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Persson, T. (2015). Determining the variability in optimal sowing date of spring cereals in South Eastern Norway (Vol. 5).
Abstract: Spring cereals are important agricultural crops in Northern Europe. The short growing season in this region necessitates early sowing. The earliest possible date is often determined by the soil water content, which usually decreases during and after snowmelt at rates varying with the weather and the soil characteristics. Tillage and sowing operations on soils with too high a water content can lead to soil compaction, increased soil erosion, and losses of nutrients and soil organic matter. Rainfall intensity also affects crop emergence, through its potentially negative effects on surface capping. The objective of this study was to determine the earliest possible sowing date of spring cereals for representative soil and climate scenarios in southeastern Norway. Criteria were set for pre-sowing tillage operations and sowing, based on the water content in differ soil layers and the incidence of rainfall. To determine the day of the year when these criteria were first met, the soil water content during the spring was simulated with the soil module in DSSAT v4.5. These simulations were performed for contrasting soil types and climate scenarios representing the period 1961-90 and 2046-65 respectively. For each combination of soil and climate, one hundred simulations with individual weather data were performed. The results provide information about the timing and variability of the optimal planting date for the current and projected climate in South Eastern Norway. No Label
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Persson, T., Höglind M, Gustavsson AM, Halling M, Jauhianen L, Niemeläinen O, Torvaldsson G, Virkajärvi P. (2013). Evaluation of the BASGRA timothy model under Nordic conditions..
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Persson, T., & Höglind, M. (2013). Assessment of harvest security of timothy under climate change condition using a set of simple criteria..
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Persson, T., Höglind, M., Gustavsson, A. - M., Halling, M., Jauhiainen, L., Niemeläinen, O., et al. (2014). Evaluation of the LINGRA timothy model under Nordic conditions. Field Crops Research, 161, 87–97.
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.
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Persson, T., & Kværnø, S. (2017). Impact of projected mid-21st century climate and soil extrapolation on simulated spring wheat grain yield in Southeastern Norway. J. Agric. Sci., 155(03), 361–377.
Abstract: The effects of soil variability on regional crop yield under projected climate change are largely unknown. In Southeastern Norway, increased temperature and precipitation are projected for the mid-21st century. Crop simulation models in combination with scaling techniques can be used to determine the regional pattern of crop yield. In the present paper, the CSM-CROPSIM-CERES-Wheat model was applied to simulate regional spring wheat yield for Akershus and Østfold counties in Southeastern Norway. Prior to the simulations, parameters in the CSM-CROPSIM-CERES-Wheat model were calibrated for the spring wheat cvars Zebra, Demonstrant and Bjarne, using cultivar trial data from Southeastern Norway and site-specific weather and soil information. Weather input data for regional yield simulations represented the climate in 1961–1990 and projections of the climate in 2046–2065. The latter were based on four Global Climate Models and greenhouse gas emission scenario A1B in the IPCC 4th Assessment Report. Data on regional soil particle size distribution, water-holding characteristics and organic matter data were obtained from a database. To determine the simulated grain yield sensitivity to soil input, the number of soil profiles used to describe the soilscape in the region varied from 76 to 16, 5 and 1. The soils in the different descriptions were selected by arranging them into groups according to similarities in physical characteristics and taking the soil in each group occupying the largest area in the region to represent other soils in that group. The simulated grain yields were higher under all four projected future climate scenarios than the corresponding average yields in the baseline conditions. On average across the region, there were mostly non-significant differences in grain yield between the soil extrapolations for all cultivars and climate projections. However, for sub-regions grain yield varied by up to 20% between soil extrapolations. These results indicate how projected climate change could affect spring wheat yield given the assumed simulated conditions for a region with similar climate and soil conditions to many other cereal production regions in Northern Europe. The results also provide useful information about how soil input data could be handled in regional crop yield determinations under these conditions.
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