Author: "Cammarano, D." - Searchworks@Jio Institute Digital Library Search Results

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122 results on '"Cammarano, D."'

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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2. Climate change effects on processing tomato in southern Italy: a simulation study

Author: Cammarano, D., Ronga, D., Pentangelo, A., Mori, M., Di Mola, I., Parisi, M., Cammarano, D., Ronga, D., Pentangelo, A., Mori, M., Di Mola, I., and Parisi, M.
Subjects: modelling, Horticulture, fertilizer, sustainability, yield, irrigation
Abstract: In the recent decades, processing tomato (Solanum lycopersicum L.) yields have increased due to the introduction of new genotypes with improved morphological, physiological and resistance traits. However, in southern Europe, yield increment was not as high as that attributed to resistance to biotic stresses such as late blight and viruses, that represent the major threats for this fruiting vegetable crop. Such effect is likely due to climate change and future projections for the Mediterranean basin indicating an increase of warm and dry periods. Crop growth and development are very sensitive to climate change and variability. In this study, we aimed to understand the projected impact of climate change on processing tomato grown in the southern Italy. A generic tomato cultivar was calibrated and evaluated using data recorded in open field cropping 'Messapico' hybrid for two consecutive years. Plants were transplanted into twin rows (3.36 plants m-2). Drip irrigation scheduling system was based restoring 100% of Etc when 40% of total available water was depleted. Two nitrogen (N) treatments were investigated (N-150 and N-200 kg ha-1). N-150 treatment of the first trial-year, representing to the typical nitrogen supply in the investigated area, was adopted for the DSSAT v4.7 model calibration (biomass: RMSE = 1584 kg ha-1, D-index = 0.93). This N rate was evaluated on the N-200 (biomass: RMSE = 1648 kg ha-1, D-index = 0.91). Contrasting Global Climate Models were compared respect to the integrated 30-years of historical weather from NASA-AgMERRA data set. The climate change variability affected full flowering and harvest dates. Simulation of the soil water content and air temperature indicates, for some years, negative impacts on the optimal crop growth due to drought and nutrient stresses which negatively impacts on fruit yield. Hence, innovative agronomic and breeding strategies are advisable to overcome the negative effects of climate changes occurring in this production area of the processing tomato.
Published: 2022

3. 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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4. 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. Variability of effects of spatial climate data aggregation on regional yield simulation by crop models

Author: Hoffmann, H., Zhao, G., van Bussel, L. G. J., Enders, A., Specka, X., Sosa, C., Yeluripati, J., Tao, F., Constantin, J., Raynal, H., Teixeira, E., Grosz, B., Doro, L., Zhao, Z., Wang, E., Nendel, C., Kersebaum, K. C., Haas, E., Kiese, R., Klatt, S., Eckersten, H., Vanuytrecht, E., Kuhnert, M., Lewan, E., Rötter, R., Roggero, P. P., Wallach, D., Cammarano, D., Asseng, S., Krauss, G., Siebert, S., Gaiser, T., and Ewert, F.
Published: 2015

6. 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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7. THERMOACOUSTIC RANGE VERIFICATION DURING PENCIL BEAM DELIVERY OF A CLINICAL PLAN TO AN ABDOMINAL IMAGING PHANTOM

Author: Patch, S., primary, Nguyen, C., additional, Dominguez-Ramirez, D., additional, Lambert, J., additional, Chirvase, C., additional, Pandey, J., additional, Bennett, C., additional, Porteous, E., additional, Ono, S., additional, Lynch, T., additional, Cohilis, M., additional, Souris, K., additional, Finch, C., additional, Lister, J., additional, Cammarano, D., additional, Janssens, G., additional, and Labarbe, R., additional
Published: 2022
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8. Publisher Correction: Processing tomato production is expected to decrease by 2050 due to the projected increase in temperature (Nature Food, (2022), 3, 6, (437-444), 10.1038/s43016-022-00521-y)

Author: Cammarano, D., Jamshidi, S., Hoogenboom, G., Ruane, A. C., Niyogi, D., and Ronga, D.
Published: 2022

9. Simulation Modeling: Applications in Cropping Systems

Author: Asseng, S., primary, Zhu, Y., additional, Basso, B., additional, Wilson, T., additional, and Cammarano, D., additional
Published: 2014
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10. 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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11. Impact of climate change on water and nitrogen use efficiencies of processing tomato cultivated in Italy

Author: Cammarano, D., primary, Ronga, D., additional, Di Mola, I., additional, Mori, M., additional, and Parisi, M., additional
Published: 2020
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12. 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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13. A spectral correction method for multi-scattering effects in close range hyperspectral imagery of vegetation scenes: application to nitrogen content assessment in wheat

Author: Rabatel, Gilles, Al Makdessi, N., Ecarnot, Martin, Roumet, Pierre, Taylor, James, Cammarano, D., Prashar, A., Hamilton, A., Information – Technologies – Analyse Environnementale – Procédés Agricoles (UMR ITAP), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-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), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-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), ANR-10-LABX-0001,AGRO,Agricultural Sciences for sustainable Development(2010), Institut National de la Recherche Agronomique (INRA)-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)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Agropolis Foundation : 1202-008, France Agrimer, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Agropolis Foundation : 1202-008, ANR-10-LABX-0001-01, and l'Agence Nationale de la Recherche (ANR)
Subjects: 0106 biological sciences, Canopy, 0211 other engineering and technologies, Spectral space, chemistry.chemical_element, Soil science, Context (language use), 02 engineering and technology, 01 natural sciences, PHENOTYPING, Partial least squares regression, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, MULTIPLE SCATTERING, Projection (set theory), 021101 geological & geomatics engineering, Mathematics, Remote sensing, Scattering, Hyperspectral imaging, 04 agricultural and veterinary sciences, General Medicine, Vegetation, 15. Life on land, Nitrogen, Close range, Data set, Optical phenomena, hyperspectral imagery, CANOPY MODELIZATION, chemistry, [SDE]Environmental Sciences, Content (measure theory), 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, canopy modeling, General Agricultural and Biological Sciences, Subspace topology, 010606 plant biology & botany
Abstract: International audience; In-field hyperspectral imagery is a promising tool for crop phenotyping or monitoring. In association with partial least square regression (PLS-R), it allows building high spatial resolution maps of the chemical content of plant leaves. However, several optical phenomena must be taken into account, due to their influence on collected spectral data. The most challenging is multiple scattering, produced when a leaf is partly illuminated by light reflection or transmission from neighboring leaves. It can induce bias in prediction results. This paper presents a method for multi-scattering correction. Its development has been based on simulation tools: a 3D canopy model of winter wheat was combined with light propagation modeling, in order to simulate the apparent reflectance of every visible leaf in the canopy for a given actual reflectance. Leaf nitrogen content (LNC) prediction has been considered. A data set of reflectance spectra associated with LNC values has been issued from real leaf measurements. A theoretical disturbance subspace representing the spectrum dispersion in the spectral space due to multi-scattering has then been built by considering polynomial combinations of the initial spectra, and a projection along this subspace has been applied to every simulated spectra. Using this strategy, a PLS-R model built on initial spectra was still satisfactory when applied to simulated spectra with multiple scattering. The method has then been applied to real plants in greenhouse and field conditions, and its prediction results compared with those of a standard PLS-R, confirming its efficiency in the presence of various lighting environments.
Published: 2017

14. 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

15. 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

16. 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

17. EPD041 - THERMOACOUSTIC RANGE VERIFICATION DURING PENCIL BEAM DELIVERY OF A CLINICAL PLAN TO AN ABDOMINAL IMAGING PHANTOM

Author: Patch, S., Nguyen, C., Dominguez-Ramirez, D., Lambert, J., Chirvase, C., Pandey, J., Bennett, C., Porteous, E., Ono, S., Lynch, T., Cohilis, M., Souris, K., Finch, C., Lister, J., Cammarano, D., Janssens, G., and Labarbe, R.
Published: 2022
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18. 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

19. Combining crop modelling and remote sensing to create yield maps for management zone delineation in small scale farming systems

Author: Zha, H., primary, Cammarano, D., additional, Wilson, L., additional, Li, Y., additional, Batchelor, W.D., additional, and Miao, Y., additional
Published: 2019
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20. Integrating geospatial tools and a crop simulation model to understand spatial and temporal variability of cereals in Scotland

Author: Cammarano, D., primary, Holland, J., additional, Basso, B., additional, Fontana, F., additional, Murgia, T., additional, Lange, C., additional, Taylor, J., additional, and Ronga, D., additional
Published: 2019
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21. 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

22. 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

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

Author: Ruiz-Ramos, M., Ferrise, Roberto, Rodríguez, A, Lorite, I. J., Bindi, Marco, Carter, T. R., Fronzek, S, Palosuo, T., Pirttioja, N., Baranowski, P., Buis, S., Cammarano, D., Chen, Y., Dumont, Bertrand, Ewert, F., Gaiser, T., Hlavinka, P., Hoffmann, H., Höhn, J. G., Jurecka, F., Kersebaum, K. C., Krzyszczak, J., Lana, M., Mechiche-Alami, A., Minet, J., Montesino Pouzols, Federico, Nendel, C., Porter, John Roy, Ruget, F., Semenov, M. A., Steinmetz, Z., Stratonovitch, P., Supit, Iwan, Tao, F., Trnka, M., de Wit, Cynthia A., Rötter, Reimund P, Ruiz-Ramos, M., Ferrise, Roberto, Rodríguez, A, Lorite, I. J., Bindi, Marco, Carter, T. R., Fronzek, S, Palosuo, T., Pirttioja, N., Baranowski, P., Buis, S., Cammarano, D., Chen, Y., Dumont, Bertrand, Ewert, F., Gaiser, T., Hlavinka, P., Hoffmann, H., Höhn, J. G., Jurecka, F., Kersebaum, K. C., Krzyszczak, J., Lana, M., Mechiche-Alami, A., Minet, J., Montesino Pouzols, Federico, Nendel, C., Porter, John Roy, Ruget, F., Semenov, M. A., Steinmetz, Z., Stratonovitch, P., Supit, Iwan, Tao, F., Trnka, M., de Wit, Cynthia A., and Rötter, Reimund P
Abstract: Adaptation of crops to climate change has to be addressed locally due to the variability of soil, climate and the specific socio-economic settings influencing farm management decisions. Adaptation of rainfed cropping systems in the Mediterranean is especially challenging due to the projected decline in precipitation in the coming decades, which will increase the risk of droughts. Methods that can help explore uncertainties in climate projections and crop modelling, such as impact response surfaces (IRSs) and ensemble modelling, can then be valuable for identifying effective adaptations. Here, an ensemble of 17 crop models was used to simulate a total of 54 adaptation options for rainfed winter wheat (Triticum aestivum) at Lleida (NE Spain). To support the ensemble building, an ex post quality check of model simulations based on several criteria was performed. Those criteria were based on the "According to Our Current Knowledge" (AOCK) concept, which has been formalized here. Adaptations were based on changes in cultivars and management regarding phenology, vernalization, sowing date and irrigation. The effects of adaptation options under changed precipitation (P), temperature (T), [CO2] and soil type were analysed by constructing response surfaces, which we termed, in accordance with their specific purpose, adaptation response surfaces (ARSs). These were created to assess the effect of adaptations through a range of plausible P, T and [CO2] perturbations. The results indicated that impacts of altered climate were predominantly negative. No single adaptation was capable of overcoming the detrimental effect of the complex interactions imposed by the P, T and [CO2] perturbations except for supplementary irrigation (sI), which reduced the potential impacts under most of the perturbations. Yet, a combination of adaptations for dealing with climate change demonstrated that effective adaptation is possible at Lleida. Combinations based o
Published: 2018

24. Modelling nitrous oxide emission of high input maize crop systems

Author: Bassu, S., Acutis, M., Amaducci, S., Argenti, G., Baranowski, P., Berti, Antonio, Bertora, C., Bindi, M., Bosco, S., Brilli, L., Cammarano, D., Doro, L., Ferrise, R., Grignani, C., Harrison, M. T., Iocola, I., Krzyszczak, J., Lai, R., Morari, Francesco, Mula, L., Nendel, C., Oygarden, L., Perego, A. ., Priesack, E., Pulina, A., Stella, T., Wu, L., Zubik, M., and Roggero, P. P.
Published: 2017

25. 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

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

Author: Ruiz-Ramos, M., primary, Ferrise, R., additional, Rodríguez, A., additional, Lorite, I.J., additional, Bindi, M., additional, Carter, T.R., additional, Fronzek, S., additional, Palosuo, T., additional, Pirttioja, N., additional, Baranowski, P., additional, Buis, S., additional, Cammarano, D., additional, Chen, Y., additional, Dumont, B., additional, Ewert, F., additional, Gaiser, T., additional, Hlavinka, P., additional, Hoffmann, H., additional, Höhn, J.G., additional, Jurecka, F., additional, Kersebaum, K.C., additional, Krzyszczak, J., additional, Lana, M., additional, Mechiche-Alami, A., additional, Minet, J., additional, Montesino, M., additional, Nendel, C., additional, Porter, J.R., additional, Ruget, F., additional, Semenov, M.A., additional, Steinmetz, Z., additional, Stratonovitch, P., additional, Supit, I., additional, Tao, F., additional, Trnka, M., additional, de Wit, A., additional, and Rötter, R.P., additional
Published: 2018
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27. 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

28. 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

29. Baseline simulation for global wheat production with CIMMYT mega-environment specific cultivars

Author: Gbegbelegbe, Sika; Cammarano, D.; Asseng, S.; Robertson, Richard D.; Chung, U.; Adam, M.; Abdalla, O.; Payne, T.; Reynolds, M.; Sonder, K.; Shiferaw, B.; Nelson, Gerald C., http://orcid.org/0000-0001-5741-3867 Robertson, Richard, Gbegbelegbe, Sika; Cammarano, D.; Asseng, S.; Robertson, Richard D.; Chung, U.; Adam, M.; Abdalla, O.; Payne, T.; Reynolds, M.; Sonder, K.; Shiferaw, B.; Nelson, Gerald C., and http://orcid.org/0000-0001-5741-3867 Robertson, Richard
Abstract: PR, IFPRI3; ISI; CRP2; A Ensuring Sustainable food production; A.1 Global Futures and Strategic Foresight, PIM; EPTD, CGIAR Research Program on Policies, Institutions, and Markets (PIM); CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS)
Published: 2017

30. 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

31. An ensemble of projections of wheat adaptation to climate change in europe analyzed with impact response surfaces

Author: Ruiz-Ramos, Margarita, Ferrise, Roberto, Rodriguez, A., Lorite, Ignacio J., Tao, F., Pirttioja, N., Fronzek, S., Palosuo, T., Carter, Timothy R., Bindi, M., Höhn, Jukka G., Kersebaum, K. C., Trnka, M., Hoffmann, H., Baranowski, P., Buis, Samuel, Cammarano, D., Deligios, P., Havlinka, P., Minet, J., Montesino, M., Porter, J., Recio, J., Ruget, Francoise, Sanz, A, Steinmetz, Z., Stratonovitch, P., Supit, I., Ventrella, D., De Wit, A., Rotter, R. P., ETSI Agrónomos, Producción Vegetal: Fitotecnia, Universidad Politécnica de Madrid (UPM), University of Florence (UNIFI), Instituto de Investigación y Formación Agraria y Pesquera (IFAPA), Environmental Impacts Group, Natural Resources Institute Finland, Finnish Environment Institute (SYKE), Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Institute of Agrosystems and Bioclimatology, Mendel University in Brno, Global Change Research Centre (CzechGlobe), Rheinische Friedrich-Wilhelms-Universität Bonn, Institute of Agrophysics Polish Academy of Sciences, Agricultural University of Lublin, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), The James Hutton Institute, University of Sassari, Université de Liège, University of Copenhagen = Københavns Universitet (KU), RIFCON GmbH, Rothamsted Research, Wageningen University, Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria (CREA‐SCA), Consiglio per la Ricerca in Agricoltura e l’analisi dell’economia agraria (CREA), MACSUR–, FACCE JPI, and by MULCLIVAR, from MINECO (CGL2012‐38923‐C02‐02), Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Instituto Andaluz de Investigación y Formación Agraria y Pesquera (IFAPA), Natural resources institute Finland, Mendel University in Brno (MENDELU), and Ruiz-Ramos, Margarita
Subjects: changement climatique, ble tendre, sensitivity analysis, soft wheat, adaptation au changement, wheat, [SDE.MCG]Environmental Sciences/Global Changes, climate, surface de réponse, Milieux et Changements globaux, europe, global change
Abstract: IRS2 TEAM:Alfredo Rodríguez(1), Ignacio J. Lorite(3), Fulu Tao(4), Nina Pirttioja(5), Stefan Fronzek(5), Taru Palosuo(4), Timothy R. Carter(5), Marco Bindi(2), Jukka G Höhn(4), Kurt Christian Kersebaum(6), Miroslav Trnka(7,8),Holger Hoffmann(9), Piotr Baranowski(10), Samuel Buis(11), Davide Cammarano(12), Yi Chen(13,4), Paola Deligios(14), Petr Hlavinka(7,8), Frantisek Jurecka(7,8), Jaromir Krzyszczak(10), Marcos Lana(6), Julien Minet(15), Manuel Montesino(16), Claas Nendel(6), John Porter(16), Jaime Recio(1), Françoise Ruget(11), Alberto Sanz(1), Zacharias Steinmetz(17,18), Pierre Stratonovitch(19), Iwan Supit(20), Domenico Ventrella(21), Allard de Wit(20) and Reimund P. Rötter(4).; An ensemble of projections of wheat adaptation to climate change in europe analyzed with impact response surfaces . International Crop Modelling Symposium
Published: 2016

32. Crop yields, soil organic carbon and soil nitrogen content change under climate change

Author: Dumont, B., Basso, B., Shcherbak, I., Asseng, S., Bassu, Simona, Boote, K., Cammarano, D., De Sanctis, Giovanni, Durand, Jean-Louis, Ewert, F., Gayler, S., Grace, P., Grant, R., Kent, J., Martre, Pierre, Nendel, C., Paustian, K., Priesack, E., Ripoche, Dominique, Ruane, A., Thorburn, P., Hatfield, J., Jones, J., Rosenzweig, C., Department of geological sciences, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, 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), Agronomie, Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Department of agronomy, University of Florida [Gainesville] (UF), The James Hutton Institute, Joint Research center, European Commission, Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Institut National de la Recherche Agronomique (INRA), Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, WESS-Water and Earth System Science Competence Cluster, Eberhard Karls Universität Tübingen = Eberhard Karls University of Tuebingen, Institute for Future Environments, Queensland University of Technology, Natural Resource Ecology Laboratory [Fort Collins] (NREL), Colorado State University [Fort Collins] (CSU), 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), Institute of landscape systems analysis, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Institute of Soil Ecology, Helmholtz-Zentrum München (HZM), Agroclim (AGROCLIM), National Aeronautics and Space Administration, Partenaires INRAE, Ecosystem sciences, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), United States Department of Agriculture (USDA), and Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research, Leibniz Association (ZALF). DEU.
Subjects: blé, maïs, comparaison de modèles, [SDE.MCG]Environmental Sciences/Global Changes, température, conduite de la culture, modèle continu, interaction sol plante climat, Milieux et Changements globaux, co2 atmosphérique, modèle de production, ComputingMilieux_MISCELLANEOUS
Abstract: International audience
Published: 2016

33. Classifying simulated wheat yield responses to changes in temperature and precipitation across a european transect

Author: Fronzek, Stefan, Pirttioja, N., Carter, Timothy R., Bindi, M., Hoffmann, H., Palosuo, T., Ruiz-Ramos, Margarita, Tao, F., Trnka, M., Acutis, M., Asseng, S., Baranowski, P., Basso, Benjamin, Bodin, P., Buis, Samuel, Cammarano, D., Deligios, P., Destain, Marie-France, Dumont, Bertrand, Ewert, Franck, Ferrise, R., FRANCOIS, Léa, Gaiser, T., Hlavinka, P., Jacquemin, Ingrid, Kersebaum, K. C., Kollas, C., Krzyszczak, J., Lorite, Ignacio J., Minet, Julien, Minguez, M. Ines, Montesino, M., Moriondo, Marco, MULLER, C, Nendel, C., Peregon, Anna, Rodríguez, A., Ruane, A. C., Ruget, Francoise, Sanna, Mattia, Semenov, M. A., Slawinski, Cezary, Stratonovitch, P., Supit, I., Waha, K., Wang, E., Wu, L., Zhao, Z., Rotter, R. P., Finnish Environment Institute (SYKE), Abeilles et Environnement (AE), Institut National de la Recherche Agronomique (INRA)-Avignon Université (AU), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Unité Mixte de Recherche sur les Herbivores - UMR 1213 (UMRH), Institut National de la Recherche Agronomique (INRA)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS), Reproduction et développement des plantes (RDP), École normale supérieure de Lyon (ENS de Lyon)-Institut National de la Recherche Agronomique (INRA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), FACCE-JPI Knowledge Hub MACSUR, Knowledge Hub FACCE MACSUR. INT. Agricultural Model Intercomparison and Improvement Project (AgMIP), USA., Institut National de la Recherche Agronomique (INRA)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, and École normale supérieure - Lyon (ENS Lyon)-Institut National de la Recherche Agronomique (INRA)-Université Claude Bernard Lyon 1 (UCBL)
Subjects: changement de température, sensitivity analysis, crop model, pluviomètre, [SDE.MCG]Environmental Sciences/Global Changes, région européenne, rendement du blé, transect, snow gauges
Abstract: Classifying simulated wheat yield responses to changes in temperature and precipitation across a european transect. International Crop Modelling Symposium
Published: 2016

34. 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

35. 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

36. 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

37. Implications of climate model biases and downscaling on crop model simulated climate change impacts

Author: Cammarano, D., primary, Rivington, M., additional, Matthews, K.B., additional, Miller, D.G., additional, and Bellocchi, G., additional
Published: 2017
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38. Baseline simulation for global wheat production with CIMMYT mega-environment specific cultivars

Author: Gbegbelegbe, S., primary, Cammarano, D., additional, Asseng, S., additional, Robertson, R., additional, Chung, U., additional, Adam, M., additional, Abdalla, O., additional, Payne, T., additional, Reynolds, M., additional, Sonder, K., additional, Shiferaw, B., additional, and Nelson, G., additional
Published: 2017
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39. The prediction of crop biomass, grain yield and grain quality using fluorescence sensing in cereals

Author: Holland, J., primary, Cammarano, D., additional, Poile, G., additional, and Conyers, M., additional
Published: 2017
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40. MagicPlate-512: A 2D silicon detector array for quality assurance of stereotactic motion adaptive radiotherapy

Author: Petasecca, M, Newall, M, Booth, J, Duncan, M, Aldosari, A, Fuduli, I, Espinoza, A, Porumb, C, Guatellli, S, Metcafe, P, Colvill, E, Cammarano, D, Carolan, M, Oborn, B, Lerch, M, Perevertaylo, V, Keall, P, and Rosenfeld, A
Subjects: 2D silicon array, QA of adaptive motion radiotherapy
Abstract: PURPOSE: Spatial and temporal resolutions are two of the most important features for quality assurance instrumentation of motion adaptive radiotherapy modalities. The goal of this work is to characterize the performance of the 2D high spatial resolution monolithic silicon diode array named "MagicPlate-512" for quality assurance of stereotactic body radiation therapy (SBRT) and stereotactic radiosurgery (SRS) combined with a dynamic multileaf collimator (MLC) tracking technique for motion compensation. METHODS: MagicPlate-512 is used in combination with the movable platform HexaMotion and a research version of radiofrequency tracking system Calypso driving MLC tracking software. The authors reconstruct 2D dose distributions of small field square beams in three modalities: in static conditions, mimicking the temporal movement pattern of a lung tumor and tracking the moving target while the MLC compensates almost instantaneously for the tumor displacement. Use of Calypso in combination with MagicPlate-512 requires a proper radiofrequency interference shielding. Impact of the shielding on dosimetry has been simulated by (GEANT)4 and verified experimentally. Temporal and spatial resolutions of the dosimetry system allow also for accurate verification of segments of complex stereotactic radiotherapy plans with identification of the instant and location where a certain dose is delivered. This feature allows for retrospective temporal reconstruction of the delivery process and easy identification of error in the tracking or the multileaf collimator driving systems. A sliding MLC wedge combined with the lung motion pattern has been measured. The ability of the MagicPlate-512 (MP512) in 2D dose mapping in all three modes of operation was benchmarked by EBT3 film. RESULTS: Full width at half maximum and penumbra of the moving and stationary dose profiles measured by EBT3 film and MagicPlate-512 confirm that motion has a significant impact on the dose distribution. Motion, no motion, and motion with MLC tracking profiles agreed within 1 and 0.4 mm, respectively, for all field sizes tested. Use of electromagnetic tracking system generates a fluctuation of the detector baseline up to 10% of the full scale signal requiring a proper shielding strategy. MagicPlate-512 is also able to reconstruct the dose variation pulse-by-pulse in each pixel of the detector. An analysis of the dose transients with motion and motion with tracking shows that the tracking feedback algorithm used for this experiment can compensate effectively only the effect of the slower transient components. The fast changing components of the organ motion can contribute only to discrepancy of the order of 15% in penumbral region while the slower components can change the dose profile up to 75% of the expected dose. CONCLUSIONS: MagicPlate-512 is shown to be, potentially, a valid alternative to film or 2D ionizing chambers for quality assurance dosimetry in SRS or SBRT. Its high spatial and temporal resolutions allow for accurate reconstruction of the profile in any conditions with motion and with tracking of the motion. It shows excellent performance to reconstruct the dose deposition in real time or retrospectively as a function of time for detailed analysis of the effect of motion in a specific pixel or area of interest.
Published: 2015

41. 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

42. 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., Rötter, R. P., Cammarano, D., Brisson, N., Basso, B., Aggarwal, P. K., Angulo, C., Bertuzzi, P., Biernath, C., Challinor, A. J., Doltra, J., Semenov, M. A., and Stratonovitch, P.
Published: 2015

43. Response of wheat growth, grain yield and water use to elevated CO2 under a Free-Air CO2 Enrichment (FACE) experiment and modelling in a semi-arid environment

Author: O'Leary, GJ, Christy, B, Nuttall, J, Huth, N, Cammarano, D, Stöckle, C, Basso, B, Shcherbak, I, Fitzgerald, G, Luo, Q, Farre-Codina, I, Palta, J, and Asseng, S
Subjects: Ecology
Abstract: © 2014 John Wiley & Sons Ltd. The response of wheat crops to elevated CO2 (eCO2) was measured and modelled with the Australian Grains Free-Air CO2 Enrichment experiment, located at Horsham, Australia. Treatments included CO2 by water, N and temperature. The location represents a semi-arid environment with a seasonal VPD of around 0.5 kPa. Over 3 years, the observed mean biomass at anthesis and grain yield ranged from 4200 to 10 200 kg ha-1 and 1600 to 3900 kg ha-1, respectively, over various sowing times and irrigation regimes. The mean observed response to daytime eCO2 (from 365 to 550 μmol mol-1 CO2) was relatively consistent for biomass at stem elongation and at anthesis and LAI at anthesis and grain yield with 21%, 23%, 21% and 26%, respectively. Seasonal water use was decreased from 320 to 301 mm (P = 0.10) by eCO2, increasing water use efficiency for biomass and yield, 36% and 31%, respectively. The performance of six models (APSIM-Wheat, APSIM-Nwheat, CAT-Wheat, CROPSYST, OLEARY-CONNOR and SALUS) in simulating crop responses to eCO2 was similar and within or close to the experimental error for accumulated biomass, yield and water use response, despite some variations in early growth and LAI. The primary mechanism of biomass accumulation via radiation use efficiency (RUE) or transpiration efficiency (TE) was not critical to define the overall response to eCO2. However, under irrigation, the effect of late sowing on response to eCO2 to biomass accumulation at DC65 was substantial in the observed data (~40%), but the simulated response was smaller, ranging from 17% to 28%. Simulated response from all six models under no water or nitrogen stress showed similar response to eCO2 under irrigation, but the differences compared to the dryland treatment were small. Further experimental work on the interactive effects of eCO2, water and temperature is required to resolve these model discrepancies.
Published: 2014

44. Statistical Analysis of Large Simulated Yield Datasets for Studying Climate Effects

Author: Makowski, D., Asseng, S., Ewert, F., Bassu, S., Durand, J.L., Martre, P., Adam, M., Aggarwal, P.K., Angulo, C., Baron, C., Basso, B., Bertuzzi, P., Biernath, C., Boogaard, H., Boote, K.J., Brisson, N., Cammarano, D., Challinor, A.J., Conijn, J.G., Corbeels, M., Deryng, D., De Sanctis, G., Doltra, J., Gayler, S., Goldberg, R., Grassini, P., Hatfield, J.L., Heng, L., Hoek, S.B., Hooker, J., Hunt, L.A., Ingwersen, J., Izaurralde, C., Jongschaap, R.E.E., Jones, J.W., Kemanian, R.A., Kersebaum, K.C., Kim, S.H., Lizaso, J., Müller, C., Naresh Kumar, S., Nendel, C., O'Leary, G.J., Olesen, J.E., Osborne, T.M., Palosuo, T., Pravia, M.V., Priesack, E., Ripoche, D., Rosenzweig, C., Ruane, A.C., Sau, F., Semenov, M.A., Shcherbak, I., Steduto, P., Stöckle, C.O., Stratonovitch, P., Streck, T., Supit, I., Tao, F., Teixeira, E., Thorburn, P., Timlin, D., Travasso, M., Roetter, R.P., Waha, K., Wallach, D., White, J.W., Williams, J.R., Wolf, J., Makowski, D., Asseng, S., Ewert, F., Bassu, S., Durand, J.L., Martre, P., Adam, M., Aggarwal, P.K., Angulo, C., Baron, C., Basso, B., Bertuzzi, P., Biernath, C., Boogaard, H., Boote, K.J., Brisson, N., Cammarano, D., Challinor, A.J., Conijn, J.G., Corbeels, M., Deryng, D., De Sanctis, G., Doltra, J., Gayler, S., Goldberg, R., Grassini, P., Hatfield, J.L., Heng, L., Hoek, S.B., Hooker, J., Hunt, L.A., Ingwersen, J., Izaurralde, C., Jongschaap, R.E.E., Jones, J.W., Kemanian, R.A., Kersebaum, K.C., Kim, S.H., Lizaso, J., Müller, C., Naresh Kumar, S., Nendel, C., O'Leary, G.J., Olesen, J.E., Osborne, T.M., Palosuo, T., Pravia, M.V., Priesack, E., Ripoche, D., Rosenzweig, C., Ruane, A.C., Sau, F., Semenov, M.A., Shcherbak, I., Steduto, P., Stöckle, C.O., Stratonovitch, P., Streck, T., Supit, I., Tao, F., Teixeira, E., Thorburn, P., Timlin, D., Travasso, M., Roetter, R.P., Waha, K., Wallach, D., White, J.W., Williams, J.R., and Wolf, J.
Abstract: Many simulation studies have been carried out to predict the effect of climate change on crop yield. Typically, in such study, one or several crop models are used to simulate series of crop yield values for different climate scenarios corresponding to different hypotheses of temperature, CO2 concentration, and rainfall changes. These studies usually generate large datasets including thousands of simulated yield data. The structure of these datasets is complex because they include series of yield values obtained with different mechanistic crop models for different climate scenarios defined from several climatic variables (temperature, CO2 etc.). Statistical methods can play a big part for analyzing large simulated crop yield datasets, especially when yields are simulated using an ensemble of crop models. A formal statistical analysis is then needed in order to estimate the effects of different climatic variables on yield, and to describe the variability of these effects across crop models. Statistical methods are also useful to develop meta-models i.e., statistical models summarizing complex mechanistic models. The objective of this paper is to present a random-coefficient statistical model (mixed-effects model) for analyzing large simulated crop yield datasets produced by the international project AgMip for several major crops. The proposed statistical model shows several interesting features; i) it can be used to estimate the effects of several climate variables on yield using crop model simulations, ii) it quantities the variability of the estimated climate change effects across crop models, ii) it quantifies the between-year yield variability, iv) it can be used as a meta-model in order to estimate effects of new climate change scenarios without running again the mechanistic crop models. The statistical model is first presented in details, and its value is then illustrated in a case study where the effects of climate change scenarios on different crops are compar
Published: 2015

45. Effect of weather data aggregation on regional crop simulation for different crops, production conditions, and response variables

Author: Zhao, G, primary, Hoffmann, H, additional, van Bussel, LGJ, additional, Enders, A, additional, Specka, X, additional, Sosa, C, additional, Yeluripati, J, additional, Tao, F, additional, Constantin, J, additional, Raynal, H, additional, Teixeira, E, additional, Grosz, B, additional, Doro, L, additional, Zhao, Z, additional, Nendel, C, additional, Kiese, R, additional, Eckersten, H, additional, Haas, E, additional, Vanuytrecht, E, additional, Wang, E, additional, Kuhnert, M, additional, Trombi, G, additional, Moriondo, M, additional, Bindi, M, additional, Lewan, E, additional, Bach, M, additional, Kersebaum, KC, additional, Rötter, R, additional, Roggero, PP, additional, Wallach, D, additional, Cammarano, D, additional, Asseng, S, additional, Krauss, G, additional, Siebert, S, additional, Gaiser, T, additional, and Ewert, F, additional
Published: 2015
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46. MagicPlate-512: A 2D silicon detector array for quality assurance of stereotactic motion adaptive radiotherapy

Author: Petasecca, M., primary, Newall, M. K., additional, Booth, J. T., additional, Duncan, M., additional, Aldosari, A. H., additional, Fuduli, I., additional, Espinoza, A. A., additional, Porumb, C. S., additional, Guatelli, S., additional, Metcalfe, P., additional, Colvill, E., additional, Cammarano, D., additional, Carolan, M., additional, Oborn, B., additional, Lerch, M. L. F., additional, Perevertaylo, V., additional, Keall, P. J., additional, and Rosenfeld, A. B., additional
Published: 2015
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47. Assessment of climate variability on optimal nitrogen fertilizer rates for precision agriculture

Author: Basso, B, Cammarano, D, Cafiero, G, Sartori, Luigi, and Basso, F.
Published: 2010

48. Discriminazione varietale attraverso analisi spettro-radiometriche nel dominio VIS-NIR

Author: Basso, B, Cammarano, D, Cafiero, G, Marino, S, and Alvino, Arturo
Published: 2010

49. 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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50. Simulating spatial and temporal variability of wheat yield and grain prtotein in Southern Italy

Author: Cammarano, D, Basso, B, Cafiero, G, Pisante, Michele, Castrignanò, A, Troccoli, A, and Buttafuoco, G.
Published: 2008

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