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.
2017
Authors
K. Marijanusic M. Manojlovic D. Bogdanovic R. Cabilovski Peder LombnæsAbstract
In order to evaluate the mineral composition of forage crops in respect to dairy cow nutrition 40 soil and corresponding plant (alfalfa, grasses and silage corn) samples were collected from 15 locations in Serbia and analyzed for the concentration of macro- (P, K, and Ca) and microelements (Mn, Cu, Zn, Fe, Co, Se, and Mo). On average, the soils were well provided with the studied elements from the aspect of plant nutrition, but the analyzed fodder crops could not secure suffi cient amounts of Cu, Zn, Se, and Ca for dairy cow nutrition. Principal components analysis was applied in order to determine the connection between the concentrations of macro- and microelements in forage crops and their grouping into components responsible for most of the variability in mineral content. The mineral composition of alfalfa was defi ned by three components (Se, Zn, and Cu) which accounted for the largest part of the established variability. The variability of mineral composition of grasses was defi ned by four components (Zn, K, Se, and P) and that of silage corn by the concentrations of Fe, Mn, and K.
Abstract
Today’s modern precision agriculture applications have a huge demand for data with high spatial and temporal resolution. This leads to the need of unmanned aerial vehicles (UAV) as sensor platforms providing both, easy use and a high area coverage. This study shows the successful development of a prototype hybrid UAV for practical applications in precision agriculture. The UAV consists of an off-the-shelf fixed-wing fuselage, which has been enhanced with multi-rotor functionality. It was programmed to perform pre-defined waypoint missions completely autonomously, including vertical take-off, horizontal flight, and vertical landing. The UAV was tested for its return-to-home (RTH) accuracy, power consumption and general flight performance at different wind speeds. The RTH accuracy was 43.7 cm in average, with a root-mean-square error of 39.9 cm. The power consumption raised with an increase in wind speed. An extrapolation of the analysed power consumption to conditions without wind resulted in an estimated 40 km travel range, when we assumed a 25 % safety margin of remaining battery capacity. This translates to a maximal area coverage of 300 ha for a scenario with 18 m/s airspeed, 50 minutes flight time, 120 m AGL altitude, and a desired 70 % of image side-lap and 85 % forward-lap. The ground sample distance with an in-built RGB camera was 3.5 cm, which we consider sufficient for farm-scale mapping missions for most precision agriculture applications.
Authors
N.P. Havill J. Elkinton J.C. Andersen Snorre Hagen Hannah J. Broadley G.J. Boettner A. CacconeAbstract
The European winter moth, Operophtera brumata, is a non-native pest in the Northeastern USA causing defoliation of forest trees and crops such as apples and blueberries. This species is known to hybridize with O. bruceata, the Bruce spanworm, a native species across North America, although it is not known if there are hybrid generations beyond F1. To study winter moth population genetics and hybridization with Bruce spanworm, we developed two sets of genetic markers, single nucleotide polymorphisms (SNPs) and microsatellites, using genomic approaches. Both types of markers were validated using samples from the two species and their hybrids. We identified 1216 SNPs and 24 variable microsatellite loci. From them we developed a subset of 95 species-diagnostic SNPs and ten microsatellite loci that could be used for hybrid identification. We further validated the ten microsatellite loci by screening field collected samples of both species and putative hybrids. In addition to confirming the presence of F1 hybrids reported in previous studies, we found evidence for multi-generation asymmetric hybridization, as suggested by the occurrence of hybrid backcrosses with the winter month, but not with the Bruce spanworm. Laboratory crosses between winter moth females and Bruce spanworm males resulted in a higher proportion of viable eggs than the reciprocal cross, supporting this pattern. We discuss the possible roles of population demographics, sex chromosome genetic incompatibility, and bacterial symbionts as causes of this asymmetrical hybridization and the utility of the developed markers for future studies.
Abstract
No abstract has been registered
Abstract
Small-area estimation is a subject area of growing importance in forest inventories. Modelling the link between a study variable Y and auxiliary variables X— in pursuit of an improved accuracy in estimators—is typically done at the level of a sampling unit. However, for various reasons, it may only be possible to formulate a linking model at the level of an area of interest (AOI). Area-level models and their potential have rarely been explored in forestry. This study demonstrates, with data (Y = stem volume per ha) from four actual inventories aided by aerial laser scanner data (3 cases) or photogrammetric point clouds (1 case), application of three distinct models representing the currency of area-level modelling. The studied AOIs varied in size from forest management units to forest districts, and municipalities. The variance explained by X declined sharply with the average size of an AOI. In comparison with a direct estimate mean of Y in an AOI, all three models achieved practically important reduction in the relative root-mean-squared error of an AOI mean. In terms of the reduction in mean-squared errors, a model with a spatial location effect was overall most attractive. We recommend the pursuit of a spatial model component in area-level modelling as promising within the context of a forest inventory.
Authors
Jihong Liu Clarke Lisa Paruch Mihaela-Olivia Dobrica Iuliana Caras Catalin Tucureanu Adrian Onu Sonya Ciulean Crina Stavaru Andre van Eerde Hege Særvold Steen Sissel Haugslien Catalina Petrareanu Catalin Lazar Ioan Popescu Ralph Bock Jean Dubuisson Norica Branza-NichitaAbstract
No abstract has been registered
Authors
Jihong Liu Clarke Andre van Eerde Lisa Paruch Inger Heldal Hege Særvold Steen Yanliang Wang Astrid Sivertsen Sissel HaugslienAbstract
No abstract has been registered
Abstract
Forest stands are important units of management. A stand-by-stand estimation of the mean and variance of an attribute of interest (Y) remains a priority in forest enterprise inventories. The advent of powerful and cost effective remotely sensed auxiliary variables (X) correlated with Y means that a census of X in the forest enterprise is increasingly available. In combination with a probability sample of Y, the census affords a modeldependent stand-level inference. It is important, however, that the sampling design affords an estimation of possible stand-effects in the model linking X to Y.We demonstrate, with simulated data, that failing to quantify non-zero stand-effects in the intercept of a linear population-level model can lead to a serious underestimation of the uncertainty in a model-dependent estimate of a stand mean, and by extension a confidence interval with poor coverage.We also provide an approximation to the variance of stand-effects in an intercept for the case when a sampling design does not afford estimation. Furthermore, we propose a method to correct a potential negative bias in an estimate of the variance of stand-effects when a sampling design prescribes few stands with small within-stand sample sizes.
Authors
Svetlana Saarela Johannes Breidenbach Pasi Raumonen Anton Grafström Göran Ståhl Mark J. Ducey Rasmus AstrupAbstract
This study presents an approach for predicting stand-level forest attributes utilizing mobile laser scanning data collected as a nonprobability sample. Firstly, recordings of stem density were made at point locations every 10th metre along a subjectively chosen mobile laser scanning track in a forest stand. Secondly, kriging was applied to predict stem density values for the centre point of all grid cells ina5m×5m lattice across the stand. Thirdly, due to nondetectability issues, a correction term was computed based on distance sampling theory. Lastly, the mean stem density at stand level was predicted as the mean of the point-level predictions multiplied with the correction factor, and the corresponding variance was estimated. Many factors contribute to the uncertainty of the stand-level prediction; in the variance estimator, we accounted for the uncertainties due to kriging prediction and due to estimating a detectability model from the laser scanning data. The results from our new approach were found to correspond fairly well to estimates obtained using field measurements from an independent set of 54 circular sample plots. The predicted number of stems in the stand based on the proposed methodology was 1366 with a 12.9% relative standard error. The corresponding estimate based on the field plots was 1677 with a 7.5% relative standard error.
Authors
Anna Sandak Jakub Sandak Athanasios Dimitriou Ingunn Burud Thomas Kringlebotn Thiis Lone Ross Graham Alan Ormondroyd Dimitrios KraniotisAbstract
No abstract has been registered