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Koeasetelmana 15 puustokoealaa puustotilavuuden muutoksen mittaukseen eri lannoituskäsittelyjen kesken.\n\n#### Kaatokoepuut\n\nKaatokoepuita kaadettu ja mitattu koealoilta vuosina 1990 ja 1995.\n\n\n### TL407 Rovaniemi\n\nRovaniemen ja Kemijärven välillä sijaitseva koemetsikkö (≈4 ha).\n\n#### Puuston mittaukset\n\nPuusto on mitattu 1990, 1995, 2000, 2005, 2010 ja 2020. Koeasetelmana 18 puustokoealaa puustotilavuuden muutoksen mittaukseen eri lannoituskäsittelyjen kesken.\n\n#### Kaatokoepuut\n\nKaatokoepuita kaadettu ja mitattu koealoilta vuosina 1990 ja 1995.\n\n\n### TL408 Oulu\n\nOulussa Ylikiimingin itäpuolella sijaitseva koemetsikkö (≈6 ha).\n\n#### Puuston mittaukset\n\nPuusto on mitattu 1991, 1996, 2001, 2006 ja 2012. 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Lisäksi koealojen vaipoilta (ulkorajoilta brutto- ja nettokoealan rajojen väliseltä alueelta) vuonna 1994.\n\n\n### TL411 Harjavalta\n\nHarjavallassa sijaitseva koemetsikkö (≈3 ha).\n\n#### Puuston mittaukset\n\nPuustot mitattu 1991, 1996 ja 2002. Koeasetelmana 12 puustokoealaa puustotilavuuden muutoksen mittaukseen eri lannoituskäsittelyjen kesken.\n\n#### Kaatokoepuut\n\nKaatokoepuita kaadettu ja mitattu koealoilta vuosina 1991 ja 1996. Lisäksi koealojen vaipoilta (ulkorajoilta brutto- ja nettokoealan rajojen väliseltä alueelta) vuonna 1994.\n\n### TL412 Harjavalta\n\nHarjavallassa sijaitseva koemetsikkö (≈3 ha).\n\n#### Puuston mittaukset\n\nPuustot mitattu 1991, 1996, 2002 ja 2006. 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Lisäksi koealojen vaipoilta (ulkorajoilta brutto- ja nettokoealan rajojen väliseltä alueelta) vuonna 1994.\n\n### TL415 Sonkajärvi\n\nSonkajärvellä sijaitseva koemetsikkö (≈5 ha).\n\n#### Puuston mittaukset\n\nPuustot mitattu 1992, 1997, 2002 ja 2007. Koeasetelmana 21 puustokoealaa puustotilavuuden muutoksen mittaukseen eri lannoituskäsittelyjen kesken.\n\n#### Kaatokoepuut\n\nKaatokoepuita kaadettu ja mitattu koealoilta vuosina 1992 ja 1997.\n\n\n### TL416 Jämsä\n\nJämsässä sijaitseva koemetsikkö (≈6 ha).\n\n#### Puuston mittaukset\n\nPuustot mitattu 1992, 1997, 2002, 2007 ja 2016. Koeasetelmana 18 puustokoealaa puustotilavuuden muutoksen mittaukseen eri lannoituskäsittelyjen kesken.\n\n#### Kaatokoepuut\n\nKaatokoepuita kaadettu ja mitattu koealoilta vuosina 1992 ja 1997.\n\n\n### TL417 Savonlinna\n\nSavonlinnassa Punkaharjulla sijaitseva koemetsikkö (≈9 ha).\n\n#### Puuston mittaukset\n\nPuustot mitattu 1993, 1997, 2002 ja 2007. Koeasetelmana 16 puustokoealaa puustotilavuuden muutoksen mittaukseen eri lannoituskäsittelyjen kesken.\n\n#### Kaatokoepuut\n\nKaatokoepuita kaadettu ja mitattu koealoilta vuosina 1992 ja 2002.\n\n### TL418 Suonenjoki\n\nSuonenjoella sijaitseva koemetsikkö (≈8 ha).\n\n#### Puuston mittaukset\n\nPuustot mitattu 1993, 1997 ja 2002. Koeasetelman 20 puustokoealasta seurattu mittauksin 16 alaa.\n\n#### Kaatokoepuut\n\nKaatokoepuita kaadettu ja mitattu koealoilta vuonna 1992.\n\n##### ______________________________________________________________________________________________________________________ \n\n### Puustonmittausaineistot ovat Excel-pohjaisia 20- ja 71-lomakkeita.\n\n### Tärkeimmät tallennetut muuttujat:\n\ntietuetunnus(20, 71), kokeen numero, mittauskerta, koealan numero, koealan pinta-ala(neliömetri), mittausajankohta(vuosi, kuukausi, päivä), puun numero, konepiste(mittauspiste koealan sisällä, -5--10), suunta(0-360), etäisyys(cm), puulaji(1-9), puujakso(0-3), puustoryhmä(1-4), läpimitta rinnankorkeudelta(mm), latvuskerros(1-6), tekninen laatu(1-9), tuhon ilmiasu(0-8), tuhon syy(0-13), tuhon aste (0-3), koepuukoodi(0-3), puun pituus(dm), puun latvusraja(dm), yläläpimitta ilmoitetulta korkeudelta(mm), tuoreoksaraja(dm, kuivaoksaraja(dm).\n\nAineiston keruussa ja tallennuksessa noudatetut standardit ja menetelmäohjeet: Metsikkökokeiden maastotyöohjeet"
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Koeasetelmana 15 puustokoealaa puustotilavuuden muutoksen mittaukseen eri lannoituskäsittelyjen kesken.\n\n**Puuston mittaus 22.5.1992**\n- Mitattu kaikki koealat.\n\n**Puuston mittaus 12.10.-17.11.1999**\n- Mitattu kaikki koealat.\n\n\n### LATER509 Posio\n\nPosion eteläpuolella sijaitseva koemetsikkö (≈4 ha).\n\n#### Puuston mittaukset\n\nPuusto on mitattu 1990. Koeasetelmana 15 puustokoealaa puustotilavuuden muutoksen mittaukseen eri lannoituskäsittelyjen kesken.\n\n**Puuston mittaus 5.11.1990**\n- Mitattu kaikki koealat.\n\n\n### LATER510 Ylitornio\n\nYlitornion Meltosjärven itäpuolella sijaitseva koemetsikkö (≈3 ha).\n\n#### Puuston mittaukset\n\nPuusto on mitattu 1990. Koeasetelmana 15 puustokoealaa puustotilavuuden muutoksen mittaukseen eri lannoituskäsittelyjen kesken.\n\n**Puuston mittaus 23.10.1990**\n- Mitattu kaikki koealat.\n\n\n### LATER511 Pelkosenniemi\n\nPelkosenniemen Luiron pohjoispuolella sijaitseva koemetsikkö (≈3 ha).\n\n#### Puuston mittaukset\n\nPuusto on mitattu 1991. Koeasetelmana 15 puustokoealaa puustotilavuuden muutoksen mittaukseen eri lannoituskäsittelyjen kesken.\n\n**Puuston mittaus 8.10.1991**\n- Mitattu kaikki koealat.\n\n\n### LATER512 Pyhtää\n\nPyhtään pohjoispuolella sijaitseva koemetsikkö (≈3 ha).\n\n#### Puuston mittaukset\n\nPuusto on mitattu 2022. Koeasetelmana 23 puustokoealaa puustotilavuuden muutoksen mittaukseen eri lannoituskäsittelyjen kesken.\n\n**Puuston mittaus 2.8.-26.9.2022**\n- Mitattu kaikki koealat, paitsi 6, 7 ja 21.\n\n\n### HARJAV505 Harjavalta/Nakkila\n\nHarjavallassa sekä Nakkilassa sijaitsevat 5 koemetsikköä (≈3/15 ha).\n\n#### Puuston mittaukset\n\nPuustot mitattu 1992 ja 1998. Koeasetelmana 25 puustokoealaa viidessä eri koemetsikössä (5 koealaa/metsikkö).\n\n**Puuston mittaus 15.10.-26.10.1992**\n- Mitattu kaikki koealat.\n\n**Puuston mittaus 1.6.-5.6.1998**\n- Mitattu kaikki koealat.\n\n\n##### ______________________________________________________________________________________________________________________ \n\n### Aineistot ovat Excel-pohjaisia 20- ja 71-lomakkeita.\n\n### Tärkeimmät tallennetut muuttujat:\n\ntietuetunnus(20, 71), kokeen numero, mittauskerta, koealan numero, koealan pinta-ala(neliömetri), mittausajankohta(vuosi, kuukausi, päivä), puun numero, konepiste(mittauspiste koealan sisällä, -5--10), suunta(0-360), etäisyys(cm), puulaji(1-9), puujakso(0-3), puustoryhmä(1-4), läpimitta rinnankorkeudelta(mm), latvuskerros(1-6), tekninen laatu(1-9), tuhon ilmiasu(0-8), tuhon syy(0-13), tuhon aste (0-3), koepuukoodi(0-3), puun pituus(dm), puun latvusraja(dm), yläläpimitta ilmoitetulta korkeudelta(mm), tuoreoksaraja(dm, kuivaoksaraja(dm).\n\nAineiston keruussa ja tallennuksessa noudatetut standardit ja menetelmäohjeet: Metsikkökokeiden maastotyöohjeet"
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"fi": "Koesarjan tavoitteena on seurata ravinnelisäyksen vaikutuksia kasvupaikkaan, kasvillisuuteen ja puustoon ojitusalueilla. Koejärjestelyt ja käsittelyt ovat vaihtelevia: lannoituskäsittelyjä on tehty eri metsänkäsittelyvaiheisiin liittyen ja perustettu usein \"tavanomaisista\" käsittelyistä poiketen esikokeiksi mm. tutkimushypoteesien testaamiseksi.\n\nTestattu kaupallisten lannoitteiden (PK, K) vaikutusta. Tärkeimpänä varttuneisiin metsiin 2000-luvun alussa perustetut kokeet. Aineisto sisältää muun muassa puustomittauksien datoja ja neulasnäytteiden tuloksia.\n\n### HANNULAPK Tornio\n\nTornion koillispuolella sijaitseva tutkimusmetsikkö (≈9 ha).\n\n#### Puuston mittaukset\n\nPuusto on mitattu kevään ja syksyn 2021 aikana. Koeasetelmana 27 puustokoealaa puustotilavuuden muutoksen mittaukseen eri lannoituskäsittelyjen kesken.\n\n**Puuston mittaus 1.5.-4.6.2021**\n- Mitattu koealat 1, 3, 4, 7, 12, 15, 16, 18, 19, 21 ja 22.\n\n**Puuston mittaus 11.8.-7.9.2021**\n- Mitattu koealat 8-11 ja 25-27.\n\n\n### HANNU1.01 Tornio\n\nTornion koillispuolella sijaitseva tutkimusmetsikkö (≈2 ha).\n\n#### Puuston mittaukset\n\nPuusto on mitattu 2001, 2008 ja 2020. Koeasetelmana 12 puustokoealaa puustotilavuuden muutoksen mittaukseen eri lannoituskäsittelyjen kesken.\n\n**Puuston mittaus 6.9.2001**\n- Mitattu kaikkien koealojen valtapuusto.\n\n**Puuston mittaus 20.-21.10.2008**\n- Puuston mittaus ylispuiden poiston jälkeen. Mitattu kaikkien koealojen alikasvos.\n\n**Puuston mittaus 25.-29.5.2020**\n- Mitattu kaikki koealat.\n\n\n### RUSKO1.01 Oulu\n\nOulun Ruskossa sijaitseva koemetsikkö (≈2 ha).\n\n#### Puuston mittaukset\n\nPuusto on mitattu 2008 ja 2021. Koeasetelmana 12 puustokoealaa puustotilavuuden muutoksen mittaukseen eri lannoituskäsittelyjen kesken.\n\n**Puuston mittaus 7.11.2008**\n- Mitattu kaikkien koealojen alikasvos.\n\n**Puuston mittaus 20.5.2021**\n- Mitattu kaikki koealat. Myös ylispuut.\n\n\n### AHTAA2.01 Raahe\n\nRaahen Vihannissa sijaitseva koemetsikkö (≈0,5 ha).\n\n#### Puuston mittaukset\n\nPuusto on mitattu 2008. Koeasetelmana 6 puustokoealaa puustotilavuuden muutoksen mittaukseen eri lannoituskäsittelyjen kesken.\n\n**Puuston mittaus 13.-17.11.2008**\n- Mitattu kaikki koealat. Koealoilta mitattu kaikki alikasvoskuuset ja -männyt.\n\n\n### OTANNEVA1 Kajaani\n\n Kajaanin Otanmäessä sijaitseva koemetsikkö (≈3 ha).\n\n#### Puuston mittaukset\n\nPuusto on mitattu 2018. Koeasetelmana 12 puustokoealaa puustotilavuuden muutoksen mittaukseen eri lannoituskäsittelyjen kesken.\n\n**Puuston mittaus 8.10.-8.11.2018**\n- Mitattu kaikki koealat.\n\n\n### OTANNEVA2 Kajaani\n\nKajaanin Otanmäessä sijaitseva koemetsikkö (≈3 ha).\n\n#### Puuston mittaukset\n\nPuusto on mitattu 2020. Koeasetelmana 12 puustokoealaa puustotilavuuden muutoksen mittaukseen eri lannoituskäsittelyjen kesken.\n\n**Puuston mittaus 29.9.-9.10.2020**\n- Mitattu kaikki koealat.\n\n\n### MUSTIKKA1 Pyhäntä\n\nPyhännällä sijaitseva koemetsikkö (≈2 ha).\n\n#### Puuston mittaukset\n\nPuusto on mitattu 2021. Koeasetelmana 9 puustokoealaa puustotilavuuden muutoksen mittaukseen eri lannoituskäsittelyjen kesken.\n\n**Puuston mittaus 22.10.2021**\n- Mitattu kaikki koealat.\n\n\n### MUSTIKKA2 Pyhäntä\n\n Pyhännällä sijaitseva koemetsikkö (≈3 ha).\n\n#### Puuston mittaukset\n\nPuusto on mitattu 2022. Koeasetelmana 12 puustokoealaa puustotilavuuden muutoksen mittaukseen eri lannoituskäsittelyjen kesken.\n\n**Puuston mittaus 11.11.2022**\n- Mitattu kaikki koealat.\n\n\n\n\n##### ______________________________________________________________________________________________________________________ \n\n### Aineistot ovat Excel-pohjaisia 20- ja 71-lomakkeita.\n\n### Tärkeimmät tallennetut muuttujat:\n\ntietuetunnus(20, 71), kokeen numero, mittauskerta, koealan numero, koealan pinta-ala(neliömetri), mittausajankohta(vuosi, kuukausi, päivä), puun numero, konepiste(mittauspiste koealan sisällä, -5--10), suunta(0-360), etäisyys(cm), puulaji(1-9), puujakso(0-3), puustoryhmä(1-4), läpimitta rinnankorkeudelta(mm), latvuskerros(1-6), tekninen laatu(1-9), tuhon ilmiasu(0-8), tuhon syy(0-13), tuhon aste (0-3), koepuukoodi(0-3), puun pituus(dm), puun latvusraja(dm), yläläpimitta ilmoitetulta korkeudelta(mm), tuoreoksaraja(dm, kuivaoksaraja(dm).\n\nAineiston keruussa ja tallennuksessa noudatetut standardit ja menetelmäohjeet: Metsikkökokeiden maastotyöohjeet"
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"fi": "# Ränskälänkorpi\n\nRänskälänkorven tutkimusinfrastruktuuri (≈25 ha) sijaitsee Etelä-Suomessa, Asikkalassa. Kohteella tutkitaan jatkuvapeitteisen ja avohakkuisiin perustuvan metsänkäsittelyn vaikutusta puuston kehitykseen, vesitalouteen sekä ojitetun suometsäekosysteemin ja ilmakehän väliseen kasvihuonekaasujen vaihtoon boreaalisella kasvillisuusvyöhykkeellä.\n\n\nPuuston mittauksia kohteella on tehty vuosina 2019, 2020, 2021 ja 2024. \n\n### Puuston mittaukset:\n\n**Puusto mitattu 4.-15.7.2019**\n-\tMitattu linjat 1 (koealat 11-15), 2 (koealat 21-25), 3 (koealat 31-35), 4 (koealat 41-45) ja 5 (koealat 51-55)\n\n**Puusto mitattu 4.-8.5.2020**\n-\tMitattu EC-koeala (sisältää koealan 61), avohakkuu (koealat 1-6) sekä linjat 6 (koealat 62-65), 7 (koealat 71-74) ja 8 (koealat 81-84)\n\n**Puusto mitattu osittaishakkuiden jälkeen 18.-19.5.2021**\n-\tMitattu EC-koeala (sisältää koealan 61) sekä linjat 1(koealat 11-15), 5(koealat 51-55), 6(koealat 62-65), 7(koealat 71-74) ja 8(koealat 81-84)\n-\tMitattu hakkuusta jäljelle jääneet puut\n-\tPituus ja latvusraja mitattu vain EC-koealalta\n\n**Puusto mitattu 21.-24.10.2024**\n-\tMitattu EC-koeala (sisältää koealan 61) sekä linjat 1 (koealat 11-15), 2 (koealat 21-25), 3 (koealat 31-35), 4 (koealat 41-45) ja 5 (koealat 51-55), 6(koealat 62-65), 7(koealat 71-73) ja 8(koealat 81-84)\n-\tKoealat 85 ja 74 poistettu, koska ne ovat kartasta poiketen mittausalueiden ulkopuolelta. Koeala 55 mitattiin, vaikka sitä ei ole hakattu. \n-\tKaikki yli rinnankorkeuden kasvaneet havupuut on otettu uusina puina mukaan. Uusien koivujen läpimittarajana oli 10 mm.\n\n\n**Aineistot ovat Excel-pohjaisia 20- ja 71-lomakkeita.**\n\n#### Tärkeimmät tallennetut muuttujat:\n\ntietuetunnus(20, 71), kokeen numero, mittauskerta, koealan numero, koealan pinta-ala(neliömetri), mittausajankohta(vuosi, kuukausi, päivä), puun numero, konepiste(mittauspiste koealan sisällä, -5--10), suunta(0-360), etäisyys(cm), puulaji(1-9), puujakso(0-3), puustoryhmä(1-4), läpimitta rinnankorkeudelta(mm), latvuskerros(1-6), tekninen laatu(1-9), tuhon ilmiasu(0-8), tuhon syy(0-13), tuhon aste (0-3), koepuukoodi(0-3), puun pituus(dm), puun latvusraja(dm).\n\nAineiston keruussa ja tallennuksessa noudatetut standardit ja menetelmäohjeet: Metsikkökokeiden maastotyöohjeet"
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"en": "The experiment aims to detect the impacts of tree stand cuttings on carbon and nutrient dynamics. Pools of C and nutrients before and 3, 5, and 10 years after cutting are measured in the tree stand, other vegetation, and surface peat soil. Also CO2, methane, and N2O fluxes, decomposition rates of various kinds of litter,as well as environmental factors such as PAR, air and soil T, precipitation, and soil moisture are monitored to enable modeling of carbon and nutrient balance.",
"fi": "Kokeissa selvitetään hiilen ja eri ravinteiden dynamiikkaa suo- ja suometsäekosysteemeissä (CRAVSUO), ja useissa kokeissa hakkuun tai muiden ympäristömuutosten vaikutuksia niihin. Puustosta, kasvillisuudesta sekä kasvupaikan aktiivisesta pintaosasta selvitetään hiilen ja ravinteiden varastot (ja niiden muutokset mahdollisen hakkuun jälkeen). Lisäksi mitataan orgaanisen aineksen hajotukseen liittyviä kaasuvirtauksia, eri kariketyyppien hajotusnopeutta ja hajotukseen liittyvää karikedynamiikkaa sekä kasvupaikan ravinnevarastomuutoksia. Useilla kokeilla toimitaan yhteistyössä Ilmatieteenlaitoksen (kasvihuonekaasumittaukset eddy covariance -menetelmällä, meteorologiset havainnot) ja Helsingin yliopiston kanssa.\n\n\n\n## LETTOSUO Tammela\n\nLettosuon tutkimusmetsikkö (≈65 ha) sijaitsee Etelä-Suomessa, Tammelassa. Kohteella tutkitaan jatkuvapeitteisen ja avohakkuisiin perustuvan metsänkäsittelyn vaikutusta puuston kehitykseen, vesitalouteen sekä ojitetun suometsäekosysteemin ja ilmakehän väliseen kasvihuonekaasujen vaihtoon boreaalisella kasvillisuusvyöhykkeellä.\n\nKohteella mitataan intensiivisesti kasvihuonekaasupäästöjä ja määritetään hiilitaseen eri komponentteja sekä maaperästä että koko ekosysteemitasolla. Lisäksi mitataan maaperän hydrologisia tunnuksia, puuston ja pintakasvillisuuden haihduntaa, puuston sekä kasvillisuuden kasvua ja muutoksia sekä vedenlaatua ja miten hakkuut vaikuttavat niihin. Alueen seuranta aloitettiin vuonna 2009 ja kummankin hakkuukäsittelyn mukainen puunkorjuu toteutettiin helmi-maaliskuussa 2016.\n\nKohteella tutkimusta suorittavat yhteistyössä Ilmatieteen laitos, Luonnonvarakeskus ja Helsingin Yliopiston Metsätieteiden laitos. Lettosuo on osa ICOS-verkostoa (Associated Ecosystem Station).\n\nTieteellisiä artikkeleita ja lisätietoa \"Tunnisteet ja tapahtumat\" -välilehden alta.\n\n \n\n\n\n### Puuston mittaukset\n\nPuusto on mitattu 2014, 2016, 2020 ja 2024. Koeasetelmana 40 puustokoealaa puustotilavuuden ja biomassan muutoksen mittaukseen säteittäisillä linjoilla keskellä olevasta eddy covariance -mittaustornista 200m etäisyydelle.\n\n**Puuston mittaus 15.10.2014**\n- Mitattu kaikki koealat.\n- Puuston mittauksessa ei lainkaan pituuskoepuita. Aineiston käsittelyssä mahdollista käyttää myöhempien mittausten pituuskoepuita.\n\n**Puuston mittaus 15.5.2016**\n- Puuston mittaus hakkuun jälkeen tehty vain harvennetuille koealoille.\n\n**Puuston mittaus 20.10.2020**\n- Mitattu kaikki koealat.\n\n**Puuston mittaus 30.10.2024**\n- Mitattu kaikki koealat.\n\n\n##### ______________________________________________________________________________________________________________________ \n\n## VESIJRAVD Vesijako\n\nPadasjoen Vesijaon tutkimusmetsiin kuuluva koemetsikkö (≈2 ha), jossa tutkitaan harvennuksen ja avohakkuun vaikutusta hiilen ja ravinteiden kiertoon ojitetulla rämeellä. Kohteella on tehty puustomittausten lisäksi kasvihuonekaasujen mittauksia kannettavalla analysaattorilla, turve- ja karikenäytteenottoja sekä kasvillisuuskartoitus.\n\n### Puuston mittaukset\n\nPuusto on mitattu 2001, 2004 ja 2020.\n\n**Puuston mittaus 13.11.2001-17.1.2002**\n- Mitattu koealat 1, 1a, 1b, 2, 3, 4 ja 7.\n\n**Puuston mittaus 15.10.2004-21.10.2004**\n- Mitattu koealat 1, 1a, 1b, 2 ja 3.\n\n**Puuston mittaus 27.8.2020**\n- Mitattu aikaisempia mittauksia pienemmät ympyräkoealat koealoista 1, 2, ja 4.\n\n##### ______________________________________________________________________________________________________________________ \n\n### Aineistot ovat Excel-pohjaisia 20- ja 71-lomakkeita.\n\n### Tärkeimmät tallennetut muuttujat:\n\ntietuetunnus(20, 71), kokeen numero, mittauskerta, koealan numero, koealan pinta-ala(neliömetri), mittausajankohta(vuosi, kuukausi, päivä), puun numero, konepiste(mittauspiste koealan sisällä, -5--10), suunta(0-360), etäisyys(cm), puulaji(1-9), puujakso(0-3), puustoryhmä(1-4), läpimitta rinnankorkeudelta(mm), latvuskerros(1-6), tekninen laatu(1-9), tuhon ilmiasu(0-8), tuhon syy(0-13), tuhon aste (0-3), koepuukoodi(0-3), puun pituus(dm), puun latvusraja(dm).\n\nAineiston keruussa ja tallennuksessa noudatetut standardit ja menetelmäohjeet: Metsikkökokeiden maastotyöohjeet"
},
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{
"id": "9c2c445d-5aa2-4ada-9529-f5e74bcec131",
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"en": "Set-up and instrumentation of the greenhouse gas measurements on experimental sites of continuous cover forestry"
},
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"en": "A set of experimental study sites was established to monitor greenhouse gas (GHG) emissions from drained peatland forests under different harvesting regimes in Finland. The purpose of these experimental sites is to study the effects of continuous cover forestry (CCF) and clear-cutting (CC) on ecosystem processes including GHG emissions and stand development on drained peatland forests. The sites represent fertile Norway spruce dominated peatland forests, where soil GHG emissions are high due to drainage that has exposed peat to decomposition in aerobic conditions. Two “flagship” sites for greenhouse gas (GHG) monitoring have been established and instrumented by the Natural Resources Institute Finland (Luke), University of Helsinki (UH) and the Finnish Meteorological Institute (FMI). The sites host continuous GHG monitoring with Eddy Covariance (EC) towers and with automatic chambers. In addition, greenhouse gas (CO2, CH4, and N2O) emissions are monitored with manually operated chambers at four sites, where effects of selection (CCF) harvests are studied with replicated treatments. These data will be used to calculate the ecosystem and soil GHG balances of the sites by using methodologies standardized earlier and compatible with the IPCC guidelines. On all experimental sites, ground water table (WT), tree growth and regeneration are monitored in different management trials. These data will form the basic data needed for designing and demonstrating optimal harvesting cycles and evaluating and generalizing the climate impacts. The results including the biological drainage capacity (evapotranspiration) of different-sized tree stands as well as the soil GHG balance of different tree stand – WT combinations will be incorporated into existing models that can be used to estimate the mitigation obtained with different management options and in different site and climatic conditions. The study sites are actively used for training and demonstration of alternative peatland management practices by host projects and by multiple stakeholders. The host projects and organizations also promote further extensions for the measurements and all complementary research activities are welcome to these study sites."
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"en": "Leppä, Korkiakoski, Nieminen, Laiho, Hotanen, Kieloaho, Korpela, Laurila, Lohila, Minkkinen, Mäkipää, Ojanen, Pearson, Penttilä, Tuovinen, Launiainen: Vegetation controls of water and energy balance of a drained peatland forest: Responses to alternative harvesting practices",
"fi": "Leppä, Korkiakoski, Nieminen, Laiho, Hotanen, Kieloaho, Korpela, Laurila, Lohila, Minkkinen, Mäkipää, Ojanen, Pearson, Penttilä, Tuovinen, Launiainen: Vegetation controls of water and energy balance of a drained peatland forest: Responses to alternative harvesting practices"
},
"description": {
"fi": "Abstract. We quantified the response of peatland water table level (WTL) and energy fluxes to harvesting of a drained peatland forest. Two alternative harvests (clear-cut and partial harvest) were carried out in a mixed-species ditch-drained peatland forest in southern Finland, where water and energy balance components were monitored for six pre-treatment and three post-treatment growing seasons. To explore the responses caused by harvestings, we applied a mechanistic multi-layer soil-plant-atmosphere transfer model. At the clear-cut site, the mean growing season WTL rose by 0.18 ± 0.02 m (error estimate based on measurement uncertainty), while net radiation, and sensible and latent heat fluxes decreased after harvest. On the contrary, we observed only minor changes in energy fluxes and mean WTL (0.05 ± 0.03 m increase) at the partial harvest site, although as much as 70% of the stand basal area was removed and leaf-area index was reduced to half. The small changes were mainly explained by increased water use of spruce undergrowth and field layer vegetation, as well as increased forest floor evaporation. The rapid establishment of field layer vegetation had a significant role in energy balance recovery at the clear-cut site. At partial harvest, chlorophyll fluorescence measurements and model-data comparison suggested the shade-adapted spruce undergrowth was suffering from light stress during the first post-harvest growing season. We conclude that in addition to stand basal area, species composition and stand structure need to be considered when controlling WTL in peatland forests with partial harvesting. Our results have important implications on the operational use of continuous cover forestry on drained peatlands. A continuously maintained tree cover with significant evapotranspiration capacity could enable optimizing WTL from both tree growth and environmental perspectives."
},
"entity_identifier": "https://doi.org/10.1016/j.agrformet.2020.108198",
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"en": "Rautakoski, Korkiakoski, Mäkelä, Koskinen, Minkkinen, Aurela, Ojanen, Lohila: Exploring temporal and spatial variation of nitrous oxide flux using several years of peatland forest automatic chamber data",
"fi": "Rautakoski, Korkiakoski, Mäkelä, Koskinen, Minkkinen, Aurela, Ojanen, Lohila: Exploring temporal and spatial variation of nitrous oxide flux using several years of peatland forest automatic chamber data"
},
"description": {
"en": "<jats:p>Abstract. The urgent need to mitigate climate change has evoked a broad interest in better understanding and estimating nitrous oxide (N2O) emissions from different ecosystems. Part of the uncertainty in N2O emission estimates still comes from an inadequate understanding of the temporal and small-scale spatial variability of N2O fluxes. Using 4.5 years of N2O flux data collected in a drained peatland forest with six automated chambers, we explored temporal and small-scale spatial variability of N2O fluxes. A Random forest with conditional inference trees was used to find immediate and time-lagged relationships between N2O flux and environmental conditions across seasons and years with different environmental conditions. The temporal variation of N2O flux was large, and the daily mean flux varied between –11 and 1760 µg N2O m⁻² h⁻¹. Three of the six measurement chambers had a maximum N2O flux of less than 400 µg N2O m⁻² h⁻¹, while the fluxes in the other three chambers exceeded 1000 µg N2O m⁻² h⁻¹. Spatial differences in the flux persisted over time, and despite the high small-scale spatial variability, the temporal patterns of the fluxes were relatively similar across the chambers. Soil moisture as well as air and soil surface temperature were the most important variables in the random forest, with lagged soil moisture also considered important. N2O flux responded to soil wetting with a time lag of 1–7 days, but the length of the time lag varied spatially and between seasons indicating interactions with other spatially and temporally variable environmental conditions. The high temporal variation in N2O flux was related to a) seasonally variable environmental conditions, with the highest N2O fluxes measured after summer dry-wet cycles and winter soil freezing, and b) to annually variable seasonal weather conditions, which lead to high year-to-year variability in N2O budget. Changes especially in the frequency of summer precipitation events and in winter temperature and snow conditions may increase the variability of annual N2O emissions if the variability in summer and winter weather conditions increases due to climate change.\n </jats:p>",
"fi": "Abstract. The urgent need to mitigate climate change has evoked a broad interest in better understanding and estimating nitrous oxide (N2O) emissions from different ecosystems. Part of the uncertainty in N2O emission estimates still comes from an inadequate understanding of the temporal and small-scale spatial variability of N2O fluxes. Using 4.5 years of N2O flux data collected in a drained peatland forest with six automated chambers, we explored temporal and small-scale spatial variability of N2O fluxes. A Random forest with conditional inference trees was used to find immediate and time-lagged relationships between N2O flux and environmental conditions across seasons and years with different environmental conditions. The temporal variation of N2O flux was large, and the daily mean flux varied between –11 and 1760 µg N2O m⁻² h⁻¹. Three of the six measurement chambers had a maximum N2O flux of less than 400 µg N2O m⁻² h⁻¹, while the fluxes in the other three chambers exceeded 1000 µg N2O m⁻² h⁻¹. Spatial differences in the flux persisted over time, and despite the high small-scale spatial variability, the temporal patterns of the fluxes were relatively similar across the chambers. Soil moisture as well as air and soil surface temperature were the most important variables in the random forest, with lagged soil moisture also considered important. N2O flux responded to soil wetting with a time lag of 1–7 days, but the length of the time lag varied spatially and between seasons indicating interactions with other spatially and temporally variable environmental conditions. The high temporal variation in N2O flux was related to a) seasonally variable environmental conditions, with the highest N2O fluxes measured after summer dry-wet cycles and winter soil freezing, and b) to annually variable seasonal weather conditions, which lead to high year-to-year variability in N2O budget. Changes especially in the frequency of summer precipitation events and in winter temperature and snow conditions may increase the variability of annual N2O emissions if the variability in summer and winter weather conditions increases due to climate change.",
"und": "<jats:p>Abstract. The urgent need to mitigate climate change has evoked a broad interest in better understanding and estimating nitrous oxide (N2O) emissions from different ecosystems. Part of the uncertainty in N2O emission estimates still comes from an inadequate understanding of the temporal and small-scale spatial variability of N2O fluxes. Using 4.5 years of N2O flux data collected in a drained peatland forest with six automated chambers, we explored temporal and small-scale spatial variability of N2O fluxes. A Random forest with conditional inference trees was used to find immediate and time-lagged relationships between N2O flux and environmental conditions across seasons and years with different environmental conditions. The temporal variation of N2O flux was large, and the daily mean flux varied between –11 and 1760 µg N2O m⁻² h⁻¹. Three of the six measurement chambers had a maximum N2O flux of less than 400 µg N2O m⁻² h⁻¹, while the fluxes in the other three chambers exceeded 1000 µg N2O m⁻² h⁻¹. Spatial differences in the flux persisted over time, and despite the high small-scale spatial variability, the temporal patterns of the fluxes were relatively similar across the chambers. Soil moisture as well as air and soil surface temperature were the most important variables in the random forest, with lagged soil moisture also considered important. N2O flux responded to soil wetting with a time lag of 1–7 days, but the length of the time lag varied spatially and between seasons indicating interactions with other spatially and temporally variable environmental conditions. The high temporal variation in N2O flux was related to a) seasonally variable environmental conditions, with the highest N2O fluxes measured after summer dry-wet cycles and winter soil freezing, and b) to annually variable seasonal weather conditions, which lead to high year-to-year variability in N2O budget. Changes especially in the frequency of summer precipitation events and in winter temperature and snow conditions may increase the variability of annual N2O emissions if the variability in summer and winter weather conditions increases due to climate change.\n </jats:p>"
},
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"en": "Korkiakoski, Ojanen, Tuovinen, Minkkinen, Nevalainen, Penttilä, Aurela, Laurila, Lohila: Partial cutting of a boreal nutrient-rich peatland forest causes radically less short-term on-site CO2 emissions than clear-cutting",
"fi": "Korkiakoski, Ojanen, Tuovinen, Minkkinen, Nevalainen, Penttilä, Aurela, Laurila, Lohila: Partial cutting of a boreal nutrient-rich peatland forest causes radically less short-term on-site CO2 emissions than clear-cutting"
},
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"fi": "Abstract. Wide-spread harvesting of forests growing on drained peatlands is foreseen to take place in Finland in the near future. A hot question is if continuous cover forestry utilizing partial cuttings could be used to mitigate carbon dioxide (CO2) emissions and the consequent climatic impact compared to traditional clear-cutting and even-aged forest management. To assess the impact of clear-cutting vs. partial cutting, we first measured CO2 exchange with the eddy covariance (EC) method for six years in a mature, nutrient-rich peatland forest in southern Finland. Part of the forest was then partially cut (74% of the stem volume), and part of it was clear-cut, and CO2 exchange of both areas was measured for another six years. Tree growth was recorded before and after the cuttings to separate the contributions of tree stand and forest floor to CO2 exchange. Before the cuttings, the site had an annual CO2 exchange close to zero, but both cutting methods turned it into a CO2 source. However, the first-year emissions from the partial cutting area (800 g CO2 m−2 yr−1) were markedly lower than the emissions after clear-cutting (3100 g CO2 m−2 yr−1). The partial cutting area remained a CO2 source during the first three years but turned into a CO2 sink after that, while the clear-cut area acted as a large, although diminishing, CO2 source for the whole measurement period. The total six-year CO2 balances before and after partial cutting did not differ significantly, while the emissions after clear-cutting were on average 2240 g CO2 m−2 yr−1 larger than before it. Combining the EC data with the tree growth measurements showed that the forest floor was losing C both before and after harvestings. In conclusion, partial cutting resulted in clearly smaller CO2 emissions than clear-cutting at least in the short term."
},
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"en": "Rautakoski, Korkiakoski, Mäkelä, Koskinen, Minkkinen, Aurela, Ojanen, Lohila: Exploring temporal and spatial variation of nitrous oxide flux using several years of peatland forest automatic chamber data",
"fi": "Rautakoski, Korkiakoski, Mäkelä, Koskinen, Minkkinen, Aurela, Ojanen, Lohila: Exploring temporal and spatial variation of nitrous oxide flux using several years of peatland forest automatic chamber data"
},
"description": {
"en": "<jats:p>Abstract. The urgent need to mitigate climate change has evoked a broad interest in better understanding and estimating nitrous oxide (N2O) emissions from different ecosystems. Part of the uncertainty in N2O emission estimates still comes from an inadequate understanding of the temporal and small-scale spatial variability of N2O fluxes. Using 4.5 years of N2O flux data collected in a drained peatland forest with six automated chambers, we explored temporal and small-scale spatial variability of N2O fluxes. A Random forest with conditional inference trees was used to find immediate and time-lagged relationships between N2O flux and environmental conditions across seasons and years with different environmental conditions. The temporal variation of N2O flux was large, and the daily mean flux varied between –11 and 1760 µg N2O m⁻² h⁻¹. Three of the six measurement chambers had a maximum N2O flux of less than 400 µg N2O m⁻² h⁻¹, while the fluxes in the other three chambers exceeded 1000 µg N2O m⁻² h⁻¹. Spatial differences in the flux persisted over time, and despite the high small-scale spatial variability, the temporal patterns of the fluxes were relatively similar across the chambers. Soil moisture as well as air and soil surface temperature were the most important variables in the random forest, with lagged soil moisture also considered important. N2O flux responded to soil wetting with a time lag of 1–7 days, but the length of the time lag varied spatially and between seasons indicating interactions with other spatially and temporally variable environmental conditions. The high temporal variation in N2O flux was related to a) seasonally variable environmental conditions, with the highest N2O fluxes measured after summer dry-wet cycles and winter soil freezing, and b) to annually variable seasonal weather conditions, which lead to high year-to-year variability in N2O budget. Changes especially in the frequency of summer precipitation events and in winter temperature and snow conditions may increase the variability of annual N2O emissions if the variability in summer and winter weather conditions increases due to climate change.\n </jats:p>",
"fi": "Abstract. The urgent need to mitigate climate change has evoked a broad interest in better understanding and estimating nitrous oxide (N2O) emissions from different ecosystems. Part of the uncertainty in N2O emission estimates still comes from an inadequate understanding of the temporal and small-scale spatial variability of N2O fluxes. Using 4.5 years of N2O flux data collected in a drained peatland forest with six automated chambers, we explored temporal and small-scale spatial variability of N2O fluxes. A Random forest with conditional inference trees was used to find immediate and time-lagged relationships between N2O flux and environmental conditions across seasons and years with different environmental conditions. The temporal variation of N2O flux was large, and the daily mean flux varied between –11 and 1760 µg N2O m⁻² h⁻¹. Three of the six measurement chambers had a maximum N2O flux of less than 400 µg N2O m⁻² h⁻¹, while the fluxes in the other three chambers exceeded 1000 µg N2O m⁻² h⁻¹. Spatial differences in the flux persisted over time, and despite the high small-scale spatial variability, the temporal patterns of the fluxes were relatively similar across the chambers. Soil moisture as well as air and soil surface temperature were the most important variables in the random forest, with lagged soil moisture also considered important. N2O flux responded to soil wetting with a time lag of 1–7 days, but the length of the time lag varied spatially and between seasons indicating interactions with other spatially and temporally variable environmental conditions. The high temporal variation in N2O flux was related to a) seasonally variable environmental conditions, with the highest N2O fluxes measured after summer dry-wet cycles and winter soil freezing, and b) to annually variable seasonal weather conditions, which lead to high year-to-year variability in N2O budget. Changes especially in the frequency of summer precipitation events and in winter temperature and snow conditions may increase the variability of annual N2O emissions if the variability in summer and winter weather conditions increases due to climate change.",
"und": "<jats:p>Abstract. The urgent need to mitigate climate change has evoked a broad interest in better understanding and estimating nitrous oxide (N2O) emissions from different ecosystems. Part of the uncertainty in N2O emission estimates still comes from an inadequate understanding of the temporal and small-scale spatial variability of N2O fluxes. Using 4.5 years of N2O flux data collected in a drained peatland forest with six automated chambers, we explored temporal and small-scale spatial variability of N2O fluxes. A Random forest with conditional inference trees was used to find immediate and time-lagged relationships between N2O flux and environmental conditions across seasons and years with different environmental conditions. The temporal variation of N2O flux was large, and the daily mean flux varied between –11 and 1760 µg N2O m⁻² h⁻¹. Three of the six measurement chambers had a maximum N2O flux of less than 400 µg N2O m⁻² h⁻¹, while the fluxes in the other three chambers exceeded 1000 µg N2O m⁻² h⁻¹. Spatial differences in the flux persisted over time, and despite the high small-scale spatial variability, the temporal patterns of the fluxes were relatively similar across the chambers. Soil moisture as well as air and soil surface temperature were the most important variables in the random forest, with lagged soil moisture also considered important. N2O flux responded to soil wetting with a time lag of 1–7 days, but the length of the time lag varied spatially and between seasons indicating interactions with other spatially and temporally variable environmental conditions. The high temporal variation in N2O flux was related to a) seasonally variable environmental conditions, with the highest N2O fluxes measured after summer dry-wet cycles and winter soil freezing, and b) to annually variable seasonal weather conditions, which lead to high year-to-year variability in N2O budget. Changes especially in the frequency of summer precipitation events and in winter temperature and snow conditions may increase the variability of annual N2O emissions if the variability in summer and winter weather conditions increases due to climate change.\n </jats:p>"
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"fi": "Aineistossa (SVESITASE) on seurattu pieniä valuma-alueita, jotka on perustettu ojitetuille rämeille. Rovaniemellä on 2 koetta (Sattasuo ja Koiraoja) ja sen lisäksi on viisi muuta koetta (Muhos, Sievi, Parkano, Mänttä-Vilppula ja Tuusula). Koekohteilla on tehty 2006-2007 kaksinkertainen ojitus, jossa ulommalla ojalla estetään ulkopuolisten vesien pääsy alueelle ja toisaalta sisempää ojaa pitkin alueelta kertyvä valumavesi kulkeutuu mittapadon kautta ulos. Valuma-alueen muodostaa käytännössä yksi sarka. Pinta-ala vaihtelee kohteen mukaan 0,5 – 4 ha. Rovaniemen Sattasuolla, Vilppulan Jaakkoinsuolla ja Tuusulan Katilassa on ollut yksi valuma-alue seurannassa. Muilla kohteilla on ollut 2-3 valuma-aluetta seurannassa. Intensiivisintä seurantaa on tehty 2006 – 2015. Valuma-alueilla on mitattu seuraavia tietoja:\n-\tPohjaveden syvyyttä kasvukaudella, 30 – 50 pohjavesiputken hila, manuaalimittaukset ja joillakin alueilla myös yksi dataloggeri\n-\tValuntamittaukset mittapadolla: Rovaniemen Sattasuolla, Vilppulassa ja Tuusulassa mittakaivo, jossa dataloggeri valunnan jatkuvaan seurantaan, muualla valuntaa mitattu mittauskampanjoin\n-\tOjaveden korkeutta manuaalimittauksin \n-\tMetsikkösadantaa sadesuppiloilla\n-\tLumen paksuutta\n-\tSääasema Rovaniemen Sattasuolla ja Vilppulassa\n-\tPuutason haihduntaa Granier-menetelmällä Rovaniemen Sattasuon kokeella ja Vilppulassa (josta huono data)\n-\tTurpeen vedenläpäisevyys, turpeen paksuus, turpeen laatu\n-\tPuuston kartoitusmittaus koko valuma-alueella (joillakin lisäksi xyz-tarkkuuspaikannus)\n-\tPuustomittaukset, katso alla\n-\tPuuston neulasten ravinnepitoisuus useana vuonna\n-\tKokeista lopetettu Tuusula, Vilppula ja Parkano 2017-2018, Rovaniemen Koiraojan koe 2022 (muutettu eri käyttöön keväällä 2025)\n-\tSievissä tehty harvennushakkuu toisella valuma-alueella keväällä 2009 ja Muhoksella samoin toisella valuma-alueella keväällä 2010. Kummatkin harvennetut valuma-alueet on tuhkalannoitettu (5t/ha) keväällä 2014.\n-\tPohjavesi- ja puustomittauksia jatkettu Muhoksella ja Sievissä 2024 saakka\n\n**Puustonmittauksia alueille tehty seuraavasti:**\n\n**SATTASUO Rovaniemi**\n- 1.9.2009 ja 17.10.2018\n\n**KOIRASUO Rovaniemi**\n- 29.5.2007, 15.6.2009 (koeala 3), 1.7.2010 (koeala 2), 30.5.2016 (koeala 2) ja 27.9.2023\n\n**NEVAJÄRVI Sievi**\n- 2.5.2006, 2.5.2009 (koeala 2), 9.6.2011, 7.10.2014 (koealojen osittainen mittaus), 8.5.2019 ja 6.10.2023\n\n**KIIMAKEI Parkano**\n- 18.12.2006, 10.5.2010 (koeala 3) ja 8.3.2017\n\n**JAAKKOINS Mänttä-Vilppula**\n- 1.4.2006 ja 6.5.2011\n\n**KATILA Tuusula**\n- 6.11.2006 ja 4.6.2013\n\n**ANSASAARI Muhos**\n- 1.8.2007, 1.6.2010 (koeala 1), 1.9.2014 (koealojen osittainen mittaus), 3.5.2019 ja 8.9.2023\n\n\n**Puustonmittausaineistot ovat Excel-pohjaisia 20- ja 71-lomakkeita.**\n\n#### Tärkeimmät tallennetut muuttujat:\n\ntietuetunnus(20, 71), kokeen numero, mittauskerta, koealan numero, koealan pinta-ala(neliömetri), mittausajankohta(vuosi, kuukausi, päivä), puun numero, konepiste(mittauspiste koealan sisällä, -5--10), suunta(0-360), etäisyys(cm), puulaji(1-9), puujakso(0-3), puustoryhmä(1-4), läpimitta rinnankorkeudelta(mm), latvuskerros(1-6), tekninen laatu(1-9), tuhon ilmiasu(0-8), tuhon syy(0-13), tuhon aste (0-3), koepuukoodi(0-3), puun pituus(dm), puun latvusraja(dm).\n\nAineiston keruussa ja tallennuksessa noudatetut standardit ja menetelmäohjeet: Metsikkökokeiden maastotyöohjeet"
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