystems may be defined as socio-ecological
persewithinagroecosystems,agronomistsshouldconcentrateon systems,orcyberneticsystemssteeredbyhumanstoattaincer-
identificationoftheleveloffunctionalbiodiversityresultinginthe tain goals (see Conway, 1987). The capacity of farmers to adapt
expressionofinterestingproperties.AspointedoutbyMain(1999), playsamajorroleinsystemresilienceand,byanalogytothecon-
whoaddressedthequestionofhowmuchbiodiversityisenough ceptofinformaleconomies(deSoto,2000),regulatorymechanisms
in the context of agroecosystems mimicking nature, the level of operateasinformalresourceflowsthatareoftenunaccountedfor
diversityconsideredadequatestronglydependsonthegoalsand inagroecosystemsanalysis(Tittonelletal.,2009).Justasnatural
criteriausedforevaluation.Moreover,interestingpropertiesmay ecosystemshavea“memory”asadirectconsequenceoftheirhis-
arise from the spatial and temporal organisation of the species tory,sodoagroecosystems,exceptthatsomeofthatmemorylies
ratherthanpurelyfromtheirnumber.Forexample,lessonscanbe inhumanagency(Tittonell,2007).
learnedfromstudiesofnaturalecosystemsaddressingagronomic A wider definition of agroecosystem diversification, more
topics:nutrientcyclingwithinacomplexlandscapemaybeuseful compatible with the socio-ecological nature of complex
T.Doréetal./Europ.J.Agronomy34 (2011) 197–210 203
agroecosystems, must consider not only species diversity, but Table2
also the diversity of agricultural practices and rural knowledge Examplesoffarmers’knowledgepotentiallyusefulinagronomy.
adaptedto/derivedfromlocalpedoclimaticconditions.Theselieat Sourcesofknowledge Keyreferences Potentialagronomic
thecoreofhumanagencyandrepresentnewsourcesofknowledge benefit
foragronomicresearch(seebelow).Agroecosystemdiversification Localecological ChalmersandFabricius Explainingchangesin
in its broadest sense thus concerns the diversity of livelihood knowledge (2007) agriculturalsystems
strategiesatacertainlocation,diverselanduse,managementand
Traditionalfarming SinghandSureja Designofsustainable
marketingstrategies,theintegrationofproductionactivities(e.g., systems (2008) farmingsystems
crop-livestock interactions), spatial and temporal associations of Abbonaetal.(2007) Understandingof
cropsandcropcultivars,andthemaintenanceofgeneticagrobio- ecologicalprocesses
diversityinthesystem.Theefficiencyofuseofnatural,economic Localknowledgeand Ballardetal.(2008) Assessmentof
and social resources in agroecosystems—which goes beyond the indicatorsfor managementpractices
partial use efficiency of a certain single input—and desirable assessingforest forforests
management
properties, such as stability and resilience, are based on one or
Farmer’sindicators Tchamitchianetal. Indicatorswith
moreofthesecategoriesofdiversity.Newavenuesforagronomy supportingdecision (2006) expandeddomainsof
tostrengthenagroecologicalintensificationshouldgobeyondthe making validity
cultivatedfieldorthemixtureofspeciesinagivenlandscape.They
shouldexploredesirablepropertiesandmechanismsthatoperate
atthescaleofcomplexsocio-ecologicalsystems,i.e.thattakeinto investigatedfarmers’knowledge.Thestudiesthathavebeencar-
accountsociologicalandecologicaldynamicsandinteractionsin riedoutinthisdomainhavemostlyassessedthevalidityofthis
agroecosystems. knowledge(e.g.,Grossman,2003;Friedmanetal.,2007;Graceetal.,
2009) or considered the local adaptation of more generic solu-
2.3. Farmers’knowledgeandlayexpertisevalorisationand tions(e.g.,Steiner,1998;Affholderetal.,2010).However,farmers’
integrationintoscientificknowledge knowledgeisnotonlyofvalueforapplicationandfortheadap-
tationofagronomicknowledgetoaparticularcase.Itcanalsobe
Farmers do not rely exclusively on the results and output of usedtoextendtheavailablescientificagronomicknowledge(see
agronomicresearchtooperatetheiragroecosystems.Theymake theexamplespresentedinTable2).Wewilldefendthispointand
use of much wider knowledge, based on their own experiences discussthevariousissuesitraisesbelow.
and on exchanges with other farmers and advisers, thus build-
ing their own expertise. This expertise is rooted in the need to 2.3.1. Valueoffarmers’knowledgeforagronomy
actwhateverthelevelofagronomicknowledgeavailable:sound We will analyse separately the lay expertise (resulting from
anddetailedorunreliableandpatchy.Itisalsodependentonthe farmers’ activities and interactions with their own systems) and
characteristics(environmental,economic,social)ofthesituationin the more traditional knowledge that some farmers or societies
whichitisconstructed.AccordingtoPrior(2003),wemayconsider havedevelopedovertime.Thevalueoflayexpertiseforagronomy
farmers to be lay experts (although this denomination entails an andfordevelopment(supporttofarmers)hasbeenrecognisedfor
antinomy):expertsbecauseoftheirexperience-basedknowledge sometime(e.g.,Barzmanetal.,1996;BaarsanddeVries,1999).
andlaybecausethisknowledgeislimitedinscopeanddoesnot Thislayexpertisecanhelptoenlargecurrentagronomicknowl-
givefarmersthebroaderanddeductiveunderstandingcharacteris- edgeinvariousways.First,farmersoperatetheiragroecosystem
ticofscientificorexpertknowledge.Recognitionofthevalueoflay evenintheabsenceofappropriateknowledge,becausetheyhave
expertiseisbothanecessityandachallengeinmanydomains,such to.Theythereforedevelopexperience-basedknowledgethatcan
as medicine (e.g., adapting treatments according to the patient’s fillinsomeofthegapsinscientificknowledge.However,asmen-
reactions,bothasobservedbydoctorsandasinterpretedbythe tioned above, this experience-based knowledge is often limited
patient) and industry (particularly for fault detection in plant or tothefarmer’sownparticularcase,whereasscientificknowledge
machineoperation).However,althoughthevalueofthislayexper- shouldbemoregeneral.
tise is recognised, it is not used to build or extend the current Second, some traditional practices are based on the observa-
scientificknowledge,buttoadaptitsapplicationinlocalsituations tion of natural ecosystems (Chalmers and Fabricius, 2007; Reed
(Henderson,2010). et al., 2007), which, as we have seen, may be of value for eco-
Farmers can observe not only their own production systems, logicalintensification.ChalmersandFabricius(2007),forexample,
butalsoothersystems(bothagriculturalandnatural)andinter- showedthatlocalexperts,usingtheirecologicalknowledge,were
actionsbetweenthesesystems.Theycanalsogainexperimental abletoputforwardexplanationsforchangesintheirsystem,some
knowledge in their own systems. They are often willing to do ofwhichwerealsoprovidedbyscientificknowledge.However,the
so and therefore carry out experiments in the operation of their local experts also had other explanations rooted in a more gen-
own agroecosystem, evaluating the response of the system to eralunderstandingofthesystem.Traditionalfarmingsystemscan
theirdecisions.Thisgeneratesdifferenttypesofknowledge.When alsobeasourceofunderstandingandinspirationforthedesignof
confronted with, observing or learning from natural ecosystems, sustainablefarmingsystems.SinghandSureja(2008)showed,for
farmers gain knowledge similar to what is generally referred to example,howtraditionalfarmingsystemscopewithharshenvi-
as local or traditional ecological knowledge (LEK or TEK, Berkes, ronmentsthroughthemanagementofawidediversityofplants
1999). Over generations, they may also build traditional knowl- providing genetic resources. Abbona et al. (2007) evaluated the
edge (not specifically ecological), refined by years of adaptation sustainabilityofatraditionalvineyardsysteminArgentina,both
(seeprevioussection).Whenexperimenting,theybuildamixture initsoriginallocationandinanewlyplantedarea.Theyshowed
of experience-based and experimental knowledge. Many studies that the traditional system, in its original location, was indeed
haveconsideredtheuseofLEK/TEK,butmosthavefocusedonthe sustainable,whereasthissystemwasnotsustainableinitsnew,
useofthisknowledgefornaturalresourcemanagement(including different location. They concluded that the efficacy of the tradi-
fisheriesandforestrysystems,whichmorecloselyresembleasub- tionalsystemwasdependentonthelocationinwhichandforwhich
sistenceharvestingactivity)ratherthanthedesignorimprovement ithadbeendevelopedovertime.Duringthisevaluationprocess,
of productive agricultural systems. Fewer studies have directly basedontheuseofindicatorsdevelopedforthisanalysisthrough
204 T.Doréetal./Europ.J.Agronomy34 (2011) 197–210
theadaptationofexistingmethods,theseauthorsgainedinsight inwhichitwasobtained(rangesofthevariablesconsidered,for
intoandanunderstandingoftheecologicalprocessesatworkin example);thisfactorcanbeusedtoanalysetheextenttowhichthe
thetraditionalvineyardsystem.Theanalysisoftraditionalfarm- knowledgeobtainedisgeneric.Certaintyreferstotheconfidence
ers’ practices therefore provided an opportunity to obtain new that can be attributed to the knowledge. Finally, precision mea-
scientific knowledge. In a different context, Ballard et al. (2008) sureshowclosetoanumericalexpressionitispossibletogetinthe
analysedtheknowledgeinvolvedinthemanagementandmoni- expressionoftheknowledge.Evencertainknowledgemaydisplay
toringactivitiesofcommunity-basedforestrygroupsandtheways alowprecisionrenderingitsusepurelyhypothetical(ventilatinga
inwhichlocalandscientificknowledgecomplementedeachother. greenhousedoesmodifyitstemperature,butthechangeisdifficult
Theyshowedthatlocalknowledgeprovidedarapidandefficient toindicatewithprecision).Artificialintelligenceprovidesaframe-
means of assessing the effects of management practices on the workforrepresentingexpertiseandanalysingtheconflictsarising
forest.Thesamewasfoundforgreenhousetomatomanagement. wheninformationfromdifferentsourcesiscompared(severallay
Tchamitchianetal.(2006)successfullyusedtheconceptof“crop expertsoracombinationoflayexpertiseandscientificknowledge;
vigour”asanindicatorintheirexpertsystemcontrollingthedaily AmgoudandKaci,2007;Bench-CaponandDunne,2007;Alsinet
greenhouseclimatefortomatoproduction.Tomatocropvigouris etal.,2008;AmgoudandPrade,2009).However,thisdomain(qual-
readily assessed by growers of greenhouse tomato crops, on the itative reasoning and argumentation) is still developing and, to
basisofasetofobservations:planttipcolourandshape,fruitload ourknowledge,itsconceptsandtoolshavenotyetbeenusedto
onthecrop,cropoverallcolour.Scientistsrelatetheseobservations mergelayexpertiseandscientificknowledgeinagronomy(there
tothegenerativetovegetativebalanceofthecropanditsability areapplicationsfordatabasefusion,assistingdebatepreparation
toperformphotosynthesis(Navarreteetal.,1997),withoutbeing andindustrialplanning).Theaddedvalueoftheseapproacheslies
abletomodelitformally. intheneedtoprovideanexplanationdetailingtheargumentssup-
Takenasawhole,localknowledgeandlayexpertisecanprovide portingapieceofknowledge,thereforeaddressingthequestions
cluestothenaturalorecologicalprocessesmostusefulinthedesign ofcertaintyandprecisionraisedabove.
ofsustainablefarmingsystems,suchasthenaturalregulationof Thequalificationoflayexpertisehasbeenshowntobeanec-
pestpopulationsbytheirpredators(Barzmanetal.,1996;Sinzogan essarystepinapproachesaimingtocombinethisexpertisewith
et al., 2004), or management of the soil and its mineral balance scientific knowledge. Going beyond the issues of the domain of
(Steiner,1998;OkobaanddeGraaff,2005;Saitoetal.,2006;Abbona validity, certainty and precision, there is the question of valida-
etal.,2007).Theycanalsobeofvalueinthedesignofassessment tionofthenewknowledgeobtained.However,classicalvalidation
methodsorindicatorsformonitoringtheecologicalperformances procedures cannot readily be applied, because the observations
ofthesefarmingsystems. underlyingtheexperience-basedknowledgeacquiredarelacking.
For example, to validate the greenhouse management rules for-
2.3.2. Qualificationandvalidationoflayexpertiseandknowledge malisedfromexpertknowledge,Tchamitchianetal.(2006)useda
expression two-stepmethodratherthanadirectvalidationoftherulesthem-
Although both interesting and challenging, the lay expertise selves, which was not possible. The first step involved checking
of farmers (or advisers) is not easy to use. First, this lay exper- thattheapplicationoftheserulesreallydidresultinthedesired
tisemustbeelicitedandrepresented.Severalmethodologieshave patternofbehaviourinthegreenhouse(asexpressedwhenbuild-
beenproposedforexpertknowledgeelicitation,eitherforspecific ingtherules),withoutquestioningtheagronomicvalidityofthis
applications,suchasplantdiseaseepidemics(HughesandMadden, behaviour.Thesecondstepinvolvedassessingthequalityofpro-
2002),orformoregeneralapplications(Cornelissenetal.,2003; ductionobtainedbyapplyingtheserules,thegoalbeingtoobtain
Leyetal.,2010).Appropriateelicitationmethodsincludetheselec- appropriate production levels from the greenhouse. Attempts at
tion of a panel of experts and the associated delimitation of the thedirectvalidationofagivenrulehaveonlymadeexplicitwhich
knowledgedomainconsidered.Thechoiceofrepresentationalso piecesofagronomicknowledgecanbeusedtosupportagivenrule.
influencestheelicitationprocess.Manyauthorsadvocatetheuse However,itwouldnothavebeenpossibletodesigntherulefrom
offuzzymodels,whichallowtheuseoflinguistictermsandare thisidentifiedscientificknowledge,generallybecausethescopes
moresuitablefortheexpressionofknowledgeinqualitativerather ofthescientificknowledgeandthatofthelayexpertiseyielding
thanquantitativeterms.Bycontrast,scientificknowledgeismost theruleweredifferent.
frequentlymodelledinquantitativeterms,particularlywhenthe
goalistorepresenttheoperationofasystemundertheinfluence
ofbothcontrolled(humandecisionsandactions)anduncontrolled 3. Methodsforsynthesizinginformation
(environment)factors.Mostoftheagronomicmodelsbuilttosim-
ulateagroecosystemsarenumericalmodelsinwhichthevariables Thethreemainresearchmethodscurrentlyusedbyagronomists
havepointvaluesratherthanintervalorprobabilisticvalues.There (Fig. 1) are various types of field experiments, on-farm inquiries
is therefore a gap between the most common representation of (e.g., Doré et al., 2008), and modelling (e.g., Rossing et al., 1997;
scientificknowledgeandthatoflayexpertise,hinderingthecom- Bergez et al., 2010). Field experiments provide validated knowl-
binationandmergingofthesetwotypesofknowledge.However, edge meeting the scientific rules for data acquisition. This basic
differencesinrepresentationarenottheonlydifficulty.Aspointed knowledgecanbesupplementedbyinquiriesprovidingdatafrom
outbyPrior(2003),layexpertsmaybewrong,eitherbecauseof real-worldagriculturalsituations(farms).Modellingcanbeused
thelimitedscopeoftheirexperienceorbecausetheirconclusions toexploretheresponseofkeyagronomicandenvironmentalvari-
arebasedonfalsepremises(misobservations,forexample,dueto ables, such as, for example, yield or nitrogen loss, to climate,
alackofknowledgeorskills).Theirknowledgeisalsosituation- croppingsystemvariablesorsocietalchanges.Thedatagenerated
dependentinthatitisobtainedinadomainoflowvariability(oneof arethenprocessed,mostlybyclassicalmethods,suchassimulation
thegoalsofagriculturalpracticesisoftentoreducevariabilityand studies,single-experimentdataanalysis,orgroupanalysis.These
diversityinagroecosystems,agoalchallengedbyecologicalinten- methodscouldprobablybecomplementedwithtwoothermeth-
sification).Layexpertiseshouldthereforebequalifiedandanalysed ods: meta-analysis, involving the statistical synthesis of results
independently,inseveraldifferentways:domainofvalidity,cer- fromaseriesofstudies,andcomparativeanalysesofagroecosys-
taintyandprecision.Thedomainofvalidityisimportantbecause tems,involvingtheuseoflarge-scalecomparisonssimilartothose
knowledgeshouldbeassociatedwithadescriptionofthedomain usedinecology(e.g.,Fortuneletal.,2009).
T.Doréetal./Europ.J.Agronomy34 (2011) 197–210 205
3.1. Meta-analysisandagronomy andwithin-studyvariances(Borensteinetal.,2009).Combina-
tionoftheindividualstudyestimatesandestimationofamean
Meta-analysis (e.g., Borenstein et al., 2009) is more powerful valueforthevariableofinterest,forexample,canbeachieved
than a simple narrative review of a series of studies, because it bycalculatingaweightedsumofindividualestimatesderived
synthesises published data in a quantitative manner and makes fromthestudiescollectedinstepii.
itpossibletoassessthebetween-studyvariabilityofavariableof v. Assessment of publication bias. Publication bias occurs when
interest. only studies with highly significant results are published. In
Both scientific researchers and decision-makers can benefit thiscase,ameta-analysiscanleadtoabiasedconclusionand
from meta-analysis in several ways (Sutton et al., 2000), as this overestimationoftheeffectofagivenfactor.The‘funnelplot’
approach provides a methodological framework for (i) explor- techniquecanbeusedtodealwiththisissue(e.g.,Borenstein
ing what has already been done on a given research topic and etal.,2009).
identifyingmoreclearlywherethegapsanduncertaintieslie,(ii) vi. Presentationoftheresultsandofthelevelofuncertainty.
generatinganoverviewofdivergentresults,(iii)guidingdecisions
basedonasystematicreviewandstatisticalanalysisofalltheavail- In the context of ecological intensification, the meta-analysis
abledatarelatedtoagiventopic,(iv)broadeningtheknowledge frameworkconstitutesaninterestingalternativetodynamiccrop
base and allowing replication for the testing of hypotheses, (v) models.Dynamiccropmodelscanbeusedbothtoassessthecon-
addingtothecumulativedevelopmentofscience. sequencesofcroppingtechniquesandenvironmentalvariablesfor
Mostmeta-analysescarriedouttodatehavebeenperformedin cropproduction(e.g.,JonesandThornton,2003)andtoassessthe
medicalscience(Normand,1999;Suttonetal.,2000).Thisapproach effect of cropping systems on key environmental variables (e.g.,
hasbeenlesssystematicallyappliedinotherareasofresearch,such Rolland et al., 2008), two key issues for ecological intensifica-
asecology(e.g.,ArnqvistandWooster,1995;Cardinaleetal.,2006), tion.However,thesemodelsincludeseveralsourcesofuncertainty
and has sometimes been applied in agriculture (e.g., Bengtsson (Monodetal.,2006)andtheirpredictionsarenotalwaysreliable
etal.,2005),animalscience(Sauvantetal.,2008)andplantpathol- (e.g.,Barbottinetal.,2008;Makowskietal.,2009).Webelievethat
ogy(Rosenbergetal.,2004).Inagronomy,meta-analysismethods meta-analysisshouldbemoresystematicallyusedbyagronomists,
have generally been used to compare the effects of different toassessandcomparetheeffectsofcroppingsystemsonproductiv-
croppingtechniquesorofdifferentcroppingsystemsonyieldor ity,risksofsoilandwaterpollution,greenhousegasemissionsand
biomassproduction.Forexample,MiguezandBollero(2005)used biodiversity.Aconsiderablebodyofexperimentaldataisavailable
ameta-analysismethodtosummariseanddescribequantitatively forsuchpurposes(e.g.,RochetteandJanzen,2005).Suchdatacould
theeffectofseveralwintercovercropsonmaizeyield.Theauthors bereviewed,combinedandanalysedwithstatisticaltechniques,to
estimated the ratio of maize yield after a winter cover crop to rankcroppingsystemsasafunctionoftheirimpactonkeyenvi-
maizeyieldwithnocoverfrom37publishedstudiescarriedout ronmentalvariables,suchaswaternitratecontent,greenhousegas
invariousregionsoftheUSAandCanada.Inanotherstudy,Miguez emissions(e.g.,N O)andthepresence/absenceofspeciesofeco-
2
etal.(2008)studiedtheeffectsofplantingdensityandnitrogen logicalinterest(e.g.,earthworms,birds).However,meta-analysis
fertiliser on the biomass production of Miscanthus × giganteus, requirestheuseofappropriatetechniquesandthevalueofameta-
using31publishedstudiesincludingbiomassmeasurementsatdif- analysismaybegreatlydecreasedifthesixstepsoutlinedabove
ferentdatesoverseveralyears.Drawingonpublishedstudieson arenotrigorouslyimplemented.
sub-SaharanAfricanagriculture,Chikowoetal.(2010)conducted
a meta-analysis of factors controlling nitrogen and phosphorus 3.2. Comparativeanalysisofagroecosystems
capture and conversion efficiencies by major cereal crops. The
meta-analysiscarriedoutbyBadgleyetal.(2007)didnotfocuson Information useful for the ecological intensification of agroe-
aspecificcroppingtechnique,butwasperformedtocomparetwo cosystems may be obtained from comparative analyses of the
agriculturalsystems:organicversusconventionalorlow-intensity. structuralandfunctionalpropertiesandperformanceofcontrast-
The authors compared the yields obtained in an organic system ing agroecosystems. Similar approaches, based on temporal or
withthoseobtainedinconventionalorlow-intensityfoodproduc- spatialcomparisons,areusedinotherfieldsofresearch,suchas
tionsystems,basedonyielddatafrom293individualstudieson plant sciences (Wright et al., 2004; Vile et al., 2005; Mauseth,
various crops. These data were used to estimate the mean yield 2006), evolution sciences (Schluessel et al., 2008) and marine
ratioforvariousfoodcategories,forbothdevelopedanddeveloping ecology(FuhrmanandSteele,2008).Thecomparativeanalysisof
countries. agroecosystemsandcomparisonsofagroecosystemswithnatural
Diverse techniques for meta-analysis are available (e.g., ecosystemsinvolvethesimultaneousanalysisofmultiplecriteria,
Borenstein et al., 2009; Sutton et al., 2000), but meta-analysis withevaluationoftheextenttowhichtheydisplayspecificsystem
shouldalwaysincludethefollowingsteps: properties. Several approaches have been proposed for this pur-
pose (e.g., Pannell and Glenn, 2000; de Bie, 2000; Xu and Mage,
i. Definitionoftheobjectiveofthemeta-analysisandofthevari- 2001;López-Ridauraetal.,2002;Giampietro,2003),basedlargely
ableofinteresttobeestimatedfromthedata(e.g.,inMiguez onconceptsformulatedmorethanadecadeago,byauthorssuch
andBollero,2005,thevariableofinterestistheratioofmaize as Conway (1987) and Marten (1988). These methods evaluate
yieldafterawintercovercroptomaizeyieldintheabsenceof indicators relating to the properties of agroecosystems, such as
acovercrop). productivity, stability and resilience. These properties are often
ii. Systematicreviewoftheliteratureand/orofthedatasetreport- interdependent and, as pointed out by Marten (1988), they are
ingvaluesofthequantitiesofinterest. notuniversalandmustberedefinedundereachnewsetofcondi-
iii. Analysisofdataquality(i.e.,qualityoftheexperimentaldesigns tions.Asdiscussedabove,studiesofthelocalknowledgesustaining
andofthemeasurementtechniques). various mechanisms of indigenous resilience across contrasting
iv. Assessment of between-study variability and heterogeneity. agroecosystems, particularly at the scale of the landscape and
Evaluation of the between-study variability of the variable of its functionality (e.g., Birman et al., 2010), are also a promising
interestandoftheheterogeneityoftheaccuracyofindividual startingpointforobtaininginformationusefulforecologicalinten-
estimatesisanimportantstepinameta-analysisandseveral sification. In the next few paragraphs, we examine briefly some
statisticalmethodshavebeenproposedtoestimatebetween- criticalissuesrelatingtothechoiceofindicatorsinmulticriteria
206 T.Doréetal./Europ.J.Agronomy34 (2011) 197–210
evaluationsandidentifyinnovativewaysoflookingattherelation- nomicsbyLeontief(1951,1966),andlaterintroducedintoecology
shipbetweenstructureandfunctioninagroecosystems. byHannon(1973).Indicators,suchasaveragemutualinformation
(AMI) and ascendency (A), were proposed by Ulanowicz (1997,
3.2.1. Comparativeanalysisbasedonmultipleindicators 2004)forcharacterisationofthedevelopmentcapacity(interms
In practice, the implementation of multicriteria analytical ofincreasedorganisation)ofecologicalsystems,andhaverecently
frameworksofteninvolvestheselectionofanumberofindicators beenusedincomparativeanalysesofagroecosystems(Rufinoetal.,
(or the use of a list of predetermined indicators) and of refer- 2009). This approach is known as ecological network analysis,
encethresholdvaluesforeachindicator.Theselectionofindicators and Rufino et al. (2009) presented a set of indicators including
is frequently biased towards the disciplinary standpoint of the AMI, A, and Finn’s cycling index, for assessment of the diversity
observerorhighlyinfluencedbycertainstakeholders,so‘quality and organisation of system components governing N flows and
control’methodsforevaluatingthechoiceofindicatorsarenec- food self-sufficiency in three smallholder crop-livestock systems
essary.Intheirexaminationofthechoiceofindicatorsindifferent fromEthiopia,KenyaandZimbabwe.Farmsystemsareconceptu-
casestudies,GrootandPacini(2010)arguedthatmulticriteriaeval- alisedasnetworks,withthehouseholdandthefarmingactivities
uationsshouldinvolvetheanalysisoffourmainsystemproperties: represented as compartments and the N flows represented as
performance,diversity,coherenceandconnectedness,whichcan connections between compartments. In this example, indicators
beapproachedfromfourdimensions:physical,ecological,produc- assessing network size, activity, cycling, organisation and diver-
tiveandsocial.Performancerelatestofunctionalpropertiesofthe sityoftheNflowswerecomparedwithindicatorsofproductivity
agroecosystem,suchascapacity,stabilityandresilience.Diversity and household food self-sufficiency. This analysis revealed that
relatestothestructuralpropertiessustainingsuchfunctions.Indi- although the amounts of N cycled were small and similar at all
cators of coherence describe the degree of interaction between sites,resourceuseefficiencyanddependenceonexternalresources
components or subsystems within an agroecosystem, and con- differed widely between these apparently ‘comparable’ agroe-
nectednessdescribesinteractionswithadjacentsystems(i.e.,other cosystems.SystemperformancewaspositivelyrelatedtoNflow
agroecosystems,urbanornaturalsystems,etc.).Whenseveralindi- network size, organisation and N cycling, consistent with the
catorsareconsideredsimultaneously,itmaybepertinenttocheck hypothesis that increasing the organisation of resource cycling
whetheralltherelevantcriteriapertainingtosystemperformance, withinresource-limitedagroecosystemsmayrenderthesesystems
diversity,coherenceorconnectednessaregivenequalimportance. moreadaptableandlessvulnerable.
Forexample,López-Ridauraetal.(2002)andPacinietal.(2003) Themainhypothesisunderlyingtheuseoftheseindicatorsis
used two sets of indicators in two independent evaluations of that agroecosystems retain the properties of the natural ecosys-
agroecosystems.Althoughbothmethodsconsideredmultiplecri- tems for which these indices were derived. Ulanowicz (2004)
teriapertainingtosystemsustainability,theyweightedthevarious calculatedthevalueofseveralindicatorsofnetworksizeandorgan-
systempropertiesand/ordimensionsofsustainabilitydifferently. isation,suchasthenumberofdifferentnodesandflows,theirroles
In general, comparative analyses based on indicators provide and their connectivity, for a number of natural ecosystems and
astaticpictureofthestatusofagroecosystemsatoneparticular agroecosystems.Thisexerciserevealedwidergapsbetweenthese
point in time, without considering the underlying feedback and systemsintermsofindicatorsoforganisationthanforthemagni-
system dynamics responsible for bringing the system to its cur- tudeofenergymatterandinformationflowwithinthem.Inother
rentstatusandforanysubsequentchangetothatstatus.Beyond words,increasingorganisationmakesitpossibletodomuchmore
comparing multiple indicators and the tradeoffs between them, with the same resources, while contributing to system stability.
the comparative analysis of agroecosystems should aim to dis- Theextentandthemannerinwhichorganisationcontributesto
til the relationships between relevant properties; e.g., between buildingresilienceinagroecosystemsisafascinatingresearcharea
performance on the one hand, and diversity, coherence and thatremainslargelyunexplored.Existingframeworksofthinking
connectednessontheother.Acommondenominatoroftheindi- about resilience in the field of ecology and nature conservation
catorsusedinmulti-criteriaevaluationsistheirinterdependence mayalsobeofinteresthere(e.g.,Walkeretal.,2010).Anindirect
and their dependence on the structural diversity of the agroe- measurementoftheorganisationofanagroecosystemisitsenergy
cosystem. This interdependence results from the co-adaptation andentropybalance.Svirezhev(2000)proposedtheuseofthermo-
of agroecosystem components over time. The structural diver- dynamicsconceptstoassessthesustainabilityofagroecosystems,
sity of agroecosystems, corresponding to the diversity of system basedontheprinciplethatanecosysteminequilibriumwithits
componentsandtheirinterrelationships,isonlyfunctionalwhen environmenthasacertain‘capacity’toabsorbanthropogenicstress
organisedinaspecificway. thatisregulatedbyitscapacitytoexpelentropybacktowardsthe
environment(the‘entropypump’).Thiscapacity,whichemerges
3.2.2. Analysingthestructureandfunctioningofagroecosystems fromvariousagroecosystemproperties,canbeusedtocharacterise
Itisoftenpostulatedthattheecologicalintensificationofagroe- thestatusofanagroecosystemwithrespecttotheadjacentnatural
cosystems may be achieved through gradual diversification to ecosystemfromwhichithasbeenderived.
capitalise on regulatory principles and mechanisms inherent to Many of the properties of agroecosystems are often interde-
natural ecosystems (see above and, for example, Altieri, 1999; pendent, together determining the vulnerability and adaptation
Gliessman,2001;Wezeletal.,2009).Knowledgeofthestructural capacity of these systems in the face of external shocks and
diversityofanagroecosystem,however,maynotbesufficientto stressors (Luers, 2005). Far from being postulates of a new the-
explainitsbehaviour,andthewayinwhichthediversecomponents ory, these properties are discussed here as operational, working
ofthesystemrelatetoeachothershouldalsobeknown.Moreover, concepts. We know that the provision of agroecosystem service
unnecessarilyhighdegreesofdiversityofsystemcomponentsand functions is regulated by the intrinsic properties of these sys-
flows within systems with poorly organised configurations may tems, the functionality of which can be influenced by design. In
leadtoredundancy(Kauffman,1995;Ulanowicz,2004).Here,we practical terms, ‘design’ implies proposing alternative configura-
examinesomemethodsforstudyingthediversityandorganisation tionsfortheorganisationofenergy,matterandinformationflows
ofsystemcomponentsbasedonthetheoryofnetworksthatmay towards,withinandfromthesysteminspaceandtime.Theexam-
beusedinthecomparativeanalysisofagroecosystems. plesexaminedhereindicatethat,uptoacertaincriticallevel,an
Indicatorsofnetworkcomplexityandorganisationhavebeen increaseinthediversityofsystemcomponentsandinterrelation-
derivedfromcommunicationscience.Theywerefirstusedineco- ships confers desirable properties on agroecosystems consistent