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.
2010
Abstract
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Authors
Solveig HaukelandAbstract
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Authors
Cecilie Mejdell Britt I.F. Henriksen Berit HansenAbstract
The welfare assessment protocol ANIPLAN Calf is developed in the project "Minimising medicine use in organic dairy herds through animal health and welfare planning" (ANIPLAN, 2007 - 2010), by its Norwegian partners; the National Veterinary Institute and Bioforsk (Norwegian Institute for Agricultural and Environmental Research). The project is funded by the CORE Organic funding body network within the context of the European Research Area. The protocol is meant to be used for health and welfare assessment in connection to welfare planning on organic dairy farms. It will be a tool for the farmer to reveal important target areas to improve the calves" welfare. The protocol is developed for advice on Norwegian dairy farms, but can easily be adjusted for other country conditions and even for conventional farms. Detailed guidelines will be developed (for Norwegian conditions) as a help to less experienced assessors/advisors.
Authors
David Bredström Petrus Jönsson Mikael RönnqvistAbstract
A cost-efficient use of harvesting resources is important in the forest industry. The main planning is carried out in an annual resource plan that is continuously revised. The harvesting operations are divided into harvesting and forwarding. The harvesting operation fells trees and puts them in piles in the harvest areas. The forwarding operation collects piles and moves them to storage locations adjacent to forest roads. These operations are conducted by machines (harvesters, forwarders and harwarders), and these are operated by crews living in cities/villages that are within some maximum distance from the harvest areas. Machines, harvest teams and harvest areas have different characteristics and properties and it is difficult to find the best possible match throughout the year. The aim of the planning is to find an annual plan with the lowest possible cost. The total cost is based on three parts: production cost, traveling cost and moving cost. The production cost is the cost for the harvesting and forwarding. The traveling cost is the cost for driving back and forwards (daily) from the home base to the harvest area and the moving cost is associated with moving the machines and equipment between harvest areas. The Forest Research Institute of Sweden (Skogforsk), together with a number of Swedish forest companies, has developed a decision support platform for the planning. One important element of this platform is that it should find high-quality plans within short computational times. One central element is an optimization model that integrates the assignment of machines to harvest areas and schedules the harvest areas during the year for each machine. The problem is complex and we propose a two-phase solution method where, first, we solve the assignment problem and, second, the scheduling. In order to be able to control the scheduling in phase 1 as well, we have introduced an extra cost component that controls the geographical distribution of harvest areas for each machine in phase 1. We have tested the solution approach on a case study from one of the larger Swedish forest companies. This case study involves 46 machines and 968 harvest areas representing a log volume of 1.33 million cubic meters. We describe some numerical results and experience from the development and tests.
Authors
Anne-Grete Roer HjelkremAbstract
Abstract The thesis is about quantification of uncertainties in complex models. Models are built to describe, explain or predict a real world outcome. It is well known that models are related with uncertainty, and that uncertainties are related to how close the simulation is to the real world outcome. Still, uncertainties are rarely quantified in dynamic models. We have focused on parameter uncertainty and output uncertainty derived from the parameters. Uncertainty originated from the empirical data is integrated into the posterior parameter distributions through the likelihood functions. Additionally, uncertainty related to the representativeness of the collected data to the population has been focused. The Bayesian statistical framework, with the Markov chain Monte Carlo algorithm random walk Metropolis was used for model calibration in the four papers. The algorithm was found simple in idea and implementation into the computer program Matlab, but challenges emerged when the method was used at complex models. In this work these challenges have been pursued together with searching for efficiency improvements in order to make as few model evaluations as possible. Paper I: explores the challenges emerging when applying Bayesian calibration to a complex deterministic dynamic model of snow depth. How prior information and new data affect the calibration process, the parameter estimates and model outputs were demonstrated. Parameter uncertainty and model uncertainty derived from the parameters were quantified, visualized and assessed. The random walk Metropolis algorithm was used and in order to reach convergence more effectively, informative priors, Sivias" likelihood, reflection at the prior boundaries and updating the proposal distribution with parts of the data gave successful results. Methods for objective and correct determination of Markov chain convergence were studied, and the use of multiple chains and the Gelman-Rubin method was found useful. Paper II: presents a dynamic model for snow cover, soil frost and surface ice. The Bayesian approach was used for model calibration and sensitivity analysis identified the non-important parameters. Paper III: shows the importance of splitting the data several times in two for model development and assessment/selection, for the model to fit well to novel data from the system and not only to the specific data at hand. Different models of ascospore maturity of Venturia inaequalis were further developed and compared by the deviance information criterion and root mean square error of prediction to show model improvements, and the analysis of variance was used to show significance of the improvements. Paper IV: examines the potential effects of selection of likelihood function when calibration a model. Since the likelihood function is rarely known for certain, but gives a reasonable quantification of how probable the data are given model outcome, it is of great importance to quantify the effect of using different likelihood functions on parameter uncertainty and on model output uncertainty derived from the parameters.
Abstract
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Authors
Solveig HaukelandAbstract
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Authors
Gunhild Børtnes Ruth MordalAbstract
Forsøk i vintersar (Satureja montana) og sommarsar (Satureja hortensis) ved Bioforsk Øst Kise og Apelsvoll 2005 - 2009
Authors
Tore SkrøppaAbstract
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Abstract
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