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- Nilsson, Mats, et al.
(författare)
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A nationwide forest attribute map of Sweden predicted using airborne laser scanning data and field data from the National Forest Inventory
- 2017
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Ingår i: Remote Sensing of Environment. - : Elsevier BV. - 0034-4257 .- 1879-0704. ; 194, s. 447-454
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Tidskriftsartikel (refereegranskat)abstract
- The National Mapping Agency in Sweden has conducted an airborne laser scanning (ALS) campaign covering almost the entire country for the purpose of creating a new national Digital Elevation Model (DEM). The ALS data were collected between 2009 and 2015 using Leica, Optech, Riegi, and Trimble scanners and have a point density of 0.5-1.0 pulses/m(2). A high resolution national raster database (12.5 m x 12.5 m cell size) with forest variables was produced by combining the ALS data with field data from the Swedish National Forest Inventory (NFI). Approximately 11500 NFI plots (10 meter radius) located on productive forest land, inventoried between 2009 and 2013, were used to create linear regression models relating selected forest variables, or transformations of the variables, to metrics derived from the ALS data. The resulting stand level relative RMSEs for predictions of stem volume, basal area, basal-area weighted mean tree height, and basal-area weighted mean stem diameter were in the ranges of 17.2-22.0%, 13.9-18.2%, 5.4-9.5%, and 8.7-13.1%, respectively. It was concluded that the predictions had an accuracy that were at least as good as data typically used in forest management planning. Above ground tree biomass was also included in the national raster database but not validated on a stand -level. An important part of the project was to make the raster database available to private forest owners, forest associations, forest companies, authorities, researchers, and the general public. Thus, all predicted forest variables can be viewed and downloaded free of charge at the Swedish Forest Agency's homepage (http://www. skogsstyrelsen.se/skogligagrunddata). (C) 2016 Elsevier Inc. All rights reserved.
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- Tedersoo, Leho, et al.
(författare)
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Global diversity and geography of soil fungi
- 2014
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Ingår i: Science. - : American Association for the Advancement of Science (AAAS). - 0036-8075 .- 1095-9203. ; 346:6213, s. artikel nr 1256688-
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Tidskriftsartikel (refereegranskat)abstract
- Fungi play major roles in ecosystem processes, but the determinants of fungal diversity and biogeographic patterns remain poorly understood. Using DNA metabarcoding data from hundreds of globally distributed soil samples, we demonstrate that fungal richness is decoupled from plant diversity. The plant-to-fungus richness ratio declines exponentially toward the poles. Climatic factors, followed by edaphic and spatial variables, constitute the best predictors of fungal richness and community composition at the global scale. Fungi show similar latitudinal diversity gradients to other organisms, with several notable exceptions. These findings advance our understanding of global fungal diversity patterns and permit integration of fungi into a general macroecological framework.
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- Elfving, Karin, et al.
(författare)
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Serologic and Molecular Prevalence of Rickettsia helvetica and Anaplasma phagocytophilum in Wild Cervids and Domestic Mammals in the Central Parts of Sweden
- 2015
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Ingår i: Vector Borne and Zoonotic Diseases. - : Mary Ann Liebert Inc. - 1530-3667 .- 1557-7759. ; 15:9, s. 529-534
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Tidskriftsartikel (refereegranskat)abstract
- Both Rickettsia helvetica and Anaplasma phagocytophilum are common in Ixodes ricinus ticks in Sweden. Knowledge is limited regarding different animal species' competence to act as reservoirs for these organism. For this reason, blood samples were collected from wild cervids (roe deer, moose) and domestic mammals (horse, cat, dog) in central Sweden, and sera were tested using immunofluorescence assay to detect antibodies against spotted fever rickettsiae using Rickettsia helvetica as antigen. Sera with a titer >= 1:64 were considered as positive, and 23.1% (104/450) of the animals scored positive. The prevalence of seropositivity was 21.5% (23/107) in roe deer, 23.3% (21/90) in moose, 36.5% (23/63) in horses, 22.1% (19/90) in cats, and 17.0% (17/100) in dogs. PCR analysis of 113 spleen samples from moose and sheep from the corresponding areas were all negative for rickettsial DNA. In roe deer, 85% (91/107) also tested seropositive for A. phagocytophilum with a titer cutoff of 1:128. The findings indicate that the surveyed animal species are commonly exposed to rickettsiae and roe deer also to A. phagocytophilum.
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- Priyashantha, Hasitha, et al.
(författare)
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Composition and properties of bovine milk: A study from dairy farms in northern Sweden; Part I. Effect of dairy farming system
- 2021
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Ingår i: Journal of Dairy Science. - : Elsevier. - 0022-0302 .- 1525-3198. ; 104:8, s. 8582-8594
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Tidskriftsartikel (refereegranskat)abstract
- This study was part of a larger project that aimed to understand the causes for increasing variation in cheese ripening in a cheese-producing region in northern Sweden. The influence of different on-farm factors on raw milk composition and properties was investigated and is described in this paper, whereas the monthly variation in the milk quality traits during 1 yr is described in our companion paper. The dairy farming systems on a total of 42 dairy farms were characterized through a questionnaire and farm visits. Milk from farm tanks was sampled monthly over 1 yr and analyzed for quality attributes important for cheese making. On applying principal component analyses to evaluate the variation in on-farm factors, different types of farms were distinguished. Farms with loose housing and automatic milking system (AMS) or milking parlor had a higher number of lactating cows, and predominantly Swedish Holstein (SH) breed. Farms associated with tiestalls had a lower number of lactating cows and breeds other than SH. Applying principal component analyses to study the variation in composition and properties of tank milk samples from farms revealed a tendency for the formation of 2 clusters: milk from farms with AMS or a milking parlor, and milk from farms with tiestall milking. The interaction between the milking system, housing system, and breed probably contributed to this grouping. Other factors that were used in the characterization of the farming systems only showed a minor influence on raw milk quality. Despite the interaction, milk from tiestall farms with various cow breeds had higher concentrations (g/100 g of milk) of fat (4.74) and protein (3.63), and lower lactose concentrations (4.67) than milk from farms with predominantly SH cows and AMS (4.32, 3.47, and 4.74 g/100 g of milk, respectively) or a milking parlor (4.47, 3.54, and 4.79 g/100 g of milk, respectively). Higher somatic cell count (195 × 103/mL) and lower free fatty acid concentration (0.75 mmol/100 g of fat) were observed in milk from farms with AMS than in milk from tiestall systems (150 × 103/mL and 0.83 mmol/100 g of fat, respectively). Type of farm influenced milk gel strength, with milk from farms with predominantly SH cows showing the lowest gel strength (65.0 Pa), but not a longer rennet coagulation time. Effects of dairy farming system (e.g., dominant breed, milking system, housing, and herd size) on milk quality attributes indicate a need for further studies to evaluate the in-depth effects of farm-related factors on milk quality attributes.
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| 7. |
- Sun, Li, et al.
(författare)
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Milking system and premilking routines have strong effect on the microbial community in bulk tank milk
- 2022
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Ingår i: Journal of Dairy Science. - : Elsevier. - 0022-0302 .- 1525-3198. ; 105:1, s. 123-139
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Tidskriftsartikel (refereegranskat)abstract
- In this study, we investigated the variation in the microbial community present in bulk tank milk samples and the potential effect of different farm management factors. Bulk tank milk samples were collected repeatedly over one year from 42 farms located in northern Sweden. Total and thermoresistant bacteria counts and 16S rRNA gene-based amplicon sequencing were used to characterize microbial community composition. The microbial community was in general heterogeneous both within and between different farms and the community composition in the bulk tank milk was commonly dominated by Pseudomonas, Acinetobacter, Streptococcus, unclassified Peptostreptococcaceae, and Staphylococcus. Principal component analysis including farm factor variables and microbial taxa data revealed that the microbial community in milk was affected by type of milking system. Milk from farms using an automatic (robot) milking system (AMS) and loose housing showed different microbial community composition compared with milk from tiestall farms. A discriminant analysis model revealed that this difference was dependent on several microbial taxa. Among farms using an automatic milking system, there were further differences in the microbial community composition depending on the brand of the milking robot used. On tiestall farms, routines for teat preparation and cleaning of the milking equipment affected the microbial community composition in milk. Total bacteria count (TBC) in milk differed between the farm types, and TBC were higher on AMS than tiestall farms (log 4.05 vs. log 3.79 TBC/mL for AMS and tiestalls, respectively). Among tiestall farms, milk from farms using a chemical agent in connection to teat preparation and a more frequent use of acid to clean the milking equipment had lower TBC in milk, than milk from farms using water for teat preparation and a less frequent use of acid to clean the milking equipment (log 3.68 vs. 4.02 TBC/mL). There were no significant differences in the number of thermoresistant bacteria between farm types. The evaluated factors explained only a small proportion of total variation in the microbiota data, however, despite this, the study highlights the effect of routines associated with teat preparation and cleaning of the milking equipment on raw milk microbiota, irrespective of type of milking system used.
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| 9. |
- Nilsson, Mats, et al.
(författare)
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Computer classification of General Habitat Categories by combining LiDAR and SPOT data
- 2010
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- General Habitat Categories (GHC) is a classification scheme developed in BioHab1, 2 and a central concept in EBONE3. A characteristic of GHC is plant height, which can be derived using Light Detection And Ranging (LiDAR) data. Computer classification of GHCs might be improved by combining spectral information in optical satellite data with LiDAR. The aim of this pilot study was to investigate to which degree airborne LiDAR improves SPOT data based classification of a selection of GHCs in a forest area in southern Sweden. Lat. 58° 30’ N Long 13° 40’ E. Managed forest with Scots pine (Pinus Sylvestris), Norway spruce (Picea Abies) and birch (Betula spp). A SPOT 5 HRG XS scene. Airborne LiDAR data with an average point density of 26 returns/m2. Photo interpretation of GHCs, 585 sample plots, in aerial DMC images. Combining LiDAR and SPOT data shows promise, considering the restrictions to this study. In a similar study, using the same dataset for classifying CORINE land cover types, overall accuracy increased from 67.1% to 77.6% when adding LiDAR data4. This means that there is potential, though the methods need improvement and further tests should include a larger test area providing adequate amounts of sample plots per GHC.
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