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.
1991
Forfattere
Tore FrognerSammendrag
Det er ikke registrert sammendrag
Forfattere
Tore SkrøppaSammendrag
Det er ikke registrert sammendrag
Forfattere
Tor J. JohansenSammendrag
Det er ikke registrert sammendrag
Forfattere
Tron EidSammendrag
This work deals with some consequences erroneous data basis might have for the planning and the management of forest holdings. The way in which the data basis, which is divided into the inventory basis, the model basis and assumptions, influences the planning process, is showed in Fig. 1. The considerations are mainly concentrated on the inventory basis, and the variables site quality, stand age, basal area and mean height. Sensitivity analysis is carried out for 10 model forests (Table 1), where consequences for plan suggestions, decisions and accomplishment of decisions are considered. Examples of how erroneous measurements might influence the property value of a forest are also given. Incorrect site quality changes both the increment and the treatments. This means that the total production of a forest might change considerably. If the site quality is one class too high or low in all stands, the production levels of the forests change by 30-40%. The largest deviations are found when the site quality is overestimated, and in the pine forests (Table 3). Incorrect site quality also influences the relative age of a stand. Fig. 2 shows how the economically mature areas, and the corresponding volumes, change due to incorrect site quality. Examples where the share of economically mature areas and volumes decrease from 50%, when the site quality is overestimated by one class, to near zero when the site quality is underestimated by one class, are not unlikely. Such deviations do, of course, also influence the balance quantity and the fellings according to economical cutting maturity (Table 4). Especially the changed rotation ages might lead to large deviations. Incorrect site quality might also insert a silvicultural program which is too intensive or extensive according to correct site quality, and the interest rate. In such cases the net present value decreases (Table 5). Also the property value is influenced by an incorrect site quality. In the model forests these values change 15-20% if the site quality is over- or underestimated by one class (Table 6). An incorrect stand age influences the forecasts due to changed increment, and due to changed time for final fellings. The changes of the increment, as a result of an overestimated or an underestimated stand age, generally decrease the balance quantity in the first case, and increase it in the second case (Table 7). In forests where the share of old stands is low, the conclusion might be the opposite, i.e. the balance quantity increase when the age is overestimated, and decreases when the age is underestimated. This is due to a changed share of mature stands. The balance quantities change by 6-8%, and the fellings according to economical maturity change about 10%, if the basal area has a bias of 10% (Table 8). The net revenues change in the same way because the mean tree, and accordingly also the prices and costs/m3, remain the same, in spite of incorrect basal area. To accomplish felling suggestions based on incorrect data, e.g. 10% too large basal area, influence the possibilities of the future. There might, for example, be too few mature stands to accomplish the suggested balance quantity strategy in the future (Fig. 3). The fellings have to be reduced some years later. This is in conflict with the decision-makers goal. Also an incorrect basal area changes the property values of the forests. 10% incorrect basal area makes them change by 7-9% (Table 9). 10% too large mean height makes the balance quantities change by 6-9% in the spruce forests, while they change by 3-5% in the Scots pine forests (Fig. 4). The deviations for the net revenues are larger because the mean tree, and accordingly the prices and costs/m3, are changed. This is particularly the case in the pine forests where the net revenues change by 12-15%. Some examples of how errors in the model basis, and how incorrect assumptions might influence the balance quantity, are also given. The balance quantities increase by 4-6% if the diameter increment is overestimated by 10%, while the balance quantities increase by 3-6% if the lower limit of the rotation ages are assumed to be 10 years too low (Fig. 5). Combinations of different errors might either strengthen or hide effects. An overestimated site quality, basal area and diameter increment at the same time will, for example in forest no. 1, lead to an overestimation of the balance quantity by 19%, while there is a very small change if the site quality is overestimated and the diameter increment is underestimated at the same time (Table 10).Taking into account the relatively moderate systematic errors assumed in the examples, that combinations of errors might appear, and that also errors in functions or errors connected to the assumptions for the treatments are quite likely, the errors appearing in practical applications are probably larger than those considered in this study.
Forfattere
Severin WoxholttSammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag
Forfattere
Mette Goul ThomsenSammendrag
Det er ikke registrert sammendrag
Forfattere
Ivar SamsetSammendrag
Rundt Islands nord- og vestkyster ligger det betydelige mengder drivtømmer. Det er mest furu og lerk, men også en del gran, edelgran, bjørk og osp. Det er tildels store tømmerdimensjoner, men også en del som er istykkerslått av isgang og bølgeslag mot strendene. Drivtømmeret på Jan Mayen ligner det vi fant på Island. Det er sannsynlig at tømmeret stammer fra Sibirs nordkyst. Det er revet løs av flom, isgang og storm, bl.a. fra områdene ved elvene, Ob, Jenisei og Lena. Derfra tar de nordøstlige overflatestrømmene tømmeret med til det når hovedstrømmer fra Beringstredet. Isen bringer tømmeret over Polhavet til havområdene mellom Spitsbergen og Grønland. Derfra bringer overflatestrømmene og nordveststormene det videre til Jan Mayens og Islands kyster. Bøndene foredler tømmeret i små sagbruk på gårdene. Lengst i nord er gårdene fraflyttet, slik at tømmerressursene samler seg opp.
Forfattere
Tron Eid L.O. ErikssonSammendrag
Nåverdien er brukt som sammenligningsgrunnlag for å vurdere lønnsomheten av tynning når det brukes ulike norske og svenske tilvekst-, pris- og kostnadsfunksjoner. De norske tilvekstfunksjonene som er brukt er laget av Blingsmo (1984), mens de svenske er laget av Agestam (1985). For en del bestand, på ulike boniteter, og for gran og furu, er det beregnet en rekke alternativer, der en vekselvis bruker norske og svenske tilvekst-, pris og kostnadsfunksjoner. Bestandssimuleringsprogrammet GAYA er brukt i beregningene. Resultatene viser at med Blingsmos tilvekstfunksjoner, og med norske pris- og kostnadsfunksjoner, vil beste alternativ med tynning gi en lavere nåverdi enn beste alternativ uten tynning. Dette gjelder alle boniteter, og både gran og furu. Med Blingsmos funksjoner, og svenske pris- og kostnadsfunksjoner, blir konklusjonene de samme. Med Agestams tilvekstfunksjoner, og med både norske og svenske pris- og kostnadsfunksjoner blir det lønnsomt å tynne i mange av bestandene. Videre viser resultatene at det for furu med Blingsmos tilvekstfunksjoner vil være mest lønnsomt å tynne så nær sluttavvirkning som mulig, det lønner seg å ha en moderat tynningsstyrke og det lønner seg å ta ut de største trærne. Med Agestams funksjoner for furu lønner det seg å tynne forholdsvis sterkt, mens det ikke lønner seg å ta ut de største trærne. Med Blingsmos tilvekstfunksjoner vil lønnsomheten i tynningsinngrepet synke forholdsvis mye dersom en innfører restriksjoner på tynningene ved å sette en maksimumsgrense for overhøyde i tråd med det som anbefales i praksis. Dersom en innfører restriksjoner på sluttavvirkningstidspunktet slik at dette kommer senere, er det mulig, også med Blingsmos tilvekstfunksjoner, å finne lønnsomme tynningsstrategier. Blingsmos tilvekstfunksjoner gir en reduksjon i tilveksten etter tynning som er tilnærmet proporsjonal med tynningsstyrken. Agestams tilvekstfunksjoner gir i en del tilfeller en økning i tilveksten etter tynning. Dette gjelder for gran når grunnflata er høg. Disse resultatene er neppe realistiske fordi funksjonene i disse tilfellene brukes utenfor det tillatte området. For furu er Agestams funksjoner brukt innenfor tillatt område, og her reduseres tilveksten etter tynning. Reduksjonen er imidlertid mindre enn med Blingsmos tilvekstfunksjoner. Forholdsvis små forskjeller i hvordan tilvekstforløpet etter en tynning spesifiseres fører altså til at konklusjonene for lønnsomheten i tynningsinngrepet kan bli forskjellig.
Forfattere
Jarle BerganSammendrag
Picea abies does not grow naturally north of the Polar circle in Norway (except for some sporadic occurrences in Finnmark). From 1973 to 1985 20 field tests were carried out at different vegetation types and altitudes in Nordland and Troms. The main objective of the investigation was to study survival, height growth and injuries to northern provenances of Norway spruce grown under various soil and climatic conditions. The climatic conditions at the experimental areas represent different kinds of heat and frost climate. According to Kielland-Lund (1981), the vegetation types represented in the investigation are: 13. ass.: Eu-Piceetum abietis a) Subass.: myrtilletosum b) Subass.: dryopteridetosum c) Subass.: athyrietosum 14. ass.: Melico nutantis-Piceetum abietis b) Subass.: typicum c) Subass.: aconitetosum The provenance codes are made up of a letter indicating the seed collection zone, and a number indicating the altitude (1=0-149 m a.s.l, 2= 150-249 m a.s.l. etc.). Fig. 1 shows the seed collection zones together with the geographical position of the field experiments. Particulars of plots and provenances are given in Tables 1 and 2. P2-Rana is used as standard provenance. The test fields are divided in two groups: 8 main fields including the 15 provenances shown in Table 2. 12 other fields includes a selection of the fifteen provenances listed in Table 2. In addition, three Finnish provenances are included in some of the experiments (see Chap. 2). The main results are given in Tables 59, 60 and 93. Provenances of Region 1 have in average for the eight main fields about 10% bigger heights than provenances from the regions 2, 3 and 4 (Table 60). As regards mean heights, P1 and P2 from Rana have turned out to be ranked among the best provenances at every experimental field, which is not always the case for P1-Drevja and P 1-Hemnes. At frosty sites and by unfavourable growth conditions at high altitudes, the results indicate that P2 from Rana is to be preferred to P1-Rana. At high levels not far from the timberline, P1 and P2 from Rana seem to be as well adapted to unfavourable local climatic conditions as local provenances in the southern part of Nordland (O4 and O5). At localities exposed to frost in the bottom of the valleys in Troms, the results show that provenances from high altitudes in the most southern part of Nordland (O4 and O5) are not to be preferred to P2-Rana. On the other hand, P3-Rana has achieved relatively good height growth and high survival at sites with unfavourable growth climate.At such areas, production of birch has to be considered instead of planting spruce. The height growth and survival of the three Finnish provenances have been good. However, there seems no reason to prefer theese provenances to P1 or P2 from Rana. At areas outside the natural bounderies of the provenances, Q1, Q2 and R1 has reached the relatively best heights and survival percentages at sites of favourable growth climate, preferentially in the outer districts of Nordland and Troms. The same can be said about O1-Vefsn and O1-Grane. The mean survival percentage of the plots varies between 80 to 90% for the different provenances (Tables 59, 60 and 93). The survival of the plants depends more on injuries by voles, vegetation pressure etc. rather than distinctions of provenances. Late summer frost is the most usual injuries by frost in North-Norway. All frost injuries specified in the tables are such injuries.