Abstract: Cattle feeding in Northern Europe is based on grass silage, but grass growth is highly dependent on weather conditions. If ensuring sufficient silage availability in every situation is prioritised, the lowest expected yield level determines the cultivated area in farmers’ decision-making. One way to manage the variation in grass yield is to increase grass production and silage storage capacity so that they exceed the annual consumption at the farm. The cost of risk management in the current and the projected future climate was calculated taking into account grassland yield and yield variability for three study areas under current and mid-21st century climate conditions. The dataset on simulated future grass yields used as input for the risk management calculations were taken from a previously published simulation study. Strategies investigated included using up to 60% more silage grass area than needed in a year with average grass yields, and storing silage for up to 6 months more than consumed in a year (buffer storage). According to the results, utilising an excess silage grass area of 20% and a silage buffer storage capacity of 6 months were the most economic ways of managing drought risk in both the baseline climate and the projected climate of 2046-2065. It was found that the silage yield risk due to drought is likely to decrease in all studied locations, but the drought risk and costs implied still remain significant. (C) 2015 IAgrE. Published by Elsevier Ltd. All rights reserved.

Abstract: This study evaluates the effect of planting three cover crops (CCs) (barley, Hordeum vulgare L.; vetch, Vicia villosa L.; rape, Brassica napus L.) on the direct emission of N(2)O, CO(2) and CH(4) in the intercrop period and the impact of incorporating these CCs on the emission of greenhouse gas (GHG) from the forthcoming irrigated maize (Zea mays L.) crop. Vetch and barley were the CCs with the highest N(2)O and CO(2) losses (75 and 47% increase compared with the control, respectively) in the fallow period. In all cases, fluxes of N(2)O were increased through N fertilization and the incorporation of barley and rape residues (40 and 17% increase, respectively). The combination of a high C:N ratio with the addition of an external source of mineral N increased the fluxes of N(2)O compared with -Ba and -Rp. The direct emissions of N(2)O were lower than expected for a fertilized crop (0.10% emission factor, EF) compared with other studies and the IPCC EF. These results are believed to be associated with a decreased NO(3)(-) pool due to highly denitrifying conditions and increased drainage. The fluxes of CO(2) were in the range of other fertilized crops (i.e., 1118.71-1736.52 kg CO(2)-Cha(-1)). The incorporation of CC residues enhanced soil respiration in the range of 21-28% for barley and rape although no significant differences between treatments were detected. Negative CH(4) fluxes were measured and displayed an overall sink effect for all incorporated CC (mean values of -0.12 and -0.10 kg CH(4)-Cha(-1) for plots with and without incorporated CCs, respectively).

Abstract: Simulation models are frequently applied to determine the production potential of forage grasses under various scenarios, including climate change. Thorough calibrations and evaluations of forage grass models can help improve their applicability. This study evaluated the ability of the Light Interception and Utilization Simulator-GRAss (LINGRA) model to predict biomass yield of timothy (Phleum pratense L. cv. Grindstad) in the Nordic countries. Variety trial data for the first and second year after establishment were obtained for seven locations: Jokioinen, Finland (60 degrees 48 ‘ N; 23 degrees 29 ‘ E), Maaninka, Finland (63 degrees 09 ‘ N; 27 degrees 18 ‘ E), Korpa, Iceland (64 degrees 09 ‘ N; 21 degrees 45 ‘ W), Srheim, Norway (58 degrees 41 ‘ N; 5 degrees 39 ‘ E), Lillerud, Sweden (59 degrees 24’ N; 13 degrees 16 ‘ E), Ostersund, Sweden (63 degrees 15 ‘ N; 14 degrees 34 ‘ E) and Ulna Sweden (63 degrees 49 ‘ N; 20 degrees 13 ‘ E) from 1992 to 2012. Two calibrations of the LINGRA model were carried out using Bayesian techniques. In the first of these (SRrheim calibration), data on biomass yield and underlying variables obtained from independent field trials at Srheim were used. In the second (Nordic calibration), biomass data from the other locations were used as well. The model was validated against the remaining set of biomass yields from all locations not included in the Nordic calibration. The observed total seasonal yield the first and second year after establishment was 913 and 991 g DM m(-2) respectively on average across the locations. The corresponding average simulated yield after the Srheim calibration was 1044 (root mean square error (RMSE) 258) and 1112 g DM m(-2) (RMSE 312), respectively. After the Nordic calibration, the simulated average total seasonal yield was 863 (RMSE 242) the first year and 927 g DM m(-2) (RMSE 271) the second year after establishment. The differences between the observed and simulated first cut yield followed the same patterns, whereas the prediction accuracy for second cut yield did not differ substantially between the calibration approaches.Using the parameter set from the Nordic region decreased the model predictability at Srheim compared with only using model parameters derived from this location. These results show that using biomass data from several locations, instead of only one specific location, in the calibration of the LINGRA model improved the overall prediction accuracy of first cut dry matter yield and total seasonal dry matter yield across an environmentally heterogeneous region. To further analyse the usefulness of including multi-site data in forage grass model calibrations, other forage grass models could be evaluated against the same dataset.