FACCE-JPI Knowledge Hub: MACSUR-Partner 25

Lead Research Organisation: Rothamsted Research
Department Name: Computational & Systems Biology


Continued pressure on agricultural land, food insecurity and required adaptation to climate change have made integrated assessment and modelling of future agro-ecosystems development increasingly important. Various modelling tools are used to support the decision making and planning in agriculture (van Ittersum et al., 2008, Brouwer & van Ittersum, 2010; Ewert et al., 2011). Crop growth simulation models are increasingly applied, particularly in climate change-related agricultural impact assessments (Rosenzweig & Wilbanks, 2010; White et al., 2011). Model-based projections of future changes in crop productivity, for instance, are made on the basis of understanding the physical and biological processes, such as how given crops respond to reduced water supply, warmer growing seasons or changed crop and soil management (Challinor et al., 2009; Challinor, 2011; Rötter et al., 2011a).

Even though most of crop growth simulation models have been developed and evaluated at field scale, and were thus not meant for large area assessments, it has become common practice to apply them in assessing agricultural impacts and adaptation to climate variability and change from field to (supra-)national scale (van der Velde et al., 2009; van Bussel, 2011). It has been hypothesized by various authors (e.g. Palosuo et al., 2011; Rötter et al., submitted; Asseng et al., in preparation) that many of those model applications involve huge uncertainties. Recently, there have been renewed efforts in improving the understanding and reporting of the uncertainties related to crop growth and yield predictions (Rötter et al., 2011a; Ferrise et al., 2011; Borgesen & Olesen, 2011). Comparison of different modelling approaches and models can reveal the uncertainties involved. Variation of model results in model intercomparisons involves also the uncertainty related to model structure, which is probably the most important source of uncertainty and most difficult to quantify. There is both, a need for quantifying the degree of uncertainty resulting from crop models as well as to determine the relative importance of their uncertainties in climate change impact assessment (e.g. Iizumi et al., 2011). That is, how much of the uncertainty can be attributed to climate models, crop models and other basic assumptions (e.g. in emission scenarios).

Such assessment of the relative importance of uncertainties and how to reduce them, is also at the core of "The Agricultural Model Intercomparison and Improvement project (AgMIP)" (www.agmip.org). AgMIP has identified three important thematic working groups cutting across trade, crop and climate modelling: they are (i) representative agricultural development pathways, (ii) scaling methods and (iii) uncertainty analysis. In that set-up, the global AgMIP initiative shows overlaps with the objectives and tasks defined for CropM, and with FACCE-MACSUR as a whole. However, CropM and FACCE-MACSUR as a whole have the ambition to go further in terms of developing climate change risk assessment methodology than AgMIP does in other parts of the globe. Also, the high density of crop and climate data in Europe will allow the analysis of scaling and model linking methods, and uncertainty which goes well beyond the capabilities of AgMIP in other world regions.

Model intercomparisons, when combined with experimental data of the compared variables, may also be used to test the performance of different models. Such intercomparisons can help to identify those parts in models that produce systematic errors and require improvements. There is currently a number of experimental data (for wheat and barley) available across Europe which may be used for model intercomparisons. Comprehensive data sets that would allow thorough comparisons are getting increasingly scarce and call for concerted efforts to develop such high quality data sets for different locations (agro-climatic conditions) and crops in Europe.

Technical Summary

Testing the validity of crop models for climate change impact requires consistent data sets for model input across a wide range of environmental conditions. In CropM we distinguish several work packages (WP). The aim of WP1 is to create a common protocol for model intercomparisons across a wide range of environmental conditions. Within WP2 the relevant data will be collected from partners and stored in a common database with web-based interface for easy access. Many methods of scaling up crop models from the field to the larger spatial and temporal scale have been developed, but systematic evaluations of these methods have rarely been performed. The aim of WP3 is to improve the application of crop models at different spatial (and temporal) scales in combination with models or data from other disciplines for assessing climate change impacts on food security. In order to responsibly inform decision making, research in WP4 will address issues involved to better identify, describe, quantify and communicate the sources of uncertainty in making projections of future crop response to climate change. Part of the uncertainty is related to climate projections, another part is related to projections regarding socio-economic, land use and other environmental factors that affect vulnerability of agriculture. In addition to the above research areas CropM will also ensure that the obtained scientific advances are communicated, shared and discussed with all CropM partners. WP5 will organise topical workshops on crop modelling, exchange of scientists and web-based courses. The engagement of CropM in cross-theme modelling excercises and the linking to decision makers will be ensured through WP6 which will support the formulation of policy questions for the different case studies.

Planned Impact

FACCE MACSUR Knowledge Hub will provide significant advance in the knowledge and methodologies for risk assessment for European agriculture and food security. The project will deliver an unprecedented detailed European fully integrated analysis. The greatest knowledge
advancement will stem from the interdisciplinary and diverse framework implemented within the project. This type of broad interdisciplinary study may lead to results that are significantly different in comparison to previous endeavours in this field. FACCE MACSUR Knowledge Hub requires a European rather than a national or local approach to achieve the objectives outlined in this proposal. Specific expertise is in certain cases only available in particular EU countries and would not be possible to find in individual countries. Additionally, the exchange skills and ToK between partners will improve the level of the scientific research and will respectively enhance the status of science in the EU. Most importantly, the dissemination of data and methodologies from this project will contribute to the objectives of the European Commission which aim to coordinate the European Science Initiative by encouraging and providing the necessary tools and support for high impact, substantial science. We will be able to highlight our results internationally and will further unify the European scientific community. Reproducible and reliable results, high impact publications and the dissemination of the results at international meetings will increase the profile of European Science and will consequently improve the ability of Europe to attract foreign researchers and result in a more competitive European Research Area. Adaptation to climate change to underpin food security is by nature transboundary.
Description We developed integration of climate change projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model ensemble with the LARS-WG weather generator, which delivers a powerful tool for the downscaling of large-scale climate projections from global climate models (GCMs) to local-scale climate scenarios for impact assessments. A subset of 18 GCMs from the CMIP5 ensemble and 3 Representative Concentration Pathways
RCP2.6, 4.5 and 8.5, were integrated with LARS-WG. For computationally demanding impact assessments, where it is not practical to explore all possible combinations of GCM × RCP, we proposed to use a climate sensitivity index to select a subset of GCMs which preserves the range of uncertainty found in CMIP5. This would allow us to quantify uncertainty in predictions of impacts resulting from the CMIP5 ensemble by conducting fewer simulation experiments. Using the Sirius wheat model, we developed a methodology for designing wheat ideotypes optimised for target environments. In a case study, we described the use of the Sirius wheat simulation model to design in silico wheat ideotypes that are optimised for future climates in Europe, sampling uncertainty in GCMs, emission scenarios, time periods and European locations with contrasting climates. Two contrasting GCMs were selected for the analysis, 'hot' HadGEM2-ES and 'cool' GISS-E2-R-CC. Despite large uncertainty in future climate projections, we were able to identify target traits for wheat improvement which may assist breeding for high-yielding wheat cultivars with increased yield stability.
Exploitation Route Our findings can be utilised by Governmental Agencies to formulate policies on climate change. Also, the wheat ideotypes concept can be integrated in breeding processes accelerating breeding for an uncertain condition of climate change.
Sectors Agriculture, Food and Drink,Environment

URL https://www.rothamsted.ac.uk/facilities-and-resources#DATAREPOSITORIESMODELSANDSOFTWARE-3
Title LARS-WG stochastic weather generator 
Description LARS-WG 6.0, a stochastic weather generator, is a computationally inexpensive downscaling tool to generate local scale climate scenarios based on global or regional climate models for impact assessments of climate change. LARS-WG has been used in more than 75 countries for research and education. The current version of LARS-WG incorporates climate projections from the CMIP5 ensemble used in the IPCC Fifth Assessment Report. LARS-WG has been well validated in diverse climates around the world. 
Type Of Technology Software 
Year Produced 2021 
Impact LARS-WG 6.0, a stochastic weather generator, is a computationally inexpensive downscaling tool to generate local scale climate scenarios based on global or regional climate models for impact assessments of climate change. LARS-WG has been used in more than 75 countries for research and education. 
URL https://zenodo.org/record/4572752
Title LARS-WG stochastic weather generator 
Description LARS-WG is a model simulating time-series of daily weather at a single site. It can be used: 1. to generate long time-series suitable for the assessment of agricultural and hydrological risk; 2. to provide the means of extending the simulation of weather to unobserved locations; 3. to serve as a computationally inexpensive tool to produce daily site-specific climate scenarios for impact assessments of climate change. LARS-WG version 5.0 includes climate scenarios based on 15 Global Climate Models (GCMs) which have been used in the IPCC 4AR (2007). This large dataset of future climate projections was produced by leading modelling groups worldwide who performed a set of coordinated climate experiments in which GCMs have been run for a common set of experiments and emission scenarios. Multi-model ensembles allow to explore the uncertainty in climate predictions resulting from structural differences in the global climate model design as well as uncertainty in variations of initial conditions or model parameters. The new version also improves simulation of extreme weather events, such as extreme daily precipitation, long dry spells and heat waves. LARS-WG has been well validated in diverse climates around the world. 
Type Of Technology Software 
Year Produced 2015 
Impact LARS-WG has been used in more than 65 countries for research and in several Universities as an educational tool. Climate scenarios generated by LARS-WG have been used in the impact assessments of climate change for the IPCC Assessment Report 5. 
URL http://www.rothamsted.ac.uk/mas-models/larswg
Title SIRIUS wheat simulation model 
Description Sirius is a wheat simulation model that calculates biomass from intercepted photosynthetically active radiation and grain growth from simple partitioning rules. Leaf area index (LAI) is developed from a simple thermal time sub-model. Phenological development is calculated from the mainstem leaf appearance rate and final leaf number, with the latter determined by responses to daylength and vernalisation. Effects of water and N deficits are calculated through their influences on LAI development and radiation-use efficiency. Sirius has been calibrated for several modern wheat cultivars and was able to simulate crop growth accurately in a wide range of conditions, including Europe, NZ/Australia and USA and under climate change 
Type Of Technology Software 
Year Produced 2015 
Impact Sirius is used in research by many scientists to understand crop responses to environmental variations, and in practice by farmers to optimize water and N management. 
URL http://www.rothamsted.ac.uk/mas-models/sirius
Title Sirius crop model 
Description Sirius is a process-based crop simulation model. Initially developed for wheat, Sirius has been extended to model inter-plant competition and other species. Sirius is a well validated model and was able to simulate accurately crop growth in a wide range of environments, including Europe, USA, New Zealand and Australia, and for experiments of climate change, e.g. Free-Air Carbon Dioxide Enrichment (FACE) experiments. 
Type Of Technology Software 
Year Produced 2021 
Impact Sirius wheat model is used as a research tool by scientists, and in practice by farmers to optimize crop management 
URL https://zenodo.org/record/4572624