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
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.
Authors
Liang Wang Alba Dieguez-Alonso Maria Nicte Polanco Olsen Alice Budai Daniel Rasse Øyvind SkreibergAbstract
No abstract has been registered
Authors
Liang Wang Alba Dieguez-Alonso Maria Nicte Polanco Olsen Alice Budai Daniel Rasse Ondřej Mašek Øyvind SkreibergAbstract
No abstract has been registered
Authors
Divina Gracia P. RodriguezAbstract
No abstract has been registered
Authors
Rasmus AstrupAbstract
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Abstract
Background The Norwegian forest resource map (SR16) maps forest attributes by combining national forest inventory (NFI), airborne laser scanning (ALS) and other remotely sensed data. While the ALS data were acquired over a time interval of 10 years using various sensors and settings, the NFI data are continuously collected. Aims of this study were to analyze the effects of stratification on models linking remotely sensed and field data, and assess the accuracy overall and at the ALS project level. Materials and methods The model dataset consisted of 9203 NFI field plots and data from 367 ALS projects, covering 17 Mha and 2/3 of the productive forest in Norway. Mixed-effects regression models were used to account for differences among ALS projects. Two types of stratification were used to fit models: 1) stratification by the three main tree species groups spruce, pine and deciduous resulted in species-specific models that can utilize a satellite-based species map for improving predictions, and 2) stratification by species and maturity class resulted in stratum-specific models that can be used in forest management inventories where each stand regularly is visually stratified accordingly. Stratified models were compared to general models that were fit without stratifying the data. Results The species-specific models had relative root-mean-squared errors (RMSEs) of 35%, 34%, 31%, and 12% for volume, aboveground biomass, basal area, and Lorey’s height, respectively. These RMSEs were 2–7 percentage points (pp) smaller than those of general models. When validating using predicted species, RMSEs were 0–4 pp. smaller than those of general models. Models stratified by main species and maturity class further improved RMSEs compared to species-specific models by up to 1.8 pp. Using mixed-effects models over ordinary least squares models resulted in a decrease of RMSE for timber volume of 1.0–3.9 pp., depending on the main tree species. RMSEs for timber volume ranged between 19%–59% among individual ALS projects. Conclusions The stratification by tree species considerably improved models of forest structural variables. A further stratification by maturity class improved these models only moderately. The accuracy of the models utilized in SR16 were within the range reported from other ALS-based forest inventories, but local variations are apparent.
Authors
Pia Heltoft ThomsenAbstract
No abstract has been registered
Abstract
No abstract has been registered