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4. 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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5. 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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6. Ecophysiological modeling of yield and yield components in winter wheat using hierarchical Bayesian analysis

Author

Poudel, Pratishtha, Bello, Nora M., Marburger, David A., Carver, Brett F., Liang, Ye, and Alderman, Phillip D.

Abstract

Yield components are widely recognized as drivers of wheat (Triticum aestivumL.) yield across environments and genotypes. In this study, we used a hierarchical Bayesian approach to model wheat grain yield in Oklahoma on an eco‐physiological basis using yield component traits thousand kernel weight (TKW) and nonyield biomass (NYB). The Bayesian approach allowed us to quantify uncertainties around the parameter values rather than obtaining a single value estimate for a parameter. The main objectives of this study were to (a) explain wheat yield as a function of component traits TKW and NYB, and thereby examine the implications for source‐sink balance; and (b) assess their association with weather conditions during key stages of wheat development. A secondary objective was to introduce Bayesian estimation for eco‐physiological modeling. Fifteen wheat genotypes planted in three locations in Oklahoma (Altus, Chickasha, and Lahoma) were evaluated across three harvest years (2017 to 2019), whereby the combination of location and year defined an environment. Results indicate that the environment explained a greater proportion of the variability in yield than genotypes or than genotype × environment (G × E) interaction; however, evidence for G × E was substantial. Yield was expected to increase with increasing TKW and NYB, which would suggest a source limitation to achieve potential yield. Yet, the contribution of early reproductive stage weather variables to the relationship between yield and NYB pointed in the direction of sink strength being compromised. In summary, our approach provides evidence for source‐sink co‐limitation in grain yield of this sample of hard red winter wheat genotypes. Yield components serve as proxy for source and sink.Association of yield components with yield is mediated by weather conditions.Wheat yield is co‐limited by source and sink.Bayesian hierarchical modeling naturally reflects hierarchy of biological systems.

Published
2022
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8. Model improvements reduce the uncertainty of wheat crop model ensembles under heat stress

Author

Maiorano, Andrea, Martre, Pierre, Asseng, S., Ewert, F., Müller, C., Rötter, R. P., Ruane, A. C., Semenov, M. A., Wallach, Daniel, Wang, E., Alderman, P. D., Kassie, B. T., Biernath, C., Basso, B., Cammarano, D., Challinor, A. J., Doltra, J., Dumont, B., Gayler, S., Kersebaum, Kimball, B. A., Koehler, A. K., Liu, L., O'Leary, G., Olesen, J. E., Ottman, Michael J., Priesack, E., Reynolds, M. P., Eyshi Rezaei, E., Stratonovitch, P., Streck, T., Thorburn, P., Waha, K., Wall, G. W., White, J. W., Zhao, Z., Zhu, Y., Écophysiologie des Plantes sous Stress environnementaux (LEPSE), 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), 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), University of Florida [Gainesville] (UF), INRES, Rheinische Friedrich-Wilhelms-Universität Bonn, Potsdam Institute for Climate Impact Research (PIK), Natural Resources Institute Finland (LUKE), NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Computational and Systems Biology Department, Rothamsted Research, 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, Agriculture, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), International Maize and Wheat Improvement Center (CIMMYT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), 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), German Research Center for Environmental Health - Helmholtz Center München (GmbH), Michigan State University [East Lansing], Michigan State University System, University of Leeds, International Center for Tropical Agriculture, Catabrian Agricultural Research and Training Center (CIFA), Department of Geological Sciences and W. K. Kellogg Biological Station, Michigan State University System-Michigan State University System, Eberhard Karls Universität Tübingen = Eberhard Karls University of Tuebingen, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), ARS/ALARC, United States Department of Agriculture, Nanjing Agricultural University, Landscape & Water Sciences, Department of Environment of Victoria, Department of Agroecology, Aarhus University [Aarhus], The School of Plant Sciences, University of Arizona, Institute of Soil Science and Land Evaluation, University of Hohenheim, and Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research, Leibniz Association (ZALF). DEU.

Subjects
Vegetal Biology, comparaison de modèles, Modélisation et simulation, modèle de simulation, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, incertitude, blé, Modeling and Simulation, température, modèle phénologique, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Biologie végétale, modèle de production, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2016

9. Inter-comparison of wheat models to identify knowledge gaps and improve process modeling

Author

Wang, E., Martre, Pierre, Asseng, S., Ewert, F., Zhao, Z., Maiorano, Andrea, Rotter, R. P., Kimball, B. A., Ottman, Michael J., Wall, G. W., White, J. W., Aggarwal, P. K., Alderman, P. D., Anothai, J., Basso, B., Biernath, C., Cammarano, D., Challinor, A. J., De Sanctis, Giacomo, Doltra, J., Fereres, E., Garcia-Vila, M., Gayler, S., Hoogenboom, G., Hunt, L. A., Izaurralde, R. C., Jabloun, M., Jones, C. D., Kersebaum, K.C., Koehler, A. K., Müller, C., Liu, L., Kumar Naresh, S., Nendel, C., O'Leary, G., Olesen, J. E., Palosuo, T., Priesack, E., Reynolds, M. P., Eyshi Rezaei, E., Ripoche, Dominique, Ruane, A. C., Semenov, M. A., Shcherbak, I., Stöckle, C., Stratonovitch, P., Streck, T., Supit, I., Tao, F., Thorburn, P., Waha, K., Wallach, Daniel, Wolf, J., Zhu, Y., Agriculture, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Écophysiologie des Plantes sous Stress environnementaux (LEPSE), 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), 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), University of Florida [Gainesville] (UF), INRES, Rheinische Friedrich-Wilhelms-Universität Bonn, Natural Resources Institute Finland (LUKE), ARS/ALARC, United States Department of Agriculture, The School of Plant Sciences, University of Arizona, CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), International Maize and Wheat Improvement Center (CIMMYT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), AgWeatherNet Program, Washington State University (WSU), Michigan State University [East Lansing], Michigan State University System, German Research Center for Environmental Health - Helmholtz Center München (GmbH), University of Leeds, International Center for Tropical Agriculture, Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA), Catabrian Agricultural Research and Training Center (CIFA), Universidad de Córdoba [Cordoba], IAS, Princeton University, Eberhard Karls Universität Tübingen = Eberhard Karls University of Tuebingen, Department of Plant Agriculture, University of Guelph, Department of Geographical Sciences, University of Maryland [College Park], University of Maryland System-University of Maryland System, Texas A and M AgriLife Research, Texas A&M University System, Department of Agroecology, Aarhus University [Aarhus], Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Potsdam Institute for Climate Impact Research (PIK), Nanjing Agricultural University, Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Landscape & Water Sciences, Department of Environment of Victoria, NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Computational and Systems Biology Department, Rothamsted Research, Institute of Soil Science and Land Evaluation, University of Hohenheim, Wageningen University and Research Centre (WUR), Institute of geographical sciences and natural resources research, Chinese Academy of Sciences [Changchun Branch] (CAS), 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, and Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research, Leibniz Association (ZALF). DEU.

Subjects
blé, Modeling and Simulation, comparaison de modèles, température, modèle phénologique, Modélisation et simulation, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, ComputingMilieux_MISCELLANEOUS, modèle de production, incertitude
Abstract

International audience

Published
2016

11. Comparison of Penman-Monteith and Priestley-Taylor Evapotranspiration Methods for Crop Modeling in Oklahoma.

Author

Akumaga, Uvirkaa and Alderman, Phillip D.

Abstract

Potential evapotranspiration (PET) is a key variable simulated by most crop simulation models using a variety of approaches. The objective of this study was to compare Priestley-Taylor (PT) and FAO-56 Penman-Monteith (FAO-56 PM) PET methods when simulating crop evapotranspiration (ET), yield, and aboveground biomass in Oklahoma. The study used data from 87 weather stations across nine climate divisions to simulate maize, sorghum, soybean, and wheat crop growth and development in Oklahoma for 1998 to 2017. Our results show that seasonal crop ET estimated by PT was lower than FAO-56 PM in most climate divisions and crops with average difference ranging from -10 to -1% for rainfed and from -21 to -1% for irrigated simulations. Differences in ET were greater for winter wheat than for maize, sorghum, and soybean. Additionally, differences in ET between methods were smaller in humid regions than in arid regions. Analysis of simulated rainfed yield and biomass showed higher values with PT for all crops except in the most humid climate divisions. However, under full irrigation, the yield differences between PT and FAO-56 PM were very low and ranged between 0-2% for all crops. In conclusion, this study confirmed that PT estimation of ET could be significantly different from FAO-56 PM especially in the arid and semiarid regions and during the winter under rainfed conditions. However, the differences in ET estimation did not affect yield and biomass simulation under full irrigation because the impact of soil water balance on the crop growth simulation was removed. [ABSTRACT FROM AUTHOR]

Published
2019
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12. Species‐genotypic parameters of the CROPGRO Perennial Forage Model: Implications for comparison of three tropical pasture grasses.

Author

Pequeno, D. N. L., Pedreira, C. G. S., Boote, K. J., Alderman, P. D., and Faria, A. F. G.

Subjects
PERENNIALS, FORAGE plants, GRASSES as feed, GENOTYPES, PLANT species, COMPUTER simulation
Abstract

Abstract: Brachiaria and Cynodon are two of the most important pasture grasses worldwide. Computer model simulations can be used to study pasture species growth and physiological aspects to identify gaps of knowledge for genetic improvement and management strategies. The objective of this research was to compare the performance relative to calibrated parameters of the CROPGRO‐Perennial Forage Model (CROPGRO‐PFM) for simulating three different species (“Marandu” palisadegrass, “Convert HD 364®” brachiariagrass and “Tifton 85” bermudagrass) grown under similar management. The field experiment consisted of two harvest frequencies, 28 and 42 days, under irrigated and rainfed conditions. Data used to calibrate the model included regular forage harvests, plant‐part composition, leaf photosynthesis, leaf area index, light interception and plant nitrogen concentration. The simulation of biomass production of the three grasses presented d‐statistic values higher than 0.80, RMSE ranging from 313 to 619 kg/ha and ratio observed/simulated ranging 0.968 to 1.027. Harvest frequency treatments of 28 and 42 days were well simulated by the model. A sensitivity analysis was conducted to evaluate the most influential parameters needed for model calibration and to contrast the grasses, showing that the differences among the three grasses are mostly driven by plant‐part composition and assimilate partitioning among plant organs. [ABSTRACT FROM AUTHOR]

Published
2018
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14. Rising temperatures reduce global wheat production

Author

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

Published
2014
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16. Development and Deployment of a Portable Field Phenotyping Platform

Author

Crain, Jared L., Wei, Yong, Barker, Jared, Thompson, Sean M., Alderman, Phillip D., Reynolds, Matthew, Zhang, Naiqian, and Poland, Jesse

Abstract

Accurate and efficient phenotyping has become the biggest hurdle for evaluating large populations in plant breeding and genetics. Contrary to genotyping, high‐throughput approaches to field‐based phenotyping have not been realized and fully implemented. To address this bottleneck, a novel, low‐cost, flexible phenotyping platform, named Phenocart, was developed and tested on a field trial consisting of 10 historical and current elite wheat (Triticum aestiviumL.) breeding lines at the International Maize and Wheat Improvement Center (CIMMYT). The lines were cultivated during the 2013 and 2014 growing cycle in Ciudad Obregon, Mexico, and evaluated multiple times throughout the growing season. The phenotyping platform was developed by integrating several sensors: a GreenSeeker for spectral reflectance, an infrared thermometer (IRT), and a global navigation satellite system (GNSS) receiver into one functional unit. The Phenocart enabled simultaneous collection of normalized difference vegetation index (NDVI) and canopy temperature (CT) with precise assignment of all measurements to plot location by georeferenced data points. Across the set of varieties, the Phenocart temperature measurements were highly correlated to a handheld IRT. In addition, CT and NDVI were both significantly correlated to yield throughout the growing season. The Phenocart is a flexible, low‐cost, and easily deployable platform to increase the amount of phenotypic data that crop breeders obtain as well as provide high‐resolution phenotypic data for genetic discovery.

Published
2016
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17. Predicting Growth of Panicum maximum: An Adaptation of the CROPGRO-Perennial Forage Model.

Author

Lara, Márcio A. S., Pedreira, Carlos G. S., Boote, Kenneth J., Pedreira, Bruno C., Moreno, Leonardo S. B., and Alderman, Phillip D.

Abstract

Warm-season grasses are economically important for cattle production in tropical regions and tools to aid in management and research on these forages would be highly beneficial both in research and the industry. This research was conducted to adapt the CROPGRO-Perennial Forage model to simulate growth of the tropical species guineagrass (Panicum maximum Jacq. cv. 'Tanzânia') and to describe model adaptation for this species. To develop the CROPGRO parameters for this species, we began with values and relationships reported in the literature. Some parameters and relationships were calibrated by comparison with observed growth, development, dry matter accumulation, and partitioning during a 17-mo experiment with Tanzania guineagrass in Piracicaba, SP, Brazil. Compared with starting parameters for palisadegrass [Brachiaria brizantha (A. Rich.) Stapf. cv. 'Xaraes'], dormancy effects of the perennial forage model had to be minimized, partitioning to storage tissue or root decreased, and partitioning to leaf and stem increased to provide for more leaf and stem growth and less root. Parameters affecting specific leaf area and senescence of plant tissues were improved. After these changes were made to the model, biomass accumulation was better simulated, mean predicted herbage yield was 6576 kg ha-1, averaged across 11 regrowth cycles of 35 (summer) or 63 d (winter), with a RMSE of 494 kg ha-1 (Willmott's index of agreement d = 0.985, simulated/observed ratio = 1.014). The model also gave good predictions against an independent data set, with similar RMSE, ratio, and d. The results of the adaptation suggest that the CROPGRO model is an efficient tool to integrate physiological aspects of guineagrass and can be used to simulate growth. [ABSTRACT FROM AUTHOR]

Published
2012
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18. Regrowth Dynamics of 'Tifton 85' Bermudagrass as Affected by Nitrogen Fertilization.

Author

Alderman, P. D., Boote, K. J., and Sollenberger, L. E.

Subjects
*REGENERATION (Biology), *BERMUDA grass, *FORAGE plants, *FEEDSTOCK, *NITROGEN fertilizers
Abstract

'Tifton 85' bermudagrass (Cynodon spp.) has been widely adopted as a forage and hay crop and is being considered as a cellulosic ethanol feedstock. The objective of this study was to evaluate the effects of N fertilization rate on Tifton 85 regrowth dynamics. A field study was conducted near Gainesville, FL, on established Tifton 85 in 2006 and 2007. The treatments were N rates of 0, 45, 90, and 135 kg N ha-1 regrowth period-1. Tissue mass, leaf:stem ratio, and tiller number and mass were measured weekly during 28-d regrowth periods. Leaf mass followed logistic time trends with the upper asymptote varying between 50 and 225 g m-2 depending on N rate and season (summer and autumn). Stem mass lagged behind leaf mass for 7 to 14 d, subsequently following linear or quadratic time trends to reach between 75 and 300 g m-2 by 28 d. Increasing N rate from 0 to 135 kg ha-1 period-1 increased tiller mass at 28 d from 1.5 to 3 g tiller-1 in summer and 1 to 1.5 g tiller-1 in autumn. Leaf:stem ratio increased to 1.0 within 14 to 21 d, followed by a subsequent decrease. Rhizome and root mass were not affected by N fertilization. Increasing N rate primarily affected mass and proportion of above-ground plant parts, with little effect on mass of below-ground parts Nitrogen nutrition index values were similar whether calculated from samples taken to a 10-cm stubble height or from samples taken to the soil surface. Regrowth was not enhanced by N rate beyond 90 kg N ha-1 regrowth period-1. [ABSTRACT FROM AUTHOR]

Published
2011
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20. Foundations for Intrusion Prevention

Author

Rubin, S., Alderman, I. D., and Vernon, M. K.

Abstract

ABSTRACTWe propose an infrastructure that helps system administrators to identify a newly published vulnerability on the site hosts, to evaluate the vulnerability threat with respect to the administrators' security priorities, and to repair the vulnerable hosts. The infrastructure foundation is the vulnerability semantics, a small set of attributes for vulnerability definition. We demonstrate that with a few attributes it is possible to define the majority of the known vulnerabilities in a way that facilitates their accurate identification, and enables the administrators to rank the vulnerabilities found according to the organization's security priorities. A large scale experiment demonstrates that our infrastructure can find significant vulnerabilities even in a site with high security awareness.

Published
2004
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21. Incidence and management of limb ischemia with percutaneous wire-guided intraaortic balloon catheters

Author

Alderman, James D., Gabliani, Gregory I., McCabe, Carolyn H., Brewer, Cynthia C., Lorell, Beverly H., Pasternak, Richard C., Skillman, John J., Steer, Michael L., and Baim, Donald S.

Abstract

In 103 patients who underwent placement of 106 percutaneous wire-guided intraaortic balloon catheters between August 1983 and January 1986, all placements were successful and the average duration of counterpulsation was 3.4 ± 1.6 days. During counterpulsation, 45 patients developed limb ischemia that required premature balloon removal in 29 patients. The development of limb ischemia was significantly related to the presence of diabetes (risk ratio 2.0), peripheral vascular disease (risk ratio 1.9), female gender (risk ratio 1.8) and the presence of a postinsertion ankle-brachial pressure index < 0.8 (risk ratio 7.9). There was no association between the development of limb ischemia and age, body surface area, balloon size (10.5F/12F) or adequacy of anticoagulation. Fifteen patients underwent vascular surgery for treatment of balloon-related limb ischemia, which was associated with one operative death. Nine patients had persistent limb ischemia (seven asymptomatic, two symptomatic) at the time of hospital discharge.

Published
1987
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22. Erratum: 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, 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

Abstract

This corrects the article DOI: 10.1038/nplants.2017.102

Published
2017
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23. 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, 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, Naresh Kumar, Soora, Nendel, Claas, O'Leary, Garry, Olesen, Jørgen E., Palosuo, Taru, Priesack, Eckart, Eyshi Rezaei, Ehsan, 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

Abstract

Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for >50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 °C to 33 °C. We derived a set of new temperature response functions that when substituted in four wheat models reduced the error in grain yield simulations across seven global sites with different temperature regimes by 19% to 50% (42% average). We anticipate the improved temperature responses to be a key step to improve modelling of crops under rising temperature and climate change, leading to higher skill of crop yield projections.

Published
2017
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