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Kauer, K., Tein, B., & Loit, E. (2014). The long-term trends in soil carbon stock and crop productivity depending on management in Estonia. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: The dynamics of soil organic carbon (SOC) content and crop productivity were studied on three still continuing field experiments situated at the experimental station of the Estonian University of Life Sciences in Tartu, Estonia. The first trial was established in 1964. The effect of mineral fertilizers and farmyard manure applied to barley and sward with different species composition on soil organic carbon content was studied. The second trial with 3-crop rotation (potato – spring wheat – spring barley) was established in 1989. Experimental factors were organic (without amendment, solid cattle manure and alternative organic fertilisers) and mineral fertilisers (0, 40, 80, 120 and 160 kg N ha-1). The third field experiment with 5-crop rotation experiment was established in 2008. Five different crops were following each other – barley undersown with red clover, red clover, winter wheat, pea and potato. Experimental factors were organic (catch crops as green manures, catch crops as green manures combined with composted cattle manure) and conventional farming systems. The conventional farming systems differed in the amounts of mineral fertilizers used: 0, 50, 100 and 150 kg N ha-1. The first goal of this research was to quantify plant C inputs to the soil in Estonian arable lands and the net primary production using crop-specific allometric relationships. Secondly, the impact of the different management scenarios on the changes in soil C stock was evaluated using plant C input data. The preliminary results of these data analysis will be presented.
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Hoffmann, H., Zhao, G., Van Bussel, L., Enders, A., Specka, X., Sosa, C., et al. (2014). Effects of climate input data aggregation on modelling regional crop yields. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: Crop models can be sensitive to climate input data aggregation and this response may differ among models. This should be considered when applying field-scale models for assessment of climate change impacts on larger spatial scales or when coupling models across scales. In order to evaluate these effects systematically, an ensemble of ten crop models was run with climate input data on different spatial aggregations ranging from 1, 10, 25, 50 and 100 km horizontal resolution for the state of North Rhine-Westphalia, Germany. Models were minimally calibrated to typical sowing and harvest dates, and crop yields observed in the region, subsequently simulating potential, water-limited and nitrogen-limited production of winter wheat and silage maize for 1982-2011. Outputs were analysed for 19 variables (yield, evapotranspiration, soil organic carbon, etc.). In this study the sensitivity of the individual models and the model ensemble in response to input data aggregation is assessed for crop yield. Results show that the mean yield of the region calculated from climate time series of 1 km horizontal resolution changes only little when using climate input data of higher aggregation levels for most models. However, yield frequency distributions change with aggregation, resembling observed data better with increasing resolution. With few exceptions, these results apply to the two crops and three production situations (potential, water-, nitrogen-limited) and across models including the model ensemble, regardless of differences among models in simulated yield levels and spatial yield patterns. Results of this study improve the confidence of using crop models at varying scales.
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Haas, E., Klatt, S., Kiese, R., Santa Barbara Ruiz, I., & Kraus, D. (2014). Parameter-induced uncertainty quantification of a regional N2O and NO3 inventory using the biogeochemical model LandscapeDNDC. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: In this study we quantify regional parameter-induced model uncertainty on nitrous oxide (N2O) emissions and nitrate (NO3) leaching from arable soils of Saxony (Germany) using the biogeochemical model LandscapeDNDC. For this we calculate a regional inventory using a joint parameter distribution for key parameters describing microbial C and N turnover processes as obtained by a Bayesian calibration study. We representatively sampled 400 different parameter vectors from the discrete joint parameter distribution comprising approximately 400,000 parameter combinations and used these to calculate 400 individual realizations of the regional inventory. The spatial domain (represented by 4042 polygons) is set up with spatially explicit soil and climate information and a region-typical 3-year crop rotation consisting of winter wheat, rape- seed, and winter barley. Average N2O emission from arable soils in the state of Saxony across all 400 realizations was 1.43 ± 1.25 [kg N / ha] with a median value of 1.05 [kg N / ha]. Using the default IPCC emission factor approach (Tier 1) for direct emissions reveal a higher average N2O emission of 1.51 [kg N / ha] due to fertilizer use. In the regional uncertainty quantification the 20% likelihood range for N2O emissions is 0.79 – 1.37 [kg N / ha] (50% likelihood: 0.46 – 2.05 [kg N / ha]; 90% likelihood: 0.11 – 4.03 [kg N / ha]). Respective quantities were calculated for nitrate leaching.
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Grosz, B., & Dechow, R. (2014). Comparison of measured and modelled soil organic carbon for a northern European long-term experiment site. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: Soil organic carbon is a key variable with regard to soil fertility influencing yield and yield security of agricultural crop production by regulating water budget and nutrient cycling. Those services might become even more relevant with respect to climate change. The sensitivity of crop yields on soil organic carbon content is influenced by site-specific conditions. To assess future vulnerability of yield security with respect to soil organic carbon contents in European croplands soil-crop models must consider the interaction of SOC and crop growth. Long term experiments that include treatments which lead to variable soil organic carbon contents can provide information on those relationships. Because the effect of soil fertility functions supported by SOC depends on a range of natural and anthropogenic factors we used various long term experiments in Sweden and Germany to evaluate the model CENTURY4.6. Thereafter we examined the impact of SOC on crop yields on site level by scenario runs modifying initial SOC levels and weather conditions. Preliminary results show differences in the modeled and observed soil organic carbon values for a range of observed long term experiments. The difference between modelled and measured of SOC stocks is up to 30% after 56 years. Overall, The use of the default values and setting were not appropriate to derive acceptable results, so the adjustment of some model parameter are required.
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Gallo, A., Mereu, V., & Spano, D. (2014). Projected climate change impact on wheat and maize in Italy. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
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.
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