Abstract: Simulation of the biogeochemical cycles of extensively and intensively managed grasslands and croplands are of particular interest due to the strong connection between ecosystem production, animal husbandry and food security. In the frame of MACSUR LiveM activities, we conducted a series of „blind tests” (i.e. uncalibrated model simulations with previously optimized model) on differently managed grasslands within Europe and Israel. We used the latest version of Biome-BGC MuSo model, the modified version of the widely used biogeochemical Biome-BGC model. Biome-BGC MuSo contains structural improvements, development of management modules, and the extension of the model to simulate herbaceouos ecosystem carbon and water cycles more faithfully. The studied ecosystems were meadows and pastures located in a variety of climate zones from the Atlantic sector to Central Europe, including Mediterranean sites. Managements were intensive and extensive grazing or mowing with or without different kind of fertilizers. Under similar options we simulated ecosystem variables, e.g. Gross Primary Production (GPP) and Net Ecosystem Exchange (NEE). Our experiences show that different sites have different sensitivity to the parameters (maximum root depth, soil parameters, etc.), but overall the model provided realistic fluxes. Experiences gained during the blind tests led us to further improve the model. Biome-BGC MuSo is available as a standalone model in personal computers, but also through virtual laboratory environment and Biome-BGC Projects database (http://ecos.okologia.mta.hu/bbgcdb) developed within the BioVeL project (http://www.biovel.eu). Scientific workflow management, web service and desktop grid technology can support model optimization in the so-called „calibrated runs” within MACSUR.

Abstract: When evaluating the performances of simulation models, the perception of the quality of the outputs may depend on the statistics used to compare simulated and observed data. In order to have a comprehensive understanding of model performance, the use of a variety of metrics is generally advocated. However, since they may be correlated, the use of two or more metrics may convey the same information, leading to redundancy. This study intends to investigate the interrelationship between evaluation metrics, with the aim of identifying the most useful set of indicators, for assessing simulation performance. Our focus is on agro-ecological modelling. Twenty-three performance indicators were selected to compare simulated and observed data of four agronomic and meteorological variables: above-ground biomass, leaf area index, hourly air relative humidity and daily solar radiation. Indicators were calculated on large data sets, collected to effectively apply correlation analysis techniques. For each variable, the interrelationship between each pair of indicators was evaluated, by computing the Spearman’s rank correlation coefficient. A definition of “stable correlation” was proposed, based on the test of heterogeneity, allowing to assess whether two or more correlation coefficients are equal. An optimal subset of indicators was identified, striking a balance between number of indicators, amount of provided information and information redundancy. They are: Index of Agreement, Squared Bias, Root Mean Squared Relative Error, Pattern Index, Persistence Model Efficiency and Spearman’s Correlation Coefficient. The present study was carried out in the context of CropM-LiveM cross-cutting activities of MACSUR knowledge hub.

Abstract: Croplands and grasslands are agricultural systems that contribute to land–atmosphere exchanges of carbon (C). We evaluated and compared gross primary production (GPP), ecosystem respiration (RECO), net ecosystem exchange (NEE) of CO2, and two derived outputs – C use efficiency (CUE=-NEE/GPP) and C emission intensity (IntC= -NEE/Offtake [grazed or harvested biomass]). The outputs came from 23 models (11 crop-specific, eight grassland-specific, and four models covering both systems) at three cropping sites over several rotations with spring and winter cereals, soybean and rapeseed in Canada, France and India, and two temperate permanent grasslands in France and the United Kingdom. The models were run independently over multi-year simulation periods in five stages (S), either blind with no calibration and initialization data (S1), using historical management and climate for initialization (S2), calibrated against plant data (S3), plant and soil data together (S4), or with the addition of C and N fluxes (S5). Here, we provide a framework to address methodological uncertainties and contextualize results. Most of the models overestimated or underestimated the C fluxes observed during the growing seasons (or the whole years for grasslands), with substantial differences between models. For each simulated variable, changes in the multi-model median (MMM) from S1 to S5 was used as a descriptor of the ensemble performance. Overall, the greatest improvements (MMM approaching the mean of observations) were achieved at S3 or higher calibration stages. For instance, grassland GPP MMM was equal to 1632 g C m−2 yr-1 (S5) while the observed mean was equal to 1763 m-2 yr-1 (average for two sites). Nash-Sutcliffe modelling efficiency coefficients indicated that MMM outperformed individual models in 92.3 % of cases. Our study suggests a cautious use of large-scale, multi-model ensembles to estimate C fluxes in agricultural sites if some site-specific plant and soil observations are available for model calibration. The further development of crop/grassland ensemble modelling will hinge upon the interpretation of results in light of the way models represent the processes underlying C fluxes in complex agricultural systems (grassland and crop rotations including fallow periods).