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.

2020

Abstract

Land-sea riverine carbon transfer (LSRCT) is one of the key processes in the global carbon cycle. Although natural factors (e.g. climate, soil) influence LSRCT, human water management strategies have also been identified as a critical component. However, few systematic approaches quantifying the contribution of coupled natural and anthropogenic factors on LSRCT have been published. This study presents an integrated framework coupling hydrological modeling, field sampling and stable isotope analysis for the quantitative assessment of the impact of human water management practices (e.g. irrigation, dam construction) on LSRCT under different hydrological conditions. By applying this approach to the case study of the Nandu River, China, we find that carbon (C) concentrations originating from different land-uses (e.g. forest, cropland) are relatively stable and outlet C variations are mainly dominated by controlled runoff volumes rather than by input C concentrations. These results indicate that human water management practices are responsible for a reduction of ∼60% of riverine C at seasonal timescales, with an even greater reduction during drought conditions. Annual C discharges have been significantly reduced (e.g. 77 ± 5% in 2015 and 39 ± 11% in 2016) due to changes in human water extraction coupled with climate variation. In addition, isotope analysis also shows that C fluxes influenced by human activities (e.g. agriculture, aquaculture) could contribute the dominant particulate organic carbon under typical climatic conditions, as well as drought conditions. This research demonstrates the substantial effect that human water management practices have on the seasonal and annual fluxes of LSRCT, especially in such small basins. This work also shows the applicability of this integrated approach, using multiple tools to quantify the contribution of coupled anthropogenic and natural factors on LSRCT, and the general framework is believed to be feasible with limited modifications for larger basins in future research.

Abstract

Cultivated organic soils account for ~7% of Norway’s agricultural land area, and they are estimated to be a significant source of greenhouse gas (GHG) emissions. The project ‘Climate smart management practices on Norwegian organic soils’ (MYR), commissioned by the Research Council of Norway (decision no. 281109), aims to evaluate GHG (e.g. carbon dioxide, methane and nitrous oxide) emissions and impacts on biomass productivity from three land use types (cultivated, abandoned and restored) on organic soils. At the cultivated sites, impacts of drainage depth and management intensity will be measured. We established experimental sites in Norway covering a broad range of climate and management regimes, which will produce observational data in high spatiotemporal resolution during 2019-2022. Using state-of-the-art modelling techniques, MYR aims to predict the potential GHG mitigation under different scenarios (e.g. different water table depth, management practices and climate pattern). Four models (BASGRA, DNDC, Coup and ECOSSE) will be further developed according to the physical/chemical properties of peat soil and then used independently in simulating biogeochemical processes and biomass dynamics in the different land uses. Robust parameterization schemes for each model to improve the predictive accuracy will be derived from a new dataset collected from multiple experimental sites in the Nordic region. Thereafter, the models will be used in the regional simulation to present the spatial heterogeneity in large scale. Eventually, a multi-model ensemble prediction will be carried out to provide scenario analyses by 2030 and 2050. By integrating experimental results and modelling, the project aims at generating useful information for recommendations on environment-friendly use of Norwegian peatlands.

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Abstract

The main objective of this paper is to present the new model BASGRA_N, to show how it was parameterized for grass swards in Scandinavia, and to evaluate its performance in predicting above-ground biomass, crude protein, cell wall content and dry matter digestibility. The model was developed to allow simulation of: (1) the impact of N-supply on the plants and their environment, (2) the dynamics of greenhouse gas emissions from grasslands, (3) the dynamics of cell-wall content and digestibility of leaves and stems, which could not be simulated with its predecessor, the BASGRA-model. To calibrate and test the model, we used field experimental data. One dataset included observations of biomass (DM) and crude protein content (CP) under different N fertilizer regimes from five sites in central and southern Sweden. The other dataset included observations of DM, and sward components as well as CP, cell wall content (NDF) and DM digestibility as affected by harvesting regime from one site in southwestern Norway. The total number of experiments was nine, of which three were used for model testing. When BASGRA_N was run with the maximum a-posteriori (MAP) parameter vector from the Bayesian calibration for the Swedish test sites, DM and CP were both simulated to an overall Pearson correlation coefficient (Rb) of minimum 0.58, Willmott's index of agreement (d) of minimum 0.69 and normalized root mean squared error (NRMSE) of maximum 0.30. Corresponding metrics for Norwegian test sites were 0.93, 0.96 and 0.27 for DM and > 0.73, > 0.61, < 0.18 for DM digestibility, NDF and CP content, respectively. We conclude that BASGRA_N can be used to simulate yield and CP responses to N with satisfactory precision, while maintaining key features from its predecessor. The results also suggest that DM digestibility and NDF can be simulated satisfactorily, which is supported by results from a recent model comparison study. Further testing of the model is needed for a few variables for which we currently do not have enough data, notably leaching and emission of N-containing compounds. Further work will include application of the model to investigate greenhouse gas mitigation options, and evaluation against independent data for the conditions for which it will be applied.

2019

Abstract

In this paper, we present a novel method for obstacle avoidance designed for a nonholonomic mobile robot. The method relies on light detection and ranging (LiDAR) readings, which are mapped into a polar coordinate system. Obstacles are taken into consideration when they are within a predefined radius from the robot. A central part of the approach is a new Heading Weight Function (HWF), in which the beams within the aperture angle of the LiDAR are virtually weighted in order to generate the best trajectory candidate for the robot. The HWF is designed to find a solution also in the case of a local-minima situation. The function is coupled with the robot’s controller in order to provide both linear and angular velocities. We tested the method both by simulations in a digital environment with a range of different static obstacles, and in a real, experimental environment including static and dynamic obstacles. The results showed that when utilizing the novel HWF, the robot was able to navigate safely toward the target while avoiding all obstacles included in the tests. Our findings thus show that it is possible for a robot to navigate safely in a populated environment using this method, and that sufficient efficiency in navigation may be obtained without basing the method on a global planner. This is particularly promising for navigation challenges occurring in unknown environments where models of the world cannot be obtained.

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Abstract

Nitrous oxide (N2O) emissions from cultivated soils correlate positively with the amount of N-fertilizer applied, but a large proportion of the annual N2O emission occurs outside the cropping season, potentially blurring this correlation. We measured the effect of split-N application (total N addition varying from 0 to 220 kg N ha−1) on N2O emissions in a spring wheat plot trial in SE Norway from the time of split-N application until harvest, and during the following winter and spring thaw period. N2O emissions were largest in the two highest N-levels, whereas yield-scaled emission (N2O intensity) was highest in the 0 N treatment. Nitrogen yield increased by 23% when adding 80 kg N ha−1 compared to adding 40 kg N ha−1 as split application, while corresponding N2O emissions were reduced by 16%. No differences in measured emissions between the N-fertilization levels were observed during the winter period or during spring thaw. Measurements of soil air composition below the snow pack revealed that N2O production continued throughout winter as the concentration in the soil air increased from 0.37 to 30.0 µL L−1 N2O over the 3 months period with continuous snow cover. However, only 7–28% of the N2O emitted during spring thaw could be ascribed to accumulated N2O, indicating de novo production of N2O in the thawing soil. The direct effect of split-N fertilizer rate on N2O emissions in sub-boreal cereal cropping was limited to the first 15–21 days after N-addition.

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Abstract

China is continually seeking to improve river water quality. Implemented in 1996, the total pollutant load control system (TPLCS) is a regulatory strategy to reduce total pollutant loads, under which a Pollutant Discharge Permit (PDP) program tracks and regulates nutrient inputs from point source polluters. While this has been promising, the input-response relationship between discharge permits and water quality targets is largely unclear – especially in China's large and complex river basins. In response, this study involved a quantitative analysis method to combine the water quality targets of the 12th Five-Year Plan (2011–2015) with allocated PDPs in the Nenjiang River Basin, China. We demonstrated our approach by applying the Soil and Water Assessment Tool (SWAT) to the Nenjiang River Basin for hydrological and water quality simulation. Ammonia nitrogen (NH3-N) was used as the primary water quality indicator. Modelling indicated that only one control section in the wider river basin did not achieve the water quality target, suggesting that the TPLCS is largely effective. The framework should be applied in other basins to study the effectiveness of PDP policies, advise further updates to the TPLCS, and ultimately aim to achieve freshwater quality targets nationally.

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Abstract

The nitrogen cycle has been radically changed by human activities1 . China consumes nearly one third of the world’s nitrogen fertilizers. The excessive application of fertilizers2,3 and increased nitrogen discharge from livestock, domestic and industrial sources have resulted in pervasive water pollution. Quantifying a nitrogen ‘boundary’4 in heterogeneous environments is important for the effective management of local water quality. Here we use a combination of water-quality observations and simulated nitrogen discharge from agricultural and other sources to estimate spatial patterns of nitrogen discharge into water bodies across China from 1955 to 2014. We find that the critical surface-water quality standard (1.0 milligrams of nitrogen per litre) was being exceeded in most provinces by the mid-1980s, and that current rates of anthropogenic nitrogen discharge (14.5 ± 3.1 megatonnes of nitrogen per year) to fresh water are about 2.7 times the estimated ‘safe’ nitrogen discharge threshold (5.2 ± 0.7 megatonnes of nitrogen per year). Current efforts to reduce pollution through wastewater treatment and by improving cropland nitrogen management can partially remedy this situation. Domestic wastewater treatment has helped to reduce net discharge by 0.7 ± 0.1 megatonnes in 2014, but at high monetary and energy costs. Improved cropland nitrogen management could remove another 2.3 ± 0.3 megatonnes of nitrogen per year—about 25 per cent of the excess discharge to fresh water. Successfully restoring a clean water environment in China will further require transformational changes to boost the national nutrient recycling rate from its current average of 36 per cent to about 87 per cent, which is a level typical of traditional Chinese agriculture. Although ambitious, such a high level of nitrogen recycling is technologically achievable at an estimated capital cost of approximately 100 billion US dollars and operating costs of 18–29 billion US dollars per year, and could provide co-benefits such as recycled wastewater for crop irrigation and improved environmental quality and ecosystem services.