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.
2018
Sammendrag
The present work focuses on an assessment of the applicability of groundwater table (GWT) measures in the modelling of soil water retention characteristics (SWRC) using artificial neural network (ANN) methods. Model development, testing, validation and verification were performed using data collected across two decades from soil profiles at full-scale research objects located in Southwest Poland. A positive effect was observed between the initial GWT position data and the accuracy of soil water reserve estimation. On the other hand, no significant effects were observed following the implementation of GWT fluctuation data over the entire growing season. The ANN tests that used data of either soil water content or GWT position gave analogous results. This revealed that the easily obtained data (temperature, precipitation and GWT position) are the most accurate modelling parameters. These outcomes can be used to simplify modelling input data/parameters/variables in the practical implementation of the proposed SWRC modelling variants.
Forfattere
Leif Sundheim Christer Magnusson Arild Sletten Per Hans Micael Wendell Guro Brodal Åshild Ergon Halvor Solheim Anne Marte Tronsmo Trond RafossSammendrag
Det er ikke registrert sammendrag
Forfattere
Rachel Cassidy Philip Jordan Marianne Bechmann Brian Kronvang Katarina Kyllmar Mairead ShoreSammendrag
Achieving an operational compromise between spatial coverage and temporal resolution in national scale river water quality monitoring is a major challenge for regulatory authorities, particularly where chemical concentrations are hydrologically dependent. The efficacy of flow-weighted composite sampling (FWCS) approaches for total phosphorus (TP) sampling (n = 26–52 analysed samples per year), previously applied in monitoring programmes in Norway, Sweden and Denmark, and which account for low to high flow discharges, was assessed by repeated simulated sampling on high resolution TP data. These data were collected in three research catchments in Ireland over the period 2010–13 covering a base-flow index range of 0.38 to 0.69. Comparisons of load estimates were also made with discrete (set time interval) daily and sub-daily sampling approaches (n = 365 to >1200 analysed samples per year). For all years and all sites a proxy of the Norwegian sampling approach, which is based on re-forecasting discharge for each 2-week deployment, proved most stable (median TP load estimates of 87–98%). Danish and Swedish approaches, using long-term flow records to set a flow constant, were only slightly less effective (median load estimates of 64–102% and 80–96%, respectively). Though TP load estimates over repeated iterations were more accurate using the discrete approaches, particularly the 24/7 approach (one sample every 7 h in a 24 bottle sampler - median % load estimates of 93–100%), composite load estimates were more stable, due to the integration of multiple small samples (n = 100–588) over a deployment.
Forfattere
Donghai Wu Philippe Ciais Nicolas Viovy Alan K. Knapp Kevin Wilcox Michael Bahn Melinda D. Smith Sara Vicca Simone Fatichi Jakob Zscheischler Yue He Xiangyi Li Akihito Ito Almuth Arneth Anna Harper Anna Ukkola Athanasios Paschalis Benjamin Poulter Changhui Peng Daniel Ricciuto David Reinthaler Guangsheng Chen Hanqin Tian Helene Genet Jiafu Mao Johannes Ingrisch Julia E.S.M. Nabel Julia Pongratz Lena R. Boysen Markus Kautz Michael Schmitt Patrick Meir Qiuan Zhu Roland Hasibeder Sebastian Sippel Shree R.S. Dangal Stephen Sitch Xiaoying Shi Yingping Wang Yiqi Luo Yongwen Liu Shilong PiaoSammendrag
Field measurements of aboveground net primary productivity (ANPP) in temperate grasslands suggest that both positive and negative asymmetric responses to changes in precipitation (P) may occur. Under normal range of precipitation variability, wet years typically result in ANPP gains being larger than ANPP declines in dry years (positive asymmetry), whereas increases in ANPP are lower in magnitude in extreme wet years compared to reductions during extreme drought (negative asymmetry). Whether the current generation of ecosystem models with a coupled carbon– water system in grasslands are capable of simulating these asymmetric ANPP responses is an unresolved question. In this study, we evaluated the simulated responses of temperate grassland primary productivity to scenarios of altered precipitation with 14 ecosystem models at three sites: Shortgrass steppe (SGS), Konza Prairie (KNZ) and Stubai Valley meadow (STU), spanning a rainfall gradient from dry to moist. We found that (1) the spatial slopes derived from modeled primary productivity and precipitation across sites were steeper than the temporal slopes obtained from interannual variations, which was consistent with empirical data; (2) the asymmetry of the responses of modeled primary productivity under normal inter-annual precipitation variability differed among models, and the mean of the model ensemble suggested a negative asymmetry across the three sites, which was contrary to empirical evidence based on filed observations; (3) the mean sensitivity of modeled productivity to rainfall suggested greater negative response with reduced precipitation than positive response to an increased precipitation under extreme conditions at the three sites; and (4) gross primary productivity (GPP), net primary productivity (NPP), aboveground NPP (ANPP) and belowground NPP (BNPP) all showed concave-down nonlinear responses to altered precipitation in all the models, but with different curvatures and mean values. Our results indicated that most models overestimate the negative drought effects and/or underestimate the positive effects of increased precipitation on primary productivity under normal climate conditions, highlighting the need for improving eco-hydrological processes in those models in the future.
Forfattere
Francisco Javier Ancin Murguzur Aitor Barbero-Lopez Sari Kontunen-Soppela Antti HaapalaSammendrag
Microbial growth on culture media is a commonplace technique to estimate the growth rate and virulence ofmicrobes, assess inhibitory effects of compounds and estimate potential damages of plant pathogens in agri-culture. Growth area measurement of solid cultures is still commonly performed as a manual process that re-quires skilled technicians and substantial time, thus warranting an automated system to reduce the workload andincrease measurement efficiency. A machine learning approach (Support Vector Machines) was developed tofully automate the area measurement process. We developed a functional model that processes images andreturns the microbial area coverage considerably faster than a manual measurement method, with minimal userinput and highly comparable results (R2= 0.88, kappa = 0.88) applicable over large datasets.
Forfattere
Anne B. NilsenSammendrag
Det er ikke registrert sammendrag
Sammendrag
Forprosjektet "Test av kommersiell teknologi for presisjonssprøyting av glyfosat i kornproduksjon" er finansiert av Forskningsmidlene for jordbruk og matindustri (tilsagnsnr. 119059)
Forfattere
Ingrid TengeSammendrag
Det er ikke registrert sammendrag
Forfattere
Bjørn Egil FløSammendrag
Det er ikke registrert sammendrag
Forfattere
Holger LangeSammendrag
Det er ikke registrert sammendrag