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280 results on '"Martre, P."'

1. Global needs for nitrogen fertilizer to improve wheat yield under climate change

Author

Martre, Pierre, Dueri, Sibylle, Guarin, Jose Rafael, Ewert, Frank, Webber, Heidi, Calderini, Daniel, Molero, Gemma, Reynolds, Matthew, Miralles, Daniel, Garcia, Guillermo, Brown, Hamish, George, Mike, Craigie, Rob, Cohan, Jean-Pierre, Deswarte, Jean-Charles, Slafer, Gustavo, Giunta, Francesco, Cammarano, Davide, Ferrise, Roberto, Gaiser, Thomas, Gao, Yujing, Hochman, Zvi, Hoogenboom, Gerrit, Hunt, Leslie A., Kersebaum, Kurt C., Nendel, Claas, Padovan, Gloria, Ruane, Alex C., Srivastava, Amit Kumar, Stella, Tommaso, Supit, Iwan, Thorburn, Peter, Wang, Enli, Wolf, Joost, Zhao, Chuang, Zhao, Zhigan, and Asseng, Senthold

Published
2024
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3. Risk of metachronous peritoneal metastases after surgery for obstructive colon cancer: Multivariate analysis from a series of 1,085 patients

Author

Abba, J., Alfarai, A., d’Annunzio, E., Arvieux, C., Badic, B., Aumont, A., Balbo, G., Baque, P., Baraket, O., Bege, T., Bellinger, J., Bert, M., Bertrand, M., Beyer-Berjot, L., Blanc, B., Brouquet, A., Brunetti, F., Cabau, M., Catheline, J.M., Cazauran, J.B., Chatelain, E., Chau, A., Codjia, T., Collard, M., Corte, H., Couchard, A.C., David, A., Dazza, M., Dejeante, C., De La Villéon, B., Denost, Q., Diaz de Cerio, J.M., Djawad-Boumediene, B., Dubuisson, V., Duchalais, E., Dufour, F., Dumaine, A.S., Esposito, F., Etienne, J.C., Eveno, C., Fayssal, E., Fernoux, P., Fixot, K., Fuks, D., Gagnat, G., Goin, G., Goudard, Y., Grégoire, E., Guillem, P., Hamel, S., Heyd, B., Huart, E., Humeau, M., Issard, J., Jafar, Y., Kadoche, D., Kahn, X., Lacaze, L., Lailler, G., Lefèvre, J.H., Lizzi, V., Loge, L., Lupinacci, R., Mabrut, J.Y., Maes, B., Maggiori, L., Mallet, L., Mariol, P., Martre, P., Mauvais, F., Messière, A.S., Michot, N., Moszkowicz, D., Munoz, N., Ortega-Deballon, P., Paquette, B., Parc, Y., Pauleau, G., Pautrat, K., Peschaud, F., Philouze, G., Pichot-Delahaye, V., Piessen, G., Pitel, S., Rat, P., Regimbeau, J.M., Rivier, P., Roussel, E., Sage, P.Y., de Saint Roman, C., Sockeel, P., Susoko, L., Tetard, O., Tortajada, P., Tranchart, A., Tresallet, C., Trilling, B., Ulloa-Severino, B., Vauchaussade de Chaumont, A., Venara, A., Cazelles, Antoine, Tarhini, Ahmad, Sabbagh, Charles, Mege, Diane, Bridoux, Valérie, Lakkis, Zaher, Voron, Thibault, Abdalla, Solafah, Lecot, Frederik, Karoui, Mehdi, and Manceau, Gilles

Published
2025
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14. A statistical analysis of three ensembles of crop model responses to temperature and CO2 concentration

Author

Makowski, D., Asseng, S., Ewert, F., Bassu, S., Durand, J.L., Li, T., Martre, P., Adam, M., Aggarwal, P.K., Angulo, C., Baron, C., Basso, B., Bertuzzi, P., Biernath, C., Boogaard, H., Boote, K.J., Bouman, B., Bregaglio, S., Brisson, N., Buis, S., Cammarano, D., Challinor, A.J., Confalonieri, R., Conijn, J.G., Corbeels, M., Deryng, D., De Sanctis, G., Doltra, J., Fumoto, T., Gaydon, D., Gayler, S., Goldberg, R., Grant, R.F., Grassini, P., Hatfield, J.L., Hasegawa, T., Heng, L., Hoek, S., Hooker, J., Hunt, L.A., Ingwersen, J., Izaurralde, R.C., Jongschaap, R.E.E., Jones, J.W., Kemanian, R.A., Kersebaum, K.C., Kim, S.-H., Lizaso, J., Marcaida, M., III, Müller, C., Nakagawa, H., Naresh Kumar, S., Nendel, C., O’Leary, G.J., Olesen, J.E., Oriol, P., Osborne, T.M., Palosuo, T., Pravia, M.V., Priesack, E., Ripoche, D., Rosenzweig, C., Ruane, A.C., Ruget, F., Sau, F., Semenov, M.A., Shcherbak, I., Singh, B., Singh, U., Soo, H.K., Steduto, P., Stöckle, C., Stratonovitch, P., Streck, T., Supit, I., Tang, L., Tao, F., Teixeira, E.I., Thorburn, P., Timlin, D., Travasso, M., Rötter, R.P., Waha, K., Wallach, D., White, J.W., Wilkens, P., Williams, J.R., Wolf, J., Yin, X., Yoshida, H., Zhang, Z., and Zhu, Y.

Published
2015
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15. Soil Organic Carbon and Nitrogen Feedbacks on Crop Yields Under Climate Change

Author

Basso, B, Dumont, B, Maestrini, B, Shcherbak, I, Robertson, G. P, Porter, J. R, Smith, P, Paustian, K, Grace, P. R, Asseng, S, Bassu, S, Biernath, C, Boote, K. J, Cammarano, D, Sanctis, G. De, Durand, J.-L, Ewert, F, Gayler, S, Hyndman, D. W, Kent, J, Martre, P, Nendel, C, Priesack, E, Ripoche, D, Ruane, A. C, Sharp, J, Thorburn, P. J, Hatfield, J. L, Jones, J. W, and Rosenzweig, C

Subjects
Earth Resources And Remote Sensing
Abstract

A critical omission from climate change impact studies on crop yield is the interaction between soil organic carbon (SOC), nitrogen (N) availability, and carbon dioxide (CO2). We used a multimodel ensemble to predict the effects of SOC and N under different scenarios of temperatures and CO2 concentrations on maize (Zea mays L.) and wheat (Triticum aestivum L.) yield in eight sites across the world. We found that including feedbacks from SOC and N losses due to increased temperatures would reduce yields by 13% in wheat and 19% in maize for a 3°C rise temperature with no adaptation practices. These losses correspond to an additional 4.5% (+3°C) when compared to crop yield reductions attributed to temperature increase alone. Future CO2 increase to 540 ppm would partially compensate losses by 80% for both maize and wheat at +3°C, and by 35% for wheat and 20% for maize at +6°C, relative to the baseline CO2 scenario.

Published
2018
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16. Diverging importance of drought stress for maize and winter wheat in Europe

Author

Webber, Heidi, Ewert, Frank, Olesen, Jørgen E., Müller, Christoph, Fronzek, Stefan, Ruane, Alex C., Bourgault, Maryse, Martre, Pierre, Ababaei, Behnam, Bindi, Marco, Ferrise, Roberto, Finger, Robert, Fodor, Nándor, Gabaldón-Leal, Clara, Gaiser, Thomas, Jabloun, Mohamed, Kersebaum, Kurt-Christian, Lizaso, Jon I., Lorite, Ignacio J., Manceau, Loic, Moriondo, Marco, Nendel, Claas, Rodríguez, Alfredo, Ruiz-Ramos, Margarita, Semenov, Mikhail A., Siebert, Stefan, Stella, Tommaso, Stratonovitch, Pierre, Trombi, Giacomo, and Wallach, Daniel

Published
2018
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20. Benchmark Data Set for Wheat Growth Models: Field Experiments and AgMIP Multi-Model Simulations.

Author

Asseng, S, Ewert, F, Martre, P, Rosenzweig, C, Jones, J. W, Hatfield, J. L, Ruane, A. C, Boote, K. J, Thorburn, P.J, and Rotter, R. P

Subjects
Meteorology And Climatology
Abstract

The data set includes a current representative management treatment from detailed, quality-tested sentinel field experiments with wheat from four contrasting environments including Australia, The Netherlands, India and Argentina. Measurements include local daily climate data (solar radiation, maximum and minimum temperature, precipitation, surface wind, dew point temperature, relative humidity, and vapor pressure), soil characteristics, frequent growth, nitrogen in crop and soil, crop and soil water and yield components. Simulations include results from 27 wheat models and a sensitivity analysis with 26 models and 30 years (1981-2010) for each location, for elevated atmospheric CO2 and temperature changes, a heat stress sensitivity analysis at anthesis, and a sensitivity analysis with soil and crop management variations and a Global Climate Model end-century scenario.

Published
2015
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22. Rising Temperatures Reduce Global Wheat Production

Author

Asseng, S, Ewert, F, Martre, P, Rötter, R. P, Lobell, D. B, Cammarano, D, Kimball, B. A, Ottman, M. J, Wall, G. W, White, J. W, Reynolds, M. P, Alderman, P. D, Prasad, P. V. V, Aggarwal, P. K, Anothai, J, Basso, B, Biernath, C, Challinor, A. J, De Sanctis, G, Doltra, J, Fereres, E, Garcia-Vila, M, Gayler, S, Hoogenboom, G, Hunt, L. A, Izaurralde, R. C, Jabloun, M, C. D. Jones, Kersebaum, K. C, Koehler, A-K, Müller, C, Naresh Kumar, S, Nendel, C, O’Leary, G, Olesen, J. E, Palosuo, T, Priesack, E, Eyshi Rezaei, E, Ruane, A. C, Semenov, M. A, Shcherbak, I, Stöckle, C, Stratonovitch, P, Streck, T, Supit, I, Tao, F, Thorburn, P. J, Waha, K, Wang, E, Wallach, D, Wolf, J, Zhao, Z, and Zhu, Y

Subjects
Meteorology And Climatology
Abstract

Crop models are essential tools for assessing the threat of climate change to local and global food production. Present models used to predict wheat grain yield are highly uncertain when simulating how crops respond to temperature. Here we systematically tested 30 different wheat crop models of the Agricultural Model Intercomparison and Improvement Project against field experiments in which growing season mean temperatures ranged from 15 degrees C to 32◦ degrees C, including experiments with artificial heating. Many models simulated yields well, but were less accurate at higher temperatures. The model ensemble median was consistently more accurate in simulating the crop temperature response than any single model, regardless of the input information used. Extrapolating the model ensemble temperature response indicates that warming is already slowing yield gains at a majority of wheat-growing locations. Global wheat production is estimated to fall by 6% for each degree C of further temperature increase and become more variable over space and time.

Published
2015
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24. Author Correction: The uncertainty of crop yield projections is reduced by improved temperature response functions

Author

Wang, Enli, Martre, Pierre, Zhao, Zhigan, Ewert, Frank, Maiorano, Andrea, Rötter, Reimund P., Kimball, Bruce A., Ottman, Michael J., Wall, Gerard W., White, Jeffrey W., Reynolds, Matthew P., Alderman, Phillip D., Aggarwal, Pramod K., Anothai, Jakarat, Basso, Bruno, Biernath, Christian, Cammarano, Davide, Challinor, Andrew J., De Sanctis, Giacomo, Doltra, Jordi, Dumont, Benjamin, Fereres, Elias, Garcia-Vila, Margarita, Gayler, Sebastian, Hoogenboom, Gerrit, Hunt, Leslie A., Izaurralde, Roberto C., Jabloun, Mohamed, Jones, Curtis D., Kersebaum, Kurt C., Koehler, Ann-Kristin, Liu, Leilei, Müller, Christoph, Kumar, Soora Naresh, Nendel, Claas, O’Leary, Garry, Olesen, Jørgen E., Palosuo, Taru, Priesack, Eckart, Rezaei, Ehsan Eyshi, Ripoche, Dominique, Ruane, Alex C., Semenov, Mikhail A., Shcherbak, Iurii, Stöckle, Claudio, Stratonovitch, Pierre, Streck, Thilo, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Waha, Katharina, Wallach, Daniel, Wang, Zhimin, Wolf, Joost, Zhu, Yan, and Asseng, Senthold

Published
2017
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27. Uncertainty in Simulating Wheat Yields Under Climate Change

Author

Asseng, S, Ewert, F, Rosenzweig, Cynthia, Jones, J. W, Hatfield, J. W, Ruane, A. C, Boote, K. J, Thornburn, P. J, Rotter, R. P, Cammarano, D, Brisson, N, Basso, B, Martre, P, Angulo, C, Bertuzzi, P, Biernath, C, Challinor, A. J, Doltra, J, Gayler, S, Goldberg, R, Grant, R, Heng, L, Hooker, J, Hunt, L. A, and Ingwersen, J

Subjects
Meteorology And Climatology, Earth Resources And Remote Sensing
Abstract

Projections of climate change impacts on crop yields are inherently uncertain1. Uncertainty is often quantified when projecting future greenhouse gas emissions and their influence on climate2. However, multi-model uncertainty analysis of crop responses to climate change is rare because systematic and objective comparisons among process-based crop simulation models1,3 are difficult4. Here we present the largest standardized model intercomparison for climate change impacts so far. We found that individual crop models are able to simulate measured wheat grain yields accurately under a range of environments, particularly if the input information is sufficient. However, simulated climate change impacts vary across models owing to differences in model structures and parameter values. A greater proportion of the uncertainty in climate change impact projections was due to variations among crop models than to variations among downscaled general circulation models. Uncertainties in simulated impacts increased with CO2 concentrations and associated warming. These impact uncertainties can be reduced by improving temperature and CO2 relationships in models and better quantified through use of multi-model ensembles. Less uncertainty in describing how climate change may affect agricultural productivity will aid adaptation strategy development and policymaking.

Published
2013
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30. Invited Review: IPCC, Agriculture and Food - A Case of Shifting Cultivation and History

Author

Porter, JR, Challinor, AJ, Henriksen, CB, Howden, SM, Martre, P, and Smith, P

Abstract

Since 1990 the Intergovernmental Panel on Climate Change (IPCC) has produced five Assessment Reports (ARs), in which agriculture as the production of food for humans via crops and livestock have featured in one form or another. A constructed data base of the ca. 2,100 cited experiments and simulations in the five ARs were analysed with respect to impacts on yields via crop type, region and whether or not adaptation was included. Quantitative data on impacts and adaptation in livestock farming have been extremely scarce in the ARs. The main conclusions from impact and adaptation are that crop yields will decline but that responses have large statistical variation. Mitigation assessments in the ARs have used both bottom-up and top-down methods but need better to link emissions and their mitigation with food production and security. Relevant policy options have become broader in later ARs and included more of the social and non-production aspects of food security. Our overall conclusion is that agriculture and food security, which are two of the most central, critical and imminent issues in climate change, have been dealt with in an unfocussed and inconsistent manner between the IPCC five ARs. This is partly a result of agriculture spanning two IPCC working groups but also the very strong focus on projections from computer crop simulation modelling. For the future, we suggest a need to examine interactions between themes such as crop resource use efficiencies and to include all production and non-production aspects of food security in future roles for integrated assessment models.

Published
2019

31. Abstracts of papers and posters safe handling of medicines

Author

Meyer, H. -J., Tromp, Th(Dick) F. J., van der Kleijn, E., Fields, Suzanne M., Moors, J. P. P., Enlund, H., Luscombe, D. K., Remond, J -Ph, Martv, S., Dhillon, S., Taylor, D., Kostrzewski, A., Bluml, B. M., Enlow, M. L., Metzler, S. E., de Vries, Philip A., Duty, C. J., Lee, H. Y., Ábrahám, Tibor, Cardoni, A. A., Sýkora, J., Szücsová, S., Reymond, J. -Ph, Marty, S, Engová, D., Sheridan, J., Vlček, J., Webb, D. G., Bates, I. P., Tabachnik A., Cass Y., Jacobs J., Vexler A., Gorodetsky, R., Whittlesea, C. M. C., Walker, R., Khan, F., Houghton, J., Phillips, I., Szymura-Oleksiak, J., Wasieczko, A., Wyska, E., Ayani, I., Errecalde, M. F., Rodriauez-Sasiain, J. M., Aouirre, C., Bellés Medall M. D., Casabó Alos V. G., Hervás Botella M. A., Cabrera Pérez A., Jiménez Torres N. V., Casterá Melchor D. E., Abad Gimeno F. J., Clark, J. E., Gomez, E. C., Cruz, A. C., Wilbur, R. L., Alfonso, I., Grout, C. H., Goldstein, R., Rivers, P., Stutz, K., Mühlcbach, S., Udeani, George, Zervopoulous, Irene, Patel, Krishna, Mullane, Michael, Radziwill, R., Dudek, J., Herbst, U., Bency, J., Muff, P., Marty, S., Rcymond, J. -Ph, Aumente M. D., Panadero M. D., Latre J. M., Torres M., Villegas M. J., Alvarez J., van Dijk, E. A., Logman, E. M., Ploeger, B., van der Schors, T. G., Steensma, D. J., Langlois-Karaga, A., Davignon, A., Bues-Charbit, M., Somme, V., Albanese, J., Durbec, O., Martin, C., Morati, N., Balansard, G., Pereira, M. E. Araújo, Nogueira, A., Silva, J. C., Mega, I., Costa, A. Gomes, Morais, J. A., Prata, M. M., Cajaraville, G., Tamés, M. J., García, B., Batel Marqucs F. J., Capela H. S., Pomingues P., Feio J. A., Siha C., Wolter, K., Fritschka, E., Schneesann, H., Stuurman, A., Gudjonsdottir, A., Angelo, H. R., Rasmuassen, M., Rasmussen, S. N., Carrera, J., Idoate, A., Modrego, A., Tejedor, I., Giráldez, J., Mangues, M. A., Farré, R., Demestre, X., Ginovart, G., Orozco, J., Julio, G., Moral, M. A., Busin, C., Bardin, C., Seroux, C., Sauvageon-Martre, H., Sraer, J. -D., Chast, F., Real, J. V., Climente, M., Font, I., Pérez, C., Ordovás, J. P., Hermenegildo, M., Catalán, J. L., Juan, J., Jiménez, N. V., Amiot, F., Clavel, S., Sarrut, B., Doreau, C., Hips, F. Zs, Soós, Gy, Petô-Nagy, G., Vincze, Z., Robays H., Freidank, A., Fischer, A., Cordovilla, H., Font, B., Ortega, A., Salek, M. S., Thomas, S., Baver, A. J., Vandenbroucke J., Ekedahl, A., Tuovinen K., Wallenius K., Enlund H., Boeke, A. W., Veenstra, E. J., van de Poll, M. A. P. C., Nonkes, K., Carlen, I., Tanner, M., Reinke, C., Escher, J., Fischer, J., Marly, S., Ooi, G. K., Cottle, A., Savage, A., Temesvári, E., Montero, M. C., Pastor, M., Valdivia, M. L., Buenestado, C., Lluch, A., Atienza, M., Santos, B., Echeverria Roca M., Fernandez Gallastegui S., Alonso Rizaldos C., Arce Trueba M. D., Booth, C. D., Aldaz, A., Lacasa, C., Cordovilla, M., Alzina, V., Sheridan, J. L., Webb, D. G., Usselmann, B., Carstens, G., Falcao, A. C., Femández de Gatta, M. M., Cobo, F. Nieto, Gorzátez, A. C. Alonso, Lanao, J. M., Dominguez -Gil, A., Burr, A., Ferreira, M. P., Rodrigues, M. O., Pereira, M. E., Marques, M. F., Vicente, M. C., Carrondo, A. P., Pires, M. A., Granja, M. A., Tuneu L., Serna J., Saló E., Cerutti P., Cardona P., Bonal J., Cosh, D. G., Abbott, F., Alderman, C. P., Mav, F. W., Peters, P. G., Scott, S. D., Jenkins, D., Cairns, C., Barber, N. D., Cammie, S. M., Burr, A. J., Brännström, I., Boya, P. Giner, Aliaga, C. Parreño, González, M. M. Negredo, Mansilla, L. Lorente, Pidrman, V., Fendrich, Z., Alberola, C., Castillo, B., Girón, C., Morell, A., López-Calull C., Carcia-Capdevila L., Sanz M., Cardona D., Castro I., Farré R., Saura R., Pérez J. M., Johnsen, Eva, Krogsgård, Ole, Pinheiro, R. L., Morais, I. A., Batel Marques F. J., Domingues P., Feio J. A., Silva C., Astdrager, C. L. L., Schnlekamp, T., de Gier, J. J., Rutten, C. W. H., Rivera, M. C., Vilanova, M., Mattei, I., Roglan, A., Serna, J., and Bonal, J.

Published
1993
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34. Shared protocols and data template in agronomic trials

Author

Cammarano, D., Martre, P., Drexler, D., Draye, X., Sessitsch, A., Pecchioni, N., Cooper, J., Willer, H., VOICU, A., and Hinsinger, P.

Subjects
Surveys and statistics, Indicators and other value-laden measures, Data template, Protocols, Data standard, agronomic data, field experiment
Abstract

Due to the overlap of many disciplines and the availability of novel technologies, modern agriculture has become a wide, interdisciplinary endeavor, especially in Precision Agriculture. The adoption of a standard format for reporting field experiments can help researchers to focus on the data rather than on re-formatting and understanding the structure of the data. This paper describes how a European consortium plans to: i) create a “handbook” of protocols for reporting definitions, methodologies and Parameters measured/calculated; and ii) how a data-template for field data was created and will be linked to the “handbook”. The overall goal of the EU-funded project Solutions for Solutions for improving Agroecosystem and Crop Efficiency for water and nutrient use (SolACE) is to help European agriculture face major challenges, such as increased rainfall variability and reduced use of N and P fertilizers in order to satisfy both economic and ecological goals. The “Handbook of Protocols” and the “Data Template” have been created to achieve a flexible, standard, and clear documentation linked with the data itself to facilitate interchange of data among project’s partners and any statistical analysis and modelling of different datasets.

Published
2018

36. Climate change impact and adaptation for wheat protein

Author

Asseng, S, Martre, P, Maiorano, A, Roetter, RP, O'Leary, GJ, Fitzgerald, GJ, Girousse, C, Motzo, R, Giunta, F, Babar, MA, Reynolds, MP, Kheir, AMS, Thorburn, PJ, Waha, K, Ruane, AC, Aggarwal, PK, Ahmed, M, Balkovic, J, Basso, B, Biernath, C, Bindi, M, Cammarano, D, Challinor, AJ, De Sanctis, G, Dumont, B, Rezaei, EE, Fereres, E, Ferrise, R, Garcia-Vila, M, Gayler, S, Gao, Y, Horan, H, Hoogenboom, G, Izaurralde, RC, Jabloun, M, Jones, CD, Kassie, BT, Kersebaum, K-C, Klein, C, Koehler, A-K, Liu, B, Minoli, S, San Martin, MM, Mueller, C, Kumar, SN, Nendel, C, Olesen, JE, Palosuo, T, Porter, JR, Priesack, E, Ripoche, D, Semenov, MA, Stockle, C, Stratonovitch, P, Streck, T, Supit, I, Tao, F, Van der Velde, M, Wallach, D, Wang, E, Webber, H, Wolf, J, Xiao, L, Zhang, Z, Zhao, Z, Zhu, Y, Ewert, F, Asseng, S, Martre, P, Maiorano, A, Roetter, RP, O'Leary, GJ, Fitzgerald, GJ, Girousse, C, Motzo, R, Giunta, F, Babar, MA, Reynolds, MP, Kheir, AMS, Thorburn, PJ, Waha, K, Ruane, AC, Aggarwal, PK, Ahmed, M, Balkovic, J, Basso, B, Biernath, C, Bindi, M, Cammarano, D, Challinor, AJ, De Sanctis, G, Dumont, B, Rezaei, EE, Fereres, E, Ferrise, R, Garcia-Vila, M, Gayler, S, Gao, Y, Horan, H, Hoogenboom, G, Izaurralde, RC, Jabloun, M, Jones, CD, Kassie, BT, Kersebaum, K-C, Klein, C, Koehler, A-K, Liu, B, Minoli, S, San Martin, MM, Mueller, C, Kumar, SN, Nendel, C, Olesen, JE, Palosuo, T, Porter, JR, Priesack, E, Ripoche, D, Semenov, MA, Stockle, C, Stratonovitch, P, Streck, T, Supit, I, Tao, F, Van der Velde, M, Wallach, D, Wang, E, Webber, H, Wolf, J, Xiao, L, Zhang, Z, Zhao, Z, Zhu, Y, and Ewert, F

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

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

Author

Liu, B., Martre, P., Ewert, F., Porter, J.R., Challinor, A.J., Müller, C., Ruane, A.C., Waha, K., Thorburn, P.J., Aggarwal, P.K., Ahmed, M., Balkovic, J., Basso, B., Biernath, C., Bindi, M., Cammarano, D., De Sanctis, G., Dumont, B., Espadafor, M., Eyshi Rezaei, E., Ferrise, R., Garcia‐Vila, M., Gayler, S., Gao, Y., Horan, H., Hoogenboom, G., Izaurralde, R.C., Jones, C.D., Kassie, B.T., Kersebaum, K.C., Klein, C., Koehler, A.‐K., Maiorano, A., Minoli, S., Montesino San Martin, M., Kumar, S.N., Nendel, C., O'Leary, G.J., Palosuo, T., Priesack, E., Ripoche, D., Rötter, R.P., Semenov, M.A., Stöckle, C., Streck, T., Supit, I., Tao, F., Van der Velde, M., Wallach, D., Wang, E., Webber, H., Wolf, J., Xiao, L., Zhang, Z., Zhao, Z., Zhu, Y., Asseng, S., Liu, B., Martre, P., Ewert, F., Porter, J.R., Challinor, A.J., Müller, C., Ruane, A.C., Waha, K., Thorburn, P.J., Aggarwal, P.K., Ahmed, M., Balkovic, J., Basso, B., Biernath, C., Bindi, M., Cammarano, D., De Sanctis, G., Dumont, B., Espadafor, M., Eyshi Rezaei, E., Ferrise, R., Garcia‐Vila, M., Gayler, S., Gao, Y., Horan, H., Hoogenboom, G., Izaurralde, R.C., Jones, C.D., Kassie, B.T., Kersebaum, K.C., Klein, C., Koehler, A.‐K., Maiorano, A., Minoli, S., Montesino San Martin, M., Kumar, S.N., Nendel, C., O'Leary, G.J., Palosuo, T., Priesack, E., Ripoche, D., Rötter, R.P., Semenov, M.A., Stöckle, C., Streck, T., Supit, I., Tao, F., Van der Velde, M., Wallach, D., Wang, E., Webber, H., Wolf, J., Xiao, L., Zhang, Z., Zhao, Z., Zhu, Y., and Asseng, S.

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°C 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 are therefor

Published
2019

38. Climate change impact and adaptation for wheat protein

Author

Asseng, S., Martre, P., Maiorano, A., Rötter, R., O'Leary, G., Fitzgerald, G., Girousse, C., Motzo, R., Giunta, F., Babar, M., Reynolds, M., Kheir, A., Thorbum, P., Waha, K., Ruane, A., Aggarwal, P., Ahmed, M., Balkovic, J., Basso, B., Biernath, C., Bindi, M., Cammarano, D., Challinor, A., De Sanctis, G., Dumont, B., Rezaei, E., Fereres, E., Ferrise, R., Garcia-Vila, M., Gayler, S., Gao, Y., Horan, H., Hoogenboom, G., Izaurralde, R., Jabloun, M., Jones, C., Kassie, B., Kersebaum, K.-C., Klein, C., Koehler, A.-K., Liu, B., Minoli, S., Martin, M.M., Müller, C., Kumar, S., Nendel, C., Oleson, J., Palosuo, T., Porter, J., Priesack, E., Ripoche, D., Semenov, M., Stöckle, C., Stratonovitch, P., Streck, T., Supit, I., Tao, F., Van der Velde, M., Wallach, D., Wang, E., Webber, H., Wolf, J., Xiao, L., Zhang, Z., Zhu, Y., Ewert, F., Asseng, S., Martre, P., Maiorano, A., Rötter, R., O'Leary, G., Fitzgerald, G., Girousse, C., Motzo, R., Giunta, F., Babar, M., Reynolds, M., Kheir, A., Thorbum, P., Waha, K., Ruane, A., Aggarwal, P., Ahmed, M., Balkovic, J., Basso, B., Biernath, C., Bindi, M., Cammarano, D., Challinor, A., De Sanctis, G., Dumont, B., Rezaei, E., Fereres, E., Ferrise, R., Garcia-Vila, M., Gayler, S., Gao, Y., Horan, H., Hoogenboom, G., Izaurralde, R., Jabloun, M., Jones, C., Kassie, B., Kersebaum, K.-C., Klein, C., Koehler, A.-K., Liu, B., Minoli, S., Martin, M.M., Müller, C., Kumar, S., Nendel, C., Oleson, J., Palosuo, T., Porter, J., Priesack, E., Ripoche, D., Semenov, M., Stöckle, C., Stratonovitch, P., Streck, T., Supit, I., Tao, F., Van der Velde, M., Wallach, D., Wang, E., Webber, H., Wolf, J., Xiao, L., Zhang, Z., Zhu, Y., and Ewert, F.

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

39. Colon sparing resection versus extended colectomy for left‐sided obstructing colon cancer with caecal ischaemia or perforation: a nationwide study from the French Surgical Association.

Author

Manceau, G., Sabbagh, C., Mege, D., Lakkis, Z., Bege, T., Tuech, J. J., Benoist, S., Lefèvre, J. H., Karoui, M., Regimbeau, J. M., Bridoux, V., Venara, A., Beyer‐Berjot, L., Codjia, T., Dazza, M., Gagnat, G., Hamel, S., Mallet, L., Martre, P., and Philouze, G.

Subjects
COLECTOMY, COLOSTOMY, COLON cancer, COLON (Anatomy), ISCHEMIA, PROGRESSION-free survival, STATISTICAL significance
Abstract

Aim: It is not known whether patients with obstructive left colon cancer (OLCC) with caecal ischaemia or diastatic perforation (defined as a blowout of the caecal wall related to colonic overdistension) should undergo a (sub)total colectomy (STC) or an ileo‐caecal resection with double‐barrelled ileo‐colostomy. We aimed to compare the results of these two strategies. Method: From 2000 to 2015, 1220 patients with OLCC underwent surgery by clinicians who were members of the French Surgical Association. Of these cases, 201 (16%) were found to have caecal ischaemia or diastatic perforation intra‐operatively: 174 patients (87%) underwent a STC (extended colectomy group) and 27 (13%) an ileo‐caecal resection with double‐end stoma (colon‐sparing group). Outcomes were compared retrospectively. Results: In the extended colectomy group, 95 patients (55%) had primary anastomosis and 79 (45%) had a STC with an end ileostomy. In the colon‐sparing group, 10 patients (37%) had simultaneous resection of their primary tumour with segmental colectomy and an anastomosis which was protected by a double‐barrelled ileo‐colostomy. The demographic data for the two groups were comparable. Median operative time was longer in the STC group (P = 0.0044). There was a decrease in postoperative mortality (7% vs 12%, P = 0.75) and overall morbidity (56% vs 67%, P = 0.37) including surgical (30% vs 40%, P = 0.29) and severe complications (17% vs 27%, P = 0.29) in the colon‐sparing group, although these differences did not reach statistical significance. Cumulative morbidity included all surgical stages and the rate of permanent stoma was 66% and 37%, respectively, with no significant difference between the two groups. Overall survival and disease‐free survival were similar between the two groups. Conclusion: The colon‐sparing strategy may represent a valid and safe alternative to STC in OLCC patients with caecal ischaemia or diastatic perforation. [ABSTRACT FROM AUTHOR]

Published
2020
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40. Soil Organic Carbon and Nitrogen Feedbacks on Crop Yields under Climate Change

Author

Basso, B., Dumont, B., Maestrini, B., Shcherbak, I., Robertson, G. P., Porter, J. R., Smith, P., Paustian, K., Grace, P. R., Asseng, S., Bassu, S., Biernath, C., Boote, K. J., Cammarano, D., De Sanctis, G., Durand, J. L., Ewert, F., Gayler, S., Hyndman, D. W., Kent, J., Martre, P., Nendel, C., Priesack, E., Ripoche, D., Ruane, A. C., Sharp, J., Thorburn, P. J., Hatfield, J. L., Jones, J. W., Rosenzweig, C., Basso, B., Dumont, B., Maestrini, B., Shcherbak, I., Robertson, G. P., Porter, J. R., Smith, P., Paustian, K., Grace, P. R., Asseng, S., Bassu, S., Biernath, C., Boote, K. J., Cammarano, D., De Sanctis, G., Durand, J. L., Ewert, F., Gayler, S., Hyndman, D. W., Kent, J., Martre, P., Nendel, C., Priesack, E., Ripoche, D., Ruane, A. C., Sharp, J., Thorburn, P. J., Hatfield, J. L., Jones, J. W., and Rosenzweig, C.

Abstract

Core Ideas: SOC decline, due to increased temperatures, reduces wheat and maize yields globally. CO2 increase to 540 ppm partially compensates yield losses due to increased temperatures. Accounting for soil feedbacks is critical when evaluating climate change impacts on crop yield. A critical omission from climate change impact studies on crop yield is the interaction between soil organic carbon (SOC), nitrogen (N) availability, and carbon dioxide (CO2). We used a multimodel ensemble to predict the effects of SOC and N under different scenarios of temperatures and CO2 concentrations on maize (Zea mays L.) and wheat (Triticum aestivum L.) yield in eight sites across the world. We found that including feedbacks from SOC and N losses due to increased temperatures would reduce yields by 13% in wheat and 19% in maize for a 3°C rise temperature with no adaptation practices. These losses correspond to an additional 4.5% (+3°C) when compared to crop yield reductions attributed to temperature increase alone. Future CO2 increase to 540 ppm would partially compensate losses by 80% for both maize and wheat at +3°C, and by 35% for wheat and 20% for maize at +6°C, relative to the baseline CO2 scenario.

Published
2018

41. Multimodel ensembles improve predictions of crop-environment-management interactions

Author

Wallach, D, Martre, P, Liu, B, Asseng, S, Ewert, F, Thorburn, PJ, van Ittersum, M, Aggarwal, PK, Ahmed, M, Basso, B, Biernath, C, Cammarano, D, Challinor, AJ, De Sanctis, G, Dumont, B, Rezaei, EE, Fereres, E, Fitzgerald, GJ, Gao, Y, Garcia-Vila, M, Gayler, S, Girousse, C, Hoogenboom, G, Horan, H, Izaurralde, RC, Jones, CD, Kassie, BT, Kersebaum, KC, Klein, C, Koehler, A-K, Maiorano, A, Minoli, S, Mueller, C, Kumar, SN, Nendel, C, O'Leary, GJ, Palosuo, T, Priesack, E, Ripoche, D, Roetter, RP, Semenov, MA, Stockle, C, Stratonovitch, P, Streck, T, Supit, I, Tao, F, Wolf, J, Zhang, Z, Wallach, D, Martre, P, Liu, B, Asseng, S, Ewert, F, Thorburn, PJ, van Ittersum, M, Aggarwal, PK, Ahmed, M, Basso, B, Biernath, C, Cammarano, D, Challinor, AJ, De Sanctis, G, Dumont, B, Rezaei, EE, Fereres, E, Fitzgerald, GJ, Gao, Y, Garcia-Vila, M, Gayler, S, Girousse, C, Hoogenboom, G, Horan, H, Izaurralde, RC, Jones, CD, Kassie, BT, Kersebaum, KC, Klein, C, Koehler, A-K, Maiorano, A, Minoli, S, Mueller, C, Kumar, SN, Nendel, C, O'Leary, GJ, Palosuo, T, Priesack, E, Ripoche, D, Roetter, RP, Semenov, MA, Stockle, C, Stratonovitch, P, Streck, T, Supit, I, Tao, F, Wolf, J, and Zhang, Z

Abstract

A recent innovation in assessment of climate change impact on agricultural production has been to use crop multimodel ensembles (MMEs). These studies usually find large variability between individual models but that the ensemble mean (e-mean) and median (e-median) often seem to predict quite well. However, few studies have specifically been concerned with the predictive quality of those ensemble predictors. We ask what is the predictive quality of e-mean and e-median, and how does that depend on the ensemble characteristics. Our empirical results are based on five MME studies applied to wheat, using different data sets but the same 25 crop models. We show that the ensemble predictors have quite high skill and are better than most and sometimes all individual models for most groups of environments and most response variables. Mean squared error of e-mean decreases monotonically with the size of the ensemble if models are added at random, but has a minimum at usually 2-6 models if best-fit models are added first. Our theoretical results describe the ensemble using four parameters: average bias, model effect variance, environment effect variance, and interaction variance. We show analytically that mean squared error of prediction (MSEP) of e-mean will always be smaller than MSEP averaged over models and will be less than MSEP of the best model if squared bias is less than the interaction variance. If models are added to the ensemble at random, MSEP of e-mean will decrease as the inverse of ensemble size, with a minimum equal to squared bias plus interaction variance. This minimum value is not necessarily small, and so it is important to evaluate the predictive quality of e-mean for each target population of environments. These results provide new information on the advantages of ensemble predictors, but also show their limitations.

Published
2018

42. Epidemiology of Distal Renal Tubular Acidosis: A Study Using Linked UK Primary Care and Hospital Data

Author

Bianic, Florence, Guelfucci, Florent, Robin, Ludovic, Martre, Catherine, Game, David, and Bockenhauer, Detlef

Abstract

Introduction:Distal renal tubular acidosis (dRTA), or RTA type 1, a rare inherited or acquired disease, is a disorder of the distal tubule caused by impaired urinary acid secretion. Due to associated conditions and nonspecific symptoms, dRTA may go undetected. This analysis aims to estimate the prevalence of dRTA in the UK Clinical Practice Research Datalink (CPRD) databases and extrapolate it to European Union Five (EU5) populations. Methods:A retrospective analysis was conducted using the CPRD GOLD database and linked Hospital Episode Statistics (HES) data to identify diagnosed and potentially undiagnosed or miscoded patients (suspected patients). Patients’ records with at least one diagnosis code for dRTA, RTA, specific autoimmune diseases, or renal disorders recorded between January 1987 and November 2017 were obtained and analyzed. An algorithm was developed to detect potentially undiagnosed/miscoded dRTA, based on associated conditions and prescriptions. Results:A total of 216 patients with diagnosis of RTA or dRTA were identified (with 98 linked to hospital data), and 447 patients were identified as having suspected dRTA. dRTA prevalence for 2017 was estimated between 0.46 (recorded cases, of which 22.1% were considered primary) and 1.60 when including suspected cases (7.6% primary) per 10,000 people. Prescription and clinical records of diagnosed patients revealed a wide range of comorbidities and a need for pharmacological treatment to manage associated symptoms. Conclusion:The study provides new estimates of dRTA prevalence in Europe and suggests that patients may often be unreported or miscoded, potentially confounding appropriate disease management.

Published
2021
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43. Uncertainty of wheat water use: Simulated patterns and sensitivity to temperature and CO₂

Author

Cammarano, D, Rötter, RP, Asseng, S, Ewert, F, Wallach, D, Martre, P, Hatfield, JL, Jones, JW, Rosenzweig, C, Ruane, AC, Boote, KJ, Thorburn, PJ, Kersebaum, KC, Aggarwal, PK, Angulo, C, Basso, B, Bertuzzi, P, Biernath, C, Brisson, N, Challinor, AJ, Doltra, J, Gayler, S, Goldberg, R, Heng, L, Hooker, JE, Hunt, LA, Ingwersen, J, Izaurralde, RC, Müller, C, Kumar, SN, Nendel, C, O'Leary, G, Olesen, JE, Osborne, TM, Palosuo, T, Priesack, E, Ripoche, D, Semenov, MA, Shcherbak, I, Steduto, P, Stöckle, CO, Stratonovitch, P, Streck, T, Supit, I, Tao, F, Travasso, M, Waha, K, White, JW, and Wolf, J

Abstract

Projected global warming and population growth will reduce future water availability for agriculture. Thus, it is essential to increase the efficiency in using water to ensure crop productivity. Quantifying crop water use (WU; i.e. actual evapotranspiration) is a critical step towards this goal. Here, sixteen wheat simulation models were used to quantify sources of model uncertainty and to estimate the relative changes and variability between models for simulated WU, water use efficiency (WUE, WU per unit of grain dry mass produced), transpiration efficiency (Teff, transpiration per kg of unit of grain yield dry mass produced), grain yield, crop transpiration and soil evaporation at increased temperatures and elevated atmospheric carbon dioxide concentrations ([CO2]). The greatest uncertainty in simulating water use, potential evapotranspiration, crop transpiration and soil evaporation was due to differences in how crop transpiration was modelled and accounted for 50% of the total variability among models. The simulation results for the sensitivity to temperature indicated that crop WU will decline with increasing temperature due to reduced growing seasons. The uncertainties in simulated crop WU, and in particularly due to uncertainties in simulating crop transpiration, were greater under conditions of increased temperatures and with high temperatures in combination with elevated atmospheric [CO2] concentrations. Hence the simulation of crop WU, and in particularly crop transpiration under higher temperature, needs to be improved and evaluated with field measurements before models can be used to simulate climate change impacts on future crop water demand.

Published
2016

44. Benchmark data set for wheat growth models: field experiments and AgMIP multi-model simulations

Author

Asseng, S, Ewert, F., Martre, P, Rosenzweig, C, Jones, J.W., Hatfield, J L, Ruane, A C, Boote, K J, Thorburn, P, Rötter, RP, Cammarano, D, Brisson, N, Basso, B, Aggarwal, PK, Angulo, C, Bertuzzi, P, Biernath, C, Challinor, AJ, Doltra, J, Gayler, S, Goldberg, R, Grant, R, Heng, L, Hooker, J, Hunt, L A, Ingwersen, J, Izaurralde, RC, Kersebaum, KC, Müller, C, Naresh Kumar, S, Nendel, C, O'Leary, G, Olesen, Jørgen Eivind, Osborne, T M, Palosuo, T, Priesack, E, Ripoche, D, Semenov, MA, Shcherbak, I, Steduto, P, Stöckle, C, Stratonovitch, P, Streck, T, Supit, I, Tao, F, Travasso, M, Waha, K, Wallach, D, White, JW, Williams, J R, Wolf, J., Department of Agricultural and Biological Engineering [Gainesville] (UF|ABE), Institute of Food and Agricultural Sciences [Gainesville] (UF|IFAS), University of Florida [Gainesville] (UF)-University of Florida [Gainesville] (UF), Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), National laboratory for agriculture and the environment, Department of agronomy, University of Florida [Gainesville] (UF), Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Plant Production Research, Agrifood Research Finland, Department of Geological Sciences and W. K. Kellogg Biological Station, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, International Water Management Institute, Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA), German Research Center for Environmental Health, University of Leeds, Catabrian Agricultural Research and Training Center (CIFA), Eberhard Karls Universität Tübingen = Eberhard Karls University of Tuebingen, Department of Renewable Resources, University of Alberta, International Atomic Energy Agency [Vienna] (IAEA), Agriculture Department, University of Reading (UOR), Department of Plant Agriculture, University of Guelph, Institute of Soil Science and Land Evaluation, University of Hohenheim, Joint Global Change Research Institute, Institute of landscape systems analysis, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Potsdam Institute for Climate Impact Research (PIK), Division of Environmental Sciences, University of Hertfordshire [Hatfield] (UH), Department of Primary Industries, Department of Agroecology, Aarhus University [Aarhus], NCAS-Climate, Walker Institute, Computational and Systems Biology Department, Rothamsted Research, Food and Agriculture Organization, Biological Systems Engineering, Washington State University (WSU), Wageningen University and Research Centre (WUR), Institute of geographical sciences and natural resources research, Chinese Academy of Sciences [Changchun Branch] (CAS), Institute for Climate and Water, Instituto Nacional de Tecnología Agropecuaria (INTA), 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, Arid-Land Agricultural Research Center, Texas A&M University System, Consultative Group on International Agricultural Research (CGIAR), Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Helmholtz Zentrum München = German Research Center for Environmental Health, Biotechnology and Biological Sciences Research Council (BBSRC)-Biotechnology and Biological Sciences Research Council (BBSRC), Institute of geographical sciences and natural resources research [CAS] (IGSNRR), Chinese Academy of Sciences [Beijing] (CAS), and Université de Toulouse (UT)-Université de Toulouse (UT)

Subjects
010504 meteorology & atmospheric sciences, [SDV]Life Sciences [q-bio], Climate change, Atmospheric sciences, 01 natural sciences, Earth System Science, [SHS]Humanities and Social Sciences, Anthesis, sensitivity analysis, wheat, Life Science, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Relative humidity, Precipitation, field experimental data, 0105 earth and related environmental sciences, 2. Zero hunger, WIMEK, Humidity, 04 agricultural and veterinary sciences, 15. Life on land, Climate Resilience, Dew point, 13. Climate action, Klimaatbestendigheid, climate change impact, Soil water, [SDE]Environmental Sciences, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Leerstoelgroep Aardsysteemkunde, Climate model, simulations, Sensitivity analysis
Abstract

International audience; The data set includes a current representative management treatment from detailed, quality-tested sentinel field experiments with wheat from four contrasting environments including Australia, The Netherlands, India and Argentina. Measurements include local daily climate data (solar radiation, maximum and minimum temperature, precipitation, surface wind, dew point temperature, relative humidity, and vapor pressure), soil characteristics, frequent growth, nitrogen in crop and soil, crop and soil water and yield components. Simulations include results from 27 wheat models and a sensitivity analysis with 26 models and 30 years (1981-2010) for each location, for elevated atmospheric CO2 and temperature changes, a heat stress sensitivity analysis at anthesis, and a sensitivity analysis with soil and crop management variations and a Global Climate Model end-century scenario.

Published
2016
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45. Similar negative impacts of temperature on global wheat yield estimated by three independent methods

Author

Liu, B., Asseng, S., Müller, C., Ewert, F., Elliott, J., Lobell, D.B., Martre, P., Ruane, A.C., Wallach, D., Jones, J.W., Rosenzweig, C., Aggarwal, P., Alderman, P.D., Anothai, J., Basso, B., Biernath, C.J., Cammarano, D., Challinor, A.J., Deryng, D., de Sanctis, G., Doltra, J., Fereres, E., Folberth, C., Garcia-Vila, M., Gayler, S., Hoogenboom, G., Hunt, L.A., Izaurralde, R.C., Jabloun, M., Jones, C.D., Kersebaum, K.C., Kimball, B.A., Koehler, A.-K., Kumar, S.N., Nendel, C., O´Leary, G., Olesen, J.E., Ottmann, M.J., Palosuo, T., Prasad, P.V.V., Priesack, E., Pugh, T.A., Reynolds, M., Rezaei, E.E., Rötter, R.P., Schmid, E., Semenov, M.A., Shcherbak, I., Stehfest, E., Stöckle, C.O., Stratonovitch, P., Streck,T., Supit, I., Tao, F., Thorburn, P.J., Waha, K., Wall, G.W., Wang, E., White, J.W., Wolf, J., Zhao, Z., and Zhu, Y.

Abstract

The potential impact of global temperature change on global crop yield has recently been assessed with different methods. Here we show that grid-based and point-based simulations and statistical regressions (from historic records), without deliberate adaptation or CO2 fertilization effects, produce similar estimates of temperature impact on wheat yields at global and national scales. With a 1 °C global temperature increase, global wheat yield is projected to decline between 4.1% and 6.4%. Projected relative temperature impacts from different methods were similar for major wheat-producing countries China, India, USA and France, but less so for Russia. Point-based and grid-based simulations, and to some extent the statistical regressions, were consistent in projecting that warmer regions are likely to suffer more yield loss with increasing temperature than cooler regions. By forming a multi-method ensemble, it was possible to quantify ‘method uncertainty’ in addition to model uncertainty. This significantly improves confidence in estimates of climate impacts on global food security.

Published
2016

46. Prognostic factors and patterns of recurrence after emergency management for obstructing colon cancer: multivariate analysis from a series of 2120 patients.

Author

Manceau, Gilles, Voron, Thibault, Mege, Diane, Bridoux, Valérie, Lakkis, Zaher, Venara, Aurélien, Beyer-Berjot, Laura, Abdalla, Solafah, Sielezneff, Igor, Lefèvre, Jeremie H, Karoui, Mehdi, On behalf of the AFC (French Surgical Association) Working Group, Codjia, T., Dazza, M., Gagnat, G., Hamel, S., Mallet, L., Martre, P., Philouze, G., and Roussel, E.

Subjects
COLON cancer, EMERGENCY management, MULTIVARIATE analysis, PROPORTIONAL hazards models, TUMOR classification, RIGHT hemicolectomy
Abstract

Purpose: At equal TNM stage, obstructing colon cancer (OCC) is associated with worse prognosis in comparison with uncomplicated cancer. Our aim was to identify prognostic factors of overall (OS) and disease-free survival (DFS) in patients treated for OCC. Methods: From 2000 to 2015, 2325 patients were treated for OCC in French surgical centers, members of the French National Surgical Association (AFC). Patients with palliative management were excluded. The main endpoints were OS and DFS. A multivariate analysis, using Cox proportional hazards regression model, was performed to determine independent prognostic factors. Results: The cohort included 2120 patients. The median of follow-up was 13.2 months. In multivariate analysis, age > 75 years, ASA score ≥ 3, ECOG score ≥ 3, right-sided colon cancer, presence of synchronous metastases, anastomotic leakage, and absence of adjuvant chemotherapy were independent OS factors. Age > 75 years, ASA score ≥ 3, right-sided colon cancer, presence of synchronous metastases, and absence of postoperative chemotherapy were independent factors of poor OS after exclusion of patients who died postoperatively. Age ≥ 75 years, ASA score ≥ 3, ECOG score ≥ 3, right-sided colon cancer, lymph node involvement, presence of vascular, lymphatic or perineural invasion, less than 12 harvested lymph nodes, and absence of adjuvant chemotherapy were independent DFS factors. Conclusions: Management of OCC should take into account prognostic factors related to the patient (age, comorbidities), tumor location, and tumor stage. Adjuvant chemotherapy administration plays an important role. For patients undergoing initial defunctionning stoma, neoadjuvant chemotherapy could be an option to improve prognosis. [ABSTRACT FROM AUTHOR]

Published
2019
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47. Thirty‐day mortality after emergency surgery for obstructing colon cancer: survey and dedicated score from the French Surgical Association.

Author

Manceau, G., Mege, D., Bridoux, V., Lakkis, Z., Venara, A., Voron, T., De Angelis, N., Ouaissi, M., Sielezneff, I., Karoui, M., Codjia, T, Dazza, M, Gagnat, G, Hamel, S, Mallet, L, Martre, P, Philouze, G, Roussel, E, Tortajada, P, and Dumaine, AS

Subjects
SURGICAL emergencies, COLON cancer, MORTALITY, HEALTH risk assessment, BOWEL obstructions
Abstract

Aim: The aim was to define risk factors for postoperative mortality in patients undergoing emergency surgery for obstructing colon cancer (OCC) and to propose a dedicated score. Method: From 2000 to 2015, 2325 patients were treated for OCC in French surgical centres by members of the French National Surgical Association. A multivariate analysis was performed for variables with P value ≤ 0.20 in the univariate analysis for 30‐day mortality. Predictive performance was assessed by the area under the receiver operating characteristic curve. Results: A total of 1983 patients were included. Thirty‐day postoperative mortality was 7%. Multivariate analysis found five significant independent risk factors: age ≥ 75 (P = 0.013), American Society of Anesthesiologists (ASA) score ≥ III (P = 0.027), pulmonary comorbidity (P = 0.0002), right‐sided cancer (P = 0.047) and haemodynamic failure (P < 0.0001). The odds ratio for risk of postoperative death was 3.42 with one factor, 5.80 with two factors, 15.73 with three factors, 29.23 with four factors and 77.25 with five factors. The discriminating capacity in predicting 30‐day postoperative mortality was 0.80. Conclusion: Thirty‐day postoperative mortality after emergency surgery for OCC is correlated with age, ASA score, pulmonary comorbidity, site of tumour and haemodynamic failure, with a specific score ranging from 0 to 5. [ABSTRACT FROM AUTHOR]

Published
2019
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48. The Hot Serial Cereal Experiment for modeling wheat response to temperature: field experiments and AgMIP-Wheat multi-model simulations

Author

Martre, P., Kimball, B.A., Ottman, M.J., Wall, G.W., White, J., Asseng, S., Ewert, F., Cammarano, D., Maiorano, Andrea, Supit, I., Martre, P., Kimball, B.A., Ottman, M.J., Wall, G.W., White, J., Asseng, S., Ewert, F., Cammarano, D., Maiorano, Andrea, and Supit, I.

Abstract

The data set reported here includes the part of a Hot Serial Cereal Experiment (HSC) experiment recently used in the AgMIP-Wheat project to analyze the uncertainty of 30 wheat models and quantify their response to temperature. The HSC experiment was conducted in an open-field in a semiarid environment in the southwest USA. The data reported herewith include one hard red spring wheat cultivar (Yecora Rojo) sown approximately every six weeks from December to August for a two-year period for a total of 11 planting dates out of the 15 of the entire HSC experiment. The treatments were chosen to avoid any effect of frost on grain yields. On late fall, winter and early spring plantings temperature free-air controlled enhancement (T-FACE) apparatus utilizing infrared heaters with supplemental irrigation were used to increase air temperature by 1.3°C/2.7°C (day/night) with conditions equivalent to raising air temperature at constant relative humidity (i.e. as expected with global warming) during the whole crop growth cycle. Experimental data include local daily weather data, soil characteristics and initial conditions, detailed crop measurements taken at three growth stages during the growth cycle, and cultivar information. Simulations include both daily in-season and end-of-season results from 30 wheat models.

Published
2017

49. The International Heat Stress Genotype Experiment for modeling wheat response to heat: field experiments and AgMIP-Wheat multi-model simulations

Author

Martre, P., Reynolds, M.P., Asseng, S., Ewert, F., Alderman, P.D., Cammarano, D., Maiorano, Andrea, Ruane, A.C., Aggarwal, P.K., Anothai, J., Supit, I., Wolf, J., Martre, P., Reynolds, M.P., Asseng, S., Ewert, F., Alderman, P.D., Cammarano, D., Maiorano, Andrea, Ruane, A.C., Aggarwal, P.K., Anothai, J., Supit, I., and Wolf, J.

Abstract

The data set contains a portion of the International Heat Stress Genotype Experiment (IHSGE) data used in the AgMIP-Wheat project to analyze the uncertainty of 30 wheat crop models and quantify the impact of heat on global wheat yield productivity. It includes two spring wheat cultivars grown during two consecutive winter cropping cycles at hot, irrigated, and low latitude sites in Mexico (Ciudad Obregon and Tlaltizapan), Egypt (Aswan), India (Dharwar), the Sudan (Wad Medani), and Bangladesh (Dinajpur). Experiments in Mexico included normal (November-December) and late (January-March) sowing dates. Data include local daily weather data, soil characteristics and initial soil conditions, crop measurements (anthesis and maturity dates, anthesis and final total above ground biomass, final grain yields and yields components), and cultivar information. Simulations include both daily in-season and end-of-season results from 30 wheat models. All data are available via DOI 10.7910/DVN/ECSFZG.

Published
2017

50. Letter : Rising temperatures reduce global wheat production

Author

Asseng, S., Ewert, F., Martre, P., Rötter, R.P., Cammarano, D., Kimball, B.A., Ottman, M.J., Wall, G.W., White, J.W., Reynolds, M.P., Alderman, P.D., Prasad, P.V.V., Lobell, D.B., Aggarwal, P.K., Anothai, J., Basso, B., Biernath, C., Challinor, A.J., De Sanctis, G., Doltra, J., Fereres, E., Garcia-Vila, M., Gayler, S., Hoogenboom, G., Hunt, L.A., Izaurralde, C., Jabloun, M., Jones, C.D., Kersebaum, K.C., Koehler, A.K., Müller, C., Naresh Kumar, S., Nendel, C., O’Leary, G., Olesen, J.E., Palosuo, T., Priesack, E., Eyshi Rezae, E., Ruane, A.C., Semenov, M.A., Shcherbak, I., Stöckle, C.O., Stratonovitch, P., Streck, T., Supit, I., Tao, T., Thorburn, P., Waha, K., Wang, E., Wallach, D., Wolf, J., Zhao, Z., and Zhu, Y.

Subjects
dryland wheat, WIMEK, growth, Soil Science Centre, adaptation, drought, PE&RC, yield, Climate Resilience, spring wheat, Plant Production Systems, Klimaatbestendigheid, Plantaardige Productiesystemen, climate-change, co2, Alterra - Centrum Bodem, heat, agriculture
Abstract

Crop models are essential tools for assessing the threat of climate change to local and global food production(1). Present models used to predict wheat grain yield are highly uncertain when simulating how crops respond to temperature(2). Here we systematically tested 30 different wheat crop models of the Agricultural Model Intercomparison and Improvement Project against field experiments in which growing season mean temperatures ranged from 15 degrees C to 32 degrees C, including experiments with artificial heating. Many models simulated yields well, but were less accurate at higher temperatures. The model ensemble median was consistently more accurate in simulating the crop temperature response than any single model, regardless of the input information used. Extrapolating the model ensemble temperature response indicates that warming is already slowing yield gains at a majority of wheat-growing locations. Global wheat production is estimated to fall by 6% for each degrees C of further temperature increase and become more variable over space and time.

Published
2015

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