FACCE MACSUR Reports (Modelling European Agriculture with Climate Change for Food Security)
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Increasing wheat yield potential and stability under climate change will require tolerance to drought during reproductive development
No full textShort periods of extreme weather, such as a spell of high temperature or drought during a sensitive stage of development, could result in substantial yield losses due to reduction of sink capacity resulting from decrease in grain number and grain size. In a modelling study (Stratonovitch & Semenov 2015), heat tolerance around flowering in wheat was identified as a key trait for increased yield potential in Europe under climate change. Ji et all (Ji et al. 2010) demonstrated cultivar specific responses of yield to drought stress around flowering in wheat. They hypothesised that carbohydrate supply to anthers may be the key in maintaining pollen fertility and grain number in wheat. It was shown in (Nuccio et al. 2015) that genetically modified varieties of maize that increase the concentration of sucrose in ear spikelets, performed better under non-drought and drought conditions in field experiments. The objective of this modelling study was to assess potential benefits of tolerance to drought during reproductive development for wheat yield potential and yield stability across Europe. We used the Sirius wheat model to optimise wheat ideotypes for 2050 (HadGEM2, RCP8.5) climate scenarios at selected European sites. At those sites where water could be limited, ideotypes sensitive to drought produced substantially lower mean yields and higher yield variability compare with tolerant ideotypes. Therefore, tolerance to drought during reproductive development will be required for wheat cultivars optimised for the future climate in Europe in order to achieve high yield potential and high yield stability
Implications of input data aggregation on upscaling of soil organic carbon changes
No full textDynamic process models are increasingly used to predict changes in soil organic carbon (SOC) stocks of agricultural soils on the large scale. This study examines the aggregation effects of climate and soil data on regional SOC modeling for varying simulation periods based on a multi model ensemble. For a NUTS2 region in central Europe (North Rhine-Westphalia) data on soil properties and daily weather available on a spatial resolution of 1 km have been aggregated to 10, 25, 50 and 100 km resolution. Soil data aggregation (DA) showed a bigger effect on modeled SOC stock changes than climate DA, which was one order of magnitude smaller. The DA effect determine the spatial resolution of model output (scale of interest). Model errors, calculated as the difference between respective DA level and 1 km outputs, were high at low model output DA level (scale of interest: 1 km) and decreased with increasing scale of interest (10-100 km). Additionally, a large variability of simulated SOC contents amongst models was observed. Contrary to model errors induced by input DA, this variability was not leveled out by increasing the scale of output data. The regionalization of SOC stocks and changes is highly influenced by input DA. Factors like the length of the modeling period, the modeling region and the type of input DA control the resulting errors. The presented study describe a detail of these relationships
Probabilistic assessment of adaptation options from an ensemble of crop models: a case study in the Mediterranean
Full text linkUncertainty about future climate change impacts increases the complexity of addressing adaptation and evaluating risks at regional level. In modelling studies, such uncertainty may arise from climate projections, field data and crop models. Approaches are required for effectively quantifying climate impacts and the effect of adaptation options, managing inherent uncertainties and communicating the results. The latter will especially benefit from adding user-friendly visualizations.In this study, a probabilistic framework for evaluating the effect of feasible adaptation strategies for winter wheat in northern Spain was applied with an ensemble of crop models. First, adaptations response surfaces (ARSs) were created. These are bi-dimensional surfaces in which the effect of an adaptation option (e.g. changes in crop yield compared to the unadapted situation) is plotted against two explanatory variables (e.g. changes in temperature and precipitation). Based on these ARSs the most effective adaptations considered here were mainly based on wheat without vernalization requirements, current and shorter cycle duration and early sowing date. Other combinations of sowing dates and cycle duration were only promising and selected when a single supplementary irrigation was applied. Then, the likelihood of staying below a critical yield threshold with different adaptation measures was calculated using ARSs and probabilistic projections of climate change. The latter are joint probabilities of changes in the same explanatory variables used for drawing the ARSs. Therefore, for these options ARSs were constructed and probabilistic climate projections superimposed. Consequent probability of effectively adapting were discussed for several options
Spatial aggregation for crop modelling at regional scales: the effects of soil variability
No full textModelling agriculture production and adaptation to the environment at regional or global scale receives much interest in the context of climate change (CC). One concern is to take into account the spatial variability of the environmental conditions (e.g. climate, soils, management practices) used as model input because the impacts of CC on cropping systems depend strongly on the site conditions [1]. For example CC effects on yield can be either negative or positive depending on the soil type [2]. Additionally, the use of different methods of upscaling and downscaling adds new sources of modelling uncertainties [3].In the present study, the effect of aggregating soil data by area majority of soil mapping units was explored for regional simulations with the soil-vegetation model CoupModel for a region inGermany (North Rhine-Westphalia). Data aggregation effects (DAE) were analysed for wheat yield, water drainage, soil carbon mineralisation and nitrogen leaching below the root zone. DAE were higher for soil C and N variables than for yield and drainage and were strongly related to the presence of specific soils within the study region. These 'key soils' were identified by a model sensitivity analysis to soils present in the region. The spatial aggregation of the key soils additionally influenced the DAE. A spatial analysis of the pattern of these key soils (i.e. presence/ absence, coverage and aggregation) can help in defining the appropriate grid-resolution that would minimize the error caused by aggregated soil input data in regional model simulations. In a second step the method will be applied and evaluated with respect to another European region(Tuscany) which is characterised by a warmer and drier climate.[1] Kersebaum, K.C., Nendel, C., 2014. Site-specific impacts of climate change on wheat production across regions ofGermany using different CO2 response functions. Eur. J. Agron. 52, 22–32. doi:10.1016/j.eja.2013.04.005[2] Folberth, C. et al, 2016. Uncertainty in soil data can outweigh climate impact signals in global crop yield simulations. Nat. Commun. 7, 11872. doi:10.1038/ncomms11872[3] Ewert et al., 2011. Scale changes and model linking methods for integrated assessment of agri-environmental systems. Agric. Ecosyst. Environ. 142, 6–17. doi:10.1016/j.agee.2011.05.01
Towards sustainable livestock production systems: Analyzing ecological constraints to grazing intensity
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Local-scale CMIP5-based climate scenarios for MACSUR2
Full text linkClimate sensitivity of GCMs was used to select 5 GCMs from the CMIP5 ensemble for impact studies in MACSUR2. Selected GCMs for MACSUR2 are EC-EARTH (7), GFDL-CM3 (8,) HadGEM2-ES (10), MIROC5 (13), and MPI-ESM-MR (15). These GCMs are evenly distributed among CMIP5 (Fig 1) and should capture, in principal, climate uncertainty of the CMIP5 ensemble. Using 5 GCMs will enable us to assess uncertainties in impacts related to uncertainty in climate projections. The selection of GCMs in MACSUR2 has a good overlap with selections of GCMs used in CORDEX and AgMIP projects. We used the LARS-WG generator to construct local-scale CMIP5-based climate scenarios for Europe (Semenov & Stratonovitch, 2015). Fifteen sites were selected in Europe for MACSUR2. For each site and each selected GCM, 100 yrs climate daily data were generated by LARS-WG for RCP4.5 and RCP8.5 emission scenarios and for baseline and 3 future periods: near-term (2021-2040), mid-term (2041-2060) and long-term (2081-2100)
Representative Agricultural Pathways for Europe
Full text linkAgricultural aspects have been covered in the scenario process on shared socio-economic pathways (SSPs), but only to a limited extent. In order to analyze the future dynamics of agricultural development they need to be complemented and specified by Representative Agricultural Pathways (RAPs), which cover different aspects of agricultural development as for example European agricultural and domestic policy, environmental policies, different livestock management systems, cropping systems or irrigation efficiencies.In this paper we will develop a general framework for RAPs where we define for each SSP the corresponding specific agricultural development. Some aspects of the above mentioned specifics can be derived from the definitions in the SSPs, as for example irrigation efficiencies which are linked to technological development. Agricultural policies on the other hand are not included in the SSP definitions. Here we will define agricultural and environmental policies, including the available funding in each area of the common agricultural policy (CAP) (pillars 1 and 2). As RAPs can only to a small degree be developed as European guidelines and implemented unilaterally, it is important to translate the overall storylines into specific scenario parameterization at national levels. Concerned by this are 1. national policies, as well as the agri-environmental schemes of the CAP in Pillar II, 2. livestock efficiencies and the development of extensive and intensive farm management, and 3. crop management systems.Additionally we will define which respresentative concentration pathways (RCPs) will match best the future agricultural and agro-economic trajectories. The following 5 preliminary RAPs for Europe will be further developed in our analysis:EU-RAP1 (Sustainable Europe) : strong CAP, strong shift on environmental regulation, no producer support, green CAP with strong mititgation componentEU-RAP2 (Middle of the road): BAU or things will stay as they are.EU-RAP3 (Fragmented Europe): Europe breaks up, rich countries support farmers with national subsidies, poor countries do not. There is no CAP anymoreEU-RAP4 (Two Europes): Europe is divided in a poor and a rich part. In the rich part a green and environmental friendly CAP will be implemented, in the poor part of Europe, the CAP will cease to existEU-RAP5(Fossil fueled Europe): free market world, strong institutions, weak on enviromental regulations, low domestic polices? Local green CAP without mitigation
World food supply and water resources: an agricultural-hydrological perspective (AgroHyd)
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Yield potentials and yield gaps in soybean production in Austria - a biophysical and economic assessment
Full text linkcontext of analysis:• stakeholders. policy relevance: CC and protein crops• research problem:• how large is the yield gap and what can be done• data• approaches• findings• discussion and outlook yield gap analysis is a daunting task• what can be learned• economics matters: prices of crop and other crops• land expansion: more land becoming more marginal• management matters a lot but – not directly observable in data• significant knowledge gaps still there• way forward:• look at other crops• explore options to improve managemen
Modelling responses of forages to climate change with a focus on nutritive value
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