Bioresource Technology 101 (2010) 157–164

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Bioresource Technology

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Typology of exogenous organic matters based on chemical and biochemicalcomposition to predict potential nitrogen mineralization

G. Lashermes a, B. Nicolardot b,1, V. Parnaudeau b,i, L. Thuriès c,2, R. Chaussod d, M.L. Guillotin e,M. Linères f, B. Mary g, L. Metzger h, T. Morvan i, A. Tricaud j, C. Villette k, S. Houot a,*

a INRA, UMR1091 Environnement et Grandes Cultures, F-78850 Thiverval-Grignon, Franceb INRA, UR1158 Agronomie Laon-Reims-Mons, F-51686 Reims, Francec Phalippou-Frayssinet, Organic Fertilizers, F-81240 Rouairoux, Franced INRA – Université de Bourgogne, UMR1229 Microbiologie des Sols et de l’Environnement, F-21065 Dijon, Francee LCA, F-17074 La Rochelle, Francef INRA, UMR1220 Transfert Sol-Plante et Cycle des Eléments Minéraux dans les Ecosystèmes Cultivés, F-33883 Villenave d’Ornon, Franceg INRA, US1158 Unité Agro-Impact, F-02007 Laon, Franceh RITTMO, F-68000 Colmar, Francei INRA, UMR1069, Soil Agro and HydroSystem, F-35000 Rennes, Francej SAS, F-45160 Ardon, Francek LDAR, F-02007 Laon, France

a r t i c l e i n f o

Article history:Received 17 April 2009Received in revised form 26 July 2009Accepted 6 August 2009Available online 1 September 2009

Keywords:Exogenous organic matterN mineralizationBiochemical fractionationTypologyHierarchical classification

0960-8524/$ - see front matter � 2009 Published bydoi:10.1016/j.biortech.2009.08.025

Abbreviations: EOM, exogenous organic matter;HEM, hemicellulose-like fraction; LIG, lignin-like fraN14, N mineralized after 14 days of incubation; N91, Nincubation; Co, organic carbon; No, organic nitrogen; MHC, hierarchical classification.

* Corresponding author. Tel.: +33 1 30 81 54 01; faE-mail address: sabine.houot@grignon.inra.fr (S. H

1 Present address: ENESAD, UMR 1210, INRA-ENESBiologie et Gestion des Adventices, 26 Bd Docteur PetCedex, France.

2 Present address: CIRAD, UPR78, Risque Environn97408 St. Denis (La Réunion), France.

a b s t r a c t

Our aim was to develop a typology predicting potential N availability of exogenous organic matters(EOMs) in soil based on their chemical characteristics. A database of 273 EOMs was constructed includinganalytical data of biochemical fractionation, organic C and N, and results of N mineralization during incu-bation of soil–EOM mixtures in controlled conditions. Multiple factor analysis and hierarchical classifica-tion were performed to gather EOMs with similar composition and N mineralization behavior. A typologywas then defined using composition criteria to predict potential N mineralization. Six classes of EOMpotential N mineralization in soil were defined, from high potential N mineralization to risk of inducingN immobilization in soil after application. These classes were defined on the basis of EOM organic N con-tent and soluble, cellulose-, and lignin-like fractions. A decision tree based on these variables was con-structed in order to easily attribute any EOM to 1 of the 6 classes.

� 2009 Published by Elsevier Ltd.

1. Introduction

Traditionally, animal manures have been used as fertilizers incrop production. More recently, in areas where animal breedinghas disappeared, other organic materials originating from urbanand industrial activities have replaced animal manures onto culti-vated soils to restore soil organic matter content and fertility.

Elsevier Ltd.

CEL, cellulose-like fraction;ction; SOL, soluble fraction;mineralized after 91 days ofFA, multiple factor analysis;

x: +33 1 30 81 53 96.ouot).AD-Université de Bourgogne,itjean, BP 87999, 21079 Dijon

emental Lié au Recyclage, F-

Exogenous organic matters (EOMs) are sources of nutrients suchas nitrogen (N), including both mineral N and organic forms under-going progressive mineralization in soil. Mineral N can be immedi-ately available for crops but can also be temporary immobilized bymicrobial biomass during EOM biodegradation in soil (Bernal et al.,1998). Consequently, EOM decomposition can either be a source ofplant available N or compete with plant for it (Flavel and Murphy,2006). On the other hand, mineral N can be potentially lost in theenvironment by volatilization, denitrification and leaching. A sus-tainable management of EOMs hence requires that the applicationof EOM supports crop production and protects water and air qual-ity (Mamo et al., 1999). It is particularly important to synchronizethe N supply from EOM with plant requirements in agriculturalsystems where other sources of mineral N such as fertilizer arelimited or excluded (Palm et al., 2001b). Thus, building tools help-ing to better anticipate the effect of EOM inputs on N dynamics insoil and to improve the management of EOM application is a realchallenge.

158 G. Lashermes et al. / Bioresource Technology 101 (2010) 157–164

Exogenous organic matter decomposition has been referred fora long time to depend upon their quality, such as their initial C to Nratio (Hadas et al., 2004; Nicolardot et al., 2001). However, this ra-tio could fail to predict C and N decomposition patterns (Recouset al., 1995; Ambus et al., 2002). Biochemical composition as deter-mined by stepwise chemical digestion of EOM has been shown togive a more complete basis to explain C and N mineralizationkinetics. In many cases, biochemical fractionation has been suc-cessfully used for the calibration of models describing soil C andN dynamics after EOM addition (Henriksen and Breland, 1999a;Pansu et al., 2003; Gabrielle et al., 2004). Some authors have testedmultifactor approaches to establish relationships between C and Nmineralization and biochemical characteristics. For differentgroups of EOM, such as wastewater sludges (Parnaudeau et al.,2004), animal wastes (Morvan et al., 2005), industrial wastewaters(Parnaudeau et al., 2006), they proposed a classification gatheringinto clusters EOMs with both similar composition criteria and min-eralization rates. All these studies pointed out that biochemicalfractions improved the prediction of C and N mineralization pat-terns and rates compared with the only knowledge of EOM originand treatment. They offered an interesting statistical method (mul-tiple factor analysis followed by hierarchical classification) to asso-ciate mineralization potential to the most discriminatingcomposition characteristics. Nevertheless, the proposed classifica-tions remained descriptive and specific to the studied group ofEOMs. In other similar work, Palm et al. (2001a) used a databaseof EOM quality to build a decision tree based on chemical compo-sition to classify and help in the management of EOMs.

The aim of our study was therefore to develop a typology pre-dicting EOM potential N mineralization in soil, from their chemicalcharacteristics, including practical guidelines to classify EOMs ofmany different origins. The assessment of N availability based oneasily determined characteristics would help to improve EOMmanagement. We used a large database including chemical andbiochemical characteristics of EOMs as well as kinetics of N miner-alization during soil incubation. Both multiple factor analysis andhierarchical classification were used to gather EOMs with similarcomposition and N mineralization behavior into classes. Thresh-olds of analytical characteristics were then defined for each classand a decision tree based on these thresholds attributed eachEOM into a single class of N availability.

2. Methods

2.1. Exogenous organic matters

A 273 EOM subset of the 440 EOM database displayed in previ-ous study (Lashermes et al., 2009) issued of several research pro-grams was used. These EOMs included a wide range of organicproducts from various origins representative of the diversity ofEOMs applied on cropped soils. Of these, 20% were wastewatersludges (group A), 23% urban composts (group B), 11% compostedmanures (group C), 5% livestock farming wastes without litter(group D), 17% non-composted livestock farming wastes with litter(group E), 2% organic products used as mulch (group F), 6% plantmaterials (group G), 3% organic and organo-mineral fertilizers(group H), 2% manufactured soil improvers (group I), 1% animalby-products (group J), 2% anaerobically digested wastes (group K)and 8% other products (group L). The wastewater sludges had var-ious origins (municipal, agro-industrial and paper mill industry)and were liquid, dewatered, thermally dried, or limed. The plantmaterials included grape marc, coffee ground, cocoa and olivecakes, and seaweeds. The animal by-products were pig bristles,feathers and wool dust.

All EOMs were air-dried and finely ground to a 1-mm particlesize before analysis. Organic C (Co) and total N were determined

using elementary analysis according ISO 10694 standard (AFNOR,1999). Mineral N (N–NHþ4 and N–NO�3 ) was determined by color-imetry after 1 M KCl extraction according to the standards ISO11732 (AFNOR, 2005b) and ISO 13395 (AFNOR, 1996). Organic N(No) was calculated as the difference between total N and mineralN. Total organic matter was determined by loss on ignition of dryresidue at 550 �C for sludges (NF EN 12879, AFNOR, 2000b) and480 �C for all other EOMs (NF EN 13039, AFNOR, 2000a).

Soluble (SOL), hemicellulose- (HEM), cellulose- (CEL), and lig-nin-like (LIG) fractions were determined using crude-fiber analysisfollowing the French standard XPU 44-162 (AFNOR, 2005a), thebiochemical fractions being expressed as g organic matter (OM)kg�1 total OM. The fractions were defined according to their solu-bility in detergents as initially used to determine forage digestibil-ity (Van Soest and Wine, 1967). The soluble organic fractionincluded both the organic matter extractable in boiling water(100 �C) and hot neutral detergent of the Van Soest extractionprocedure.

2.2. N mineralization of EOMs during incubations

Mineral N dynamics in soil after EOM addition was assessed inincubation of soil–EOM mixtures under controlled laboratory con-ditions during 91 days. Most EOMs (87%) were incubated at 28 �Cand the remaining at 15 �C. A temperature function specific to or-ganic residue decomposition (Nicolardot et al., 2001) was used toconvert the time scales of EOM incubated at 15 �C into an equiva-lent time scale at 28 �C. The added amount of EOM varied from 2 to40 g kg�1 dry soil. In all cases, a control treatment (soil withoutEOM addition) was carried out. Since experimental data came fromdifferent previous studies, a total of 20 soils were used for the incu-bations with various characteristics: clay, 15–280 g kg�1 dry soil;silt, 17–802 g kg�1 dry soil; CaCO3, 0–725 g kg�1 dry soil, organicC, 0.9–25.8 g kg�1 dry soil. Soil moisture content was adjusted to75–100% of the soil water holding capacity. For 159 EOMs, mineralN was added at the beginning of the incubation to prevent EOMdecomposition from being limited by mineral N availability (Re-cous et al., 1995; Henriksen and Breland, 1999b). No mineral Nwas added for the remaining 114 EOMs. Most of them presenteda high enough initial concentration of mineral N to prevent N lim-itation that finally occurred for only 11 EOMs. Three or four repli-cates were performed for each EOM at each sampling date. MineralN was extracted in soil–EOM mixtures by 1 M KCl and N–NHþ4 andN–NO�3 concentrations in the extracts were determined by color-imetry. The mineral N coming from EOM organic N mineralizationwas calculated at each sampling date as the difference betweenmineral N content in soil–EOM mixtures and in control soil assum-ing that soil origin did not influence the N mineralization of EOM(Coppens, 2005) and was expressed as g N kg�1 EOM dry matter(DM). In this work, only the N mineralized after 14 and 91 dayswere considered (N14 and N91, respectively).

2.3. Statistical analyzes

Two thirds of the initial data collection (181 EOMs) were ran-domly chosen among each EOM group (from groups A to L) to per-form statistical analyzes and elaborate the typology. The last third(92 EOMs) was kept for the validation of the typology.

A first multiple factor analysis (noted MFA1) was performedwith the SPAD software (DECISIA, 1999) on the first collection (cal-ibration data) of 181 EOMs. The detailed method has been previ-ously described (Escofier and Pages, 1998; Parnaudeau et al.,2004, 2006; Morvan et al., 2005). A multiple factor analysis corre-sponds to a principal component analysis with several sets of vari-ables in which the influence of each set is balanced. Two sets ofvariables were used: the first one included the composition

G. Lashermes et al. / Bioresource Technology 101 (2010) 157–164 159

variables (Co and No contents; Co:No ratio; biochemical fractions);the second the N mineralization data (N14 and N91). Based onthe results of MFA1, an ascending hierarchical classification (notedHC1) was carried out using the algorithm of Ward to gather themost comparable EOMs in terms of N mineralization behaviorand analytical composition into groups or ‘‘clusters”. The clustersconstitute the different classes of N availability and the most dis-criminating composition characteristics explaining EOM N releasein the classes are identified.

To elaborate the typology, the chemical composition of EOMswas then carefully revised in each cluster in order to define (1)thresholds for each composition criterion associated to the behav-ior of potential N mineralization; (2) the decision tree used to attri-bute each EOM to only one class of N mineralization behavior. Eachstep of the decision tree was based on the comparison between thechemical composition of an EOM and the thresholds defined in thedecision tree. Thus, each EOM was progressively attributed to asingle class of N availability.

A second multiple factor analysis (noted MFA2) coupled with asecond ascending hierarchical classification (noted HC2) had to becarried out on a subset of 78 EOMs of the calibration data collec-tion that had N14 and N91 within the ranges of 0–20 and 0–25 g N kg�1 EOM-DM, respectively, to better define the criteriaused for their discrimination. This second MFA was performedwith the same sets of variables as previously.

Finally, once the composition criteria and the decision tree weredefined for all classes of N mineralization, the proposed typologywas tested back with the 181 EOMs used for its definition and val-idated with the remaining 92 EOM data collection.

3. Results and discussion

3.1. Short description of the database

Chemical characteristics (No, Co:No) and biochemical fractions(SOL, HEM, CEL and LIG) were very variable within the databaseand even among each group of EOM (Fig. 1). The potential N min-eralization at 28 �C also differed greatly: 69% EOMs induced net Nmineralization with 0–94 g mineralized N kg�1 EOM-DM after91 days in incubation, while 31% induced net N immobilizationwith �0.01 to �16 g immobilized N kg�1 EOM-DM. Both compostsof urban and animal waste origins (groups B and C) presented lowN mineralization or even induced N immobilization. Mulches,manufactured soil improvers and anaerobically digested wastes(groups F, I and K) also presented low and similar potential N min-eralization. On the contrary, wastewater sludges (group A), plantmaterials (group G), fertilizers (group H), animal by-products(group J), livestock farming wastes without litter (group D) andin some extent livestock farming wastes with litter (group E) pre-sented very variable potential N mineralization. For some groups(F, I and K) all EOMs presented close values of N mineralized after91 days, which could be explained by the origin and treatmentwhile for the remaining groups, neither the origin nor the treat-ment were useful criteria to predict potential N mineralization insoil. The N mineralization data (N91) were significantly positivelycorrelated with the organic N content (r = 0.72, P < 0.001), the HEMand SOL fractions (r = 0.49 and 0.33, P < 0.001, respectively) andsignificantly negatively correlated with the CEL and LIG fractions(r = �0.40 and �0.36, P < 0.001, respectively) and with Co:No

(r = �0.35, P < 0.001).

3.2. Multiple factor analyses and hierarchical classification of EOMs

The aim was to distribute EOMs among classes of potential Nmineralization based on their chemical and biochemical composi-

tions. In previous studies on more limited number and type ofEOMs (Parnaudeau et al., 2004, 2006; Morvan et al., 2005), suchclassification gathered EOMs with similar N and C mineralization.Considering the large database used in the present work, includingboth N and C mineralization would have led to too many classes ofEOMs since homogeneous patterns of N mineralization were heter-ogeneous for C mineralization. Therefore, only N mineralizationdata and composition variables were used to elaborate thetypology.

Fig. 2 shows the projection of the two sets of variables and the181 EOMs of the calibration data collection on the plane defined bythe first two components of the first multiple factor analysis(MFA1). The two first component factors accounted for 67.5% of to-tal inertia. The N mineralization variables (N14 and N91) contrib-uted to 52% of the first component and only 5% of the secondone. Among the group of composition variables, the organic N con-tent mainly contributed to the first component (16%). Organic C(27%), the SOL fraction (24%), CEL, HEM and Co:No (14%) contrib-uted to the second component. The LIG fraction only contributedto the third component (65%). The first component separated EOMswith high organic N content and potential N mineralization (on theleft side) from EOMs with low organic N content and potential Nmineralization (on the right side). The second principal componentseparated EOMs with large SOL fraction in the upper part of theplane from EOMs with large organic C content, large CEL andHEM fractions and larger Co:No ratio in the lower part.

The hierarchical classification based on MFA1 (HC1) defined a7-cluster partition of EOMs, presented in Fig. 2. These clusters of-fered a graduate decrease in potential N mineralization as de-scribed by N14 and N91 with the following ranges of variation:

– cluster 1.1: N14 and N91 P 40 g N kg�1 EOM-DM;– cluster 1.2: 20 6 N14 6 30 and 25 6 N91 6 40 g N kg�1 EOM-

DM;– cluster 1.3: �15 6 N14 6 15 and 0 6 N91 6 20 g N kg�1 EOM-

DM;– cluster 1.4: �5 6 N14 6 10 and 0 6 N91 6 15 g N kg�1 EOM-

DM;– cluster 1.5 and cluster 1.7: �5 6 N14 6 5 and �5 6 N916 5 g N kg�1 EOM-DM;

– cluster 1.6: �10 6 N14 6 0 and �10 6 N91 6 0 g N kg�1 EOM-DM.

Among the composition variables, the No content was the mostcorrelated with N mineralization. This variable clearly differenti-ated EOMs of cluster 1.1 (No > 90 g kg�1 EOM-DM) and cluster1.2 (65 < No < 90 g kg�1 EOM-DM) from the other clusters(No < 65 g kg�1 EOM-DM). The CEL fraction and the Co:No ratio,both variables that contributed to the second component ofMFA1 separated EOMs of cluster 1.6 (Co:No > 20 and CEL fraction>400 g kg�1 EOM-OM, respectively) that always induced soil Nimmobilization. The potential N mineralization of EOMs in theremaining clusters 1.3, 1.4, 1.5 and 1.7 overlapped so they werestudied with the second multiple factor analysis (MFA2).

Fig. 3 shows the projection of the variables and the 78 EOMscharacterized by intermediate potential N mineralization (N14and N91 ranging 0–20 and 0–25 g kg�1 EOM-DM, respectively)and which overlapped in the clusters 1.3, 1.4, 1.5 and 1.7 afterthe first classification, on the plane defined by the first two compo-nents of MFA2. The two first component factors accounted for 72%of total inertia. As in MFA1, the N mineralization variables (N14and N91) mostly contributed to the first component (52%). Amongthe composition variables, No and the LIG fractions both contrib-uted for 12% to the first component. For this component, EOMswith large N91 and organic N were separated on the left side fromEOMs with large LIG fractions on the right side. The variables Co

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Fig. 1. Distribution of mineralized N after 91 days (N91) of incubation of EOM–soil mixtures in laboratory conditions at 28 �C and of the main characteristics of the 273 usedEOMs: organic C (Co), organic N (No), Co:No ratio, soluble (SOL), hemicellulose- (HEM), cellulose- (CEL) and lignin-like (LIG) fractions. The EOMs were gathered into groups of:wastewater sludges (A), urban composts (B), composted manures (C), livestock farming wastes without litter (D), livestock farming wastes with litter (E), mulches (F), plantmaterials (G), fertilizers (H), manufactured soil improvers (I), animal by-products (J), anaerobically digested wastes (K) and other products (L). Upper and lower lines of eachbox correspond to 25th and 75th percentiles, upper and lower bars correspond to 10th and 90th percentiles. The horizontal continuous line in the box is the median andclosed circles are extreme experimental data.

Principal Component 1 - 47.06%-8 -6 -4 -2 0 2 4

Principal Component 2 - 20.46%

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4 Cluster 1.1Cluster 1.2Cluster 1.3Cluster 1.4Cluster 1.5Cluster 1.6Cluster 1.7

SOL

N14No

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Fig. 2. Projection of the variables and the 181 EOMs of the calibration data collection on the plane defined by the first two components of the first multiple factor analysis(MFA1). The variables are represented by arrows. The coordinates of each variable are the correlation coefficients with the two first principal components. The variables arebetter represented in the plane when arrow points are close to the circle. When they are correctly represented, the correlations between variables are greater when the anglebetween their directions is smaller. The affiliation of EOMs to the 7 clusters defined by the hierarchical classification (HC1) is represented with the different symbols. Thesevariables have been defined in Fig. 1 except N14 corresponding to N mineralized after 14 days of incubation of EOM–soil mixtures in laboratory conditions at 28 �C.

160 G. Lashermes et al. / Bioresource Technology 101 (2010) 157–164

(29%) and SOL (24%) mostly contributed to the second componentthat separated EOMs with large SOL fractions on the upper part of

the plane from EOMs with large Co contents in the lower part of theplane.

Principal Component 1 - 49,14%-4 -2 0 2

Principal Component 2 - 23,97%

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Cluster 2.1Cluster 2.2Cluster 2.3

SOL

N14

N91 No HEM

CoCEL Co:No

LIG

Fig. 3. Projection of the variables and the 78 EOMs on the plane defined by the twofirst components of the second multiple factor analysis (MFA2) based on the samevariables as in MFA1 and Fig. 2. The 78 EOMs were issued from the calibration datacollection and had N14 and N91 within the ranges of 0–20 and 0–25 g N kg�1 EOM-DM, respectively. Interpretation is the same as for Fig. 2.

G. Lashermes et al. / Bioresource Technology 101 (2010) 157–164 161

The second ascending hierarchical classification (HC2) based onMFA2 defined a 3-cluster partition of the 78 EOMs, cluster 2.1 tocluster 2.3 (Fig. 3), characterized by the following ranges of poten-tial N mineralization:

– cluster 2.1: 0 6 N14 6 15 and 10 6 N91 6 25 g N kg�1 EOM-DM;– cluster 2.2: 0 6 N14 6 10 and 0 6 N91 6 15 g N kg�1 EOM-DM;– cluster 2.3: 0 6 N14 6 5 and 0 6 N91 6 5 g N kg�1 EOM-DM.

The No content and the LIG fraction differentiated EOMs of clus-ter 2.1 (organic N > 30 g kg�1 EOM-DM and LIG < 200 g kg�1 EOM-OM) from those of cluster 2.3 (organic N < 30 g kg�1 EOM-DM andLIG > 200 g kg�1 EOM-OM). The EOMs of cluster 2.2 had intermedi-ate organic N content, ranging from 10 to 40 g kg�1 EOM-DM andLIG fraction lower than 200 g kg�1 EOM-OM. Although Co contentand the SOL fraction were the main contributing variables of thesecond MFA2 component, the ranges of Co content were similaramong the three clusters and only the SOL fraction was used to dif-ferentiate the clusters, ranging 450–700 g kg�1 EOM-OM in cluster2.1, 550–800 g kg�1 EOM-OM in cluster 2.2 and 200–450 in thecluster 2.3.

3.3. Calibration of EOM typology

Based on MFA and HC results, a typology of EOMs was proposedand a decision tree using the most discriminating chemical andbiochemical analytical parameters was constructed to attributeeach EOM to only 1 class of potential N mineralization (Fig. 4).The decision tree included several steps in which thresholds weregiven for the discriminating composition criteria: the No content,the CEL, LIG and SOL fractions were selected while the Co:No ratioand the Co content were not retained because their distributionsoverlapped between clusters. The progression in the decision tree

step by step made it possible to attribute each EOM to a single classof N mineralization potential by comparison of its chemical com-position with the thresholds.

A total of 6 classes were defined, characterized by a gradient inN availability, from the class 1 including EOMs with very high po-tential N mineralization to the class 6 with EOMs inducing risk of Nimmobilization:

– class 1: N14 and N91 P 40 g N kg�1 EOM-DM;– class 2: 20 6 N14 6 30 and 25 6 N91 6 40 g N kg�1 EOM-DM;– class 3: 0 6 N14 6 15 and 10 6 N91 6 25 g N kg�1 EOM-DM;– class 4: 0 6 N14 6 10 and 0 6 N91 6 15 g N kg�1 EOM-DM;– class 5: �10 6 N14 6 5 and �5 6 N91 6 5 g N kg�1 EOM-DM;– class 6: �10 6 N14 6 0 and �10 6 N91 6 0 g N kg�1 EOM-DM.

The classes 1, 2 and 6 corresponded to clusters 1.1, 1.2 and 1.6issued of HC1, respectively. All EOMs of clusters 1.5 and 1.7 weregathered into the class 5 because of their similarities. The classes3 and 4 corresponded to the clusters 2.1 and 2.2 of HC2, respec-tively. The remaining EOMs of the cluster 2.3 were also includedinto the class 5. The chemical characteristics and the potential Nmineralization after 91 days of incubation are presented in Fig. 5for the 6 classes.

In class 6, the large CEL fraction content could explain the Nimmobilization observed during the microbial degradation of thisfraction after EOM addition to soil (Annabi et al., 2007). The class5 was the largest one with 123 EOMs characterized by variouscompositions leading to the same potential N mineralization.For 75% of EOMs in this class, a large LIG fraction (>200 g kg�1

EOM-OM) probably explained the very low N mineralization po-tential. As mentioned earlier by Hattori and Mukai (1986) andParnaudeau et al. (2004) even if LIG concentrations only representrelatively small proportions of EOM organic matter, it could beenough to significantly reduce EOM decomposition. Among theremaining EOMs of the class 5, some had the same CEL, LIG andSOL proportions than EOMs of the class 3 but had also lower or-ganic N content (<30 g kg�1 EOM-DM) that limited their potentialN mineralization.

For the diversified panel of EOMs studied, the No content wasfinally the best predictor for N mineralization of EOM and this sin-gle criterion explained 52% of the variability in the measured po-tential N mineralization (N91). The biochemical fractions havebeen previously related to potential N availability for plant materi-als (Müller et al., 1988; Jensen et al., 2005) as well as composts andmanures (Flavel and Murphy, 2006). The Co:No ratio has also beenshown to be a better predictor of N availability in plant materials(Vigil and Kissel, 1991; Trinsoutrot et al., 2000), organic fertilizersand their raw materials (Pansu et al., 2003), animal wastes (Mor-van et al., 2005), and wastewater sludges (Barbarika et al., 1985;Parnaudeau et al., 2004). In this study, the Co:No ratio, which variedfrom 1 to 102 among the complete data collection, increased fromclass 1 (median of 4) to class 6 (median of 30). Nevertheless, thisparameter was insufficient to predict potential N mineralization.Indeed, Co:No ratios ranging between 8 and 25 corresponded toEOM inducing positive N mineralization or net N immobilization.Similar range of Co:No ratios considered as break point betweenN mineralization and immobilization have been reported by Trin-soutrot et al. (2000) for plant materials, Parnaudeau et al. (2006)for industrial wastewaters and Pansu et al. (2003) for organic fer-tilizers and their raw materials but were lower than the Co:No ratioof 40 proposed for plant materials by Vigil and Kissel (1991). Moregenerally, it was impossible to predict potential N mineralizationfrom only one parameter, either the Co:No ratio or the biochemicalfractions (Ambus et al., 2002) and multifactor approaches wereneeded (Ruffo and Bollero, 2003; Morvan et al., 2005; Parnaudeauet al., 2006).

No > 90

65 < No 90

No 65 CEL > 400

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Class 2

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Yes

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450 < SOL 700

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EOM input

Fig. 4. Decision tree based on chemical and biochemical compositions of EOMs attributing each EOM into 1 of the 6 classes of the final typology: No in g kg�1 EOM-DM,biochemical fractions in g kg�1 EOM organic matter (OM).

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Fig. 5. Distribution of the mineralized N after 91 days (N91) of incubation of EOM–soil mixtures in laboratory conditions at 28 �C and main characteristics of the 273 studiedEOMs (biochemical fractions, Co, No and Co:No) within the 6 classes of the final typology made from the 273 EOMs. Upper and lower lines of each box correspond to 25th and75th percentiles, upper and lower bars correspond to 10th and 90th percentiles. The horizontal continuous line in the box is the median and closed circles are extremeexperimental data.

162 G. Lashermes et al. / Bioresource Technology 101 (2010) 157–164

3.4. Validation of the typology

The decision tree constructed to attribute each EOM to 1 class ofpotential N mineralization based on analytical characteristics wastested first with the 181 EOMs used for the elaboration of thetypology, then with the remaining 92 EOMs of the validation data

collection. The two data collections used for calibration and valida-tion were randomly chosen among each EOM groups (A to L). Theactual measured N14 and N91 of each EOM was compared to thepotential N mineralization determined from the classification. Adifference of ±5 g N kg�1 EOM-DM was accepted in the validationof the class attribution. The percentages of EOMs that were not

G. Lashermes et al. / Bioresource Technology 101 (2010) 157–164 163

well classified were calculated for each class in the calibration andthe validation data sets. For classes 1, 3, 5 and 6, similar or evenbetter percentages of EOMs were well classified (98–100%) bythe typology when tested against the validation data collectioncompared to the calibration data collection. For classes 2 and 4,only 68% and 75% of EOMs were well classified, respectively. How-ever in class 2, it corresponded to 1 EOM from a total of only 4EOMs in the validation data collection. In class 4, the 2 EOMs (froma total of 8) not well classified presented larger N mineralizationthan expected in this class. Finally the composition criteria usedin the decision tree were relevant enough to predict potential Nmineralization or immobilization and the typology was validated.

All classes included various types of EOMs and within one group(A to L), EOMs were distributed within up to 5 classes (Table 1).Only the anaerobically digested wastes and the manufactured soilimprovers were gathered into one single class (class 5).

3.5. Application of the EOM typology

Classically the potential N availability of an EOM is assessedthrough rather expensive and time consuming laboratory incuba-tions in controlled conditions. The typology was elaborated to fas-ter estimate this potential N mineralization from easily availableanalytical characteristics. For some EOM groups, information aboutorigin and treatment may be sufficient to predict N release pat-terns of EOM application to soil, but very often this is not enough.The proposed classification offered to predict N release patterns. Itmade it possible the comparison and discrimination of a wide pa-nel of EOMs in standard conditions and could be used for new

Table 1Numbers (and percentages) of each type of EOMs within the 6 classes of the typology.

Types of EOMs Classes of the typology

1 2

Wastewater sludges (A) 7 (64%)Urban composts (B)Composted manures (C)Livestock farming wastes with litter (D)Livestock farming wastes without litter (E) 1 (14%) 2 (18%)Mulches (F)Plant materials (G) 1 (9%)Fertilizers (H) 3 (43%) 1 (9%)Soil improvers (I)Animal by-products (J) 3 (43%)Anaerobically digested wastes (K)Other products (L)

Total number of EOMs 7 11

Table 2Classes of potential N availability of EOMs: estimation of the N released in field conditiolaboratory incubations. Reference values of potential N mineralized are proposed for each

Class Potential N mineralization in soil Mineral N release f

2 months after soil

Range of variation

1 Very high N mineralization P402 High N mineralization [20; 30]3 Medium N mineralization [0; 15]4 Medium to low N mineralization [0; 10]5 Low N mineralization [�10; 5]

Risk of induced N immobilization in soil6 Risk of induced N immobilization in soil [�10; 0]

a The predicted N mineralized or immobilized 2 months and 1 year after EOM applicaduring laboratory incubations, after 14 and 91 days of incubation, respectively. Only testimated to be equivalent to 14 and 91 days at 28 �C after conversion of lab-days to fioutdoor conditions.

types of EOM. It could be a useful tool for EOM end-users for takinginto account the N availability from EOM application in their man-agement of N fertilisation. The classification only concerned organ-ic N availability. In all cases, the mineral N content of EOMs shouldbe also accounted to evaluate the overall available N when EOM isapplied in field conditions.

The transcription of these experimental results to field condi-tions is a real challenge. In this study, the laboratory time scalewas transposed to field time scale using a temperature functionspecific to organic residue decomposition (Nicolardot et al., 2001)and using average daily temperature data recorded during 14 yearsin the Paris basin (France). One year in outdoor conditions wouldthus be equivalent to 92 days at 28 �C and 2 months in field condi-tions would correspond to 14 days at 28 �C without considerationof variability induced by the period of EOM application. This con-version scale was applied to transpose N14 and N91 measured inincubation into field indications 2 months and 1 year after EOMapplication, respectively. The N mineralization potential were con-verted in kg N Mg�1 DM and one reference value was given foreach class as the median potential N mineralization measuredwithin the EOM of the class (Table 2). Such transcription was pro-posed as potentially useful for the management of EOM in fieldconditions. Nevertheless, both the proposed classification andexperimental measurement of N availability during soil incubationcorrespond to laboratory estimations measured at constant tem-perature of 28 �C, constant water content and with ground EOMs.The influence on EOM decomposition of soil moisture, of repeatedcycles of drying and rewetting known to enhance microbial activ-ity (Sørensen, 1974), and of EOM grinding (Bremer et al., 1991)

3 4 5 6

15 (56%) 17 (89%) 14 (8%) 1 (4%)1 (5%) 63 (35%) 6 (21%)

2 (7%) 20 (11%) 5 (18%)2 (7%) 31 (17%) 11 (39%)5 (19%) 6 (3%)

2 (1%) 3 (11%)13 (7%)

2 (7%) 1 (1%)7 (4%)

1 (5%) 2 (7%)6 (3%)

1 (4%) 18 (10%)

27 19 181 28

ns (in kg N Mg�1 EOM-DM) based on the extrapolation of the N mineralized duringclass of EOM.

rom EOM (kg N Mg�1 DM)

applicationa 1 year after soil applicationa

Reference value Range of variation Reference value

60 P40 6030 [25; 40] 3010 [10; 25] 155 [0; 15] 100 [�5; 5] 0

-5 [�10; 0] �5

tion to soil correspond to the amounts of N mineralized or immobilized observedhe temperature effect was considered: the duration of 2 months and 1 year wereeld-days, taking into account the temperature differences between laboratory and

164 G. Lashermes et al. / Bioresource Technology 101 (2010) 157–164

were not considered and remained to be better understood and ac-counted for.

4. Conclusions

A six-class typology was proposed predicting ranges of poten-tial N availability from very large potential to risk of induced Nimmobilization in soil after EOM application. The organic N con-tent, the soluble, cellulose-, and lignin-like fractions were foundas key variables for the prediction of potential N availability. Basedon thresholds for these composition criteria, a decision tree wasbuilt to help the end-user to attribute any EOM to 1 class of N min-eralization. Indicative guideline values of mineral N release afterEOM application were given. The mineral N content of the EOMshould be added to evaluate the overall available N for crops.

Acknowledgements

This work was financially supported by ADEME (French Envi-ronment and Energy Management Agency). We would like to thankGuillaume Bodineau for his help in the creation of the database aswell as Pascal Mathieu for his interesting comments.

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