Abstract: Eddy covariance data from four European grassland sites are used to probabilistically invert the CARAIB (CARbon Assimilation In the Biosphere) dynamic vegetation model (DVM) with 10 unknown parameters, using the DREAM((ZS)) (DiffeRential Evolution Adaptive Metropolis) Markov chain Monte Carlo (MCMC) sampler. We focus on comparing model inversions, considering both homoscedastic and heteroscedastic eddy covariance residual errors, with variances either fixed a priori or jointly inferred together with the model parameters. Agreements between measured and simulated data during calibration are comparable with previous studies, with root mean square errors (RMSEs) of simulated daily gross primary productivity (GPP), ecosystem respiration (RECO) and evapotranspiration (ET) ranging from 1.73 to 2.19, 1.04 to 1.56 g C m(-2) day(-1) and 0.50 to 1.28 mm day(-1), respectively. For the calibration period, using a homoscedastic eddy covariance residual error model resulted in a better agreement between measured and modelled data than using a heteroscedastic residual error model. However, a model validation experiment showed that CARAIB models calibrated considering heteroscedastic residual errors perform better. Posterior parameter distributions derived from using a heteroscedastic model of the residuals thus appear to be more robust. This is the case even though the classical linear heteroscedastic error model assumed herein did not fully remove heteroscedasticity of the GPP residuals. Despite the fact that the calibrated model is generally capable of fitting the data within measurement errors, systematic bias in the model simulations are observed. These are likely due to model inadequacies such as shortcomings in the photosynthesis modelling. Besides the residual error treatment, differences between model parameter posterior distributions among the four grassland sites are also investigated. It is shown that the marginal distributions of the specific leaf area and characteristic mortality time parameters can be explained by site-specific ecophysiological characteristics.

Abstract: Integrated conservation and management of agricultural areas affected by the current global warming represents a priority at international level following the implementation of the principles of sustainable agriculture and adaptation measures. Transylvanian Plain (TP), with an area of 395,616 ha is of great agricultural importance for Romania, but with an afforestation degree of only 6.8% and numerous degradation phenomena of farmland, it has the lowest degree of sustainability to climate change. Monitoring of agro-climatic indicators and their evolution in between 2008-2014 and the analysis of the obtained data underlie the technological development of recommendations tailored to current favorable conditions for the main crops. Results obtained show that: the thermal regime of the soils in TP is of mesic type and the hydric regime is ustic; multiannual average of temperature in soil at 10 cm depth is 11.40ºC, respectively at 50 cm depth is 10.24ºC; the average yearly air temperature is 11.17ºC; multiannual average of soil moisture is 0.227 m3/m3; Multiannual average value of precipitation is 466.52 mm. During the studied period, compared with data series available (1961-1990; 1901-2000), clear decrease of the average quantities of rainfall especially during critical periods for crops, and increases in average temperatures for the entire year can be noticed. Between June and August the highest temperature difference were recorded, differences of +3.09°C to +3.65°C. There is an increase phenomenon of drought and heat; determined indicators show that most values, 61.11%, are commensurate with a semiarid climate. Aggression peaks are in February-April, July, and October-November, and for the whole period, in 19.43% of the cases are favorable and very favorable conditions for triggering erosion. Recommended agro-technical measures to limit and counteract the effects of drought, as a climatic phenomenon with major risk to agriculture in TP, refer to: i) use of a biological material resistant to water stress and heat; ii) use of management practices favorable for accumulation of, conservation and the efficient use of water from rainfall; iii) operating a system of conservation agriculture based on soil protection and desertification avoidance.

Abstract: Process-based crop models that synthesise the latest scientific understanding of biophysical processes are currently the primary scientific tools available to assess potential impacts of climate change on crop production. Important obstacles are still present, however, and must be overcome for improving crop modelling application in integrated assessments of risk, of sustainability and of crop-production resilience in the face of climate change (e.g. uncertainty analysis, model integration, etc.). The research networks MACSUR and AGMIP organised the CropM International Symposium and Workshop in Oslo, on 10-12 February 2014, and present this CR Special, discussing the state-of-the-art-as well as future perspectives-of crop modelling applications in climate change risk assessment, including the challenges of integrated assessments for the agricultural sector.