Heid L., Darsonville O., Conil S., Klumpp K., Longdoz B.
ICOS Belgium Science Conference, 20 octobre 2017
Comparison of aboveground biomass
production efficiency for a grassland
and a forest with similar edaphic
and climatic conditions
CONTEXT
&
OBJECTIVES
•Evolution in ecosystem management
- Intensification, soil changes (structure, fertility,…)
•Climate change (Temperature, Pluviometry)
- Drift & extreme events (drought, heat wave, flood,…)
•Need of better understanding of ecosystem C balance
- Strategies in C allocation, sequestration & emission
(Trumbore, 2006)
- Uncertainties around the spatio-temporal variability of
theses strategies (Campioli et al., 2013)
3 cultures 30% espaces hétérogènes 13% prairies 16% forêt 26% milieux semi-naturels, zones humides et aquatiques 10% bâti 5% +0.5% /an -0.7%/an -0.7%/an +0.1%/an +0.5%/an Building 5% Crops 30% Forests 26% Grasslands 16% Mixed 13% Humid zones 10%
GPP Rautoaerial Rautosoil
NEE=
GPP
+
Reco
GPP = Rauto + BP + exudates + COV BPE = BP/GPP Vicca et al., 2012aBPE (leaves, wood) bBPE (roots)
Compounds of Interest Forest : aerial Structural C
Grassland: Digestible C CIPE= CIBP/GPP 4 CIP + (réserves, SS ,…) Rhetsoil Biomass Production Efficiency Compound of Interest Production Efficiency
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•Only annual BPE/aBPE
•Large variability in annual BPE/aBPE
-Ecosystem type, Nutriments availability, Mean
annual temperature, Specie, Age, Management,
[CO2]
⇛ Seasonal variability of aBPE ? Which drivers ? ⇛ Comparing aBPE evolutions of Forest vs Grassland
MATERIAL ET METHOD
29/06/2016 7
Sites
Grassland
Forest
• Beech (88%) stand ~55 ans
• Soil type: calcisol (7.1 <pH<8)
• Stem density: 800 stems/ha
• Height: 20-25m
• LAI = 8
• Type of soil: calcisol (7.1 <pH<8)
• Vegetation: Bromus hordaceus (53.8%), Bellis
perennis (16.8%) Dates Event 30/03/2014 Fertilization (NPK) 13/04/2014 Grazing (Part 1) 05/06/2014 Mowing (Part 2) 12/07/2014 Grazing (Part 1 +2)
29/06/2016 8
CO2 Flux and weather measurements
• « Traditional » equipment with LI-7200 enclosed
• Data processing : Frequency correction (Fratini); Flux quality
selection (Mauder, u*threshold), Gapfilling & Partitioning (Reinschtein)
9 Biomass measurements 80 cm 80 cm = 0.64 m² x 10 dispatched randomly on the 5.1ha Dates Sampling 23/02/2014 Cleaning 1 16/04/2014 Growth 1 (Cleaning 2) 23/05/2014 Growth 2 (Cleaning 3) 05/06/2014 Cleaning 4 24/07/2014 Growth 3 (Cleaning 5) 11/09/2014 Growth 4 (Cleaning 6) 06/11/2014 Growth 5
Cages excluding grazing Dendrometers Litter traps
Allometric equations fitted on the site (diameter, age, height)
Lab analyses of cleaning biomass for C content
Lab analyses of wood micro-cores for wood density and C content
Lab analyses of collected
litter for C content
Lab Biochemical analyses for (non-) structural C
RESULTS
GPP Rautoaerial Rautosoil
NEE=
GPP
+
Reco
GPP = Rauto + BP + exudates + COV 11 CIP + (réserves, SS ,…) Rhetsoil -549gC m-2 -1639 gC m-2 1089 gC m-2 aBP aBPE 652 gC m-2 0.39GPP -484 gC m-2 -1538 gC m-2 1054 gC m-2 aBP aBPE 400 gC m-2 0.25 12 -549gC m-2 -1639 gC m-2 1089 gC m-2 652 gC m-2 0.39
NEE=
GPP
+
Reco
Reco GPP = Rauto + BP + exudates + COV1. Forest : more assimilated C dedicated to aboveground biomass
2. Grassland : Belowground or Rauto ? (soil ingrowth cores)
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Intra-annual variability (Forest)
0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 10.04-05.05 06.05-02.06 03.06-30.06 01.07-28.07 29.07-25.08 26.08-22.09 22.09-23.11 Abo v eg ro und w BP (gC.m -2 .d -1 ) aBP aBP - d fixed 1. Density correction up to 22%
2. Can’t be neglected for studies on C allocation seasonal variability
0 50 100 150 200 250 300 350 400 C co n ten t (gC /m²) GPP aBP bois aBP feuille
Avril- Mai Juin Juillet Août Septembre Octobre
1 0.31 0.46 0.55 0.32 0.07 0.00 0.20 0.40 0.60 0.80 1.00 1.20 1 2 3 4 5 6 aBPE
1. Large intra-annual variability
2. No apparent influence of meteo or soil conditions
3. Seasonal bell-shaped phenology similar to the photoperiod
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Management: Fertilization Grazing Mowing
Intra-annual variability (Grassland)
0.00 0.10 0.20 0.30 0.40 0.50 0.60 22/02-16/04 17/04-23/05 05/06-24/07 25/07-11/09 12/09-06/11 aBPE
1. Larger variability ⇔ forest 2. Impact of mowing ?
16 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 Pr odu cti on eff ic ie ncy NSC C_Cel+L C_Hem
April Mai June July August Sept
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