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621 results on '"Palosuo, T."'

1. Implications of crop model ensemble size and composition for estimates of adaptation effects and agreement of recommendations

Author

Rodríguez, A., Ruiz-Ramos, M., Palosuo, T., Carter, T.R., Fronzek, S., Lorite, I.J., Ferrise, R., Pirttioja, N., Bindi, M., Baranowski, P., Buis, S., Cammarano, D., Chen, Y., Dumont, B., 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, M., Nendel, C., Porter, J.R., Ruget, F., Semenov, M.A., Steinmetz, Z., Stratonovitch, P., Supit, I., Tao, F., Trnka, M., de Wit, A., and Rötter, R.P.

Published
2019
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3. Temperature and precipitation effects on wheat yield across a European transect : a crop model ensemble analysis using impact response surfaces

Author

Pirttioja, N., Carter, T. R., Fronzek, S., Bindi, M., Hoffmann, H., Palosuo, T., Ruiz-Ramos, M., Tao, F., Trnka, M., Acutis, M., Asseng, S., Baranowski, P., Basso, B., Bodin, P., Buis, S., Cammarano, D., Deligios, P., Destain, M.-F., Dumont, B., Ewert, F., Ferrise, R., François, L., Gaiser, T., Hlavinka, P., Jacquemin, I., Kersebaum, K. C., Kollas, C., Krzyszczak, J., Lorite, I. J., Minet, J., Minguez, M. I., Montesino, M., Moriondo, M., Müller, C., Nendel, C., Öztürk, I., Perego, A., Rodríguez, A., Ruane, A. C., Ruget, F., Sanna, M., Semenov, M. A., Slawinski, C., Stratonovitch, P., Supit, I., Waha, K., Wang, E., Wu, L., Zhao, Z., and Rötter, R. P.

Published
2015

5. 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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10. Natural-Rubber-Latex Allergy

Author

Turjanmaa, K., Alenius, H., Mäkinen-Kiljunen, S., Reunala, T., Palosuo, T., Kanerva, Lasse, editor, Wahlberg, Jan E., editor, Elsner, P., editor, and Maibach, Howard I., editor

Published
2000
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15. Contributor contact details

Author

Kohjiya, Emeritus S., primary, Ikeda, Y., additional, Cornish, K., additional, Puskas, J.E., additional, Chiang, K., additional, Barkakaty, B., additional, Phinyocheep, P., additional, Toki, S., additional, Tohsan, A., additional, Kato, A., additional, Kokubo, Y., additional, Tsushi, R., additional, Nakao, T., additional, Kohjiya, Emeritus S., additional, Nair, A.B., additional, Joseph, R., additional, Nunes, R.C.R., additional, Poompradub, S., additional, Hirata, Y., additional, Kondo, H., additional, Ozawa, Y., additional, Hashim, A.S., additional, Ong, S.K., additional, Fukahori, Y., additional, Isayev, A., additional, and Palosuo, T., additional

Published
2014
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16. 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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49. Comparing the performance of 11 crop simulation models in predicting yield response to nitrogen fertilization

Author

Salo, T. J., Palosuo, T., Kersebaum, K. C., Nendel, C., Angulo, C., Ewert, F., Bindi, M., Calanca, P., Klein, T., Moriondo, M., Ferrise, R., Olesen, J. E., Patil, R. H., Ruget, F., Taká?, J., Hlavinka, P., Trnka, M., Rötter, R. P., Natural Resources Institute Finland, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Institute of Crop Science and Resource Conservation (IRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Department of Agrifood Production and Environmental Sciences (DISPAA), University of Florence (UNIFI), Agroscope, Istituto di Biometeorologia [Firenze] (IBIMET), Consiglio Nazionale delle Ricerche (CNR), Department of agroecology, Aarhus University [Aarhus], Department of Agroecology, Department of Agronomy, University of Agricultural Sciences, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), National Agricultural and Food Centre, Soil Science and Conservation Research Institute, Global Change Research Centre, Czech Academy of Sciences [Prague] (ASCR), Institute of Agrosystems and Bioclimatology, Mendel University in Brno, The authors wish to acknowledge the financial assistance provided under the umbrella of COST action 734 'Impacts of Climate Change and Variability on European Agriculture (CLIVAGRI)' and the work of individual researchers was funded by various bodies: T. Palosuo, T. Salo and R. Rotter, the strategic project MODAGS funded by MTT Agrifood Research Finland, and projects FACCE MACSUR and NORFASYS (decision nos. 268277 and 292944) funded by the Ministry of Agriculture and Forestry and the Academy of Finland, respectively, K. C. Kersebaum performed parts of the current study under the umbrella of FACCE MACSUR funded by the German Federal Office for Agriculture and Food and COST ES1106, C. Nendel was supported by ZALF in-house funds, J.E. Olesen and R.H. Patil, CRES funded by Danish Strategic Research Council, P. Hlavinka was supported by the Ministry of Education, Youth and Sports of CR within the National Sustainability Program I (NPU I), grant number LO1415, M. Trnka, Crop modelling as a tool for increasing the production potential and food security of the Czech Republic funded by National Agency for Agricultural Research (QJ1310123) and project LD 13030 - Water resources in the Czech Agriculture under the Climate Change conditions - CZECH-AGRIWAT, Natural resources institute Finland, Institute of Crop Science and Resource Conservation [Bonn] (INRES), Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Czech Academy of Sciences [Prague] (CAS), Mendel University in Brno (MENDELU), and Salo, Tapio J.

Subjects
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences, hordeum vulgare, 010504 meteorology & atmospheric sciences, engineering.material, modèle de simulation, 01 natural sciences, finlande, Genetics, Precipitation, Leaf area index, 3d, 0105 earth and related environmental sciences, 2. Zero hunger, Moisture, Phenology, Simulation modeling, 04 agricultural and veterinary sciences, 15. Life on land, Agricultural sciences, fertilisation azotée, Agronomy, Phenotyping, 13. Climate action, 040103 agronomy & agriculture, engineering, 0401 agriculture, forestry, and fisheries, Environmental science, Animal Science and Zoology, minéralisation, Fertilizer, Hordeum vulgare, Crop simulation model, Agronomy and Crop Science, Sciences agricoles, azote du sol, point cloud
Abstract

SUMMARYEleven widely used crop simulation models (APSIM, CERES, CROPSYST, COUP, DAISY, EPIC, FASSET, HERMES, MONICA, STICS and WOFOST) were tested using spring barley (Hordeum vulgare L.) data set under varying nitrogen (N) fertilizer rates from three experimental years in the boreal climate of Jokioinen, Finland. This is the largest standardized crop model inter-comparison under different levels of N supply to date. The models were calibrated using data from 2002 and 2008, of which 2008 included six N rates ranging from 0 to 150 kg N/ha. Calibration data consisted of weather, soil, phenology, leaf area index (LAI) and yield observations. The models were then tested against new data for 2009 and their performance was assessed and compared with both the two calibration years and the test year. For the calibration period, root mean square error between measurements and simulated grain dry matter yields ranged from 170 to 870 kg/ha. During the test year 2009, most models failed to accurately reproduce the observed low yield without N fertilizer as well as the steep yield response to N applications. The multi-model predictions were closer to observations than most single-model predictions, but multi-model mean could not correct systematic errors in model simulations. Variation in soil N mineralization and LAI development due to differences in weather not captured by the models most likely was the main reason for their unsatisfactory performance. This suggests the need for model improvement in soil N mineralization as a function of soil temperature and moisture. Furthermore, specific weather event impacts such as low temperatures after emergence in 2009, tending to enhance tillering, and a high precipitation event just before harvest in 2008, causing possible yield penalties, were not captured by any of the models compared in the current study.

Published
2016

50. 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

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