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.
2017
Sammendrag
Seed biology is important for emergence in the field and for future weed infestations. This chapter focuses on seed biology, germination, dormancy and efforts in predicting weed emergence from seeds from a European perspective. It presents a brief overview of population dynamics in time and space, the factors influencing the dynamics and how population dynamics can be modelled. Emergence from the seed-bank starts with germination, pre-emergence growth and finally emergence. In addition to seeds, vegetatively propagated material is briefly mentioned. Dormancy influences under what conditions that germination can occur and regulates timing of germination. Population dynamics are important for understanding the whole system and are often based on the life-cycle of weeds: seed-bank, seedlings, adult plants, seed production and dispersal. Challenges in modelling emergence and population dynamics are large, due to differences between and within populations of species, variability in species response and there being many weed species in the same field with contrasting characteristics.
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
Fields experiments were conducted during two growing seasons (2010–2011 and 2012–2013) at three seeding dates to identify stink bug (Hemiptera: Pentatomidae) species and to determine their seasonal population density fluctuation and damage caused to three common bean (Phaseolus vulgaris L.) cultivars “Ica Pijao,” “Cubacueto 25–9,” and “Chévere.” Stink bug species observed were Nezara viridula (L.), Piezodorus guildinii (Westwood), Chinavia rolstoni (Rolston), Chinavia marginatum (Palisot de Beauvois), and Euschistus sp. The most prevalent species was N. viridula in both seasons. The largest number of stink bugs was found in beans seeded at the first (mid September) and third (beginning of January) seeding dates. Population peaked at BBCH 75 with 1.75, 0.43, and 1.25 stink bugs/10 plants in 2010–2011 and with 2.67, 0.45, and 1.3 stink bugs/10 plants in 2012–2013 in the fields seeded the first, second, and third seeding dates, respectively. The lowest numbers of stink bugs were found in beans seeded at the second (mid November) seeding date. A significant negative correlation between relative humidity and number of stink bugs was found in 2010–2011, and a similar tendency was observed in 2012–2013. The highest seed and pod damage levels occurred in cv. “Chévere” and the lowest in cv. “ICA Pijao” during both seasons. Results suggest that cv. “ICA Pijao” and the second (mid November) seeding date is the best choice to reduce stink bug damage.
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag
Forfattere
Ranjana Pathak A Sundaram Lance Cadle-Davidson Knut Asbjørn Solhaug Arne Stensvand Hans Ragnard Gislerød Aruppillai SuthaparanSammendrag
Det er ikke registrert sammendrag
Forfattere
Anne-Grete Roer Hjelkrem Mats Höglind Marcel van Oijen Jürgen Schellberg Thomas Gaiser Frank EwertSammendrag
Proper parameterisation and quantification of model uncertainty are two essential tasks in improvement and assessment of model performance. Bayesian calibration is a method that combines both tasks by quantifying probability distributions for model parameters and outputs. However, the method is rarely applied to complex models because of its high computational demand when used with high-dimensional parameter spaces. We therefore combined Bayesian calibration with sensitivity analysis, using the screening method by Morris (1991), in order to reduce model complexity by fixing parameters to which model output was only weakly sensitive to a nominal value. Further, the robustness of the model with respect to reduction in the number of free parameters were examined according to model discrepancy and output uncertainty. The process-based grassland model BASGRA was examined in the present study on two sites in Norway and in Germany, for two grass species (Phleum pratense and Arrhenatherum elatius). According to this study, a reduction of free model parameters from 66 to 45 was possible. The sensitivity analysis showed that the parameters to be fixed were consistent across sites (which differed in climate and soil conditions), while model calibration had to be performed separately for each combination of site and species. The output uncertainty decreased slightly, but still covered the field observations of aboveground biomass. Considering the training data, the mean square error for both the 66 and the 45 parameter model was dominated by errors in timing (phase shift), whereas no general pattern was found in errors when using the validation data. Stronger model reduction should be avoided, as the error term increased and output uncertainty was underestimated.
Forfattere
Therese With BergeSammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag