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.
2008
Sammendrag
Arealer med høstkorn som jordarbeides før tilsåing er mer erosjonsutsatt ved avrenningsepisoder om høsten og gjennom vinterperioden. Redusert jordarbeiding som lett høstharving eller direktesåing reduserte jordtapene fra høstkornareal betydelig.
Forfattere
Marianne BechmannSammendrag
Fosfor er begrensende næringsstoff for algevekst i de fleste jordbrukspåvirkede innsjøer. Fosfortap fra jordbruksarealer er en viktig kilde for fosfor, men ikke alle arealer har like stor risiko for fosfortap. Fosforindeksen er utviklet for å definere forskjeller i risiko for P tap og for å øke forståelsen for hvilke arealer og hvilke tiltak som bør prioriteres.
Forfattere
Tore Krogstad Anne Falk ØgaardSammendrag
P-AL er høyt i dypere lag i leirjord, mens innholdet av vannløselig P er tilnærmet null. Det er klare indikasjoner på at det skjer en transport av P fra øvre sjikt ned til grøftedybde og at økt gjødsling øker transporten av P nedover i profilene.
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
We investigated whether the stand age affects the life span of tree and understory fine roots (<1mm) in three Norway spruce (Picea abies) stands: 30, 60 and 120-yr-old. In each stand 9 minirhizotrons were installed and images were collected once in a month throughout the growing season during the three years. Norway spruce fine roots in the 30-yr old stand had a life span 401 ± 27 and 341 ± 68 days, and understory 409 ± 162 and 349 ± 142 days, estimated by using the Kaplan Meier survival analysis (KM) and Weibull distribution, respectively...
Forfattere
Tore SkrøppaSammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag
Forfattere
Lukas Gudmundsson Holger LangeSammendrag
Many time series analysis methods depend on equally spaced observations with no data point missing. If this condition is met, powerful techniques are available that identify temporal structures such as trends, periodic phenomena or nonlinear dynamics. Unfortunately, most observations of natural systems, in particular over longer periods of time such as decades, are prone to sampling errors leading to missing points in the observations. Singular System Analysis (SSA) is a powerful tool to extract the dynamics contained in time series at arbitrary temporal scales. In its original formulation, however, SSA relies as well on data without missing values. Recently several extensions to SSA have been proposed which are designed to fill the gaps, exploiting the dynamics contained in the sampled parts of the series to estimate the structure of the signal at the position of missing values. SSA consists of two steps: Decomposition and reconstruction. For the decomposition the time series under investigation is embedded into a trajectory matrix and decomposed with singular value decomposition. The reconstruction (of selected components) of the time series employs the left and right singular values to obtain additive components of the time series. In the original variant of SSA both steps are dependent on gap free data sets. In order to evaluate the power of SSA for time series with missing values we simulate 1000 series of different processes - ARMA(2,3) and red noise contaminated sine waves. Several gap–schemes (continuous, periodic, and uniformly distributed) are used to create time series with up to 50% (artificially) missing values. SSA is applied on all surrogate series. The decomposition as well as the reconstruction is compared systematically to the gap free benchmark. In addition we evaluate the ability of SSA to capture periodic phenomena in the presence of missing values and whether periodical gaps lead to the identification of spurious periods. We demonstrate that SSA successfully reproduces the signal part of time series (i.e. components with large eigenvalues) for up to 30% missing values. For less significant components with higher rank numbers, the presence of gaps is increasingly deleterious. A number of distributed smaller gaps, a situation most likely to occur in observations, spoils the analysis to a much lesser degree than a single large gap. Thus, these new variants of SSA substantially enlarge the set of observational time series amenable to the analysis, and allows for obtaining precise estimates of the signal at the position of missing data points.
Sammendrag
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Sammendrag
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