Crop modelling for integrated assessment of risk to food production from climate change

Environmental Modelling & Software xxx (2014) 1e17

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Crop modelling for integrated assessment of risk to food production from climate change* € tter b, M. Bindi c, H. Webber a, M. Trnka d, e, K.C. Kersebaum f, F. Ewert a, *, R.P. Ro J.E. Olesen g, M.K. van Ittersum h, S. Janssen i, M. Rivington j, M.A. Semenov k, D. Wallach l, J.R. Porter m, n, D. Stewart o, p, J. Verhagen q, T. Gaiser a, T. Palosuo b, F. Tao b, C. Nendel f,  d, S. Asseng s P.P. Roggero r, L. Bartosova a

University of Bonn, Institute of Crop Science and Resource Conservation (INRES), Crop Science Group, Katzenburgweg 5, 53115, Bonn, Germany €nnrotinkatu 5, 50100, Mikkeli, Finland MTT Agrifood Research Finland, Plant Production Research, Lo c University of Florence, Department of Agri-food Production and Environmental Sciences, Piazzale delle Cascine 18, 50144, Firenze, Italy d Department of Agrosystems and Bioclimatology, Mendel University in Brno, Zemedelska 1, 613 00, Brno, Czech Republic e Global Change Research Centre, Academy of Sciences of the Czech Republic, v.v.i., B elidla 986/4b, 603 00, Brno, Czech Republic f Leibniz Centre for Agricultural Landscape Research, Institute of Landscape Systems Analysis, Eberswalder Str. 84, 15374, Müncheberg, Germany g Department of Agroecology, Aarhus University, Blichers All e 20, P.O. Box 50, 8830, Tjele, Denmark h Plant Production Systems Group, Wageningen University, P.O. Box 430, 6700 AK, Wageningen, The Netherlands i Earth Informatics, Alterra, Wageningen University, P.O. Box 47, 6700 AA, Wageningen, The Netherlands j The James Hutton Institute, Craigiebuckler, Aberdeen, AB15 8QH, UK k Computational and Systems Biology Department, Rothamsted Research, Harpenden, Herts, AL5 2JQ, UK l INRA, UMR 1248 Agrosyst emes et d eveloppement territorial (AGIR), 31326, Castanet-Tolosan Cedex, France m Natural Resources Institute, University of Greenwich, Greenwich, UK n Faculty of Sciences, University of Copenhagen, Denmark o The James Hutton Institute, Dundee, DD2 5DA, Scotland, UK p Bioforsk, Norwegian Institute for Agricultural and Environmental Research, Nord Holt, Tromsø, Norway q Wageningen University and Research Centre, Plant Research International, P.O. Box 616, 6700 AP, Wageningen, The Netherlands r Nucleo di Ricerca sulla Desertificazione and Dipartimento di Agraria, University of Sassari, viale Italia 39, 07100, Sassari, Italy s Agricultural & Biological Engineering Department, University of Florida, Gainesville, FL, 32611, USA b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 7 February 2014 Received in revised form 7 November 2014 Accepted 2 December 2014 Available online xxx

The complexity of risks posed by climate change and possible adaptations for crop production has called for integrated assessment and modelling (IAM) approaches linking biophysical and economic models. This paper attempts to provide an overview of the present state of crop modelling to assess climate change risks to food production and to which extent crop models comply with IAM demands. Considerable progress has been made in modelling effects of climate variables, where crop models best satisfy IAM demands. Demands are partly satisfied for simulating commonly required assessment variables. However, progress on the number of simulated crops, uncertainty propagation related to model parameters and structure, adaptations and scaling are less advanced and lagging behind IAM demands. The limitations are considered substantial and apply to a different extent to all crop models. Overcoming these limitations will require joint efforts, and consideration of novel modelling approaches. © 2014 Elsevier Ltd. All rights reserved.

Keywords: Uncertainty Scaling Integrated assessment Risk assessment Adaptation Crop models

1. Introduction

* Thematic Issue for Environmental Modelling and Software “Agricultural systems modelling and software: current status and future prospects”. * Corresponding author. E-mail address: [email protected] (F. Ewert).

The use of dynamic, process-based crop and cropping system simulation models for climate change impact and risk assessment studies has become increasingly important (Tubiello and Ewert, € tter et al., 2002; Challinor et al., 2009a; White et al., 2011; Ro 2012a; Angulo et al., 2013b). Initiated by the pioneering work of

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F. Ewert et al. / Environmental Modelling & Software xxx (2014) 1e17

de Wit (1965) and Monteith (1965a), crop model development spans a period of nearly five decades (Donatelli et al., 2002; van Ittersum et al., 2003; Boote et al., 2013). Presently, a range of models at differing degrees of model complexity and emphasis on different research questions, crops and regions has become available (Challinor et al., 2009a; Soussana et al., 2010; White et al., 2011; Asseng et al., 2013). Most early crop models were not primarily developed for largearea climate change impact studies, but for application at the plot or field scale, with single crops and a limited range of management options over one or a few seasons. They were developed to integrate and document current understanding of crop physiology and its ability to quantify the effects of environment and basic management on crop productivity. More recently, emphasis has been placed on improving model flexibility to support the simulation of different crops, cropping systems and production situations (Donatelli et al., 2002; Keating et al., 2003; Adam et al., 2012a, 2012b; Brown et al., in this issue). Early applications of crop models in climate change impact studies were mainly site-based, referring to individual fields, to estimate the impacts of possible climate change on selected crops (Bindi et al., 1996; Semenov et al., 1996). Later efforts tried to assess climate change impacts for larger areas such as regions, nations, large watersheds and/or globally € tter et al., 1995; Easterling et al., (Rosenzweig and Parry, 1994; Ro 2007). Recently, crop modelling studies for climate impact research have become more elaborated (Eckersten et al., 2001; Tao € tter et al., 2012a; Elliott et al., et al., 2009b; Iizumi et al., 2011; Ro 2013; Hawkins et al., 2013b; Rosenzweig and Neofotis, 2013; Tao and Zhang, 2013a) and crop models for large area application were developed (Challinor et al., 2004; Bondeau et al., 2007; Tao et al., 2009a). To better understand the risks of climate change for crop and food production explicit attention has been given to issues of model uncertainty with specific emphasis on multi-model €tter et al., 2012b; ensemble simulations (Palosuo et al., 2011; Ro Asseng et al., 2013), up-scaling (Ewert et al., 2011), adaptations (Howden et al., 2007; Moriondo et al., 2010a; Lobell et al., 2011b) and the impact of extreme events (Challinor et al., 2005; Asseng et al., 2011; Moriondo et al., 2011; Eitzinger et al., 2013; Lobell et al., 2013; Tao and Zhang, 2013a; Teixeira et al., 2013). The complexity of climate change impacts and adaptations for managing climate risks and improving food security calls for more integrated modelling and quantitative assessment approaches that go beyond the sole biophysical aspects of crop and cropping systems as recently stressed by Wheeler and von Braun (2013) and the IPCC 2014 Working Group II report (Porter et al., 2014). During the 1990s, a few examples of integrated regional assessment modelling were reported in which crop model output was utilized systematically in assessing agricultural land use potential and constraints, and for optimizing land and resource use to meet multiple regional €tter et al., 2005), development goals (van Ittersum et al., 2004; Ro though not yet in the context of climate change. Later, integrated assessment modelling (IAM), see definitions in Jakeman and Letcher (2003) and Laniak et al. (2013), increasingly received attention in climate impact research (e.g. Lehtonen et al., 2010; Nelson et al., 2013) with crop models forming an integral part of the modelling chain (Bland, 1999; Harris, 2002; van Ittersum et al., 2008; Ewert et al., 2009; Bergez et al., in this issue). As part of this model integration, a number of issues (e.g. scale of application, integration of sub-models, uncertainty propagation) have become apparent that must be addressed to achieve a sound conceptual, methodological and technical integration of crop models within IAM for climate change risk assessment. Yet, the information on such limitations is fragmented and solid conclusions for crop modelling have not been drawn. This points to the need for a comprehensive overview of recent advances in crop modelling

contrasted with the requirements on crop models for use in IAM of risks to food production from climate change. Accordingly, the present study aims to (i) review the state of the art in crop modelling and (ii) characterize the demand of IAM on crop modelling for assessments of climate change risks to food production in the context of food security. The main focus is on food cropping systems, though it is expected that many issues explored here will also apply to arable systems for feed, fibre and bio-energy production and grasslands. First, the context of climatic change risk for crops and cropping systems is reviewed. Following this, a framework for conceptualizing integrated assessment modelling for climate change risk to food production is provided with a description of the current state of the art of crop models, as relevant for climate impact assessments. The final section summarizes the key requirements of IAM for crop models and their current state of development to meet these demands. Finally, key challenges and priorities for crop model improvement and development to better serve climate change risk assessment are identified and conclusions for future research are drawn. 2. Risks to food production from climate change 2.1. Framing climate risks Historical weather records show that global warming is causing changes in temperature and rainfall patterns and has increased the frequency and severity of extreme events (Lamb, 1995; Trenberth, 2011; Coumou and Rahmstorf, 2012; Field et al., 2012; Liu and Allan, 2013). Such changes are also projected by climate models for future conditions (Meehl et al., 2007a; Solomon et al., 2007; Rummukainen, 2012; Sloth Madsen et al., 2012; Taylor et al., 2012). How climate change and extreme weather events translate into agricultural risk depends on the magnitude, likelihood (frequency), and certainty of the impacts, as well as the system's vulnerability (i.e. ability to cope with its consequences) (e.g. Parry, 2007). As such, large impacts associated with increased climate variability (Lobell et al., 2011a; Gourdji et al., 2013; Lobell et al., 2013) and increases in mean temperatures for crops now grown at or near their thermal optimal (Porter and Semenov 2005, Hatfield et al., 2011; S anchez et al., 2014) pose an immediate source of risk to food production. Risk will, however, vary between crops and regions and with people's socio-economic conditions (Kates et al., 2012; Dow et al., 2013). The high degree of uncertainty in knowing what the mean and variation in climate variables will be in the future (Rummukainen, 2012; Sloth Madsen et al., 2012; Rummukainen, 2014) and what the resulting impacts on crops in combination with elevated CO2 concentrations €tter et al., 2013) poses considerable will be (Asseng et al., 2013; Ro challenges for planning and investments in development (Trnka et al., 2014). 2.2. Observed climate changes and impacts on crops While the past centuries have experienced relatively warm and cool periods, as well as periods with more variable and extreme weather than at present (Lamb, 1995; Büntgen et al., 2011), the recent rate of global warming is unprecedented. Globally, there is unequivocal evidence that recent decades have experienced record high temperatures (e.g. IPCC, 2007; Stocker et al., 2013) and mounting evidence that extreme events are occurring more frequently than they did during most of the 20th century (Alexander et al., 2006; Trenberth, 2011; Field et al., 2012; Handmer et al., 2012; Hansen et al., 2012; Seneviratne et al., 2012). These changes have had distinct implications for agricultural

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development, productivity and food security (Lamb, 1995; Coumou and Rahmstorf, 2012). In a global analysis, Lobell and Gourdji (2012) found that warming (average temperatures over the growing season of major cereals increased by about 0.75
C between 1980 and 2011) decreased wheat and maize yields by about 5% while elevated [CO2] increased yields of C3 crops by þ3%. Growing evidence suggests that at a regional scale, crop phenology (e.g. Siebert and Ewert, 2012; Palosuo et al., 2013), crop productivity (Battisti and Naylor, 2009; Reidsma et al., 2009; Schlenker and Roberts, 2009; Brisson et al., 2010; Schlenker and Lobell, 2010; Lobell et al., 2011a; Hawkins et al., 2013a; Lobell et al., 2013; Iizumi et al., 2014) and the relative area share of different crops (Elsgaard et al., 2012) have already been impacted in some regions by increasing temperatures and/or days with extreme high temperatures, with some investigation done into the relative sensitivity of crop productivity to climate, management and socio-economic factors across regions (Reidsma et al., 2009; Sacks and Kucharik, 2011; Himanen et al., 2013). In some high-yielding cereal production regions of the USA and China, farmers have successfully adapted cropping to warming trends by using later maturing crop cultivars and adapting sowing practices, for example (Sacks and Kucharik, 2011; Tao and Zhang, 2013b), and in some cases adaptation has even resulted in productivity gains (Ewert, 2012). In other world regions, there are few incentives and considerable constraints for farmers to adapt crop management to warming trends (Cairns et al., 2013; Palosuo et al., 2013; Iizumi et al., 2014; Webber et al., 2014a). Most climate impact research for crop production has focused on changes in temperature and precipitation, with other changes receiving little attention though potentially causing large negative impacts on crop production. For example, high ozone concentration near the earth surface can be harmful to crops (e.g. Lobell and Gourdji, 2012) and its concentration has increased considerably and will continue to do so in many areas around the world (Stevenson et al., 2006; Fagnano and Maggio, 2010). 2.3. Expected sources of future climate risks Climate projections reported in the IPCC Fifth Assessment Report (Stocker et al., 2013) suggest increased global temperatures of 0.3
e4.8
C by the end of the century across the CMIP5 (Climate Model Intercomparison Project Phase 5) group of global climate models (GCM) (Taylor et al., 2012). The variation in projected temperature increases is attributed to a combination of factors including variation in future socio-economic scenarios, as well as how individual GCMs represent and parameterize the biophysical drivers affecting the global climate system at a resolution that is computationally feasible (Rummukainen, 2014). Variation in projections, €nen and Ra €ty, 2013), inparticularly for climatic extremes (R€ aisa creases when moving from global to regional projections (Tebaldi et al., 2006; Rummukainen, 2014). The increased uncertainty is largely attributed to model structure and the parameterization of regional climate models (RCM) needed to capture regional-scale drivers of climate (e.g. West African monsoon, Baron et al., 2005). € m et al. (2013) For example, Sloth Madsen et al. (2012) and Kjellstro agreed that most climate models project the strongest warming for northeastern Europe up to the 2040s with increased precipitation in the north (mainly during winter) and decreased precipitation in the south. However, these projections are associated with large uncertainties in the spatial patterns of changes in temperature and precipitation for both summer and winter months as demonstrated in Sloth Madsen et al. (2012) using 11 RCMs (Fig. 1). Globally, in the IPCC Special Report on Climate Extremes, Field et al. (2012) assessed that the length, intensity and/or frequency of heat waves is very likely to increase (Tebaldi et al., 2006; Kharin

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et al., 2007; Meehl et al., 2007b; Orlowsky and Seneviratne, 2012). Changes in climate extremes can result from either changes in means, variance and/or a combination of the mean and distribution of a climate variable (Field et al., 2012), and studies considering only the changes in mean may reach incorrect conclusions about how changes will evolve (Ballester et al., 2010). Few model comparisons have been conducted at global scale to assess the uncertainties in projections for extreme events as compared to mean changes (Tebaldi et al., 2006; Kharin et al., 2007; Orlowsky and Seneviratne, 2012) with the conclusion that uncertainty in this respect is largely unknown. In addition to different model structures and parameters, uncertainty derives from the likely poor representation of some processes related to extremes in climate models (Field et al., 2012). 3. Framing integrated assessment for modelling climate change risk to food production Food security, climate change, loss of biodiversity, decrease of suitable land and water resources are among the challenges facing the global food system (Aggarwal et al., 2010; Hanjra and Qureshi, 2010; Foley et al., 2011; Beddington et al., 2012; Müller and LotzeCampen, 2012; Wheeler and von Braun, 2013). Interconnected in a multitude of aspects, all challenges are characterized by high levels of complexity (Pahl-Wostl, 2007) arising from interactions of biophysical, economic, political and social factors at various scales, as well as, competing value systems (Cash et al., 2003; Ericksen et al., 2009; McIntyre et al., 2009). Perhaps no problem more clearly exemplifies this complexity than the challenge of obtaining global food security (includes food production, access to food, stability of production and access, and food utilization) under climate change. This reality has led Porter et al. (2014) in the IPCC AR5 to state that assessments of climate change impacts on food security require the use of knowledge beyond biophysical impact models. Presently, many knowledge gaps exist regarding climate change impacts on food security, particularly related to impacts on food access (incomes, markets, trade) and changing nutritional status (Wheeler and von Braun, 2013). More broadly, consideration of a range of information and values is critical for dealing with uncertainty and understanding climate risks to food production, environmental quality and the dependent human systems, and for exploring possible adaptations (Beddington et al., 2012; Harrison et al., 2013). For example, increased climate variability is likely to lead to higher variability in crop performance (Gornall et al., 2010; Trnka et al., 2011). Coupled with input and output price fluctuations (HuchetBourdon and Korinek, 2010), various production risks may increase considerably (Bindi and Olesen, 2011) and profitability of agriculture may become more uncertain. Further, the high degree of uncertainty in expected temperature and precipitation changes implies investment risks for deciding on long term changes in farming technology such as adopting irrigation or switching to new crops, requiring new machinery, infrastructure, agronomic and market knowledge (Lehtonen et al., 2010). Integrated assessment (IA) is an attempt to improve the understanding of such challenges by providing a multi-level and interdisciplinary framework that brings together and synthesizes scientific knowledge from relevant disciplines (Jakeman and Letcher, 2003; Laniak et al., 2013) as well as explicitly making space for the inclusion of stakeholders and their values (Rotmans and Van Asselt, 1996; Harris, 2002; Parker et al., 2002; Matthews et al., 2008a; Sterk et al., 2011; Nguyen et al., 2014). The dominant tools of integrated assessments consist of modelling frameworks (IAM) (Rotmans and Van Asselt, 1996). Using a mix of disciplinary models and employing a systems approach (Ewert et al., 2009), IAM can quantitatively assess various sustainability

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Fig. 1. Projected changes in mean summer (JuneeAugust) temperature for the scenario period 2031e2050 as compared with the reference period 1975e1994. Note that similar patterns are seen for the five models using the ECHAM5 GCM. (source: Sloth Madsen et al., 2012, Fig. 2).

indicators that are used by decision makers in formulating policy responses (Alkan Olsson et al., 2009). Ideally, IAM bridge the necessary systems and scales to capture relevant system and scale interactions (Parker et al., 2002; Ewert et al., 2011), though in reality most fail to do so and focus on one scale only (van Ittersum et al., 2008). Recent exceptions are the IAM studies in the SEAMLESS (van Ittersum et al., 2008; Ewert et al., 2009) and CLIMSAVE (Harrison et al., 2013) projects. On their own, crop models can be used directly to investigate the impacts of climate change on potential crop productivity via perturbations in the mean and variability of climate variables such as temperature and precipitation (White et al., 2011) as well as their interaction with increasing atmospheric CO2 concentration. Input climate data may originate from either direct or derived use of outputs from GCM/RCMs (Hawkins et al., 2013b) or weather generators (Semenov and Barrow, 1997; Semenov and Stratonovitch, 2010). Most global or large area impact studies (Rosenzweig and Parry, 1994) have assumed optimal agronomic management which in all but the most intensive systems will bear little resemblance to the reality (economic or cultural) of actual cropping systems (Rosenzweig et al., 2013). Such crop model studies treat climate, soils and management factors as exogenous input variables to the system and do not account for dynamic feedbacks between crop productivity and input variables. Depending on the model employed, it may reflect limitations of the model, and in other cases, limitations of the simulation methodology or data availability. This is contrasted with IAM (Fig. 2) which can in theory both simulate dynamic feedbacks between crop growth, soil and water quality, and management, as well as explicitly account for the realistic range of factors (climate, markets, economics, resource availability and policies) that will influence future food production. For example, crop productivity is a function of cropping system management (Reidsma et al., 2009, 2010), with the historical yield

increases of past decades being largely driven by technology development (Reynolds et al., 1999; Ewert et al., 2005, 2006). As such, studies on future climate risk to crop productivity must be able to approximate future management, as influenced by a range of socio-economic, biophysical and subjective factors, to varying degrees affected by climate change themselves (Schmidhuber and Tubiello, 2007) (Fig 3). The need for IAM is further supported when climate risks to farms and total production are sought, as negative impacts on productivity create upward pressure on prices, with various feedbacks, such as resulting shifts in consumption, crop choices, areas, crop management, land degradation, resource availability, regulations and trade affecting production and supply of each crop (Britz et al., 2012; Nelson et al., 2013). Assessing the impacts of cropping system adaptations (Howden et al., 2007; Hall et al., 2012; Vermeulen et al., 2012; Kahiluoto et al., 2014) necessitates IAM, as possible adaptations range from incremental adjustments of current management to changes at the farm or even regional scale through adapting land use and income sources (Rickards and Howden, 2012) depending on perceived risks (Dow et al., 2013) or timeframes considered (Vermeulen et al., 2013). The value and potential of IA of climate impacts on agriculture and land use is increasingly stressed, with examples found at regional (Claessens et al., 2012; Wolf et al., 2012), national (Lehtonen et al., 2010; Kalaugher et al., 2013), continental (Ewert et al., 2005; Rounsevell et al., 2005; Wolf et al., 2012) and global scales (Rosenzweig and Parry, 1994; Fischer et al., 2005; Nelson et al., 2013). In IAM for crop production, common model components simulate crop growth, farm management and resource allocation, market and trade factors, and income at varying process/ decision levels (van Ittersum et al., 2008; Ewert et al., 2009). The dependence of agriculture on land and water resources, and its potentially negative impacts on them, results in the common inclusion of land use, erosion, hydrology and nutrient models in IAM for food production (Britz et al., 2012). The mix of disciplinary

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Fig. 2. Integrated Assessment and Modelling aims to capture context relevant interactions between different system components (embodied in different disciplinary models) at different spatial scales (implying different organizational levels with specific spatial and temporal scales). The case of climate risk for food production is illustrated here with arrows indicating directional linkages. Note that for crop productivity spatial scaling is indicated only for up-scaling, as crop productivity at larger scales is currently conceptually understood as aggregate productivity, though it is commonly approximated at larger spatial extents using regional mean characteristics.

models included in an IAM depends on the scale, region and particular problem considered. 4. Crop model application for climate change impact assessment 4.1. Types and structure of crop models Dynamic, process-based crop growth simulation models have been developed since the sixties (de Wit, 1965; Monteith, 1965a; Duncan et al., 1967) to better understand and manage crops and, increasingly, cropping systems. These models formalize our understanding and quantitative knowledge about how crops grow in response to weather, soil and management conditions and crop genetic characteristics. They provide information and are used at varying spatial scales, including field, regional and global scales € ckle et al., 2003; van (Jones et al., 2003; Keating et al., 2003; Sto Ittersum et al., 2003; Challinor et al., 2004; Bondeau et al., 2007; Tao et al., 2009a, 2009b; Kersebaum and Nendel, 2014). More complex crop and cropping system models are able to integrate processes of carbon (C), nitrogen (N) and water balance from planting to maturity, providing estimates of final yield and biomass production, as well as, daily values of crop and soil components (Boote et al., 2013). In addition, a few models also account for phosphorus (P) dynamics (e.g. EPIC Williams and Singh, 1995) and estimates of greenhouse gas fluxes (e.g. DNDC Li et al., 1992). Further, some models include crop-weed interactions (Kropff and Van Laar, 1993) and damage by pests (Kropff et al., 1995) and pathogens, but these are rather exceptions (Savary et al., 2006). Crop models differ in the level of detail at which bio-physical processes (e.g. phenology, photosynthesis, respiration, transpiration and soil evaporation) are simulated, and which production constraints are addressed (i.e. potential, and water and N limiting

productivities) (van Ittersum and Rabbinge, 1997; Rivington and Koo, 2010). The degree to which crop models can be considered deterministic versus semi-empirical varies along a spectrum with all widely applied models containing at least some empirical parameterization (i.e. including one or more simplified processes that require crop or region specific calibration). Most crop models apply either simple canopy radiation use efficiency (Monteith, 1977) or more detailed leaf photosynthesis approaches (Farquhar et al., 1980; Yin and Van Laar, 2005) to determine daily biomass production. Alternatively, the AquaCrop model (Steduto et al., 2009; Raes et al., in this issue) translates transpiration into biomass using conservative, crop-specific parameters. Functions for simulating potential evapotranspiration include the PriestleyeTaylor equation (Priestley and Taylor, 1972) or more physically based approaches such as the PenmaneMonteith equation (Monteith, 1965b). Similarly, soil water routines in crop models can range from simple capacity approaches (e.g. single bucket Sinclair and Amir, 1992; or multiple tipping buckets Ritchie, 1998) to more complex solutions of the Richards equation (Richards, 1931). Depending on model structure and level of process detail, crop models require different input variables (weather, soil and management) and simulate different outputs related to crops (biomass growth, leaf area index, evapotranspiration, grain yield and quality, etc.) and soils (soil moisture, soil nitrogen, nitrogen leaching, etc.). The models' scale of application determines if the outputs are meant to be representative of field, regional or global scales. 4.2. Recent developments in crop models for simulating climate change impacts Crop models typically respond to variability and change in weather and climate related to temperature, precipitation and radiation, and atmospheric [CO2] concentration (Table 1). The

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Fig. 3. Crop management is influenced by bio-physical, socio-economic and subjective factors (preferences/perceptions), many of which are sensitive to some degree, either directly or indirectly, to climate and weather.

different approaches used to simulate the response of photosynthesis and transpiration to increasing [CO2] have been reviewed before (Tubiello and Ewert, 2002) and are not repeated here. Presently, most crop models account to some degree for the effect of increasing [CO2]. All crop models consider temperature effects on various eco-physiological processes, including phenology, light Table 1 Requirements and appropriateness of crop models to simulate effects of relevant climate change factors. Requirements

Representation by major crop models

Ambient [CO2] effect

All major crop models include [CO2] effect but often in simplified form and based on old experimental data or not tested All major crop models represent temperature effects at different levels of detail, though often not tested Specific heat stress impacts (e.g. floret mortality leaf senescence) not considered explicitly (except for a few of the major models) and not tested yet Some models consider frost damage but are not tested Few of the major models explicitly includes O3 stress (except, AFRCWHEAT2-O3, LINTULCC) All crop models include effect of water and drought stress. Lack of oxygen in the root zone is only considered by a few models (HERMES, MONICA, Lintul, WOFOST) Only considered in a few crop models (CERES, SUCROS) Rarely taken into account, exceptions are available Detailed models for cereal lodging exist, but rarely integrated in crop models

Temperature

Heat stress

Early/Late frost damage Tropospheric O3 effect Drought stress and excess water

Diffuse radiation Effect of snow and hail Lodging due to strong winds and rain

utilization/photosynthesis/respiration processes and evapotranspiration. Only a few consider heat stress effects (occurring when maximum temperatures surpass critical thresholds) on accelerating leaf senescence and maturity of wheat (Asseng et al., 2011), or affecting floret mortality/spikelet fertility of various cereals, groundnut, sunflower etc. for periods of even a few hours around flowering (Matsui et al., 1997; Challinor et al., 2005; Moriondo et al., nchez et al., 2014; van Oort et al., 2014). While many 2011; Sa models contain optimum temperature ranges for photosynthesis, none explicitly consider damage done to the photosynthetic process by high temperatures (e.g. Al-Khatib and Paulsen, 1999). As discussed, crop models include soil water balance calculations at various degrees of detail and consider the impact of crop water shortage. However, there are distinct differences in how models distribute roots over depth, calculate soil water dynamics and estimate crop water extraction from soil (Wu and Kersebaum, 2008). Water shortage stress is generally simulated by using a stress index that can be the ratio of supply to demand rate, constrained to a maximum value of 1 (Ritchie, 1985) or a fraction of available soil water in the rooted soil (Stapper, 1984; Amir and Sinclair, 1991; Bindi et al., 2005; Steduto et al., 2009). The stress index is then applied in various ways to reduce biomass production by limiting leaf area expansion or accelerating leaf senescence, and/or, to reduce the photosynthetic rate or radiation use efficiency. Only a few models consider excess water and oxygen deficiency in the root zone, soil salinity and aluminium toxicity impacts on root and crop growth (Supit et al., 1994; Asseng et al., 1995; Kersebaum, 2007). Globally, the 1980's witnessed decreasing trends of solar radiation and alterations in the proportion of its' diffuse and direct components (Stanhill and Cohen, 2001). The decrease has been largely attributed to increases in aerosols (Stanhill and Cohen, 2001; Liang and Xia, 2005). However, there are indications that in the 1990's the trend was reversed for most world regions where adequate observational facilities exist (Wild et al., 2005). As a consequence of variations in direct solar radiation, the diffused

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light fraction also varies, which can change the radiation use efficiency of crops (Sinclair et al., 1992). This effect is only considered in some crop models (Ritchie, 1985; Ritchie et al., 1985; Spitters, 1986; Keating et al., 2001). Very few efforts are known to consider effects of frost, snow, hail, flood and wind in crop models. Finally, though it is known that even a few hours of elevated ozone concentrations can be very damaging to crops (e.g. Lobell and Gourdji, 2012), only a few models consider ozone effects on crop growth (Ewert and Porter, 2000; Ferretti et al., 2007). We briefly acknowledge other model developments not directly related to climate change impacts but indirectly improving the performance of models for a wider range of conditions. For example, a number of models simulate crop N stress under dry soils (e.g. STICS Brisson et al., 1998, van Ittersum et al., 2003; HERMES Kersebaum 2007) which is modelled using convectionediffusion approaches in conjunction with root length density distributions which can reflect N stress effects resulting in lower N use efficiency (Kersebaum, 1995; Svendsen et al., 1995; Brisson et al., 1998). Also, effects of other nutrients [e.g. P uptake and response to P-deficient soil and P fertilization into APSIM (Keating et al., 2003) and the DSSAT crop models (Dzotsi et al., 2010)] have been considered, as has the sensitivity of growth and transpiration to soil salinity (e.g. CROPGRO model, Webber et al., 2010). Other recent developments also refer to the modelling of crop management (Martin et al., 2012; Moore et al., in this issue) with links to farm systems models (Bergez et al. in this issue, Holzworth et al., in this issue), integration of biotic constraints due to plant diseases (Whish et al., in this issue), improved model calibration routines (Archontoulis et al., in this issue) and approaches for multi-scale application (Angulo et al., €ckle et al., in this issue). Such broadening of crop 2013a, 2013b; Sto model capabilities and applications is reflected in the evolution of a new generation of agricultural systems simulation models (Bergez et al., in this issue, Holzworth et al., in this issue). Progress has been made in testing crop models with field experiments, under a wide range of crops and growing conditions, as the foundation for climate change assessments, indicated by numerous publications. Effects of [CO2] on crops have been widely tested with Free Air Carbon-dioxide Enrichment (FACE) experiments (Grant et al., 1995; Kartschall et al., 1995; Bindi et al., 1996; Tubiello et al., 1999; Jamieson et al., 2000; Grossman-Clarke et al., 2001; Ewert et al., 2002; Asseng et al., 2004; Bannayan et al., 2005) and experiments with elevated [CO2] in open top chambers (Ewert et al., 1999; Rodriguez et al., 2001), also in interaction with ozone effects (Ewert et al., 1999). Several models have been tested with detailed field experimentation of wheat grown under drought (Jamieson et al., 1998; Asseng et al., 2004) and deficit irrigation in cotton (Farahani et al., 2009) and maize (Heng et al., 2009). However, multi-site, multi-year experiments to explicitly study the effects of climate variability and change are still scarce. 5. Requirements of IAM for crop modelling 5.1. General demands The role of crop models in IAM varies with the particular focus of the study and the scale considered. Examples are known where €tter crop models have been used as part of IAM at the farm (e.g. Ro and Van Keulen, 1997; Janssen and van Ittersum, 2007), catchment (e.g. Dono et al., 2013b), regional (e.g. Lu and Van Ittersum, 2004) and the global level (e.g. Fischer et al., 2005; Nelson et al., 2013). While process-based crop models are currently the primary scientific tools available to quantitatively evaluate potential impacts of climate change on cropping systems (White et al., 2011), important obstacles must be overcome before crop models (and their associated study methodologies) are truly able to accomplish this task

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€ tter et al., 2011a). In a recent review of (Challinor et al., 2009a; Ro crop models used in climate impact studies, White et al. (2011) highlighted that: i) most of the studies focused on a few crops (i.e. mainly wheat and maize, and to a lesser extent soybean and rice); ii) only a few studies considered more than conventional adaptation options (i.e. planting dates and cultivars) examining different tillage practices or crop rotations; and iii) risk was quantified mainly in relation to variability in yield or effects of water deficits. For biotic factors affecting variability, none of the publications provided a quantitative analysis of the effects of pests, two papers modelled the effects of disease (i.e. rice blast, Luo et al., 1998a, b), and only one paper examined the effects of weeds (i.e. red rice, Lago et al., 2008). The authors demonstrated that only 6% of the 221 analyzed studies partially or fully considered heat stress, shown in recent studies (Asseng et al., 2011; Semenov and Shewry, 2011) to be a serious limitation, together with inadequate process descriptions of drought stress, to the application of many crop models in climate impact studies. Expanding from such analysis, specifically considering crop models as part of IAM, important demands of IAM on crop models consist of i) scale and regional coverage, ii) number of crops, iii) responsiveness to climate change, iv) assessment variables, v) crop management for adaptation, vi) uncertainty and error propagation, vii) data demand and availability and viii) model integration (Table 2). Excepting the demands regarding the responsiveness to climate change factors, which have been discussed above (Section 4), these demands are described in the following section. 5.2. Specific demands and representation in crop models 5.2.1. Scale and regional coverage Depending on the issue and application, estimates of environmental, management and technology impacts on crop production might be required at various scales from field and farm to the global level, with multi-scale application of crop models frequently demanded in IAM (Robertson et al., 2013). For example, adaptation options such as changes in crop management should be evaluated at the farm level (Tittonell et al., 2007; van Wijk et al., 2009; Patt et al., 2010), particularly in low intensity situations. However, soil data are generally available at a much coarser resolution, and management data is rarely spatially explicit and therefore requires disaggregation (Ewert et al., 2011). To be useful to policy makers, production generally needs to be aggregated to larger administrative or economic units (Fischer et al., 2005; Wolf et al., 2012). However, Ewert et al. (2011) found that moving between scales was likely to add considerably to the uncertainty of IAM results. This finding was based on two case studies for IAM, in which the authors reviewed available scaling approaches such as extrapolation, interpolation and aggregation of input data, modification of model structure or parameters, derivation of response functions, and nested models (Ewert et al., 2011). Regarding the temporal scale, evaluation of the impacts of climate and adapted management on soil and water quality typically requires crop model simulations of biomass production for many years, with periods of at least 15 years suggested in the study of van Wijk et al. (2009) and van Wart et al. (2013a). Crop models have been designed to operate at the plot and field scale, and establishing guidelines for their application at larger scales is an active and on-going research area (van Bussel et al., 2011a; Nendel et al., 2013; van Wart et al., 2013b). Currently, the most promising areas of investigation for regional scale modelling include the use of simpler models with lower data demands (Adam et al., 2011; Angulo et al., 2013b) or even statistical models (Ewert et al., 2005; Rounsevell et al., 2005; Hermans et al., 2010) and various aggregation methods (Angulo et al., 2013b; Nendel et al.,

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Table 2 Requirements and appropriateness of crop models in IAM frameworks. Requirements

Representation by major crop models

General

Specific

Scale and regional coverage

Simulations at regional, continental and globals scale; multiscale simulations;multi-year to capture climate variability, long term dynamics; consistent regional coverage Multiple crops, i.e. 20 and more depending on the region Ambient [CO2] effect, temperature, heat stress, frost damage, tropospheric O3 effect, drought and excess water stress, effect of snow and hail, lodging due to wind and rain Crop yield, biomass, N and P cycle and losses, micronutrients deficiencies, carbon cycling and organic matter accumulation, soil salinization, acidification, waterlogged soils Field workability, tillage practice, crop rotation, fertilization, sowing date, irrigation, change of cultivar, change of crop, water savings techniques, nutrient optimization, pest, disease and weeds Outputs of crop yield and different processes

Crops Responsiveness to relevant climate change factors Assessment variables

Management for adaptation

Uncertainty and error propagation Data demand and availability Integration into IA framework

Calibration, evaluation and application

Conceptual, methodological and technical

2013; van Wart et al., 2013b). While assessments of crop management should generally be conducted at the field/farm level (Wheeler and von Braun, 2013), this is difficult due to the lack of spatially explicit management data (Ewert et al., 2011). Methods for the delineation of areas considered to have homogeneous climates with respect to the impact of input variables on simulated crop growth were recently reviewed (van Wart et al., 2013b), though most climate impact studies using crop models use political boundaries and/or provide no justification for their choice of data aggregation methods (Angulo et al., 2013b). Finding a balanced spatial resolution between site and meteorological data is challenging since recent regional scaling studies have shown that the highest resolution input does not always provide the best match to regional yields (Nendel et al., 2013). Regarding temporal scale, many crop models are capable of multi-year simulations. However, most process-based crop models simulate on a daily time step, and multi-year large area simulations can require considerable computing time. Economic models generally only require final yields in response to management and climate (Münch et al., 2014). However, van Bussel et al. (2011b) determined significant effects (overestimation) on crop growth when temporally aggregated weather data were used as inputs to crop models as compared to daily time series, likely related to the effect of averaging precipitation across sites. Furthermore, few models are able to simulate the consequence of long-term dynamics of some internal variables (e.g. soil organic matter) on soil physical and chemical fertility (e.g. soil hydraulic properties). This has led to the use of crop model simulations for developing statistical response curves from simulations as inputs to economic models (Claessens et al., 2012) or probability distributions (e.g. Dono et al., 2013a, b). The downside of such approaches, however, is the loss of the ability to capture feedbacks on potentially new management options (Ewert et al., 2011). As for spatial coverage, in a review of about 1200 publications between 2002 and 2011 (Trnka, personal communication), 20% of studies covered regions in both Americas and 21% covered parts of

Few crop models have been applied at different scales but not in a consistent way; no simultaneous cross-scale applications; no consistent regional coverage with comparable simulation quality Typically only major crops are simulated Major crop models generally responsive to many climate change factors, with capability in certain models for specific factors (e.g. lodging due to wind and rain) Crop models can simulate many assessment variables, but need to be calibrated for these; few models simulate comprehensive sets of assessment variables; other options are to link crop models to other model components designed and calibrated for these assessment variables Need for model improvement and testing for many management options; low intensity systems poorly represented

Possible for stand alone models (never done for crop models in IAM) Available for limited number of sites, usually representative of optimal management in agronomic experiments (barely done for larger areas due to data limitations); limited data for climate change situations Weak conceptual integration and various issues for methodological integration; no examples found with full dynamic interactions of relevant feedbacks due to excessive computing requirements and lack of conceptual and methodological integration

Asia (e.g. China and India were dominant with 9% and 6%). Africa was studied in 18% of the publications, Australia, and Oceania in 3%. The highest number of individual studies was found in Europe (27%) with 8% covering the whole of Europe (Trnka, personal communication). 5.2.2. Crops Global IAM generally requires information about twenty or more crops to represent key agricultural commodity crops (Nelson et al., 2013). In some regions, it is expected that climate change and expanded market opportunities and support (Thomas et al., 2007) will result in crops being grown in areas where they are currently not grown, such as maize in Northern Germany, Sweden and Finland (Olesen et al., 2011; Elsgaard et al., 2012) and tree crops at northern latitudes (Moriondo et al., 2013a, 2013b). Adaptation of crop rotations requires also the consideration of perennial crops, like alfalfa (Hlavinka et al., 2014) for which parameter values are rarely available even in generic crop growth models, although APSIM does include an alfalfa model (Monks et al., 2009). White et al. (2011) found that most model evaluations and testing in climate studies have been strongly biased towards wheat, maize, rice and soybean ignoring most of the commodity classes required in the global economic trade models. Only a few crop models allow simulations of more crops, and none fully matches the demands of economic models. 5.2.3. Assessment variables Assessing the sustainability and climate resilience of different crop production systems under future climates requires comprehensive accounting of the effects of agricultural land use and crop management on the immediate plant growth environment such as soil and water quality (e.g. soil acidity and salinity, groundwater depth, nitrogen leaching) and on gaseous emissions (e.g. ammonia, nitrous oxide and carbon dioxide). This requires that system behaviour be investigated across a range of assessment variables.

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Crop models must either themselves generate these assessment variables, or pass required output variables to other models in the IAM who will simulate them (Lehtonen et al., 2010). Most of the major crop models simulate the N cycle, though many do not account for P and K. Again, most are capable of simulating long-term effects of carbon cycling but relatively few take account of soil constraints such as salinity or acidity (Boote et al., 2013). However, in an IAM appropriate soil organic matter or hydrology models may be available to simulate such assessment variables. It is expected that hydrological and erosion models can be coupled to an IAM to account for a wider range of externalities such as soil loss due to changes in both timing of full canopy cover with warmer temperature and rainfall intensity, with resultant impacts on crop productivity (O'Neal et al., 2005). In these cases, the critical job for a crop model is to correctly simulate biomass production, water and nutrient uptake, though most models are generally not calibrated for the latter two variables. For example, in a recent maize model comparison of 23 models, large discrepancies were reported in simulated future transpiration amounts (Bassu et al., 2014). Some field-scale crop models also include outputs on N dynamics and yield quality aspects, including sugar and acid concentrations (Bindi and Maselli, 2001), grain protein (Asseng et al., 2002), grain protein composition (Martre et al., 2006) and undersized grains (Asseng et al., 2008). Outputs of particular importance for applications linking climate change impacts and adaptation to mitigation are C dynamics of both vegetation and soil related to CO2 fluxes (e.g. Gervois et al., 2008) and emissions of other greenhouse gases (CH4 and N2O) (e.g. N2O, Saggar, 2010). 5.2.4. Crop management for adaptation With the increasing acceptance of future climate change, the application of crop models in climate change impact assessments for informing adaptation strategies (Howden et al., 2007; Webber et al., 2014b) and for managing risk (Challinor et al., 2009a; Kahiluoto et al., 2014) has received more attention. Matthews et al. (2013) suggest that crop modelling can contribute to climate change impact assessments by: i) determining where and how well crops of the future will grow; ii) contributing to crop improvement programs; iii) identifying what future crop management practices will be appropriate; and iv) assessing risk to crop production in the face of present climate variability. However, while there are already uncertainties regarding potential impact that cannot fully be quantified, adaptation emerges from a dynamic co-evolution of bio-physical and socio-economic processes of which quantitative assessments can never be exhaustive (Ison, 2010). Within IAM, crop models need to capture feedbacks from the farming systems models. This implies they can simulate the yield, biomass, nitrogen and water use responses of crops to the altered management and soil conditions generated by other IAM models (van Ittersum and Rabbinge, 1997). Particularly for low intensity farming systems, they should be responsive to varying labour inputs for weeding, crop management impacts on plant diseases, the influence of soil improvement practices on soil water, C, N and crop growth. In world regions where strong gender differences exist in crop choices or division of manual labour (Giller et al., 2009), climate risk assessments should at least acknowledge if the knowledge and skills required to carry out the adaptations evaluated are, in reality, gender specific (Doss and Morris, 2000). Management options for intensive systems are represented to varying degrees in many crop models. Among the suggested adaptation strategies, planting date is the most frequently explored option. Changes in cropping systems (cultivars or rotation) are also investigated (Nendel et al., 2014), whereas new practices (e.g. type and distribution of fertilizers and tillage practices), interacting

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practices (e.g. planting dates and irrigation management) and improved crop characteristics are investigated only marginally (White et al., 2011). Recently, crop models have been applied with new cultivars and management practices to understand the changes needed in terms of growth duration and/or (drought or heat) stress tolerance and material input (water and nutrient) use to maintain or increase crop production under changed climatic conditions (e.g. € tter et al., 2011b; Ludwig and Asseng, 2010; Moriondo et al., 2011; Ro Semenov and Shewry, 2011; Nendel et al., 2014). On the contrary, key processes for low intensity systems are not well represented (e.g. labour invested in weeding) (Hertel and Lobell, 2013). Likewise, many models are not responsive to various mulching, species mixtures, intercropping and reduced tillage technologies. Climate impact study methodologies so far often fail to accommodate or recognize the reality of cropping system management and adaptations; for instance, they often optimize crop, cultivar or sowing dates to climatic factors, and ignore other drivers and constraints facing farmers (labour shortage, lack of capital or knowledge, cultivation schedule, damage from wildlife, gender, etc.) (Bryan et al., 2009; Gbetibouo et al., 2010; van Oort et al., 2012). In addition to the technical limitations of crop models, Patt et al. (2010) attributed the difficulties in modelling adaptations in IAM to the highly disaggregated nature of real adaptations that are undertaken at relatively local scales and based on a host of consid€ter, 2008). On the erations and imperfect knowledge (Patt and Schro other hand, the costs of adaptations are largely valued at national and international scales. Simplified approaches to simulate adaptations at such large scales are either implicit, to minimize damages, or explicit with gross approximations of both the costs and potential of adaptations; both methods are considered unsatisfactory, suggesting there may be little scope for global IAM to support adaptation decision making (Patt et al., 2010). Rather, Patt et al. (2010) suggested adaptation actions could be better supported through use of small-scale IAM tools to facilitate and guide discussions among stakeholders (Hedger et al., 2006; Matthews et al., 2008b, 2011). However, in IAM for climate risk assessment there is especially a need for new integrative assessment approaches and tools that link analysis at the global level with that at the farm, where the final decisions on agricultural production and resource management are taken (Mandryk et al., 2012). Our review suggests that linking robust agro-ecosystem models and farming system models, in combination with sound socio-economic data and meaningful future scenarios, could form the basis needed to understand the types of adaptation decisions farmers could make under future climatic and economic conditions. The uncertainty in future conditions (climatic, economic, etc) and farmer decision making under risk suggests a potential role for new approaches combining statistical bio-economic farming systems modelling and crop models in risk assessment (e.g. Lehtonen et al., 2010; Moriondo et al., 2010b). 5.2.5. Uncertainty and error propagation Consideration of uncertainty in IAM is complex, as it includes different types of uncertainty such as numerical uncertainty that can be statistically quantified (probability theory), scenario uncertainty, imperfect understanding of the issue (Refsgaard et al., 2007; Gabbert et al., 2010) with some potentially relevant unknowns €tter et al., 2012a). Assuming frequently ignored (Knutti, 2010; Ro that crop models are scientifically sound representations of crop growth and development (Walker et al., 2003), it is only quantification of the numerical uncertainty of their simulations that crop models must supply for IAM. However, even quantifiable uncertainty derives from many sources: its associated data, parameter,

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structure (Walker et al., 2003), and data scaling methods. Likewise, to quantify the uncertainty (in particular, bias and error) that emerges from crop models' inclusion in the IAM modelling chain and feedbacks introduced by linking to other sub-models also requires the assumption that the entire system represented (feedbacks and interactions) is based on sound scientific understanding, which is more difficult to assess for complex systems. With so many potential sources of uncertainty, Gabbert et al. (2010) and Laniak et al. (2013) have suggested that users of the IAM should determine what uncertainty information is important for them, and uncertainty analysis should then derive these measures. Currently, in crop modelling studies there are uncertainties associated with individual models and data (Palosuo et al., 2011; Angulo et al., 2013b; Asseng et al., 2013), with basically three ways of evaluating uncertainty in crop models. The first, which is the traditional that cannot be replaced by another, is through comparison of simulated and observed values, with the assumption that past errors are representative of the uncertainty in future simulations and is commonly done for crop models (Wallach et al., 2013). A major difficulty with this approach in the context of IAM of future climate effects is that past errors may not be representative of future errors (Refsgaard et al., 2014). A second approach is to evaluate the contribution of specific sources of error to model uncertainty (Walker et al., 2003; Palosuo et al., 2011). The major effort in this respect has been to evaluate the effects of uncertainty in model parameter values on uncertainty in predictions, through sensitivity analysis, or to use a Bayesian approach (for example, Wallach et al., 2012). Also, input uncertainty can be quantified through a sensitivity analysis in which, like for parameters, inputs are varied in multi-simulations. The third approach is to determine structural uncertainty by repeating the same simulation with multiple models representing a range of process modelling approaches. The use of model ensembles in crop model studies has been used to quantify uncertainty (Challinor et al., 2009b; Palosuo €tter et al., 2012b; Asseng et al., 2013; Bassu et al., et al., 2011; Ro 2014) indicating large uncertainties due to crop models. Asseng et al. (2013) suggested that at least five models are needed for robust climate change impact assessments of yield impacts for increases of up to 3
C and 540 ppm of CO2. Fewer models are needed for smaller changes and more models for greater changes in temperature. However, it is expected that the differences in the scale of data between sub-model components will also contribute to IAM uncertainty, as will the linkages between components if feedbacks exist (Ewert et al., 2011). Some caution is needed, however, to check whether and to what extent the sample of the ensemble is representative for the larger population of models/range of plausible simulation runs (Tebaldi and Knutti, 2007, Knutti, 2010). The ensemble modelling approach is easily extended to chains of models (for example, climate models feeding climate projections into crop models, and the latter feeding relative yield changes into economic models) (e.g. Nelson et al., 2013), and is therefore applicable to IAM. Although still rarely done, see Tao et al. (2009b) and Iizumi et al. (2011) for recent exceptions, this would be a way to understand model uncertainty introduced to IAM by crop models (Hoffmann and Rath, 2013). 5.2.6. Data demands and availability Input data need to be available to execute models at required scales. To be used in climate risk assessments, models need to be calibrated and evaluated at the places where they will be applied. It would be almost impossible to perform a calibration/evaluation of the entire IAM, but as its ultimate purpose is to facilitate policy decisions, it is important to acknowledge and communicate clearly any lack of formal testing and embrace IAM as discussion support tools (Parker et al., 2002).

One challenge in IAM is the need to have spatially explicit management data to capture management by soil interactions, which is rarely available at fine resolution to represent spatial variability in management (nor is fine resolution soil information available). Such data could improve the ability to simulate and evaluate cropping system adaptations at regional and global scales (Nelson et al., 2013). Also, other than crop yield and biomass production, many processes that crop models can simulate (e.g. N cycling or soil organic C dynamics) have not been adequately tested, often due to a lack of long-term data series which limits their utility in assessing other ecosystem services. An international network of long term agronomic experimental infrastructures is being developed through national and international projects to support model testing and development with long term data series (e.g. www. anaee.com, www.expeeronline.eu, www.icfar.it, www. globalresearchalliance.org). Individual models can be calibrated and evaluated for key crops, crop rotations and some locations, and it is increasingly recognized as critical to have quality datasets for € tter et al., 2011a; Rosenzweig et al., 2013). such purposes (Ro However, this is currently not feasible for many global applications. Finally, calibration and evaluation of the entire IAM is generally impossible due to large data requirements, and alternate measures should be devised to evaluate them appropriately (Parker et al., 2002), such as sub-model testing and plausibility analysis, among others (Bennett et al., 2013). 5.2.7. Integration Crop models need to conceptually link with the other knowledge contained in the IAM by providing required information to other sub-models and responding to information on management and relevant environmental feedbacks affecting crop growth. Methodologically, it is required that outputs from different model types (e.g. farm management model), which may be in the form of statistical probability distribution functions, can be linked in crop models which generally require discrete input values. Technically, crop models need to be compatible with the units and (spatial and temporal) scale of information coming from other IAM components. Crop models should also be available at the correct level of process detail such that they can capture the most important feedbacks (usually required at a daily time-step). Few examples are found in the literature offering a coherent basis for the conceptual linking (Harris, 2002) of models in IAM (Ewert et al., 2011; Janssen et al., 2011). Some of the challenging aspects of model integration for climate risk assessment relate to linking the farm and regional or global scales, as is the case for evaluating food production under adapted management (van Ittersum et al., 2008). Britz et al. (2012) discussed the conceptual challenge of different model components overlapping in endogenous variables, considered here for nitrogen leaching. The IAM may use output of the hydrology model to represent nitrogen leached, though the crop model will use its own value to determine nitrogen use and therefore yield, which the farming systems model would determine at optimal fertilization levels. Beyond redesigning component models such that they can be consistently integrated, crop model calibration for IAM should also include other assessment variables, particularly when they are simulated in more than one sub-model (e.g. ET, soil nitrogen content). The combined conceptual and technical challenges of linking crop models with other different disciplinary models has often led to IAM being suitable to limited problem constellations like food production (Fischer et al., 2005) or agricultural land use change (Britz et al., 2011), integrated river basin management (Gaiser et al., 2008) or food security in water scarce environments (Krol et al., 2003). However, generic modelling platforms using crop models have also been developed (van Ittersum et al., 2008; Ewert et al., 2009; Donatelli et al., 2012;

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Harrison et al., 2013; Wenkel et al., 2013; Audsley et al., in press) to respond to the need for science to support societal decision making with tools and processes adapted to complex problems, rather than trying to fit problems to the available tools (Parker et al., 2002; Meinke et al., 2009; Britz et al., 2012). However, their construction requires considerable technical effort to bring about model integration (Ewert et al., 2009; Britz et al., 2012). Another technical issue for crop model integration concerns capturing the relevant processes in the crop model without introducing excessive model run-times. One alternative approach is to use simplified versions of complex process based crop models (sometimes called metamodels or model emulators) which emulate the responses of the original process based model (Ferrise et al., 2011; Britz et al., 2012). The approach was demonstrated by Audsley et al. (in press) and Harrison et al. (2013), though it leads to a number of outputs with the reliability of the crop models being degraded in order to gain speed. It is likely that significant improvement of run time could be achieved through optimization of the whole model but a such step is often viewed as laborious and costly and does not lead to improvement of the model core capabilities. 5.3. Limitations in crop model development for use in IAM This qualitative evaluation shows the major limitations of crop models to be used in IAM for climate change risks to food production. There is little diversity among models with respect to the scale of application and factors considered, although model outcomes can differ significantly. Most models can be applied at field scale for potential, water and nitrogen limited conditions for the major crops and crop rotations. Calibration and evaluation are also typically performed and data for climate change situations is improving but still limited (Refsgaard et al., 2014). The scope of crop models can be characterised as too narrow to be applied in IAM (Fig. 4). Technically, expanding crop models to account for these limitations presents several challenges, including a lack of experimental data or scientific understanding of some of the processes involved. However, this review has highlighted that it is not possible to prioritize which of these limitations are most important to overcome, but rather that the demands on crop models in IAM is a function of the specific application. For example, in some low intensity farming situations, improving model response to pest, disease and weeds may be critical, whereas in

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other systems, these factors are less important or controlled by farmers. Further, data availability for model calibration and evaluation can vary depending on crop, scale, process and region. Quality checked national and international databases on soils, weather and crop management would greatly contribute to effective IAM. However, a few generic issues emerged which are anticipated to be important across applications and constitute challenging scientific questions. The first is related to the cross-scale nature of IA. Crop models have originally been developed for application at the field scale, with recent efforts focussing on how to appropriately use them at larger scales. The result is often that the suitability of model structures, parameters and data inputs depend on scale, partly because some of the major determining factors and processes that affect crop yield vary with scale. This raises the question as to whether more than one crop model (structure, parameters and/or data) should used in IAs conducted at multiple scales, with the requirement that their results converge when aggregated (or disaggregated) to the same scale. A second challenge is that of uncertainty and error propagation resulting from the combined use of emission scenarios/concentration pathways, climate projections and impact projections by crop models in the IAM. Further uncertainty is introduced with assumptions of technology development (see, Ewert et al., 2005) for considering adaptation options, that should be in line with the shared socio-economic pathways (Kriegler et al., 2012) chosen for the economic modelling in the IAM chain. Crop modelling studies are increasingly showing the large uncertainty in climate change impact studies attributable to model choice (Asseng et al., 2013; Bassu et al., 2014). However, the quantification of the uncertainty resulting from crop models in IAMs represents a computational and conceptual challenge due to the numerous combinations across various model components. The final challenge identified relates to the conceptual integration of crop models with farm systems, economic and environmental models in IAM e particularly in capturing feedbacks between system components under future conditions. For example, beyond the many uncertainties in future conditions, future management (e.g. nitrogen fertilization) will influence crop productivity in response to new climates. However, modelling crop response to climate requires estimating how crop management will change (i.e. nitrogen fertilization), in response to climate change, technological options available and changes in markets. Collectively, these are daunting tasks that need to be tackled, e.g. in the integrated regional

Fig. 4. A qualitative assessment of the ability of crop models to satisfy the demands placed on them by Integrated Assessment and Modelling of climate change risks to food production.

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assessments underway in projects such as MACSUR (see, www. macsur.eu) and AgMIP (www.agmip.org). It is expected that progress will be made to address these limitations through the joint international activities of e.g. MACSUR and AgMIP in developing and implementing consistent future agro-technological scenarios and multi-model ensemble runs for crops models embedded in broader IA models. 6. Conclusion Climate change represents increased risk to food production as more frequent extreme events like heat waves and droughts cause large negative impacts on crops, and the high degree of uncertainty in future climate conditions makes planned adaptation difficult. The global food system faces challenges related to food security, climate change, loss of biodiversity, decrease of suitable land and water resources. Understanding climate change risks to food production is an important part of addressing these challenges. It requires consideration of complex interactions of bio-physical, economic, political and social factors at various scales. Integrated assessments and modelling (IAM) provides a multi-level and interdisciplinary framework that brings together and synthesizes scientific knowledge from relevant disciplines. Crop models are seen as essential components of IAM but need to comply with the demands of IAM. There has been considerable progress in modelling climate change impacts on crops, but that progress largely refers to the improved responsiveness of crop models to climate change factors. This also includes recent efforts to improve model simulations of the effects of extreme events. However, many other aspects relevant for climate change risk assessment are less well represented. Large gaps still exist (Fig. 4) for the number of crops and assessment variables modelled, the multi-scale application of crop models including the consistent regional coverage, the consideration of management activities including technological development to evaluate adaptation options and the propagation of different sources of uncertainties. Even larger gaps exist for the integration of different data and modelling techniques in IAM (Fig. 4). Substantial progress is needed in different areas to address these challenges. Prioritisation of research activities to fill these gaps is difficult and will depend on the specific IA study. However, some generic issues that span across IA studies were identified. These relate to cross-scale application of crop models, uncertainty propagation and conceptual and methodological integration of crop models into IAM. Finally, the existence and maintenance of long-term observation networks at a regional and global scale is crucial for model improvement and reliable model integration. Any progress on these issues will be beneficial to improve the use of crop models in IAM of climate change risks to food production. Nevertheless, the limitations to be overcome are substantial and it is unlikely that the required progress can be achieved by any individual research group. Concerted international efforts are required to guide this process of crop model improvement including data gathering for IAM. These efforts should go beyond the crop model community and include expertise from other IAM modellers. Also, present approaches of using crop models across scales, and the adequacy of current assessment variables and management options needs revision. Alternative methods of combinations of statistical modelling with crop modelling, for example, may be promising and need further exploration. This may eventually lead to new types of crop models and a novel generic crop modelling methodology for future IAM. Application-tailored approaches to extend crop models and/or combine them with other tools to address specific problems of single IA studies will

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