Publications
NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.
2021
Authors
Sigridur DalmannsdottirAbstract
No abstract has been registered
Abstract
Current forage production on tile drained peat soil is challenged by low drainage efficiency and large GHG emissions. Alternative methods need to be evaluated to sustain agricultural usage while protecting peat C and N stocks. Peat inversion is a valid method when the peat layer is less than 1.5 m deep and lies on top of a self-draining mineral soil. The peat body is covered by the underlying mineral soil while maintaining connectivity to the self-draining subsoil through tilted mineral soil layers. We studied the effect of inversion of previously tile drained peat with forage production on dry matter yield (DMY), methane (CH4) and nitrous oxide (N2O) emissions and peat degradation. The field experiment was carried out in adjacent fields with inverted and tile drained nutrient poor peat in Western Norway during 2014-2018. At both fields the surface was slightly graded towards open ditches surrounding the field. The thickness of the mineral cover layer of the inverted peat varied between 80-100 cm on top of the graded surface (upper site) and 40-50 cm closer to the ditches (lower site). Coarse silt and fine sand dominated the texture of the cover layer and content of organic matter was very low (0.5 % tot. C). The texture was finer (higher content of silt and clay) at the lower site compared to the upper site. Mean DMY for 4 ley years at the inverted (upper site) and tile drained peat was 12.2 and 10.3 t ha-1 y-1, respectively. Mean methane emissions in tile drained peat were 200, 140, 209 and 55 kg CH4-C ha-1 in 2015, 2016, 2017 and 2018, respectively, whereas the CH4 exchange in inverted peat was small. In inverted peat, we found up to 50 vol% CH4 in the soil air close to the buried peat, which strongly decreased towards the soil surface at both inverted sites. Nitrous oxide emissions in fertilized tile drained peat were 4.3, 9.5, 9.8 and 5.3 kg N2O-N ha-1 in 2015-2018, respectively. In inverted peat (upper site) N2O emissions were 3.6, 3.6, 8.5 and 2.7 kg N2O-N ha-1 these years. In lower site, measured in 2017 and 2018, the emissions were 10.3 and 4.5 kg N2O-N ha-1, respectively for the two years. N2O-emissions were small in unfertilized plots both at tile drained and inverted peat. Depth profiles of N2O in soil air indicated that N2O is produced in the mineral layer and not in the buried peat. Continuously monitored O2 profiles showed O2-concentrations of 0-5 vol% in the top of the buried peat and much higher concentrations (5-20 vol %) in the tile drained peat. Dark chamber measurements in 2018 showed a CO2-flux of 1.43, 1.49 and 2.35 kg ha-1 h-1 CO2-C after 1.st cut and 1.4, 1.25 and 2.01 kg ha-1 h-1 CO2-C after 2.cut in inverted upper site, inverted lower site and tile drained peat, respectively. The larger respiration measured at tile drained peat most probably derives from larger heterotrophic respiration, as the mass of roots was lower in tile drained than in inverted peat. Results from this field experiment suggest that inversion of tile drained peat reduces the CH4 emissions and degradation of the peat. N2O emissions is fertilizer induced in both tile drained and inverted nutrient poor peat, and is determined by soil and weather conditions at the time of fertilization. The large variation in emissions between years can be explained by different weather conditions. 2017 was a wet year and 2018 a very dry year.
Abstract
Process-based grass models (PBGMs) are widely used for predicting grass growth under potential climate change and different management practices. However, accurate predictions using PBGMs heavily rely on field observations for data assimilation. In data-limited areas, performing robust and reliable estimates of grass growth remains a challenge. In this paper, we incorporated satellite-based MODIS data products, including leaf area index, gross primary production and evapotranspiration, as an additional supplement to field observations. Popular data assimilation methods, including Bayesian calibration and the updating method ensemble Kalman filter, were applied to assimilate satellite derived information into the BASic GRAssland model (BASGRA). A range of different combinations of data assimilating methods and data availability were tested across four grassland sites in Norway, Finland and Canada to assess the corresponding accuracy and make recommendations regarding suitable approaches to incorporate MODIS data. The results demonstrated that optimizing the model parameters that are specific for grass species and cultivar should be targeted prior to updating model state variables. The MODIS derived data products were capable of constraining model’s simulations on phenological development and biomass accumulation by parameter optimization with its performance exceeding model outputs driven by default parameters. By integrating even a small number of field measurements into the parameter calibration, the model’s predictive accuracy was further improved - especially at sites with obvious biases in the input MODIS data. Overall, this comparative study has provided flexible solutions with the potential to strengthen the capacity of PBGMs for grass growth estimation in practical applications.
Abstract
No abstract has been registered
Authors
Marit JørgensenAbstract
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Abstract
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Abstract
No abstract has been registered
Authors
Vibeke Lind Angela Dagmar Schwarm Marcello Mele Alice Cappucci Giulia Foggi Özge Sizmaz Eleni Tsiplakou Alberto Stanislao Atzori Joni Van Mullem Nico PeirenAbstract
No abstract has been registered
Authors
Vibeke LindAbstract
No abstract has been registered
Abstract
No abstract has been registered