Vincent BADEAU, Renaud RABASTENS (INRA Nancy) Manuel NICOLAS, Erwin ULRICH (ONF Fontainebleau)

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1 First results of Biosoil in France Vincent BADEAU, Renaud RABASTENS (INRA Nancy) Manuel NICOLAS, Erwin ULRICH (ONF Fontainebleau) BIOSOIL Conference Brussels, 9th November 9

2 First comparisons between two campaigns LEVEL PLOTS: Renaud RABASTENS (Master s degree Nancy University months) Floristic inventories homogenization of species names inconsistencies changes in floristic composition LEVEL PLOTS: Soil analyses inconsistencies potential changes Manuel NICOLAS & Erwin ULRICH National Forest Office RENECOFOR network Soil analyses ( sites)

3 First Campaign (99-99) 99) ICP-Forests level plots general informations physical environment soil description floristic description dendrometric measurements profile pit sample / mandatory layer L+F / H / M / M / M / M Regional Offices plots plots plots plots plots

4 Second Campaign (-) ) BIOSOIL m m m² sub-samples composite sample / depth (±kg) INRA Arras (analyses) Field operations National Forest Inventory visited sites / sampled INRA Orléans + JRC (results)

5 Floristic inventories st nd st campaign 9 species (. / plot) nd campaign species (. / plot). /. +.. /. +.. / /. +.. /. +. % % species common to both campaign L & K *** Ellenberg N *** T ** R * Gégout C/N ** ph & S/T = F =

6 ΔpH HO M (Camp_ Camp_) Soil analyses % analyses with ΔpH ph >. Sols calcaires Sols bruns Sols acides Sols podzolisés Sols lessivés Sols hydromorphes Sols alluviaux

7 Soil analyses. ph HO calc nd campaign.. hydr allu. acid less podz brun.... st campaign

8 [N] st campaign Nitrogen (g/kg) calc brun acid podz less hydr alluv calc brun acid podz less hydr alluv ]-, -] ]-, -] ]-, -.] ]-., -.] ]-.,.[ [.,.[ [., [ [, [ [, [ ]-, -] ]-, -] ]-, -.] ]-., -.] ]-.,.[ [.,.[ [., [ [, [ [, [ Δ [N] M Δ [N] M [N] st campaign Freq (%) Freq (%) Δ [N] Δ [N]

9 Carbon (g/kg) Freq (%) calc brun acid podz less hydr alluv calc brun acid podz less hydr alluv ]-, -] ]-, -] ]-.,.[ [, [ [, [ Δ [C] M [C] st campaign Δ [C] 9 M [C] st campaign Freq (%) ]-, -] ]-, -] ]-, -] ]-, [ [, [ [, [ [, [ Δ [C] Δ [C]

10 Ca couche - cm Calcium Ca couche - cm Ca couche - cm

11 Magnesium Mg couche - cm Mg couche - cm Mg couche - cm

12 K couche - cm Potassium K couche - cm K couche - cm

13 Manganese Mn couche - cm Mn couche - cm Mn couche - cm

14 Aluminium Al couche - cm Al couche - cm Al couche - cm

16 Conclusions for the level plots Overall quality of Biosoil campaign probably > ICP-forest campaign (more species + spatial variability + ) Floristic inventories: temporal trends are consistent with previous french results but more specific analyses should be done Soil analyses: - huge variability in the evolution of the concentrations - global positif shift for all the elements and all the layers Temporal and spatial trends can not be clearly identified

17 Level II plots Manuel NICOLAS & Erwin ULRICH RENECOFOR Network National Forest Office

18 st campaign 99 / 99 - plots sampled Biosoil campaign / - plots re-sampled st campaign nd campaign G G G G G Same protocol for both campaigns: samples grouped into composites for analysis x depths (- ; - ; - cm)

19 Level II General results : spatial variability.. ΔpH HO for all sites and all depths CPS : ph at - cm CPS EPC EPC EPC HET HET PM c PS a SP SP 99 On level II, variability is also important But spatial variability can be identified and taken into account for trend assessment e.g. CPS at - cm: ΔpH = +. for one cluster, what can be explained by spatial variability of calcareous layer depth under sandy soil

20 Level II General results : statistical test Example : [H + ] graph for PS a plot at - cm depth n.s. Dashed red line = laboratory detection threshold Error bars = laboratory measurement uncertainty Non parametric test (Wilcoxon Mann Whitney for unpaired samples) applied in the most conservative case given by uncertainties Exchangeable elements ph ph CaCl HO Al H Ca Mg K Al+Mn+H Ca+Mg+K ECEC S/T C N C/N Sum CPS EPC EPC EPC HET HET PM c PS a SP SP Sum significant changes (p.) over tests, %

21 Level II Results : No changes detected in eutrophic/calcic soils (eg HET ) [Al ech ] [Ca ech ] [Mg ech ] [K ech ].. S/T cm nd campaign cm cm st campaign

22 Level II Results : Hypothetic change to nutrient unbalance in acid soils (eg PS a) [Al ech ]. [Ca ech ] [Mg ech ] [K ech ].. S/T % *. + % * - cm nd campaign cm.. - cm st campaign Slight increase of [Mg] & [K] and decrease of [Ca] to be confirmed with further data

23 Level II Results : Surprising significant nutrient loss on EPC plot (Andosol) [Al ech ] [Ca ech ] [Mg ech ] [K ech ]. S/T + % * % *.. - cm nd campaign 99 + % * % * % * - cm + % * cm st campaign This might be explained by the loss of nutrients inherited from former agricultural inputs

24 Level II Discussion on increase of bulk density Bulk density has increased on plots / re-sampled plots This could be due to soil compaction for of them (Norway spruce plantations) nd campaign E.g: EPC plot % % % st campaign - cm - cm - cm e = cm e = cm e = cm Excessive soil sampled at the bottom 99/9 / Δh = -9, cm e =, cm e =, cm e =, cm Replicates and knowledge of harvesting events are necessary to identify the cause(s) of bulk density increase. If soil compaction, vertical movements must be integrated: - In the interpretation of concentration changes - In the calculation of nutrient stocks But how the sampling method could be improved to better integrate management effects in long term monitoring?

25 Conclusions for the level plots Overall quality of Biosoil campaign = st campaign High variability in the evolution of the concentrations BUT spatial variability can be identified and taken into account Temporal trends can be identified Only re-sampled plots Temporal trends can not be generalised Spatial trends can not be identified

Only healthy soil can grow a nutrient dense food. You are what you eat!

Understanding How Cation Nutrients & Soil Structure are Related By Michael Martin Meléndrez Only healthy soil can grow a nutrient dense food. You are what you eat! Soil Must be able to hold onto water,