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159 results on '"Doltra, Jordi"'

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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4. Desherbatge mecànic del blat de moro amb binadores

Author

Serra Gironella, Joan, Betbese, Josep Anton, Llenes Espigares, Josep Maria, Montagut Casanovas, Núria, Fañé Bolibar, Joan, Solanelles Batlle, Francesc, Estadella Servalls, Alejandro, Doltra, Jordi, Recacha Borrell, Judit, Producció Vegetal, and Cultius Extensius Sostenibles

Abstract

Les binadores són les màquines de desherbatge mecànic que permeten obtenir les eficàcies més altes en el control de les herbes del blat de moro (en condicions favorables, superiors al 70%). Consten d’un bastidor al qual s’acoplen cossos amb relles per treballar el terreny situat entre les línies del cultiu. La seva principal limitació és el control de les herbes situades dins i a prop de la fila del cultiu i sobretot l’eliminació de les espècies perennes. És una eina adequada per incorporar en les estratègies de desherbatge combinat mecànic i químic que permet la reducció de l’ús d’herbicides. Se’n pot augmentar la precisió amb la incorporació de l’autoguiatge SSNG RTK i/o càmeres o visors. Les binadores de precisió permeten una major velocitat de treball i un millor control de les herbes properes al cultiu. info:eu-repo/semantics/publishedVersion

Published
2023

6. How Modelers Model: the Overlooked Social and Human Dimensions in Model Intercomparison Studies

Author

Albanito, Fabrizio, primary, McBey, David, additional, Harrison, Matthew, additional, Smith, Pete, additional, Ehrhardt, Fiona, additional, Bhatia, Arti, additional, Bellocchi, Gianni, additional, Brilli, Lorenzo, additional, Carozzi, Marco, additional, Christie, Karen, additional, Doltra, Jordi, additional, Dorich, Christopher, additional, Doro, Luca, additional, Grace, Peter, additional, Grant, Brian, additional, Léonard, Joël, additional, Liebig, Mark, additional, Ludemann, Cameron, additional, Martin, Raphael, additional, Meier, Elizabeth, additional, Meyer, Rachelle, additional, De Antoni Migliorati, Massimiliano, additional, Myrgiotis, Vasileios, additional, Recous, Sylvie, additional, Sándor, Renáta, additional, Snow, Val, additional, Soussana, Jean-François, additional, Smith, Ward N., additional, and Fitton, Nuala, additional

Published
2022
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8. The Uncertainty of Crop Yield Projections Is Reduced by Improved Temperature Response Functions

Author

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

Subjects
Meteorology And Climatology
Abstract

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

Published
2017
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12. Statistical Analysis of Large Simulated Yield Datasets for Studying Climate Effects

Author

Makowski, David, primary, Asseng, Senthold, additional, Ewert, Frank, additional, Bassu, Simona, additional, Durand, Jean-Louis, additional, Martre, Pierre, additional, Adam, Myriam, additional, Aggarwal, Pramod K., additional, Angulo, Carlos, additional, Baron, Christian, additional, Basso, Bruno, additional, Bertuzzi, Patrick, additional, Biernath, Christian, additional, Boogaard, Hendrik, additional, Boote, Kenneth J., additional, Brisson, Nadine, additional, Cammarano, Davide, additional, Challinor, Andrew J., additional, Conijn, Sjakk J. G., additional, Corbeels, Marc, additional, Deryng, Delphine, additional, De Sanctis, Giacomo, additional, Doltra, Jordi, additional, Gayler, Sebastian, additional, Goldberg, Richard, additional, Grassini, Patricio, additional, Hatfield, Jerry L., additional, Heng, Lee, additional, Hoek, Steven, additional, Hooker, Josh, additional, Hunt, Tony L. A., additional, Ingwersen, Joachim, additional, Izaurralde, Cesar, additional, Jongschaap, Raymond E. E., additional, Jones, James W., additional, Kemanian, Armen R., additional, Kersebaum, Christian, additional, Kim, Soo-Hyung, additional, Lizaso, Jon, additional, Müller, Christoph, additional, Kumar, Naresh S., additional, Nendel, Claas, additional, O'Leary, Garry J., additional, Olesen, Jorgen E., additional, Osborne, Tom M., additional, Palosuo, Taru, additional, Pravia, Maria V., additional, Priesack, Eckart, additional, Ripoche, Dominique, additional, Rosenzweig, Cynthia, additional, Ruane, Alexander C., additional, Sau, Fredirico, additional, Semenov, Mickhail A., additional, Shcherbak, Iurii, additional, Steduto, Pasquale, additional, Stöckle, Claudio, additional, Stratonovitch, Pierre, additional, Streck, Thilo, additional, Supit, Iwan, additional, Tao, Fulu, additional, Teixeira, Edmar I., additional, Thorburn, Peter, additional, Timlin, Denis, additional, Travasso, Maria, additional, Rötter, Reimund, additional, Waha, Katharina, additional, Wallach, Daniel, additional, White, Jeffrey W., additional, Williams, Jimmy R., additional, and Wolf, Joost, additional

Published
2015
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13. The International Heat Stress Genotype Experiment for Modeling Wheat Response to Heat: Field Experiments and AgMIP-Wheat Multi-Model Simulations

Author

Martre, Pierre, Reynolds, Matthew P, Asseng, Senthold, Ewert, Frank, Alderman, Phillip D, Cammarano, Davide, Maiorano, Andrea, Ruane, Alexander C, Aggarwal, Pramod K, Anothai, Jakarat, Basso, Bruno, Biernath, Christian, Challinor, Andrew J, De Sanctis, Giacomo, Doltra, Jordi, Dumont, Benjamin, Fereres, Elias, Garcia-Vila, Margarita, Gayler, Sebastian, Hohenheim, Gerrit, Hunt, Leslie A, Izaurralde, Roberto C, Jabloun, Mohamed, Jones, Curtis D, Kassie, Belay T, Kersebaum, Kurt T, Koehler, Ann-Kristin, Mueller, Christoph, Kumar, Soora Naresh, Liu, Bing, Lobell, David B, Nendel, Claas, O’Leary, Garry, Olesen, Jørgen E, Palosuo, Taru, Priesack, Eckart, Rezaei, Ehsan Eyshi, Ripoche, Dominique, Roetter, Reimund P, Semenov, Mikhail A, Stoeckle, Claudio, Stratonovitch, Pierre, Streck, Thilo, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Waha, Katharina, Wang, Enli, White, Jeffrey W, Wolf, Joost, Zhao, Zhigan, and Zhu, Yan

Subjects
Meteorology And Climatology
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.

Published
2017
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14. Uncertainty of Wheat Water Use: Simulated Patterns and Sensitivity to Temperature and CO2

Author

Cammarano, Davide, Roetter, Reimund P, Asseng, Senthold, Ewert, Frank, Wallach, Daniel, Martre, Pierre, Hatfield, Jerry L, Jones, James W, Rosenzweig, Cynthia E, Ruane, Alex C, Boote, Kenneth J, Thorburn, Peter J, Kersebaum, Kurt Christian, Aggarwal, Pramod K, Angulo, Carlos, Basso, Bruno, Bertuzzi, Patrick, Biernath, Christian, Brisson, Nadine, Challinor, Andrew J, Doltra, Jordi, Gayler, Sebastian, Goldberg, Richie, Heng, Lee, and Steduto, Pasquale

Subjects
Meteorology And Climatology
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
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15. Statistical Analysis of Large Simulated Yield Datasets for Studying Climate Effects

Author

Makowski, David, Asseng, Senthold, Ewert, Frank, Bassu, Simona, Durand, Jean-Louis, Martre, Pierre, Adam, Myriam, Aggarwal, Pramod K, Angulo, Carlos, Baron, Chritian, Basso, Bruno, Bertuzzi, Patrick, Biemath, Christian, Boogaard, Hendrik, Boote, Kenneth J, Brisson, Nadine, Cammarano, Davide, Challinor, Andrew J, Conijn, Sjakk J. G, Corbeels, Marc, Deryng, Delphine, De Sanctis, Giacomo, Doltra, Jordi, Gayler, Sebastian, Goldberg, Richard A, Grassini, Patricio, Hatfield, Jerry L, Heng, Lee, Hoek, Steven, Hooker, Josh, Hunt, Tony L. A, Ingwersen, Joachim, Izaurralde, Cesar, Jongschaap, Raymond E. E, and Rosenzweig, Cynthia

Subjects
Statistics And Probability, Meteorology And Climatology
Abstract

Many studies have been carried out during the last decade to study the effect of climate change on crop yields and other key crop characteristics. In these studies, one or several crop models were used to simulate crop growth and development for different climate scenarios that correspond to different projections of atmospheric CO2 concentration, temperature, and rainfall changes (Semenov et al., 1996; Tubiello and Ewert, 2002; White et al., 2011). The Agricultural Model Intercomparison and Improvement Project (AgMIP; Rosenzweig et al., 2013) builds on these studies with the goal of using an ensemble of multiple crop models in order to assess effects of climate change scenarios for several crops in contrasting environments. These studies generate large datasets, including thousands of simulated crop yield data. They include series of yield values obtained by combining several crop models with different climate scenarios that are defined by several climatic variables (temperature, CO2, rainfall, etc.). Such datasets potentially provide useful information on the possible effects of different climate change scenarios on crop yields. However, it is sometimes difficult to analyze these datasets and to summarize them in a useful way due to their structural complexity; simulated yield data can differ among contrasting climate scenarios, sites, and crop models. Another issue is that it is not straightforward to extrapolate the results obtained for the scenarios to alternative climate change scenarios not initially included in the simulation protocols. Additional dynamic crop model simulations for new climate change scenarios are an option but this approach is costly, especially when a large number of crop models are used to generate the simulated data, as in AgMIP. Statistical models have been used to analyze responses of measured yield data to climate variables in past studies (Lobell et al., 2011), but the use of a statistical model to analyze yields simulated by complex process-based crop models is a rather new idea. We demonstrate herewith that statistical methods can play an important role in analyzing simulated yield data sets obtained from the ensembles of process-based crop models. Formal statistical analysis is helpful to estimate the effects of different climatic variables on yield, and to describe the between-model variability of these effects.

Published
2015

17. Multimodel Ensembles of Wheat Growth: Many Models are Better than One

Author

Martre, Pierre, Wallach, Daniel, Asseng, Senthold, Ewert, Frank, Jones, James W, Rotter, Reimund P, Boote, Kenneth J, Ruane, Alexander C, Thorburn, Peter J, Cammarano, Davide, Hatfield, Jerry L, Rosenzweig, Cynthia, Aggarwal, Pramod K, Angulo, Carlos, Basso, Bruno, Bertuzzi, Patrick, Biernath, Christian, Brisson, Nadine, Challinor, Andrew J, Doltra, Jordi, Gayler, Sebastian, Goldberg, Richie, Grant, Robert F, Heng, Lee, Hooker, Josh, Hunt, Leslie A, Ingwersen, Joachim, Izaurralde, Roberto C, Kersebaum, Kurt Christian, Kumar, Soora Naresh, Nendel, Claas, O'Leary, Garry, Olesen, Jorgen E, Osborne, Tom M, Palosuo, Taru, and Priesack, Eckart

Subjects
Meteorology And Climatology, Earth Resources And Remote Sensing
Abstract

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop model scan give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 2438 for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.

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

Author

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

Published
2017
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21. 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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22. Reviews and syntheses: Review of causes and sources of N2O emissions and NO3 leaching from organic arable crop rotations

Author

Hansen, Sissel, Frøseth, Randi Berland, Stenberg, Maria, Stalenga, Jarosław, Olesen, Jørgen E., Krauss, Maike, Radzikowski, Paweł, Doltra, Jordi, Nadeem, Shahid, Torp, Torfinn, Pappa, Valentini, and Watson, Christine A.

Subjects
VDP::Landbruks- og Fiskerifag: 900::Landbruksfag: 910, Air and water emissions, Soil quality
Abstract

The emissions of nitrous oxide (N2O) and leaching of nitrate (NO3) from agricultural cropping systems have considerable negative impacts on climate and the environment. Although these environmental burdens are less per unit area in organic than in non-organic production on average, they are roughly similar per unit of product. If organic farming is to maintain its goal of being environmentally friendly, these loadings must be addressed. We discuss the impact of possible drivers of N2O emissions and NO3 leaching within organic arable farming practice under European climatic conditions, and potential strategies to reduce these. Organic arable crop rotations are generally diverse with the frequent use of legumes, intercropping and organic fertilisers. The soil organic matter content and the share of active organic matter, soil structure, microbial and faunal activity are higher in such diverse rotations, and the yields are lower, than in non-organic arable cropping systems based on less diverse systems and inorganic fertilisers. Soil mineral nitrogen (SMN), N2O emissions and NO3 leaching are low under growing crops, but there is the potential for SMN accumulation and losses after crop termination, harvest or senescence. The risk of high N2O fluxes increases when large amounts of herbage or organic fertilisers with readily available nitrogen (N) and degradable carbon are incorporated into the soil or left on the surface. Freezing/thawing, drying/rewetting, compacted and/or wet soil and mechanical mixing of crop residues into the soil further enhance the risk of high N2O fluxes. N derived from soil organic matter (background emissions) does, however, seem to be the most important driver for N2O emission from organic arable crop rotations, and the correlation between yearly total N-input and N2O emissions is weak. Incorporation of N-rich plant residues or mechanical weeding followed by bare fallow conditions increases the risk of NO3 leaching. In contrast, strategic use of deep-rooted crops with long growing seasons or effective cover crops in the rotation reduces NO3 leaching risk. Enhanced recycling of herbage from green manures, crop residues and cover crops through biogas or composting may increase N efficiency and reduce N2O emissions and NO3 leaching. Mixtures of legumes (e.g. clover or vetch) and non-legumes (e.g. grasses or Brassica species) are as efficient cover crops for reducing NO3 leaching as monocultures of non-legume species. Continued regular use of cover crops has the potential to reduce NO3 leaching and enhance soil organic matter but may enhance N2O emissions. There is a need to optimise the use of crops and cover crops to enhance the synchrony of mineralisation with crop N uptake to enhance crop productivity, and this will concurrently reduce the long-term risks of NO3 leaching and N2O emissions.

Published
2019
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23. Quantification of modelling uncertainties in an ensemble of carbon simulations in grasslands and croplands

Author

Sandor, Renata, Bellocchi, Gianni, Ehrhardt, Fiona, Bhatia, A., Brilli, Lorenzo, de Antoni Migliorati, Massimiliano, Carozzi, Marco, Doltra, Jordi, Dorich, Chris, Doro, Luca, Fitton, Nuala, Fuchs, K, Gongadze, Kate, Grace, Pete, Grant, B., Giacomini, S.J., Klumpp, Katja, Léonard, L, Liebig, M., Martin, Raphaël, Massad, Raia Silvia, Merbold, Lutz, Newton, P., Pattey, Elizabeth, Rees, B., Rolinski, Susanne, Sharp, Johanna, Smith, P., Smith, W., Snow, Val, Soussana, Jean-François, Zhang, Q, Recous, Sylvie, 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), Agricultural Research Institute, Direction scientifique Environnement, Forêt et Agriculture, Institut National de la Recherche Agronomique (INRA), Indian Agricultural Research Institute (IARI), Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Queensland University of Technology, Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Catabrian Agricultural Research and Training Center (CIFA), Natural Resource Ecology Laboratory [Fort Collins] (NREL), Colorado State University [Fort Collins] (CSU), Università degli Studi di Sassari, Collège de Direction (CODIR), Fractionnement des AgroRessources et Environnement (FARE), Université de Reims Champagne-Ardenne (URCA)-Institut National de la Recherche Agronomique (INRA), and CN-MIP

Subjects
flux de carbon, flux de carbone, modélisation, grandes cultures, prairies, ensemble, [SDV]Life Sciences [q-bio], prairie, [SDE]Environmental Sciences, [SDV.IDA]Life Sciences [q-bio]/Food engineering, grande culture, [SPI.GPROC]Engineering Sciences [physics]/Chemical and Process Engineering, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2019

24. 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

25. Review of key causes and sources for N2O emissions and NO3-leaching from organic arable crop rotations

Author

Hansen, Sissel, Berland Frøseth, Randi, Stenberg, Maria, Stalenga, Jarosław, Olesen, Jørgen E., Krauss, Maike, Radzikowski, Paweł, Doltra, Jordi, Nadeem, Shahid, Torp, Torfinn, Pappa, Valentini, and Watson, Christine A.

Abstract

The emissions of nitrous oxide (N2O) and leaching of nitrate (NO3) have considerable negative impacts on climate and the environment. Although these environmental burdens are on average less per unit area in organic than in non-organic production, they are not smaller per unit of product. If organic farming is to maintain its goal of being an environmentally friendly production system, these emissions should be mitigated. We discuss the impact of possible triggers within organic arable farming practice for the risk of N2O emissions and NO3 leaching under European climatic conditions, and possible strategies to reduce these. Organic arable crop rotations can be characterised as diverse with frequent use of legumes, intercropping and organic fertilizers. The soil organic matter content and share of active organic matter, microbial and faunal activity are higher, soil structure better and yields lower, than in non-organic, arable crop rotations. Soil mineral nitrogen (SMN), N2O emissions and NO3 leaching are low under growing crops, but there is high potential for SMN accumulation and losses after crop termination or crop harvest. The risk for high N2O fluxes is increased when large amounts of herbage or organic fertilizers with readily available nitrogen (N) and carbon are incorporated into the soil or left on the surface. Freezing/thawing, drying/rewetting, compacted and/or wet soil and mixing with rotary harrow further enhance the risk for high N2O fluxes. These complex soil N dynamics mask the correlation between total N-input and N2O emissions from organic arable crop rotations. Incorporation of N rich plant residues or mechanical weeding followed by bare fallow increases the risk of nitrate leaching. In contrast, strategic use of deep-rooted crops with long growing seasons in the rotation reduces nitrate leaching risk. Reduced tillage can reduce N leaching if yields are maintained. Targeted treatment and use of herbage from green manures, crop residues and catch crops will increase N efficiency and reduce N2O emissions and NO3 leaching. Continued regular use of catch crops has the potential to reduce NO3 leaching but may enhance N2O emissions. A mixture of legumes and non-legumes (for instance grasses or cereals) are as efficient a catch crop as monocultures of non-legume species.

Published
2018

26. Reviews and syntheses: Review of causes and sources of N<sub>2</sub>O emissions and NO<sub>3</sub> leaching from organic arable crop rotations

Author

Hansen, Sissel, primary, Berland Frøseth, Randi, additional, Stenberg, Maria, additional, Stalenga, Jarosław, additional, Olesen, Jørgen E., additional, Krauss, Maike, additional, Radzikowski, Paweł, additional, Doltra, Jordi, additional, Nadeem, Shahid, additional, Torp, Torfinn, additional, Pappa, Valentini, additional, and Watson, Christine A., additional

Published
2019
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27. Supplementary material to "Review of key causes and sources for N<sub>2</sub>O emissions and NO<sub>3</sub>-leaching from organic arable crop rotations"

Author

Hansen, Sissel, primary, Berland Frøseth, Randi, additional, Stenberg, Maria, additional, Stalenga, Jarosław, additional, Olesen, Jørgen E., additional, Krauss, Maike, additional, Radzikowski, Paweł, additional, Doltra, Jordi, additional, Nadeem, Shahid, additional, Torp, Torfinn, additional, Pappa, Valentini, additional, and Watson, Christine A., additional

Published
2018
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28. Review of key causes and sources for N<sub>2</sub>O emissions and NO<sub>3</sub>-leaching from organic arable crop rotations

Author

Hansen, Sissel, primary, Berland Frøseth, Randi, additional, Stenberg, Maria, additional, Stalenga, Jarosław, additional, Olesen, Jørgen E., additional, Krauss, Maike, additional, Radzikowski, Paweł, additional, Doltra, Jordi, additional, Nadeem, Shahid, additional, Torp, Torfinn, additional, Pappa, Valentini, additional, and Watson, Christine A., additional

Published
2018
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29. 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

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

Author

Martre, Pierre, Kimball, Bruce A., Ottman, Michael J., Wall, Gerard W., White, Jeffrey W., Asseng, Senthold, Ewert, Frank, Cammarano, Davide, Maiorano, Andrea, Aggarwal, Pramod K., Anothai, Jakarat, Basso, Bruno, Biernath, Christian, Challinor, Andrew J., De Sanctis, Giacomo, Doltra, Jordi, Dumont, Benjamin, Fereres, Elias, Garcia-Vila, Margarita, Gayler, Sebastian, Hoogenboom, Gerrit, Hunt, Leslie A., Izaurralde, Roberto C., Jabloun, Mohamed, Jones, Curtis D., Kassie, Belay T., Kersebaum, Kurt C., Koehler, Ann-Kristin, Müller, Christoph, Kumar, Soora Naresh, Liu, Bing, Lobell, David B., Nendel, Claas, O'Leary, Garry, Olesen, Jørgen E., Palosuo, Taru, Priesack, Eckart, Rezaei, Ehsan Eyshi, Ripoche, Dominique, Rötter, Reimund P., Semenov, Mikhail A., Stöckle, Claudio, Stratonovitch, Pierre, Streck, Thilo, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Waha, Katharina, Wang, Enli, Wolf, Joost, Zhao, Zhigan, Zhu, Yan, Martre, Pierre, Kimball, Bruce A., Ottman, Michael J., Wall, Gerard W., White, Jeffrey W., Asseng, Senthold, Ewert, Frank, Cammarano, Davide, Maiorano, Andrea, Aggarwal, Pramod K., Anothai, Jakarat, Basso, Bruno, Biernath, Christian, Challinor, Andrew J., De Sanctis, Giacomo, Doltra, Jordi, Dumont, Benjamin, Fereres, Elias, Garcia-Vila, Margarita, Gayler, Sebastian, Hoogenboom, Gerrit, Hunt, Leslie A., Izaurralde, Roberto C., Jabloun, Mohamed, Jones, Curtis D., Kassie, Belay T., Kersebaum, Kurt C., Koehler, Ann-Kristin, Müller, Christoph, Kumar, Soora Naresh, Liu, Bing, Lobell, David B., Nendel, Claas, O'Leary, Garry, Olesen, Jørgen E., Palosuo, Taru, Priesack, Eckart, Rezaei, Ehsan Eyshi, Ripoche, Dominique, Rötter, Reimund P., Semenov, Mikhail A., Stöckle, Claudio, Stratonovitch, Pierre, Streck, Thilo, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Waha, Katharina, Wang, Enli, Wolf, Joost, Zhao, Zhigan, and Zhu, Yan

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
2018

31. Physical robustness of canopy temperature models for crop heat stress simulation across environments and production conditions

Author

Webber, Heidi, primary, White, Jeffrey W., additional, Kimball, Bruce A., additional, Ewert, Frank, additional, Asseng, Senthold, additional, Eyshi Rezaei, Ehsan, additional, Pinter, Paul J., additional, Hatfield, Jerry L., additional, Reynolds, Matthew P., additional, Ababaei, Behnam, additional, Bindi, Marco, additional, Doltra, Jordi, additional, Ferrise, Roberto, additional, Kage, Henning, additional, Kassie, Belay T., additional, Kersebaum, Kurt-Christian, additional, Luig, Adam, additional, Olesen, Jørgen E., additional, Semenov, Mikhail A., additional, Stratonovitch, Pierre, additional, Ratjen, Arne M., additional, LaMorte, Robert L., additional, Leavitt, Steven W., additional, Hunsaker, Douglas J., additional, Wall, Gerard W., additional, and Martre, Pierre, additional

Published
2018
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32. 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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33. Erratum: The uncertainty of crop yield projections is reduced by improved temperature response functions

Author

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

Published
2017
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34. The uncertainty of crop yield projections is reduced by improved temperature response functions

Author

Commonwealth Scientific and Industrial Research Organisation (Australia), Chinese Academy of Sciences, China Scholarship Council, Ministry of Education of the People's Republic of China, Institut National de la Recherche Agronomique (France), European Commission, International Food Policy Research Institute (US), CGIAR (France), Department of Agriculture (US), Federal Ministry of Education and Research (Germany), Deutsche Gesellschaft für Internationale Zusammenarbeit, Danish Council for Strategic Research, Federal Ministry of Food and Agriculture (Germany), Finnish Ministry of Agriculture and Forestry, National Natural Science Foundation of China, Helmholtz Association, Grains Research and Development Corporation (Australia), Texas AgriLife Research, Texas A&M University, National Institute of Food and Agriculture (US), Wang, Enli, Martre, Pierre, Zhao, Zhigan, Ewert, Frank, Maiorano, Andrea, Rötter, Reimund P., Kimball, Bruce A., Ottman, Michael J., Wall, Gerard W., White, Jefrrey W., Reynolds, Matthew, Alderman, Phillip, Aggarwal, Pramod K., Anothai, Jakarat, Basso, Bruno, Biernath, Christian, Cammarano, Davide, Challinor, Andrew J., De Sanctis, Giacomo, Doltra, Jordi, Dumont, Benjamin, Fereres Castiel, Elías, García Vila, Margarita, Gayler, Sebastian, Hoogenboom, Gerrit, Hunt, Leslie A., Izaurralde, Roberto C., Jabloun, Mohamed, Jones, Curtis D., Kersebaum, Kurt C., Koehler, Ann-Kristin, Liu, Leilei, Müller, Christoph, Kumar, Soora Naresh, Nendel, Claas, O'Leary, Garry, Olesen, Jørgen E., Palosuo, Taru, Priesack, Eckart, Rezaei, Ehsan Eyshi, Ripoche, Dominique, Ruane, Alexander C., Semenov, Mikhail A., Shcherbak, Iurii, Stöckle, Claudio, Stratonovitch, Pierre, Streck, Thilo, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Waha, Katharina, Wallach, Daniel, Wang, Zhimin, Wolf, Joost, Zhu, Yan, Asseng, Senthold, Commonwealth Scientific and Industrial Research Organisation (Australia), Chinese Academy of Sciences, China Scholarship Council, Ministry of Education of the People's Republic of China, Institut National de la Recherche Agronomique (France), European Commission, International Food Policy Research Institute (US), CGIAR (France), Department of Agriculture (US), Federal Ministry of Education and Research (Germany), Deutsche Gesellschaft für Internationale Zusammenarbeit, Danish Council for Strategic Research, Federal Ministry of Food and Agriculture (Germany), Finnish Ministry of Agriculture and Forestry, National Natural Science Foundation of China, Helmholtz Association, Grains Research and Development Corporation (Australia), Texas AgriLife Research, Texas A&M University, National Institute of Food and Agriculture (US), Wang, Enli, Martre, Pierre, Zhao, Zhigan, Ewert, Frank, Maiorano, Andrea, Rötter, Reimund P., Kimball, Bruce A., Ottman, Michael J., Wall, Gerard W., White, Jefrrey W., Reynolds, Matthew, Alderman, Phillip, Aggarwal, Pramod K., Anothai, Jakarat, Basso, Bruno, Biernath, Christian, Cammarano, Davide, Challinor, Andrew J., De Sanctis, Giacomo, Doltra, Jordi, Dumont, Benjamin, Fereres Castiel, Elías, García Vila, Margarita, Gayler, Sebastian, Hoogenboom, Gerrit, Hunt, Leslie A., Izaurralde, Roberto C., Jabloun, Mohamed, Jones, Curtis D., Kersebaum, Kurt C., Koehler, Ann-Kristin, Liu, Leilei, Müller, Christoph, Kumar, Soora Naresh, Nendel, Claas, O'Leary, Garry, Olesen, Jørgen E., Palosuo, Taru, Priesack, Eckart, Rezaei, Ehsan Eyshi, Ripoche, Dominique, Ruane, Alexander C., Semenov, Mikhail A., Shcherbak, Iurii, Stöckle, Claudio, Stratonovitch, Pierre, Streck, Thilo, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Waha, Katharina, Wallach, Daniel, Wang, Zhimin, Wolf, Joost, Zhu, Yan, and Asseng, Senthold

Abstract

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

Published
2017

35. Crop model improvement reduces the uncertainty of the response to temperature of multi-model ensembles

Author

Maiorano, Andrea, primary, Martre, Pierre, additional, Asseng, Senthold, additional, Ewert, Frank, additional, Müller, Christoph, additional, Rötter, Reimund P., additional, Ruane, Alex C., additional, Semenov, Mikhail A., additional, Wallach, Daniel, additional, Wang, Enli, additional, Alderman, Phillip D., additional, Kassie, Belay T., additional, Biernath, Christian, additional, Basso, Bruno, additional, Cammarano, Davide, additional, Challinor, Andrew J., additional, Doltra, Jordi, additional, Dumont, Benjamin, additional, Rezaei, Ehsan Eyshi, additional, Gayler, Sebastian, additional, Kersebaum, Kurt Christian, additional, Kimball, Bruce A., additional, Koehler, Ann-Kristin, additional, Liu, Bing, additional, O’Leary, Garry J., additional, Olesen, Jørgen E., additional, Ottman, Michael J., additional, Priesack, Eckart, additional, Reynolds, Matthew, additional, Stratonovitch, Pierre, additional, Streck, Thilo, additional, Thorburn, Peter J., additional, Waha, Katharina, additional, Wall, Gerard W., additional, White, Jeffrey W., additional, Zhao, Zhigan, additional, and Zhu, Yan, additional

Published
2017
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36. Canopy temperature for simulation of heat stress in irrigated wheat in a semi-arid environment: A multi-model comparison

Author

Webber, Heidi, primary, Martre, Pierre, additional, Asseng, Senthold, additional, Kimball, Bruce, additional, White, Jeffrey, additional, Ottman, Michael, additional, Wall, Gerard W., additional, De Sanctis, Giacomo, additional, Doltra, Jordi, additional, Grant, Robert, additional, Kassie, Belay, additional, Maiorano, Andrea, additional, Olesen, Jørgen E., additional, Ripoche, Dominique, additional, Rezaei, Ehsan Eyshi, additional, Semenov, Mikhail A., additional, Stratonovitch, Pierre, additional, and Ewert, Frank, additional

Published
2017
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37. A Comparative Nitrogen Balance and Productivity Analysis of Legume and Non-legume Supported Cropping Systems: The Potential Role of Biological Nitrogen Fixation

Author

Iannetta, Pietro P. M., primary, Young, Mark, additional, Bachinger, Johann, additional, Bergkvist, Göran, additional, Doltra, Jordi, additional, Lopez-Bellido, Rafael J., additional, Monti, Michele, additional, Pappa, Valentini A., additional, Reckling, Moritz, additional, Topp, Cairistiona F. E., additional, Walker, Robin L., additional, Rees, Robert M., additional, Watson, Christine A., additional, James, Euan K., additional, Squire, Geoffrey R., additional, and Begg, Graham S., additional

Published
2016
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38. Similar estimates of temperature impacts on global wheat yield by three independent methods

Author

Liu, Bing, primary, Asseng, Senthold, additional, Müller, Christoph, additional, Ewert, Frank, additional, Elliott, Joshua, additional, Lobell, David B., additional, Martre, Pierre, additional, Ruane, Alex C., additional, Wallach, Daniel, additional, Jones, James W., additional, Rosenzweig, Cynthia, additional, Aggarwal, Pramod K., additional, Alderman, Phillip D., additional, Anothai, Jakarat, additional, Basso, Bruno, additional, Biernath, Christian, additional, Cammarano, Davide, additional, Challinor, Andy, additional, Deryng, Delphine, additional, Sanctis, Giacomo De, additional, Doltra, Jordi, additional, Fereres, Elias, additional, Folberth, Christian, additional, Garcia-Vila, Margarita, additional, Gayler, Sebastian, additional, Hoogenboom, Gerrit, additional, Hunt, Leslie A., additional, Izaurralde, Roberto C., additional, Jabloun, Mohamed, additional, Jones, Curtis D., additional, Kersebaum, Kurt C., additional, Kimball, Bruce A., additional, Koehler, Ann-Kristin, additional, Kumar, Soora Naresh, additional, Nendel, Claas, additional, O’Leary, Garry J., additional, Olesen, Jørgen E., additional, Ottman, Michael J., additional, Palosuo, Taru, additional, Prasad, P. V. Vara, additional, Priesack, Eckart, additional, Pugh, Thomas A. M., additional, Reynolds, Matthew, additional, Rezaei, Ehsan E., additional, Rötter, Reimund P., additional, Schmid, Erwin, additional, Semenov, Mikhail A., additional, Shcherbak, Iurii, additional, Stehfest, Elke, additional, Stöckle, Claudio O., additional, Stratonovitch, Pierre, additional, Streck, Thilo, additional, Supit, Iwan, additional, Tao, Fulu, additional, Thorburn, Peter, additional, Waha, Katharina, additional, Wall, Gerard W., additional, Wang, Enli, additional, White, Jeffrey W., additional, Wolf, Joost, additional, Zhao, Zhigan, additional, and Zhu, Yan, additional

Published
2016
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39. Multi-wheat-model ensemble responses to interannual climate variability

Author

Ruane, Alex C., primary, Hudson, Nicholas I., additional, Asseng, Senthold, additional, Cammarano, Davide, additional, Ewert, Frank, additional, Martre, Pierre, additional, Boote, Kenneth J., additional, Thorburn, Peter J., additional, Aggarwal, Pramod K., additional, Angulo, Carlos, additional, Basso, Bruno, additional, Bertuzzi, Patrick, additional, Biernath, Christian, additional, Brisson, Nadine, additional, Challinor, Andrew J., additional, Doltra, Jordi, additional, Gayler, Sebastian, additional, Goldberg, Richard, additional, Grant, Robert F., additional, Heng, Lee, additional, Hooker, Josh, additional, Hunt, Leslie A., additional, Ingwersen, Joachim, additional, Izaurralde, Roberto C., additional, Kersebaum, Kurt Christian, additional, Kumar, Soora Naresh, additional, Müller, Christoph, additional, Nendel, Claas, additional, O'Leary, Garry, additional, Olesen, Jørgen E., additional, Osborne, Tom M., additional, Palosuo, Taru, additional, Priesack, Eckart, additional, Ripoche, Dominique, additional, Rötter, Reimund P., additional, Semenov, Mikhail A., additional, Shcherbak, Iurii, additional, Steduto, Pasquale, additional, Stöckle, Claudio O., additional, Stratonovitch, Pierre, additional, Streck, Thilo, additional, Supit, Iwan, additional, Tao, Fulu, additional, Travasso, Maria, additional, Waha, Katharina, additional, Wallach, Daniel, additional, White, Jeffrey W., additional, and Wolf, Joost, additional

Published
2016
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41. Review of key causes and sources for N2O emissions and NO3-leaching from organic arable crop rotations.

Author

Hansen, Sissel, Frøseth, Randi Berland, Stenberg, Maria, Stalenga, Jarosław, Olesen, Jørgen E., Krauss, Maike, Radzikowski, Paweł, Doltra, Jordi, Nadeem, Shahid, Torp, Torfinn, Pappa, Valentini, and Watson, Christine A.

Subjects
SOIL leaching, LEACHING & the environment, CROP rotation, NITROUS oxide, ANESTHETICS
Abstract

The emissions of nitrous oxide (N2O) and leaching of nitrate (NO3) have considerable negative impacts on climate and the environment. Although these environmental burdens are on average less per unit area in organic than in non-organic production, they are not smaller per unit of product. If organic farming is to maintain its goal of being an environmentally friendly production system, these emissions should be mitigated. We discuss the impact of possible triggers within organic arable farming practice for the risk of N2O emissions and NO3 leaching under European climatic conditions, and possible strategies to reduce these. Organic arable crop rotations can be characterised as diverse with frequent use of legumes, intercropping and organic fertilizers. The soil organic matter content and share of active organic matter, microbial and faunal activity are higher, soil structure better and yields lower, than in non-organic, arable crop rotations. Soil mineral nitrogen (SMN), N2O emissions and NO3 leaching are low under growing crops, but there is high potential for SMN accumulation and losses after crop termination or crop harvest. The risk for high N2O fluxes is increased when large amounts of herbage or organic fertilizers with readily available nitrogen (N) and carbon are incorporated into the soil or left on the surface. Freezing/thawing, drying/rewetting, compacted and/or wet soil and mixing with rotary harrow further enhance the risk for high N2O fluxes. These complex soil N dynamics mask the correlation between total N-input and N2O emissions from organic arable crop rotations. Incorporation of N rich plant residues or mechanical weeding followed by bare fallow increases the risk of nitrate leaching. In contrast, strategic use of deep-rooted crops with long growing seasons in the rotation reduces nitrate leaching risk. Reduced tillage can reduce N leaching if yields are maintained. Targeted treatment and use of herbage from green manures, crop residues and catch crops will increase N efficiency and reduce N2O emissions and NO3 leaching. Continued regular use of catch crops has the potential to reduce NO3 leaching but may enhance N2O emissions. A mixture of legumes and non-legumes (for instance grasses or cereals) are as efficient a catch crop as monocultures of non-legume species. [ABSTRACT FROM AUTHOR]

Published
2018
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42. Cereal yield and quality as affected by N availability in organic and conventional crop rotations in Denmark: a combined modeling and experimental approach

Author

Doltra, Jordi, Lægdsmand, Mette, and Olesen, Jørgen Eivind

Subjects
Cereals, pulses and oilseeds, Air and water emissions
Abstract

The effects of nitrogen (N) availability related to fertilizer type, catch crop management, and rotation composition on cereal yield and grain N were investigated in four organic and one conventional cropping systems in Denmark using the FASSET model. The four-year rotation studied was: spring barley–(faba bean or grass-clover)– potato–winter wheat. Experiments were done at three locations representative of the different soil types and climatic conditions in Denmark. The three organic systems that included faba bean as the N fixing crop comprised a system with manure (stored pig slurry) and undersowing catch crops (OF+C+M), a system with manure but without undersowing catch crops (OF−C + M), and a system without manure and with catch crops (OF + C−M). A grass-clover green manure was used asNfixing crop in the other organic system with catch crops (OG+C+M). Cuttings of grass-clover were removed from the plots and an equivalent amount of total-N in pig slurry was applied to the cropping system. The conventional rotation included mineral fertilizer and catch crops (CF+C+F), although only non-legume catch crops were used. Measurements of cereal dry matter (DM) at harvest and of grain N contents were done in all plots. On average the FASSET model was able to predict the yield and grain N of cereals with a reasonable accuracy for the range of cropping systems and soil types studied, having a particularly good performance on winter wheat. Cereal yields were better on the more loamy soil. DM yield and grain N content were mainly influenced by the type and amount of fertilizer-N at all three locations. Although a catch crop benefit in terms of yield and grain N was observed in most of the cases, a limited N availability affected the cereal production in the four organic systems. Scenario analyses conducted with the FASSET model indicated the possibility of increasing N fertilization without significantly affecting N leaching if there is an adequate catch crop management. This would also improve yields of cereal production of organic farming in Denmark.

Published
2011

44. Soil and plant nitrogen dynamics of a tomato crop under different fertilization strategies

Author

Doltra, Jordi, Muñoz, P, Antón, A, and Ariño, J

Subjects
fungi, food and beverages
Abstract

A field experiment was conducted in 2007 to investigate the effects of the N fertilizer source on the soil and plant N dynamics of a tomato crop grown in a sandy loam soil. The fertilization treatments were: mineral N-fertilization applied by fertigation (TM); organic N-fertilization (TO) with compost obtained from the organic fraction of urban waste (OFUW); and a combined treatment (TC) with half organic and half mineral N. Compost was incorporated in November 2006. Plants were drip irrigated with well water. The nitrate concentrations in the irrigation solutions were determined on a weekly basis. Soil samples were taken before planting and at harvest from depths of up to 90 cm in order to determine moisture and N-NO3- levels. The estimated amounts of total N-NO3- available in the different treatments, including the initial content in the 0-90 cm soil layer, were 560 (TC), 570 (TO) and 610 (TM) kg N ha-1. The N contents of plants sampled on three occasions during the growing period and those of marketable fruits were also analyzed. Total marketable yield was determined at the end of the harvest period. The EU-Rotate_N model was used to predict the effects of the applied treatments. The model was calibrated using data from a previous experiment. No differences between treatments were observed with respect to yield or N content in marketable fruits. The amount of N left in the field at the end of the cropping period was significantly lower in TO than in TC and TM. Simulated plant growth, marketable yield and N uptake were similar to observed data. N leaching predicted by the model for the TM treatment was 50% higher than in the TC and TO treatments. However, an underestimation of residual N contents was found in all treatments. It was concluded that tomato fertilization with compost obtained from the OFUW could offer a suitable strategy for reducing N leaching without affecting yield when there is sufficient mineral N available throughout the growing period. The EU-Rotate_N model may be a valuable tool for evaluating N fertilizer practices for tomato crops.

Published
2010

45. The development of the EU-Rotate_N and its use to test strategies for N use across Europe

Author

Rahn, C.R., Zhang, K., Lillywhite, R., Ramos, C., Doltra, Jordi, De Pas, J.M., Riley, H., Fink, M., Nendel, C., Thorup-Kristensen, Kristian, Pedersen, Anders, Piro, F., Venezia, A., Firth, C., Schmutz, U., Rayns, F., and Strohmeyer, K.

Subjects
fungi, food and beverages
Abstract

A new model EU-ROTATE_N has been written which enables the assessment of the economic and environmental performance of nitrogen applied to crop rotations in either conventional or organic cropping for a wide range of crops and growing conditions in Europe. The model can simulate root development, the mineralization and release of N from soil organic matter and crop residues, the effect of freezing conditions, and water movement. New routines have also been added to estimate the effects of sub-optimal rates of N and spacing on the marketable outputs and gross margins. Model performance was tested against experimental results and broadly simulated the patterns of crop growth, N response and N losses. The model provides a mechanism for comparing the relative effects of differing cropping and fertilisation practices on yield gross margin and losses of nitrogen through leaching. The running of a number of scenarios has demonstrated that nitrogen management in field vegetable rotations can be improved in Europe by following at least Good Agricultural Practice. The model also provides the potential for suggesting improvements which have a minimal effect on gross margin whilst reducing nitrogen losses.

Published
2010

46. SIMULATING WINTER WHEAT YIELD AND NITROGEN LEACHING FROM ORGANIC AND CONVENTIONAL CROP ROTATIONS

Author

Doltra, Jordi, Ølsen, Jørgen E., Lægdsmand, Mette, Grignani, C., Acutis, M., Zavattaro, L., Bechini, L., Bertora, C., Marino Gallina, P., and Sacco, D.

Subjects
Crop combinations and interactions, Crop health, quality, protection, Cereals, pulses and oilseeds, Air and water emissions
Abstract

This work evaluates the effects of growing a green manure crop or a legume crop in winter wheat yield and on nitrate leaching, in an irrigated crop rotation. Results are also compared with a conventional cropping system. Simulations with the field version of the FASSET model (Berntsen et al., 2003) were done to evaluate the performance of the model to predict winter wheat dry matter yield. The simulated leaching from the different cropping systems was evaluated during the rotation period.

Published
2009

47. 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, Russel, 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, and Harrison, Matthew T.

Subjects
GREENHOUSE gas mitigation, FOOD security, CLIMATE change, AGRICULTURAL productivity, CROP yields, CROP rotation
Abstract

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 potential of using process‐based model ensembles to predict jointly productivity and N2O emissions at field scale is discussed. [ABSTRACT FROM AUTHOR]

Published
2018
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48. Multimodel ensembles of wheat growth: many models are better than one

Author

Martre, Pierre, primary, Wallach, Daniel, additional, Asseng, Senthold, additional, Ewert, Frank, additional, Jones, James W., additional, Rötter, Reimund P., additional, Boote, Kenneth J., additional, Ruane, Alex C., additional, Thorburn, Peter J., additional, Cammarano, Davide, additional, Hatfield, Jerry L., additional, Rosenzweig, Cynthia, additional, Aggarwal, Pramod K., additional, Angulo, Carlos, additional, Basso, Bruno, additional, Bertuzzi, Patrick, additional, Biernath, Christian, additional, Brisson, Nadine, additional, Challinor, Andrew J., additional, Doltra, Jordi, additional, Gayler, Sebastian, additional, Goldberg, Richie, additional, Grant, Robert F., additional, Heng, Lee, additional, Hooker, Josh, additional, Hunt, Leslie A., additional, Ingwersen, Joachim, additional, Izaurralde, Roberto C., additional, Kersebaum, Kurt Christian, additional, Müller, Christoph, additional, Kumar, Soora Naresh, additional, Nendel, Claas, additional, O'leary, Garry, additional, Olesen, Jørgen E., additional, Osborne, Tom M., additional, Palosuo, Taru, additional, Priesack, Eckart, additional, Ripoche, Dominique, additional, Semenov, Mikhail A., additional, Shcherbak, Iurii, additional, Steduto, Pasquale, additional, Stöckle, Claudio O., additional, Stratonovitch, Pierre, additional, Streck, Thilo, additional, Supit, Iwan, additional, Tao, Fulu, additional, Travasso, Maria, additional, Waha, Katharina, additional, White, Jeffrey W., additional, and Wolf, Joost, additional

Published
2014
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49. A better nitrogen use to improve organic wheat production

Author

Doltra, Jordi and Doltra, Jordi

Abstract

Different crop management strategies are investigated in representative soils and climatic conditions of Denmark to enhance the yield and quality of organic wheat. The agronomic practices include the use of manure, catch crops and the composition of the crop rotation. Among these, manure application was the main factor affecting yields. Model predictions indicate that wheat yield could be improved by increasing manure nitrogen.

Published
2010

50. SIMULATING WINTER WHEAT YIELD AND NITROGEN LEACHING FROM ORGANIC AND CONVENTIONAL CROP ROTATIONS

Author

Grignani, C., Acutis, M., Zavattaro, L., Bechini, L., Bertora, C., Marino Gallina, P., Sacco, D., Doltra, Jordi, Ølsen, Jørgen E., Lægdsmand, Mette, Grignani, C., Acutis, M., Zavattaro, L., Bechini, L., Bertora, C., Marino Gallina, P., Sacco, D., Doltra, Jordi, Ølsen, Jørgen E., and Lægdsmand, Mette

Abstract

This work evaluates the effects of growing a green manure crop or a legume crop in winter wheat yield and on nitrate leaching, in an irrigated crop rotation. Results are also compared with a conventional cropping system. Simulations with the field version of the FASSET model (Berntsen et al., 2003) were done to evaluate the performance of the model to predict winter wheat dry matter yield. The simulated leaching from the different cropping systems was evaluated during the rotation period.

Published
2009

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