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.
2003
Authors
Grete StokstadAbstract
Several factors influence the value of a lamb carcass throughout the slaughtering season, and therefore have implications for the optimal slaughtering time of lambs. The expected price of the carcass varies through the season due to: Variations in the weight of the lambs, and the growth through the season. The classification of the carcass, i.e., the price per kg changes as the lambs grow. The prices of the various quality changes throughout the season. The quality of the grazing fields limits the possible weight gain and influences the classification of lams. The grazing resources are in general limited, and will affect the possibility of fattening lambs in the fall. The objective with this study is to come up with a tool to help in determining when to slaughter which lambs in the fall when resources are limited. In order to make good decisions, the first step is to calculate the profitability of various slaughtering decisions. I use known characteristics of the lambs as weight, sex etc. to determine expected value of the carcass if slaughtered at various point in time in the future. In order to determine expected quality for the carcasses I have used a multinomial ordered probit regression model to determine the probability for obtaining a particular classification. A linear programming model is used to choose the best alternatives given limited grassing resources. The model can be used to determine optimal slaughtering decisions given a particular group of lambs and resources. By limiting the possible choices in the model, the model user may also investigate the losses associated with alternative slaughtering schemes. In this paper I describe the forecasting models for determining the value of the carcass, I describe the general linear programming model and show some results from running the model.
Abstract
No abstract has been registered
Authors
Ali Temiz Morten Eikenes U.C. Yildiz Fred G. Evans Bjørn JacobsenAbstract
No abstract has been registered
Authors
Halvor B. Gjærum Mary Namaganda Kåre LyeAbstract
No abstract has been registered
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No abstract has been registered
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This report contains all papers presented at the OECD Expert meeting in Oslo October 7th - 9th 2002, in addition to the list of participants. The topic of the meeting was the development of landscape indicators. In brief, the Expert Meeting agreed that interested OECD Member countries should consider the following recommendations; • Invest in the scientific understanding and further development of an indicator framework for agricultural landscapes, representing the linkages between landscape structure, function and management, • Build upon the existing national and international experiences in policy monitoring, evaluation and predictive scenarios, • Encourage pro-active collaboration, information exchange and methodological integration, • Contribute to, and cooperate with, other international initiatives related to developing agricultural landscape indicators, • Establish an informal expert network to follow up recommendations of the meeting.
Authors
Helge Lundekvam Eirik Romstad Lillian ØygardenAbstract
Norwegian agriculture depends heavily on governmental subsidies, due to small farming units and high costs. Due to a limited home market, many agricultural productions are also quantum regulated. Milk and grain production was regulated starting in the 1950 using region specific prices. At the level of three counties in south-eastern Norway, this policy resulted in an increase in the grain producing area from 30 to 80% of total agricultural area causing a similar reduction in grassland area over a'30 year period. The change in land use caused by this policy more than doubled the estimated soil losses by water erosion. During the late; seventies extensive land levelling in the same region stimulated by subsidies lead to an estimated two-three fold increase in soil erosion. The increase was especially high when former ravine landscapes used for pasture were levelled and turned into arable land that was ploughed in autumn. Very visible erosion and increasing negative offsite effects on water quality together with overproduction put an end to the subsidies for land levelling. Erosion research was started around 1980 and the results from this research lead to the introduction of several kinds of payments in the early 1990 to encourage more sustainable agricultural production. Since the policy changed there has been changes in cultivating systems and a reduction in soil erosion has been estimated. Thus, farmers' behaviour and soil erosion in Norway is strongly influenced by agricultural and environmental policy.
Authors
Celine ReboursAbstract
No abstract has been registered
Abstract
The rationale for stand growth modelling is often either grounded in a search for improved scientific understanding or in support for management decisions. The ultimate goal under the first task is seen in mechanistic models, i.e. models that represent the stand structure realistically and predict future growth as a function of the current status of the stand. Such mechanistic models tend to be over-parameterized with respect to the data actually available for a given stand. Calibration of these models may lead to non-unique representations and unreliable predictions. Empirical models, the second major line of growth modelling, typically match available data sets as well as do process-based models. They have less degrees of freedom, hence mitigate the problem of non-unique calibration results, but they employ often parameters without physiological or physical meaning. That is why empirical models cannot be extrapolated beyond the existing conditions of observations. Here we argue that this widespread dilemma can be overcome by using interactive models as an alternative approach to mechanistic (algorithmic) models. Interactive models can be used at two levels: a) the interactions among trees of a species or ecosystem and b) the interactions between forest management and a stand structure, e.g. in thinning trials. In such a model data from a range of sources (scientific, administrative, empirical) can be incorporated into consistent growth reconstructions. Interactive selection among such growth reconstructions may be theoretically more powerful than algorithmic automatic selection. We suggest a modelling approach in which this theoretical conjecture can be put to a practical test. To this end growth models need to be equipped with interactive visualization interfaces in order to be utilized as input devices for silvicultural expertise. Interactive models will not affect the difficulties of predicting forest growth, but may be at their best in documenting and disseminating silvicultural competence in forestry.
Authors
Staffan JacobsonAbstract
No abstract has been registered