FACCE MACSUR Reports (Modelling European Agriculture with Climate Change for Food Security)
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Multi-scale Modelling of Adapting European Farming Systems
Full text linkEuropean farming systems are challenged by an increasing global population, income growth, dietary changes and last, but not least, by a changing climate threatening future harvests, especially through increased frequency and severity of extreme events such as drought and heat waves. Therefore, there is a clear need to sustainably intensify and effectively adapt agricultural systems to climate change. Yet, increase in food production and adaptation are just two of many claims on agriculture, which is also supposed to meet growing demands on feed, fibre and fuel and to play a key role in mitigating climate change. The multiple claims on ecosystem services expected from agri-ecological systems call for an integrated assessment and modelling (IAM) of agricultural systems to adequately evaluate the multiple dimensions of the potential impacts as well as promising adaptation and mitigation options. This includes agriculture's responses to global change in the context of other sustainability aspects. Biophysical and socioeconomic analyses need to be integrated across different disciplines and spatiotemporal scales. In recent years the agricultural systems modelling community has made great efforts to use harmonized climate change, socio-economic and agricultural development scenarios and run them through a chain of models, e.g. by selected ensembles of biophysical and economic models at multiple scales, from farm to global. In phase 2 (2015-17) the European MACSUR knowledge hub has put its main focus on the regional (sub-national) level in the EU, with due consideration of the whole farm context.
The aim of this paper is to compare three regional cases from the pool of MACSUR case studies across Europe, i.e. North Savo region in Finland, the Mostviertel region in Austria and the Oristanese region in Sardinia (Italy) representing different European farming systems along a north-south climatic gradient in Europe. These case studies represent a sample of some prominent farming systems, though only a fraction of a much larger diversity of farming and environmental conditions prevailing in Europe. We describe how adaptation options are analysed within an integrated set of linked models or model outputs combining information from different spatial scales, i.e. from region-specific crop, animal and farm level models to an analysis at regional and national level changes in agriculture and food production. First results show that adaptation to climate change affects agricultural production and farm income very differently. For some regions, e.g. in Finland there are both negative and positive effects while for the Sardinian case study adaptation to climate change have negative effects on farm income.
Biophysical models, especially crop simulation models are first applied to analyse climate change impacts on yield, water use, biomass etc. and provide the outputs (i.e. delta changes) as input to economic models that contain the regional specificities of the case studies. Likewise, biophysical models are applied to analyse effects of various adaptation and mitigation options to provide information on effects of management changes on reducing damage/loss or taking opportunities from climate (adaptation) or reducing greenhouse gas emissions (mitigation). The economic models analyse economic impacts, for example the viability of management changes at farm and regional scales. Farm and regional scale economic models, backed by more detailed data and regional expert knowledge, can supply better representations of developments in each of the regions than this could be done by larger-scale (e.g. EU-wide or global) models. Sector or national economy-wide models are less specific in technical changes in agriculture, productivity changes, or in its use of inputs, due to higher level of aggregation. Nevertheless the market level view offered by sector models put the farm level changes and adaptations in a wider global context. Agricultural markets are highly integrated globally and the analyses for the case study regions also require information on global and European market developments. For example, significant changes in food demand due to changes in tastes and preferences, including aspects of climate change mitigation, may imply major changes for regional production structures. In MACSUR, this information – although not fully implemented in the case studies yet – is provided by the economic agricultural sector model CAPRI. The main strength of CAPRI in this context is that it is a global model with European focus. As such CAPRI can capture global developments and translate them to the regional level in the EU. The coupled analysis using global, EU and national level models side by side with farm level models provides unique results and much more insights on future possibilities and challenges for farmers and the food chain, than separating and restricting the analyses to either low or high aggregation level analyses.
Market and policy changes often dominate longer term climate change considerations in the decision making of food chain actors, even if unfavourable weather events have become more common in recent years. Socio-economic scenarios from global to national and regional levels are needed to put adaptation and mitigation strategies in a wider context. Models, especially those that are able to accommodate biophysical, economic and policy changes are needed to show the value added from adaptations to climate change.
Benefits and costs of mitigation strategies may be highly dependent on market developments. The current integrated assessment and modelling approach of MACSUR focusses on adaptation scenarios. It will be extended for the analysis and impact of mitigation policies in a later phase
Drivers and trends for agricultural soil management – a foresight study for Germany
No full textClimate change is a strong driving force for agricultural soil management. However, adaptation pathways of agricultural management to climate change also depend on other, interacting driving forces. These include socio-economic drivers (consumer demand, factor costs, policies, farm(er)s' attributes), bio-physical drivers (land availability, soil degradation, resource scarcities) technological drivers (ICT & robotics, tillage, biomass utilization, research & monitoring). A decent understanding of such driving forces and how they might be translated into trends of soil management is necessary to inform scenario development and modelling for analyzing climate change adaptation in terms of yields, economic performance and environmental integration. We conducted a foresight review of driving forces and trends for soil management in Germany as an example. We distinguished between quantitative trends (namely intensification vs extensification) and qualitative trends in soil management. While quantitative trends have been addressed in modelling studies since long, qualitative trends imply a higher degree of uncertainty in terms of their characteristics and implications. We differentiate such qualitative trends into five categories: (i) Crops and rotations, (ii) mechanical pressures, (iii) inputs into the soil, (iv) spatial patterns of cropping systems, (v) general behavior concerning soil management. We outline possible developments of such management categories including preliminary uncertainty estimation and consequences for the integration of productivity performance with environmental concerns
Modelling nitrous oxide emissions of high input maize crop systems
No full textArable soils are a large source of nitrous oxide (N2O) emissions and several factors may affect the processes responsible of its production (nitrification and denitrification). In particular, forage crop systems for dairy farming are among the cropping systems with highest N input, mainly because they are based on high yielding forage grasses such as maize. A number of options have been explored to decrease the emissions but they remain site specific and are related to climatic, soil and local availability of management options. Moreover, guidelines for estimating N2O emission from agricultural soils does not take into account different crops, soils, climate and management, all of which are known to affect nitrification-denitrification and N2O production and emission.Process-based models represent a promising route to capture the spatial and temporal variability of N2O emissions, along with the effects of crop management. Nevertheless, the testing and comparison of these models have been limited to only a few works, with studies mainly based on biogeochemical models rather than process-based crop models. Furthermore, a multi-model ensemble analysis, which proved to be the best option for crop system analysis, has not been done extensively for the simulation of N2O emissions to addressing the various options for mitigations practices related to maize crop fertilization systems.Our objective is to evaluate the performances of several process-based models in simulating N2O emissions under different type, amount, rate of N fertilizer, i) quantify N2O emission, as a function of nitrogen inputs, across a wide range of soil types and environmental contexts; ii) assess the uncertainty in simulating N2O emissions, and iii) identify efficient mitigation of N-fertilized maize systems
Challenges and research gaps in the area of integrated climate change risk assessment for European agriculture and food security: FACCE MACSUR Policy Brief 3
Full text linkPriorities in addressing research gaps and challenges should follow the order of importance, which in itself would be a matter of defining goals and metrics of importance, e.g. the extent, impact and likelihood of occurrence. For improving assessments of climate change impacts on agriculture for achieving food security and other sustainable development goals across the European continent, the most important research gaps and challenges appear to be the agreement on goals with a wide range of stakeholders from policy, science, producers and society, better reflection of political and societal preferences in the modelling process, and the reflection of economic decisions in farm management within models. These and other challenges could be approached in phase 3 of MACSUR.Climate change will affect human well-being and welfare through the impact on agricultural production of food, feed, and bioeconomy resources and as well as on the ecosystem and social services of rural agriculture. Associations among the many facets of agricultural production are non-linear and involve synergies and tradeoffs. In addition, these associations may vary across a heterogeneous, large spatial and political arena like Europe. For improved assessments of climate change impacts, existing modelling and assessment methodologies will have to be extended (or in specific cases new ones developed) to accommodate these heterogeneities and interactions.Assessments at spatial scales of farm level or greater must include socio-economic aspects at time-scales greater than one year. At these scales, within-year and production-unit (plants, animals, plots) variation is dampened and variation in political settings, consumer attitudes and national economies, availability of resources, and value of products move to the fore.FACCE MACSUR researchers identified needs for research to improve integrated assessments for information of policy, producers, consumers in five areas: (a) assessment criteria, (b) generalization of existing and new knowledge, (c) political and societal settings, (d) on-farm processes (generation of outputs from available resources, including their variation and disturbances), and (e) assessing implications of sub-optimal and technology-improved food production for global food security
Modelling climate change adaptation in European agriculture: Challenges and priorities.
No full textUncertainty in climate change impact projections originates mainly from the inadequacies in structure and parameters of the impact model, climate change scenarios and other input data. Previous studies tried to account for the uncertainty from one or two of the major sources. Here, we developed a super-ensemble-based probabilistic projection to account for the uncertainties from three major sources comprehensively. We demonstrated the approach by assessing projected climate change impact on barley growth and yield in the Boreal and Mediterranean climatic zones in Europe using eight crop models and multiple sets of crop model parameters under three representative climate change scenarios for the 2050s. We further quantified and compared the contribution of crop model structure, crop model parameter and climate change scenario to the mean squared error using the multivariate analysis of variance. The projected changes in barley yield due to climate change by the 2050s ranged from -45.8% to +26.3% at Jokioinen, Finland and from -54.8% to +78.6% at Lleida, Spain, relative to 1981-2010 level. Based on the super-ensemble probabilistic projection, the median of simulated yield change was -3.8% and +7.5%, and the probability of yield decrease was 0.57 and 0.43 in the 2050s, at Jokioinen and Lleida, respectively. The contribution of crop model structure, crop model parameters, and climate change scenarios to the mean squared error was, respectively, 37%, 53%, and 2% at Jokioinen, and 46%, 40%, and 2% at Lleida, for our setting with just three different climate scenarios. The super-ensemble-based probabilistic approach can provide more useful information and better understanding of the uncertainties in climate change impact projections. and stakeholder interaction (e.g. communication with, relevance for). For operational and tactical strategies (changes in practice in response to daily, monthly, or seasonal variation in conditions) most challenges were technical, relating to limitations in the processes and mechanisms represented in models. For longer term strategic climate change adaptation, uncertainty about future socio-economic context (e.g. prices and regulation) and the impact of new adaptation options (e.g. appearance of new technologies) were highlighted. Progressively novel and far-reaching strategies increasingly challenge the scope of existing models. Whilst models vary in capacity, most modellers reported a potential to better characterise adaptation. However, costs (e.g. trade-offs with processing speed) and the fact that adaptation lies beyond the initial remit of many models mean that strategic prioritisation of adaptation as a focus for modelling is key to facilitating model development to support effective stakeholder choices
A scenario-neutral approach to understanding the regional land use change and food supply consequences of future climate and socio economic change
No full textEurope’s ability to feed its population depends on the balance of agricultural productivity (future climate, yields and land suitability) and demand (socio-economic and technology change such as population, food choice, imports, & environmental choices). Given the widely recognised future uncertainty in both of these, this presentation uses the IMPRESSIONS Integrated Assessment Platform (IAP), The IAP contains meta-models of optimal cropping and crop and forest yields derived from the outputs of the previously developed complex models (Audsley et al; 2015).The profitability of each land use is modelled for every soil in every 10 minute grid across Europe. Land use in a grid is then allocated based on profit thresholds. The model iterates the price of six commodity groups until demand is satisfied or cannot be met. The model has been systematically run with perturbations against the baseline of five key variables: annual temperature, annual precipitation, atmospheric carbon dioxide levels, European population, and finally, plant yield changes due to restrictions or genetic and technological developments. The land use results for each scenario are aggregated into 8 climatically-distinct regions. Contour plots are used to display impact response surfaces that demonstrate differing regional sensitivities and tipping points, affording insights into the regional opportunities and threats that the future may offer to European agriculture and forestry.
Multi-model approach for assessing sunflower food value chain in Tanzania
No full textSunflower is one of the major oilseeds produced in Tanzania, but due to insufficient domestic production more than half of the country's demand is imported. The improvement of sunflower food value chain (FVC) is important to ensure an increase on production, availability and quality of edible oil in Tanzania. Therefore, a conceptual framework can allow the combined use of different models to provide insights about the sunflower FVC. This research focuses on identifying the European models participating in the MACSUR project that can provide a better understanding of the sunflower FVC in Tanzania, especially within a context of food security improvement. A FVC scheme for Tanzania was designed with the main steps of sunflower production. Thereafter, the models used in two MACSUR themes (CropM and TradeM) were selected and placed along each step of the FVC. As result, the sunflower FVC in Tanzania was organized in five steps, namely natural resources (1), production (2), processing (3), trade (4) and consumption (5). The step 1 uses environmental indicators to analyse soil parameters (calculated using LPJmL), and part of the outputs will provide data for the step 2 of the FVC. In the production step, data from step 1, together with other inputs, will be used to run crop models (as HERMES, MONICA and APSIM) to analyse the impact on sunflower yield. Thereafter, outputs from step 2 can be used as input for socio-economic models (as MODAM or MagPIE) to estimate production costs and profit (step 3) and also to determine the market opportunity for the sunflower oil and the by-products (trade and consumption). Due to the large range of models, it is possible to assess significant part of the FVC, reducing the necessity of assumptions and improving the understanding of the FVC
Bringing together grassland and farm scale modelling. Part 1. Characterizing grasslands in farm scale modelling
Full text linkThis report provides an overview of how grasslands are represented in six different farmscale models represented in MACSUR. A survey was conducted, followed by a workshop in which modellers discussed the results of the survey, and identified research challenges and knowledge gaps. The workshop was attended by grassland as well as livestock specialists. The investigated models differed largely with respect to how grasslands were represented, e.g. as regards weather and management factors accounted for, spatial and temporal resolution, and output variables. All models had grassland modules that simulate DM yield and herbage N content (or crude protein (CP) content = N content x 6.25). Many models also simulate P content, whereas only one simulate K content. About half of the model simulate herbage energy value and/or herbage fibre content and fibre and/or dry matter digestibility. Critical input data required from grassland models to simulate ruminant productivity and GHG emissions at farm scale was identified by the workshop participants. The different types of input data required were ranked in order of importance as regards their influence on important system outputs. For simulation of ruminant productivity and GHG emissions, herbage DM yield was ranked as the most important input variable from grassland models, followed by CP content together with at least one variable describing herbage fibre characteristics. These findings suggest that work on improving the ability of the current grassland models with respect to simulation of fibre/energy should be prioritized in farm-scale modelling aiming at quantifying livestock production and GHG emissions under different management regimes and climate conditions. More work is also needed on model evaluation, a task that has not been prioritized yet for some models
Contribution of uncertainties from model structure, parameters and climate scenarios in climate change impact projections
No full textUncertainty in climate change impact projections originates mainly from the inadequacies in structure and parameters of the impact model, climate change scenarios and other input data. Previous studies tried to account for the uncertainty from one or two of the major sources. Here, we developed a super-ensemble-based probabilistic projection to account for the uncertainties from three major sources comprehensively. We demonstrated the approach by assessing projected climate change impact on barley growth and yield in the Boreal and Mediterranean climatic zones in Europe using eight crop models and multiple sets of crop model parameters under three representative climate change scenarios for the 2050s. We further quantified and compared the contribution of crop model structure, crop model parameter and climate change scenario to the mean squared error using the multivariate analysis of variance. The projected changes in barley yield due to climate change by the 2050s ranged from -45.8% to +26.3% at Jokioinen, Finland and from -54.8% to +78.6% at Lleida, Spain, relative to 1981-2010 level. Based on the super-ensemble probabilistic projection, the median of simulated yield change was -3.8% and +7.5%, and the probability of yield decrease was 0.57 and 0.43 in the 2050s, at Jokioinen and Lleida, respectively. The contribution of crop model structure, crop model parameters, and climate change scenarios to the mean squared error was, respectively, 37%, 53%, and 2% at Jokioinen, and 46%, 40%, and 2% at Lleida, for our setting with just three different climate scenarios. The super-ensemble-based probabilistic approach can provide more useful information and better understanding of the uncertainties in climate change impact projections
Using impact response surfaces to analyse the likelihood of impacts on crop yield under a changing climate.
No full textMost studies of future climate change impacts rely on estimates based on a limited set of projections of future climate. This way, it is not possible to determine whether one estimate is more or less likely than another. However, if future climate outcomes can be expressed probabilistically, this makes it possible to express impacts in terms of likelihoods, as demonstrated in this study.The approach involves overlaying joint probability density functions (pdfs) that describe uncertainties in projections of temperature and precipitation change over future time periods (using RCP-based climate model simulations) with impact response surfaces (IRSs). The IRS shows the modelled sensitivity of crop yield across a wide range of systematic changes in the same climate variables relative to the baseline (1981-2010). The likelihood of falling short of a target yield threshold is then calculated by integrating across the area of the pdf where yields are below the threshold. The WOFOST crop model was run for a locally grown cultivar of spring barley in south-west Finland assuming contrasting clay loam and sandy soils. IRSs were constructed for seven future CO2 concentrations representing time periods during the 21st century, so that the time-evolution of impact likelihoods with respect to mean yield levels and reliability can be presented. The effectiveness of adaptation options was demonstrated with simulations for cultivars with different development rates.The approach is an efficient way to summarise results and communicate them to a wider audience. Results indicate that the CO2 fertilisation effect counteracts the decline in yields with higher temperatures, and that a future switch to later maturing cultivars would lower the likelihood of a shortfall and produce higher yields