Publikasjoner
NIBIOs ansatte publiserer flere hundre vitenskapelige artikler og forskningsrapporter hvert år. Her finner du referanser og lenker til publikasjoner og andre forsknings- og formidlingsaktiviteter. Samlingen oppdateres løpende med både nytt og historisk materiale. For mer informasjon om NIBIOs publikasjoner, besøk NIBIOs bibliotek.
2020
Sammendrag
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Forfattere
Frode Sundnes Marianne Karlsson Froukje Maria Platjouw Nicholas Clarke Øyvind Kaste Salar ValiniaSammendrag
While the role of forestry in mitigating climate change is increasingly subject to political commitment, other areas, such as water protection, may be at risk. In this study, we ask whether surface waters are sufficiently safeguarded in relation to the 2015 launch of a series of measures to intensify forest management for mitigation of climate change in Norway. First, we assess how impacts on water are accounted for in existing regulations for sustainable forestry. Secondly, we provide an overview of the impacts of forestry on water quality relevant to three support schemes: afforestation on new areas, increased stocking density in existing forests, and forest fertilisation. Lastly, we assess the uncertainties that exist with regard to surface waters in the implementation of these measures. We find that the safeguards in place are adequate to protect water resources at the point of initiation, but there is a large degree of uncertainty as to the long-term effect of these mitigation measures.
Sammendrag
Soil respiration is an important ecosystem process that releases carbon dioxide into the atmosphere. While soil respiration can be measured continuously at high temporal resolutions, gaps in the dataset are inevitable, leading to uncertainties in carbon budget estimations. Therefore, robust methods used to fill the gaps are needed. The process-based non-linear least squares (NLS) regression is the most widely used gap-filling method, which utilizes the established relationship between the soil respiration and temperature. In addition to NLS, we also implemented three other methods based on: 1) artificial neural networks (ANN), driven by temperature and moisture measurements, 2) singular spectrum analysis (SSA), relying only on the time series itself, and 3) the expectation-maximization (EM) approach, referencing to parallel flux measurements in the spatial vicinity. Six soil respiration datasets (2017–2019) from two boreal forests were used for benchmarking. Artificial gaps were randomly introduced into the datasets and then filled using the four methods. The time-series-based methods, SSA and EM, showed higher accuracies than NLS and ANN in small gaps (<1 day). In larger gaps (15 days), the performance was similar among NLS, SSA and EM; however, ANN showed large errors in gaps that coincided with precipitation events. Compared to the observations, gap-filled data by SSA showed similar degree of variances and those filled by EM were associated with similar first-order autocorrelation coefficients. In contrast, data filled by both NLS and ANN exhibited lower variance and higher autocorrelation than the observations. For estimations of the annual soil respiration budget, NLS, SSA and EM resulted in errors between −3.7% and 5.8% given the budgets ranged from 463 to 1152 g C m−2 year−1, while ANN exhibited larger errors from −11.3 to 16.0%. Our study highlights the two time-series-based methods which showed great potential in gap-filling carbon flux data, especially when environmental variables are unavailable.
Sammendrag
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Forfattere
Jiangsan Zhao Dmitry Kechasov Boris Rewald Gernot Bodner Michel Verheul Nicholas Clarke Jihong Liu ClarkeSammendrag
Hyperspectral imaging has many applications. However, the high device costs and low hyperspectral image resolution are major obstacles limiting its wider application in agriculture and other fields. Hyperspectral image reconstruction from a single RGB image fully addresses these two problems. The robust HSCNN-R model with mean relative absolute error loss function and evaluated by the Mean Relative Absolute Error metric was selected through permutation tests from models with combinations of loss functions and evaluation metrics, using tomato as a case study. Hyperspectral images were subsequently reconstructed from single tomato RGB images taken by a smartphone camera. The reconstructed images were used to predict tomato quality properties such as the ratio of soluble solid content to total titratable acidity and normalized anthocyanin index. Both predicted parameters showed very good agreement with corresponding “ground truth” values and high significance in an F test. This study showed the suitability of hyperspectral image reconstruction from single RGB images for fruit quality control purposes, underpinning the potential of the technology—recovering hyperspectral properties in high resolution—for real-world, real time monitoring applications in agriculture any beyond.
Sammendrag
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Sammendrag
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Sammendrag
Perennial versus short term (<3 years) grass vegetation cover is likely to have considerable differences in root density and thus carbon (C) inputs to soil. Carbon inputs are important to maintain soil organic carbon (SOC) and may even increase it. In Norway and Scandinavia, the SOC content in soil is often higher than in other parts of Europe, due to the cold climate and high precipitation (i.e. slower turnover rates for soil organic matter) and a dominance of animal production systems with a large amount of grassland. Here we aimed to evaluate differences in SOC content, down to 60 cm depth, of a long-term grassland (without ploughing for decades) and a short-term grassland (frequently renewed by ploughing) under contrasting climate, soil and management conditions. Quantification of SOC was carried out on three long-term experimental sites on an extended latitude gradient in West and North Norway. The samples were taken from 4 depth increments (0-5, 5-20, 20-40 and 40-60 cm) in treatments that have not been ploughed for at least 43 years, and in treatments that were ploughed every third year until 2011. Preliminary results suggest that there is no significant difference in SOC storage down to 60 cm between long-term and short-term grasslands.
Sammendrag
Several scientific groups have concluded that the use of biochar as an on-farm management tool for carbon sequestration should be further investigated. Review articles also pinpoint the use of biochar to reduce greenhouse gas emissions from the entire agricultural production, and this should be studied using whole-chain models. Biochar is added to animal diets with the main purpose of enhancing animal health. There are indications that biochar fed to ruminants may reduce enteric methane emission. Twenty-four ewe lambs were fed one of two diets, a control diet (no biochar) and a biochar diet (1.4% biochar). There were no differences in dry matter intake and average daily growth rate between animals. An expected reduction in enteric methane emissions from animals fed the biochar diet was not detected. We conclude that the effect on enteric methane emissions may depend on structure and properties of the biochar offered. We suggest further research on biomass and pyrolysis of biochar to accommodate several properties as a feed additive for farm animals.
Sammendrag
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