FACCE MACSUR Reports (Modelling European Agriculture with Climate Change for Food Security)
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Wanting it all - is a stakeholders' Vision for Europen comaptaible with meeting Europe's food demand under high end climate change
Full text linkResponding to climate change requires a desirable endpoint or vision against which to plan adaptation and mitigation and to determine `success'. Climate change impact, adaptation and vulnerability (CCIAV) models are useful tools to assess the consequences of adaptation in reducing the potential impacts of climate change. However, to date most CCIAV studies have had two significant limitations - (1) the lack of a clearly defined Vision and (2) a lack of contextualisation of adaptation according to the constraints (and opportunities) of alternative socio-economic futures. This paper describes how a European integrated assessment platform (the IMPRESSIONS integrated assessment platform or IAP), downscaled European Shared Socio-economic Pathways (Euro-SSPs) and a structured stakeholder engagement process of adaptation planning and Visioning have been brought together to provide a rich understanding of the adaptation challenges facing Europe. A scenario-neutral multi-dimensional Vision for Europe in 2100 (encapsulating such factors as equity, lifestyle, governance, resilience, environment, food, water and energy) was derived by stakeholders, who then developed preliminary adaptation, mitigation and transformational pathways to achieve the Vision within the context of the individual Euro-SSPs. The multi-sectoral IMPRESSIONS IAP was then used to assess the ability of the pathways to achieve selected indicators of the Vision. The results demonstrate the very different challenges and opportunities for Europe over the coming century posed by the Euro-SSPs and the synergies and trade-offs between meeting Europe's food demand under climate change and other desirable aspects of the Vision
The impact of climate change on maize phenology in Poland under 10 different RCM scenarios
No full textMany of physiological processes that influences yielding are heavily dependent on phenological development. Climate change will impact not only planting and harvesting date of crops but also the length of particular phenological phase. Therefore study of impact of climate change on agriculture should begin with determine how climate change will effect on crop phenology.The aim of the study was to assess how maize phenology will change according to different RCM scenarios. The length of the development stages was estimated by phenological model implemented in HERMES model. The model was calibrated and validated for silage maize varieties FAO 230-250 using 13-year data set (2004-2016) from experimental site, located in Grabów (Masovian Voivodeship, Central Poland). Calibration has been made by establishment - in crop parameter file - measured temperature sum for each development phase.The analyses were performed for the time series from 1971-2050 from RCM scenarios biased, using local experimental data from the station located at experimental site. The RCM simulations were represented by ten Regional Models based on five different Global Models. Two Representative Concentration Pathways (RCP 4.5 and RCP8.5) were analysed and compared.Based on RCP4.5 the mean monthly air temperature for period between April and September will increase by 1.1˚C while RCP8.5 predicts 1.2˚C air temperature growth. In dependence on RCM scenario, changes in monthly mean air temperature between the base line period (1971-2020) and future climate (2021-2050) in RCP 4.5 range from 0.5˚C to 1.5˚C. Then according to RCP 8.5 the temperature will increase between 0.6˚C and 1.7˚C. The results demonstrate how to use different RCM and its basing can impact on maize phenology
Book of Abstracts
Full text linkConference Organising CommitteeMartin Banse Thünen Institute, GermanyFloor Brouwer Wageningen University and Research, NetherlandsKatharina Brüser Leibniz Centre for Agricultural Landscape Research, GermanyNandor Fodor University of Leeds, UKChristine Foyer University of Leeds, UKRichard Kipling Aberystwyth University, UKMartin Köchy Thünen Institute, GermanyClaas Nendel Institute of Landscape Systems Analysis, GermanyDaniel Sandars Cranfield University, UKNigel Scollan Queen's University, UKFranz Sinabell Austrian Institute of Economic Research, AustriaKairsty Topp Scotland's Rural College, UK
Assessing the role of farm-level adaptation in limiting the local economic impacts of more frequent extreme weather events in Dutch arable farming systems.
Full text linkThe expected increase in extreme events frequency is likely to considerably affect future crop productivity. Appropriate adaptation measures in agricultural systems should be identified according to the main climate risks expected in a region and taking into account the role of decisions made at the farm level. Yet, there is limited understanding of the interplay between local production capabilities, regional climatic changes and more general socio-economic conditions. We propose a method that combines local productivity factors, economic factors, crop-specific sensitivity to climatic extremes, and climate change scenarios, to assess future economic impacts of extreme events on agricultural systems. Our assessment is spatially explicit and uses discounted time series of cash flows taking into account expected impacts on yield and crop quality, to estimate changes in the expected net present value of agricultural systems. We also assess the economic feasibility of a portfolio of adaptation measures by considering their initial investments, annual costs, and effectiveness in reducing crop damage. We apply the method to investigate potential economic impacts of extreme events in arable farming systems in the Netherlands in period around 2050. We find that the expected increase in frequency can substantially undermine the economic viability of Dutch arable farming systems. The results indicate considerable differences among regions: some regions are severely impacted by all extremes, while others consistently demonstrate high resilience. Though the exact magnitude of the impacts remains highly uncertain, adaptation measures should nevertheless be regarded as no-regret strategies, since they alleviate both economic impacts and uncertainty around impact magnitude
Sensitivity of a grassland model ensemble to climate change factors: the MACSUR approach.
Full text linkIn grassland modelling, understanding feedbacks between grassland ecosystems and the atmosphere in the context of regional scale climatic changes is essential for the accurate quantification of ecosystem water and carbon (C) fluxes. Different grassland models respond differently to environmental conditions and climatic circumstances. To test the sensitivity of different models to changes in input variables, ensemble modelling approaches are used because they generate an expanded envelope of possible systemic outputs. Here, an ensemble modelling approach was applied to explore water and C fluxes from grasslands in Europe. Seven grassland models were run at nine long-term grassland sites representing a broad gradient of geographic and climatic conditions. It was assessed the sensitivity to climate change factors including precipitation (P), temperature (T) and atmospheric CO2 concentration [CO2]. Baseline weather series (including [CO2]=380 ppm) were modified by changing T and P by -25%, -10%, -5%, +5%, +10%, +25% of the observed standard deviation and [CO2] by +5%, +10%, +15%, +25%, +50%, +100%. The obtained multi-model responses for each driver showed different levels of sensitivity. Soil temperature and gross primary production (GPP) displayed strong sensitivity to air temperature and precipitation. Based on the multi-model median of model responses, altered scenarios of precipitation had an important effect on modelled evapotranspiration from grassland swards. In general, yield biomass and GPP increased with elevated levels of [CO2]. Rising T and [CO2] had a fundamental effect on the C cycling of terrestrial ecosystems. This study demonstrates the use of ensemble modelling to address critical issues of uncertainty associated with individual model predictions, and provides increased understanding of water and C fluxes in grasslands under climate change
Recovering the costs of irrigation water with different pricing methods under Climate Change: insights from a Mediterranean case study
No full textClimate change (CC) is likely to increase water requirements of crops, and thus irrigation water uses. European Water Framework Directive (WFD) asks to fully cover the costs for water services, while minimizing adverse environmental, social and economic impacts. Preference is given to pricing instruments that establish a direct linkage between water use and cost, i.e. the volumetric system. In Italy, most of the irrigation schemes are managed by Reclamation and Irrigation Boards (RIBs). RIBs impose fees on the associated farmers usually aimed to cover only water distribution costs (WDC), through pricing systems often different from the volumetric. The present analysis focuses on an area of insular Italy (Sardinia), where a RIB supplies irrigation water to the associated farms. Currently, an area-based pricing system is adopted that makes farmers cover part of the WDC. The rest is supplemented by Regional Authorities with a contribution to compensate for local orographic and climatic disadvantages. Our objective is to assess the economic, social and environmental impacts of alternative pricing systems, including the volumetric, under a scenario of near-future (2020-2030) CC. The simulations deal with four levels of cost recovery, that start from the current level and gradually come to cover the full cost for water services. We do this through an economic model calibrated using Positive Mathematical Programming (PMP), that accounts for the abolition of milk quotas, the 2014-2020 CAP reform and the trend of expansion of bioenergy crops that affected the study area in the last years. The results are expected to provide local stakeholders and policy-makers with useful indications for implementing the WFD, while not discharging the issues related to an effective adaptation to CC
Comparing the performance of nutritive value predictions in three timothy models
No full textGrasslands are the main source of energy and nutrients in ruminant production systems. Nutritive value of grasslands is in most feeding systems described based on energy, i.e. digestibility and cell wall content, and crude protein content of the feed and plays a significant role in the profitability of these production systems. Timothy (Phleum pratense L.) is a widely used forage grass grown either in pure stands or in mixtures with other forage grasses and legumes in cold-temperate regions of the world. Timothy management practices, including cultivar selection, cutting frequency, and fertilization are adapted to the climate and soil conditions as well as to the animal production system this grass is part of. Models exist that can simulate phenological development, dry matter growth, digestibility and nutritive value of timothy as a function of the weather, soil, and management practices. These models differ in how they represent plant processes related to nutritive value. An analysis of these differences is needed to identify the correct process representation, and requires comparing model outputs against data from experiments conducted under different climate, soil, and management conditions. The overall goal of this study was to compare the ability of three simulation models, BASGRA, CATIMO and STICS, to predict fibre and crude protein concentrations along with digestibility. Datasets covering a wide range of climate and soil conditions, cultivars, and management practices in major timothy grass production regions of Canada, Finland, Norway, and Sweden were used for model calibration and validation. Simulations results were then analysed to better understand the strengths and the weaknesses of the modeling approaches used in the evaluated models
When and why to predict using the mean or median of a crop multi-model ensemble
No full textThe systematic use of crop multi-model ensembles (MMEs) has recently become widespread. In these studies, it has often been noted that ensemble predictors, in particular the mean (emean) or median (emedian) of the ensemble simulated values, are in close agreement with observations. If this is the case in general, using ensemble predictors could be an important pathway to improved model predictions and as a consequence to more widespread use of crop models. However, only a single study has specifically targeted the quality of ensemble predictors, and that was based on only limited data.The purpose of this study was to analyze the behavior of the ensemble predictors over a much wider range of situations, and to propose a random effects statistical model of model error to explain and generalize the empirical findings. We analyze the results of applying MMEs to simulate five separate experiments, each designed to study the effects of a specific range of environmental conditions.The basic finding, which confirms and extends previous studies, is that emedian and emean are the best or among the best predictors for every experiment and every response variable considered. Emedian in most cases is preferred to emean, but the differences are small. The empirical results also show that emedian and emean in general have high skill values. Finally, the results show that the skill values increase with the number of models in the ensemble.The statistical model shows how these conclusions depend on overall bias of the models in the ensemble, and the variances of the random model effect, the treatment effect and their interaction
Modelling the impact of soil management on soil functions
No full textSoils are the central resource for the production of biomass and due to an increasing soil loss and an increasing demand for food and energy there is an enormous pressure on soils. Besides the importance of soils for biomass production there are other essential soil functions we would like to preserve. To render agricultural productions efficient and sustainable we need to develop model tools that are in the position to quantitatively predict the impact of a multitude of management measures on soil productivity and soil functions. These functions are considered as emergent properties produced by soils as complex systems. The major challenge is to handle the multitude of physical, chemical and biological processes interacting in a non-linear manner. There is a large number of validated models for specific soil processes. However, it is not possible to simulate soil functions by coupling all the relevant processes at the detailed (i.e. molecular) level where they are well understood. A new systems perspective is required to evaluate the ensemble of soil functions and their sensitivity towards external forcing. A second challenge is that soils are spatially heterogeneous systems by nature. Soil processes are highly dependent on the local soil properties and, hence, any model to predict soil functions needs to account for the specific site conditions. We propose a new model strategy based on a thorough analysis of the interactions between physical, chemical and biological processes considering their site-specific characteristics. Coupling of the observed nonlinear interaction may define the stability and the sensitivity of the systems with respect to soil productivity and soil functions. The presented approach has been developed in the framework of the BonaRes project funded by the by the German Ministry of Education and Research
Assessment of climate change impacts on SOC dynamic in rainfed cereal cropping systems managed with contrasting tillage practices using a multi model approach
Full text linkConservation tillage (i.e., reduced- RT and no till - NT) is frequently proposed as mitigation practices as it can contribute to increase soil organic carbon (SOC) compared to conventional mouldboard ploughing (CT). In this study, we assessed the long-term effects of different tillage management practices on crop yield and SOC stock dynamics in Mediterranean rainfed cereal cropping systems at current and future climate scenarios. We relied on data obtained from long term experiments (LTEs) coming from ICFAR network coupled with four simulation models (APSIM, DSSAT, EPIC, SALUS). Two LTEs dataset were used: AN (Ancona, Marche, 1994-2015) characterized by a two-year durum wheat-maize rotation (NT vs CT: 40 cm deep mouldboard ploughing) and PI2 (Pisa, Toscana) based on a maize continuous crop from 1994 to 1998 followed by a durum wheat-maize rotation (RT: 15 cm disc tillage; vs CT: 30 cm deep ploughing). Climate scenarios were generated by setting up a statistical model using predictors from ERA40 reanalysis and seasonal indices of temperature and precipitation from E-OBS gridded data for the period 1958-2010. The statistical downscaling model was applied to CMCC-CM predictors to obtain climate scenarios at local scale over the period 1971-2000 and 2021-2050 (RCP45 and RCP85 emission scenarios). The multi-model mean was able to better reproduce and with less uncertainty SOC dynamics than a single model, hence better SOC predictions are also expected to occur in the future assessment. Overall, our study showed a decrease of SOC stocks in both sites and tillage systems in future scenarios. However, even if conservation tillage was more affected by climate change losing more SOC than CT, these systems were still able to stock more soil organic carbon also under future scenarios