Predicting and mitigating the net greenhouse gas emissions of crop rotations in Western Europe   总被引：1，自引：0，他引：1  

Simon Lehuger  Benoît GabriellePatricia Laville  Matieyendou LamboniBenjamin Loubet  Pierre Cellier 《Agricultural and Forest Meteorology》2011,151(12):1654-1671

Nitrous oxide, carbon dioxide and methane are the main biogenic greenhouse gases (GHGs) contributing to net greenhouse gas balance of agro-ecosystems. Evaluating the impact of agriculture on climate thus requires capacity to predict the net exchanges of these gases in a systemic approach, as related to environmental conditions and crop management. Here, we used experimental data sets from intensively monitored cropping systems in France and Germany to calibrate and evaluate the ability of the biophysical crop model CERES-EGC to simulate GHG exchanges at the plot-scale. The experiments involved major crop types (maize-wheat-barley-rapeseed) on loam and rendzina soils. The model was subsequently extrapolated to predict CO₂ and N₂O fluxes over entire crop rotations. Indirect emissions (IE) arising from the production of agricultural inputs and from use of farm machinery were also added to the final greenhouse gas balance. One experimental site (involving a maize-wheat-barley-mustard rotation on a loamy soil) was a net source of GHG with a net GHG balance of 670 kg CO₂-C eq ha⁻¹ yr⁻¹, of which half were due to IE and half to direct N₂O emissions. The other site (involving a rapeseed-wheat-barley rotation on a rendzina) was a net sink of GHG for −650 kg CO₂-C eq ha⁻¹ yr⁻¹, mainly due to high C returns to soil from crop residues. A selection of mitigation options were tested at one experimental site, of which straw return to soils emerged as the most efficient to reduce the net GHG balance of the crop rotation, with a 35% abatement. Halving the rate of N inputs only allowed a 27% reduction in net GHG balance. Removing the organic fertilizer application led to a substantial loss of C for the entire crop rotation that was not compensated by a significant decrease of N₂O emissions due to a lower N supply in the system. Agro-ecosystem modeling and scenario analysis may therefore contribute to design productive cropping systems with low GHG emissions.  相似文献   

Multivariate global sensitivity analysis for dynamic crop models   总被引：2，自引：0，他引：2  

Matieyendou Lamboni  David Makowski  Simon Lehuger  Benoit Gabrielle  Herv Monod 《Field Crops Research》2009,113(3):312-320

Dynamic crop models are frequently used in ecology, agronomy and environmental sciences for simulating crop and environmental variables at a discrete time step. They often include a large number of parameters whose values are uncertain, and it is often impossible to estimate all these parameters accurately. A common practice consists in selecting a subset of parameters by global sensitivity analysis, estimating the selected parameters from data, and setting the others to some nominal values. For a discrete-time model, global sensitivity analyses can be applied sequentially at each simulation date. In the case of dynamic crop models, simulations are usually computed at a daily time step and the sequential implementation of global sensitivity analysis at each simulation date can result in several hundreds of sensitivity indices, with one index per parameter per simulation date. It is not easy to identify the most important parameters based on such a large number of values. In this paper, an alternative method called multivariate global sensitivity analysis was investigated. More precisely, the purposes of this paper are (i) to compare the sensitivity indices and associated parameter rankings computed by the sequential and the multivariate global sensitivity analyses, (ii) to assess the value of multivariate sensitivity analysis for selecting the model parameters to estimate from data. Sequential and multivariate sensitivity analyses were compared by using two dynamic models: a model simulating wheat biomass named WWDM and a model simulating N₂O gaseous emission in crop fields named CERES-EGC. N₂O measurements collected in several experimental plots were used to evaluate how parameter selection based on multivariate sensitivity analysis can improve the CERES-EGC predictions.The results showed that sequential and multivariate sensitivity analyses provide modellers with different types of information for models which exhibit a high variability of sensitivity index values over time. Conversely, when the parameter influence is quite constant over time, the two methods give more similar results. The results also showed that the estimation of the parameters with the highest sensitivity indices led to a strong reduction of the prediction errors of the model CERES-EGC.  相似文献