Uncertainty, ignorance and ambiguity in cropmodelling for African agricultural adaptation

Stephen Whitfield

Received: 4 October 2012 /Accepted: 18 May 2013 /Published online: 12 June 2013# Springer Science+Business Media Dordrecht 2013

Abstract Drawing on social constructivist approaches to interpreting the generation ofknowledge, particularly Stirling’s (Local Environ 4(2):111–135, 1999) schema of incom-plete knowledge, this paper looks critically at climate-crop modelling, a research disciplineof growing importance within African agricultural adaptation policy. A combination ofinterviews with climate and crop modellers, a meta-analysis survey of crop modellingconducted as part of the CGIAR’s Climate Change Agriculture and Food Security(CCAFS) programme in 2010, and peer-reviewed crop and climate modelling literatureare analysed. Using case studies from across the crop model production chain as illustrationsit is argued that, whilst increases in investment and growth of the modelling endeavour areundoubtedly improving observational data and reducing ignorance, the future of agricultureremains uncertain and ambiguous. The expansion of methodological options, assumptionsabout system dynamics, and divergence in model outcomes is increasing the space and needfor more deliberative approaches to modelling and policy making. Participatory and delib-erative approaches to science-policy are advanced in response. The discussion highlights theproblem that, uncertainty and ambiguity become hidden within the growing complexity ofconventional climate and crop modelling science, as such, achieving the transparency andaccessibility required to democratise climate impact assessments represents a significantchallenge. Suggestions are made about how these challenges might be responded to withinthe climate-crop modelling community.

1 Introduction

Climate adaptation policy, particularly in African agriculture, is increasingly influenced bythe work of international research bodies and technology developers that attempt to generateand respond to projections of change. Meteorological research centres and organisations

Climatic Change (2013) 120:325–340DOI 10.1007/s10584-013-0795-3

S. Whitfield (*)Institute of Development Studies, Library Road, Brighton BN1 9RE, UKe-mail: s.whitfield@ids.ac.uk

S. WhitfieldWorld Agroforestry Centre, UN Avenue, Nairobi, Kenya

within the Consultative Group on International Agricultural Research (CGIAR) have madeclimate-crop modelling a central tenet of their action-research strategies and policy outreach.Crop modelling is at the centre of collaborative work within the Climate Change, Agricultureand Food Security (CCAFS) group of the CGIAR consortium on ‘Adaptation to ProgressiveClimate Change’, and is being carried out as part of the International Crops Research Institutefor the Semi Arid Tropics’ (ICRISAT) Global Theme on Agroecosystems work, theInternational Food Policy Research Institute’s (IFPRI’s) IMPACT 2009 project, FANRPAN’sStrengthening Evidence-Based Climate Change Adaptation Policies (SECCAP) programme,and the Kenya Agricultural Research Institute’s Climate Change Unit, amongst others.

These are initiatives and projects with policy significance and are supported by anemergent evidence-based policy discourse in African agriculture (Whitfield 2012). Climateand crop model projections are increasingly being used to guide national crop breedingprograms and the promotion of new cash crops and forest management strategies withinAfrican agriculture. Technocratic approaches to framing challenges in African agriculture(e.g. the use of climate models) often go hand in hand with the advancement of technocraticsolutions. The Water Efficient Maize for Africa (WEMA) initiative, which adopts geneticmodification tools in the development of ‘climate change-ready’maize, is one example of anintervention that is justified through climate and crop model projections. Projects such asWEMA are breaking new technological and political ground in Africa and necessarily drawon evidence, within a discourse of evidence-based policy, to prove their legitimacy. In thisway, models and projections become attached to a highly politicised field, in which it may bedifficult to disentangle the strategic use of evidence to justify policy from policy that isresponsive to evidence (Pawson 2006).

The predictive limitations of crop and climatemodels are recognised andwarnings about over-reliance or uncritical interpretation of model outputs within a decision-making process areregularly made in crop model literature (Challinor et al. 2010; Thornton et al. 2010; Robertsonet al. 2012). However, there is an evident tension between suggestions that these limitations resultfrom the relative simplicity of models (i.e. their inability to incorporate all of the factors thatinteract in determining the future) (Challinor 2010) and acknowledgements that there is aninherent ‘unknowability’ about the future (Challinor et al. 2009). Whilst the former can beaddressed through the growth of the modelling endeavour (and is undeniably driving a trend ofmore science, more scientists, and more scientific complexity in the global project of climate andcrop modelling), the latter argument might question the justifiability of such endeavour.

Through the contributions of social constructivist scholars, it has come to bewidely acceptedthat all knowledge is generated through a combination of experience and experiment (Fischer2000; Tsouvalis et al. 2000) and all is subject to value-judgements and the influence of theinstitutional and societal context, in which it is generated (Kuhn 1986; Longino 1990). As aresult, greater recognition has been given to the contextualised knowledge of ‘non-experts’,developed through real experience of societal behaviour and values, as an important source ofinformation about the realities of risk (De Marchi 2003; Renn 2008).

1.1 Risk governance

The challenge in adopting this socially constructed understanding of risk within processes ofassessment, management and policy—combined here under the all encompassing concept ofgovernance—is to make space for multiple knowledge bases. The trajectory of debates inrisk studies over the past two decades has led to a realisation that ‘risk and governance arenot separate concepts, but belong to spheres of investigation and practical interest that arestrictly intertwined’ (De Marchi 2003: 171). Challenges to the conventional monopoly of

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‘experts’ over the definition and measurement of risk, has resulted in growing calls for, andeven international agreements about, the need for public participation in both knowledgeproduction and policy design (e.g. Cartagena Protocol on Biosafety). ‘Risk governance’ hascome to represent a popular umbrella term that covers a range of different models andframeworks that emphasize knowledge exchange and communication.

The ‘analytic-deliberative’1 prototype is the most commonly referred to model of riskgovernance (Renn 1999; Burgess et al. 2007; Renn and Schweizer 2009). Common methodsof facilitating participation within this model are deliberative juries and workshops, the aimof which are to determine ‘best’ knowledge through information exchange and consensusbuilding. Renn and Schweizer (2009) suggest that the functional virtue of participatory riskgovernance is in achieving legitimacy and social acceptance of policies, and the function ofcommunication and knowledge exchange is further understood by others as a way ofbuilding social resilience and reducing vulnerability (Sellke and Renn 2010). In a sense,the analytic-deliberative model presupposes that communication is achievable.

The post-modernmodel of risk governance places less emphasis on pre-defined stakeholdersor pre-framed discussions, but rather achieves participation through open (and often anony-mous) discussion forums (Schmidt et al. 2008). The focus within such approaches is less onlegitimizing information and building consensus, and more on acknowledging plural rational-ities and the idea that risk is differently constructed within and across society. Such approachesto deliberation have been used informally, and are often established and moderated by cam-paign groups, as such they are often spaces for constructing a specific type of risk knowledgeand reinforcing socio-cultural barriers (Kasperson et al. 1988; Renn et al. 2010).

The ‘scientific citizenship’ model (Irwin 2001; Weldon 2004; Leach et al. 2005) reflectsthe growing emphasis placed on the value of societal ethics and citizen values in the processof knowledge generation. This model of risk governance shares many of the traits of theanalytic-deliberative model of risk governance, but places greater emphasis on society-ledscience, such that participants play a greater role in framing issues as well as discussingappropriate methods, scales of analysis and analysis criteria, and has been labelled the ‘co-production of science’ (Jasanoff 2004; Wynne 2006).2

1.2 Ignorance, uncertainty and ambiguity

Three descriptions of incomplete knowledge, based on work by Stirling (1999), are utilisedin this research in order to describe the nature of incomplete knowledge in crop and climatemodelling: ignorance, uncertainty, and ambiguity. Ignorance describes that gap in knowl-edge that results from the unexplained and may relate to potential impacts which are not yetidentified (let alone ascribed a probability of occurrence) or observed dynamics for whichthere are not yet any tested theories. Uncertainty pertains to that component of incompleteknowledge that is characterised by an understanding of processes but ignorance of likeli-hoods. It is reflected, for example, in the presentation of multiple alternative outcomes, therelative probabilities (or probability distributions) of which are unknown or strongly disput-ed. A third description, ‘ambiguity’, reflects the idea that alternative knowledges areintrinsically tied to (or constructed on the basis of) alternative framings of the issue. In thecase of ambiguity, differences in outcomes may result from different perspectives, aboutwhich it may not be possible to make judgements about legitimacy or about which no onemay claim the outright authority to judge legitimacy. These descriptions are not mutually

1 Based on the National Research Council’s model of democratic policy making (Stern and Fineberg 1996)2 See Ferretti and Pavone (2009) for two recent case studies of this model of risk governance

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exclusive. Knowledge about a particular system could be both uncertain and ambiguous if,for example there are alternative legitimate theories about system dynamics and inputs, eachof which is associated with probabilistically-unknown outcomes.

Box 1: Descriptions of Incomplete Knowledge (based on Stirling 1999)

Ignorance: unexplained and unidentified system dynamics (those about which there may be either no, oruntested, theories)

Uncertainty: the nature of system dynamics may be explained, but the relative likelihood of alternativeoutcomes is unknown or strongly disputed.

Ambiguity: alternative outcomes result from different framings of ‘the system’ (e.g. value-based methodo-logical choices)

1.3 The link between the nature of knowledge and models of risk governance

It is increasingly recognised that the quality of evidence depends not only on its lack ofignorance, but also on the clarity of understanding of its uncertainties and ambiguities(Webster 2003) so that action is based on more than just the completeness of knowledge,but on a pluralistic approach to understanding and interpreting the nature of its incomplete-ness. The nature of incomplete knowledge has important implications for the governance ofknowledge gaps. Areas of ignorance might represent a need for systematic and scientificendeavours to model, identify, quantify and hypothesise about. Uncertainties and ambigu-ities, however, must necessarily be addressed through more pluralistic approaches of riskgovernance in which ‘non-experts’ can have input in the making of assumptions, the framingof systems and challenges, and the interpretation of results.

An analytic-deliberative model of risk governance, which focuses on building consensusand closing in on best knowledge, fits most closely with incomplete knowledge associated withuncertainty and ignorance. Participation within this model takes place within structures and pre-framed discussion, leaving little room for the negotiation of ambiguities. By contrast, a post-modern approach to risk governance responds much more directly to ambiguities, but may noteasily facilitate the findings of policy solutions in cases where stakeholders from alternativecultures must necessarily debate modelling assumptions. A scientific citizenship approachmight best facilitate the inclusion of ‘non-experts’ in the generation of knowledge within atargeted knowledge production process. The method of participatory modelling (Huber-Sannwald et al. 2006) is an example of scientific citizenship, which offers a useful approachfor better aligning projection assumptions with policy utility in the projection outputs.

1.4 Outline

A combination of interviews with climate and crop modellers, a meta-analysis survey of cropmodelling conducted as part of the CGIAR’s CCAFS programme in 2010, and peer-reviewedcrop and climate modelling literature are analysed in order to identify the changing the nature ofthis knowledge generating process. This research was conducted as part of PhD research basedat the World Agroforestry Centre in Nairobi. The focus country for the research is Kenya, acountry which hosts two CGIAR centres and invests a large amount into crop breedingprogrammes (including genetic modification) through its national agricultural research centres.International investment in research and climate change adaptation interventions in Kenya arerepresentative of an Africa-wide trend that sees increasing emphasis placed on climate and cropmodelling.

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The findings of the primary research are described in Section 3, which begins with asimple explanation of the process of creating a climate-crop model projection, and thefollowing sections draw on interview and survey responses about particular componentsof this production chain in identifying incomplete knowledge. The discussion sectionconsiders the implications for achieving risk governance in agricultural adaptation in Africa.

2 Methodology

A combination of qualitative methods was adopted in this research. “Qualitative methods areconcerned with how the world is viewed, experienced and constructed by social actors. Theyprovide access to the motives, aspirations and power relationships that account for how places,people, and events are made and represented” (Smith 2000: 660). As a field that deals largely inquantitative research, the crop and climate modelling community, a framework in which it isviewed as a community of social interaction and construction, and applies methods as such, canpotentially gain important insight into the process behind the figures. Publications through whichclimate modelling projects communicate outputs tend to be framed within a common discoursethat is institutionalised within the modelling community and usually deal with incompleteknowledge in quantitative terms (e.g. probabilistic analysis of alternative outcomes). A qualitativemixed method approach, inclusive of interviews, is capable of revealing the values and judge-ments (many of which are so institutionalised that they are not acknowledged) that easily becomehidden in the technical outputs of climate modellers. Whilst a review of this literature is sufficientfor identifying ignorance and uncertainty, getting at ambiguity requires a more in depth approach.

Six completed crop modelling projects3 from the past 10 years, which have at least partialfocus on the future of maize agriculture in Kenya (some focus on multiple crops and/or broadergeographic regions), were selected for an initial review in order to identify the key stagesinvolved in producing crop projections and the most common models and datasets drawn on insuch projects. These projects represent some of the major action-research initiatives conductedby CGIAR institutions and other influential initiatives in African agriculture. An extendedliterature reviewwas later conducted in order to draw out some of the key sources of incompleteknowledge with regards to Kenyan climate and crop projections. A strategy for searching andscreening relevant literature within two major citation indexes4 was adopted and produced atotal of 27 papers; sections of IPCC reports relevant to East Africa were also reviewed.

Structured interviews with actors involved in each stage of the crop projection productionchain were informed by the literature review and the results of a 2010 meta-analysis survey ofcrop modellers conducted by the CCAFS group of the CGIAR.5 A total of 16 interviews wereconducted with academics at the UK and Kenya Meteorological Departments, CGIAR institu-tions, and the Kenya Agricultural Research Institution’s Climate Change Unit (respondents areanonymous and assigned a code number fromCS1 to CS16). Respondents were identified from

3 Parry et al. (2004), Tatsumi et al. (2011), Fischer et al. (2009), Thornton et al. (2011, 2009), and Nelson et al.(2009)4 Web of Knowledge and Science Direct;

Search in ‘title and abstract’ (SD) or ‘topic’ (WoK): ((climate OR precipitation OR temperature OR cropOR yield OR productivity OR agricult* OR maize) AND (process OR trend OR projection OR forecast))AND model AND (Kenya OR “East* Africa”) AND (uncertain* OR assum* OR unknown)

Screening criteria: Specific focus on Kenya of East Africa; Refers to projections of climate or maize cropmodels; Published since 1992; Identifies sources of uncertainty in projections5 A description of the methodology and results of this survey can be found at http://ccafs.cgiar.org/news/decision-support/crop-modelling-climate-change-and-food-security

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the reference lists of six key reports. Thirty authors were contacted by email and nine agreed tobe interviewed, a further seven interviews resulted from suggestions and introductions made byinitial respondents. Questions focused on methodological decisions and assumptions as well asapproaches to identifying, quantifying and communicating uncertainty within the contributionsthat the respondents make within the climate and crop modelling community. Interviews weretranscribed and manually coded according to both climate/crop model production process(see below) and category of incomplete knowledge, and analysed qualitatively.

3 Results

3.1 Mapping the production of climate/crop projections

Figure 1 is a distillation of the elements of similar diagrams presented by Tatsumi et al.(2011), Thornton et al. (2009), and Nelson et al. (2009) and it provides a schematicrepresentation of the stages involved in producing a crop projection. These stages represent

Fig. 1 Simplified climate-crop model production chain

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the main points of entry in the tracing of assumptions and the status of knowledge in thepresented review.

In crop modelling, information that describes limits on photosynthetic potential and wateravailability (daily rainfall and maximum and minimum daily temperatures), soil moisturecapacities and mineral properties, plant phenological development requirements (i.e. theresponse of the plant to water availability, radiation, and daily temperatures), and agriculturalpractices are all essential inputs to the models calculation of yield. Each of these sets of datahas a production chain of its own, which continues to branch (as hinted at within thediagram) well beyond the scope of this paper. In the production chain of the required inputdata for running a crop model (downscaled rainfall and temperature data, soil data, and cropand land management data) a repetitive pattern of observation-assumption-computation-projection can be identified. The computation of future greenhouse gas emissions, a keyinput parameter for the running of crop models, for example, is achieved through theprocessing of data on observed emissions (or emissions drivers) based on assumptions abouthow future behaviour. Similarly global scale climate projections undergo a computationalprocess of downscaling based on assumptions about inter-scalar climate relationships; thisproduces a projection of local and daily weather.

The following discussion looks at developments across the four main components of thecrop projections chain: observation data, assumptions, computations, and projections, inorder to discuss how increased capacity, endeavour and complexity in all four are changingthe nature of climate impact knowledge.

3.2 Ignorance about Africa’s agro-climate future

Improvements in observational data are helping to reveal new understandings about Africanclimate and crop system processes, and gradually explaining some of the previouslyunexplained dynamics. New soil mapping initiatives (e.g. the Global Soil Map Initiativeand the Kenya Soil Survey), the growth of the global climate model intercomparison project(CMIP), and the development of downscaling statistical packages are all examples of waysin which ignorance about Africa’s agro-climatic future are being reduced Table 1.

Improved observational data has undoubtedly facilitated the growth in complexity ofcomputations, both in terms of the number of parameters and dynamic relationships capturedwithin models, and the number of different models themselves. Since the IPCC’s firstassessment report in 1990, global climate models have developed to cumulatively incorpo-rate the effects of (in chronological order): radiative forcing and long term GHGs; atmo-spheric chemicals and aerosols; terrestrial carbon cycling; tropospheric ozone; cryosphericalbedo; coupled ocean atmosphere dynamics (e.g. ENSO); cloud cover, thickness andheight; and much more (Le Treut et al. 2007). A diversity of climate models have beendeveloped reflecting a range of capabilities, temporal and spatial scales of concern, levels ofcomplexity and data input requirements. The growth of the global climate modelintercomparison project (CMIP), a key source of data utilised in the IPCC assessmentreports, since its beginnings in 1995, is indicative of the growth of the global project ofclimate modelling. The initial CMIP1 and CMIP2 experiments, run in the mid 1990sinvolved the participation of 25 models, representing almost the entirety of developedcoupled climate models at the time. These two experiments collected present day controlruns (CMIP1) and 1 % per year CO2 increase experiment data (CMIP2) (Meehl et al. 2000;Lambert and Boer 2001). By comparison, the current CMIP5 experiment, the data fromwhich will be utilised in the IPCC’s fifth assessment report, will be participated in by morethan 50 models (including high resolution atmosphere models and earth system models as

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well as AOGCMs) from 20 modelling groups, and will involve the running of AOGCMs inover 30 different configurations (characterised by different emissions scenarios (now de-scribed as Representative Concentration Pathways); specified CO2 concentration increases;aerosol projections; volcanic simulations etc.) in both long-term and near-term experiment(Taylor et al. 2012).

Despite improvements in observational data and the subsequent growth of options andcomplexity in the production of crop model input data, such as that described for soil andclimate information, certain sources of ignorance about climatic and agricultural systemshave persisted. Trends in El Nino Southern Oscillation (ENSO), its affect on Indian OceanSea Surface Temperatures and the links between these temperatures and rainfall patterns(onset, intensity, length and event frequency) in East Africa, for example, form a complexrelationship about which there are low levels of academic and climate model agreement anda steady flow of new ideas being presented in academic literature (Indeje et al. 2000;Conway et al. 2007; Williams and Funk 2011). Whilst Indian Ocean sea surface tempera-tures are known to be a key driver of rainfall across East Africa (Ummenhofer et al. 2009),identifying this relationship within historical records and accurately describing the processesthat drive this relationship continue to be difficult to disentangle from a number of otherocean–atmosphere dynamics (Conway et al. 2007).

Table 1 Main improvements in climate-crop model relevant observational data over past two decades (basedon literature review—references included)

Observed data Main improvements References

Emissionstrends anddrivers

• Better quantification of emissions for disaggregatedsources

Nakicenovic et al. (2000), Le Treutet al. (2007), and Van Vuuren etal. (2011)• More accurate monitoring of current emissions

• Socio-economic analyses of local,national, and global trends

Weather/climate

• Increasing global coverage of weather stations IPCC (2001), Jones et al. (2001),Brázdil et al. (2005), and Barnettet al. (2001)

• Improved accuracy and reliability of data collectionequipment (including remote and real-time reporting)

• Improved techniques for deriving historical data fromproxies

Cropmanagementpractices

• Increased number of local-level studies of crop man-agement practices

Parry et al. (2004), Fischer et al.(2005), Deressa et al. (2009), andSacks et al. (2010)• Participatory approaches to identify locally-relevant

trends and drivers of change

• National level socio-economic analyses of land man-agement determinants

Soils • Increasing global coverage of soil data collectionstations

Nachtergaele (1999), Dirmeyer(2000), Sanchez et al. (Sanchezet al. 2009)• Increased detail in collected data (common collection

of more parameters)

• Improvements to the temporal and spatial resolutionof data sets

Crop yieldresponses

• Increased number of field trials using controlledenvironments or collecting detailed environmentaldata

Thornton et al. (1995), Heng et al.(2009), and Tingem andRivington (2009)

• Improved geographical, crop-type, and environmentalcondition coverage of studies

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3.3 Uncertainty about the agro-climate future

Projection output data is, to different extents, sensitive to the observational data and theassumptions on which they are based, and as the number of models and input data sourcesgrows, so to do the number of projections. Although the conventional approach may be tointerpret the distribution of these different projections as indicative of their probability (i.e.assuming that the most common projection is the most likely), this is problematic, particularlygiven the subjectivity of assumptions and the persistence of a certain level of ignorance.

A popular approach to both calculating and reducing uncertainty is to do multiple runs ofa model with perturbed parameters, in order to explore uncertainty in the definition ofparticular parameters within a model (e.g. uncertainty in the relationships that describe SST-rainfall teleconnections), or runs of multiple models with identical inputs, often described asan ensemble. The cumulative distribution of outcomes in the former case essentially de-scribes intra-model uncertainty, and in the latter the distribution describes inter-modeluncertainty. Although the resultant distributions of outcomes are often considered to repre-sent probability distributions, where there is some level of ignorance or ambiguity within themodel outputs, it is a misinterpretation of the result of multiple-model runs to assume that themean or modal outcome is the most accurate or most likely. In cases where the knowledgegap is largely composed of ambiguity or ignorance, and where ensembles do not explore thefull breadth of this knowledge gap, the extremes of multiple model output distributions (andeven those values that sit outside of the distribution) may be just as legitimate, and just aslikely, as those at the centre.

3.4 Ambiguity about the agro-climate future

The existence of ambiguities in crop modelling are reflected in the making of assumptions, notonly those that determine how observed data is transformed into projections (methodologicaldecisions), but in the judgements that are made about the accuracy and reliability of the data(e.g. the assumptions made in the reverse production process) and in the translation of pro-jections into policy, which is introduced here and discussed further in the following sections.

The result of increases in the complexity of computations and the expanding number ofmodels and data sources, is an ever-increasing number of options in, and approaches to, thewhole exercise of crop modelling. “Over the last few years, the options available to cropmodellers in terms of climate data,… have grown so much and the consequence is thatproject possibilities have just increased exponentially” (CS6 Interview). Evident throughoutthe discussion thus far are some of the many methodological choices involved in generatinga crop projection, choices which are increasing in scope as the various contributing disci-plines expand in size and complexity. A selection of those choices, alluded to in thepreceding discussion and identified by interview respondents, are summarised in Table 2.

Across the six projects reviewed, alternative approaches to describing land managementpractices are evident including climate-triggered planting dates (Thornton et al. 2010); landutilisation type databases (Tatsumi et al. 2011); local surveys (Thornton et al. 2009); andland use change models (Parry 2004; Nelson et al. 2009). Respondent CS9 suggests that ‘thecrudeness of land management data …. [mean that] there are endless assumptions andoptions’. Many studies utilise data on current land management practices as input data forcrop modelling, and as this data improves in resolution, coverage and detail, the need forcertain assumptions (e.g. the assumption that management practices in location A are thesame as those in location B, 40 miles away, about which there is published information) isreduced. The necessity of other assumptions, however, is less readily removed through

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observational data improvements. In using information for observations of current landmanagement as a direct input into crop models, for example, a key assumption is madeabout the insignificance or lack of future changes to land management practices (and/or

Table 2 Methodological dilemmas in climate-crop modelling (based on interview responses)

Projections Methodological dilemmas

GHG emissions • Should emissions scenario include/exclude non-energy-based emissions/ natural-emissions? (CS5)

• Should scenario selection reflect ‘most likely’ future, be used to assess futureinterventions, or offer represent best and worst cases? (CS5, CS11)

• What are the likelihoods of alternative ideas about population, economic andenergy efficiency trends and future policy interventions? (CS11, CS12)

• How should the sensitivity of the chosen climate model to alternative emissionsscenarios be interpreted? (CS1, CS11)

Climate projections • Which models should be included or excluded? (CS1, CS2, CS6, CS8)

• Inclusion/exclusion should be based on what criteria? (e.g. data availability,spatial resolution, capture of ocean–atmosphere flux, dependence on fluxadjustments, ability to reproduce observed SST teleconnections…) (CS2, CS6,CS12, CS15)

•What do model ensemble distributions/outliers represent? (e.g. does mean = morelikely?) (CS1, CS2, CS6, CS15)

Downscaled weatherdata

• What assumptions about the relationship between modelled GCM climates andlocal observations are acceptable? (CS7, CS9)

• Acceptability should be based on what criteria/methods? (statistical significance,local and detailed climate models driven by boundary conditions, identificationof key climate mechanisms (indicators)) (CS7, CS9, CS14)

• What spatial/temporal resolution is of sufficient policy relevance? (CS7, CS14)

• How should resolution benefits be weighed against interpolation errors? (CS14)

Crop and landmanagement data

• On what basis should land management inputs be determined? (primary datacollection, participatory modelling, assumptions about economic rationality,meteorological determinism…) (CS3, CS10)

• What should be assumed about future changes in land management practices?(assume no change, socio-economic response functions linked to predictionsabout socio-economic change, model ‘optimal’ management strategies…) (CS4,CS10, CS13)

• What physiology properties should be included/excluded in defining plantcharacteristics? (CS3, CS10)

• What level of detail/accuracy about plant physiology is necessary? (CS13)

Soil data • Which properties should be included/excluded in describing soil characteristics?(CS3, CS10)

• Inclusion/exclusion should be based on what criteria? (functionality, dataavailability, resolution of available data, statistical significance identified inparticular crop studies…) (CS10, CS13, CS16)

• Which soil data sets should be used? (CS10, CS16)

• How should uncertainty created by errors in and the resolution and age of datasetsbe interpreted? (CS10)

Crop projection • Which crop models should be included or excluded? (CS2, CS8)

• Inclusion/exclusion should be based on what criteria? (e.g. data requirements/availability, computational capacity, spatial resolution, reliance on statisticaladjustments, inclusion of crop responses to atmospheric CO2, necessity ofclimate downscaling....) (CS2, CS8)

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drivers of change) (Interviews CS3, CS10). An alternative to making this potentially problem-atic assumption is to introduce yet another model that incorporates, for example, the impact ofprojected changes to the socio-economic influences on land management decisions (e.g. Parry2004; Nelson et al.2009). The complexity and large error margins in such attempts, however,reflect the complexity of individual decision making, and themselves require assumptions to bemade about the relative significance and likely future direction of different drivers of change(Interviews CS3, CS10). Although the use of ‘optimal’ land management strategies or climate-derived planting dates may appear to be objective solutions to ambiguity of managementpractices, they are not themselves without necessary (albeit hidden) assumptions (InterviewsCS4, CS10, CS13). One might challenge such an approach by highlighting the problems ofassuming rationality in decision making in a world in which there may be alternative orcompeting rationalities (i.e. those decisions in which yield optimisation is sacrificed in pursuitof other benefits) or imperfect decision making (i.e. decisions made on the basis of incompleteinformation).

As with emissions data, generating land management input data depends on the subjec-tive construction of a future scenario, a process in which multiple and interacting drivingforces of change may be framed in or out and justified on the basis of different values andperspectives. Of course, it may be pragmatically argued that certain assumptions are morerealistic than others and often methodological decisions are made for reasons of accessibilityand availability (e.g. access to particular datasets or models), not just judgements aboutaccuracy. Similarly, methods might be chosen in spite of their flaws rather than in denial ofthem, because of their virtues. The ability of models to simulate observed data, for example,is commonly used as a proxy for model accuracy because it is the most efficient way ofcomparing performance across models rather than because of assumptions about its infalli-bility as a method (Interview CS2). The argument here, however, is that deciding onmethodological approaches to generating crop projections inevitably involves some degreeof value-based choice. Value judgements necessarily enter into a process of framing thelimits and determining the drivers of the change that is being modelled, with significantimplications for the ‘evidence’ that the process generates, and how it is interpreted.

It is only through interpretation mechanisms that the ‘evidence’ produced in climate and cropmodelling finds utility, and so it is important to think critically about how these are ‘constructed’,who is constructing them, and how certain interpretations (or mechanisms of interpretation) findlegitimacy or become privileged over others. This is the focus of the following discussion.

4 Discussion

4.1 Responding to incomplete knowledge

4.1.1 Precaution

“The models aren’t very good at predicting trends yet… so we need to implement adaptationstrategies for moderately severe cases” (Interview Respondent CS14). This approach tousing climate impact projections was a popular response by interview respondents and isbased on a particular framing of incomplete knowledge, one in which it is reducible to aprobability distribution. Where projections are divergent, moderate change becomes akin tomost likely change as though a single peaking probability distribution fills the gap betweentwo extremes—and so in the face of ‘uncertainty’ (the label that is often assigned genericallyacross the knowledge gap), precaution becomes the only sensible prescription.

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An in depth understanding of the nature of incomplete knowledge sheds more light on theappropriateness of a precautionary approach. Were incomplete knowledge in crop andclimate modelling largely characterised by ignorance, then reducing knowledge gaps andclosing in on more accurate projections could be realistically achieved through increasedinvestment in modelling. In such instance, a precautionary approach, which advocateswaiting for more complete evidence, would be legitimate. However, as incomplete knowl-edge in climate and crop modelling is characterised by uncertainty and ambiguity, there islittle prospect that knowledge will become complete enough for action simply throughinvesting in conventional modelling that does little to address these characteristics. In fact,‘complete enough’ is itself another ambiguous judgement over which the climate and cropmodelling community should not hold a monopoly.

4.1.2 Participation/deliberation

Where knowledge is characterised by uncertainty and ambiguity, policy makers and otherstakeholders may need more than just a summary of evidence (and its completeness), butinstead must engage in the process of creating and understanding knowledge, and thusunderstand where projections come from, the potential sources of biases and error withinthem, and the meaning behind the reductionist probabilities that are attached to projectionsand forecasts (Webster 2003; Swart et al. 2009; Pidgeon and Fischhoff 2011).

Participatory modelling is a scientific citizenship methodology that aims to integrate localand scientific knowledge within the modelling endeavour by involving a range of stakeholdersin deliberatively determining key system dynamics and defining the interrelationships betweenthem. Participatory models might range from complex, formula-driven descriptions of a systemfor which participants determine the key questions for the model to answer and provide theinput data (e.g. Anselme et al. 2010), to very simple qualitative descriptions of the system basedon a few loosely-described and stakeholder-derived system component relationships andthresholds (such as those described by Huber-Sannwald et al. 2006).

A number of different methodologies for participatory modelling are described withinpeer-reviewed literature, and practitioners wishing to implement such an approach, may findit useful to base their strategies on guidelines offered by: Anselme et al. (2010), Huber-Sannwald et al. (2006), Voinov and Bousquet (2010), and Whitfield and Reed (2011). Thebest approach to participatory modelling implemented will depend on the stakeholdersinvolved and the stakeholder-defined nature of the problem, however the key rules for bestpractice in stakeholder participation outlined by Reed (2008) are recommended.

4.2 The challenge of democratising agricultural adaptation planning

Essentially complex models act to fractionate the knowledge gap. For example, the task ofparameterizing terrestrial carbon cycling in a simpler model, and making the assumptionsinvolved in it, may, in more complex models, become separated into tasks of parameterizingsoil carbon storage capacities, sequestration rates, numerous biogeochemical feedbacks, etc.(Randall et al. 2007). Incomplete knowledge, and assumptions become widely dispersedacross a large international and interdisciplinary set of experts, making modelling assump-tions very difficult to trace, even by those involved in the modelling process (Shackley et al.1998; Lahsen 2005). Lahsen (2005) points out that the complexity and resultant dislocatednature of knowledge bases makes the endeavour to involve modellers themselves incommunicating uncertainty, and facilitating co-production of knowledge, incredibly diffi-cult. The vast majority of the research activity that contributes this modelling takes place

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within institutions outside of Africa. The result is that even the experts find themselvesdetached from the production of the model, and the detailed understanding of the knowledgegaps in the process is not held by, or accessible to, any one scientist, research group or evenacademic discipline.

Problematically, this complexity and resulting dislocated knowledge base leaves the dooropen for politically motivated and biased claims and offers a source of legitimacy that isincreasingly captured by climate change deniers. This has implications for trust in and theacceptability of climate model output-based policies, and makes challenges to the accuracyof climate change projections difficult to defend against (Bulkeley 2001).

Particularly on the African continent, the messages about climate change that currentlyreach stakeholders such as smallholder farmers are incomplete and very difficult to processand act on. The complexity of conventional climate and crop modelling makes it incrediblydifficult for stakeholders to participate in the whole chain, from framing to projections andeventually interpretation of evidence. Whilst addressing ignorance might legitimize invest-ments in expanding conventional modelling and incorporating more parameters and systemdynamics within models, such an approach might be counter-productive in terms of dealingwith uncertainty and ambiguity. Adding complexity to models inevitably makes them lessaccessible to stakeholders.

4.3 Meeting the challenge

Meeting the particular challenge of democratising the science-policy space around agricul-tural adaptation will require:

1. More openness about the nature of climate and crop knowledge across the wholeproduction chain of climate-crop modelling, including the acknowledgement of fram-ings, methodological choices, values and assumptions, even if they are institutionalisedand widely accepted amongst the climate-crop modelling community

2. Finding ways to communicate these sources of incomplete knowledge beyond theclimate crop modelling community, to stakeholders such as farmers, in qualitative andunderstandable ways

3. Moving beyond a linear model of evidence speaking to policy to an acknowledgementof, and reflection on, the way in which policy also frames evidence

4. Involving stakeholders in deliberating over the framings, choices, values, and assump-tions that enter into both science and policy in an integrated way

From an evidence-based policy perspective it is easy to justify a focus on ‘gettingbetter observations… [to] provide better data for model calibration’ (Interview CS8)such that this ‘uncertainty’ is reduced and, over time, adaptation strategies can bebetter fit to a shrinking probability distribution curve. But due to inherent uncer-tainties, indeterminacies and value-judgements in climate and crop science, develop-ments in modelling should not be seen as progressing the technology to a statusbeyond ‘reality-based social and policy heuristics’ (Wynne and Shackley 1994), asthough becoming more than this (becoming ‘predictive truth machines’) is an achiev-able end. Instead, from an understanding that social and policy heurism is theirprimary function, climate models should be developed with policy in mind—i.e.designed and developed to serve particular policy purposes. If the purpose is toimprove adaptation policy then it is necessary to structure the modelling process soas to facilitate the participation of those that are to adapt; to produce knowledge thatis not necessarily complete, but is relevant and usable.

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5 Conclusion

Because of an inevitable reliance on assumptions, not only in the construction of climatemodels, but also in the evaluation and legitimization of their outputs, it is the utility, ratherthan the accuracy of outputs that should become the focus of critical reflection. Reducingignorance (particularly through improvements in observed data) about the agro-climate systemundoubtedly goes someway to improving the utility of model projections, but it can only go sofar. Whilst complex models undoubtedly produce policy relevant information, they do not holda monopoly over such information (both in terms of their techniques and outputs). Not onlydoes a more democratic approach to knowledge, one which draws on the experiential knowl-edge of ‘non-expert’ stakeholders, help to reduce certain areas of ignorance, it also helps toimprove the credibility of assumptions, choices, and outputs and, in doing so enhances thepolicy-utility of the evidence base that it generates. The suggestions made may go some waytowards better democratising the science-policy process and produce more socially appropriate,trusted and effective approaches to agricultural climate adaptation in Africa.

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