Abstract: Agroecosystems are highly sensitive to climate change. To predict and describe the processes, interactions and feedbacks in the plant-soil-system a model accounting for both compartments at an appropriate level of complexity is required.To describe the processes of crop development, crop growth, water flux, heat transport, and carbon cycling three process models were coupled and adjusted to each other: the one-dimensional soil water, heat and CO2 transport model SOILCO2, the carbon turnover model RothC, and the plant growth model SUCROS. Thereby, the main focus was on the full description of the CO2 flux into the atmosphere via plant and soil processes and finally on simulating the net ecosystem exchange. Additionally, the model was modified to work at the temporal resolution between 0.5 and 24 hours.For a first model evaluation a winter wheat data set obtained within the TERENO Rur catchment (North Rhine-Westphalia, Germany) during 2009 was used. For model initialisation soil carbon fractions were available. Plant specific parameters and soil properties were taken from literature. Measured soil water contents, soil temperatures, crop measurements, autotrophic, and heterotrophic chamber-based respiration measurements were used for validation and calibration.The coupled agroecosystem model AgroC described the crop development and heat transport well. Minor adjustments had to be made for carbon cycling, and to adapt the model to site specific conditions the soil hydraulic coefficients for soil water transport had to be determined by inverse modelling.

Abstract: Agriculture is unique among economic sectors in the nature of impacts from climate change. The production activity that transforms inputs into agricultural outputs involves direct use of weather inputs (temperature, solar radiation available to the plant, and precipitation). Previous studies of the impacts of climate change on agriculture have reported substantial differences in outcomes such as prices, production, and trade arising from differences in model inputs and model specification. This article presents climate change results and underlying determinants from a model comparison exercise with 10 of the leading global economic models that include significant representation of agriculture. By harmonizing key drivers that include climate change effects, differences in model outcomes were reduced. The particular choice of climate change drivers for this comparison activity results in large and negative productivity effects. All models respond with higher prices. Producer behavior differs by model with some emphasizing area response and others yield response. Demand response is least important. The differences reflect both differences in model specification and perspectives on the future. The results from this study highlight the need to more fully compare the deep model parameters, to generate a call for a combination of econometric and validation studies to narrow the degree of uncertainty and variability in these parameters and to move to Monte Carlo type simulations to better map the contours of economic uncertainty.

Abstract: Agriculture is one of the most important sectors for global economy. Its high vulnerability to climate conditions cause a serious concern for the consequence determined by the incoming climate changes. The increase in temperature and decrease in rainfall, projected for the next decades in the Mediterranean Basin, may cause a significant impact on crop development and production. In this contest, the assessment of the climate change impacts on crop growth and yield is necessary in order to identify the crops and areas more vulnerable and suggest adaptation strategies to cope with climate change. The use of crop simulation models, such as those implemented in DSSAT-CSM (Decision Support System for Agrotechnology Transfer – Cropping System Model) software, version 4.5., is the most common approach for the assessment of climate change impacts on crop development and yields. These models are often used at field scale. However, recent studies have been carried out at both regional and continental scale. In this work, CSM-CERES-Wheat and CSM-CERES-Maize crop models, parameterized at Italian scale for different varieties of durum wheat, common wheat and maize, were applied to assess climate change impacts on crop phenology and productivity. Dynamically downscaled climate data, using by the Regional Climate Model COSMO-CLM, and RCP 4.5 and 8.5 scenarios were used for impact assessment. Moreover, some adaptation strategies were evaluated. Results, analyzed at regional level, will be discussed.