Urban and livestock wastes in the tropics: characterization and modeling of their transformations in soil to better choose their potential utilization

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Urban and livestock wastes in the tropics:

characterization and modeling of their transformations in soil to

better choose their potential utilization

La Réunion Island

Laurent THURIES

1

_{, Aurora del Sol NAVARRO-PRADA}

1,2

_{, Nantenaina RABETOKOTANY}

1,3

1

_{CIRAD, Recycling & Risks Unit, St Denis, La Réunion Island}

2

_{Danone Baby Nutrition,Villefranche-sur-Saône, France}

(2)

La Réunion?

(3)

(4)

OK, but also…

Vinasses (rhum) spreading

Manure… overflow

Pict. PF Chaballier

(5)

The situation (1/2)

• Various organic materials (OM) from different origins

– traditional (agri-food industry)

– new or emerging (urban)

• Uses?

– Soil inputs

• Product status: organic amendments & fertilizers

• Waste status: with legal constraints

• Where?

– (trad) needs for market gardening (high), pastures

– (new) needs for sugar cane (increasing price for inorganic fertilizers)

• Increasing production of organic « wastes »

Compost Fertiliser Manure Litter GWC Slurry

– Other

• Landfills

• Energy

(6)

• Characterizing of an OM by lab methods is generally:

– Expensive

(ex. potential nitrogen transformation: 2000 €)

– Time consuming

(ex. plant fibres: 1 week; pot N transf. : 6 months)

– Not environmentally friendly

(utilization of solvents, chemicals)

The situation (2/2)

• Lack of references on ‘new’ OM in terms of their C & N

transformations (models)

Potential transformation of N

Sequential extraction of fibres

(7)

FAO 2010 Dakar; Urban and peri-urban horticulture in the century of cities: Lessons, challenges, opportunities

Questions

N° Name Flow AOM = added organic matter Analytical solution RAOMF at time t Parameters m1 Consecutive humification 1st_{order 2 CM,} 3 parameters ( ) ( ) t k mR H mL H t k k mR H mL mR mL mR H mL e k k k k e k k k k k − + − − + + − + − kmL, kmR: 1st order k. mineralization constants of labile (L) and resistant (R) compartments kH: humification constant. m2 Exchange 1st order 2 CM m t t m e k e k 1 2 2 1 2 2 1 1 λ λ λ λ λ λ λ λ − + − − + kH, kD: humification and decomposition constants, km: mineralization constant (λ1, λ2: roots of 2nd order linear differential equation

f(kH, kD, km)) m3 Consecutive decomposition 1st_{order 2 CM,} 3 parameters ( ) kt D m m L t k D m D m L D m e k k k P e k k k k P − − − − + − − 1 kD , km: decomposition and mineralization constants

PL: labile AOM fraction

m4 Parallel 1st order 2 CM, 3 parameters t mR k L t mL k e P e L P − − − + (1 ) k_PmL, kmR: see m1 above L: see m3 above m5 Parallel 1st order 3 CM, 4 parameters

(

)

S t mR k S L t mL k L P e P P e P + − − + − − 1 kmL, kmR, PL: see m4 above

PS: stable AOM fraction

m6 Parallel 1 st order 3 CM, 2 parameters

(

)

S rt S L lt L P e P P e P + − − + − − 1 PL, PS: see m4 and m5 above l, h= constants (fixed

values of kmL and kmR for all

AOM) m7 2nd_{order kinetic} model t k (1 ) 1 1 α α − +

k: 2nd_{order kinetic constant,} α: fraction of AOM becoming microbial biomass m8 1 st_{order plus 0} order model Pe PL kmt t k L mL 0 1− + + − _P_L,_k_mL_{: see m4 above}

km0 : 0 order kinetic constant

• Is it possible to develop some new tools for a rapid,

inexpensive and environmentally friendly

characterization of various OM?

• Is it possible to use this data as input in models of OM

transformations (C & N)?

(8)

Composts

Fertilisers

Develop a method for a rapid characterization of various OM, to:

predict the transformations of the OM added to soil

(modeling)

(re-)direct the production of organic fertilizers

help in decision making (return to soils, energy?)

Manures

Litters GWC

Slurries

Objectives & Applications

Management of OM (organic wastes from the agriculture /town),

material choice & transformation process

• Agriculture/Urban sector:

La Réunion, Madagascar & Sénégal; environmental and sanitary

hazards (trace metals) in peri-urban situations

(Project ISARD)

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Organic Materials (La Réunion)

• Livestock effluents

– Raw

(10)

• Urban wastes

– Raw

• Green wastes

• Sewage sludges

– Transformed by composting (+ mixtures)

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X

N

P

Spectra acquisition

Spectral data matrix

Control of the spectral population

N

X

q

Y

k

N

Chemometry: Calibration development

regressions: MLR, PLS, neuron netw..

Model validation

Database maintenance

SEP=

Y-Y)²

N

i i

( ^

∑

Lignin

Ref.

Pred.

^

_{Y = b}

0 + b

1 X

1 +…b

i

X

i

+ b

p

X

p

Spectra

Spect.

Lab references

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– Functional compartments = f°(

measurable

fractions)

0

0.2

0.4

0.6

0.8

1

Fumier Cacao Café Compost

Lignine Cellulose Hcelluloses Soluble Cendres

chemistry

C, N

C

0 0.1 0.2 0.3 0.4 0.5 0.6 0 30 60 90 120 150 180 210 C m in g g-1

N

0 50 100 150 200 0 30 60 90 120 150 180 210 Nm in kg h a -1

TAO

Model

biochemistry

Ex. composted manure

Chemical and biochemical characteristics are used as input data for the

modeling of C and N transformations of OM added to soils :

The TAO model: Transformation of Added Organics

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Dupille 2009

Fumiers Fermes de Référence 2009 TAO Bioch

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0 30 60 90 120 150 180 210 temps (jours à 28°C) C-CO 2 g g-1 C a jou té 1 2 5 6 7 8 11 12 22 24 14 15 18 19 19bis 20 18bis 26 28

C min° predictions of manures

sampling

preparation

acquisition

_treatment

TAO

modeling

prediction

NIR

modeling

SOL HCEL CEL LIG Cend C N

NIRS + TAO modeling

+ ref data

NIR predictions of some important characteristics of OM

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– « Improving the characterization of effluents by means of new methods and

models, for a better agronomic consideration »

NIR characterization at a laboratory level

NIR characterization in situ

Reproducers

White chicken

turkeys

_{Portable NIR}

NIR models DM, OM, N

R² = 0,9875

SECV = 1,17

RPDcv = 9,00

NIR model classified as excellent

(R²>0.9, RPD>3) (Saeys et al., 2005)

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– « Organic materials from livestock or town under tropical climates: the use of

Near Infrared Spectroscopy to choose their potential utilizations in agriculture

and/or energy »

Collaborations: LRI, IRD & INRA

Urban/Agricultural sectors

C & N wide range

C:N

Sugar scum (lime)

ECUM

5 Slaughter sludge

PROCESS

8 Goat manure

FUMCAB

9 Dairy manure

FUMBOV

6 Dairy slurry

LISBOV

6 Chicken manure

LITV

10 Chicken manure

LIT Av

10 Chicken manure

LITCOUV

11 Chicken manure compost

COMREPRO

8 Chicken manure compost

CFV

11 Centrifuged pig slurry SJ

LISPc SJ

17 Compost of centrifuged pig slurry CLPv SJ

9 Composted pig manure

CLP Co

15 Green waste compost +SS

CDVB

10 Green waste compost

CDV SP

17 Green waste compost

CDV LP

11

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Urban/Agricultural sectors

0% 20% 40% 60% 80% 100% ECUM PROC ESS FUMC AB FUM BOV LISB OV _LITV LITA V LITC OUV COMR EPRO CF V LISPc SJ CLPv SJ CLP Co CDV B CDV SP CDV LP SOL HEM CEL LIG ASH

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Urban/Agricultural sectors

Potential mineralization of C & N

– effluents (agricultural & agro-industrial)

– effluent composts

– green waste composts +/- sewage sludge

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 0 30 60 90 120 150 180 210 time (d) Cmin (C-CO 2 g.g -1 added C) ECUM PROCESS LITAV LITCOUV LISBOV FUMBOV LITV COMREPRO LISPc SJ FUMCAB CDV_LP CLP Co CDV SP CLPv SJ CFV Co CDVB

raw or +/- uncomposted

composts

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Urban/Agricultural sectors

TAObioch

PL

PS

Sugar scum (lime)

ECUM

0.20 0.15

Slaughter sludge

PROCESS

0.39 0.06

Goat manure

FUMCAB

0.22 0.41

Dairy manure

FUMBOV

0.18 0.29

Dairy slurry

LISBOV

0.20 0.31

Chicken manure

LITV

0.28 0.08

Chicken manure

LIT Av

0.26 0.07

Chicken manure

LITCOUV

0.24 0.18

Chicken manure compost

COMREPRO

0.17 0.29

Chicken manure compost

CFV

0.25 0.23

Centrifuged pig slurry SJ

LISPc SJ

0.05 0.46

Compost of centrifuged pig slurry CLPv SJ

0.18 0.43

Composted pig manure

CLP Co

0.16 0.42

Green waste compost +SS

CDVB

0.23 0.33

Green waste compost

CDV SP

0.13 0.32

Green waste compost

CDV LP

0.26 0.13

min

0.05 0.06

max

0.39 0.46

mean

0.21 0.26

Calculated with

fibers + C & N

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C min

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0 30 60 90 120 150 180 210

C

m

in g g-1

NIR predictions or Lab data

PL, PR, PS =

f°

(NDSoluble, Hémicelluloses, Cellulose,Lignine, MO, Nt)

PL, PR, PS =

f°

(NDSoluble, Hemicelluloses, Cellulose,Lignine, MO, Nt)

L R MO apport 1-P’_L-P_S P’_L rR lL S PS

Modèle TAO : 1er_{ordre 3 compartiments,}

L, labile R, résistant S, stable lcte minéralisation de L rcte minéralisation de R TAO L R MO apport 1-P’_L-P_S P’_L rR lL S PS L R MO apport 1-P’_L-P_S P’_L rR lL S PS

Modèle TAO : 1er_{ordre 3 compartiments,}

L, labile R, résistant S, stable lcte minéralisation de L rcte minéralisation de R TAO

Usefulness of NIR predictions for the TAO model

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Cmin with TAO & TAO-NIR

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 30 60 90 120 150 180 210 ECUM p_ECUM p_ECUM- NIR 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 30 60 90 120 150 180 210 FUMCAB p_FUMCAB p_FUMCAB-NIRopt 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 30 60 90 120 150 180 210 LISBOV p_LISBOV p_LISBOV-NIR 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 30 60 90 120 150 180 210 CDVB p_CDVB p_CDVB-NIRopt

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Nmin with TAO

CLPv SJ Coli 0 0.1 0.2 0.3 0.4 0.5 0.6 0 30 60 90 120 150 180 210 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 N tot Ap N org total N reorg Nmin ap Nmin_Ap CLP Co Coli 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0 30 60 90 120 150 180 210 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1 N tot Ap N org total N reorg Nmin ap Nmin_Ap CFV Co Coli 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0 30 60 90 120 150 180 210 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 N tot Ap N org total N reorg Nmin ap Nmin_Ap LITV Av Coli 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0 30 60 90 120 150 180 210 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 N tot Ap N org total N reorg Nmin ap Nmin_Ap

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Conclusions

• Both versions of the TAO model can be useful to make

OM mineralization predictions :

– faster and

– cheaper

than reference analyses:

• ~5min vs 1 week-6months

• and ~10€ vs 120€-2000~€

• Potential utilizations: selection of the most appropriate

usages of OM (agronomic or energy according to a

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UR Recycling & Risks

Special thanks to my students & colleagues in La Réunion

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• Thuriès L., Pansu M., Larré-Larrouy M-C., Feller C. (2002) ‘Biochemical composition and mineralization kinetics of organic inputs in a sandy soil’ Soil Biology and Biochemistry, 34, 239-250.

• Pansu M., Thuriès L. (2003) ‘Kinetics of C and N mineralization, N immobilization and N volatilization of organic inputs in soil’ Soil Biology and Biochemistry, 35, 37-48.

• Pansu M., Thuriès L., Larré-Larrouy M-C., Bottner P. (2003) ‘Predicting N transformations from organic inputs in soil in relation to incubation time and biochemical composition’ Soil Biology and Biochemistry, 35, 353-363.

• Thuriès L., Bastianelli D., Davrieux F., L. Bonnal, R. Oliver, Pansu M., Feller C. (2005) ‘Prediction by NIRS of the composition of plant raw materials from the organic fertiliser industry and of crop residues from tropical agrosystems.’ Journal of Near Infrared Spectroscopy, 13, 187–199.