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27 results on '"Sharp, Joanna"'

1. Residual correlation and ensemble modelling to improve crop and grassland models

Author

Sándor, Renáta, Ehrhardt, Fiona, Grace, Peter, Recous, Sylvie, Smith, Pete, Snow, Val, Soussana, Jean-François, Basso, Bruno, Bhatia, Arti, Brilli, Lorenzo, Doltra, Jordi, Dorich, Christopher D., Doro, Luca, Fitton, Nuala, Grant, Brian, Harrison, Matthew Tom, Skiba, Ute, Kirschbaum, Miko U.F., Klumpp, Katja, Laville, Patricia, Léonard, Joel, Martin, Raphaël, Massad, Raia Silvia, Moore, Andrew D., Myrgiotis, Vasileios, Pattey, Elizabeth, Rolinski, Susanne, Sharp, Joanna, Smith, Ward, Wu, Lianhai, Zhang, Qing, and Bellocchi, Gianni

Published
2023
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7. APSIM – Evolution towards a new generation of agricultural systems simulation

Author

Holzworth, Dean P., Huth, Neil I., deVoil, Peter G., Zurcher, Eric J., Herrmann, Neville I., McLean, Greg, Chenu, Karine, van Oosterom, Erik J., Snow, Val, Murphy, Chris, Moore, Andrew D., Brown, Hamish, Whish, Jeremy P.M., Verrall, Shaun, Fainges, Justin, Bell, Lindsay W., Peake, Allan S., Poulton, Perry L., Hochman, Zvi, Thorburn, Peter J., Gaydon, Donald S., Dalgliesh, Neal P., Rodriguez, Daniel, Cox, Howard, Chapman, Scott, Doherty, Alastair, Teixeira, Edmar, Sharp, Joanna, Cichota, Rogerio, Vogeler, Iris, Li, Frank Y., Wang, Enli, Hammer, Graeme L., Robertson, Michael J., Dimes, John P., Whitbread, Anthony M., Hunt, James, van Rees, Harm, McClelland, Tim, Carberry, Peter S., Hargreaves, John N.G., MacLeod, Neil, McDonald, Cam, Harsdorf, Justin, Wedgwood, Sara, and Keating, Brian A.

Published
2014
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10. S-map parameters for APSIM

Author

Vogeler, Iris, primary, Lilburne, Linda, additional, Webb, Trevor, additional, Cichota, Rogerio, additional, Sharp, Joanna, additional, Carrick, Sam, additional, Brown, Hamish, additional, and Snow, Val, additional

Published
2022
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12. Ensemble modelling of carbon fluxes in grasslands and croplands

Author

Sándor, Renáta, Ehrhardt, Fiona, Grace, Peter, Recous, Sylvie, Smith, Pete, Snow, Val, Soussana, Jean-François, Basso, Bruno, Bhatia, Arti, Brilli, Lorenzo, Doltra, Jordi, Dorich, Christopher D., Doro, Luca, Fitton, Nuala, Grant, Brian, Harrison, Matthew Tom, Kirschbaum, Miko U.F., Klumpp, Katja, Laville, Patricia, Léonard, Joel, Martin, Raphaël, Massad, Raia-Silvia, Moore, Andrew, Myrgiotis, Vasileios, Pattey, Elizabeth, Rolinski, Susanne, Sharp, Joanna, Skiba, Ute, Smith, Ward, Wu, Lianhai, Zhang, Qing, Bellocchi, Gianni, Sándor, Renáta, Ehrhardt, Fiona, Grace, Peter, Recous, Sylvie, Smith, Pete, Snow, Val, Soussana, Jean-François, Basso, Bruno, Bhatia, Arti, Brilli, Lorenzo, Doltra, Jordi, Dorich, Christopher D., Doro, Luca, Fitton, Nuala, Grant, Brian, Harrison, Matthew Tom, Kirschbaum, Miko U.F., Klumpp, Katja, Laville, Patricia, Léonard, Joel, Martin, Raphaël, Massad, Raia-Silvia, Moore, Andrew, Myrgiotis, Vasileios, Pattey, Elizabeth, Rolinski, Susanne, Sharp, Joanna, Skiba, Ute, Smith, Ward, Wu, Lianhai, Zhang, Qing, and Bellocchi, Gianni

Abstract

Croplands and grasslands are agricultural systems that contribute to land–atmosphere exchanges of carbon (C). We evaluated and compared gross primary production (GPP), ecosystem respiration (RECO), net ecosystem exchange (NEE) of CO2, and two derived outputs - C use efficiency (CUE=-NEE/GPP) and C emission intensity (IntC= -NEE/Offtake [grazed or harvested biomass]). The outputs came from 23 models (11 crop-specific, eight grassland-specific, and four models covering both systems) at three cropping sites over several rotations with spring and winter cereals, soybean and rapeseed in Canada, France and India, and two temperate permanent grasslands in France and the United Kingdom. The models were run independently over multi-year simulation periods in five stages (S), either blind with no calibration and initialization data (S1), using historical management and climate for initialization (S2), calibrated against plant data (S3), plant and soil data together (S4), or with the addition of C and N fluxes (S5). Here, we provide a framework to address methodological uncertainties and contextualize results. Most of the models overestimated or underestimated the C fluxes observed during the growing seasons (or the whole years for grasslands), with substantial differences between models. For each simulated variable, changes in the multi-model median (MMM) from S1 to S5 was used as a descriptor of the ensemble performance. Overall, the greatest improvements (MMM approaching the mean of observations) were achieved at S3 or higher calibration stages. For instance, grassland GPP MMM was equal to 1632 g C m−2 yr-1 (S5) while the observed mean was equal to 1763 m-2 yr-1 (average for two sites). Nash-Sutcliffe modelling efficiency coefficients indicated that MMM outperformed individual models in 92.3 % of cases. Our study suggests a cautious use of large-scale, multi-model ensembles to estimate C fluxes in agricultural sites if some site-specific plant and soil observations are avai

Published
2020

13. Ensemble modelling of carbon fluxes in grasslands and croplands

Author

Sándor, Renáta, primary, Ehrhardt, Fiona, additional, Grace, Peter, additional, Recous, Sylvie, additional, Smith, Pete, additional, Snow, Val, additional, Soussana, Jean-François, additional, Basso, Bruno, additional, Bhatia, Arti, additional, Brilli, Lorenzo, additional, Doltra, Jordi, additional, Dorich, Christopher D., additional, Doro, Luca, additional, Fitton, Nuala, additional, Grant, Brian, additional, Harrison, Matthew Tom, additional, Kirschbaum, Miko U.F., additional, Klumpp, Katja, additional, Laville, Patricia, additional, Léonard, Joel, additional, Martin, Raphaël, additional, Massad, Raia-Silvia, additional, Moore, Andrew, additional, Myrgiotis, Vasileios, additional, Pattey, Elizabeth, additional, Rolinski, Susanne, additional, Sharp, Joanna, additional, Skiba, Ute, additional, Smith, Ward, additional, Wu, Lianhai, additional, Zhang, Qing, additional, and Bellocchi, Gianni, additional

Published
2020
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16. Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N2O emissions

Author

Ehrhardt, Fiona, Soussana, Jean François, Bellocchi, Gianni, Grace, Peter, McAuliffe, Russell, Recous, Sylvie, Sándor, Renáta, Smith, Pete, Snow, Val, de Antoni Migliorati, Massimiliano, Basso, Bruno, Bhatia, Arti, Brilli, Lorenzo, Doltra, Jordi, Dorich, Christopher D., Doro, Luca, Fitton, Nuala, Giacomini, Sandro J., Grant, Brian, Harrison, Matthew T., Jones, Stephanie K., Kirschbaum, Miko U.F., Klumpp, Katja, Laville, Patricia, Léonard, Joël, Liebig, Mark, Lieffering, Mark, Martin, Raphaël, Massad, Raia S., Meier, Elizabeth, Merbold, Lutz, Moore, Andrew D., Myrgiotis, Vasileios, Newton, Paul, Pattey, Elizabeth, Rolinski, Susanne, Sharp, Joanna, Smith, Ward N., Wu, Lianhai, Zhang, Qing, Ehrhardt, Fiona, Soussana, Jean François, Bellocchi, Gianni, Grace, Peter, McAuliffe, Russell, Recous, Sylvie, Sándor, Renáta, Smith, Pete, Snow, Val, de Antoni Migliorati, Massimiliano, Basso, Bruno, Bhatia, Arti, Brilli, Lorenzo, Doltra, Jordi, Dorich, Christopher D., Doro, Luca, Fitton, Nuala, Giacomini, Sandro J., Grant, Brian, Harrison, Matthew T., Jones, Stephanie K., Kirschbaum, Miko U.F., Klumpp, Katja, Laville, Patricia, Léonard, Joël, Liebig, Mark, Lieffering, Mark, Martin, Raphaël, Massad, Raia S., Meier, Elizabeth, Merbold, Lutz, Moore, Andrew D., Myrgiotis, Vasileios, Newton, Paul, Pattey, Elizabeth, Rolinski, Susanne, Sharp, Joanna, Smith, Ward N., Wu, Lianhai, and Zhang, Qing

Abstract

Simulation models are extensively used to predict agricultural productivity and greenhouse gas emissions. However, the uncertainties of (reduced) model ensemble simulations have not been assessed systematically for variables affecting food security and climate change mitigation, within multi-species agricultural contexts. We report an international model comparison and benchmarking exercise, showing the potential of multi-model ensembles to predict productivity and nitrous oxide (N2O) emissions for wheat, maize, rice and temperate grasslands. Using a multi-stage modelling protocol, from blind simulations (stage 1) to partial (stages 2–4) and full calibration (stage 5), 24 process-based biogeochemical models were assessed individually or as an ensemble against long-term experimental data from four temperate grassland and five arable crop rotation sites spanning four continents. Comparisons were performed by reference to the experimental uncertainties of observed yields and N2O emissions. Results showed that across sites and crop/grassland types, 23%–40% of the uncalibrated individual models were within two standard deviations (SD) of observed yields, while 42 (rice) to 96% (grasslands) of the models were within 1 SD of observed N2O emissions. At stage 1, ensembles formed by the three lowest prediction model errors predicted both yields and N2O emissions within experimental uncertainties for 44% and 33% of the crop and grassland growth cycles, respectively. Partial model calibration (stages 2–4) markedly reduced prediction errors of the full model ensemble E-median for crop grain yields (from 36% at stage 1 down to 4% on average) and grassland productivity (from 44% to 27%) and to a lesser and more variable extent for N2O emissions. Yield-scaled N2O emissions (N2O emissions divided by crop yields) were ranked accurately by three-model ensembles across crop species and field sites. The pote

Published
2018

17. C-MIP: An international model inter-comparison simulating organic carbon dynamics in bare fallow soils

Author

Farina, Roberta, Ehrhardt, Fiona, Bellocchi, Gianni, Chenu, Claire, Soussana, Jean-François, Abdalla, Mohamed, Álvaro-Fuentes, Jorge, Blauer, Mira, Brilli, Lorenzo, Chakrabarti, Bidisha, CLIVOT, Hugues, De Antoni, Max, di bene, claudia, Dorich, Cristopher, Ferchaud, Fabien, Nuala, Fitton, Francaviglia, Rosa, Franko, Uwe, Grant, Brian B, Guenet, Bertrand, Harrison, Matthew T., Kirschbaum, Miko U.F., Kuka, Katrin, Lehtonen, Aleksi, Martin, Raphaël, Meier, Elizabeth Anne, Menichetti, Lorenzo, Mula, Laura, Nendel, Claas, Rolinski, Susanne, Sharp, Joanna, Shepherd, Anita, Smith, Ward, Snow, Val, Taghizadeh-Toosi, Arezoo, Tsutskikh, Elena, Zhang, Qing, Recous, Sylvie, Research Centre for the Soil-Plant System, Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria (CREA), Collège de Direction, Institut National de la Recherche Agronomique (INRA), Unité Mixte de Recherche sur l'Ecosystème Prairial - UMR (UREP), Institut National de la Recherche Agronomique (INRA)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS), Institute of Biological and Environmental Sciences, (SFIRC), Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Université Paris-Saclay, Consejo Superior de Investigaciones Científicas, Istituto di Biometeorologia [Firenze] (IBIMET), Consiglio Nazionale delle Ricerche (CNR), Indian Agricultural Research Institute (IARI), UR 1158 AgroImpact Agroressources et Impacts environnementaux, Queensland University of Technology, Colorado State University [Fort Collins] (CSU), Helmholtz Centre for Environmental Research (UFZ), Agriculture Canada, Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université Paris-Saclay-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Centre National de la Recherche Scientifique (CNRS), Tasmanian Institute of Agriculture, Landcare Research, Julius Kühn-Institut (JKI), Natural Resources Institute Finland, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Swedish University of Agricultural Sciences (SLU), Dipartimento di Agraria e Nucleo Ricerca Desertificazione, Università degli Studi di Sassari, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Potsdam Institute for Climate Impact Research (PIK), The New Zealand Institute for Plant and Food Research, Rothamsted Research, Agriculture and Agri-Food [Ottawa] (AAFC), AgResearch Ltd, Aarhus University, Institute of Atmospheric Physics [Beijing] (IAP), Chinese Academy of Sciences [Beijing] (CAS), Fractionnement des AgroRessources et Environnement - UMR-A 614 (FARE), Université de Reims Champagne-Ardenne (URCA)-Institut National de la Recherche Agronomique (INRA)-SFR Condorcet, Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)-Centre National de la Recherche Scientifique (CNRS)-Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)-Centre National de la Recherche Scientifique (CNRS), Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria ( CREA ), Institut National de la Recherche Agronomique ( INRA ), Unité de Recherche sur l'Ecosystème Prairial, ( SFIRC ), Ecologie fonctionnelle et écotoxicologie des agroécosystèmes ( ECOSYS ), Institut National de la Recherche Agronomique ( INRA ) -AgroParisTech, Istituto di Biometeorologia [Firenze] ( IBIMET ), Consiglio Nazionale delle Ricerche ( CNR ), Indian Agricultural Research Institute ( IARI ), Colorado State University, Helmholtz Centre for Environmental Research ( UFZ ), LSCE, Centre National de la Recherche Scientifique ( CNRS ), Julius Kühn-Institut ( JKI ), Commonwealth Scientific and Industrial Research Organisation, Swedish University of Agricultural Sciences ( SLU ), Leibniz Centre for Agricultural Landscape Research, Potsdam Institute for Climate Impact Research ( PIK ), Agriculture and Agri-Food Canada ( AAFC ), Institute of Atmospheric Physics [Beijing] ( IAP ), Chinese Academy of Sciences [Beijing] ( CAS ), Fractionnement des AgroRessources et Environnement - UMR-A 614 ( FARE ), Université de Reims Champagne-Ardenne ( URCA ) -Institut National de la Recherche Agronomique ( INRA ) -SFR Condorcet, and Université de Reims Champagne-Ardenne ( URCA ) -Université de Picardie Jules Verne ( UPJV ) -Centre National de la Recherche Scientifique ( CNRS ) -Université de Reims Champagne-Ardenne ( URCA ) -Université de Picardie Jules Verne ( UPJV ) -Centre National de la Recherche Scientifique ( CNRS )

Subjects
[ SDV ] Life Sciences [q-bio], [SDV]Life Sciences [q-bio]
Abstract

C-MIP: An international model inter-comparison simulating organic carbon dynamics in bare fallow soils. 6th International Symposium on Soil Organic Matter

Published
2017

19. Predicting nitrogen supply from dairy effluent applied to contrasting soil types.

Author

Norris, Matthew, Johnstone, Paul R., Dexter, Moira M., Selbie, Diana R., Houlbrooke, David J., Sharp, Joanna M., and Hedderley, Duncan I.

Subjects
SOIL classification, SILT loam, PHOSPHORUS in water, HOT water, DAIRY cattle, NITROGEN
Abstract

A closed incubation assay was conducted to identify attributes that can be used to predict nitrogen (N) supply from dairy cattle effluents applied to contrasting soil types. The experimental design included six slurry (DM 7.9–13%) and six solid (DM 15.9–42.5%) effluents applied to a Horotiu sandy loam or a Templeton silt loam with treatments incubated at 70% of field capacity at 20°C for 2, 5, 14, 21, 35, 42, 63, 96, 119 and 175 days. After 175 days, inorganic N supply (INS) ranged from −38.6 to 82.8% of total effluent N applied while net N mineralisation (NNM) ranged from −107.4 to 65.1% of organic effluent N applied. The best predictor of INS at day 175 was the log ratio of total C to water extractable N (P < 0.001, r = −0.85) and the log ratio of DM to water extractable N (P < 0.001, r = −0.85) (water extractable N = water soluble N + hot water extractable N). These measures were inversely related to INS and effective at explaining both net mineralisation and immobilisation effects. Importantly, correlations between INS and C:N ratio measures were improved considerably when total N was substituted for a labile N measure, in this case water extractable N. We were unable to identify an effluent measure to consistently predict NNM. [ABSTRACT FROM AUTHOR]

Published
2019
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21. Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N2O emissions

Author

Ehrhardt, Fiona, primary, Soussana, Jean‐François, additional, Bellocchi, Gianni, additional, Grace, Peter, additional, McAuliffe, Russel, additional, Recous, Sylvie, additional, Sándor, Renáta, additional, Smith, Pete, additional, Snow, Val, additional, de Antoni Migliorati, Massimiliano, additional, Basso, Bruno, additional, Bhatia, Arti, additional, Brilli, Lorenzo, additional, Doltra, Jordi, additional, Dorich, Christopher D., additional, Doro, Luca, additional, Fitton, Nuala, additional, Giacomini, Sandro J., additional, Grant, Brian, additional, Harrison, Matthew T., additional, Jones, Stephanie K., additional, Kirschbaum, Miko U. F., additional, Klumpp, Katja, additional, Laville, Patricia, additional, Léonard, Joël, additional, Liebig, Mark, additional, Lieffering, Mark, additional, Martin, Raphaël, additional, Massad, Raia S., additional, Meier, Elizabeth, additional, Merbold, Lutz, additional, Moore, Andrew D., additional, Myrgiotis, Vasileios, additional, Newton, Paul, additional, Pattey, Elizabeth, additional, Rolinski, Susanne, additional, Sharp, Joanna, additional, Smith, Ward N., additional, Wu, Lianhai, additional, and Zhang, Qing, additional

Published
2017
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22. Review and analysis of strengths and weaknesses of agro-ecosystem models for simulating C and N fluxes

Author

Brilli, Lorenzo, primary, Bechini, Luca, additional, Bindi, Marco, additional, Carozzi, Marco, additional, Cavalli, Daniele, additional, Conant, Richard, additional, Dorich, Cristopher D., additional, Doro, Luca, additional, Ehrhardt, Fiona, additional, Farina, Roberta, additional, Ferrise, Roberto, additional, Fitton, Nuala, additional, Francaviglia, Rosa, additional, Grace, Peter, additional, Iocola, Ileana, additional, Klumpp, Katja, additional, Léonard, Joël, additional, Martin, Raphaël, additional, Massad, Raia Silvia, additional, Recous, Sylvie, additional, Seddaiu, Giovanna, additional, Sharp, Joanna, additional, Smith, Pete, additional, Smith, Ward N., additional, Soussana, Jean-Francois, additional, and Bellocchi, Gianni, additional

Published
2017
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23. Judging a brand by its covers: calendars, diaries and books can be branded from cover to cover and can guarantee a lifespan of at least a year, providing a level of exposure that many pay millions for

Author

Sharp, Joanna

Subjects
Diaries -- Marketing, Books -- Marketing, Sales promotions, Company marketing practices, Advertising, marketing and public relations, Business, Business, international
Abstract

Practicality, versatility and staying power are all qualities associated with successful incentives. Finding them wrapped up neatly in a single product is something of a dream for buyers, but it [...]

Published
2004

24. Sources of variability in the effectiveness of winter cover crops for mitigating N leaching

Author

Teixeira, Edmar I., primary, Johnstone, Paul, additional, Chakwizira, Emmanuel, additional, Ruiter, John de, additional, Malcolm, Brendon, additional, Shaw, Naomi, additional, Zyskowski, Robert, additional, Khaembah, Edith, additional, Sharp, Joanna, additional, Meenken, Esther, additional, Fraser, Patricia, additional, Thomas, Steve, additional, Brown, Hamish, additional, and Curtin, Denis, additional

Published
2016
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26. More than child's play: toys were traditionally the preserve of children but the range of playful products and gadgets available today makes them suitable incentives for little kids and big kids alike

Author

Sharp, Joanna

Subjects
Business enterprises -- Target marketing, Toys -- Influence, Toys -- Usage, Toys -- Marketing, Children as consumers -- Behavior, Children as consumers -- Analysis, Advertising agencies -- Target marketing, Advertising agencies -- Services, Company marketing practices, Advertising, marketing and public relations, Business, Business, international
Abstract

Kids love toys. No wonder manufacturers so frequently offer free toys with their products--it's one way to ensure that even the most frugal parent is pestered into making a purchase. [...]

Published
2004

27. Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N 2 O emissions.

Author

Ehrhardt F, Soussana JF, Bellocchi G, Grace P, McAuliffe R, Recous S, Sándor R, Smith P, Snow V, de Antoni Migliorati M, Basso B, Bhatia A, Brilli L, Doltra J, Dorich CD, Doro L, Fitton N, Giacomini SJ, Grant B, Harrison MT, Jones SK, Kirschbaum MUF, Klumpp K, Laville P, Léonard J, Liebig M, Lieffering M, Martin R, Massad RS, Meier E, Merbold L, Moore AD, Myrgiotis V, Newton P, Pattey E, Rolinski S, Sharp J, Smith WN, Wu L, and Zhang Q

Subjects
Computer Simulation, Food Supply, Uncertainty, Agriculture methods, Crops, Agricultural physiology, Models, Biological, Nitrous Oxide metabolism
Abstract

Simulation models are extensively used to predict agricultural productivity and greenhouse gas emissions. However, the uncertainties of (reduced) model ensemble simulations have not been assessed systematically for variables affecting food security and climate change mitigation, within multi-species agricultural contexts. We report an international model comparison and benchmarking exercise, showing the potential of multi-model ensembles to predict productivity and nitrous oxide (N 2 O) emissions for wheat, maize, rice and temperate grasslands. Using a multi-stage modelling protocol, from blind simulations (stage 1) to partial (stages 2-4) and full calibration (stage 5), 24 process-based biogeochemical models were assessed individually or as an ensemble against long-term experimental data from four temperate grassland and five arable crop rotation sites spanning four continents. Comparisons were performed by reference to the experimental uncertainties of observed yields and N 2 O emissions. Results showed that across sites and crop/grassland types, 23%-40% of the uncalibrated individual models were within two standard deviations (SD) of observed yields, while 42 (rice) to 96% (grasslands) of the models were within 1 SD of observed N 2 O emissions. At stage 1, ensembles formed by the three lowest prediction model errors predicted both yields and N 2 O emissions within experimental uncertainties for 44% and 33% of the crop and grassland growth cycles, respectively. Partial model calibration (stages 2-4) markedly reduced prediction errors of the full model ensemble E-median for crop grain yields (from 36% at stage 1 down to 4% on average) and grassland productivity (from 44% to 27%) and to a lesser and more variable extent for N 2 O emissions. Yield-scaled N 2 O emissions (N 2 O emissions divided by crop yields) were ranked accurately by three-model ensembles across crop species and field sites. The potential of using process-based model ensembles to predict jointly productivity and N 2 O emissions at field scale is discussed., (© 2017 John Wiley & Sons Ltd.)

Published
2018
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