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1. S-map parameters for APSIM

Author

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

Subjects
Process-based modelling, Digital spatial soil information, Sensitivity analysis, Science
Abstract

Agroecosystem models have become an important tool for impact assessment studies, and their results are often used for management and policy decisions. Soil information is a key input for these models, yet site-specific soil property data are often not available, and soil databases are increasingly being used to provide input parameters. For New Zealand, the digital spatial soil information system S-map provides geospatial data on a range of soil characteristics, including estimates of soil water properties. We describe a protocol for how properties from S-map can be used as input parameters for the APSIM (Agricultural Production Systems sIMulator) framework. Finally, we investigate how changes in the physical description of soil layers, and soil organic matter pools, affect the various outputs of APSIM. • This paper presents a description of how information from S-map, a digital soil map of New Zealand, can be used for building a soil description for APSIM. • A sensitivity analysis shows the effect of soil layering and the set-up setup, size, and distribution of SOM pools on model outputs, including plant growth and N leaching

Published
2022
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2. A protocol to build soil descriptions for APSIM simulations

Author

Rogerio Cichota, Iris Vogeler, Joanna Sharp, Kirsten Verburg, Neil Huth, Dean Holzworth, Neal Dalgliesh, and Val Snow

Subjects
APSIM soil parameterisation, Science
Abstract

Soil processes have a major impact on agroecosystems, controlling water and nutrient cycling, regulating plant growth and losses to the wider environment. Process-based agroecosystem simulation models generally encompass detailed descriptions of the soil, including a wide number of parameters that can be daunting to users with a limited soil science background. In this work we review and present an abridged description of the models used to simulate soil processes in the APSIM (Agricultural Production Systems sIMulator) framework. Such a resource is needed because this information is currently spread over multiple publications and some elements have become outdated. We list and briefly describe the parameters, and establish a protocol with guidelines, for building a soil description for APSIM. This protocol will promote consistency, enhancing the quality of the science done employing APSIM, and provide an easier pathway for new users. This compilation should also be of relevance to users of other models that require detailed soil information. • This paper presents a brief description of the models for simulating soil processes in the APSIM model. • The method stablishes guidelines to define the parameters for building a soil description for APSIM.

Published
2021
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3. Residual correlation and ensemble modelling to improve crop and grassland models.

Author

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

Published
2023
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5. Sensitivity analysis of soil parameters in the Agricultural Production Systems sIMulator (APSIM)

Author

Iris Vogeler, Joanna Sharp, Rogerio Cichota, and Linda Lilburne

Subjects
Soil Science, Environmental Science (miscellaneous), Earth-Surface Processes
Abstract

Context The performance of process-based agroecosystem simulation models is highly sensitive to the numerous input parameters, many associated with high variability and uncertainty. Aims Our aims were to: (1) test the accuracy of the Agricultural Production Systems sIMulator (APSIM) model regarding the prediction of soil water storage and movement in a pasture system with a free draining pumice soil based on site-specific soil hydraulic properties; (2) identify sensitive soil hydraulic properties on model outputs; and (3) identify the influence of uncertainty in the description of soil properties on various model outputs. Methods We carried out a sensitivity analysis (SA) to identify sensitive soil hydraulic parameters. We set up APSIM to simulate a pasture system on a free-draining pumice soil in New Zealand. The model was first established with site-specific soil hydraulic properties and outputs were compared with measured soil moisture status and drainage. Next, the model’s sensitivity to the soil hydraulic parameters was assessed for various outputs linked to production and environmental outcomes. Key results Varying the various hydraulic parameters affected soil moisture status, but it had generally little effect on drainage, N leaching, and pasture production in this system. Conclusions The results suggest that for well-drained soils in a high precipitation zone with no water limitation, the model has low sensitivity to soil hydraulic parameters. Further analysis is required for different soils and for drier conditions. Implications For well-drained soils and under non-limiting water conditions the use of general data from databases, rather than site specific measurement of hydraulic properties is justified.

Published
2022
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7. APSIM - Evolution towards a new generation of agricultural systems simulation.

Author

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

Published
2014
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8. Does Particulate Organic Matter Fraction Meet the Criteria for a Model Soil Organic Matter Pool?

Author

Denis Curtin, Joanna Sharp, Weiwen Qiu, and Michael H. Beare

Subjects
chemistry.chemical_classification, Chemistry, Soil organic matter, Soil Science, Fraction (chemistry), 04 agricultural and veterinary sciences, Fractionation, 010501 environmental sciences, Biodegradation, Silt, 01 natural sciences, chemistry.chemical_compound, Loam, Environmental chemistry, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Sodium polytungstate, Organic matter, 0105 earth and related environmental sciences
Abstract

There is a well-recognized need for improved fractionation methods to partition soil organic matter into functional pools. Physical separation based on particle size is widely used, yielding particulate organic matter (POM, i.e., free or “uncomplexed” organic matter > 50 μm) as the most labile fraction. To evaluate whether POM meets criteria for an ideal model pool, we examined whether it is: 1) unique, i.e., found only in the > 50 μm fraction and 2) homogeneous, rather than a composite of different subfractions. Following ultrasonic dispersion, sand (> 50 μm) along with coarse (20–50 μm) and fine (5–20 μm) silt fractions were isolated from a silt loam soil under long-term pasture at Lincoln, New Zealand. The sand and silt fractions contained 20% and 21% of total soil C, respectively. We adopted a sequential density separation procedure using sodium polytungstate with density increasing step-wise from 1.7 to 2.4 g cm−3 to recover organic matter (light fractions) from the sand and silt fractions. Almost all (ca. 90%) the organic matter in the sand fraction and a large proportion (ca. 60%–70%) in the silt fractions was recovered by sequential density separation. The results suggested that POM is a composite of organo-mineral complexes with varying proportions of organic and mineral materials. Part of the organic matter associated with the silt fractions shared features in common with POM. In a laboratory bio-assay, biodegradability of POM varied depending on land use (pasture > arable cropping). We concluded that POM is neither homogeneous nor unique.

Published
2019
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9. Ensemble modelling of carbon fluxes in grasslands and croplands

Author

Elizabeth Pattey, Ute Skiba, Brian Grant, Gianni Bellocchi, Katja Klumpp, Pete Smith, Matthew T. Harrison, Val Snow, Sylvie Recous, Joanna Sharp, Fiona Ehrhardt, Arti Bhatia, Joël Léonard, Raphaël Martin, Andrew D. Moore, Jordi Doltra, Renata Sándor, Lorenzo Brilli, Jean-François Soussana, Peter Grace, Susanne Rolinski, Ward Smith, Vasileios Myrgiotis, Raia Silvia Massad, Qing Zhang, Miko U. F. Kirschbaum, Lianhai Wu, Nuala Fitton, Luca Doro, Bruno Basso, Christopher D. Dorich, Patricia Laville, Producció Vegetal, Cultius Extensius Sostenibles, Unité Mixte de Recherche sur l'Ecosystème Prairial - UMR (UREP), VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Direction scientifique Environnement, Forêt et Agriculture, Institut National de la Recherche Agronomique (INRA), Queensland University of Technology, Fractionnement des AgroRessources et Environnement (FARE), Université de Reims Champagne-Ardenne (URCA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), University of Aberdeen, AgResearch Grasslands, Collège de Direction (CODIR), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Indian Agricultural Research Institute (IARI), Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Catabrian Agricultural Research and Training Center (CIFA), Università degli Studi di Sassari, Agriculture and Agri-Food [Ottawa] (AAFC), British Geological Survey (BGS), Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Transfrontalière BioEcoAgro (Transfrontalière BioEcoAgro), Université d'Artois (UA)-Université de Liège-Université de Picardie Jules Verne (UPJV)-Université du Littoral Côte d'Opale (ULCO)-Université de Lille-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Computer Laboratory [Cambridge], University of Cambridge [UK] (CAM), Scotland's Rural College (SRUC), Potsdam Institute for Climate Impact Research (PIK), Plant & Food Research, Natural Environment Research Council (NERC), Rothamsted Research, CN-MIP by Joint Programming Initiative 'FACCE' Models4Pastures by Joint Programming Initiative 'FACCE' MACSUR by Joint Programming Initiative 'FACCE' COMET-Global by Joint Programming Initiative 'FACCE' MAGGNET by Joint Programming Initiative 'FACCE' French Embassy in Budapest (Hungary) by way of 'Make Our Planet Great Again' programme Federal Ministry of Education & Research (BMBF)01LN1317AEuropean Union (EU)EC EVK2-CT2001-00105GOCE-CT-2003-505572017841SRUC, Queensland University of Technology [Brisbane] (QUT), Università degli Studi di Firenze = University of Florence (UniFI), Università degli Studi di Sassari = University of Sassari [Sassari] (UNISS), Agriculture and Agri-Food (AAFC), Transfrontalière BioEcoAgro - UMR 1158 (BioEcoAgro), Université d'Artois (UA)-Université de Liège-Université de Picardie Jules Verne (UPJV)-Université du Littoral Côte d'Opale (ULCO)-Université de Lille-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-JUNIA (JUNIA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL), and Biotechnology and Biological Sciences Research Council (BBSRC)

Subjects
0106 biological sciences, [SDE.MCG]Environmental Sciences/Global Changes, Soil Science, Growing season, Atmospheric sciences, 01 natural sciences, Grassland, Temperate climate, 2. Zero hunger, Biomass (ecology), geography, geography.geographical_feature_category, Primary production, 04 agricultural and veterinary sciences, Soil carbon, 15. Life on land, Crop rotation, 13. Climate action, [SDE]Environmental Sciences, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Ecosystem respiration, Agronomy and Crop Science, 010606 plant biology & botany
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 available for model calibration. The further development of crop/grassland ensemble modelling will hinge upon the interpretation of results in light of the way models represent the processes underlying C fluxes in complex agricultural systems (grassland and crop rotations including fallow periods). info:eu-repo/semantics/acceptedVersion

Published
2020
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10. Sources of variability in the effectiveness of winter cover crops for mitigating N leaching

Author

E. D. Meenken, Denis Curtin, P. M. Fraser, N Shaw, E.N. Khaembah, Steve Thomas, Brendon J. Malcolm, Joanna Sharp, Paul Johnstone, R.F. Zyskowski, Hamish E. Brown, John de Ruiter, Edmar Teixeira, and E. Chakwizira

Subjects
0106 biological sciences, Ecology, Sowing, 04 agricultural and veterinary sciences, Nitrate leaching, Crop rotation, 01 natural sciences, Agronomy, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Water holding capacity, Environmental science, Animal Science and Zoology, Leaching (agriculture), Crop management, Cover crop, Agronomy and Crop Science, 010606 plant biology & botany
Abstract

The effectiveness of growing winter cover crops to mitigate nitrogen (N) leaching can be widely variable, even within a single climatic zone. We sought to investigate key drivers of this variability for the Canterbury Plains of New Zealand. First, through an analysis of local field experiments, we quantified the local variability in the effectiveness of cover crops to reduce N leaching. We then calibrated and applied a biophysical model to isolate the impact of potential drivers of this variability. Crop management (cover crop sowing date), soil water holding capacity (WHC) and inter-annual weather variability (30 years of historical climate) were selected as main factors to be investigated. The analysis of local literature showed that, compared to fallow treatments, winter cover crops reduced N leaching by an average (±95% CI) of 17 ± 8.2 kg N ha−1. This represented a median N leaching reduction of ∼50% with a wide interquartile range (6–75%). The modelling study showed that the delay in sowing dates consistently reduced the average effectiveness of cover crops, from >80% for March- to

Published
2016
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11. Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N2O emissions

Author

Fiona Ehrhardt, Mark A. Liebig, Raphaël Martin, Jordi Doltra, Russel McAuliffe, Val Snow, Joël Léonard, Andrew D. Moore, Stephanie K. Jones, Lutz Merbold, Pete Smith, Lianhai Wu, Elizabeth A. Meier, Paul C. D. Newton, Arti Bhatia, Gianni Bellocchi, Massimiliano De Antoni Migliorati, Miko U. F. Kirschbaum, Ward Smith, Brian Grant, Renáta Sándor, Joanna Sharp, Lorenzo Brilli, Nuala Fitton, Jean-François Soussana, Elizabeth Pattey, Luca Doro, Katja Klumpp, Christopher D. Dorich, Bruno Basso, Raia Silvia Massad, Sylvie Recous, Patricia Laville, Qing Zhang, Matthew T. Harrison, Sandro José Giacomini, Susanne Rolinski, Mark Lieffering, Peter Grace, Vasileios Myrgiotis, Collège de Direction (CODIR), 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), Queensland University of Technology, Agresearch Ltd, Fractionnement des AgroRessources et Environnement (FARE), Université de Reims Champagne-Ardenne (URCA)-Institut National de la Recherche Agronomique (INRA), University of Aberdeen, Michigan State University [East Lansing], Michigan State University System, Indian Agricultural Research Institute (IARI), Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Catabrian Agricultural Research and Training Center (CIFA), Colorado State University [Fort Collins] (CSU), Università degli Studi di Sassari, Universidade Federal de Santa Maria = Federal University of Santa Maria [Santa Maria, RS, Brazil] (UFSM), Agriculture and Agri-Food [Ottawa] (AAFC), Tasmanian Institute of Agriculture, Scotland's Rural College (SRUC), Manaaki Whenua – Landcare Research [Lincoln], Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Agroressources et Impacts environnementaux (AgroImpact), USDA-ARS : Agricultural Research Service, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Potsdam Institute for Climate Impact Research (PIK), New Zealand Institute for Crop and Food Research, Partenaires INRAE, Rothamsted Research, Chinese Academy of Sciences (CAS), ANR, European Project: 277610,EC:FP7:KBBE,FP7-JPROG-2011-RTD,FACCE CSA(2011), Queensland University of Technology [Brisbane] (QUT), Università degli Studi di Firenze = University of Florence (UniFI), Università degli Studi di Sassari = University of Sassari [Sassari] (UNISS), Agriculture and Agri-Food (AAFC), Biotechnology and Biological Sciences Research Council (BBSRC), and Universidade Federal de Santa Maria (UFSM)

Subjects
biogeochemical models, 010504 meteorology & atmospheric sciences, [SDV]Life Sciences [q-bio], Climate change, Atmospheric sciences, 01 natural sciences, Grassland, [SDV.IDA]Life Sciences [q-bio]/Food engineering, Environmental Chemistry, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, [SPI.GPROC]Engineering Sciences [physics]/Chemical and Process Engineering, Agricultural productivity, 0105 earth and related environmental sciences, General Environmental Science, agriculture, 2. Zero hunger, Global and Planetary Change, geography, geography.geographical_feature_category, Ecology, Ensemble forecasting, nitrous oxide, Crop yield, Simulation modeling, 04 agricultural and veterinary sciences, benchmarking, climate change, greenhouse gases, soil, yield, Climate change mitigation, Agronomy, 13. Climate action, greenhouse gas, Greenhouse gas, [SDE]Environmental Sciences, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science
Abstract

International audience; 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 1SD 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 grass-land 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.

Published
2018
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12. Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N

Author

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

Subjects
Crops, Agricultural, Nitrous Oxide, Uncertainty, Agriculture, Computer Simulation, Models, Biological, Food Supply
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

Published
2017

13. Estimating the organic carbon stabilisation capacity and saturation deficit of soils: a New Zealand case study

Author

S.J. McNeill, Michael H. Beare, Denis Curtin, Mike Dodd, Roger L. Parfitt, Joanna Sharp, and H. S. Jones

Subjects
Total organic carbon, Soil water, Environmental Chemistry, Soil science, Ecosystem, Soil carbon, Saturation (chemistry), Biogeosciences, Geology, Earth-Surface Processes, Water Science and Technology
Abstract

The capacity of a soil to sequester organic carbon can, in theory, be estimated as the difference between the existing soil organic C (SOC) concentration and the SOC saturation value. The C saturation concept assumes that each soil has a maximum SOC storage capacity, which is primarily determined by the characteristics of the fine mineral fraction (i.e. 0. The median saturation deficit for both Allophanic and non-Allophanic soils was 12 mg C g−1 at 0–15 cm depth and 15 mg C g−1 at 15–30 cm depths. Improving predictions of the saturation deficit of soils may be important to developing and deploying effective SOC sequestration strategies.

Published
2014
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14. C and N models Intercomparison – benchmark and ensemble model estimates for grassland production

Author

Pete Smith, Sandro José Giacomini, Gianni Bellocchi, Joël Léonard, M. de Antoni Migliorati, Renata Sándor, Chris Dorich, Arti Bhatia, Ward Smith, Paul C. D. Newton, Jordi Doltra, Raia Silvia Massad, Fiona Ehrhardt, Miko U. F. Kirschbaum, Elizabeth Pattey, Matthew T. Harrison, Qing Zhang, Andrew D. Moore, Russell McAuliffe, Mark A. Liebig, Susanne Rolinski, Raphaël Martin, Brian Grant, Vasileios Myrgiotis, Mark Lieffering, Lorenzo Brilli, Jean-François Soussana, Luca Doro, Katja Klumpp, Bruno Basso, Elizabeth A. Meier, Patricia Laville, Sylvie Recous, Val Snow, Peter Grace, Lutz Merbold, Joanna Sharp, Nuala Fitton, Stephanie K. Jones, Lianhai Wu, Institute for Soil Sciences and Agricultural Chemistry (ATK TAKI), Centre for Agricultural Research [Budapest] (ATK), Hungarian Academy of Sciences (MTA)-Hungarian Academy of Sciences (MTA), UR 0874 Unité de recherche sur l'Ecosystème Prairial, Institut National de la Recherche Agronomique (INRA)-Unité de recherche sur l'Ecosystème Prairial (UREP)-Ecologie des Forêts, Prairies et milieux Aquatiques (EFPA), Institut National de la Recherche Agronomique (INRA), Collège de Direction (CODIR), Department of Geological Sciences, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, Indian Agricultural Research Institute (IARI), DISPAA, Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Queensland University of Technology, Cantabrian Agricultural Research and Training Centre, Natural Resource Ecology Laboratory [Fort Collins] (NREL), Colorado State University [Fort Collins] (CSU), Desertification Research Group, University of Sassari, Institute of Biological and Environmental Sciences, University of Aberdeen, Soil Department, Federal University of Santa Maria, Agriculture and Agri-Food [Ottawa] (AAFC), Tasmanian Institute of Agriculture, Scotland's Rural College (SRUC), Manaaki Whenua – Landcare Research [Lincoln], Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Agroressources et Impacts environnementaux (AgroImpact), United States Department of Agriculture, Lincoln Research Centre, AgResearch Ltd, Grasslands Research Centre, Agriculture and Food, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Department of Environmental Systems Science [ETH Zürich] (D-USYS), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Mazingira Centre, International Livestock Research Institute, Black Mountain Laboratories, CSIRO Agriculture Flagship, Fractionnement des AgroRessources et Environnement (FARE), Université de Reims Champagne-Ardenne (URCA)-Institut National de la Recherche Agronomique (INRA), Potsdam Institute for Climate Impact Research (PIK), Plant & Food Research, Sustainable Soils and Grassland Systems, Rothamsted Research, Institute of Atmospheric Physics [Beijing] (IAP), Chinese Academy of Sciences [Beijing] (CAS), ANR, European Project: 277610, 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), Collège de Direction, University of Florence (UNIFI), Scotland's Rural College (SCUR), Landcare Research, Unité d'Agronomie de Laon-Reims-Mons (AGRO-LRM), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology in Zürich [Zürich] (ETH Zürich), Fractionnement des AgroRessources et Environnement - UMR-A 614 (FARE), Université de Reims Champagne-Ardenne (URCA)-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)-Institut National de la Recherche Agronomique (INRA), New Zealand Institute for Plant and Food Research Limited, 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), Unité de recherche sur l'Ecosystème Prairial (UREP), Università degli Studi di Firenze = University of Florence (UniFI), Queensland University of Technology [Brisbane] (QUT), Università degli Studi di Sassari = University of Sassari [Sassari] (UNISS), Agriculture and Agri-Food (AAFC), and Biotechnology and Biological Sciences Research Council (BBSRC)-Biotechnology and Biological Sciences Research Council (BBSRC)

Subjects
010504 meteorology & atmospheric sciences, Calibration (statistics), [SDV]Life Sciences [q-bio], ANPP, Context (language use), Atmospheric sciences, 01 natural sciences, Grassland, Physics::Geophysics, Benchmark (surveying), C-N MIP, Quantitative Biology::Populations and Evolution, model intercomparaison, 0105 earth and related environmental sciences, 2. Zero hunger, geography, geography.geographical_feature_category, Ensemble forecasting, Ecology, Simulation modeling, 04 agricultural and veterinary sciences, General Medicine, 15. Life on land, grassland production, 13. Climate action, Greenhouse gas, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Arable land
Abstract

Much of the uncertainty in crop and grassland model predictions of how arable and grassland systems respond to changes in management and environmental drivers can be attributed to differences in the structure of these models. This has created an urgent need for international bench- marking of models, in which uncertainties are estimated by running several models that simulate the same physical and management conditions (ensemble modelling) to generate expanded envelopes of uncertainty in model predictions (Asseng et al. , 2013). Simulations of C and N fluxes, in particular, are inherently uncertain because they are driven by complex interactions (Sandor et al. , 2016) and complicated by considerable spatial and temporal variability in the measurements. In this context, the Integrative Research Group of the Global Research Alliance (GRA) on Agricultural Greenhouse Gases promotes a coordinated activity across multiple international projects (e.g. C and N Models Inter-comparison and Improvement to assess management options for GHG mitigation in agrosystems worldwide (C-N MIP) and Models4Pastures of the FACCE-JPI, https://www.faccejpi.com) to benchmark and compare simulation models that estimate C – N related outputs (including greenhouse gas emissions) from arable crop and grassland systems (http://globalresearchalliance.org/e/model- intercomparison-on-agricultural-ghg-emissions). This study presents some preliminary results on the uncertainty of outputs from 12 grassland models, whereas exploring differences in model response when increasing data resources are used for model calibration.

Published
2016
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15. A multi-model assessment of C cycling and soil C sequestration in grasslands and croplands

Author

Renata Sandor, Fiona Ehrhardt, Bruno Basso, Gianni Bellocchi, Arti Bhatia, Lorenzo Brilli, Massimiliano de Antoni Migliorati, Jordi Doltra, Chris Dorich, Luca Doro, Nuala Fitton, Sandro José Giacomini, Peter Grace, Brian Grant, Harrison, Matthew T., Jones, Stephanie K., Kirschbaum, Miko U. F., Katja Klumpp, Patricia Laville, Joël Léonard, Mark Liebig, Mark Lieffering, Raphaël Martin, Russel Mcauliffe, Elizabeth Meier, Lutz Merbold, Moore, Andrew D., Vasileios Myrgiotis, Paul Newton, Elizabeth Pattey, Sylvie Recous, Susanne Rolinski, Joanna Sharp, Raia Silvia Massad, Pete Smith, Ward Smith, Val Snow, Lianhai Wu, Wen Zhang, Jean-François Soussana, 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), Collège de Direction, Institut National de la Recherche Agronomique (INRA), Michigan State University [East Lansing], Michigan State University System, Indian Agricultural Research Institute (IARI), University of Florence (UNIFI), Queensland University of Technology, Cantabrian Agricultural Research and Training Centre, Colorado State University [Fort Collins] (CSU), University of Sassari, University of Aberdeen, Federal University of Santa Maria, Agriculture and Agri-Food [Ottawa] (AAFC), Tasmanian Institute of Agriculture, Scotland's Rural College (SCUR), Landcare Research, Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Unité d'Agronomie de Laon-Reims-Mons (AGRO-LRM), United States Department of Agriculture - Agricultural Research Service, AgResearch Ltd, CSIRO, International Livestock Research Institute, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), AgResearch Grasslands, Agricuture and Agri-Food Canada, Fractionnement des AgroRessources et Environnement - UMR-A 614 (FARE), Université de Reims Champagne-Ardenne (URCA)-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)-Institut National de la Recherche Agronomique (INRA), Potsdam Institute for Climate Impact Research (PIK), New Zealand Institute for Plant and Food Research Limited, Rothamsted Research, Chinese Academy of Sciences (CAS), CN-MIP, 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), Unité de Recherche sur l'Ecosystème Prairial, Institut National de la Recherche Agronomique ( INRA ), Michigan State University, Indian Agricultural Research Institute ( IARI ), University of Florence, Colorado State University, Agriculture and Agri-Food Canada ( AAFC ), Scotland's Rural College, Ecologie fonctionnelle et écotoxicologie des agroécosystèmes ( ECOSYS ), Institut National de la Recherche Agronomique ( INRA ) -AgroParisTech, Unité d'Agronomie de Laon-Reims-Mons ( AGRO-LRM ), Commonwealth Scientific and Industrial Research Organisation, 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 ), Potsdam Institute for Climate Impact Research ( PIK ), Chinese Academy of Sciences ( CAS ), Collège de Direction (CODIR), Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Scotland's Rural College (SRUC), Manaaki Whenua – Landcare Research [Lincoln], Agroressources et Impacts environnementaux (AgroImpact), USDA-ARS : Agricultural Research Service, Fractionnement des AgroRessources et Environnement (FARE), Université de Reims Champagne-Ardenne (URCA)-Institut National de la Recherche Agronomique (INRA), and Plant & Food Research

Subjects
modelling, field crop, [ SDV ] Life Sciences [q-bio], grandes cultures et prairies, carbon, [SDV]Life Sciences [q-bio], prairie, grande culture, carbone, grassland, modélisation
Abstract

A multi-model assessment of C cycling and soil C sequestration in grasslands and croplands. 6th International Symposium on Soil Organic Matter ; Harpenden (Royaume Uni)

Published
2017

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

Author

Marco Carozzi, Raia Silvia Massad, Joanna Sharp, Fiona Ehrhardt, Katja Klumpp, Roberta Farina, Richard T. Conant, Nuala Fitton, Lorenzo Brilli, Jean-François Soussana, Sylvie Recous, Luca Bechini, Peter Grace, Giovanna Seddaiu, Ileana Iocola, Gianni Bellocchi, Pete Smith, D. Cavalli, Roberto Ferrise, Joël Léonard, Luca Doro, Marco Bindi, Rosa Francaviglia, Ward Smith, Chris Dorich, Raphaël Martin, Department of Agri-Food Production and Environmental Sciences, University delgi Studi di Firenze, Istituto di Biometeorologia [Firenze] (IBIMET), Consiglio Nazionale delle Ricerche (CNR), Department of Agricultural and Environmental Sciences, University degli Study di Milano, Universita degli Study di Firenze, Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Università degli Studi di Milano [Milano] (UNIMI), Natural Resource Ecology Laboratory [Fort Collins] (NREL), Colorado State University [Fort Collins] (CSU), Desertification Research Centre, Department of Agricultural Sciences, University of Sassari, Blackland Research & Extension Center, Texas A&M University System, Collège de Direction (CODIR), Institut National de la Recherche Agronomique (INRA), Research Centre for the Soil-Plant System, Consiglio per la Ricerca in Agricoltura e l’analisi dell’economia agraria (CREA), University degli study di Firenze, Institute of Biological and Environmental Sciences, University of Aberdeen, Queensland University of Technology, Università degli Studi di Sassari, 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), Agroressources et Impacts environnementaux (AgroImpact), Fractionnement des AgroRessources et Environnement (FARE), Université de Reims Champagne-Ardenne (URCA)-Institut National de la Recherche Agronomique (INRA), Plant & Food Research, Agriculture and Agri-Food [Ottawa] (AAFC), European Project: 618105, Università degli Studi di Firenze = University of Florence (UniFI), National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR), Università degli Studi di Milano = University of Milan (UNIMI), Università degli Studi di Sassari = University of Sassari [Sassari] (UNISS), Consiglio per la Ricerca in Agricoltura e l’analisi dell’economia agraria = Council for Agricultural Research and Economics (CREA), Queensland University of Technology [Brisbane] (QUT), Agriculture and Agri-Food (AAFC), Università degli studi di Milano [Milano], Collège de Direction, Unité d'Agronomie de Laon-Reims-Mons (AGRO-LRM), Fractionnement des AgroRessources et Environnement - UMR-A 614 (FARE), Université de Reims Champagne-Ardenne (URCA)-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)-Institut National de la Recherche Agronomique (INRA), New Zealand Institute for Plant and Food Research Limited, 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
Biogeochemical cycle, Environmental Engineering, 010504 meteorology & atmospheric sciences, Biogeochemical model, [SDE.MCG]Environmental Sciences/Global Changes, Agricultural engineering, Carbon sequestration, 01 natural sciences, DayCent, Pedo-climate, N cycle, Environmental Chemistry, Ecosystem, Waste Management and Disposal, 0105 earth and related environmental sciences, 2. Zero hunger, Soil organic matter, Simulation modeling, Environmental engineering, 04 agricultural and veterinary sciences, 15. Life on land, Pollution, Management, 13. Climate action, Greenhouse gas, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, DSSAT, Environmental science, C cycle
Abstract

Biogeochemical simulation models are important tools for describing and quantifying the contribution of agricultural systems to C sequestration and GHG source/sink status. The abundance of simulation tools developed over recent decades, however, creates a difficulty because predictions from different models show large variability. Discrepancies between the conclusions of different modelling studies are often ascribed to differences in the physical and biogeochemical processes incorporated in equations of C and N cycles and their interactions. Here we review the literature to determine the state-of-the-art in modelling agricultural (crop and grassland) systems. In order to carry out this study, we selected the range of biogeochemical models used by the CN-MIP consortium of FACCE-JPI (http://www.faccejpi.com): APSIM, CERES-EGC, DayCent, DNDC, DSSAT, EPIC, PaSim, RothC and STICS. In our analysis, these models were assessed for the quality and comprehensiveness of underlying processes related to pedo-climatic conditions and management practices, but also with respect to time and space of application, and for their accuracy in multiple contexts. Overall, it emerged that there is a possible impact of ill-defined pedo-climatic conditions in the unsatisfactory performance of the models (46.2%), followed by limitations in the algorithms simulating the effects of management practices (33.1%). The multiplicity of scales in both time and space is a fundamental feature, which explains the remaining weaknesses (i.e. 20.7%). Innovative aspects have been identified for future development of C and N models. They include the explicit representation of soil microbial biomass to drive soil organic matter turnover, the effect of N shortage on SOM decomposition, the improvements related to the production and consumption of gases and an adequate simulations of gas transport in soil. On these bases, the assessment of trends and gaps in the modelling approaches currently employed to represent biogeochemical cycles in crop and grassland systems appears an essential step for future research.

Published
2017
Full Text
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17. Global Research Alliance on agricultural greenhouse gases - benchmark and ensemble crop and grassland model estimates

Author

Renata Sándor, Ehrhardt, F., Bruno Basso, Bathia, A., Gianni Bellocchi, Brilli, L., Laura Maritza Cardenas, Massimiliano de Antoni Migliorati, Bregon, J. D., Dorich, C., Lucas Doro, Fitton, N., Giacomini, S., Peter Grace, Grant, Brian B., Harrison, M., Stephanie Jones, Miko Kirschbaum, Katja Klumpp, Laville, P., Joël Léonard, Liebig, M., Lieffering, M., Raphaël Martin, Mcauliffe, R., Elizabeth Anne Meier, Lutz Merbold, Andrew Moore, Vasilis Myrgiotis, Elizabeth Pattey, Zhang, Q., Sylvie Recous, Suzanne Rolinski, Joanna Sharp, Raia Silvia Massad, Smith, P., Ward Smith, Val Snow, Soussana, J. F., Institute for Soil Sciences and Agricultural Chemistry (ATK TAKI), Centre for Agricultural Research [Budapest] (ATK), Hungarian Academy of Sciences (MTA)-Hungarian Academy of Sciences (MTA), Department of Earth and Environmental Sciences [East Lansing], Michigan State University [East Lansing], Michigan State University System-Michigan State University System, 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), Dipartimento di Scienze delle Produzioni Agroalimentari e dell'Ambiente (DISPAA), University of Florence (UNIFI), Department of Sustainable Soils and Grassland Systems, Rothamsted Research, Institute for Future Environments, Queensland University of Technology, Texas A and M AgriLife Research, Texas A&M University System, Institute of Biological and Environmental Sciences, (SFIRC), Universidade Federal de Santa Maria (UFSM), Science and Technology Branch, Environment and Climate Change Canada, Tasmanian Institute of Agriculture (TIA), University of Tasmania (UTAS), Soil Science and Systems Team, Scotland's Rural College (SCUR), Ecosystems and Global Change Team, Landscare Research, Unité d'Agronomie de Laon-Reims-Mons (AGRO-LRM), Institut National de la Recherche Agronomique (INRA), Agricultural Research Service (ARS), United States Department of Agriculture, CSIRO Ecosystem Sciences, Livestock Systems and Environment, International Livestock Research Institute, Agriculture & Food, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), School of GeoSciences, University of Edinburgh, Ottawa Research and Development Centre, Agriculture and Agri-Food [Ottawa] (AAFC), 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), Climate Impacts and Vulnerabilities - Research Domain II, Potsdam Institute for Climate Impact Research (PIK), Modelling, Sustainable Production, New Zealand Institute for Plant and Food Research Limited, Farm Systems and Environment, AgResearch Ltd, UR 0874 Unité de recherche sur l'Ecosystème Prairial, Institut National de la Recherche Agronomique (INRA)-Unité de recherche sur l'Ecosystème Prairial (UREP)-Ecologie des Forêts, Prairies et milieux Aquatiques (EFPA), Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Universidade Federal de Santa Maria = Federal University of Santa Maria [Santa Maria, RS, Brazil] (UFSM), University of Tasmania [Hobart, Australia] (UTAS), Scotland's Rural College (SRUC), Agroressources et Impacts environnementaux (AgroImpact), USDA-ARS : Agricultural Research Service, Fractionnement des AgroRessources et Environnement (FARE), Université de Reims Champagne-Ardenne (URCA)-Institut National de la Recherche Agronomique (INRA), Plant & Food Research, and ProdInra, Archive Ouverte

Subjects
[SDV] Life Sciences [q-bio], agricultural greenhouse gases, grassland model, [SDV]Life Sciences [q-bio]
Abstract

CT3 Biogéochimie, physique et écologie des solsEnjS4 Bouclage des cycles N et P et stockage de carboneTyp_Proj_Bourse de thèse/Post-DocTyp_Proj_Projet ANR; Uncertainties in the response of crop and grassland models to management and environmental drivers can be attributed to differences in the structure of different models. This has created an urgent need for international benchmarking of models, where uncertainties are estimated by running several models that simulate the same physical and management conditions (ensemble modelling) to generate expanded envelopes of uncertainty (e.g. Asseng et al., 2013). Simulations of the agricultural C and N fluxes, in particular, are inherently uncertain because they are driven by complex interactions (e.g. Sándor et al., 2016) and characterized by considerable spatial and temporal variability in the measurements. In this context, the Integrative Research Group of the Global Research Alliance (GRA) on Agricultural Greenhouse Gases promotes a coordinated activity across multiple international projects (e.g. C-N MIP and Models4Pastures of the FACCE-JPI, https://www.faccejpi.com) to benchmark and compare simulation models that estimate C-N related outputs (including greenhouse gas emissions) from arable crop and grassland systems (http://globalresearchalliance.org/e/model-intercomparison-on-agricultural-ghg-emissions). This study presents some preliminary results on the uncertainty of outputs from 12 grassland models while exploring model differences when models were calibrated with increasing data resources.

Published
2016

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