Review and analysis of strengths and weaknesses of agro-ecosystem models for simulating C and N fluxes
2017
Brilli, Lorenzo | Bechini, Luca | Bindi, Marco | Carozzi, Marco | Cavalli, Daniele | Conant, Richard | Dorich, Cristopher | Doro, Luca | Ehrhardt, Fiona | Farina, Roberta | Ferrise, Roberto | Nuala, Fitton | Francaviglia, Rosa | Grace, Peter | Locola, Ileana | Klumpp, Katja | Léonard, Joël | Martin, Raphaël | Massad, Raia Silvia | Recous, Sylvie | Seddaiu, Giovanna | Sharp, Joanna | Smith, Pete | Smith, Ward N. | Soussana, Jean-François | Bellocchi, Gianni | Department of Agri-Food Production and Environmental Sciences ; Università degli Studi di Firenze = University of Florence (UniFI) | Istituto di Biometeorologia [Firenze] (IBIMET) ; National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR) | Department of Agricultural and Environmental Sciences ; Università degli Studi di Milano = University of Milan (UNIMI) | Department of Agri-Food Production and Environmental Sciences ; Università degli Studi di Firenze = University of Florence (UniFI) | Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS) ; Institut National de la Recherche Agronomique (INRA)-AgroParisTech | Department of Agricultural and Environmental Sciences ; Università degli Studi di Milano = University of Milan (UNIMI) | Natural Resource Ecology Laboratory [Fort Collins] (NREL) ; Colorado State University [Fort Collins] (CSU) | Desertification Research Centre, Department of Agricultural Sciences ; Università degli Studi di Sassari = University of Sassari [Sassari] (UNISS) | 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 = Council for Agricultural Research and Economics (CREA) | Department of Agri-Food Production and Environmental Sciences ; Università degli Studi di Firenze = University of Florence (UniFI) | Institute of Biological and Environmental Sciences ; University of Aberdeen | Queensland University of Technology [Brisbane] (QUT) | Desertification Research Centre, Department of Agricultural Sciences ; Università degli Studi di Sassari = University of Sassari [Sassari] (UNISS) | 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) ; Institut National de la Recherche Agronomique (INRA) | 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 (AAFC) | European Project: 618105
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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