Abstract: Conference presentation PDF

Abstract: • Comparison of two methods for soil organic C quantification is presented. • Springer–Klee wet digestion and dry combustion with automated analyser were compared. • Soil samples were collected from three different sites in a Southern Italy area. • Recoveries close to one were observed for whole dataset and for data grouped per site. • The strong agreement between the methods would enable direct comparison of results. Abstract Soil organic carbon (SOC) is the largest carbon pool in the terrestrial biosphere and it is among the most important factors responsible for conservation of soil quality. Automated dry combustion techniques are gradually replacing traditional quantification methods based on wet digestion chemistry. Critical comparison of different methods is fundamental to reevaluate archives of SOC data and accurately assess and model long-term carbon stock variation and should be performed for different soil types and management conditions. Two analytical methods, the Springer–Klee wet digestion and the dry combustion using an automated analyser, were compared for soils typical of a Mediterranean environment in Southern Italy. Soil samples were collected from three sites, at two depths. Soils were fine textured (from clay–loam to clay) with total carbonate ranging from 6.6 to 16.7 g 100 g− 1. SOC content varied from 6.92 to 28.86 g kg− 1 (as average of the two methods), with values and ranges typical of Southern Europe. On average, Springer–Klee method gave slightly higher values and showed greater data variability. This behaviour, in agreement with other studies, can be attributed to the reaction of K2Cr2O7 with other soil constituents and to analytical constraints. Our results suggest high consistency between Springer–Klee and dry combustion techniques and show recoveries close to one both for the whole dataset and for data grouped per experimental site or soil depth. Linear regression equations between the two methods were slightly affected by different soil types (P = 0.0621). The best fitting of the relationship was a linear regression passing through the origin for the whole dataset (Radj2 = 0.965; RPD = 3.41). The strong overall agreement observed between the two methods would enable the direct comparison of new data set with those already existing in Southern Italy for soils with similar characteristics.

Abstract: Working with ensembles of crop models is a recent but important development in crop modeling which promises to lead to better uncertainty estimates for model projections and predictions, better predictions using the ensemble mean or median, and closer collaboration within the modeling community. There are numerous open questions about the best way to create and analyze such ensembles. Much can be learned from the field of climate modeling, given its much longer experience with ensembles. We draw on that experience to identify questions and make propositions that should help make ensemble modeling with crop models more rigorous and informative. The propositions include defining criteria for acceptance of models in a crop MME, exploring criteria for evaluating the degree of relatedness of models in a MME, studying the effect of number of models in the ensemble, development of a statistical model of model sampling, creation of a repository for MME results, studies of possible differential weighting of models in an ensemble, creation of single model ensembles based on sampling from the uncertainty distribution of parameter values or inputs specifically oriented toward uncertainty estimation, the creation of super ensembles that sample more than one source of uncertainty, the analysis of super ensemble results to obtain information on total uncertainty and the separate contributions of different sources of uncertainty and finally further investigation of the use of the multi-model mean or median as a predictor.