2021
Abstract
The Norwegian Genetic Resource Centre estimated the inbreeding rate and thereby the effective population size for all of Norway’s cattle breeds at risk, using the method by Gutierrez et al 2008. When the conservation efforts began in 1990, these breeds were very small in number. However, the current population status shows that the breeds have been wisely managed.
Abstract
No abstract has been registered
Abstract
Information about the distribution of a study object (e.g., species or habitat) is essential in face of increasing pressure from land or sea use, and climate change. Distribution models are instrumental for acquiring such information, but also encumbered by uncertainties caused by different sources of error, bias and inaccuracy that need to be dealt with. In this paper we identify the most common sources of uncertainties and link them to different phases in the modeling process. Our aim is to outline the implications of these uncertainties for the reliability of distribution models and to summarize the precautions needed to be taken. We performed a step-by-step assessment of errors, biases and inaccuracies related to the five main steps in a standard distribution modeling process: (1) ecological understanding, assumptions and problem formulation; (2) data collection and preparation; (3) choice of modeling method, model tuning and parameterization; (4) evaluation of models; and, finally, (5) implementation and use. Our synthesis highlights the need to consider the entire distribution modeling process when the reliability and applicability of the models are assessed. A key recommendation is to evaluate the model properly by use of a dataset that is collected independently of the training data. We support initiatives to establish international protocols and open geodatabases for distribution models.
Authors
Hui Tang Kjetil Schanke Aas Eirik Aasmo Finne Inge Althuizen Rosie A. Fisher Hans Tømmervik Ane Victoria Vollsnes Anders Bryn Sonya Rita Geange Sunniva Indrehus Vigdis Vandvik Jarle Werner Bjerke Terje Koren Berntsen Frode StordalAbstract
No abstract has been registered
Abstract
The size and location of agricultural fields that are in active use and the type of use during the growing season are among the vital information that is needed for the careful planning and forecasting of agricultural production at national and regional scales. In areas where such data are not readily available, an independent seasonal monitoring method is needed. Remote sensing is a widely used tool to map land use types, although there are some limitations that can partly be circumvented by using, among others, multiple observations, careful feature selection and appropriate analysis methods. Here, we used Sentinel-2 satellite image time series (SITS) over the land area of Norway to map three agricultural land use classes: cereal crops, fodder crops (grass) and unused areas. The Multilayer Perceptron (MLP) and two variants of the Convolutional Neural Network (CNN), are implemented on SITS data of four different temporal resolutions. These enabled us to compare twelve model-dataset combinations to identify the model-dataset combination that results in the most accurate predictions. The CNN is implemented in the spectral and temporal dimensions instead of the conventional spatial dimension. Rather than using existing deep learning architectures, an autotuning procedure is implemented so that the model hyperparameters are empirically optimized during the training. The results obtained on held-out test data show that up to 94% overall accuracy and 90% Cohen’s Kappa can be obtained when the 2D CNN is applied on the SITS data with a temporal resolution of 7 days. This is closely followed by the 1D CNN on the same dataset. However, the latter performs better than the former in predicting data outside the training set. It is further observed that cereal is predicted with the highest accuracy, followed by grass. Predicting the unused areas has been found to be difficult as there is no distinct surface condition that is common for all unused areas.
Abstract
Aim Many thematic land cover maps, such as maps of vegetation types, are based on field inventories. Studies show inconsistencies among field workers in such maps, explained by inter-observer variation in classification and/or spatial delineation of polygons. In this study, we have tested a new method to assess the accuracy of these two components independently. Location Four study sites dominated by different ecosystems in southeast Norway. Methods We have used a vegetation-based land cover classification system adapted to a map scale of 1:5,000. First, a consensus map, a map that can be considered an approximation of a flawless map, was established. Secondly, the consensus map was adapted to test the accuracy of classification and polygon delineation independently. We used 10 field workers to generate a consensus map, and 14 new field workers (in pairs) to test the accuracy (n = 7). Results The results show that the accuracy of polygon delineation is lower than that of land cover classification. This is in contrast with previous studies, but previous research designs have not enabled a separation of the two accuracy components. Conclusion We recommend strengthening the training and harmonization of field workers in general, and increasing the emphasis on polygon delineation.
Abstract
Abandonment of agricultural land is a process described from different regions of many industrialized countries. Given the current focus on land use, land use change and food security, it appears highly relevant to develop improved tools to identify and monitor the dynamics of agricultural land abandonment. In particular, the temporal aspect of abandonment needs to be assessed and discussed. In this study, we used the detailed information available through the Norwegian subsidy claim database and analyzed the history of use of unique land parcels through a fourteen-year period. We developed and tested five different statistics identifying these land parcels, their temporal dynamics and the extent of occurrence. What became apparent was that a large number of land parcels existing in the database as agricultural land were taken out of production, but then entered into production again at a later stage. We believe that this approach to describe the temporal dynamics of land abandonment, including how it can be measured and mapped, may contribute to the understanding of the dynamics in land abandonment, and thus also contribute to an improved understanding of the food production system.
2020
Authors
François Lefèvre Ricardo Alía Kjersti Bakkebø Fjellstad Lars Graudal Silvio Daniele Oggioni Mari Rusanen Giovanni Giuseppe Vendramin Michele BozzanoAbstract
No abstract has been registered
Authors
Kjersti Bakkebø FjellstadAbstract
No abstract has been registered
Authors
Cathrine Brekke Linn Fenne Groeneveld Theodorus Meuwissen Nina Svartedal Steffen Weigend Peer BergAbstract
The aim of this study was to evaluate genetic diversity within and between lines at the Norwegian live poultry gene bank as well as assessing the conservation value in an international context. Eight lines including the national breed, Jærhøns, were genotyped with the 600K Affymetrix® Axiom® Chicken Genotyping Array. The white egg layers were generally more inbred than the brown layers. Comparative analyses were carried out with 72 international populations of different origins. The lines that were last bred for commercial production in Norway, Norbrid, are clearly separated from the rest of the international set and more closely related to the current commercial lines. The brown egg layer Norbrid 7 has the highest relative contribution to genetic diversity. The Norwegian genebank lines are of conservation value in a national and international perspective, as they all add genetic diversity to the global set.