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.
2007
Authors
Klaus Mittenzwei Wendy Fjellstad Wenche Dramstad Ola Flaten Arnt Kristian Gjertsen Maria L. Loureiro Sjur Spildo PrestegardAbstract
No abstract has been registered
Authors
Klaus Mittenzwei Wendy Fjellstad Wenche E. Dramstad Ola Flaten Arnt Kristian Gjertsen Maria Loureiro Sjur Spildo PrestegardAbstract
In recent years the objectives of agricultural policy have shifted from a principal focus on production and income towards agriculture\"s provision of public goods summarized by the term ‘multifunctionality\". Agricultural sector models, which are important tools for policy advice, need to be adjusted in order to maintain their relevance and reliability in accordance with policy changes. This paper investigates the strengths and limitations of incorporating multifunctionality indicators in the agricultural sector model Common Agricultural Policy Regional Impact Analysis (CAPRI) by reviewing the existing literature and incorporating such indicators in the model. Multifunctionality indicators are developed and implemented for four selected aspects of multifunctionality: food security, landscape, environmental concerns and rural viability. By running different policy reform scenarios, it is shown that indicators closely related to the underlying economic variables of the sector model may provide useful to describe the effects of policy reforms on agriculture\"s multifunctionality. However, these indicators do not completely cover the selected aspects of multifunctionality. In order to yield a broader coverage, this paper proposes to strengthen interdisciplinary research by linking agricultural sector models with other model systems like farm-based economical-ecological models, regional economic models or landscape information systems.
Authors
Einar Heegaard Rune Halvorsen Økland Harald Bratli Wenche Dramstad Gunnar Engan Oddvar Pedersen Heidi SolstadAbstract
No abstract has been registered
2006
Abstract
There is increasing awareness of the need to monitor trends in our constantly changing agricultural landscapes. Monitoring programmes often use remote sensing data and focus on changes in land cover/land use in relation to values such as biodiversity, cultural heritage and recreation.Although a wide range of indicators is in use, landscape aesthetics is a topic that is frequently neglected. Our aim was to determine whether aspects of landscape content and configuration could be used as surrogate measures for visual landscape quality in monitoring programmes based on remote sensing.In this paper, we test whether map-derived indicators of landscape structure from the Norwegian monitoring programme for agricultural landscapes are correlated with visual landscape preferences. Two groups of people participated: (1) locals and (2) non-local students.Using the total dataset, we found significant positive correlations between preferences and spatial metrics, including number of land types, number of patches and land type diversity. In addition, preference scores were high where water was present within the mapped image area, even if the water itself was not visible in the images.When the dataset was split into two groups, we found no significant correlation between the preference scores of the students and locals. Whilst the student group preferred images portraying diverse and heterogeneous landscapes, neither diversity nor heterogeneity was correlated with the preference scores of the locals.We conclude that certain indicators based on spatial structure also have relevance in relation to landscape preferences in agricultural landscapes. However, the finding that different groups of people prefer different types of landscape underlines the need for care when interpreting indicator values
Authors
Rune Halvorsen Økland Harald Bratli Wenche E. Dramstad A. Edvardsen Gunnar Engan Wendy Fjellstad Einar Heegaard Oddvar Pedersen Heidi SolstadAbstract
Knowledge of variation in vascular plant species richness and species composition in modern agricultural landscapes is important for appropriate biodiversity management. From species lists for 2201 land-type patches in 16 1-km2 plots five data sets differing in sampling-unit size from patch to plot were prepared.Variation in each data set was partitioned into seven sources: patch geometry, patch type, geographic location, plot affiliation, habitat diversity, ecological factors, and land-use intensity.Patch species richness was highly predictable (75% of variance explained) by patch area, within-patch heterogeneity and patch type. Plot species richness was, however, not predictable by any explanatory variable, most likely because all studied landscapes contained all main patch types ploughed land, woodland, grassland and other open land and hence had a large core of common species.Patch species composition was explained by variation along major environmental complex gradients but appeared nested to lower degrees in modern than in traditional agricultural landscapes because species-poor parts of the landscape do not contain well-defined subsets of the species pool of species-rich parts.Variation in species composition was scale dependent because the relative importance of specific complex gradients changed with increasing sampling-unit size, and because the amount of randomness in data sets decreased with increasing sampling-unit size. Our results indicate that broad landscape structural changes will have consequences for landscape-scale species richness that are hard or impossible to predict by simple surrogate variables.
2005
Abstract
No abstract has been registered
2004
Authors
Klaus Mittenzwei Maria Loureiro Wenche Dramstad Wendy Fjellstad Ola Flaten Arnt Kristian Gjertsen Sjur Spildo PrestegardAbstract
No abstract has been registered
2003
Abstract
No abstract has been registered
Abstract
At present there are nearly 20 000 milk producers in Norway, and approximately 10 per cent of them are members of the Norwegian Dairy Financial Recording (NDFR). The NDFR is an important basis for production and financial advice given by the dairies. There is a great interest among milk producers and advisors in comparing results from different farms to find out why some are doing well and some are doing not so well, and to learn from those doing well. Gross margin (GM) per litre of milk produced is the traditional indicator for efficiency. This data, as other data on milk production, indicate that there is a wide variation in gross margin per litre of milk between farms with seemingly similar conditions for producing milk. This is interpreted as a potential for improving the efficiency of many producers. However, for many reasons gross margin per litre of milk is not an ideal indicator. A new version of the NDFR contains more information, for instance information on fixed costs of roughages produced on the farm. It is hoped that the new version of the NDFR makes it a better tool for improving the profitability of milk production. In an ongoing project we try to use the NDFR to analyse who are doing well and why. We use a combination of Data Envelopment Analysis (DEA) and statistical analysis. For each farm we produce an efficiency index, and then we apply statistical methods to find factors that can explain the index. So far we have only very preliminary results. Management factors are important, but the NDFR data-base have very little information on management factors. It is planned to collect such data for a sample of farmers and include that in the study at a later stage.
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.