Sensitivity analysis for models of greenhouse gas emissions at farm level. Case study of N(2)O emissions simulated by the CERES-EGC model.

Modelling complex systems such as farms often requires quantification of a large number of input factors. Sensitivity analyses are useful to reduce the number of input factors that are required to be measured or estimated accurately. Three methods of sensitivity analysis (the Morris method, the rank regression and correlation method and the Extended Fourier Amplitude Sensitivity Test method) were compared in the case of the CERES-EGC model applied to crops of a dairy farm. The qualitative Morris method provided a screening of the input factors. The two other quantitative methods were used to investigate more thoroughly the effects of input factors on output variables. Despite differences in terms of concepts and assumptions, the three methods provided similar results. Among the 44 factors under study, N(2)O emissions were mainly sensitive to the fraction of N(2)O emitted during denitrification, the maximum rate of nitrification, the soil bulk density and the cropland area.

[1]  Benoit Gabrielle,et al.  Predicting in situ soil N2O emission using NOE algorithm and soil database , 2005 .

[2]  H. Steinfeld,et al.  Livestock's long shadow: environmental issues and options. , 2006 .

[3]  A. Saltelli,et al.  A quantitative model-independent method for global sensitivity analysis of model output , 1999 .

[4]  A. Saltelli,et al.  Sensitivity Anaysis as an Ingredient of Modeling , 2000 .

[5]  Stefano Tarantola,et al.  Variance‐Based Methods , 2008 .

[6]  Matieyendou Lamboni,et al.  Multivariate global sensitivity analysis for dynamic crop models , 2009 .

[7]  C. Fortuin,et al.  Study of the sensitivity of coupled reaction systems to uncertainties in rate coefficients. I Theory , 1973 .

[8]  Zong-ci Zhao,et al.  Climate change 2001, the scientific basis, chap. 8: model evaluation. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change IPCC , 2001 .

[9]  J. Soussana,et al.  Mitigating the greenhouse gas balance of ruminant production systems through carbon sequestration in grasslands. , 2010, Animal : an international journal of animal bioscience.

[10]  Benoit Gabrielle,et al.  Process‐based modeling of nitrous oxide emissions from wheat‐cropped soils at the subregional scale , 2006 .

[11]  William N. Venables,et al.  Modern Applied Statistics with S , 2010 .

[12]  Andrea Saltelli,et al.  An effective screening design for sensitivity analysis of large models , 2007, Environ. Model. Softw..

[13]  David Makowski,et al.  Bayesian calibration of the nitrous oxide emission module of an agro-ecosystem model , 2009 .

[14]  J. Soussana,et al.  A review of farm level modelling approaches for mitigating greenhouse gas emissions from ruminant livestock systems , 2007 .

[15]  G. Velthof,et al.  Technical and policy aspects of strategies to decrease greenhouse gas emissions from agriculture , 2001, Nutrient Cycling in Agroecosystems.

[16]  A. Saltelli,et al.  The role of sensitivity analysis in ecological modelling , 2007 .

[17]  Stefano Tarantola,et al.  Sensitivity Analysis as an Ingredient of Modeling , 2000 .

[18]  Max D. Morris,et al.  Factorial sampling plans for preliminary computational experiments , 1991 .

[19]  Evaluation of greenhouse gas emissions and design of mitigation options: a whole farm approach based on farm management data and mechanistic models , 2008 .