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102. Physical and Socio-economic Indicators

Author

Agnew, Maureen D., primary, Goodess, Clare M., additional, Hemming, Debbie, additional, Giannakopoulos, Christos, additional, Salem, Skander Ben, additional, Bindi, Marco, additional, Bradai, Mohamed Nejmeddine, additional, Dibari, Camilla, additional, El-Askary, Hesham, additional, El-Fadel, Mutasem, additional, El-Raey, Mohamed, additional, Ferrise, Roberto, additional, Grünzweig, José M., additional, Harzallah, Ali, additional, Hattour, Abdallah, additional, Hatzaki, Maria, additional, Kanas, Dina, additional, Kostopoulou, Effie, additional, Lionello, Piero, additional, Oweis, Theib, additional, Pino, Cosimo, additional, Psiloglou, Basil, additional, Abed, Salah Sahabi, additional, Sánchez-Arcilla, Agustín, additional, Senouci, Mohamed, additional, Taleb, Mohamed Zoheir, additional, and Tanzarella, Annalisa, additional

Published
2012
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103. Introduction

Author

Agnew, Maureen D., primary, Goodess, Clare M., additional, Hemming, Debbie, additional, Giannakopoulos, Christos, additional, Salem, Skander Ben, additional, Bindi, Marco, additional, Bradai, Mohamed Nejmeddine, additional, Congedi, Letizia, additional, Dibari, Camilla, additional, El-Askary, Hesham, additional, El-Fadel, Mutasem, additional, Ferrise, Roberto, additional, Grünzweig, José M., additional, Harzallah, Ali, additional, Hattour, Abdallah, additional, Hatzaki, Maria, additional, Kanas, Dina, additional, Kostopoulou, Effie, additional, Lionello, Piero, additional, Oweis, Theib, additional, Pino, Cosimo, additional, Reale, Marco, additional, Sánchez-Arcilla, Agustín, additional, and Senouci, Mohamed, additional

Published
2012
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104. Integration of the Climate Impact Assessments with Future Projections

Author

Goodess, Clare M., primary, Agnew, Maureen D., additional, Giannakopoulos, Christos, additional, Hemming, Debbie, additional, Salem, Skander Ben, additional, Bindi, Marco, additional, Bradai, Mohamed Nejmeddine, additional, Congedi, Letizia, additional, Dibari, Camilla, additional, El-Askary, Hesham, additional, El-Fadel, Mutasem, additional, El-Raey, Mohamed, additional, Ferrise, Roberto, additional, Founda, Dimitra, additional, Grünzweig, José M., additional, Harzallah, Ali, additional, Hatzaki, Maria, additional, Kay, Gillian, additional, Lionello, Piero, additional, Aranda, César Mösso, additional, Oweis, Theib, additional, Sierra, Joan Pau, additional, Psiloglou, Basil, additional, Reale, Marco, additional, Sánchez-Arcilla, Agustín, additional, Senouci, Mohamed, additional, Tanzarella, Annalisa, additional, and Varotsos, Konstantinos V., additional

Published
2012
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105. Synthesis and the Assessment of Adaptation Measures

Author

Goodess, Clare M., primary, Agnew, Maureen D., additional, Hemming, Debbie, additional, Giannakopoulos, Christos, additional, Bindi, Marco, additional, Dibari, Camilla, additional, El-Askary, Hesham, additional, El-Fadel, Mutasem, additional, El-Hattab, Mamdouh, additional, El-Raey, Mohamed, additional, Ferrise, Roberto, additional, Grünzweig, José M., additional, Harzallah, Ali, additional, Kanas, Dina, additional, Lionello, Piero, additional, Aranda, César Mösso, additional, Oweis, Theib, additional, Sierra, Joan Pau, additional, Reale, Marco, additional, Sánchez-Arcilla, Agustín, additional, Senouci, Mohamed, additional, Sommer, Rolf, additional, and Tanzarella, Annalisa, additional

Published
2012
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106. Climate Impact Assessments

Author

Hemming, Debbie, primary, Agnew, Maureen D., additional, Goodess, Clare M., additional, Giannakopoulos, Christos, additional, Salem, Skander Ben, additional, Bindi, Marco, additional, Bradai, Mohamed Nejmeddine, additional, Congedi, Letizia, additional, Dibari, Camilla, additional, El-Askary, Hesham, additional, El-Fadel, Mutasem, additional, El-Raey, Mohamed, additional, Ferrise, Roberto, additional, Grünzweig, José M., additional, Harzallah, Ali, additional, Hattour, Abdallah, additional, Hatzaki, Maria, additional, Kanas, Dina, additional, Lionello, Piero, additional, McCarthy, Mark, additional, Aranda, César Mösso, additional, Oweis, Theib, additional, Sierra, Joan Pau, additional, Psiloglou, Basil, additional, Reale, Marco, additional, Sánchez-Arcilla, Agustín, additional, Senouci, Mohamed, additional, and Tanzarella, Annalisa, additional

Published
2012
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108. Understanding effects of genotype × environment × sowing window interactions for durum wheat in the Mediterranean basin

Author

Padovan, Gloria, primary, Martre, Pierre, additional, Semenov, Mikhail A., additional, Masoni, Alberto, additional, Bregaglio, Simone, additional, Ventrella, Domenico, additional, Lorite, Ignacio J., additional, Santos, Cristina, additional, Bindi, Marco, additional, Ferrise, Roberto, additional, and Dibari, Camilla, additional

Published
2020
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109. Evaluating the Potential of Legumes to Mitigate N2O Emissions From Permanent Grassland Using Process‐Based Models

Author

Fuchs, Kathrin, primary, Merbold, Lutz, additional, Buchmann, Nina, additional, Bellocchi, Gianni, additional, Bindi, Marco, additional, Brilli, Lorenzo, additional, Conant, Richard T., additional, Dorich, Christopher D., additional, Ehrhardt, Fiona, additional, Fitton, Nuala, additional, Grace, Peter, additional, Klumpp, Katja, additional, Liebig, Mark, additional, Lieffering, Mark, additional, Martin, Raphaël, additional, McAuliffe, Russell, additional, Newton, Paul C. D., additional, Rees, Robert M., additional, Recous, Sylvie, additional, Smith, Pete, additional, Soussana, Jean‐François, additional, Topp, Cairistiona F. E., additional, and Snow, Val, additional

Published
2020
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111. Uncertainties in simulating N uptake, net N mineralization, soil mineral N and N leaching in European crop rotations using process-based models

Author

Yin, Xiaogang, primary, Kersebaum, Kurt-Christian, additional, Beaudoin, Nicolas, additional, Constantin, Julie, additional, Chen, Fu, additional, Louarn, Gaëtan, additional, Manevski, Kiril, additional, Hoffmann, Munir, additional, Kollas, Chris, additional, Armas-Herrera, Cecilia M., additional, Baby, Sanmohan, additional, Bindi, Marco, additional, Dibari, Camilla, additional, Ferchaud, Fabien, additional, Ferrise, Roberto, additional, de Cortazar-Atauri, Inaki Garcia, additional, Launay, Marie, additional, Mary, Bruno, additional, Moriondo, Marco, additional, Öztürk, Isik, additional, Ruget, Françoise, additional, Sharif, Behzad, additional, Wachter-Ripoche, Dominique, additional, and Olesen, Jørgen E., additional

Published
2020
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116. Phenological Model Intercomparison for Estimating Grapevine Budbreak Date (Vitis vinifera L.) in Europe

Author

Leolini, Luisa, primary, Costafreda-Aumedes, Sergi, additional, A. Santos, João, additional, Menz, Christoph, additional, Fraga, Helder, additional, Molitor, Daniel, additional, Merante, Paolo, additional, Junk, Jürgen, additional, Kartschall, Thomas, additional, Destrac-Irvine, Agnès, additional, van Leeuwen, Cornelis, additional, C. Malheiro, Aureliano, additional, Eiras-Dias, José, additional, Silvestre, José, additional, Dibari, Camilla, additional, Bindi, Marco, additional, and Moriondo, Marco, additional

Published
2020
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120. Modelling biological N fixation and grass-legume dynamics with process-based biogeochemical models of varying complexity

Author

Fitton, Nuala, Bindi, Marco, Brilli, Lorenzo, Cichota, Rogerio, Dibari, Camila, Fuchs, Kathrin, Huguenin-Elie, Olivier, Klumpp, Katja, Lieffering, Mark, Lüscher, Andreas, Martin, Raphaël, McAuliffe, Russel, Merbold, Lutz, Newton, Paul, Rees, Robert M., Smith, Pete, Topp, Cairistiona F.E., and Snow, Valerie

Subjects
Nitrogen uptake, Species composition, Model validation, Overyielding
Abstract

Grasslands comprised of grass-legume mixtures could become a substitute for nitrogen fertiliser through biological nitrogen fixation (BNF) which in turn can reduce nitrous oxide emissions directly from soils without negative impacts on productivity. Models can test how legumes can be used to meet environmental and production goals, but many models used to simulate greenhouse gas (GHG) emissions from grasslands have either a poor representation of grass-legume mixtures and BNF, or poor validation of these features. Our objective is to examine how such systems are currently represented in two process-based biogeochemical models, APSIM and DayCent, when compared against an experimental dataset with different grass-legume mixtures at three nitrogen (N) fertiliser rates. Here, we propose a novel approach for coupling DayCent, a single species model to APSIM, a multi-species model, to increase the capability of DayCent when representing a range of grass-legume fractions. While dependent on specific assumptions, both models can capture the key aspects of the grass-legume growth, including biomass production and BNF and to correctly simulate the interactions between changing legume and grass fractions, particularly mixtures with a high clover fraction. Our work suggests that single species models should not be used for grass-legume mixtures beyond about 30% legume content, unless using a similar approach to that adopted here.

Published
2019

121. An overview on Climate Change effects on viticulture

Author

Merante, Paolo, Leolini, Luisa, Bindi, Marco, and Moriondo, Marco

Subjects
Climate change effects on grapevine, Climate change impacts, Wine regions
Abstract

Over the last decades, Climate Change has become the paramount challenge for the current agricultural sector. The global warming, the increase of precipitation variability and the increasing frequency of extreme events are leading to detrimental consequences on crop performances. In particular, the viticulture sector is threatened by the approaching climate change, which is predicted to be particularly evident on the narrow geographical areas and the specific climatic niches of the main wine regions. Indeed, the warmer temperature and the extremes events are expected to have strong repercussions on phenology patterns, yield variations and quality changes of the most important grapevine varieties. Under these conditions, finding new solutions for maintaining high quality productions will be essential. To this regard, this work offers an overview of the present and future effects of climate change on viticulture in different regions, by analysing the specific impact of changing conditions on the main biological aspects of grapevine such as phenology, yield and quality. Accordingly, a literature review on the most recent studies, which are at the forefront in analysing climate change impacts on viticulture, will be essential for discussing the most important findings to proper deal with the relevant problems of climate change in this sector.

Published
2019
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122. Modelling strategies for estimating vine development and growth under different environmental conditions

Author

Leolini, Luisa, Merante, Paolo, Bindi, Marco, and Moriondo, Marco

Subjects
Climate change, Grapevine modelling, viticulture, crop growth models
Abstract

Viticulture is an important part of the agricultural sector with a high economic and environmental relevance especially in the most traditional wine regions. In these areas, the specific Terroir characterized by the complex of climate, soil and human factors contribute to define the peculiar features of grapes and the wines’ high quality. Any changes in environmental conditions and agricultural practices may affect the vintage quality with strong consequences on wine industry and the socioeconomic aspects of the winemaking sector. In this context, crop simulation models, which are able to represent vine development and growth, can be useful tools for predicting present and future performances under different environmental conditions. On this basis, the main characteristics of two crop models will be described/introduced: UNIFI.GrapeML a model library that collect specific modelling strategies for grapevine and CropSyst a multi-years, multi-crop model. Although these models are based on different modelling strategies, they include the main physiological processes of the plant and are able to reproduce the pivotal climate-plant-soil interactions. Accordingly, UNIFI.GrapeML and CropSyst simulation model will be applied for estimating the production cycle of different grapevine varieties under changing environmental conditions.

Published
2019
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123. Global wheat production with 1.5 and 2.0°C above pre-industrial warming

Author

Liu, Bing, Martre, Pierre, Ewert, Frank, Porter, John R., Challinor, Andy J., Müller, Christoph, Ruane, Alex C., Waha, Katharina, Thorburn, Peter J., Aggarwal, Pramod K., Ahmed, Mukhtar, Balkovič, Juraj, Basso, Bruno, Biernath, Christian, Bindi, Marco, Cammarano, Davide, De Sanctis, Giacomo, Dumont, Benjamin, Espadafor, Mónica, Eyshi Rezaei, Ehsan, Ferrise, Roberto, Garcia-Vila, Margarita, Gayler, Sebastian, Gao, Yujing, Horan, Heidi, Hoogenboom, Gerrit, Izaurralde, Roberto C., Jones, Curtis D., Kassie, Belay T., Kersebaum, Kurt C., Klein, Christian, Koehler, Ann-Kristin, Maiorano, Andrea, Minoli, Sara, Montesino San Martin, Manuel, Naresh Kumar, Soora, Nendel, Claas, O’Leary, Garry J., Palosuo, Taru, Priesack, Eckart, Ripoche, Dominique, Rötter, Reimund P., Semenov, Mikhail A., Stöckle, Claudio, Streck, Thilo, Supit, Iwan, Tao, Fulu, Van der Velde, Marijn, Wallach, Daniel, Wang, Enli, Webber, Heidi, Wolf, Joost, Xiao, Liujun, Zhang, Zhao, Zhao, Zhigan, Zhu, Yan, and Asseng, Senthold

Subjects
1.5°C warming, climate change, extreme low yields, food security, model ensemble, wheat production
Published
2019

124. Climate change impact and adaptation for wheat protein

Author

Asseng, Senthold, Martre, Pierre, Maiorano, Andrea, Roetter, Reimund P., O'Leary, Garry J., Fitzgerald, Glenn J., Girousse, Christine, Motzo, Rosella, Giunta, Francesco, Babar, M. Ali, Reynolds, Matthew P., Kheir, Ahmed M. S., Thorburn, Peter J., Waha, Katharina, Ruane, Alex C., Aggarwal, Pramod K., Ahmed, Mukhtar, Balkovic, Juraj, Basso, Bruno, Biernath, Christian, Bindi, Marco, Cammarano, Davide, Challinor, Andrew J., De Sanctis, Giacomo, Dumont, Benjamin, Rezaei, Ehsan Eyshi, Fereres, Elias, Ferrise, Roberto, Garcia-Vila, Margarita, Gayler, Sebastian, Gao, Yujing, Horan, Heidi, Hoogenboom, Gerrit, Izaurralde, R. Cesar, Jabloun, Mohamed, Jones, Curtis D., Kassie, Belay T., Kersebaum, Kurt-Christian, Klein, Christian, Koehler, Ann-Kristin, Liu, Bing, Minoli, Sara, San Martin, Manuel Montesino, Mueller, Christoph, Kumar, Soora Naresh, Nendel, Claas, Olesen, Jørgen Eivind, Palosuo, Taru, Porter, John R., Priesack, Eckart, Ripoche, Dominique, Semenov, Mikhail A., Stockle, Claudio, Stratonovitch, Pierre, Streck, Thilo, Supit, Iwan, Tao, Fulu, Van der Velde, Marijn, Wallach, Daniel, Wang, Enli, Webber, Heidi, Wolf, Joost, Xiao, Liujun, Zhang, Zhao, Zhao, Zhigan, Zhu, Yan, Ewert, Frank, Asseng, Senthold, Martre, Pierre, Maiorano, Andrea, Roetter, Reimund P., O'Leary, Garry J., Fitzgerald, Glenn J., Girousse, Christine, Motzo, Rosella, Giunta, Francesco, Babar, M. Ali, Reynolds, Matthew P., Kheir, Ahmed M. S., Thorburn, Peter J., Waha, Katharina, Ruane, Alex C., Aggarwal, Pramod K., Ahmed, Mukhtar, Balkovic, Juraj, Basso, Bruno, Biernath, Christian, Bindi, Marco, Cammarano, Davide, Challinor, Andrew J., De Sanctis, Giacomo, Dumont, Benjamin, Rezaei, Ehsan Eyshi, Fereres, Elias, Ferrise, Roberto, Garcia-Vila, Margarita, Gayler, Sebastian, Gao, Yujing, Horan, Heidi, Hoogenboom, Gerrit, Izaurralde, R. Cesar, Jabloun, Mohamed, Jones, Curtis D., Kassie, Belay T., Kersebaum, Kurt-Christian, Klein, Christian, Koehler, Ann-Kristin, Liu, Bing, Minoli, Sara, San Martin, Manuel Montesino, Mueller, Christoph, Kumar, Soora Naresh, Nendel, Claas, Olesen, Jørgen Eivind, Palosuo, Taru, Porter, John R., Priesack, Eckart, Ripoche, Dominique, Semenov, Mikhail A., Stockle, Claudio, Stratonovitch, Pierre, Streck, Thilo, Supit, Iwan, Tao, Fulu, Van der Velde, Marijn, Wallach, Daniel, Wang, Enli, Webber, Heidi, Wolf, Joost, Xiao, Liujun, Zhang, Zhao, Zhao, Zhigan, Zhu, Yan, and Ewert, Frank

Published
2019

125. Global wheat production with 1.5 and 2.0°C above pre‐industrial warming

Author

National Science Foundation (US), National Natural Science Foundation of China, International Food Policy Research Institute (US), CGIAR (France), Institut National de la Recherche Agronomique (France), Federal Ministry of Education and Research (Germany), Biotechnology and Biological Sciences Research Council (UK), China Scholarship Council, Department of Agriculture and Water Resources (Australia), Ministero delle Politiche Agricole Alimentari e Forestali, Gorgan University, Victoria State Government, National Institute of Food and Agriculture (US), Federal Ministry of Food and Agriculture (Germany), German Research Foundation, Academy of Finland, LabEx Agro, Natural Resources Institute Finland, Liu, Bing, Martre, Pierre, Ewert, Frank, Porter, John R., Challinor, Andrew J., Müller, Christoph, Ruane, Alexander C., Waha, Katharina, Thorburn, Peter, Aggarwal, Pramod K., Ahmed, Mukhtar, Balkovič, Jurajb, Basso, Bruno, Biernath, Christian, Bindi, Marco, Cammarano, Davide, De Sanctis, Giacomo, Dumont, Benjamin, Espadafor, Mónica, Rezaei, Ehsan Eyshi, Ferrise, Roberto, García Vila, Margarita, Gayler, Sebastian, Gao, Yujing, Horan, Heidi, Hoogenboom, Gerrit, Izaurralde, Roberto C., Jones, Curtis D., Kassie, Belay T., Kersebaum, Kurt C., Klein, Christian, Koehler, Ann-Kristin, Maiorano, Andrea, Minoli, Sara, Montesino San Martin, Manuel, Kumar, Soora Naresh, Nendel, Claas, O'Leary, Garry, Palosuo, Taru, Priesack, Eckart, Ripoche, Dominique, Rötter, Reimund P., Semenov, Mikhail A., Stöckle, Claudio, Streck, Thilo, Supit, Iwan, Tao, Fulu, Van der Velde, Marijn, Wallach, Daniel, Wang, Enli, Webber, Heidi, Wolf, Joost, Xiao, Liujun, Zhang, Zhao, Zhao, Zhigan, Zhu, Yan, Asseng, Senthold, National Science Foundation (US), National Natural Science Foundation of China, International Food Policy Research Institute (US), CGIAR (France), Institut National de la Recherche Agronomique (France), Federal Ministry of Education and Research (Germany), Biotechnology and Biological Sciences Research Council (UK), China Scholarship Council, Department of Agriculture and Water Resources (Australia), Ministero delle Politiche Agricole Alimentari e Forestali, Gorgan University, Victoria State Government, National Institute of Food and Agriculture (US), Federal Ministry of Food and Agriculture (Germany), German Research Foundation, Academy of Finland, LabEx Agro, Natural Resources Institute Finland, Liu, Bing, Martre, Pierre, Ewert, Frank, Porter, John R., Challinor, Andrew J., Müller, Christoph, Ruane, Alexander C., Waha, Katharina, Thorburn, Peter, Aggarwal, Pramod K., Ahmed, Mukhtar, Balkovič, Jurajb, Basso, Bruno, Biernath, Christian, Bindi, Marco, Cammarano, Davide, De Sanctis, Giacomo, Dumont, Benjamin, Espadafor, Mónica, Rezaei, Ehsan Eyshi, Ferrise, Roberto, García Vila, Margarita, Gayler, Sebastian, Gao, Yujing, Horan, Heidi, Hoogenboom, Gerrit, Izaurralde, Roberto C., Jones, Curtis D., Kassie, Belay T., Kersebaum, Kurt C., Klein, Christian, Koehler, Ann-Kristin, Maiorano, Andrea, Minoli, Sara, Montesino San Martin, Manuel, Kumar, Soora Naresh, Nendel, Claas, O'Leary, Garry, Palosuo, Taru, Priesack, Eckart, Ripoche, Dominique, Rötter, Reimund P., Semenov, Mikhail A., Stöckle, Claudio, Streck, Thilo, Supit, Iwan, Tao, Fulu, Van der Velde, Marijn, Wallach, Daniel, Wang, Enli, Webber, Heidi, Wolf, Joost, Xiao, Liujun, Zhang, Zhao, Zhao, Zhigan, Zhu, Yan, and Asseng, Senthold

Abstract

Efforts to limit global warming to below 2°C in relation to the pre‐industrial level are under way, in accordance with the 2015 Paris Agreement. However, most impact research on agriculture to date has focused on impacts of warming >2°C on mean crop yields, and many previous studies did not focus sufficiently on extreme events and yield interannual variability. Here, with the latest climate scenarios from the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) project, we evaluated the impacts of the 2015 Paris Agreement range of global warming (1.5 and 2.0°C warming above the pre‐industrial period) on global wheat production and local yield variability. A multi‐crop and multi‐climate model ensemble over a global network of sites developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP) for Wheat was used to represent major rainfed and irrigated wheat cropping systems. Results show that projected global wheat production will change by −2.3% to 7.0% under the 1.5°C scenario and −2.4% to 10.5% under the 2.0°C scenario, compared to a baseline of 1980–2010, when considering changes in local temperature, rainfall, and global atmospheric CO2 concentration, but no changes in management or wheat cultivars. The projected impact on wheat production varies spatially; a larger increase is projected for temperate high rainfall regions than for moderate hot low rainfall and irrigated regions. Grain yields in warmer regions are more likely to be reduced than in cooler regions. Despite mostly positive impacts on global average grain yields, the frequency of extremely low yields (bottom 5 percentile of baseline distribution) and yield inter‐annual variability will increase under both warming scenarios for some of the hot growing locations, including locations from the second largest global wheat producer—India, which supplies more than 14% of global wheat. The projected global impact of warming <2°C on wheat production is therefore not

Published
2019

126. Climate change impact and adaptation for wheat protein

Author

International Food Policy Research Institute (US), CGIAR (France), European Commission, Institut National de la Recherche Agronomique (France), National Natural Science Foundation of China, Federal Ministry of Food and Agriculture (Germany), Biotechnology and Biological Sciences Research Council (UK), Innovation Fund Denmark, China Scholarship Council, Ministero delle Politiche Agricole Alimentari e Forestali, Academy of Finland, Finnish Ministry of Agriculture and Forestry, Federal Ministry of Education and Research (Germany), Department of Agriculture and Water Resources (Australia), University of Melbourne, Grains Research and Development Corporation (Australia), National Institute of Food and Agriculture (US), German Research Foundation, Gorgan University, Asseng, Senthold, Martre, Pierre, Maiorano, Andrea, Rötter, Reimund P., O'Leary, Garry, Fitzgerald, Glenn J., Girousse, Christine, Motzo, Rosella, Giunta, Francesco, Babar, M. Ali, Reynolds, Matthew, Kheir, Ahmed, M .S., Thorburn, Peter, Waha, Katharina, Ruane, Alexander C., Aggarwal, Pramod K., Ahmed, Mukhtar, Balkovič, Juraj, Basso, Bruno, Biernath, Christian, Bindi, Marco, Cammarano, Davide, Challinor, Andrew J., De Sanctis, Giacomo, Dumont, Benjamin, Rezaei, Ehsan Eyshi, Fereres Castiel, Elías, Ferrise, Roberto, García Vila, Margarita, Gayler, Sebastian, Gao, Yujing, Horan, Heidi, Hoogenboom, Gerrit, Izaurralde, Roberto C., Jabloun, Mohamed, Jones, Curtis D., Kassie, Belay T., Kersebaum, Kurt C., Klein, Christian, Koehler, Ann-Kristin, Liu, Bing, Minoli, Sara, Montesino San Martin, Manuel, Müller, Christoph, Kumar, Soora Naresh, Nendel, Claas, Olesen, Jørgen E., Palosuo, Taru, Porter, John R., Priesack, Eckart, Ripoche, Dominique, Semenov, Mikhail A., Stöckle, Claudio, Stratonovitch, Pierre, Streck, Thilo, Supit, Iwan, Tao, Fulu, Van der Velde, Marijn, Wallach, Daniel, Wang, Enli, Webber, Heidi, Wolf, Joost, Xiao, Liujun, Zhang, Zhao, Zhao, Zhigan, Zhu, Yan, Ewert, Frank, International Food Policy Research Institute (US), CGIAR (France), European Commission, Institut National de la Recherche Agronomique (France), National Natural Science Foundation of China, Federal Ministry of Food and Agriculture (Germany), Biotechnology and Biological Sciences Research Council (UK), Innovation Fund Denmark, China Scholarship Council, Ministero delle Politiche Agricole Alimentari e Forestali, Academy of Finland, Finnish Ministry of Agriculture and Forestry, Federal Ministry of Education and Research (Germany), Department of Agriculture and Water Resources (Australia), University of Melbourne, Grains Research and Development Corporation (Australia), National Institute of Food and Agriculture (US), German Research Foundation, Gorgan University, Asseng, Senthold, Martre, Pierre, Maiorano, Andrea, Rötter, Reimund P., O'Leary, Garry, Fitzgerald, Glenn J., Girousse, Christine, Motzo, Rosella, Giunta, Francesco, Babar, M. Ali, Reynolds, Matthew, Kheir, Ahmed, M .S., Thorburn, Peter, Waha, Katharina, Ruane, Alexander C., Aggarwal, Pramod K., Ahmed, Mukhtar, Balkovič, Juraj, Basso, Bruno, Biernath, Christian, Bindi, Marco, Cammarano, Davide, Challinor, Andrew J., De Sanctis, Giacomo, Dumont, Benjamin, Rezaei, Ehsan Eyshi, Fereres Castiel, Elías, Ferrise, Roberto, García Vila, Margarita, Gayler, Sebastian, Gao, Yujing, Horan, Heidi, Hoogenboom, Gerrit, Izaurralde, Roberto C., Jabloun, Mohamed, Jones, Curtis D., Kassie, Belay T., Kersebaum, Kurt C., Klein, Christian, Koehler, Ann-Kristin, Liu, Bing, Minoli, Sara, Montesino San Martin, Manuel, Müller, Christoph, Kumar, Soora Naresh, Nendel, Claas, Olesen, Jørgen E., Palosuo, Taru, Porter, John R., Priesack, Eckart, Ripoche, Dominique, Semenov, Mikhail A., Stöckle, Claudio, Stratonovitch, Pierre, Streck, Thilo, Supit, Iwan, Tao, Fulu, Van der Velde, Marijn, Wallach, Daniel, Wang, Enli, Webber, Heidi, Wolf, Joost, Xiao, Liujun, Zhang, Zhao, Zhao, Zhigan, Zhu, Yan, and Ewert, Frank

Abstract

Wheat grain protein concentration is an important determinant of wheat quality for human nutrition that is often overlooked in efforts to improve crop production. We tested and applied a 32‐multi‐model ensemble to simulate global wheat yield and quality in a changing climate. Potential benefits of elevated atmospheric CO2 concentration by 2050 on global wheat grain and protein yield are likely to be negated by impacts from rising temperature and changes in rainfall, but with considerable disparities between regions. Grain and protein yields are expected to be lower and more variable in most low‐rainfall regions, with nitrogen availability limiting growth stimulus from elevated CO2. Introducing genotypes adapted to warmer temperatures (and also considering changes in CO2 and rainfall) could boost global wheat yield by 7% and protein yield by 2%, but grain protein concentration would be reduced by −1.1 percentage points, representing a relative change of −8.6%. Climate change adaptations that benefit grain yield are not always positive for grain quality, putting additional pressure on global wheat production.

Published
2019

127. Territorio agricolo e cambiamenti globali

Author

Bindi, Marco and Dibari, Camilla

Subjects
Agricultural, lcsh:NA9000-9428, agro-alimentary production, lcsh:Architecture, climatic changes, lcsh:NA1-9428, lcsh:Aesthetics of cities. City planning and beautifying
Abstract

Economic, cultural, political, technology and environmental factors influence the agricultural system and territory. In particular, the climatic changes - in action and the changes forecast in the future above all - will be considerable repercussions on the agricultural territory and his productive capacities. It’s possible to introduce some arrangement strategies to reduce the negative impacts., Ri-Vista, Vol 10 No 2 (2008)

Published
2015

128. Impacts of 1.5°C global warming on natural and human systems

Author

Hoegh Guldberg, Ove, Jacob, Daniela, Taylor, Michael, Bindi, Marco, Brown, Sally, Camilloni, Ines Angela, Diedhiou, Arona, Djalante, Riyanti, Ebi, Kristie L., Engelbrecht, Francois, Guiot, Joel, Hijioka, Yasuaki, Mehrotra, Shagun, Payne, Antony, Seneviratne, Sonia I., Thomas, Adelle, Warren, Rachel, Zhou, Guangsheng, and Masson Delmotte, Valerie

Subjects
IMPACTS, purl.org/becyt/ford/1 [https], purl.org/becyt/ford/1.5 [https], CLIMATE CHANGE, PATHWAYS, PARIS AGREEMENT
Abstract

This Report responds to the invitation for IPCC ?... to provide a Special Report in 2018 on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways? contained in the Decision of the 21st Conference of Parties of the United Nations Framework Convention on Climate Change to adopt the Paris Agreement. The IPCC accepted the invitation in April 2016, deciding to prepare this Special Report on the impacts of global warming of1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty. Fil: Hoegh Guldberg, Ove. University of Queensland; Australia Fil: Jacob, Daniela. Climate Service Center; Alemania Fil: Taylor, Michael. University of West Indies; Jamaica Fil: Bindi, Marco. Università degli Studi di Firenze; Italia Fil: Brown, Sally. University of Southampton; Reino Unido Fil: Camilloni, Ines Angela. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina Fil: Diedhiou, Arona. Institut de Recherche Pour Le Développement; Senegal Fil: Djalante, Riyanti. No especifíca; Fil: Ebi, Kristie L.. No especifíca; Fil: Engelbrecht, Francois. No especifíca; Fil: Guiot, Joel. No especifíca; Fil: Hijioka, Yasuaki. No especifíca; Fil: Mehrotra, Shagun. No especifíca; Fil: Payne, Antony. No especifíca; Fil: Seneviratne, Sonia I.. No especifíca; Fil: Thomas, Adelle. No especifíca; Fil: Warren, Rachel. No especifíca; Fil: Zhou, Guangsheng. No especifíca

Published
2018

129. LIFE PASTORALP: a project for alpine pasture vulnerability assessment

Author

Argenti, Giovanni, Bassignana, Mauro, Bellocchi, Gianni, Dibari, Camilla, Filippa, Gianluca, Poggio, Laura, Staglianò, Nicolina, Bindi, Marco, DISPAA, Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Institut Agricole Régional d'Aoste, Partenaires INRAE, Unité Mixte de Recherche sur l'Ecosystème Prairial - UMR (UREP), Institut National de la Recherche Agronomique (INRA)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS), Agenzia Regionale per la Protezione dell'Ambiente della Valle d'Aosta, and Parco Nazionale del Gran Paradiso

Subjects
pâturages, changement climatique, pâturage, [SDV]Life Sciences [q-bio], Alpes, alpes, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2018

131. Modelling biological N fixation and grass-legume dynamics with process-based biogeochemical models of varying complexity

Author

Fitton, Nuala, primary, Bindi, Marco, additional, Brilli, Lorenzo, additional, Cichota, Rogerio, additional, Dibari, Camila, additional, Fuchs, Kathrin, additional, Huguenin-Elie, Olivier, additional, Klumpp, Katja, additional, Lieffering, Mark, additional, Lüscher, Andreas, additional, Martin, Raphael, additional, McAuliffe, Russel, additional, Merbold, Lutz, additional, Newton, Paul, additional, Rees, Robert M., additional, Smith, Pete, additional, Topp, Cairistiona F.E., additional, and Snow, Valerie, additional

Published
2019
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132. Effects of input data aggregation on simulated crop yields in temperate and Mediterranean climates

Author

Maharjan, Ganga Ram, primary, Hoffmann, Holger, additional, Webber, Heidi, additional, Srivastava, Amit Kumar, additional, Weihermüller, Lutz, additional, Villa, Ana, additional, Coucheney, Elsa, additional, Lewan, Elisabet, additional, Trombi, Giacomo, additional, Moriondo, Marco, additional, Bindi, Marco, additional, Grosz, Balazs, additional, Dechow, Rene, additional, Kuhnert, Mathias, additional, Doro, Luca, additional, Kersebaum, Kurt-Christian, additional, Stella, Tommaso, additional, Specka, Xenia, additional, Nendel, Claas, additional, Constantin, Julie, additional, Raynal, Hélène, additional, Ewert, Frank, additional, and Gaiser, Thomas, additional

Published
2019
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133. Global wheat production with 1.5 and 2.0°C above pre‐industrial warming

Author

Liu, Bing, primary, Martre, Pierre, additional, Ewert, Frank, additional, Porter, John R., additional, Challinor, Andy J., additional, Müller, Christoph, additional, Ruane, Alex C., additional, Waha, Katharina, additional, Thorburn, Peter J., additional, Aggarwal, Pramod K., additional, Ahmed, Mukhtar, additional, Balkovič, Juraj, additional, Basso, Bruno, additional, Biernath, Christian, additional, Bindi, Marco, additional, Cammarano, Davide, additional, De Sanctis, Giacomo, additional, Dumont, Benjamin, additional, Espadafor, Mónica, additional, Eyshi Rezaei, Ehsan, additional, Ferrise, Roberto, additional, Garcia‐Vila, Margarita, additional, Gayler, Sebastian, additional, Gao, Yujing, additional, Horan, Heidi, additional, Hoogenboom, Gerrit, additional, Izaurralde, Roberto C., additional, Jones, Curtis D., additional, Kassie, Belay T., additional, Kersebaum, Kurt C., additional, Klein, Christian, additional, Koehler, Ann‐Kristin, additional, Maiorano, Andrea, additional, Minoli, Sara, additional, Montesino San Martin, Manuel, additional, Naresh Kumar, Soora, additional, Nendel, Claas, additional, O’Leary, Garry J., additional, Palosuo, Taru, additional, Priesack, Eckart, additional, Ripoche, Dominique, additional, Rötter, Reimund P., additional, Semenov, Mikhail A., additional, Stöckle, Claudio, additional, Streck, Thilo, additional, Supit, Iwan, additional, Tao, Fulu, additional, Van der Velde, Marijn, additional, Wallach, Daniel, additional, Wang, Enli, additional, Webber, Heidi, additional, Wolf, Joost, additional, Xiao, Liujun, additional, Zhang, Zhao, additional, Zhao, Zhigan, additional, Zhu, Yan, additional, and Asseng, Senthold, additional

Published
2019
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134. Decline in climate resilience of European wheat

Author

Kahiluoto, Helena, primary, Kaseva, Janne, additional, Balek, Jan, additional, Olesen, Jørgen E., additional, Ruiz-Ramos, Margarita, additional, Gobin, Anne, additional, Kersebaum, Kurt Christian, additional, Takáč, Jozef, additional, Ruget, Francoise, additional, Ferrise, Roberto, additional, Bezak, Pavol, additional, Capellades, Gemma, additional, Dibari, Camilla, additional, Mäkinen, Hanna, additional, Nendel, Claas, additional, Ventrella, Domenico, additional, Rodríguez, Alfredo, additional, Bindi, Marco, additional, and Trnka, Mirek, additional

Published
2018
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135. The response of process-based agro-ecosystem models to within-field variability in site conditions

Author

Wallor, Evelyn, primary, Kersebaum, Kurt-Christian, additional, Ventrella, Domenico, additional, Bindi, Marco, additional, Cammarano, Davide, additional, Coucheney, Elsa, additional, Gaiser, Thomas, additional, Garofalo, Pasquale, additional, Giglio, Luisa, additional, Giola, Pietro, additional, Hoffmann, Munir P., additional, Iocola, Ileana, additional, Lana, Marcos, additional, Lewan, Elisabet, additional, Maharjan, Ganga Ram, additional, Moriondo, Marco, additional, Mula, Laura, additional, Nendel, Claas, additional, Pohankova, Eva, additional, Roggero, Pier Paolo, additional, Trnka, Mirek, additional, and Trombi, Giacomo, additional

Published
2018
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136. Contribution of crop model structure, parameters and climate projections to uncertainty in climate change impact assessments

Author

Tao, Fulu, Rötter, Reimund, Palosuo, Taru, Hernández Díaz-Ambrona, Carlos Gregorio, Minguez Tudela, Maria Ines, Semenov, Mikhail A., Kersebaum, K.C., Nendel, Claas, Specka, Xenia, Hoffmann, Holger, Ewert, Frank, Dambreville, Anaelle, Martre, Pierre, Rodríguez Fernández, Lucía, Ruiz Ramos, Margarita, Gaiser, Thomas, Höhn, Jukka, Salo, Tapio, Ferrise, Roberto, Bindi, Marco, Cammarano, Davide, Schulman, Alan H, Tao, Fulu, Rötter, Reimund, Palosuo, Taru, Hernández Díaz-Ambrona, Carlos Gregorio, Minguez Tudela, Maria Ines, Semenov, Mikhail A., Kersebaum, K.C., Nendel, Claas, Specka, Xenia, Hoffmann, Holger, Ewert, Frank, Dambreville, Anaelle, Martre, Pierre, Rodríguez Fernández, Lucía, Ruiz Ramos, Margarita, Gaiser, Thomas, Höhn, Jukka, Salo, Tapio, Ferrise, Roberto, Bindi, Marco, Cammarano, Davide, and Schulman, Alan H

Abstract

Climate change impact assessments are plagued with uncertainties from many sources, such as climate projections or the inadequacies in structure and parameters of the impact model. Previous studies tried to account for the uncertainty from one or two of these. Here, we developed a triple-ensemble probabilistic assessment using seven crop models, multiple sets of model parameters and eight contrasting climate projections together to comprehensively account for uncertainties from these three important sources. We demonstrated the approach in assessing climate change impact on barley growth and yield at Jokioinen, Finland in the Boreal climatic zone and Lleida, Spain in the Mediterranean climatic zone, for the 2050s. We further quantified and compared the contribution of crop model structure, crop model parameters and climate projections to the total variance of ensemble output using Analysis of Variance (ANOVA). Based on the triple-ensemble probabilistic assessment, the median of simulated yield change was 4% and +16%, and the probability of decreasing yield was 63% and 31% in the 2050s, at Jokioinen and Lleida, respectively, relative to 1981–2010. The contribution of crop model structure to the total variance of ensemble output was larger than that from downscaled climate projections and model parameters. The relative contribution of crop model parameters and downscaled climate projections to the total variance of ensemble output varied greatly among the seven crop models and between the two sites. The contribution of downscaled climate projections was on average larger than that of crop model parameters. This information on the uncertainty from different sources can be quite useful for model users to decide where to put the most effort when preparing or choosing models or parameters for impact analyses. We concluded that the triple-ensemble probabilistic approach that accounts for the uncertainties from multiple important sources provide more comprehensive informati

Published
2018

137. Classifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change

Author

Fronzek, S., Pirtioja, Nina, Carter, Timothy, Bindi, Marco, Hoffmann, Holger, Palosuo, Taru, Ruiz Ramos, Margarita, Tao, Fulu, Trnka, M., Acutis, Marco, Asseng, Senthold, Baranowski, Piotr, Basso, Bruno, Bodin, Per, Buis, Samuel, Cammarano, Davide, Deligios, Paola, Destain, Marie-France, Dumont, Benjamin, Ewert, Frank, Ferrise, Roberto, François, Louis, Gaiser, Thomas, Hlavinka, P., Jacquemin, I., Kersebaum, K.C., Kollas, Chris, Krzyszczak, Jaromir, Lorite, I. J., Minet, Julien, Minguez Tudela, Maria Ines, Montesino, Manuel, Moriondo, Marco, Müller, Christoph, Nendel, Claas, Öztürk, Isik, Perego, Alessia, Rodríguez Sánchez, Alfredo, Ruane, Alex C., Ruget, François, Sanna, Mattia, Semenov, Mikhail A., Slawinski, Cezary, Stratonovitch, Pierre, Supit, Iwan, Waha, Katharina, Wang, E., Wu, Lianhai, Zhao, Z., Röter, R.P., Fronzek, S., Pirtioja, Nina, Carter, Timothy, Bindi, Marco, Hoffmann, Holger, Palosuo, Taru, Ruiz Ramos, Margarita, Tao, Fulu, Trnka, M., Acutis, Marco, Asseng, Senthold, Baranowski, Piotr, Basso, Bruno, Bodin, Per, Buis, Samuel, Cammarano, Davide, Deligios, Paola, Destain, Marie-France, Dumont, Benjamin, Ewert, Frank, Ferrise, Roberto, François, Louis, Gaiser, Thomas, Hlavinka, P., Jacquemin, I., Kersebaum, K.C., Kollas, Chris, Krzyszczak, Jaromir, Lorite, I. J., Minet, Julien, Minguez Tudela, Maria Ines, Montesino, Manuel, Moriondo, Marco, Müller, Christoph, Nendel, Claas, Öztürk, Isik, Perego, Alessia, Rodríguez Sánchez, Alfredo, Ruane, Alex C., Ruget, François, Sanna, Mattia, Semenov, Mikhail A., Slawinski, Cezary, Stratonovitch, Pierre, Supit, Iwan, Waha, Katharina, Wang, E., Wu, Lianhai, Zhao, Z., and Röter, R.P.

Abstract

Crop growth simulation models can differ greatly in their treatment of key processes and hence in their response to environmental conditions. Here, we used an ensemble of 26 process-based wheat models applied at sites across a European transect to compare their sensitivity to changes in temperature (−2 to +9°C) and precipitation (−50 to +50%). Model results were analysed by plotting them as impact response surfaces (IRSs), classifying the IRS patterns of individual model simulations, describing these classes and analysing factors that may explain the major differences in model responses. The model ensemble was used to simulate yields of winter and spring wheat at four sites in Finland, Germany and Spain. Results were plotted as IRSs that show changes in yields relative to the baseline with respect to temperature and precipitation. IRSs of 30-year means and selected extreme years were classified using two approaches describing their pattern. The expert diagnostic approach (EDA) combines two aspects of IRS patterns: location of the maximum yield (nine classes) and strength of the yield response with respect to climate (four classes), resulting in a total of 36 combined classes defined using criteria pre-specified by experts. The statistical diagnostic approach (SDA) groups IRSs by comparing their pattern and magnitude, without attempting to interpret these features. It applies a hierarchical clustering method, grouping response patterns using a distance metric that combines the spatial correlation and Euclidian distance between IRS pairs. The two approaches were used to investigate whether different patterns of yield response could be related to different properties of the crop models, specifically their genealogy, calibration and process description. Although no single model property across a large model ensemble was found to explain the integrated yield response to temperature and precipitation perturbations, the application of the EDA and SDA approaches revealed th

Published
2018

138. Simulation of soil organic carbon effects on long-term winter wheat (Triticum aestivum) production under varying fertilizer inputs

Author

Ghaley, Bhim Bahadur, Wosten, Henk, Olesen, Jørgen Eivind, Schelde, Kirsten, Baby, Sanmohan, Karki, Yubaraj K., Borgesen, Christen D., Smith, Pete, Yeluripati, Jagadeesh, Ferrise, Roberto, Bindi, Marco, Kuikman, Peter, Lesschen, Jan-Peter, Porter, John Roy, Ghaley, Bhim Bahadur, Wosten, Henk, Olesen, Jørgen Eivind, Schelde, Kirsten, Baby, Sanmohan, Karki, Yubaraj K., Borgesen, Christen D., Smith, Pete, Yeluripati, Jagadeesh, Ferrise, Roberto, Bindi, Marco, Kuikman, Peter, Lesschen, Jan-Peter, and Porter, John Roy

Published
2018

139. Classifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change

Author

Fronzek, Stefan, Pirttioja, Nina, Carter, Timothy R., Bindi, Marco, Hoffmann, Holger, Palosuo, Taru, Ruiz-Ramos, Margarita, Tao, Fulu, Trnka, Miroslav, Acutis, Marco, Asseng, Senthold, Baranowski, Piotr, Basso, Bruno, Bodin, Per, Buis, Samuel, Cammarano, Davide, Deligios, Paola, Destain, Marie-France, Dumont, Benjamin, Ewert, Frank, Ferrise, Roberto, François, Louis, Gaiser, Thomas, Hlavinka, Petr, Jacquemin, Ingrid, Kersebaum, Kurt Christian, Kollas, Chris, Krzyszczak, Jaromir, Lorite, Ignacio J., Minet, Julien, Minguez, M. Ines, Montesino San Martin, Manuel, Moriondo, Marco, Müller, Christoph, Nendel, Claas, Öztürk, Isik, Perego, Alessia, Rodríguez, Alfredo, Ruane, Alex C., Ruget, Françoise, Sanna, Mattia, Semenov, Mikhail A., Slawinski, Cezary, Stratonovitch, Pierre, Supit, Iwan, Waha, Katharina, Wang, Enli, Wu, Lianhai, Zhao, Zhigan, Rötter, Reimund P., Fronzek, Stefan, Pirttioja, Nina, Carter, Timothy R., Bindi, Marco, Hoffmann, Holger, Palosuo, Taru, Ruiz-Ramos, Margarita, Tao, Fulu, Trnka, Miroslav, Acutis, Marco, Asseng, Senthold, Baranowski, Piotr, Basso, Bruno, Bodin, Per, Buis, Samuel, Cammarano, Davide, Deligios, Paola, Destain, Marie-France, Dumont, Benjamin, Ewert, Frank, Ferrise, Roberto, François, Louis, Gaiser, Thomas, Hlavinka, Petr, Jacquemin, Ingrid, Kersebaum, Kurt Christian, Kollas, Chris, Krzyszczak, Jaromir, Lorite, Ignacio J., Minet, Julien, Minguez, M. Ines, Montesino San Martin, Manuel, Moriondo, Marco, Müller, Christoph, Nendel, Claas, Öztürk, Isik, Perego, Alessia, Rodríguez, Alfredo, Ruane, Alex C., Ruget, Françoise, Sanna, Mattia, Semenov, Mikhail A., Slawinski, Cezary, Stratonovitch, Pierre, Supit, Iwan, Waha, Katharina, Wang, Enli, Wu, Lianhai, Zhao, Zhigan, and Rötter, Reimund P.

Published
2018

140. Adaptation response surfaces for managing wheat under perturbed climate and CO2 in a Mediterranean environment

Author

Ruiz-Ramos, M., Ferrise, Roberto, Rodríguez, A, Lorite, I. J., Bindi, Marco, Carter, T. R., Fronzek, S, Palosuo, T., Pirttioja, N., Baranowski, P., Buis, S., Cammarano, D., Chen, Y., Dumont, Bertrand, Ewert, F., Gaiser, T., Hlavinka, P., Hoffmann, H., Höhn, J. G., Jurecka, F., Kersebaum, K. C., Krzyszczak, J., Lana, M., Mechiche-Alami, A., Minet, J., Montesino Pouzols, Federico, Nendel, C., Porter, John Roy, Ruget, F., Semenov, M. A., Steinmetz, Z., Stratonovitch, P., Supit, Iwan, Tao, F., Trnka, M., de Wit, Cynthia A., Rötter, Reimund P, Ruiz-Ramos, M., Ferrise, Roberto, Rodríguez, A, Lorite, I. J., Bindi, Marco, Carter, T. R., Fronzek, S, Palosuo, T., Pirttioja, N., Baranowski, P., Buis, S., Cammarano, D., Chen, Y., Dumont, Bertrand, Ewert, F., Gaiser, T., Hlavinka, P., Hoffmann, H., Höhn, J. G., Jurecka, F., Kersebaum, K. C., Krzyszczak, J., Lana, M., Mechiche-Alami, A., Minet, J., Montesino Pouzols, Federico, Nendel, C., Porter, John Roy, Ruget, F., Semenov, M. A., Steinmetz, Z., Stratonovitch, P., Supit, Iwan, Tao, F., Trnka, M., de Wit, Cynthia A., and Rötter, Reimund P

Abstract

Adaptation of crops to climate change has to be addressed locally due to the variability of soil, climate and the specific socio-economic settings influencing farm management decisions. Adaptation of rainfed cropping systems in the Mediterranean is especially challenging due to the projected decline in precipitation in the coming decades, which will increase the risk of droughts. Methods that can help explore uncertainties in climate projections and crop modelling, such as impact response surfaces (IRSs) and ensemble modelling, can then be valuable for identifying effective adaptations. Here, an ensemble of 17 crop models was used to simulate a total of 54 adaptation options for rainfed winter wheat (Triticum aestivum) at Lleida (NE Spain). To support the ensemble building, an ex post quality check of model simulations based on several criteria was performed. Those criteria were based on the "According to Our Current Knowledge" (AOCK) concept, which has been formalized here. Adaptations were based on changes in cultivars and management regarding phenology, vernalization, sowing date and irrigation. The effects of adaptation options under changed precipitation (P), temperature (T), [CO2] and soil type were analysed by constructing response surfaces, which we termed, in accordance with their specific purpose, adaptation response surfaces (ARSs). These were created to assess the effect of adaptations through a range of plausible P, T and [CO2] perturbations. The results indicated that impacts of altered climate were predominantly negative. No single adaptation was capable of overcoming the detrimental effect of the complex interactions imposed by the P, T and [CO2] perturbations except for supplementary irrigation (sI), which reduced the potential impacts under most of the perturbations. Yet, a combination of adaptations for dealing with climate change demonstrated that effective adaptation is possible at Lleida. Combinations based o

Published
2018

141. Simulation of soil organic carbon effects on long-term winter wheat (Triticum aestivum) production under varying fertilizer inputs

Author

Ghaley, Bhim B., Wösten, Henk, Olesen, Jørgen E., Schelde, Kirsten, Baby, Sanmohan, Karki, Yubaraj K., Børgesen, Christen D., Smith, Pete, Yeluripati, Jagadeesh, Ferrise, Roberto, Bindi, Marco, Kuikman, Peter, Lesschen, Jan Peter, Porter, John R., Ghaley, Bhim B., Wösten, Henk, Olesen, Jørgen E., Schelde, Kirsten, Baby, Sanmohan, Karki, Yubaraj K., Børgesen, Christen D., Smith, Pete, Yeluripati, Jagadeesh, Ferrise, Roberto, Bindi, Marco, Kuikman, Peter, Lesschen, Jan Peter, and Porter, John R.

Abstract

Soil organic carbon (SOC) has a vital role to enhance agricultural productivity and for mitigation of climate change. To quantify SOC effects on productivity, process models serve as a robust tool to keep track of multiple plant and soil factors and their interactions affecting SOC dynamics. We used soil-plant-atmospheric model viz. DAISY, to assess effects of SOC on nitrogen (N) supply and plant available water (PAW) under varying N fertilizer rates in winter wheat (Triticum aestivum) in Denmark. The study objective was assessment of SOC effects on winter wheat grain and aboveground biomass accumulation at three SOC levels (low: 0.7% SOC; reference: 1.3% SOC; and high: 2% SOC) with five nitrogen rates (0–200 kg N ha−1) and PAW at low, reference, and high SOC levels. The three SOC levels had significant effects on grain yields and aboveground biomass accumulation at only 0–100 kg N ha−1 and the SOC effects decreased with increasing N rates until no effects at 150–200 kg N ha−1. PAW had significant positive correlation with SOC content, with high SOC retaining higher PAW compared to low and reference SOC. The mean PAW and SOC correlation was given by PAW% = 1.0073 × SOC% + 15.641. For the 0.7–2% SOC range, the PAW increase was small with no significant effects on grain yields and aboveground biomass accumulation. The higher winter wheat grain and aboveground biomass was attributed to higher N supply in N deficient wheat production system. Our study suggested that building SOC enhances agronomic productivity at only 0–100 kg N ha−1. Maintenance of SOC stock will require regular replenishment of SOC, to compensate for the mineralization process degrading SOC over time. Hence, management can maximize realization of SOC benefits by building up SOC and maintaining N rates in the range 0–100 kg N ha−1, to reduce the off-farm N losses depending on the environmental zones, land use and the production system.

Published
2018

142. Evaluating the Potential of Legumes to Mitigate N2O Emissions From Permanent Grassland Using Process‐Based Models.

Author

Fuchs, Kathrin, Merbold, Lutz, Buchmann, Nina, Bellocchi, Gianni, Bindi, Marco, Brilli, Lorenzo, Conant, Richard T., Dorich, Christopher D., Ehrhardt, Fiona, Fitton, Nuala, Grace, Peter, Klumpp, Katja, Liebig, Mark, Lieffering, Mark, Martin, Raphaël, McAuliffe, Russell, Newton, Paul C. D., Rees, Robert M., Recous, Sylvie, and Smith, Pete

Subjects
GRASSLAND soils, LEGUMES, GRASSLANDS, FERTILIZER application, NITROGEN fertilizers, NITROUS oxide
Abstract

A potential strategy for mitigating nitrous oxide (N2O) emissions from permanent grasslands is the partial substitution of fertilizer nitrogen (Nfert) with symbiotically fixed nitrogen (Nsymb) from legumes. The input of Nsymb reduces the energy costs of producing fertilizer and provides a supply of nitrogen (N) for plants that is more synchronous to plant demand than occasional fertilizer applications. Legumes have been promoted as a potential N2O mitigation strategy for grasslands, but evidence to support their efficacy is limited, partly due to the difficulty in conducting experiments across the large range of potential combinations of legume proportions and fertilizer N inputs. These experimental constraints can be overcome by biogeochemical models that can vary legume‐fertilizer combinations and subsequently aid the design of targeted experiments. Using two variants each of two biogeochemical models (APSIM and DayCent), we tested the N2O mitigation potential and productivity of full factorial combinations of legume proportions and fertilizer rates for five temperate grassland sites across the globe. Both models showed that replacing fertilizer with legumes reduced N2O emissions without reducing productivity across a broad range of legume‐fertilizer combinations. Although the models were consistent with the relative changes of N2O emissions compared to the baseline scenario (200 kg N ha−1 yr−1; no legumes), they predicted different levels of absolute N2O emissions and thus also of absolute N2O emission reductions; both were greater in DayCent than in APSIM. We recommend confirming these results with experimental studies assessing the effect of clover proportions in the range 30–50% and ≤150 kg N ha−1 yr−1 input as these were identified as best‐bet climate smart agricultural practices. Key Points: A partial substitution of fertilizer nitrogen with symbiotically fixed nitrogen could mitigate nitrous oxide (N2O) emissions in grasslands by around 130 Gg yr−1Experimentally testing this mitigation option is challenging so modeling offers means to identify the optimum legume/fertilizer combinationThe models showed that net benefits to N2O mitigation and yield can be achieved across a wide range of legume/fertilizer combinations [ABSTRACT FROM AUTHOR]

Published
2020
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143. Crop Residue Management as a Strategy of Adaptation and Mitigation to Climate Change

Author

Ventrella, Domenico, Giglio, Luisa, Bindi, Marco, Basso, Bruno, Bonciarelli, Umberto, Dallamarta, Anna, Danuso, Francesco, Doro, Luca, Ferrise, Roberto, Fornaro, Francesco, Garofalo, Pasquale, Ginaldi, Fabrizio, Iocola, Ileana, Merante, Paolo, Mula, Laura, Onofri, Andrea, Orlandini, Simone, Pasqui, Massimiliano, Tomozeiu, Rodica, Villani, Giulia, Vonella, Alessandro Vimorio, and Roggero, Pier Paolo

Published
2017
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144. Multi-model uncertainty analysis in predicting grain N for crop rotations in Europe

Author

Yin, Xiaogang, Kersebaum, Kurt Christian, Kollas, Chris, Baby, Sanmohan, Beaudoin, Nicolas, Manevski, Kiril, Palosuo, Taru, Nendel, Claas, Wu, Lianhai, Hoffmann, Munir, Hoffmann, Holger, Sharif, Behzad, Armas-Herrera, Cecilia M., Bindi, Marco, Charfeddine, Monia, Conradt, Tobias, Constantin, Julie, Ewert, Frank, Ferrise, Roberto, Gaiser, Thomas, de Cortazar-Atauri, Iñaki Garcia, Giglio, Luisa, Hlavinka, Petr, Lana, Marcos, Launay, Marie, Louarn, Gaëtan, Manderscheid, Remy, Mary, Bruno, Mirschel, Wilfried, Moriondo, Marco, Öztürk, Isik, Pacholski, Andreas, Ripoche-Wachter, Dominique, Rötter, Reimund P., Ruget, Françoise, Trnka, Mirek, Ventrella, Domenico, Weigel, Hans Joachim, Olesen, Jørgen E., Unité d'Agronomie de Laon-Reims-Mons (AGRO-LRM), Institut National de la Recherche Agronomique (INRA), Department of Agroecology, Aarhus University [Aarhus], Institute of Landscape Systems Analysis, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Potsdam Institute for Climate Impact Research (PIK), Natural Resources Institute Finland, Rothamsted Research, Crop Production Systems in the Tropics, Georg-August-Universität Göttingen, INRES, Rheinische Friedrich-Wilhelms-Universität Bonn, Department of Agri-food Production and Environmental Sciences, University of Florence (UNIFI), Unità di ricerca per i sistemi colturali degli ambienti caldo-aridi, Agricultural Research Council (CRA), UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, UE Agroclim (UE AGROCLIM), Global Change Research Centre (CzechGlobe), Mendel University in Brno, Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Thünen Institute of Biodiversity, Istituto di Biometeorologia [Firenze] (IBIMET), Consiglio Nazionale delle Ricerche (CNR), EurochemAgro, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), FACCE MACSUR 2812ERA147/CARBIOCIAL 01LL0902M/ KULUNDA 01LL0905L /NORFASYS 268277 292944/MACSUR D.M. 24064/7303/15/ QJ1310123, Agroressources et Impacts environnementaux (AgroImpact), Natural resources institute Finland, Georg-August-University [Göttingen], Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Agroclim (AGROCLIM), and Mendel University in Brno (MENDELU)

Subjects
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Irrigation, 010504 meteorology & atmospheric sciences, Model calibration, Soil Science, Single Year simulation, Plant Science, 01 natural sciences, Model ensemble, Crop, continuous simulation, model ensemble, Grain N, Uncertainty analysis, 0105 earth and related environmental sciences, Mathematics, 2. Zero hunger, model calibration, grain n, Crop yield, Simulation modeling, Continuous simulation, Model inter-comparison, Single year simulation, 04 agricultural and veterinary sciences, Crop rotation, model inter-comparison, single year simulation, Tillage, Mean absolute percentage error, Agronomy, Continous simulation, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Agronomy and Crop Science, Model intercomparison
Abstract

Realistic estimation of grain nitrogen (N; N in grain yield) is crucial for assessing N management in crop rotations, but there is little information on the performance of commonly used crop models for simulating grain N. Therefore, the objectives of the study were to (1) test if continuous simulation (multi-year) performs better than single year simulation, (2) assess if calibration improves model performance at different calibration levels, and (3) investigate if a multi-model ensemble can substantially reduce uncertainty in reproducing grain N. For this purpose, 12 models were applied simulating different treatments (catch crops, CO2 concentrations, irrigation, N application, residues and tillage) in four multi-year rotation experiments in Europe to assess modelling accuracy. Seven grain and seed crops in four rotation systems in Europe were included in the study, namely winter wheat, winter barley, spring barley, spring oat, winter rye, pea and winter oilseed rape. Our results indicate that the higher level of calibration significantly increased the quality of the simulation for grain N. In addition, models performed better in predicting grain N of winter wheat, winter barley and spring barley compared to spring oat, winter rye, pea and winter oilseed rape. For each crop, the use of the ensemble mean significantly reduced the mean absolute percentage error (MAPE) between simulations and observations to less than 15%, thus a multi–model ensemble can more precisely predict grain N than a random single model. Models correctly simulated the effects of enhanced N input on grain N of winter wheat and winter barley, whereas effects of tillage and irrigation were less well estimated. However, the use of continuous simulation did not improve the simulations as compared to single year simulation based on the multi-year performance, which suggests needs for further model improvements of crop rotation effects.

Published
2017

145. Model comparison and improvement: Links established with other consortia - Report on Task H1-XC1 - Sub-task XC1.3

Author

Bellocchi, Gianni, Brilli, Lorenzo, Ferrise, Roberto, Dibari, Camilla, and Bindi, Marco

Subjects
GeneralLiterature_REFERENCE(e.g.,dictionaries,encyclopedias,glossaries), ComputingMilieux_MISCELLANEOUS
Abstract

XC1 has established links to other research activities and consortia on model comparison and improvement. They include the global initiatives AgMIP (http://www.agmip.org) and GRA (http://www.globalresearchalliance.org), and the EU-FP7 project MODEXTREME (http://modextreme.org). These links have allowed sharing and communication of recent results and methods, and have created opportunities for future research calls.

Published
2017

146. Applying adaptation response surfaces for managing wheat under perturbed climate and elevated CO2 in a Mediterranean environment

Author

RUIZ RAMOS, MARGARITA, Ferrise, Roberto, Rodríguez, Alfredo, Lorite, Ignacio J., Bindi, Marco, Carter, Timothy R., Fronzek, Stefan, Palosuo, Taru, Pirttioja, Nina, Baranowski, Piotr, Buis, Samuel, Cammarano, Davide, Chen, Y., Dumont, Benjamin, Ewert, Frank, Gaiser, Thomas, Hlavinka, Petr, Hoffmann, Holger, Höhn, J. G., Jurecka, F., Kersebaum, Kurt Christian, Krzyszczak, J., Lana, Marcos, Mechiche-Alami, A., Minet, Julien, Montesino, M., Nendel, Claas, Porter, John R., RUGET, Françoise, Semenov, Mikhael A., Steinmetz, Z., Stratonovitch, Pierre, Supit, I., Tao, Fulu, Trnka, Miroslav, de Wit, A., and Rötter, Reimund

Subjects
Climate Change, Agriculture, Food Security, Joint Programming Initiative, crop modelling
Abstract

This study developed Adaptation Response Surfaces and applied them to a study case in North East Spain on winter crops adaptation, using rainfed winter wheat as reference crop. Crop responses to perturbed temperature, precipitation and CO2 were simulated by an ensemble of crop models. A set of combined changes on cultivars (on vernalisation requirements and phenology) and management (on sowing date and irrigation) were considered as adaptation options and simulated by the crop model ensemble. The discussion focused on two main issues: 1) the recommended adaptation options for different soil types and perturbation levels, and 2) the need of applying our current knowledge (AOCK) when building a crop model ensemble. The study has been published Agricultural Systems (Available online 25 January 2017, https://doi.org/10.1016/j.agsy.2017.01.009), and the text below consists on extracts from that paper.

Published
2017
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147. Assessment of climate change impacts on SOC dynamic in rainfed cereal cropping systems managed with contrasting tillage practices using a multi model approach

Author

Iocola, I., Bassu, S., FARINA, ROBERTA, Antichi, Daniele, Basso, Bruno, Bindi, Marco, Dallamarta, A., Danuso, F., DORO, Luca, Ferrise, Roberto, Giglio, L., Ginaldi, F., Mazzoncini, M., Mula, L., ORSINI, Roberto, Pasqui, Massimiliano, Seddaiu, Giovanna, Tomozeiu, R., Ventrella, Domenico, Villani, G., and Roggero, Pier Paolo

Abstract

Conservation 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.

Published
2017
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149. Probabilistic assessment of adaptation options from an ensemble of crop models: a case study in the Mediterranean

Author

Ferrise, Roberto, RUIZ RAMOS, MARGARITA, Rodríguez, Alfredo, Lorite, I.J., Pirttioja, N., Fronzek, S., Palosuo, T., Carter, T.R., Bindi, Marco, Höhn, J.G., Baranowski, P., Buis, S., Cammarano, Davide, Nendel, Claas, Hlavinka, P., Hoffmann, Holger, Jurecka, F., Kersebaum, Kurt Christian, Krzyszczak, J., Lana, Marcos, Mechiche-Alami, A., Minet, J., Montesino, M., Porter, J.R., Ruget, F., Steinmetz, Z., Stratonovitch, P., Supit, I., Tao, F., Trnka, Miroslav, de Wit, A., Rötter, Reimund, Y. Chen, B. Dumont, Ewert, Frank, Gaiser, Thomas, and M. A. Semenov

Subjects
Agricultura
Abstract

Uncertainty 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.

Published
2017

150. Needs on model improvement: Report on Task H1-XC1 - Sub-task XC1.1. - Modelling European Agriculture with Climate Change for Food Security (MACSUR)2017 Scientific Conference, 22-24 May, 2017 in Berlin

Author

Ferrise, Roberto, Bellocchi, Gianni, Brilli, Lorenzo, Dibari, Camilla, and Bindi, Marco

Subjects
agriculture, livestock modelling, gap analysis, food security, Joint Programming Initiative, grassland modelling, crop modelling, climate change
Abstract

The need to answer new scientific questions can be satisfied by an increased knowledge of physiological mechanisms which, in turn, can be used for improving the accuracy of simulations of process-based models. In this context, this report highlights areas that need to be further improved to facilitate the operational use of simulation models. It describes missing approaches within simulation models which, if implemented, would likely improve the representation of the dynamics of processes underlying different compartments of crop and grassland systems (e.g. plant growth and development, yield production, GHG emissions), as well as of the livestock production systems. The following rationale has been used in the organization of this report. We first briefly introduced the need to improve the reliability of existing models. Then, we indicated climate change and its influence on the global carbon balance as the main issue to be addressed by existing crop and grassland (section 2), and livestock (section 3) models. In section 2, among the major aspects that if implemented may reduce the uncertainty inherent to model outputs, we suggested: i) quantifying the effects of climate extremes on biological systems; ii) modelling of multi-species sward; iii) coupling of pest and disease sub-models; iv) improvement of the carry-over effect. In section 3, as the most important aspects to consider in livestock models we indicated: i) impacts and dynamics of pathogens and disease; ii) heat stress effects on livestock; iii) effects on grassland productivity and nutritional values; iv) improvement of GHG emissions dynamics. In Section 4, remarks are made concerning the need to implement the suggested aspects into the existing models.

Published
2017

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