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Albanito F, McBey D, Harrison M, Smith P, Ehrhardt F, Bhatia A, Bellocchi G, Brilli L, Carozzi M, Christie K, Doltra J, Dorich C, Doro L, Grace P, Grant B, Léonard J, Liebig M, Ludemann C, Martin R, Meier E, Meyer R, De Antoni Migliorati M, Myrgiotis V, Recous S, Sándor R, Snow V, Soussana JF, Smith WN, Fitton N. How Modelers Model: the Overlooked Social and Human Dimensions in Model Intercomparison Studies. Environ Sci Technol 2022; 56:13485-13498. [PMID: 36052879 PMCID: PMC9494747 DOI: 10.1021/acs.est.2c02023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 08/23/2022] [Accepted: 08/25/2022] [Indexed: 06/15/2023]
Abstract
There is a growing realization that the complexity of model ensemble studies depends not only on the models used but also on the experience and approach used by modelers to calibrate and validate results, which remain a source of uncertainty. Here, we applied a multi-criteria decision-making method to investigate the rationale applied by modelers in a model ensemble study where 12 process-based different biogeochemical model types were compared across five successive calibration stages. The modelers shared a common level of agreement about the importance of the variables used to initialize their models for calibration. However, we found inconsistency among modelers when judging the importance of input variables across different calibration stages. The level of subjective weighting attributed by modelers to calibration data decreased sequentially as the extent and number of variables provided increased. In this context, the perceived importance attributed to variables such as the fertilization rate, irrigation regime, soil texture, pH, and initial levels of soil organic carbon and nitrogen stocks was statistically different when classified according to model types. The importance attributed to input variables such as experimental duration, gross primary production, and net ecosystem exchange varied significantly according to the length of the modeler's experience. We argue that the gradual access to input data across the five calibration stages negatively influenced the consistency of the interpretations made by the modelers, with cognitive bias in "trial-and-error" calibration routines. Our study highlights that overlooking human and social attributes is critical in the outcomes of modeling and model intercomparison studies. While complexity of the processes captured in the model algorithms and parameterization is important, we contend that (1) the modeler's assumptions on the extent to which parameters should be altered and (2) modeler perceptions of the importance of model parameters are just as critical in obtaining a quality model calibration as numerical or analytical details.
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Affiliation(s)
- Fabrizio Albanito
- Institute
of Biological and Environmental Sciences, School of Biological Science, University of Aberdeen, 23 Street Machar Drive, Aberdeen AB24 3UU, U.K.
| | - David McBey
- Institute
of Biological and Environmental Sciences, School of Biological Science, University of Aberdeen, 23 Street Machar Drive, Aberdeen AB24 3UU, U.K.
| | - Matthew Harrison
- Tasmanian
Institute of Agriculture, University of
Tasmania, Newnham Drive, Launceston, Tasmania 7248, Australia
| | - Pete Smith
- Institute
of Biological and Environmental Sciences, School of Biological Science, University of Aberdeen, 23 Street Machar Drive, Aberdeen AB24 3UU, U.K.
| | - Fiona Ehrhardt
- INRAE,
CODIR, Paris 75007, France
- RITTMO
AgroEnvironnement, Colmar 68000, France
| | - Arti Bhatia
- ICAR-Indian
Agricultural Research Institute, New Delhi 110012, India
| | - Gianni Bellocchi
- Université
Clermont Auvergne, INRAE, VetAgro Sup, UREP, Clermont-Ferrand 63000, France
| | - Lorenzo Brilli
- CNR-IBE,
National Research Council Institute for the BioEconomy, Via Caproni 8, Florence 50145, Italy
| | - Marco Carozzi
- UMR
ECOSYS, INRAE, AgroParisTech, Université Paris-Saclay, Thiverval-Grignon 78850, France
| | - Karen Christie
- Tasmanian
Institute of Agriculture, University of
Tasmania, 16-20 Mooreville Road, Burnie, Tasmania 7320, Australia
| | - Jordi Doltra
- Sustainable
Field Crops Programme, Institute of Agrifood
Research and Technology (IRTA) Mas Badia, La Tallada d’Empordà, Girona 17134, Spain
| | - Christopher Dorich
- Natural
Resource Ecology Lab, Colorado
State University, Fort Collins, Colorado 80521, United States
| | - Luca Doro
- Texas A&M AgriLife Research, Blackland
Research and Extension Center, Temple, Texas 76502, United States
- Desertification Research Centre, University
of Sassari, Sassari 07100, Italy
| | - Peter Grace
- Queensland University of Technology, Brisbane, Queensland 4000, Australia
| | - Brian Grant
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, Ontario K1A 0C6, Canada
| | - Joël Léonard
- BioEcoAgro
Joint Research Unit, INRAE, Barenton-Bugny 02000, France
| | - Mark Liebig
- USDA-ARS Northern Great Plains Research
Laboratory, P.O. Box 459, Mandan, North Dakota 58554, United States
| | | | - Raphael Martin
- Université
Clermont Auvergne, INRAE, VetAgro Sup, UREP, Clermont-Ferrand 63000, France
| | - Elizabeth Meier
- CSIRO Agriculture
and Food, St
Lucia, Queensland 4067, Australia
| | - Rachelle Meyer
- Faculty of Veterinary & Agricultural
Sciences, University of Melbourne, Parkville, Victoria 3010, Australia
| | - Massimiliano De Antoni Migliorati
- Queensland University of Technology, Brisbane, Queensland 4000, Australia
- Department of Environment and Science, Dutton Park, Queensland 4102, Australia
| | | | - Sylvie Recous
- Université
de Reims Champagne-Ardenne, INRAE, FARE Laboratory, Reims 51100, France
| | - Renáta Sándor
- Agricultural Institute, Centre for Agricultural Research,
ELKH, Martonvásár 2462, Hungary
| | - Val Snow
- AgResearch, P.O. Box 4749, Christchurch 8140, New
Zealand
| | | | - Ward N. Smith
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, Ontario K1A 0C6, Canada
| | - Nuala Fitton
- Institute
of Biological and Environmental Sciences, School of Biological Science, University of Aberdeen, 23 Street Machar Drive, Aberdeen AB24 3UU, U.K.
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Farina R, Sándor R, Abdalla M, Álvaro-Fuentes J, Bechini L, Bolinder MA, Brilli L, Chenu C, Clivot H, De Antoni Migliorati M, Di Bene C, Dorich CD, Ehrhardt F, Ferchaud F, Fitton N, Francaviglia R, Franko U, Giltrap DL, Grant BB, Guenet B, Harrison MT, Kirschbaum MUF, Kuka K, Kulmala L, Liski J, McGrath MJ, Meier E, Menichetti L, Moyano F, Nendel C, Recous S, Reibold N, Shepherd A, Smith WN, Smith P, Soussana JF, Stella T, Taghizadeh-Toosi A, Tsutskikh E, Bellocchi G. Ensemble modelling, uncertainty and robust predictions of organic carbon in long-term bare-fallow soils. Glob Chang Biol 2021; 27:904-928. [PMID: 33159712 DOI: 10.1111/gcb.15441] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Accepted: 10/26/2020] [Indexed: 06/11/2023]
Abstract
Simulation models represent soil organic carbon (SOC) dynamics in global carbon (C) cycle scenarios to support climate-change studies. It is imperative to increase confidence in long-term predictions of SOC dynamics by reducing the uncertainty in model estimates. We evaluated SOC simulated from an ensemble of 26 process-based C models by comparing simulations to experimental data from seven long-term bare-fallow (vegetation-free) plots at six sites: Denmark (two sites), France, Russia, Sweden and the United Kingdom. The decay of SOC in these plots has been monitored for decades since the last inputs of plant material, providing the opportunity to test decomposition without the continuous input of new organic material. The models were run independently over multi-year simulation periods (from 28 to 80 years) in a blind test with no calibration (Bln) and with the following three calibration scenarios, each providing different levels of information and/or allowing different levels of model fitting: (a) calibrating decomposition parameters separately at each experimental site (Spe); (b) using a generic, knowledge-based, parameterization applicable in the Central European region (Gen); and (c) using a combination of both (a) and (b) strategies (Mix). We addressed uncertainties from different modelling approaches with or without spin-up initialization of SOC. Changes in the multi-model median (MMM) of SOC were used as descriptors of the ensemble performance. On average across sites, Gen proved adequate in describing changes in SOC, with MMM equal to average SOC (and standard deviation) of 39.2 (±15.5) Mg C/ha compared to the observed mean of 36.0 (±19.7) Mg C/ha (last observed year), indicating sufficiently reliable SOC estimates. Moving to Mix (37.5 ± 16.7 Mg C/ha) and Spe (36.8 ± 19.8 Mg C/ha) provided only marginal gains in accuracy, but modellers would need to apply more knowledge and a greater calibration effort than in Gen, thereby limiting the wider applicability of models.
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Affiliation(s)
- Roberta Farina
- Research Centre for Agriculture and Environment, CREA - Council for Agricultural Research and Economics, Rome, Italy
| | - Renata Sándor
- Centre for Agricultural Research, Agricultural Institute, Martonvásár, Hungary
- Université Clermont Auvergne, INRAE, VetAgro Sup, UREP, Clermont-Ferrand, France
| | | | | | | | | | | | - Claire Chenu
- Université Paris Saclay, INRAE, AgroParisTech, Paris, France
| | - Hugues Clivot
- INRAE, BioEcoAgro, Barenton-Bugny, France
- Université de Lorraine, INRAE, LAE, Colmar, France
| | | | - Claudia Di Bene
- Research Centre for Agriculture and Environment, CREA - Council for Agricultural Research and Economics, Rome, Italy
| | | | | | | | | | - Rosa Francaviglia
- Research Centre for Agriculture and Environment, CREA - Council for Agricultural Research and Economics, Rome, Italy
| | - Uwe Franko
- Helmholtz Centre for Environmental Research, Halle, Germany
| | - Donna L Giltrap
- Manaaki Whenua - Landcare Research, Palmerston North, New Zealand
| | - Brian B Grant
- Ottawa Research and Development Centre, Agriculture and Agri-Food, Ottawa, ON, Canada
| | - Bertrand Guenet
- Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France
- Laboratoire de Géologie de l'ENS, PSL Research University, Paris, France
| | | | | | - Katrin Kuka
- JKI - Federal Research Centre for Cultivated Plants, Braunschweig, Germany
| | | | - Jari Liski
- Finnish Meteorological Institute, Helsinki, Finland
| | - Matthew J McGrath
- Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France
| | | | | | | | - Claas Nendel
- Leibniz Centre for Agricultural Landscape Research, Müncheberg, Germany
- University of Potsdam, Potsdam, Germany
| | - Sylvie Recous
- Université de Reims Champagne Ardenne, INRAE, FARE, Reims, France
| | | | - Anita Shepherd
- University of Aberdeen, Aberdeen, UK
- formerly Rothamsted Research, North Wyke, UK
| | - Ward N Smith
- Ottawa Research and Development Centre, Agriculture and Agri-Food, Ottawa, ON, Canada
| | | | | | - Tommaso Stella
- Leibniz Centre for Agricultural Landscape Research, Müncheberg, Germany
| | | | - Elena Tsutskikh
- Leibniz Centre for Agricultural Landscape Research, Müncheberg, Germany
| | - Gianni Bellocchi
- Université Clermont Auvergne, INRAE, VetAgro Sup, UREP, Clermont-Ferrand, France
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3
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Sándor R, Ehrhardt F, Brilli L, Carozzi M, Recous S, Smith P, Snow V, Soussana JF, Dorich CD, Fuchs K, Fitton N, Gongadze K, Klumpp K, Liebig M, Martin R, Merbold L, Newton PCD, Rees RM, Rolinski S, Bellocchi G. The use of biogeochemical models to evaluate mitigation of greenhouse gas emissions from managed grasslands. Sci Total Environ 2018; 642:292-306. [PMID: 29902627 DOI: 10.1016/j.scitotenv.2018.06.020] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2018] [Revised: 05/15/2018] [Accepted: 06/02/2018] [Indexed: 06/08/2023]
Abstract
Simulation models quantify the impacts on carbon (C) and nitrogen (N) cycling in grassland systems caused by changes in management practices. To support agricultural policies, it is however important to contrast the responses of alternative models, which can differ greatly in their treatment of key processes and in their response to management. We applied eight biogeochemical models at five grassland sites (in France, New Zealand, Switzerland, United Kingdom and United States) to compare the sensitivity of modelled C and N fluxes to changes in the density of grazing animals (from 100% to 50% of the original livestock densities), also in combination with decreasing N fertilization levels (reduced to zero from the initial levels). Simulated multi-model median values indicated that input reduction would lead to an increase in the C sink strength (negative net ecosystem C exchange) in intensive grazing systems: -64 ± 74 g C m-2 yr-1 (animal density reduction) and -81 ± 74 g C m-2 yr-1 (N and animal density reduction), against the baseline of -30.5 ± 69.5 g C m-2 yr-1 (LSU [livestock units] ≥ 0.76 ha-1 yr-1). Simulations also indicated a strong effect of N fertilizer reduction on N fluxes, e.g. N2O-N emissions decreased from 0.34 ± 0.22 (baseline) to 0.1 ± 0.05 g N m-2 yr-1 (no N fertilization). Simulated decline in grazing intensity had only limited impact on the N balance. The simulated pattern of enteric methane emissions was dominated by high model-to-model variability. The reduction in simulated offtake (animal intake + cut biomass) led to a doubling in net primary production per animal (increased by 11.6 ± 8.1 t C LSU-1 yr-1 across sites). The highest N2O-N intensities (N2O-N/offtake) were simulated at mown and extensively grazed arid sites. We show the possibility of using grassland models to determine sound mitigation practices while quantifying the uncertainties associated with the simulated outputs.
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Affiliation(s)
- Renáta Sándor
- INRA, VetAgro Sup, UCA, Unité Mixte de Recherche sur l'Écosystème Prairial (UREP), 63000 Clermont-Ferrand, France; Agricultural Institute, CAR HAS, 2462 Martonvásár, Hungary
| | | | - Lorenzo Brilli
- University of Florence, DISPAA, 50144 Florence, Italy; IBIMET-CNR, 50145 Florence, Italy
| | - Marco Carozzi
- Agroscope Research Station, Climate Agriculture Group, Zurich, Switzerland
| | - Sylvie Recous
- FARE Laboratory, INRA, Université de Reims Champagne-Ardenne, 51100 Reims, France
| | - Pete Smith
- Institute of Biological & Environmental Sciences, School of Biological Sciences, University of Aberdeen, Aberdeen AB24 3UU, United Kingdom
| | - Val Snow
- AgResearch - Lincoln Research Centre, Private Bag 4749, Christchurch 8140, New Zealand
| | | | | | - Kathrin Fuchs
- Department of Environmental Systems Science, Institute of Agricultural Sciences, ETH Zürich, 8092 Zurich, Switzerland
| | - Nuala Fitton
- Institute of Biological & Environmental Sciences, School of Biological Sciences, University of Aberdeen, Aberdeen AB24 3UU, United Kingdom
| | - Kate Gongadze
- Rothamsted Research, Sustainable Soil and Grassland Systems Department, United Kingdom
| | - Katja Klumpp
- INRA, VetAgro Sup, UCA, Unité Mixte de Recherche sur l'Écosystème Prairial (UREP), 63000 Clermont-Ferrand, France
| | | | - Raphaël Martin
- INRA, VetAgro Sup, UCA, Unité Mixte de Recherche sur l'Écosystème Prairial (UREP), 63000 Clermont-Ferrand, France
| | - Lutz Merbold
- Department of Environmental Systems Science, Institute of Agricultural Sciences, ETH Zürich, 8092 Zurich, Switzerland; Mazingira Centre, International Livestock Research Institute, 00100 Nairobi, Kenya
| | - Paul C D Newton
- AgResearch Grasslands Research Centre, Private Bag 11008, Palmerston North 4442, New Zealand
| | - Robert M Rees
- Scotland's Rural College, EH9 3JG Edinburgh, United Kingdom
| | - Susanne Rolinski
- Potsdam Institute for Climate Impact Research, 14473 Potsdam, Germany
| | - Gianni Bellocchi
- INRA, VetAgro Sup, UCA, Unité Mixte de Recherche sur l'Écosystème Prairial (UREP), 63000 Clermont-Ferrand, France.
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4
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Ehrhardt F, Soussana JF, Bellocchi G, Grace P, McAuliffe R, Recous S, Sándor R, Smith P, Snow V, de Antoni Migliorati M, Basso B, Bhatia A, Brilli L, Doltra J, Dorich CD, Doro L, Fitton N, Giacomini SJ, Grant B, Harrison MT, Jones SK, Kirschbaum MUF, Klumpp K, Laville P, Léonard J, Liebig M, Lieffering M, Martin R, Massad RS, Meier E, Merbold L, Moore AD, Myrgiotis V, Newton P, Pattey E, Rolinski S, Sharp J, Smith WN, Wu L, Zhang Q. Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N 2 O emissions. Glob Chang Biol 2018; 24:e603-e616. [PMID: 29080301 DOI: 10.1111/gcb.13965] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2017] [Revised: 10/01/2017] [Accepted: 10/10/2017] [Indexed: 06/07/2023]
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 (N2 O) emissions for wheat, maize, rice and temperate grasslands. Using a multi-stage modelling protocol, from blind simulations (stage 1) to partial (stages 2-4) and full calibration (stage 5), 24 process-based biogeochemical models were assessed individually or as an ensemble against long-term experimental data from four temperate grassland and five arable crop rotation sites spanning four continents. Comparisons were performed by reference to the experimental uncertainties of observed yields and N2 O emissions. Results showed that across sites and crop/grassland types, 23%-40% of the uncalibrated individual models were within two standard deviations (SD) of observed yields, while 42 (rice) to 96% (grasslands) of the models were within 1 SD of observed N2 O emissions. At stage 1, ensembles formed by the three lowest prediction model errors predicted both yields and N2 O emissions within experimental uncertainties for 44% and 33% of the crop and grassland growth cycles, respectively. Partial model calibration (stages 2-4) markedly reduced prediction errors of the full model ensemble E-median for crop grain yields (from 36% at stage 1 down to 4% on average) and grassland productivity (from 44% to 27%) and to a lesser and more variable extent for N2 O emissions. Yield-scaled N2 O emissions (N2 O emissions divided by crop yields) were ranked accurately by three-model ensembles across crop species and field sites. The potential of using process-based model ensembles to predict jointly productivity and N2 O emissions at field scale is discussed.
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Affiliation(s)
| | | | | | - Peter Grace
- Queensland University of Technology, Brisbane, Qld, Australia
| | | | | | - Renáta Sándor
- UMR Ecosystème Prairial, INRA, Clermont-Ferrand, France
- HAS, CAR, Agricultural Institute, Martonvásár, Hungary
| | - Pete Smith
- Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen, UK
| | - Val Snow
- Lincoln Research Centre, AgResearch, Lincoln, New Zealand
| | | | - Bruno Basso
- Department of Geological Sciences, Michigan State University, East Lansing, MI, USA
| | - Arti Bhatia
- Indian Agricultural Research Institute, New Delhi, India
| | | | - Jordi Doltra
- Cantabrian Agricultural Research and Training Center (CIFA), Muriedas, Spain
| | | | - Luca Doro
- Desertification Research Centre, University of Sassari, Sassari, Italy
| | - Nuala Fitton
- Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen, UK
| | - Sandro J Giacomini
- Soil Department, Federal University of Santa Maria (UFSM), Santa Maria, Brazil
| | - Brian Grant
- Ottawa Research and Development Center, Agriculture and Agri-Food Canada, Ottawa, ON, Canada
| | | | | | | | - Katja Klumpp
- UMR Ecosystème Prairial, INRA, Clermont-Ferrand, France
| | - Patricia Laville
- INRA, UMR ECOSYS, Université Paris-Saclay, Thiverval-Grignon, France
| | | | - Mark Liebig
- USDA Agricultural Research Service, Mandan, ND, USA
| | - Mark Lieffering
- AgResearch, Grasslands Research Centre, Palmerton North, New Zealand
| | | | - Raia S Massad
- INRA, UMR ECOSYS, Université Paris-Saclay, Thiverval-Grignon, France
| | | | - Lutz Merbold
- ETH Zurich, Institute of Agricultural Sciences, Zurich, Switzerland
- International Livestock Research Institute (ILRI), Mazingira Centre, Nairobi, Kenya
| | - Andrew D Moore
- Agriculture & Food, Black Mountain Science and Innovation Precinct, CSIRO, Canberra, ACT, Australia
| | | | - Paul Newton
- AgResearch, Grasslands Research Centre, Palmerton North, New Zealand
| | - Elizabeth Pattey
- Ottawa Research and Development Center, Agriculture and Agri-Food Canada, Ottawa, ON, Canada
| | - Susanne Rolinski
- Potsdam Institute for Climate Impact Research (PIK), Potsdam, Germany
| | - Joanna Sharp
- New Zealand Institute for Plant and Food Research, Christchurch, New Zealand
| | - Ward N Smith
- Ottawa Research and Development Center, Agriculture and Agri-Food Canada, Ottawa, ON, Canada
| | - Lianhai Wu
- Sustainable Soils and Grassland Systems, Rothamsted Research, Devon, UK
| | - Qing Zhang
- LAPC, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
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5
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Brilli L, Bechini L, Bindi M, Carozzi M, Cavalli D, Conant R, Dorich CD, Doro L, Ehrhardt F, Farina R, Ferrise R, Fitton N, Francaviglia R, Grace P, Iocola I, Klumpp K, Léonard J, Martin R, Massad RS, Recous S, Seddaiu G, Sharp J, Smith P, Smith WN, Soussana JF, Bellocchi G. Review and analysis of strengths and weaknesses of agro-ecosystem models for simulating C and N fluxes. Sci Total Environ 2017; 598:445-470. [PMID: 28454025 DOI: 10.1016/j.scitotenv.2017.03.208] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2016] [Revised: 03/21/2017] [Accepted: 03/22/2017] [Indexed: 05/21/2023]
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.
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Affiliation(s)
- Lorenzo Brilli
- Università degli Studi di Firenze, Department of Agri-Food Production and Environmental Sciences, 50144 Florence, Italy; IBIMET-CNR, Via Caproni 8, 50145 Firenze, Italy.
| | - Luca Bechini
- Università degli Studi di Milano, Department of Agricultural and Environmental Sciences, Milan, Italy
| | - Marco Bindi
- Università degli Studi di Firenze, Department of Agri-Food Production and Environmental Sciences, 50144 Florence, Italy
| | - Marco Carozzi
- INRA, AgroParisTech, UMR1402 EcoSys, 78850 Thiverval-Grignon, France
| | - Daniele Cavalli
- Università degli Studi di Milano, Department of Agricultural and Environmental Sciences, Milan, Italy
| | - Richard Conant
- NREL, Colorado State University, Fort Collins, CO 80523, USA
| | | | - Luca Doro
- Desertification Research Centre, Department of Agricultural Sciences, University of Sassari, 07100 Sassari, Italy; Texas A&M AgriLife Research, Blackland Research & Extension Center, Temple, (TX), USA
| | | | - Roberta Farina
- CREA-RPS, Research Centre for the Soil-Plant System, Via della Navicella 2-4, 00184 Roma, Italy
| | - Roberto Ferrise
- Università degli Studi di Firenze, Department of Agri-Food Production and Environmental Sciences, 50144 Florence, Italy
| | - Nuala Fitton
- Institute of Biological and Environmental Sciences, University of Aberdeen, St Machar Drive, AB24 3UU Aberdeen, UK
| | - Rosa Francaviglia
- CREA-RPS, Research Centre for the Soil-Plant System, Via della Navicella 2-4, 00184 Roma, Italy
| | - Peter Grace
- Queensland University of Technology, Brisbane, Australia
| | - Ileana Iocola
- Desertification Research Centre, Department of Agricultural Sciences, University of Sassari, 07100 Sassari, Italy
| | | | - Joël Léonard
- INRA, UR 1158 AgroImpact, site de Laon, F-02000 Barenton-Bugny, France
| | | | | | | | - Giovanna Seddaiu
- Desertification Research Centre, Department of Agricultural Sciences, University of Sassari, 07100 Sassari, Italy
| | - Joanna Sharp
- New Zealand Institute for Plant and Food Research, 7608 Lincoln, New Zealand
| | - Pete Smith
- Institute of Biological and Environmental Sciences, University of Aberdeen, St Machar Drive, AB24 3UU Aberdeen, UK
| | - Ward N Smith
- Agriculture and Agri-Food Canada, Ottawa, Ontario K1A 0C6, Canada
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