•Statistical models were developed to emulate ensembles of process-based crop models.•They describe the between-crop model variability of the simulated yield data.•They can be used to compute mean ...yield loss and probabilities of yield loss.•Their interests were illustrated for maize, wheat, and rice.
Ensembles of process-based crop models are increasingly used to simulate crop growth for scenarios of temperature and/or precipitation changes corresponding to different projections of atmospheric CO2 concentrations. This approach generates large datasets with thousands of simulated crop yield data. Such datasets potentially provide new information but it is difficult to summarize them in a useful way due to their structural complexities. An associated issue is that it is not straightforward to compare crops and to interpolate the results to alternative climate scenarios not initially included in the simulation protocols. Here we demonstrate that statistical models based on random-coefficient regressions are able to emulate ensembles of process-based crop models. An important advantage of the proposed statistical models is that they can interpolate between temperature levels and between CO2 concentration levels, and can thus be used to calculate temperature and CO2 thresholds leading to yield loss or yield gain, without re-running the original complex crop models. Our approach is illustrated with three yield datasets simulated by 19 maize models, 26 wheat models, and 13 rice models. Several statistical models are fitted to these datasets, and are then used to analyze the variability of the yield response to CO2 and temperature. Based on our results, we show that, for wheat, a CO2 increase is likely to outweigh the negative effect of a temperature increase of +2°C in the considered sites. Compared to wheat, required levels of CO2 increase are much higher for maize, and intermediate for rice. For all crops, uncertainties in simulating climate change impacts increase more with temperature than with elevated CO2.
•Crop model ensemble size and composition affect the ensemble outputs.•Recommendations on adaptation are sensitive to model ensemble composition and size.•The new EOA index effectively measures the ...confidence level of recommendations.•Effective adaptation of wheat in the Mediterranean is feasible with high confidence.•The EOA index can be applied to assess confidence in many other contexts.
Climate change is expected to severely affect cropping systems and food production in many parts of the world unless local adaptation can ameliorate these impacts. Ensembles of crop simulation models can be useful tools for assessing if proposed adaptation options are capable of achieving target yields, whilst also quantifying the share of uncertainty in the simulated crop impact resulting from the crop models themselves. Although some studies have analysed the influence of ensemble size on model outcomes, the effect of ensemble composition has not yet been properly appraised. Moreover, results and derived recommendations typically rely on averaged ensemble simulation results without accounting sufficiently for the spread of model outcomes. Therefore, we developed an Ensemble Outcome Agreement (EOA) index, which analyses the effect of changes in composition and size of a multi-model ensemble (MME) to evaluate the level of agreement between MME outcomes with respect to a given hypothesis (e.g. that adaptation measures result in positive crop responses). We analysed the recommendations of a previous study performed with an ensemble of 17 crop models and testing 54 adaptation options for rainfed winter wheat (Triticum aestivum L.) at Lleida (NE Spain) under perturbed conditions of temperature, precipitation and atmospheric CO2 concentration. Our results confirmed that most adaptations recommended in the previous study have a positive effect. However, we also showed that some options did not remain recommendable in specific conditions if different ensembles were considered. Using EOA, we were able to identify the adaptation options for which there is high confidence in their effectiveness at enhancing yields, even under severe climate perturbations. These include substituting spring wheat for winter wheat combined with earlier sowing dates and standard or longer duration cultivars, or introducing supplementary irrigation, the latter increasing EOA values in all cases. There is low confidence in recovering yields to baseline levels, although this target could be attained for some adaptation options under moderate climate perturbations. Recommendations derived from such robust results may provide crucial information for stakeholders seeking to implement adaptation measures.
This paper presents the development of quantitative, spatially explicit and alternative scenarios of future agricultural land use in Europe (the 15 European Union member states, Norway and ...Switzerland). The scenarios were constructed to support analyses of the vulnerability of ecosystem services, but the approach also provides an exploration of how agricultural land use might respond to a range of future environmental change drivers, including climate and socio-economic change. The baseline year was 2000 and the scenarios were constructed for 3 years (2020, 2050 and 2080) at a spatial resolution of 10 min latitude and longitude. Time slices were defined for the climate scenarios as the 10 years before 2020, 2050 and 2080. The scenarios were based on an interpretation of the four storylines of the Special Report on Emission Scenarios (SRES) of the Intergovernmental Panel on Climate Change (IPCC) using a simple supply/demand model of agricultural area quantities at the European scale and the disaggregation of these quantities using scenario-specific, spatial allocation rules. The scenarios demonstrate the importance of assumptions about technological development for future agricultural land use in Europe. If technology continues to progress at current rates then the area of agricultural land would need to decline substantially. Such declines will not occur if there is a correspondingly large increase in the demand for agricultural goods, or if political decisions are taken either to reduce crop productivity through policies that encourage extensification or to accept widespread overproduction. For the set of parameters assumed here, cropland and grassland areas (for the production of food and fibre) decline by as much as 50% of current areas for some scenarios. Such declines in production areas would result in large parts of Europe becoming surplus to the requirement of food and fibre production. Although it is difficult to anticipate how this land would be used in the future, it seems that continued urban expansion, recreational areas (such as for horse riding) and forest land use would all be likely to take up at least some of the surplus. Furthermore, whilst the substitution of food production by energy production was considered in these scenarios, surplus land would provide further opportunities for the cultivation of bioenergy crops.
This paper presents a range of future, spatially explicit, land use change scenarios for the EU15, Norway and Switzerland based on an interpretation of the global storylines of the Intergovernmental ...Panel on Climate Change (IPCC) that are presented in the special report on emissions scenarios (SRES). The methodology is based on a qualitative interpretation of the SRES storylines for the European region, an estimation of the aggregate totals of land use change using various land use change models and the allocation of these aggregate quantities in space using spatially explicit rules. The spatial patterns are further downscaled from a resolution of 10
min to 250
m using statistical downscaling procedures. The scenarios include the major land use/land cover classes urban, cropland, grassland and forest land as well as introducing new land use classes such as bioenergy crops.
The scenario changes are most striking for the agricultural land uses, with large area declines resulting from assumptions about future crop yield development with respect to changes in the demand for agricultural commodities. Abandoned agricultural land is a consequence of these assumptions. Increases in urban areas (arising from population and economic change) are similar for each scenario, but the spatial patterns are very different. This reflects alternative assumptions about urban development processes. Forest land areas increase in all scenarios, although such changes will occur slowly and largely reflect assumed policy objectives. The scenarios also consider changes in protected areas (for conservation or recreation goals) and how these might provide a break on future land use change. The approach to estimate new protected areas is based in part on the use of models of species distribution and richness. All scenarios assume some increases in the area of bioenergy crops with some scenarios assuming a major development of this new land use.
Several technical and conceptual difficulties in developing future land use change scenarios are discussed. These include the problems of the subjective nature of qualitative interpretations, the land use change models used in scenario development, the problem of validating future change scenarios, the quality of the observed baseline, and statistical downscaling techniques.
Global food and feed demands have been projected to double in the 21st century, which will further increase the pressure on the use of land, water and nutrients. At the same time, the political ...decisions to support renewable energy sources are accelerating the use of biomass, including grain, sugar, oilseed, and lignocellulosic crops for biofuel and power generation. Government directives — incited by climate change, high oil prices and geo-political tensions — promote partial replacement of fossil fuel by biofuels. Prices and availability of commodities used as staple food and feed are becoming already affected by the growing demand for bioenergy. Many implications of this demand for biofuel on the resource base (land, water, biodiversity), environment, rural economy, food prices and social impacts are unknown. The present study reviews and discusses the opportunities and limits of crops and resources for food, feed and biofuel production. There are gaps in our knowledge regarding the global capacity for sustainable plant-based bioenergy production, while maintaining food security; commercial biomass production will compete with food crops for arable land and scarce fresh water resources. The rapidly growing demand for food, feed and fuel will require a combination of further increases in crop yields (ca. 2% per annum) and a doubling or tripling of resource-use efficiencies, especially of nitrogen-use efficiency and water productivity in production systems with high external inputs, over the next 20 to 30 years. Adaptation of cropping systems to climate change and a better tolerance to biotic and abiotic stresses by genetic improvement and by managing diverse cropping systems in a sustainable way will be of key importance. An integrated assessment of resource-use efficiencies, ecological services and economic profitability may guide the choice of crop species and cultivars to be grown in a target environment and region, depending on the added value for specific purposes: food, feed or fuel. To avoid negative impacts on food security, governments should give high priority to 2nd, 3rd and 4th generation technologies for bioenergy.
Crop simulation models are widely applied at large scale for climate change impact assessment or integrated assessment studies. However, often a mismatch exists between data availability and the ...level of detail in the model used. Good modelling practice dictates to keep models as simple as possible, but enough detail should be incorporated to capture the major processes that determine the system's behaviour. The objective of this study was to investigate the effect of the level of detail incorporated in process-based crop growth models on simulated potential yields under a wide range of climatic conditions. We conducted a multi-site analysis and identified that by using a constant radiation use efficiency (
RUE) value under a wide range of climatic conditions, the description of the process of biomass production may be over-simplified, as the effects of high temperatures and high radiation intensities on this parameter are ignored. Further, we found that particular attention should be given to the choice of the light interception approach in a crop model as determined by leaf area index (
LAI) dynamics. The two
LAI dynamics approaches considered in this study gave different simulated yields irrespective of the characteristics of the location and the light interception approaches better explained the differences in yield sensitivity to climatic variability than the biomass production approaches. Further analysis showed that differences between the two
LAI dynamics approaches for simulated yields were mainly due to different representations of leaf senescence in both approaches. We concluded that a better understanding and modelling of leaf senescence, particularly its onset, is needed to reduce model uncertainty in yield simulations.
The phenological development of cereal crops from emergence through flowering to maturity is largely controlled by temperature, but also affected by day length and potential physiological stresses. ...Responses may vary between species and varieties. Climate change will affect the timing of cereal crop development, but exact changes will also depend on changes in varieties as affected by plant breeding and variety choices. This study aimed to assess changes in timing of major phenological stages of cereal crops in Northern and Central Europe under climate change. Records on dates of sowing, flowering, and maturity of wheat, oats and maize were collected from field experiments conducted during the period 1985–2009. Data for spring wheat and spring oats covered latitudes from 46 to 64°N, winter wheat from 46 to 61°N, and maize from 47 to 58°N. The number of observations (site–year–variety combinations) varied with phenological phase, but exceeded 2190, 227, 2076 and 1506 for winter wheat, spring wheat, spring oats and maize, respectively. The data were used to fit simple crop development models, assuming that the duration of the period until flowering depends on temperature and day length for wheat and oats, and on temperature for maize, and that the duration of the period from flowering to maturity in all species depends on temperature only. Species-specific base temperatures were used. Sowing date of spring cereals was estimated using a threshold temperature for the mean air temperature during 10 days prior to sowing. The mean estimated temperature thresholds for sowing were 6.1, 7.1 and 10.1°C for oats, wheat and maize, respectively. For spring oats and wheat the temperature threshold increased with latitude. The effective temperature sums required for both flowering and maturity increased with increasing mean annual temperature of the location, indicating that varieties are well adapted to given conditions. The responses of wheat and oats were largest for the period from flowering to maturity. Changes in timing of cereal phenology by 2040 were assessed for two climate model projections according to the observed dependencies on temperature and day length. The results showed advancements of sowing date of spring cereals by 1–3 weeks depending on climate model and region within Europe. The changes were largest in Northern Europe. Timing of flowering and maturity were projected to advance by 1–3 weeks. The changes were largest for grain maize and smallest for winter wheat, and they were generally largest in the western and northern part of the domain. There were considerable differences in predicted timing of sowing, flowering and maturity between the two climate model projections applied.
Climate change is anticipated to affect European agriculture, including the risk of emerging or re-emerging feed and food hazards. Indirectly, climate change may influence such hazards (e.g. the ...occurrence of mycotoxins) due to geographic shifts in the distribution of major cereal cropping systems and the consequences this may have for crop rotations. This paper analyses the impact of climate on cropping shares of maize, oat and wheat on a 50-km square grid across Europe (45–65°N) and provides model-based estimates of the changes in cropping shares in response to changes in temperature and precipitation as projected for the time period around 2040 by two regional climate models (RCM) with a moderate and a strong climate change signal, respectively. The projected cropping shares are based on the output from the two RCMs and on algorithms derived for the relation between meteorological data and observed cropping shares of maize, oat and wheat. The observed cropping shares show a south-to-north gradient, where maize had its maximum at 45–55°N, oat had its maximum at 55–65°N, and wheat was more evenly distributed along the latitudes in Europe. Under the projected climate changes, there was a general increase in maize cropping shares, whereas for oat no areas showed distinct increases. For wheat, the projected changes indicated a tendency towards higher cropping shares in the northern parts and lower cropping shares in the southern parts of the study area. The present modelling approach represents a simplification of factors determining the distribution of cereal crops, and also some uncertainties in the data basis were apparent. A promising way of future model improvement could be through a systematic analysis and inclusion of other variables, such as key soil properties and socio-economic conditions, influencing the comparative advantages of specific crops.
Agriculture is interrelated with the socio-economic and natural environment and faces increasingly the problem of managing its multiple functions in a sustainable way. Growing emphasis is on adequate ...policies that can support both agriculture and sustainable development. Integrated Assessment and Modelling (IAM) can provide insight into the potential impacts of policy changes. An increasing number of Integrated Assessment (IA) models are being developed, but these are mainly monolithic and are targeted to answer specific problems. Approaches that allow flexible IA for a range of issues and functions are scarce. Recently, a methodology for policy support in agriculture has been developed that attempts to overcome some of the limitations of earlier IA models. The proposed framework (SEAMLESS-IF) integrates relationships and processes across disciplines and scales and combines quantitative analysis with qualitative judgments and experiences. It builds on the concept of systems analysis and attempts to enable flexible coupling of models and tools. The present paper aims to describe progress in improving flexibility of IAM achieved with the methodology developed for SEAMLESS-IF. A brief literature review identifying limitations in the flexibility of IAM is followed by a description of the progress achieved with SEAMLESS-IF. Two example applications are used to illustrate relevant capabilities of SEAMLESS-IF. The examples refer to (i) the impacts on European agriculture of changes in world trade regulations and (ii) regional impacts of the EU Nitrates Directive in combination with agro-management changes. We show that improving the flexibility of IAM requires flexibility in model linking but also a generic set up of all IA steps. This includes problem and scenario definition, the selection and specification of indicators and the indicator framework, the structuring of the database, and the visualization of results. Very important is the flexibility to integrate, select and link models, data and indicators depending on the application. Technical coupling and reusability of model components is greatly improved through adequate software architecture (SEAMLESS-IF uses OpenMI). The use of ontology strongly supports conceptual consistency of model linkages. However, the scientific basis for linking models across disciplines and scales is still weak and requires specific attention in future research. We conclude that the proposed framework significantly advances flexibility in IAM and that it is a good basis to further improve integrated modelling for policy impact assessment in agriculture.
•We assessed response of biomass to combination of management and climate variables.•Temperature increase directly influence crop yield under lowest level of soil fertility.•Water availability become ...to main limiting factor of yield under high soil fertility.•Millet production may decline under all fertilization methods due to climate change.
Effects of climate variability and change on yields of pearl millet have frequently been evaluated but yield responses to combined changes in crop management and climate are not well understood. The objectives of this study were to determine the combined effects of nutrient fertilization management and climatic variability on yield of pearl millet in the Republic of Niger. Considered fertilization treatments refer to (i) no fertilization and the use of (ii) crop residues, (iii) mineral fertilizer and (iv) a combination of both. A crop simulation model (DSSAT 4.5) was evaluated by using data from field experiments reported in the literature and applied to estimate pearl millet yields for two historical periods and under projected climate change. Combination of crop residues and mineral fertilizer resulted in higher pearl millet yields compared to sole application of crop residues or fertilizer. Pearl millet yields showed a strong response to mean temperature under all fertilization practices except the combined treatment in which yields showed higher correlation to precipitation. The crop model reproduced reported yields well including the detected sensitivity of crop yields to mean temperature, but underestimated the response of yields to precipitation for the treatments in which crop residues were applied. The crop model simulated yield declines due to projected climate change by −11 to −62% depending on the scenario and time period. Future crop yields in the combined crop residues+fertilizer treatment were still larger than crop yields in the control treatment with baseline climate, underlining the importance of crop management for climate change adaptation. We conclude that nutrient fertilization and other crop yield limiting factors need to be considered when analyzing and assessing the impact of climate variability and change on crop yields.
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