Carbon sequestration due to the abandonment of croplands in the former USSR since 1990 Nicolas VUICHARD (1) Luca BELELLI (1) Irina KURGANOVA (2) Philippe.

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Carbon sequestration due to the abandonment of croplands in the former USSR since 1990 Nicolas VUICHARD (1) Luca BELELLI (1) Irina KURGANOVA (2) Philippe CIAIS (3) Pascalle Smith (3) Riccardo VALENTINI (1) (1)University of Tuscia – Viterbo (Italy) (2)Inst. of Physiochemical and Biological Problems in Soil Science – Pushchin (Russia) (3)LSCE/IPSL – Saclay (France)

General overview Soil carbon changes are impacted by time agriculture recovering grasslandgrassland Soil carbon Climaxic ecosystem + Climate ++ Management (harvest, tillage, fert.) + Climate ++ Land-use legacy 50’s90’s

Cropland’s abandonment ► Due to 1990’s economic crisis, important area of arable land has been abandoned Arable and permanent crops in CIS (former USSR) source FAOSTAT - 20 Mha Abandoned cropland areas from 1990 to 2000 (Hurtt et al., 2006)

C gain estimate from plot to regional scale Soil carbon inventories Flux measurements Ecosystem modeling associated with climatology and land-use change scenarios At the plot scale At the regional scale Difficult to extrapolate estimate Model-based uncertain estimate Easy to extrapolate estimate Data-based precise estimates

Eddy-Flux measurements 5 years 10 yearsmore than 100 years Hak 1 natural steppe NEE: gC m -2 yr -1 Hak 3 Cultivated from 1958 to 1993 NEE: -146 gC m -2 yr -1 Hak 2 Cultivated from 1958 to 1998 NEE: -201 gC m -2 yr -1 Years after land-use change Flux data from Belelli Hak3 Hak1 NEE anomaly with Hak1 (gC m -2 yr -1 ) Hak2 Years after land-use change

Soil carbon inventories NEE (gC m -2 yr -1 ) Years after land-use change Carbon accumulation in soils of abandoned croplands C accumulation (tC ha -1 ) Years after land-use change Carbon balance of abandoned land

Soil carbon inventory based regional estimate C gain : 131 TgC from 1993 to 2000 over 21.5 Mha Abandonned land (Mha) NEE (TgC yr -1 )

Assessment of a model-based regional estimate Gridded climate and soil forcing Generic Crop Model LUE growth Biomass allocation and yield Water and Nitrogen balance No soil C balance scale : field / seasonal cycle wheat maize soybean Terrestrial Biosphere Model Natural ecosystem functioning + disturbances scale : local => regional => global 1 year => 1000 years Krinner et al. (2005) Brisson et al. (2002) By using Orchidee-Stics LAI

Simple land-use change scenario Scenario time agriculture grassland recovering grassland 2000 Model improvements for steppes ► Modification of photosynthetic parameters values ► Changes in the allocation scheme Flux data from Belelli et al. (submitted) LAI data retrieved from FAPAR product (JRC-ISPRA) Orchidee Orchidee-Stics Orchidee

Simple land-use change scenario Scenario time agriculture grassland recovering grassland 2000 N-fertilizer addition statistics from USDA (on arable land of former USSR) C export management ► Harvest = grains + straw –> We export all the harvested biomass out of the field ► Tillage -> few information on intensity –> We assume that tillage induces a 30% decrease of the residence time of the soil carbon Orchidee Orchidee-Stics Orchidee

NEE (gC m -2 yr -1 ) Results Map of the C gain from 1993 to 2000 per m 2 Spatial mean annual NEE Abandonment of cultivation Croplands 100% instant. aband. Realistic aband. scenario Croplands 100% instant. aband cropland gC m recovering grassland

Results (2) Map of the C gain from 1993 to 2000 Abandoned cropland area from 1993 to 2000 C gain from 1993 to 2000 per m 2 C gain : 92 TgC in 8 years over 17 Mha No fertilization during cultivation => +6.1% No tillage (no impact on soil decomposition) => -5.4% 10% of straw remains on plot => -12.7%

Estimates comparison and prediction C gain between 1993 and 2000 Extrapolation for the period Model-based estimate 116 TgC in 8 years over 21.5 Mha Data-based estimate 131 TgC in 8 years over 21.5 Mha Data-based estimate 217 TgC in 10 years over 22.8 Mha

Conclusions Good agreement between our 2 regional estimates The abandonment of crops leads to a substantial gain of C Improvements can be done ► model approach To better quantify the management practices ► data approach To obtain C accumulation for the first years after land- use change

Thank you ! Acknowledgments to: Hurtt et al. (global land-use change map) FAPAR team from JRC-ISPRA

Assessment of the amount of carbon sequestered At the plot scale At the regional scale Soil carbon inventory- based studies Flux study based on eddy-covariance measurements Soil carbon data derived estimate Estimate based on ecosystem modeling with land-use change scenarios

Annual NEP (gC m -2 y -1 ) PeriodHak1Hak2Hak3 Jul 2002-Jun X Jan 2004-Dec X-146 EC based estimate Flux data from Belelli

Scenario time agriculture grassland recovering grassland 2000 Abandoned cropland areas from 1990 to 2000 (Hurtt et al., 2006) Simple land-use change scenario Region of interest 80% of the abandoned croplands are located between 20°-40°E and 40°- 60°N

Model improvements for steppes On the Hak1 site (54.5°N 90°E) ► Modification of photosynthetic parameters values ► Changes in the allocation scheme Flux data from Belelli et al. (submitted) LAI data retrieved from FAPAR product (JRC-ISPRA)

Hypothesis / Simplification Fertilization C export management of croplands ► Harvest = grains + straw –> We export all the harvested biomass out of the field ► Tillage -> few information on intensity –> We assume that tillage induces a 30% decrease of the time of residence of the soil carbon statistics from USDA ( on arable land of former USSR)

Assessment of a regional estimate Vegetation map Agricultural practices Gridded climate/soil forcing LAI Vegetation height Irrigation Nitrogen stress index Generic Crop Model LUE growth Biomass allocation and yield Water and Nitrogen balance No soil C balance scale : field / seasonal cycle wheat maize soybean Terrestrial Biosphere Model Natural ecosystem functioning + disturbances scale : local => regional => global 1 year => 1000 years Krinner et al. (2005)Brisson et al. (2002) By using Orchidee-Stics mechanistic model