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
Bjørn Langerud Martin SandvikSammendrag
Det er ikke registrert sammendrag
Forfattere
Tor J. JohansenSammendrag
Det er ikke registrert sammendrag
Forfattere
Hans Grønlien G. Tengesdal Ivar FæsteSammendrag
På oppdrag fra NLVF ved Erik Eid Hohle, gjennomførte Institutt for bygningsteknikk, NLH, og Seksjon treteknologi, NISK, tørkeforsøk med brenselflis i Sel i Gudbrandsdalen sommeren 1988. Flisa ble tørket i ei universaltørke for høy, flis og korn. Dette er ei kaldluft-tørke. Tørka er konstruert ved Institutt for bygningsteknikk og er basert også på tilleggsvarme fra solinnstråling. Flisvirket besto av bjørk (3%), or (10%), gran (12%) og furu (75%). Hogging og transport av ialt 72 lm3 flis tok 9,5 timer med to mann, en prestasjon på ca. 7,5 lm3 pr. time. Tørkinga startet 4. juli. Gjennomsnittsfuktigheten i flisa var da 37,8%. Flisa ble tørket i et 80 - 90 cm tykt lag. Mottrykket i flisa var ca. 150 Pa (15 mm VS). Lufthastigheten gjennom flishaugen var ca. 18 cm/sek. Allerede etter ett døgn var fuktigheten i bunnsjiktet nede på ca. 14%, mens midt- og toppsjiktet hadde tørket ubetydelig. Etter to døgn var også fuktigheten i midtsjiktet kommet under 15%. Ved avslutningen av tørkinga etter fem døgn den 8. juli var middelfuktigheten 13,4%, men fremdeles ble det registrert fuktighet på 35% ett sted i toppsjiktet. Det totale vanninnholdet i flisa var da redusert fra ca. 7 tonn til 1,8 tonn. Tørrvekta av hele flispartiet er beregnet til 11,5 tonn. Den effektive brennverdien pr. kg var 3,0 kWh (10,8 MJ/kg) før tørkinga, mens den etter tørking var steget til 4,5 kWh/kg (16,2 MJ/kg), en total økning på 4100 kWh. Forbruket av elektrisk energi (vifte) var ca. 700 kWh. Tørrstofftapet i tørkeperioden var 0,32%. Etter lagring av flisa i 4 måneder hadde tørrstofftapet økt med ytterligere 0,38%. Ved omrøring i flisa i tørkeperioden var mengden av soppsporer i lufta over flisa betydelig over det nivå som regnes for helsefarlig. Etter 4 måneders lagring ble det registrert ubetydelige mengder soppsporer i lufta, selv ved omrøring i flisa.
Sammendrag
Det er ikke registrert sammendrag
Forfattere
Bjørn Langerud Pasi Puttonen Erik TroengSammendrag
Det er ikke registrert sammendrag
Forfattere
Tor J. JohansenSammendrag
Det er ikke registrert sammendrag
Forfattere
Tron EidSammendrag
This work deals with some effects erroneous measurements might have for the inventory results, the forecasts and the priorities of treatments in forest stands. The following variables are evaluated; site quality, total age, tariff number, number of trees/ha, basal area, mean height, conditions for logging and hauling, hauling distance and timber quality. The consequences of errors are considered by means of sensitivity analysis for 14 different stands (Table 2). Some considerations on how and why erroneous measurements appear, are done in chapter 2.3.Incorrect site quality or total age influences the total production, partly because the increment changes and partly because the treatments change. Fig. 1 shows an example of how a 20% incorrect site quality during a period of 35 years makes the volume change by 7-9%. The increment and the volume also change if the total age is incorrectly estimated (10%). The volumes change by 1% and 9% depending on the number of years in the time period between the \"inventory\" and the final fellings (Table 5).Incorrect site quality or total age might also influence the treatments of a stand, e.g. changed rotations age because the value increment has changed. An incorrect site quality might also insert \"incorrect\" regeneration methods. This is most likely in stands of medium site quality. An example shows that the net present value decreases about 30% if planting is selected instead of natural regeneration because the decision-maker believe that the site quality is F17, instead of F14.Incorrectly estimated basal area or mean height directly influence the volume. 10% biased basal area means that the volume changes 10% (Table 9 and 10). Approximately the same bias for the volume appears with 10% error for the mean height (Table 11 and 13). There is, however, quite big differences between these two variables with respect to other consequences.A biased basal area has small (Table 9) or none (Table 10) effects on the prices and costs/m3 because the size of the \"mean tree\" is little or not effected. A biased mean height has larger effects because the size of the \"mean tree\" changes. Cases where the net revenue/ha increases by 30-40%, if the mean height is 10% too high, is quite likely (Table 11 and 13).Incorrect tariff number or number of trees/ha do not influence the volume. The changes of the net revenues/ha are accordingly quite small (Table 7 and 8). This is particularly the situation for Norway spruce where both prices and costs/m3 increase if the tariff number or the number of trees/ha are positively biased.The change for Scots pine is larger because the prices/m3 decrease while the costs/m3 increase. For the costs/m3, errors have similar effects for Norway spruce and Scots pine, i.e. a positively biased tariff number or number of trees/ha increase the costs/m3, while a positively biased basal area or mean height decrease them.For the prices/m3, however, there is an opposite effect for tariff number, number of trees/ha and basal area, i.e. if incorrect measurements for one of these variables make the mean diameter increase, the prices decrease for spruce while they increase for pine (Fig. 2). A positively biased mean height makes the prices higher for both tree species in the considered stands.Two procedures to decide the volume/ha, the \"mean tree\" and the number of trees/ha are described in chapter 2.3.3. (see Table 1). The selected procedure is irrelevant for the volume, because this volume in both cases are based on the basal area and the mean height. The differences between the procedures appear when the \"mean tree\" is decided; the effects of errors are different because the number of trees/ha is measured in \"the number of trees procedure\", while the tariff number is measured in \"the tariff number procedure\".With a 10% biased tariff number in a spruce stand, the mean diameter change about 10% and the number of trees/ha change about 15%. There are more significant changes for pine, i.e. examples where the mean diameter changes more than 30% (Table 6). With a 10% biased number of trees/ha, the mean diameter changes about 5% for both tree species. The big changes of the variables describing the \"mean tree\" of \"the tariff number procedure\", make the changes for prices and costs/m3, and accordingly also the net revenue/ha, larger than the changes of \"the number of trees procedure\" (Table 7 and 8).An incorrect basal area has a small effect on the \"mean tree\" in both procedures (Table 9 and 10). For an incorrectly estimated mean height, however, the consequences are severe for \"the tariff number procedure\". This is accordingly also the situation for the prices and costs/m3, and the net revenue/ha (Table 11 and 13). Effects of erroneous measurements in cutting class II (age class, i.e. young stands) are considered for the variables regulated number of trees/ha and site quality.If these variables are biased, the volume increment change, accordingly also the volume of the final fellings. With a positive bias of 30% for the regulated number of trees/ha, there are examples where the volume of the final felling is more than 15% too high (Fig. 3). If the site quality is one 3-meter class too high or low, the volume of the final felling might have a bias of 30% (Table 14).The changes of the costs/m3 and net revenues/m3 are quite small if the conditions for logging and hauling are incorrectly classified (Table 15). The changes are larger if the timber quality is incorrectly classified. A 10% bias for the share of pulpwood, means that the net revenue/m3 changes 5.5% and 15.6% for spruce and pine stands respectively (Table 16). The large change for pine is due to the big difference between the price/m3 for pulpwood and sawtimber.
Forfattere
Alhaji JengSammendrag
The investigations have shown that Norwegian alum shales have the capacity of producing large amounts of acidity. The amounts produced, though strongly correlated with the amount of pyrite in the material, also depends upon the quality of the pyrite, i.e. its weatherability. A shale sample collected from a road-cut near Brumunddal (BRUM 1) contains much higher amounts of pyrite than samples from Storting and Munchsgate, Oslo (STORT and MUNCH respectively), but the relationship between % pyrite remaining and acid produced is, for example, much stronger in STORT than in BRUM 1. The pyrite in STORT and MUNCH appears as minute grains, finely distributed in the material, whereas in BRUM 1 it appears more as concretions. This is a determining factor in the rate of weathering and potentiality of acid production, with finer grained pyrite weathering more easily than coarser grained. Another important factor in the weathering of shales is the presence of carbonates and other buffering components. It is demonstrated in these investigations that calcareous shales like the MUNCH sample whether at a slower rate than acid shales like STORT & BRUM1, because the acid generated, which is mainly responsible for the weathering, is consumed at a faster rate and more completely in the former than in the two latter samples. As such, small amounts of acid, Fe and A1 were found in solution for MUNCH during the 100-day weathering period, contrary to what was found in the acid samples. The liming trial further demonstrates this fact.
Forfattere
Alhaji S. JengSammendrag
Det er ikke registrert sammendrag
Forfattere
Ivar GjerdeSammendrag
Det er ikke registrert sammendrag