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Kahiluoto, H.; Kaseva, J.; Hakala, K.; Himanen, S.J.; Jauhiainen, L.; Rötter, R.P.; Salo, T.; Trnka, M. |
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Title |
Cultivating resilience by empirically revealing response diversity |
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Journal Article |
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Year |
2014 |
Publication |
Global Environmental Change |
Abbreviated Journal |
Glob. Environ. Change |
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25 |
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186-193 |
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Keywords |
generic approach; climate change; food security; agrifood systems; cultivars; adaptive capacity; climate-change; functional diversity; plant-communities; genetic diversity; biodiversity; ecosystems; management; redundancy; evenness; weather |
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Abstract  |
Intensified climate and market turbulence requires resilience to a multitude of changes. Diversity reduces the sensitivity to disturbance and fosters the capacity to adapt to various future scenarios. What really matters is diversity of responses. Despite appeals to manage resilience, conceptual developments have not yet yielded a break-through in empirical applications. Here, we present an approach to empirically reveal the ‘response diversity’: the factors of change that are critical to a system are identified, and the response diversity is determined based on the documented component responses to these factors. We illustrate this approach and its added value using an example of securing food supply in the face of climate variability and change. This example demonstrates that quantifying response diversity allows for a new perspective: despite continued increase in cultivar diversity of barley, the diversity in responses to weather declined during the last decade in the regions where most of the barley is grown in Finland. This was due to greater homogeneity in responses among new cultivars than among older ones. Such a decline in the response diversity indicates increased vulnerability and reduced resilience. The assessment serves adaptive management in the face of both ecological and socioeconomic drivers. Supplier diversity in the food retail industry in order to secure affordable food in spite of global price volatility could represent another application. The approach is, indeed, applicable to any system for which it is possible to adopt empirical information regarding the response by its components to the critical factors of variability and change. Targeting diversification in response to critical change brings efficiency into diversity. We propose the generic procedure that is demonstrated in this study as a means to efficiently enhance resilience at multiple levels of agrifood systems and beyond. (C) 2014 The Authors. Published by Elsevier Ltd. |
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0959-3780 |
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CropM |
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no |
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MA @ admin @ |
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4525 |
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Bindi, M.; Palosuo, T.; Trnka, M.; Semenov, M.A. |
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Title |
Modelling climate change impacts on crop production for food security INTRODUCTION |
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Journal Article |
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Year |
2015 |
Publication |
Climate Research |
Abbreviated Journal |
Clim. Res. |
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65 |
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3-5 |
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Keywords |
Crop production; Climate change impact and adaptation assessments; Upscaling; Model ensembles |
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Process-based crop models that synthesise the latest scientific understanding of biophysical processes are currently the primary scientific tools available to assess potential impacts of climate change on crop production. Important obstacles are still present, however, and must be overcome for improving crop modelling application in integrated assessments of risk, of sustainability and of crop-production resilience in the face of climate change (e.g. uncertainty analysis, model integration, etc.). The research networks MACSUR and AGMIP organised the CropM International Symposium and Workshop in Oslo, on 10-12 February 2014, and present this CR Special, discussing the state-of-the-art-as well as future perspectives-of crop modelling applications in climate change risk assessment, including the challenges of integrated assessments for the agricultural sector. |
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2016-10-31 |
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0936-577x |
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Editorial Material |
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CropM, ftnotmacsur |
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MA @ admin @ |
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4785 |
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Kersebaum, K.-C.; Wallor, E.; Ventrella, D.; Cammarano, D.; Choucheney, E.; Ewert, F.; Ferrise, R.; Gaiser, T.; Garofalo, P.; Giglio, L.; Giola, P.; Hoffmann, M.; Laan, M.; Lewan, E.; Maharjan, G.R.; Moriondo, M.; Mula, L.; Nendel, C.; Pohankova, E.; Roggero, P.P.; Trnka, M.; Trombi, G. |
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Title |
Comparison of site sensitivity of crop models using spatially variable field data from Precision Agriculture |
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Report |
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2017 |
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FACCE MACSUR Reports |
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10 |
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C1.1-D2 |
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Site conditions and soil properties have a strong influence on impacts of climate change on crop production. Vulnerability of crop production to changing climate conditions is highly determined by the ability of the site to buffer periods of adverse climatic situations like water scarcity or excessive rainfall. Therefore, the capability of models to reflect crop responses and water and nutrient dynamics under different site conditions is essential to assess climate impact even on a regional scale. To test and improve sensitivity of models to various site properties such as soil variability and hydrological boundary conditions, spatial variable data sets from precision farming of two fields in Germany and Italy were provided to modellers. For the German 20 ha field soil and management data for 60 grid points for 3 years (2 years wheat, 1 year triticale) were provided. For the Italian field (12 ha) information for 100 grid points were available for three growing seasons of durum wheat. Modellers were asked to run their models using a) the model specific procedure to estimate soil hydraulic properties from texture using their standard procedure and use in step b) fixed values for field capacity and wilting point derived from soil taxonomy. Only the phenology and crop yield of one grid point provided for a basic calibration. In step c) information for all grid points of the first year (yield, soil water and mineral N content for Germany, yield, biomass and LAI for Italy) were provided. First results of five out of twelve participating models are compared against measured state variables analysing their site specific response and consistency across crop and soil variables. (Main text to be published in a peer-reviewed journal) |
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CropM |
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MA @ admin @ |
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4951 |
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Yin, X.G.; Kersebaum, K.C.; Kollas, C.; Manevski, K.; Baby, S.; Beaudoin, N.; Ozturk, I.; Gaiser, T.; Wu, L.H.; Hoffmann, M.; Charfeddine, M.; Conradt, T.; Constantin, J.; Ewert, F.; de Cortazar-Atauri, I.G.; Giglio, L.; Hlavinka, P.; Hoffmann, H.; Launay, M.; Louarn, G.; Manderscheid, R.; Mary, B.; Mirschel, W.; Nende, C.; Pacholskin, A.; Palosuo, T.; Ripoche-Wachter, D.; Rotter, R.P.; Ruget, F.; Sharif, B.; Trnka, M.; Ventrella, D.; Weigel, H.J.; Olesen, J.E.; Yin, X.; Kersebaum, K.C.; Kollas, C.; Manevski, K.; Baby, S.; Beaudoin, N.; Ozturk, I.; Gaiser, T.; Wu, L.; Hoffmann, M.; Charfeddine, M.; Conradt, T.; Constantin, J.; Ewert, F.; de Cortazar-Atauri, I.G.; Giglio, L.; Hlavinka, P.; Hoffmann, H.; Launay, M.; Louarn, G.; Manderscheid, R.; Mary, B.; Mirschel, W.; Nende, C.; Pacholskin, A.; Palosuo, T.; Ripoche-Wachter, D.; Roetter, R.P.; Ruget, F.; Sharif, B.; Trnka, M.; Ventrella, D.; Weigel, H.-J.; Olesen, J.E. |
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Title |
Performance of process-based models for simulation of grain N in crop rotations across Europe |
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Journal Article |
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Year |
2017 |
Publication |
Agricultural Systems |
Abbreviated Journal |
Agric. Syst. |
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Volume |
154 |
Issue |
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63-77 |
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Keywords |
Calibration, Crop model, Crop rotation, Grain N content, Model evaluation, Model initialization; Climate-Change; Winter-Wheat; Nitrogen-Fertilization; Agroecosystem; Models; Multimodel Ensembles; Yield Response; Use Efficiency; Soil-Moisture; Oilseed Rape; Elevated Co2 |
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The accurate estimation of crop grain nitrogen (N; N in grain yield) is crucial for optimizing agricultural N management, especially in crop rotations. In the present study, 12 process-based models were applied to simulate the grain N of i) seven crops in rotations, ii) across various pedo-climatic and agro-management conditions in Europe, under both continuous simulation and single year simulation, and for iv) two calibration levels, namely minimal and detailed calibration. Generally, the results showed that the accuracy of the simulations in predicting grain N increased under detailed calibration. The models performed better in predicting the grain N of winter wheat (Triticum aestivum L.), winter barley (Hordewn vulgare L.) and spring barley (Hordeum vulgare L.) compared to spring oat (Avena saliva L.), winter rye (Secale cereale L.), pea (Piswn sativum L.) and winter oilseed rape (Brassica napus L.). These differences are linked to the intensity of parameterization with better parameterized crops showing lower prediction errors. The model performance was influenced by N fertilization and irrigation treatments, and a majority of the predictions were more accurate under low N and rainfed treatments. Moreover, the multi-model mean provided better predictions of grain N compared to any individual model. In regard to the Individual models, DAISY, FASSET, HERMES, MONICA and STICS are suitable for predicting grain N of the main crops in typical European crop rotations, which all performed well in both continuous simulation and single year simulation. Our results show that both the model initialization and the cover crop effects in crop rotations should be considered in order to achieve good performance of continuous simulation. Furthermore, the choice of either continuous simulation or single year simulation should be guided by the simulation objectives (e.g. grain yield, grain N content or N dynamics), the crop sequence (inclusion of legumes) and treatments (rate and type of N fertilizer) included in crop rotations and the model formalism. |
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2017-06-12 |
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0308-521x |
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CropM, ft_macsur |
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MA @ admin @ |
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4963 |
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Pohanková, E.; Hlavinka, P.; Kersebaum, K.C.; Dubrovský, M.; Fischer, M.; Balek, J.; Žalud, Z.; Hlavácová, M.; Trnka, M. |
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Pilot study: Field crop rotations modeling under present and future conditions in the Czech Republic using HERMES model |
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2015 |
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FACCE MACSUR Reports |
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5 |
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Sp5-75 |
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The aim of this study is to compare the water and organic material balance, yields and other aspects estimated within crop rotations by the Hermes crop model for present and future climatic conditions in the Czech Republic. Moreover, this is a pilot study for the complex and continuous crop rotations modeling (using both single crop models and ensembles) in connection with transient climate change scenarios. For this purpose, three locations representing important agricultural regions of the Czech Republic (with different climatic conditions) were selected. The crop rotation (including spring barley, silage maize, winter wheat, winter rape, and winter wheat in the listed order) was simulated from 1981-2080. The period 1981-2010 was covered by measured meteorological data, and the period 2011-2080 was represented by a transient synthetic weather series from the weather generator M&Rfi. The generated data was based on five circulation models representing an ensemble of 18 CMIP3 global circulation models to preserve to a large degree the uncertainty of the original ensemble. Two types of crop management were compared, and the influences of soil quality, increasing atmospheric CO2 and magnitude of adaptation measure (in the form of sowing date changes) were also considered. According to the results, if a “dry” scenario (such as GFCM21) would occur, then all the C3 crops produced in drier regions would be devastated in a significant number of seasons; for example, by the 2070s, up to 19.5%, 21.5% and 47.0% of seasons with winter rape, spring barley and winter wheat, respectively, would have a yield level below 50% of the present yield. Negative impacts are likely even on premium-quality soils regardless of the use of a flexible sowing date and accounting for increasing CO2 concentrations. Moreover, in some cases, the use of catch crops can have negative impacts, exacerbating the soil water deficit for the subsequent crops. This study (submitted to Climate Research journal) will be used as a pilot for subsequent activities. In this area, following calculations (the same set of stations and updated climate scenarios) using growth models ensemble (currently includes 12 modeling approaches) started to estimate uncertainty aspects. Consequently, the analysis within wider range of conditions (across continents) and farming methods will be conducted. No Label |
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MACSUR Science Conference 2015 »Integrated Climate Risk Assessment in Agriculture & Food«, 8–9+10 April 2015, Reading, UK |
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MA @ admin @ |
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2190 |
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