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<figure><img src="image_29.png" /></figure>
# Chapitre 3. Effet du pois et du blé en conditions contrôlées sur certaines communautés microbiennes impliquées dans le cycle de l'azote
Introduction

Les plantes cultivées influencent la minéralisation de l'azote dans le sol (Van der Krift et Berendse, 2001 ; Parkin et al., 2002 ; Gan et al., 2010). En effet, au cours de leur croissance, les plantes modifient la disponibilité en carbone et en azote dans le sol via le processus de rhizodéposition (exsudation de N minéral et de molécules organiques solubles, production de litière racinaire) et le prélèvement de N minéral (Jensen, 1996c ; Wichern et al., 2008 ; Bell et al., 2015). De plus, les plantes restituent après la fin du cycle de culture des résidus végétaux qui constituent de la matière organique fraîche (Jensen, 1997 ; Trinsoutrot et al., 2000 ; Chen et al., 2014). Ainsi, en modifiant la disponibilité relative en C et N dans le sol, les plantes vont influencer la taille et les activités des communautés microbiennes du sol dont les activités impliquées dans la décomposition-minéralisation de N (Kuzyakov et Bol, 2006, Dijkstra et al., 2013, Bell et al., 2015). Ces effets pourront conduire soit à des phénomènes d'immobilisation nette ou de minéralisation nette de N et moduleront donc ainsi la biodisponibilité en N pour les plantes.

Concernant le cas des Légumineuses telles que le pois, il est généralement admis que ces cultures, en plus de leur capacité à fixer le N atmosphérique, ont un effet positif sur la minéralisation nette de N au cours de leur croissance. Ceci permet ainsi d'augmenter la teneur en azote minéral du sol pour la culture qui leur succède (Gan et al., 2010 ; Jensen, 1996a ; Maidl et al., 1996). De plus, les résidus de Légumineuses influencent positivement la minéralisation nette de N et peuvent contribuer à augmenter la disponibilité en N minéral pour la culture en place (Jensen, 1997 ; Soon et Arshad, 2002 ; Engstrom et Linden, 2012). Les effets positifs des Légumineuses sur la minéralisation nette de N pourraient résulter de la qualité de leur rhizodépôts (avec de C/N autour de 17 ; Wichern et al., 2007a,b) et de leurs résidus (C/N moyen de 44 pour les résidus aériens (Soon et Arshad, 2002 ; Lupwayi et al., 2006 ; Kumar et Goh, 2002 ; Gan et al., 2011 ; Engstrom et Linden, 2012 ; Jensen, 1996b, 1997 ; Arcand et al., 2014). Ces C/N relativement bas sont susceptibles de modifier les activités protéase et d'ammonification (Biederbeck et al., 2005 ; Piotrowska-Długosz et Wilczewski, 2014) qui réalisent deux étapes importantes de la minéralisation : l'activité protéase est considérée comme l'activité limitant la minéralisation de l'azote (Jan et al., 2009) et l'activité d'ammonification est responsable de la dernière étape de minéralisation de l'azote (Geisseler et al., 2010).
## Expérimentation 1

<figure><img src="image_30.png" /></figure>

## Expérimentation 2

<figure><img src="image_31.png" /><figcaption>**Figure 1.** Schéma du dispositif expérimental en conditions contrôlées. Expérimentation 1 : culture du pois. Expérimentation 2 : culture du blé dans quatre sols différents</figcaption></figure>

Même si l'effet des plantes sur la minéralisation de N et sur certaines des activités enzymatiques impliquées dans la minéralisation de N a été décrit (Carpenter-Boggs et al., 2000 ; Dinesh et al., 2004 ;
Sakurai et al., 2007 ; Ordonez et al., 2009 ; Churka Blum et al., 2013), les mécanismes fins par lesquels les plantes, au cours de leur croissance, influencent ces activités microbiennes restent mal caractérisés (Cheng, 2009 ; Gan et al., 2010 ; Bell et al., 2015). La mesure des traits fonctionnels est une approche qui s'est révélée fructueuse pour comprendre comment les plantes modifient les processus écosystémiques (Cadotte et al., 2011 ; Garnier et Navas, 2012 ; Bardgett et al., 2014). Bien que les traits fonctionnels racinaires soient sous-étudiés comparativement aux traits fonctionnels aériens, l'analyse de ces traits offre une l'opportunité de comprendre plus finement les interactions entre le développement du système racinaire, les communautés microbiennes et les processus de minéralisation de N, dans la mesure où les racines constituent les organes par lesquels les flux de N et C s'effectuent entre la plante et le sol (Pérez-Harguindeguy et al., 2013).

Afin de mieux comprendre quels traits racinaires du pois et du blé influencent la taille des communautés microbiennes et les activités impliquées dans la décomposition et la minéralisation de N, des expérimentations en conditions contrôlées ont été mises en place (Figure 1). Dans une première expérimentation, nous avons analysé les relations entre les traits fonctionnels racinaires du pois au cours de son développement et les activités protéase et d'ammonification. Dans une seconde expérimentation, nous avons étudié, grâce à la mesure des traits fonctionnels racinaires, comment le blé influence au cours de son développement la biomasse microbienne et les activités protéases dans des sols amendés avec deux types de résidus de pois i.e. des litières racinaires et aériennes ou des rhizodépôts. A différents stades de développement (stades végétatifs et reproducteurs) du pois et du blé, des plantes ont été prélevées et des traits fonctionnels racinaires liés à la rhizodéposition et au prélèvement de nutriments ont été mesurés. Des mesures de disponibilité en C et N dans le sol, d'abondance microbienne (abondance de gènes ADNr 16S et *apr*, abondance microbienne C et N) et d'activité protéase et d'ammonification ont également été effectuées.

Les résultats et conclusions de ces travaux font l'objet d'un premier article accepté dans la revue Soil Biology and Biochemistry (Romillac et al., 2015) et d'un second article actuellement en préparation pour la revue Pedobiologia.

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# Influence of root traits modulating soil bioavailable C and N on ammonification activity along pea development

Nicolas Romillac<sup>a,b</sup>, Séverine Piutti<sup>a,b</sup>, Bernard Amiaud<sup>c,d</sup>, Sophie Slezack-Deschaumes<sup>a,b</sup>

Publié dans *Soil Biology and Biochemistry* (2015), **90**, 148-151

<footnote><sup>a</sup> Université de Lorraine, Laboratoire Agronomie et Environnement, UMR 1121, TSA 40602, F-54518 Vandœuvre cedex, France</footnote>

<footnote><sup>b</sup> INRA, Laboratoire Agronomie et Environnement, UMR 1121, TSA 40602, F-54518 Vandœuvre cedex, France</footnote>

<footnote><sup>c</sup> Université de Lorraine, UMR 1137, Ecologie et Ecophysiologie Forestière, F-54500 Vandoeuvre lès Nancy, France</footnote>

<footnote><sup>d</sup> INRA, UMR 1137, Ecologie et Ecophysiologie Forestière, F-54280 Champenoux, France</footnote>
**Abstract**

Plant functional traits are useful tools for understanding plant impacts on soil nitrogen (N) mineralization. The objective of this study was to examine the root traits that govern the influence of *Pisum sativum* L. on potential protease and ammonification activities, which are two key microbial activities involved in N mineralization. Ammonification activity was greater during pea reproductive than vegetative stages, whereas potential protease activity did not vary along pea development. Ammonification activity was more strongly affected by root architecture traits (total root length and percentage of fine roots) than by root growth traits (root dry matter content). Pea root traits appear to affect ammonification activity in a complex manner involving variations in rhizodeposition that modulate carbon and N availability for soil microorganisms.

**Keywords:** *Pisum sativum*, ammonification activity, protease activity, root traits

Plants influence soil nitrogen (N) mineralization during their growth by controlling N uptake as well as the release of carbon (C) and N rhizodeposits to the soil (Laungani and Knops, 2012; van der Krift and Berendse, 2001). These rhizodeposits can be used as an energy source by soil microorganisms to produce enzymes implicated in soil organic N degradation, including protease and ammonification activities (Bardgett et al., 2014; Dijkstra et al., 2013). The subsequent increase in soil N mineralization may therefore have a positive effect on the resulting soil N availability. However, the release of these rhizodeposits may also improve net N immobilization, thus decreasing soil N availability (Dijkstra et al., 2013; Orwin et al., 2010). Therefore, identifying the mechanisms by which plants modify the microbial activities involved in N mineralization is important for better predicting how plant species alter soil N availability. Plant trait-based approaches may be helpful for understanding such mechanisms (Powell et al., 2013). In particular, the analysis of root traits may be a critical tool because roots are the key organs that drive nutrient acquisition, nutrient cycling and rhizodeposition (Bardgett et al., 2014; York et al., 2013).

The aim of our study was to identify the root traits that govern the potential influence of pea (*Pisum sativum* L.) on soil potential protease and ammonification activities. Pea was chosen because compared with other crops, this plant species releases readily mineralizable N and
easily metabolizable C rhizodeposits that are assumed to have a positive effect on soil N mineralization (Evans et al., 1996; Mayer et al., 2003; Wichern et al., 2007a, 2007b). Therefore, according to the “economic rules” regulating microbial enzyme production (Allison and Vitousek, 2005), the elevated assimilable N resources in pea rhizodeposits would not require that microorganisms produce proteolytic enzymes to decompose proteinaceous organic N from soil organic matter. As opposed to vegetative stages, during pea reproductive stages, the relative excess in N (i.e., amino acids) rather than C in the rhizodeposits would promote mineralization of amino acids via ammonification activities for their C content rather than their N (Farrell et al., 2013, 2014). Thus, we hypothesized that root traits modulating N rhizodeposition processes (Roumet et al., 2008) and soil C availability more strongly influence potential protease and ammonification activities than do traits related to root growth.

A spring pea cultivar (*P. sativum* L., cv. Kayanne) was grown under controlled conditions (supplementary information). Soil and roots were sampled (n=4) at two vegetative (3-leaf and 5-leaf) and two reproductive (first flowers and early seed filling) stages. The dry masses of roots and aerial parts were measured. Root morphology was analyzed by digital scanning using WinRHIZO (Arsenault et al., 1995). Four root architecture traits related to N interception and rhizodeposition were determined: total root length, specific root length (SRL), fine root length percentage and root mean diameter. In addition, seven root growth traits were estimated: root dry matter content (RDMC), root mass fraction (RMF), root tissue density (RTD), total root C and N contents, and water-soluble root C and N contents. We determined the following soil characteristics: the total mineral N content in 2 M KCl extracts, soil N (HW-N) and C (HW-C) contents in hot water extracts, the microbial biomass C (MBC) and N (MBN), and the abundances of the total bacterial and protease communities using quantitative PCR and ammonification and protease activities. Analyses were conducted using standard methods, which are outlined in the supplementary information. Redundancy analyses (RDA) were performed to assess the relationship between protease and ammonification activities, and three sets of the most significant (after forward selection, Legendre and Legendre, 1998) explanatory variables, i.e., root architecture traits, root growth traits and soil variables. Then, variation partitioning was performed to quantify the relative contributions of the most significant explanatory variables to the variations in ammonification and protease activities (Peres-Neto et al., 2006). These analyses were performed using the vegan 2.0-9 package (Oksanen, 2013) of R 3.0.0 (R Core Team, 2013).
The results showed potential protease activity did not change during pea development. It should be underlined that protease activities have been estimated by using the conventional method described by Ladd and Butler (1972), reflecting more potential activities rather than native activities. We cannot exclude that the use of isotope pool dilution techniques to determine native gross protein depolymerisation, such as those developed by Wanek et al. (2010), would have influenced the results. In contrast to protease activity, ammonification activity evolved as a function of pea phenology, with a significant increase at the beginning of seed filling (H' = 9.5, *p* < 0.05, supplementary information, Table 1). During pea reproductive stages, changes in ammonification activity may be associated with an increase in the C-to-N ratio of pea rhizodeposits that would favor C limitations for soil microorganisms (Kopacek et al., 2013). Mineralization of excess amino acids in pea rhizodeposits could then alleviate such soil C limitations (Jensen, 1996; Farrell et al., 2014). The significant decrease in soil HW-C/N (H' = 12.90, *p* < 0.01, supplementary information, Table 1) between the 5-leaf and flowering pea stages supports these findings. This statement is also reinforced by our observation of a significant negative correlation between ammonification activity and HW-C (r = -0.73, *p* < 0.01).

Based on RDA analyses (Fig. 1 and 2), no root trait was associated with potential protease activity, likely because this activity did not change during pea development. By contrast, several root traits that varied throughout pea development (supplementary information, Table 1) significantly influenced ammonification activity. The RDMC and covarying total root length and percentage of fine roots showed direct influences on ammonification activity (Fig. 1), whereas the RMF and percentage of fine roots influenced this microbial activity through their impact on soil HW-C (Fig. 2). Using variation partitioning, we show that a combination of these roots traits and HW-C explained 53% of the variation in ammonification activity. Plant root traits contributed to 30% of the variation in soil enzyme activities. We observed that RDMC contributed to only 9% of the variation in ammonification activity, although this root growth trait was positively correlated with ammonification activity (r = 0.86, *p* < 0.001). By contrast, the percentage of fine roots that was positively correlated with ammonification activity (r = 0.74, *p* < 0.01) contributed to 21% of the variation in this microbial activity. Finally, we show that if soil HW-C alone does not explain the variation in ammonification activity, this soil variable is positively influenced by the RMF (r = 0.95, *p* < 0.001), which is an indicator of the relative allocation of C to the roots (Poorter et al., 2012). In this case,
<figure><img src="image_32.png" /></figure>

**Fig. 1** Redundancy analysis of enzymatic activities in relation to the root growth related traits (A and C) and root architecture related traits (B and D). In RDA distance biplots (A and B), phenological stages are denoted for: 3-leaf with filled triangle (▲); 5-leaf with filled circle (●); first flowers with empty triangle (△); early seed filling with empty circles (○). In correlation biplots (C and D), solid lines are roots traits (explanatory variables), dashed lines are enzymatic activities (dependent variables). When only one explanatory variable was selected, RDA is constructed with only one constrained ordination axe (RDA 1), the second axe being an unconstrained residual axe (PC 1). The RDAs were significant at *P* < 0.01 and explained 32 % (A and C) and 38 % (B and D) of the variation

according to our hypothesis, the variation in ammonification activity is likely to be controlled
primarily by root architecture traits that modulate the soil bioavailable C and N and
secondarily by root growth traits. These results suggest that changes in pea biomass
partitioning during reproductive stages could limit belowground C allocation and alter soil C
status. The increase in the percentage of fine roots may then provide significant amounts of
amino acids to the soil because fine roots are assumed to have high turnover and high amino
<figure><img src="image_33.png" /><figcaption>**Fig. 2** Redundancy analysis of soil hot-water soluble C in relation to root-growth related traits (A and C) and root-architecture related traits (B and D). In RDA distance biplots (A and B), phenological stages are denoted for: 3-leaf with filled triangle (▲); 5-leaf with filled circle (●); first flowers with empty triangle (△); early seed filling with empty circles (○). In correlation biplots (C and D), solid lines are roots traits (explanatory variables), dashed line is soil hot-water soluble C (dependent variable). When only one explanatory variable was selected, RDA is constructed with only one constrained ordination axe (RDA 1), the second axe being an unconstrained residual axe (PC 1). The RDAs were significant at *P* < 0.001 and explained 88 % (A and C) and 82 % (B and D) of the variation</figcaption></figure>

acid contents (Eissenstat et al., 2000; Jones et al., 2005). Microorganisms can mineralize amino acids via ammonification activity, which may alleviate soil C limitations.

Our results highlight that a suite of root traits can impact pea effects on the soil enzyme activities implicated in N mineralization, including ammonification activity. This work reinforces the importance of considering root traits to increase our understanding of the impact of plant species on soil N mineralization. However, our findings must be extended to other crops because plant traits vary substantially across plant species.
Acknowledgments

This study was funded by the French Minister of Higher Education and Research.

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**Supplementary information**

**Appendix A: Materials and methods**

*Plant material, growth and harvest conditions*

One seed of spring pea (*Pisum sativum* L., cv. Kayanne) was sown in a PVC tube (15 cm depth, 8 cm diameter) with a closed bottom containing 450 g of soil (Bouzule, Champenoux, France, 48° 74' N, 6° 35' E) that had been sieved at 5 mm. The soil (Vertic Cambisol (clayic): FAO-WRB classification) had the following characteristics: 62% clay, 35% silt, 3% sand, 26 g kg⁻¹ organic carbon, 2.6 g kg⁻¹ total N, and pH (water) 7.1. Due to its high clay content, soil was mixed with 10% sand (w/w) in order to facilitate the later separation of roots from the soil and to limit the probability of roots damage and loss. Plants were grown to maturity in a climate-controlled chamber under a photoperiod of 16 h and a temperature of 22.5 °C/18 °C (day/night) without fertilization. Artificial lighting (MH Daylight 400 W, Osram, Munich, Germany) provided 300 µmol m⁻² s⁻¹ of photosynthetically active radiation. The soil was maintained at 80% of its water-holding capacity by weighing the tubes and watering them with tap water 5 times per week. Four random pea plants were harvested at the 3-leaf, 5-leaf, first flowers and early seed filling stages, corresponding to 10, 22, 49 and 59 days after emergence, respectively. The root systems were carefully removed from the PVC tube. The roots were manually separated from the soil, and the detached roots were collected using tweezers. The roots were gently cleaned under tap water, and root traits were immediately measured. The soil was stored at 4 °C until processing (within the week after sampling). Soil subsamples were frozen at -20 °C immediately after sampling and before soil DNA extraction.

*Root trait measurements*

Before digital scanning, the entire root system was suspended in 1 cm of deionized water in a 40 x 30 cm clear acrylic tray using tweezers. Then, the roots were scanned at a resolution of 400 dpi using a flatbed Epson Expression 10000XL scanner (Epson, Suwa, Japan). WinRHIZO Reg v.2005c software (Regent Instruments, Quebec, Canada) was used to analyze the scanned images using Regent's and Tennant's methods (Arsenault et al., 1995).

The total root length and the projected root surface area of each root system were determined. These values were subsequently used to calculate the root volume, root mean diameter (mm) and root length distribution per root diameter class (there were 10 root diameter classes, each with an increase of 0.1 mm; the tenth class referred to roots > 0.9 mm in diameter).
The root fresh weight (g) was determined after a 24 h rehydration in deionized water. Before being weighed, roots were carefully dried with filter paper. Then, the roots were frozen at -80 °C and freeze-dried on a Lyovac GT2 (Steris, Mentor, OH, USA), and their dry weight (g) was measured. The plant aerial parts were dried for 72 h at 60 °C and weighed.

The root dry matter content (RDMC, mg g-1) was calculated as the ratio of the root dry mass to the root fresh mass. The root mass fraction (RMF, g g-1) was calculated as the ratio of the whole root dry mass to the total plant dry mass. The root tissue density (RTD, cm3 g-1) was calculated as the ratio of the root volume to the total root dry mass. The specific root length (SRL, m g-1) was calculated as the ratio of the root length to the root dry mass. Finally, the fine root length percentage was calculated as the percentage of the total root length represented by roots with a diameter < 0.2 mm (Eissenstat, 1992). Dry roots were powdered using an MM 400 ball mill (Retsch, Haan, Germany), and the total root C and N contents were determined using a Flash EA 1112 elemental autoanalyzer (Thermo Fisher Scientific, Waltham, MA, USA). The water-soluble root C and N contents were determined according to Nelson and Lym (2003). Briefly, 500 mg of powdered roots was incubated in 15 ml of deionized water for 1 h at 60 °C and was 150 filtered through a Whatman 42 filter (Sigma-Aldrich, Saint-Louis, MO, USA). The water-soluble root C and N contents were determined using a TOC-V CSH Total Organic Carbon analyzer (Shimadzu, Kyoto, Japan).

## Soil N and C contents

Ten grams of fresh homogenized soil was shaken for 1 h with 100 ml of 2 M KCl solution and was then filtered through a Whatman 42 filter. The total mineral N content in the KCl extract was determined using a SAN++ CFA molecular absorption spectrometer (Skalar Analytical, Breda, The Netherlands). The soil residues were dried for 24 h at ambient temperature. Then, the soil C and N were extracted with hot water according to Vong et al. (2007). The total C and N in the hot water extracts (HW-C and HW-N, respectively) were measured using a TOC-V CSH (Shimadzu) and were used to calculate the hot water C/N ratio (HW-C/N).

## Quantification of 16S rDNA and protease community abundances

The abundance of the total bacterial and protease communities were measured using quantitative PCR to amplify the 16S rDNA and *apr* genes, respectively (Lee et al., 1996; Bach et al., 2002). A portion of the 16S rDNA gene was amplified using the 341f and 534r primers (Lopez-Gutierrez et al., 2004), and the aprI and aprII primers were used to amplify part of the *apr* gene (Bach et al., 2002).
Soil DNA was extracted using the Fast DNA® 171 SPIN Kit for Soil (MP Biomedical, Santa Anna, CA, USA) according to the manufacturer's instructions. Standards for the quantification were obtained from cloned 16S rDNA and *apr* fragments ligated into PCR8®/GW/TOPO® plasmids (Life Technologies, Carlsbad, CA, USA). For 16S rDNA, the reaction mixture contained 10 ng of template DNA, 1x Power SYBR® Green PCR Master Mix (Applied Biosystems, Foster City, CA, USA), 1 µM of each primer and 250 ng of T4Gene 32 (MP Biomedicals, Strasbourg, France) in a final volume of 10 µl. The PCR conditions were as follows: 95 °C for 10 min and 35 cycles of 15 s at 95 °C, 30 s at 60 °C, and 30 s at 72 °C. For the *apr* gene, the reaction components included 10 ng of template DNA, 1x Power SYBR® Green PCR Master Mix, 1.75 µM of each primer and 150 ng of T4Gene 32 in a final volume of 20 µl. The PCR conditions were as follows: 95 °C for 10 min and 40 cycles of 20 s at 95 °C, 30 s at 53 °C, and 60 s at 72 °C (Gschwendtner et al., 2010). Each sample, control and standard was amplified in triplicate in different PCR plates. PCR was performed in a StepOnePlus™ thermocycler (Applied Biosystems). Cycling, data collection and calculations were performed with StepOne™ Software v.2.1. The specificity of the amplified products was confirmed using a melting curve analysis and the electrophoresis of the qPCR products on a 1.5% (w/v) agarose gel.

## Soil microbial biomass C and N

The microbial biomass C (MBC) and the microbial biomass N (MBN) were estimated using the fumigation extraction method (Vance et al., 1987). The extracts were frozen at -20 °C until analysis. The extractible C and N concentrations were determined using a TOC-V CSH (Shimadzu). The microbial biomass C and N were determined by the following calculation: the C and N contents in the non-fumigated soil samples were subtracted from the C and N contents in fumigated soil samples, and the result was divided by an extraction coefficient (0.45 for C (Vance et al., 1987) or 0.54 for N (Brookes et al., 1985)).

## Ammonification and protease activities

Potential ammonification activity was determined according to Bonde et al. (2001). Briefly, 1 g of fresh soil was mixed with 1.8 ml of deionized water and 0.2 ml of a 10 mM L arginine solution. The mixture was incubated for 1.5 h at 22 °C without further agitation. Ice-cold 2M KCl was used to recover extractible N after the incubation. Samples were centrifuged at 16,000g for 10 min at 4 °C, and the NH4+ concentration in the supernatants was determined using a SAN++ CFA molecular absorption spectrometer (Skalar Analytical). For the controls, the procedure was as for the assays with the
exception that arginine was added after the incubation and the addition of 2M KCl. Ammonification activity was expressed as µg NH4+ g-1 dry soil h-1.

Potential protease activity was determined according to Ladd and Butler (1972). One gram of fresh soil was incubated for 4 h at 45 °C with 2.5 ml of 0.1 M Tris-HCl buffer (pH 8) and 2.5 ml of 2% sodium caseinate solution. The addition of 5 ml of 10% trichloroacetic acid stopped the reaction. After centrifugation at 16,000g for 2 min, 0.5 ml of the supernatant was mixed with 0.75 ml of 1.4 M sodium carbonate and 0.25 ml of 16.5% Folin-Ciocalteu reagent for 5 min. A standard curve was prepared using tyrosine and the concentration of tyrosine in each sample was determined colorimetrically using a Cary® 300 spectrophotometer (Varian, Palo Alto, CA, USA) at 680 nm. For the controls, the procedure was the same as for the assays, with the exception that sodium caseinate was added after the incubation and the addition of trichloroacetic acid. Protease activity was expressed as µg Tyr g-1 dry soil h-1.

## Statistical analysis

All of the examined variables were analyzed using a Kruskal-Wallis test with the plant growth stage as a factor. The means were grouped using the kruskalmc function in the pgirmess package v. 1.5.8 (Giraudoux, 2013) to allow for multiple comparisons. To separate the influences of plant root traits and soil variables on enzymatic activities, the measured variables were divided into 4 groups: root growth traits (RTD, RDMC, RMF, total root C and N contents, and water-soluble root C and N contents), root architecture traits (SRL, root length, fine roots length percentage, and average diameter), soil variables (16S rRNA abundance, *apr* abundance, mineral N, HW-C, HW-N, HW-C/N, and microbial biomass C and N) and enzyme activities (ammonification activity and protease activity). The relationships between the different subsets of selected variables were analyzed using a redundancy analysis (RDA), a constrained ordination procedure based on the principal component analysis that allows assessments of the relationship between a set of explanatory variables and a set of response variables. Prior to the RDA, the set of explanatory variables underwent forward selection using the ordistep function (Oksanen et al., 2013) to select the most significant variables and avoid multicollinearity (Legendre and Legendre, 1998). To quantify the contribution of each of the three sets of selected variables (plant root growth traits, plant root architecture traits and soil variables) to the variations in ammonification and protease activities, variation partitioning was performed (Peres-Neto et al., 2006). The RDAs and variation partitioning were performed using the vegan 2.0-9 package (Oksanen, 2013).
The adjusted R² values were calculated using Ezekiel's formula (Ezekiel, 1930). The significance of the RDA and variation partitioning results was assessed using a Monte-Carlo permutation test with 1,000 permutations. Some correlations between variables were analyzed using the Pearson test. R 3.0.0 software (R Core Team 2013) was used for all the statistical analyses.
<table><caption>Appendix B: Table 1 Pea root functional traits, soil enzymatic activities and soil variables at 4 phenological stages</caption><thead><tr><th></th><th></th><th>3 leaves</th><th>5 leaves</th><th>First flowers</th><th>Early seed filling</th><th>H'</th></tr></thead><tbody><tr><td rowspan="6">Root growth traits</td><td>Root tissue density (g cm<sup>-3</sup>)</td><td>0.05 ± 0.00<sup>a</sup></td><td>0.05 ± 0.00<sup>a</sup></td><td>0.07 ± 0.00<sup>ab</sup></td><td>0.08 ± 0.00<sup>b</sup></td><td>11.00*</td></tr><tr><td>Root dry matter content (mg g<sup>-1</sup>)</td><td>52.82 ± 0.28<sup>a</sup></td><td>55.68 ± 1.36<sup>a</sup></td><td>63.99 ± 0.96<sup>ab</sup></td><td>76.43 ± 4.10<sup>b</sup></td><td>11.17*</td></tr><tr><td>Root mass fraction (g g<sup>-1</sup>)</td><td>0.34 ± 0.01<sup>b</sup></td><td>0.29 ± 0.03<sup>b</sup></td><td>0.09 ± 0.01<sup>ab</sup></td><td>0.08 ± 0.00<sup>a</sup></td><td>11.17*</td></tr><tr><td>Total C (%)</td><td>37.51 ± 0.52<sup>a</sup></td><td>37.10 ± 0.57<sup>a</sup></td><td>38.24 ± 0.80<sup>a</sup></td><td>38.06 ± 0.62<sup>a</sup></td><td>1.37 NS</td></tr><tr><td>Total N (%)</td><td>4.92 ± 0.16<sup>b</sup></td><td>3.66 ± 0.14<sup>ab</sup></td><td>2.55 ± 0.11<sup>ab</sup></td><td>2.21 ± 0.08<sup>a</sup></td><td>11.69**</td></tr><tr><td>Water soluble C (%)</td><td>8.76 ± 1.08<sup>a</sup></td><td>6.46 ± 0.20<sup>a</sup></td><td>6.96 ± 0.14<sup>a</sup></td><td>6.92 ± 1.07<sup>a</sup></td><td>5.34 NS</td></tr><tr><td rowspan="4">Root architecture traits</td><td>Specific root length (m g<sup>-1</sup>)</td><td>524.70 ± 3.00<sup>a</sup></td><td>666.76 ± 58.36<sup>a</sup></td><td>847.91 ± 68.98<sup>ab</sup></td><td>990.31 ± 55.43<sup>b</sup></td><td>8.94*</td></tr><tr><td>Length (m)</td><td>29.34 ± 5.60<sup>a</sup></td><td>37.03 ± 1.40<sup>ab</sup></td><td>53.31 ± 10.09<sup>ab</sup></td><td>71.94 ± 0.48<sup>b</sup></td><td>7.80*</td></tr><tr><td>Fine roots length (%)</td><td>3.36 ± 0.16<sup>a</sup></td><td>14.22 ± 1.17<sup>a</sup></td><td>25.30 ± 2.45<sup>ab</sup></td><td>32.92 ± 0.85<sup>b</sup></td><td>12.11**</td></tr><tr><td>Mean diameter (mm)</td><td>0.67 ± 0.03<sup>b</sup></td><td>0.80 ± 0.24<sup>b</sup></td><td>0.48 ± 0.01<sup>ab</sup></td><td>0.41 ± 0.01<sup>a</sup></td><td>11.60**</td></tr><tr><td rowspan="2">Enzymatic activities</td><td>Protease activity (µg Tyr h<sup>-1</sup> g<sup>-1</sup> dry soil)</td><td>152±12<sup>a</sup></td><td>137±6<sup>a</sup></td><td>133±10<sup>a</sup></td><td>158±13<sup>a</sup></td><td>2.38 NS</td></tr><tr><td>Ammonification activity (µg N-NH<sub>4</sub><sup>+</sup> h<sup>-1</sup> g<sup>-1</sup> dry soil)</td><td>0.88±0.88<sup>a</sup></td><td>0.43±0.26<sup>a</sup></td><td>1.78±0.48<sup>a</sup></td><td>11.62±1.46<sup>b</sup></td><td>9.50*</td></tr><tr><td rowspan="7">Soil variables</td><td>16S rDNA (copies g-1 dry soil)</td><td>2.21 ± 10<sup>5</sup> ± 8.45 ± 10<sup>4ab</sup></td><td>2.65 ± 10<sup>6</sup> ± 2.32 ± 10<sup>5b</sup></td><td>1.86 ± 10<sup>5</sup> ± 3.59 ± 10<sup>4a</sup></td><td>3.81 ± 10<sup>5</sup> ± 4.72 ± 10<sup>4ab</sup></td><td>11.47**</td></tr><tr><td>apr (copies g-1 dry soil)</td><td>1.95 ± 10<sup>3</sup> ± 8.63 ± 10<sup>2ab</sup></td><td>3.53 ± 10<sup>4</sup> ± 4.81 ± 10<sup>3b</sup></td><td>1.41 ± 10<sup>3</sup> ± 1.91 ± 10<sup>2a</sup></td><td>3.19 ± 10<sup>3</sup> ± 1.17 ± 10<sup>3ab</sup></td><td>8.71*</td></tr><tr><td>Mineral N (µgN g-1 dry soil)</td><td>27.74 ± 1.05<sup>a</sup></td><td>20.43 ± 1.39<sup>a</sup></td><td>21.26 ± 2.36<sup>a</sup></td><td>25.69 ± 3.80<sup>a</sup></td><td>3.13 NS</td></tr><tr><td>Hot-water soluble C (µgC g-1 dry soil)</td><td>580.25 ± 13.05<sup>b</sup></td><td>560.83 ± 19.25<sup>b</sup></td><td>301.64 ± 9.55<sup>a</sup></td><td>245.29 ± 19.36<sup>a</sup></td><td>10.49*</td></tr><tr><td>Hot-water soluble N (µgN g-1 dry soil)</td><td>52.63 ± 1.94<sup>ab</sup></td><td>51.13 ± 3.26<sup>a</sup></td><td>83.21 ± 2.21<sup>b</sup></td><td>78.07 ± 4.83<sup>ab</sup></td><td>10.06*</td></tr><tr><td>Hot-water soluble C/N</td><td>10.56 ± 0.40<sup>b</sup></td><td>11.04 ± 0.42<sup>b</sup></td><td>3.63 ± 0.07<sup>a</sup></td><td>3.13 ± 0.05<sup>a</sup></td><td>12.90**</td></tr><tr><td>Microbial biomass C (µgC g-1 dry soil)</td><td>265.74 ± 7.98<sup>b</sup></td><td>219.55 ± 12.20<sup>ab</sup></td><td>191.63 ± 1.72<sup>a</sup></td><td>239.09 ± 19.71<sup>ab</sup></td><td>8.94*</td></tr><tr><td></td><td>Microbial biomass N (µgN g-1 dry soil)</td><td>39.62 ± 2.26<sup>a</sup></td><td>29.29 ± 2.26<sup>a</sup></td><td>26.82 ± 2.82<sup>a</sup></td><td>37.59 ± 3.90<sup>a</sup></td><td>6.71 NS</td></tr></tbody></table>
**Appendix C: Table 2** Selection of explanatory variables for RDA. AIC is the Akaike Information Criteria. The *p* value is the significance of the F-statistics associated with all variables tested using permutation tests. Variables are selected as explanatory if *p* < 0.05 (*). In case of significance equality, the variable with the lowest AIC is selected

<table><thead><tr><th rowspan="2">Root growth traits</th><th colspan="2">Enzymatic activities</th><th colspan="2">Hot-water soluble C</th></tr><tr><th>AIC</th><th>p value</th><th>AIC</th><th>p value</th></tr></thead><tbody><tr><td>Root dry matter content (mg g<sup>-1</sup>)</td><td>11.103</td><td>0.005*</td><td>0.874</td><td>0.001*</td></tr><tr><td>Total N (%)</td><td>5.496</td><td>0.090</td><td>-28.904</td><td>0.998</td></tr><tr><td>Total C (%)</td><td>6.490</td><td>0.150</td><td>-28.908</td><td>0.955</td></tr><tr><td>Root mass fraction (g g<sup>-1</sup>)</td><td>7.521</td><td>0.405</td><td>0.962</td><td>0.001*</td></tr><tr><td>Root tissue density (g cm<sup>-3</sup>)</td><td>7.805</td><td>0.495</td><td>-31.087</td><td>0.208</td></tr><tr><td>Water soluble N (%)</td><td>7.674</td><td>0.500</td><td>-29.410</td><td>0.550</td></tr><tr><td>Water soluble C (%)</td><td>7.773</td><td>0.505</td><td>-29.166</td><td>0.681</td></tr></tbody></table>

<table><thead><tr><th rowspan="2">Root architecture traits</th><th colspan="2">Enzymatic activities</th><th colspan="2">Hot-water soluble C</th></tr><tr><th>AIC</th><th>p value</th><th>AIC</th><th>p value</th></tr></thead><tbody><tr><td>Length (m)</td><td>8.193</td><td>0.015*</td><td>-19.240</td><td>0.883</td></tr><tr><td>Fine roots length (%)</td><td>8.839</td><td>0.045*</td><td>0.962</td><td>0.001*</td></tr><tr><td>Mean diameter (mm)</td><td>6.296</td><td>0.500</td><td>-22.556</td><td>0.109</td></tr><tr><td>Specific root length (m g<sup>-1</sup>)</td><td>7.792</td><td>0.815</td><td>-19.397</td><td>0.734</td></tr></tbody></table>

<table><thead><tr><th rowspan="2">Soil variables</th><th colspan="2">Enzymatic activities</th></tr><tr><th>AIC</th><th>p value</th></tr></thead><tbody><tr><td>Hot-water soluble C (µgC g<sup>-1</sup> dry soil)</td><td>6.645</td><td>0.025*</td></tr><tr><td>Microbial biomass N (µgN g<sup>-1</sup> dry soil)</td><td>5.526</td><td>0.100</td></tr><tr><td>Mineral N (µgN g<sup>-1</sup> dry soil)</td><td>2.030</td><td>0.145</td></tr><tr><td>Microbial biomass C (µgC g<sup>-1</sup> dry soil)</td><td>1.023</td><td>0.395</td></tr><tr><td>Hot-water soluble N (µgN g<sup>-1</sup> dry soil)</td><td>0.822</td><td>0.455</td></tr><tr><td>Hot-water soluble C/N</td><td>0.543</td><td>0.540</td></tr><tr><td>16S rDNA (copies g<sup>-1</sup> dry soil)</td><td>0.310</td><td>0.730</td></tr><tr><td>apr (copies g<sup>-1</sup> dry soil)</td><td>0.261</td><td>0.765</td></tr></tbody></table>
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Combined effects of pea residues and wheat root functional traits

on soil protease activity

Nicolas Romillac<sup>a,b</sup>, Séverine Piutti<sup>a,b</sup>, Bernard Amiaud<sup>c,d</sup>, Sophie Slezack-Deschaumes<sup>a,b</sup>

Mis en forme pour **Pedobiologia**

<footnote><sup>a</sup> Université de Lorraine, Laboratoire Agronomie et Environnement, UMR 1121, TSA 40602, F-54518 Vandœuvre cedex, France</footnote>

<footnote><sup>b</sup> INRA, Laboratoire Agronomie et Environnement, UMR 1121, TSA 40602, F-54518 Vandœuvre cedex, France</footnote>

<footnote><sup>c</sup> Université de Lorraine, UMR 1137, Ecologie et Ecophysiologie Forestière, F-54500 Vandœuvre lès Nancy, France</footnote>

<footnote><sup>d</sup> INRA, UMR 1137, Ecologie et Ecophysiologie Forestière, F-54280 Champenoux, France</footnote>
Introduction

Grain legumes may have beneficial yield effects to the succeeding cereal crop (Arcand et al., 2014; Beckie et Brandt, 1997; Stevenson and van Kessel, 1996). These effects have been partly attributed to an improvement of the soil N supply to the succeeding crop (Mayer et al., 2003, Arcand et al., 2013, 2014). Indeed, legumes N inputs have been shown to be higher than other crops (Soon and Arshad, 2002). These inputs include crop residues which usually present higher N concentrations and lower C/N ratio than cereals (Arcand et al., 2014; Nicolardot et al., 2001; Soon and Arshad, 2002). This also includes N rhizodeposits that represent a significant part of soil N pools, mainly immobilized in microbial biomass and as labile microbial residues (Jensen, 1996; Mayer et al., 2003). Due to the chemical nature of residues of grain legumes and their C/N stoichiometry, it is assumed that they can favor N mineralization and therefore return more nutrients to the subsequent crop (Gan et al., 2010; Manzoni et al., 2008; Soon and Arshad, 2002; Stevenson and van Kessel, 1996). However, biochemical characteristics of plant residues are not the only factors that influence soil N dynamic. Indeed, subsequent crops will also exert a control of N mineralization (Mayer et al., 2003; Yuan et al., 2011). During their growth, plants influence soil microbial communities inhabiting the rhizosphere that are the major decomposers of organic matter and thus, affect N mineralization (Zhu et al., 2014; Kuzyakov, 2010). As soil microorganisms are often C limited in soil, simple organic compounds released by roots (exudation) constitute an important source of C and nutrients for these microbial communities (Nguyen, 2003). The root exudates will have an impact on soil microbial biomass, changing its composition, increasing its size and its activity (De Graaff et al., 2010; Nguyen and Henry, 2002, Pascault et al., 2013). This microbial activation has further consequences in the decomposition of soil organic matter and plant residues (Dijkstra et al., 2013, Kuzyakov, 2010). Indeed, by increasing microbial biomass and activity, C compounds released by roots can create microbial N demand, especially during the early steps of residue decomposition that require mineral N (Manzoni et al., 2008; Zhu et al., 2014). Furthermore, plant N uptake can further decrease the N availability for soil microorganisms (Bell et al., 2015). Therefore, to meet their N requirement, microorganisms could synthesize extracellular enzymes implicated in the breakdown of complex organic nitrogen (Averill and Finzi, 2011; DeAngelis et al., 2008; Dijkstra et al., 2013). Then, after the mining of N, this nutrient can be immobilized in the microbial biomass and it can become available for plants through the turnover of microorganisms (Kuzyakov and Xu, 2013; Phillips et al., 2011).

Among extracellular enzymes that can be induced to convert complex organic N into bioavailable compounds, proteases are assumed to play a major role (Geisseler and Horwath, 2008). Indeed, proteins constitute the most abundant form of organic N in soil and residues, with potential high turnover rates (Kögel-Knaber, 2002). Furthermore, the degradation of proteins is considered as a key
limiting step in N mineralization that could further influence soil-derived N supply (Jones et al., 2004; Jan et al., 2009). Different mechanisms regulating soil extracellular enzymes such as proteases have been suggested that allow microorganisms to potentialize N and energy that are invested to produce these enzymes (Allison and Vitousek, 2005; Averill and Finzi, 2011; Geisseler et al., 2008). Indeed, even if these enzymes can be produced constitutively, proteases appear to be mainly up-regulated in response to addition of substrate-limited N (Geisseler and Horwath, 2008, 2009). By contrast, the presence of mineral N or easily metabolizable sources of N seems to have contradictory effects on the regulation of soil proteases (Beg et al., 2002, Geisseler and Horwath, 2008).

Considering all these elements, the ways by which plants such as legumes impact N mineralization could be broadly divided into those that modify the balance between N input / output and those that alter soil microbial communities driving N mineralization. However, our understanding of how plants alter microbial activities implicated in N mineralization remains poorly understood (Grigulis et al., 2013). To get a better understanding, plant trait-based approaches can be relevant (Powell et al., 2013). Few studies showed that soil microbial communities' composition and microbial activities implicated in soil N mineralization were explained by root traits (Klumpp et al., 2009; Legay et al., 2014; Romillac et al., 2015). These results highlight that root traits could be a critical tool to understand and quantify the relative influence of plants on the functioning of soil microbial communities (York et al., 2013; Bardgett et al., 2014).

The objectives of our study were i) to progress in the understanding of root traits that govern the potential influence of wheat on soil protease activity and, ii) to evaluate the potential influence of the residual effects of pea (crop residues and N rhizodeposits) on the interaction between wheat and soil protease activity. An experiment in controlled conditions was conducted on a spring wheat cultivar. Vegetative and reproductive periods of wheat were considered since plant N needs, root system architecture and rhizodeposition are assumed to evolve between vegetative and reproductive stages. Extracellular protease activities and root traits related to root growth processes, N interception and rhizodeposition processes were analyzed at each sampling date. Soil variables (i.e. soil inorganic N, organic extractible C and N, size of microbial biomasses N) were also measured, as they are a) putative drivers of extracellular protease and b) possibly affected by root patterns and the presence of pea residues. We hypothesized that (1) pea residues have a higher impact on extracellular protease activity than wheat, as pea residues are potential substrates for proteases, (2) root traits indirectly alter extracellular protease through modifications of soil driving factors.
# Material and methods

## Soil

The soil used was a Vertic Cambisol (clayic) (FAO-WRB classification) soil taken from the 0-20 cm layer at the research farm "Bouzule" in north-east of France (48° 74' N, 6° 35' E). After sampling, the soil was air dried, sieved at 5 mm and mixed with 10 % sand (w/w). The soil has the following characteristics: 62% clay, 35% silt, 3% sand, 26 g kg⁻¹ organic carbon, 2.6 g kg⁻¹ total N, and pH (water) 7.1.

## Preparation of soil and pea residues

Pea (*Pisum sativum* L., cv. Kayanne) were grown in a climate-controlled chamber, as described in Romillac et al. (2015). At maturity, all visible roots and root fragments were manually separated from the soil and collected. The aerial parts of the plants were also harvested. The soil containing rhizodeposits (cultivated soil) was stored at 18°C in the dark during 24h before processing.

During the growing period of pea, a non-cultivated soil maintained at 80% of its water-holding capacity was stored in the dark in the climate-controlled chamber.

Pea residues (root and aerial parts) were dried at 60°C and cut into 2-3 mm pieces with scissors. The mix of root and aerial residues contained 39.6 % total C, 1.31 % total N, 41.2 mg g⁻¹ soluble C and 4.43 mg g⁻¹ soluble N. Soil properties of the cultivated and non-cultivated soils are listed in table 1.

<table><caption>**Table1** Control soil (C) and soil previously cropped with pea (P) characteristics before planting. Values are average ± SEM. n: number of replicates, MBN: microbial biomass N, P soil: soil previously cropped, C soil: control soil. Significance: n.s. = non significant., * p < 0.05, ** p < 0.01, *** p < 0.001</caption><thead><tr><th>Variables</th><th>P soil</th><th>C soil</th><th>X<sup>2</sup></th></tr></thead><tbody><tr><td>n</td><td>8</td><td>8</td><td></td></tr><tr><td>Protease activity<br>(µg Tyr h<sup>-1</sup> g<sup>-1</sup> dry soil)</td><td>45.1 ± 3.7</td><td>50.8 ± 5.9</td><td>0.40 n.s.</td></tr><tr><td>Mineral N<br>(µg N g<sup>-1</sup> dry soil)</td><td>37.3 ± 3.0</td><td>63.2 ± 2.4</td><td>11.29***</td></tr><tr><td>MBN<br>(µg C g<sup>-1</sup> dry soil)</td><td>56.8 ± 1.3</td><td>51.4 ± 1.5</td><td>3.98*</td></tr><tr><td>Soluble C<br>(µg C g<sup>-1</sup> dry soil)</td><td>28.6 ± 3.1</td><td>24.7 ± 1.3</td><td>0.70 n.s.</td></tr><tr><td>Soluble N<br>(µg N g<sup>-1</sup> dry soil)</td><td>14.6 ± 0.8</td><td>37.7 ± 0.8</td><td>11.29***</td></tr></tbody></table>
Experimental design

The experiment was composed of four treatments: non-cultivated soil (control, C), non-cultivated soil with pea residues (R treatment), cultivated soil with pea rhizodeposits (P treatment) and cultivated soil with pea rhizodeposits and residues (P+R treatment). Pea residues were homogeneously mixed into the soil at a rate of 1 g of C kg⁻¹ of dry soil equivalent. C and P soils were also homogeneously mixed before potting.

One seed of spring wheat (Triticum aestivum L., cv. Sensas) was sown in a PVC tube (15 cm depth, 8 cm diameter) with a closed bottom containing 450 g of one of the four soil types i.e. C, R, P or P+R soil type. Plants were grown up to maturity as described in Romillac et al. (2015). The soil was maintained at 80 % of its water-holding capacity by weighting the tubes and watering them with tap water 5 times a week. Between 3-leaf and first tiller stages, plants received 375 mg N tube⁻¹ (equivalent to 50 units ha⁻¹) as NH₄NO₃ (Sigma Aldrich, Saint Louis, USA).

Wheat plants were randomly harvested at 3 leaf-unfolded, first tillers, 2 nodes, early flowering, and maturity stages, which correspond to 14, 22, 28, 41 and 89 days after emergence, respectively. The root systems were carefully removed from the PVC tubes. The roots were manually separated from the soil, and the detached roots were collected using tweezers. After gently shaking the roots, soil aggregates that remained attached were collected. The roots were gently cleaned under tap water, and root traits were immediately measured. The soil was stored at 4 °C until processing (within the week after sampling).

Measurement of root traits

Before digital scanning, roots were suspended in 1 cm of deionized water in a 40 x 30 cm clear acrylic tray using tweezers. Then, roots were scanned at 400 dpi with an Epson Expression 10000XL scanner (Epson, Suwa, Japan). The WinRHIZO Reg v.2005c software (Regent Instruments, Quebec, Canada) was used to analyse scanned images using the Regent's and the Tennant's methods (Arsenault et al. 1995) in order to evaluate the total root length and the projected root surface area of each root system. The root volume (cm³), root surface area (cm²), root mean diameter (mm) and root length distribution per root diameter classes (10 root diameter classes, 0.1 mm each, the tenth for roots > 0.9 mm diameter) were subsequently calculated.

Root fresh weight (g) was determined after 24 h rehydration in deionized water and drying with filter paper. Afterwards, roots were frozen at -80°C, freeze-dried on a Lyovac GT2 (Steris, Mentor, USA), and weighted to measure the root dry weight (g). Plant aerial parts were dried 72 h at 60 °C and weighted.
Specific Root Length (SRL, m g⁻¹) was calculated as the ratio of root length to root dry mass.
Root Dry Matter Content (RDMC, mg g⁻¹) was calculated as the ratio of root dry mass to root fresh mass. Finally, the fine root percentage was calculated as the percentage of the total root length represented by root length with a diameter < 0.2 mm (Eissenstat, 1992).

Dry roots were powdered using a ball mill MM 400 (Retsch, Haan, Germany) and the total root N and C contents was estimated using an element autoanalyser Flash EA 1112 (ThermoFisherScientific, Waltham, USA). Total Root C/N (T-C/N) was subsequently calculated.

Water-soluble Root N (S-RNC) and C (S-RCC) contents were determined according to Nelson and Lym (2003). Briefly, 500 mg of powdered roots were incubated in 15 ml of deionized water during 1 h at 60 °C and filtered through a Whatman 42 filter (Sigma-Aldrich, Saint-Louis, USA). S-RNC and S-RCC were determined using a Total Organic Carbon analyzer TOC-V CSH (Shimadzu, Kyoto, Japan).

### Soil N and C contents

Soil mineral N was extracted with 2 M KCl (1:10 w/v) on an end-over-end shaker for 1 hour and then filtered through a Whatman 42 filter. Total mineral N content in KCl extract was determined using a molecular absorption spectrometer SAN++ CFA (Skalar Analytical, Breda, The Netherlands).

Soil soluble C and N were extracted with 0.5 M K₂SO₄ (1:5 w/v) on an end-over-end shaker for 40min and then filtered through a Whatman No 42 filter (Bell et al., 2015). C and N in K₂SO₄ extract were determined using a TOC-V CSH (Shimadzu, Kyoto, Japan).

### Soil N microbial biomass (MBN)

MBN was estimated by the fumigation extraction method (Vance et al. 1987). The extracts were frozen at -20 °C until analysis. The extractible N concentration was determined using a TOC-V CSH (Shimadzu, Kyoto, Japan). MBN was determined by subtracting N content in the non-fumigated samples to N content in fumigated soil samples, respectively, and divided by an extraction coefficient of 0.54 (Brookes et al. 1985).

### Potential protease activity

Potential protease activity was determined according to Ladd and Butler (1972). Briefly, 1g of fresh homogenised soil was incubated for 4 h at 45 °C with 2.5 ml of 0.1 M Tris-HCl buffer (pH 8) and 2.5 mL 2% sodium caseinate solution. The addition of 5mL 10% trichloroacetic acid stopped the reaction. After centrifugation at 16,000 g for 2 min, 0.5 mL of supernatant was mixed with 0.75 mL 1.4M sodium
carbonate and 0.25ml 16.5% Folin-Ciocalteu reagent during 5 min. A standard curve was prepared using tyrosine and the concentration of tyrosine in each sample was determined colorimetrically using a Cary® 300 spectrophotometer (Varian, Palo Alto, CA, USA) at 680 nm. For the controls, the procedure was the same as for the assay, with the exception that sodium caseinate was added after the incubation and the addition of trichloroacetic acid. Protease activity was expressed as µg Tyr g⁻¹ dry soil h⁻¹.

## Statistical analysis

Exploratory analysis of the data was performed using principal component analysis (PCA) performed with the package RcmdrPlugin.FactoMineR v1.5-0 (Husson et al., 2015).

All variables were submitted to a Kruskal-Wallis test with the plant growth stage or the soil treatment as factors. The means were grouped by the function "kruskalmc", package pgirmess 1.5.8 (Giraudoux 2013), to allow multiple comparisons.

In order to disentangle the influence of plant root traits and soil variables on enzymatic activities, measured variables were grouped in 2 sets (root functional traits and soil variables) and one stand alone variable (protease activity). Each set was submitted to forward selection using the function "ordistep" (Oksanen et al. 2013) in order to select the most significant variables and avoid multicollinearity (Legendre and Legendre 1998). The relationships between the different subsets of selected variables were analyzed using redundancy analysis (RDA), a constrained ordination procedure based on principal component analysis which allows assessing the relationship between a set of explanatory variables and a set of response variables. RDAs were performed using package vegan 2.0-9 (Oksanen 2013). The adjusted R² were calculated using Ezekiel's formula (Ezekiel, 1930). The significance of the RDAs and variation partitioning was tested by Monte-Carlo permutation test with 1,000 permutations.

Some correlations between variables were analyzed using Pearson test. The R 3.0.0 software (R Core Team 2013) was used for all statistical analysis.

## Results

### Evolution of the protease activity

Potential protease activity ranged from about 35 to 94 µg Tyr h⁻¹ g⁻¹ dry soil (Fig. 1). This activity was significantly influenced by soil treatment (χ²=39.07, p<0.001). The addition of pea residues (R and P+R treatments) increased potential protease activity by 84 and 90% for R and P+R respectively. The means
of protease activity were 83.1±5.35 µg Tyr h⁻¹ g⁻¹ dry soil and 74.2±4.39 µg Tyr h⁻¹ g⁻¹ dry soil in R and P+R treatments, respectively as compared to 45.5±3.59 µg Tyr h⁻¹ g⁻¹ dry soil and 39.7±2.39 µg Tyr h⁻¹ g⁻¹ dry soil in control soil and soil containing pea rhizodeposits, respectively. The presence of pea rhizodeposits had no significant influence on potential protease activity as compared to the control (Fig. 1.).

Potential protease activity changed significantly ($\chi^2=19.88$, p<0.01) along the phenology of wheat. In control and pea rhizodeposits amended soils, protease activity only slightly evolved along wheat phenology. In control soil, a significant change in protease activity was observed between 3-leaf and early flowering stages ($\chi^2=< 11.13$, p<0.05) whereas, in pea rhizodeposits amended soils, a significant decrease was observed between early flowering and maturity stages ($\chi^2=16.35$, p<0.05). By contrast, in soils amended with pea residues, activity increased strongly at 3-leaf stage (from 55.84±6.47 to 93.90±7.88 and from 40.85±6.46 to 71.26±7.50 µg Tyr h⁻¹ g⁻¹ dry soil, for soils R and P+R, respectively,). Then, protease activity decreased significantly at maturity of wheat (47.63±8.62 and 50.99±9.10 µg Tyr h⁻¹ g⁻¹ dry soil for R and P+R soils respectively) ($\chi^2=14.84$ and 12.71 for soils R and P+R, respectively, p<0.05).

## Evolution of the soil variables

PCA represented 77% of the total variability on the first two axes (PC1 and PC2). This analysis showed that soil variables (MBN, soil soluble C and N and mineral N) were affected by both wheat phenology and the presence of pea residues into the soil (Fig. 2). PC1, which explained 44% of the total variability, enabled the discrimination between control soil and soils containing pea rhizodeposits and pea residues. Furthermore, PC2, which accounted for 33% of the total variability, opposed samples analyzed at 3-leaf and first tillers stages to those of the other stages. Across soil treatments, soils containing pea rhizodeposits (P and P+R treatments) have lower mineral and soluble N contents (Tables 1 and 2). Mineral N decreased among soil treatments in the following order: C > R > P > P+R whereas soluble N content decreased by 41% in soils containing pea rhizodeposits (P and P+R) compared to other soils (C and R). MBN was influenced by the presence of pea residues, but only in soils previously cropped in pea, being 1.3 times highest in soil P+R compared to soil P. However, soil soluble C was not influenced by the soil treatments (Tables 1 and 2).

All variables but soil soluble N were influenced by the wheat development stage (Table 2). Soil mineral N globally decreased by a factor 2.6 along wheat development, from 3 leaves to maturity, but with a temporary increase at stage first tillers. Soil soluble C increased 2.7 times between stages first tillers and 2 nodes but was otherwise unaffected by wheat development.
<figure><img src="image_34.png" /><figcaption>**Figure 1** Potential protease activity at different wheat phenological stages and in different soil conditions. Soil treatments: A, control soil; B, soil amended with pea residues; C, soil previously cropped with pea; D, soil previously cropped with pea amended with pea residues. Wheat stages: 0d, before sowing; 14d, 3 leaves unfolded; 22d, first tillers; 28d, 2 nodes; 41d, early flowering; 89d, maturity. Different letters indicate significant differences between means (P < 0.05)</figcaption></figure>

## Evolution of wheat root traits

PCA showed that root functional traits evolved mostly according to wheat phenology and were not influenced by soil treatments (Fig.3.) with the exception of SRL. The PC1, which explained 50% of the total variation, clearly discriminated root traits along wheat development and showed a shift during plant growth. Root traits from 3-leaf to 2 nodes stages displayed PC1 scores different from those of root traits at flowering and maturity. S-RNC, S-RCC and root N uptake showed a similar trend of evolution since the values of these traits tend to decrease along wheat phenology (Table 3). S-RNC significantly decreased at early flowering stage whereas root N uptake decreased successively at 2 nodes stage and maturity. S-RCC was quite stable over time even if a significant decrease was observed between 2-nodes and maturity stages. By contrast, root dry weight, total root length, RDMC, % of fine roots and root C/N ratio increased along wheat phenology (Table 3). Root dry weight and total root length increased until early flowering and then decreased while RDMC, % of fine roots and root C/N ratio increased from 3 leaves stage to maturity. The only exception was SRL, which was correlated with
the third axe of the PCA (13% of the total variation), and did not vary across wheat phenology but was
slightly increased by the presence of pea rhizodeposits (Table 3).

<figure><img src="image_35.png" /><figcaption>**Figure 2** Comparison of soil variables by Principal Component Analysis (A, correlation biplot and B, distance biplot) from 14 to 89 days after sowing. First two axes are presented. Individuals are colored according to phenological stage. Squares (■) represent centroid of the phenological stage or soil treatment treatments. C soil, control soil, R soil, soil amended with pea residues, P soil, soil previously cropped with pea, P+R soil, P soil amended with pea residues</figcaption></figure>
<table><caption>**Table 2** Average ± SEM of the soil variables in the 4 soil treatments and along wheat phenology. Different letters indicate significant differences between means (P < 0.05). n: number of replicates, MBN: microbial biomass N. Soil treatments: C soil, control soil, R soil, soil amended with pea residues, P soil, soil previously cropped with pea, P+R soil, P soil amended with pea residues. Significance: n.s. = non significant., * p < 0.05, ** p < 0.01, *** p < 0.001</caption><thead><tr><th rowspan="2">n</th><th colspan="5">Soil treatments</th><th colspan="5">Wheat development stages</th></tr><tr><th>C</th><th>R</th><th>P</th><th>P+R</th><th>χ<sup>2</sup></th><th>3 leaves</th><th>First tillers</th><th>2 nodes</th><th>Early flowering</th><th>Maturity</th><th>χ<sup>2</sup></th></tr></thead><tbody><tr><td>Mineral N<br>(µgN g<sup>-1</sup> dry soil)</td><td>32.1 ± 3.2a</td><td>27.4 ± 2.2ab</td><td>20.5 ± 1.6bc</td><td>17.3 ± 1.3c</td><td>21.4***</td><td>26.1 ± 1.7ab</td><td>32.8 ± 3.1b</td><td>23.4 ± 2.9ab</td><td>20.5 ± 2.5a</td><td>19.1 ± 2.4a</td><td>17.3**</td></tr><tr><td>Soluble C<br>(µgC g<sup>-1</sup> dry soil)</td><td>55.3 ± 5.9</td><td>61.1 ± 9.0</td><td>57.5 ± 7.6</td><td>68.0 ± 7.9</td><td>1.5n.s.</td><td>31.7 ± 1.2a</td><td>27.6 ± 1.2a</td><td>72.5 ± 8.8b</td><td>74.43 ± 6.0b</td><td>96.9 ± 5.5b</td><td>47.9***</td></tr><tr><td>Soluble N<br>(µgN g<sup>-1</sup> dry soil)</td><td>22.6 ± 1.6a</td><td>19.8 ± 1.7a</td><td>14.0 ± 1.2b</td><td>11.0 ± 0.6b</td><td>34.0***</td><td>16.6 ± 1.8</td><td>15.9 ± 1.8</td><td>17.1 ± 2.1</td><td>18.1 ± 1.9</td><td>16.2 ± 2.0</td><td>9.0n.s.</td></tr><tr><td>MBN<br>(µgC g<sup>-1</sup> dry soil)</td><td>44.6 ± 3.8ab</td><td>54.1 ± 4.1ab</td><td>41.9 ± 2.2a</td><td>55.7 ± 2.6b</td><td>12.6**</td><td>59.4 ± 2.0b</td><td>52.7 ± 3.3b</td><td>45.2 ± 3.2ab</td><td>50.9 ± 5.2ab</td><td>36.8 ± 2.3a</td><td>27.3***</td></tr></tbody></table>

Relationship between protease activity, soil variables and root functional traits

Redundancy analyses (RDA) were used to assess the relationships between potential protease activity, soil variables and root functional traits. The RDA involving soil variables as explanatory factor explained 17% of the protease activity variation. MBN was the only soil significant explanatory variable (RDA r²=0.17, Fig. 4). Indeed, potential protease activity was positively related to MBN (r=0.66, p<0.001).

Protease activity was not related to any of the root traits that have been measured. However, RDMC and total root length were the most explanatory root traits for MBN. MBN was negatively correlated to RDMC (r=-0.40, p<0.001) and total root length (r=-0.42, p<0.001) and the RDA explained 22% of the variation (Fig. 5).

Discussion

In this study, we developed a mechanistic approach based on plant root traits in order to have a better understanding of the combined influence of wheat along its growth and pea residues (crop residues and rhizodeposits) inputs on soil potential protease activity. More precisely, we analyzed the relative effects of i) soil properties i.e. soil nutrient contents and microbial biomass N and ii) root traits including both their direct and indirect effects via soil properties, on potential soil protease activity.
<figure><img src="image_36.png" /><figcaption>**Figure 3** Comparaison of root functional traits by Principal Component Analysis (A, correlation biplot and B, distance biplot) from 14 to 89 days after sowing. Axes 1 and 3 are presented. Axis 2 eigenvalue: 19%. Individuals are colored according to phenological stage. Squares (■) represent centroid of the phenological stage or soil treatment treatments. WS-C, water soluble C; WS-N, water soluble N; SRL, specific root length; RDMC, root dry matter content; C soil, control soil; R soil, soil amended with pea residues; P soil, soil previously cropped with pea; P+R soil, P soil amended with pea residues</figcaption></figure>
<figure><img src="image_37.png" /></figure>

**Figure 4** Redundancy analysis correlation biplots of protease activity in relation to soil variables. Only significant explanatory variables selected through a forward procedure are represented. Blue line is the explanatory soil variable, red line is protease activity (dependent variable). As only one dependent variable was selected, RDA was constructed with only one constrained ordination axe (RDA 1), the second axe being an unconstrained residual axe (PC 1). The RDA was significant at *P* < 0.001 and explained 17% of the variation

Regulation of protease activity

Including all treatments, mean values of potential protease activity were similar to those observed in
other studies (Alef and Kleiner, 1987; Geisseler et al., 2012; Stark et al., 2008; Vranova et al., 2013).
We evidenced that protease activity was significantly increased in soils amended with pea residues.
The same level of protease activity was observed in both residues amended treatments i.e. whether
residues were combined or not with pea rhizodeposits (R and P+R soils). In these treatments, after a
significant increase at 3-leaf stage of wheat, potential protease activity was quite stable until
decreasing at wheat maturity. By contrast, in control and pea rhizodeposits treatments (C and R soils),
we showed that potential protease activity was low but changed significantly along wheat phenology,
contrary to what is observed along pea phenology (Romillac et al., 2015). These results would confirm
with the idea that protease activity are mainly regulated in soils by the availability of protein substrates
(Geisseler and Horwath, 2008; Geisseler et al., 2009). Indeed, crop residues represent the major way
by which proteins enter into the soil (Jan et al., 2009; Jones et al., 2005), and, in particular in the case
of pea residues that contain at least twice as much proteins than cereal residues (Jensen et al., 2012;
Munier-Jolain and Salon, 2005). Despite a confounding effect of the wheat development cannot be
ruled out, the increased protease activity seemed to be short lived. The increase in protease activity
was detected 14 days after residues incorporation at stage 3 leaved and was still there at 41 days after
residue incorporation (stage early flowering) but had disappeared at 89 days after residue
incorporation (wheat maturity). This short term effect of the pea residues is in accordance with the
fact that pea residues N mineralization in particularly fast when compared to other crops and occur
primarily between the first 40-70 days after soil incorporation (Jensen, 1997; Lupwayi, 2006).

We also observed that the presence of pea N rhizodeposits did not influence the induction of protease activity. It has been shown that these rhizodeposits are immobilized in microbial residues and represent a significant pool of labile organic N with a positive effect on net N mineralization (Gransee

<figure><img src="image_38.png" /><figcaption>**Figure 5** Redundancy analysis correlation biplots of microbial biomass N in relation to root functional traits. Only significant explanatory variables selected through a forward procedure are represented. Blue line are root functional traits (explanatory variable), red line is protease activity (dependent variable). RDMC: root dry matter content. The RDA was significant at *P* < 0.001 and explained 22% of the variation</figcaption></figure>
<table><caption>**Table 3** Average ± SEM of the root functional traits in the 4 soil treatments and along wheat phenology. Different letters indicate significant differences between means (P < 0.05). n: number of replicates. Soil treatments: C soil, control soil, R soil, soil amended with pea residues, P soil, soil previously cropped with pea, P+R soil, P soil amended with pea residues. Significance: n.s. = non significant., * p < 0.05, ** p < 0.01, *** p < 0.001</caption><thead><tr><th rowspan="2">n</th><th colspan="5">Soil treatments</th><th colspan="5">Wheat development stage</th></tr><tr><th>C</th><th>R</th><th>P</th><th>P+R</th><th>Chi²</th><th>3 leaves</th><th>First tillers</th><th>2 nodes</th><th>Early flowering</th><th>Maturity</th><th>Chi²</th></tr><tr><th></th><th>19</th><th>20</th><th>19</th><th>20</th><th></th><th>16</th><th>16</th><th>16</th><th>16</th><th>16</th><th></th></tr></thead><tbody><tr><td>Root dry matter content (mg g<sup>-1</sup>)</td><td>189 ± 19</td><td>181 ± 13</td><td>172 ± 16</td><td>184 ± 15</td><td>0.82 n.s.</td><td>111 ± 2a</td><td>136 ± 3a</td><td>155 ± 4ab</td><td>209 ± 3b</td><td>302 ± 7c</td><td>71.8***</td></tr><tr><td>C/N</td><td>35.5 ± 5.8</td><td>35.4 ± 3.8</td><td>37.7 ± 6.3</td><td>49.3 ± 10.7</td><td>2.94 n.s.</td><td>23.5 ± 0.9a</td><td>25.5 ± 0.6a</td><td>27.3 ± 0.5ab</td><td>39.0 ± 1.3ab</td><td>83.3 ± 12.5b</td><td>62.9***</td></tr><tr><td>Water soluble C (%)</td><td>2.34 ± 0.19</td><td>1.94 ± 0.14</td><td>2.04 ± 0.16</td><td>1.80 ± 0.17</td><td>6.05 n.s.</td><td>2.04 ± 0.08ab</td><td>2.16 ± 0.13ab</td><td>2.46 ± 0.15a</td><td>2.09 ± 0.21ab</td><td>1.36 ± 0.25b</td><td>17.4**</td></tr><tr><td>Water soluble N (%)</td><td>0.42 ± 0.05</td><td>0.39 ± 0.06</td><td>0.37 ± 0.04</td><td>0.27 ± 0.03</td><td>5.50 n.s.</td><td>0.58 ± 0.03b</td><td>0.47 ± 0.07b</td><td>0.43 ± 0.04b</td><td>0.22 ± 0.03a</td><td>0.11 ± 0.03a</td><td>54.4***</td></tr><tr><td>Length (m)</td><td>14.3 ± 16.0</td><td>14.2 ± 1.5</td><td>17.5 ± 1.9</td><td>15.5 ± 1.4</td><td>1.95 n.s.</td><td>6.61 ± 0.46a</td><td>12.6 ± 0.9ab</td><td>18.1 ± 1.3bc</td><td>21.6 ± 1.8cd</td><td>18.0 ± 1.3bcd</td><td>45.0***</td></tr><tr><td>Fine roots length (%)</td><td>64 ± 3.4</td><td>63 ± 2.6</td><td>64 ± 2.9</td><td>64 ± 2.6</td><td>0.07 n.s.</td><td>47 ± 1.1a</td><td>61 ± 1.1b</td><td>60 ± 1.3b</td><td>72 ± 1.1bc</td><td>83 ± 0.8c</td><td>68.7***</td></tr><tr><td>Root dry mass (mg)</td><td>71.8 ± 7.2</td><td>72.3 ± 7.2</td><td>76.7 ± 8.0</td><td>72.1 ± 6.8</td><td>0.12 n.s.</td><td>32.9 ± 2.5a</td><td>54.3 ± 3.8ab</td><td>87.0 ± 6.4bc</td><td>107.4 ± 5.0c</td><td>83.8 ± 3.3b</td><td>56.5***</td></tr><tr><td>Specific root length (mg<sup>-1</sup>)</td><td>19.9 ± 0.8a</td><td>20.3 ± 1.2ab</td><td>23.0 ± 0.9b</td><td>22.0 ± 0.9ab</td><td>8.91*</td><td>20.4 ± 0.6</td><td>23.5 ± 1.1</td><td>21.2 ± 0.7</td><td>20.0 ± 1.3</td><td>21.6 ± 1.7</td><td>4.4 n.s.</td></tr><tr><td>N uptake (mgN mg<sup>-1</sup> root d<sup>-1</sup>)</td><td>0.49 ± 0.07</td><td>0.53 ± 0.09</td><td>0.44 ± 0.07</td><td>0.42 ± 0.06</td><td>0.76 n.s.</td><td>0.83 ± 0.06c</td><td>0.75 ± 0.03c</td><td>0.36 ± 0.05b</td><td>0.35 ± 0.03b</td><td>0.05 ± 0.01a</td><td>61.4***</td></tr></tbody></table>

and Wittenmayer, 2000; Jensen, 1996; Mayer et al., 2004). As microbial residues consist mainly of polymers including proteins (Geisseler et al., 2012), we could have expected that these compounds would be additional substrates for proteases. Then, the lack of induction of protease activity in response to pea rhizodeposits suggest that potential protease activity in soils is not only regulated by the availability of protein substrates but could also depend of the quality of the protein substrates (Jan et al., 2009). In pure culture studies, it has been shown that microbial extracellular proteases can present broad or narrow substrates specificity (Gao et al., 2013; Waghmare, 2015). However, even if microbial proteases constitute one of the most important groups of hydrolytic enzymes, little is actually known about the functionalities of soil proteases and, in particular how the large diversity of soil proteins influences their activities (Bach et al., 2002; Fuka et al., 2008; Gupta et al., 2002, Jan et al., 2009; Sakurai et al., 2007). More works are needed to have a better understanding of mechanisms contributing to protease regulation in soils.

## Relative effects of plant root traits and soil properties on potential protease activity

Based on RDA analysis, root traits have no contribution to variation in potential protease activity, likely because this activity was only influenced by the presence of residues. These results are in accordance with previous study of Legay et al. (2014) showing that plant traits have no direct effects on microbial activities implicated in N mineralization such as nitrification. However, plant traits could have indirect
effects on potential protease activity through their impact on C and N bioavailability and soil microbial biomass (Legay et al., 2014; Orwin et al., 2010; Romillac et al., 2015; de Vries et al., 2012). We observed that potential protease activity was positively related to MBN. This observation is in accordance with those of Asmar et al. (1992) and Geisseler and Horwath (2008). Furthermore, we found that MBN was negatively associated with total root length and RDMC. Such relationships between RDMC and nitrifier gene abundance have already been observed (Legay et al., 2014). As, RDMC and total root length are known to be linked to soil nutrient availability (Perez-Ramos et al., 2012; Birouste et al., 2014), the relationships between RDMC / total root length and MBN could result from changes in soil bioavailable C and N. In particular, in soils amended with pea residues in which potential protease activity was significantly increased, we found that the C/N ratio of soil soluble fractions was higher than the C/N ratio observed in control and pea rhizodeposits amended soils (data not shown). This higher C/N ratio could create N limiting conditions and microbes may benefit from producing enzymes to obtain it (Allison and Vitousek, 2005; Fornara et al., 2011). The moderate contribution of MBN to explain variation in potential protease activity (22% of the variation) suggests also that only a fraction of protease activity in soil is produced constitutively by soil microorganisms. This result reinforced our hypothesis that the increase in protease activity observed in residue amended soils is mainly due to substrate induction. The increase of protease activity between stages first tillers and stages early flowering could be also due to substrate induction due to release of structural compound such as protein by the decaying roots of the wheat (van Noordwijk et al., 1994). The regulation of protease activity allows soil microorganisms to mobilize complex N resources from residues to meet their needs.

**Conclusion**

The results of this study show that the production of potential protease activities in the presence of substrates is mostly decoupled from the growth of microbial biomass. Therefore, substrate induction seems to be the dominant mechanism that regulates protease synthesis. Our results highlight that root traits have a moderate contribution in the variation of protease activity, through their influence on N microbial biomass. The impact of root traits seemed to be related to variations in C and N availability for soil microorganisms. More works are needed to evaluate whether the observed effects are confirmed considering different soils and plant species. Additional studies are also needed to analyze the influence of substrate quality (i.e. proteins) on soil protease activity.
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Résumé du Chapitre 3

<figure><img src="image_39.png" /><figcaption>**Figure 1** Schéma représentant les effets du pois et du blé sur les activités potentielles protéase et d'ammonification mis en évidence dans notre étude. RDMC : teneur racinaire en matière sèche ; RMF : fraction de masse racinaire</figcaption></figure>

L'expérimentation en conditions contrôlées a permis de mieux comprendre l'effet du pois et du blé sur
deux activités microbiennes impliquées dans la décomposition – minéralisation de N (Figure 1). Nous
avons notamment mis en évidence l'influence directe et/ou indirecte de certains traits racinaires en
lien avec l'acquisition de N et la rhizodéposition sur ces activités microbiennes.

Ainsi, concernant l'activité potentielle d'ammonification, celle-ci varie au cours du développement du
pois, avec une activité plus importante aux stades reproducteurs du pois, où le pois présente de forts
besoins en N (Salon et al., 2001 ; Voisin et al., 2007). Ces variations de l'activité d'ammonification sont
expliquées par une combinaison de traits racinaires liés à la croissance ou l'acquisition de N et la
rhizodéposition tels que le RDMC, le RMF, le pourcentage et la longueur de racines fines. Ces traits
influencent soit directement, soit indirectement via le pool de C soluble du sol, l'activité potentielle
d'ammonification.
Nous avons suggéré que l'augmentation de l'activité d'ammonification aux stades reproducteurs du pois est corrélée à une diminution de la teneur du sol en carbone soluble et à la modification du profil de rhizodéposition du pois (libération d'acides aminés). La diminution de la teneur en carbone soluble du sol conduirait à une limitation des microorganismes en carbone (Kopacek et al., 2013 ; Jones and Murphy, 2007 ; Farrell et al., 2014). Les microorganismes utiliseraient alors les acides aminés libérés par le processus de rhizodéposition afin de répondre à leurs besoins en carbone. L'azote minéral ainsi libéré serait alors disponible pour la plante.

Nous avons observé que l'activité potentielle protéase, contrairement à l'activité d'ammonification ne varie pas au cours du développement du pois et évolue faiblement au cours du développement du blé. En revanche, cette activité était fortement stimulée par l'enfouissement des résidus du pois. L'induction de l'activité protéase en réponse à l'apport de résidus est corrélée à une augmentation de la biomasse microbienne azotée.

Aucun des traits fonctionnels racinaires mesurés n'ont permis d'expliquer les variations d'activité protéase. En revanche, nous avons montré que les traits racinaires influencent indirectement l'activité protéase via leurs effets sur la biomasse microbienne azotée. Ainsi, la biomasse microbienne azotée est influencée par le RDMC et la longueur racinaire. Néanmoins, nous avons observé que les variations de la biomasse microbienne azotée n'expliquent qu'une part modérée des variations de l'activité protéase au cours du développement du pois. La synthèse des protéases est donc majoritairement découplée de la croissance de la biomasse microbienne. Les variations d'activité protéase au cours du développement du blé semblent donc plus probablement contrôlées par les disponibilités relatives en carbone et azote soluble du sol et par la disponibilité en substrat.

Ces résultats suggèrent des différences de régulation des activités protéase et d'ammonification. Tandis que l'ammonification semble contrôlée par la disponibilité relative en carbone et en azote soluble (Geisseler et al., 2010), la synthèse des protéases semble aussi conditionnée par la présence d'un substrat (Geisseler et Horwath, 2008).

La mesure de traits fonctionnels racinaires permet d'améliorer la compréhension des interactions plantes-microorganismes. Néanmoins, les traits fonctionnels racinaires n'expliquaient qu'une fraction de la variation des activités enzymatiques. D'autres traits fonctionnels comme par exemple les traits fonctionnels aériens pourraient être pris en compte. De plus, ces études réalisées en microcosme, ne prennent pas en compte l'ensemble des facteurs qui peuvent structurer les communautés microbiennes et modifier leurs activités enzymatiques. Il est donc important d'analyser le fonctionnement des communautés microbiennes en conditions de plein champ.
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# Chapitre 4. Effet en conditions réelles de l'introduction du pois dans une rotation de céréales et colza sur les communautés microbiennes et les adventices
Introduction

Les plantes cultivées agissent comme des filtres pour les autres communautés (microbiennes, végétales et autres) qui vivent dans les agroécosystèmes. Selon leurs caractéristiques écologiques, elles modifient le milieu en capturant (interception de la lumière, prélèvement des nutriments et de l'eau) ou libérant (rhizodéposition, litière racinaire) des ressources au-cours de leur cycle de développement, mais également sur des durées plus longues, au travers de l'enfouissement de leurs résidus et de reliquats de nutriments (N, S...) sous forme minérale. Elles déterminent également la nature (type d'herbicides, de travail du sol) et la date (au-travers de la date de semis, de la date de récolte) des pratiques agricoles, dont certaines sont assimilables à des perturbations.

L'ensemble de ces modifications influencent la composition des communautés végétales et microbiennes, les communautés d'adventices étant plus sensibles à la disponibilité en lumière et en éléments minéraux ainsi qu'aux perturbations susceptibles de les détruire et les communautés microbiennes étant plus sensibles à la disponibilité en nutriments organiques et minéraux. A leur tour, les modifications des communautés végétales et microbiennes sont susceptibles d'entraîner des modifications des fonctions écosystémiques, comme la compétition entre la culture et ses adventices et la minéralisation de la matière organique.

Comparé à d'autres plantes cultivées comme le colza ou les céréales d'hiver, le pois de printemps a un cycle de développement court, est relativement peu compétitif pour la lumière (Kostrzewska et al., 2012 ; Seibutis et Deveikyte, 2006), n'est pas fertilisé en azote et prélève peu d'azote minéral dans les sols mais présente des besoins en soufre intermédiaire entre ceux du colza et ceux des céréales. Il produit peu de résidus et de rhizodépôts (Swinnen, 1994 ; Carrouée et al., 1999 ; Gan et al., 2009) mais ceux-ci ont des teneurs en azote et en soufre supérieure à ceux des céréales (Jensen, 1996, 1997 ; Soon et Arshad, 2002 ; Wichern et al., 2008 ; Gan et al., 2011 ; Arcand et al., 2013, Churka Blum et al., 2013) qui les rend facilement dégradables pour les microorganismes. Sur le plan des perturbations, il se différencie surtout par sa date de semis mais aussi par les familles d'herbicides utilisables, différentes de celles utilisables sur céréales. Le pois est donc susceptible d'entraîner des modifications de l'abondance et de la composition des communautés adventices et microbiennes impliqués dans la minéralisation de l'azote et du soufre, pouvant modifier le niveau de compétition entre les cultures et les adventices ainsi que la minéralisation nette d'azote et de soufre (Figure 1).
<figure><img src="image_40.png" /><figcaption>**Figure 1** Schéma conceptuel de l'effet du pois sur les communautés microbiennes et végétales adventices</figcaption></figure>

Au chapitre précédent nous avons mis en évidence l'effet du pois sur les abondances bactériennes et sur l'activité protéase, au cours de son développement et sous la culture de blé suivante. Cet effet résulte principalement des modifications de la disponibilité en C et N pour les communautés microbiennes. Ces résultats concernant l'effet de l'introduction du pois dans une rotation et son arrière-effet sur la culture suivante ont été obtenus en conditions contrôlées. L'allocation de carbone au système racinaire peut être différente entre les conditions contrôlées et les conditions naturelles au champ (Wichern et al., 2007). De plus, la sphère d'influence du système racinaire pour des cultures en conditions contrôlées est nettement supérieure du fait de la limitation du volume de sol. Dans ces conditions, les effets rhizosphère peuvent être exacerbés par rapport à la situation en conditions naturelles. Les relations peuvent être également différentes au champ en raison de la variabilité de température et de pluviométrie, qui peut conduire à des observations contrastées d'année en année (Schjonning et al., 2007 ; Yin et al., 2010 ; Vaughn et Young, 2010 ; Liu et al., 2010 ; Reardon et al., 2014). L'effet de filtres environnementaux tels que le climat et les conditions édaphiques sur les communautés microbiennes, ainsi que leurs interactions avec des espèces végétales de durée de cycle et avec des exigences en nutriments variables sont mal connues notamment à une échelle locale comme celle de la parcelle agricole. Afin de mieux comprendre les effets de l'introduction du pois dans un système de culture, il est donc nécessaire d'évaluer en conditions naturelles, d'une part ses effets en comparaison d'autres cultures et, d'autre part, ses arrière-effets sur l'abondance et les activités des communautés microbiennes. Les différentes cultures se succédant au sein de la rotation présentant
des exigences contrastées en N et S, des minéraux considérés comme limitants pour la croissance et
le développement des plantes, il apparaît pertinent de suivre l'abondance et l'activité des
communautés microbiennes responsables de l'activité protéase et de l'activité arylsulfatase, deux
enzymes fréquemment corrélées respectivement à la minéralisation brute de N (Zaman et al., 1999;
Mishra et al., 2005) et à l'immobilisation brute de S (Dedourge et al., 2003, 2004; Vong et al., 2003,
2008).

Afin de comprendre les effets du pois, en comparaison avec d'autres plantes cultivées, sur l'abondance et la composition des communautés microbiennes, ainsi que sur les fonctions écosystémiques qu'elles réalisent (minéralisation de l'azote et du soufre), nous avons utilisé une partie des données issues d'une expérimentation au champ comparant deux rotations : colza d'hiver/blé d'hiver/orge d'hiver et colza d'hiver/blé d'hiver/orge d'hiver/pois de printemps/blé d'hiver. L'abondance et la composition des communautés microbiennes, leurs activités protéase et arylsulfatase et les flux nets de minéralisation potentielle d'azote et de soufre ont été mesurées sous toutes les cultures sur plusieurs années. L'activité d'ammonification potentielle n'a pas été mesurée sur les échantillons de sol issus de cette expérimentation au champ, et ne pourra pas être traitée dans les chapitres suivants.

Le premier chapitre correspond à un article en préparation pour PLoS One. Il présente les effets du colza, de l'orge et du pois, au-cours de leur développement, en interaction avec les conditions climatiques, sur l'abondance et l'activité (arylsulfatase et protéase) des communautés microbiennes, ainsi que sur la résultante en termes de flux nets de minéralisation de l'azote et du soufre.

Le deuxième chapitre présente les effets du pois, à différents stades de la succession culturale (1 an ou 3 ans après l'enfouissement des résidus), sur l'abondance des communautés microbiennes, la composition taxonomique de la communauté bactérienne (approche de pyroséquençage), les activités arylsulfatase et protéase, ainsi que la résultante en terme de flux nets de minéralisation de l'azote et du soufre.

Au-niveau des adventices, il a été montré dans la littérature que l'abondance (Deveikyte et al., 2009 ; Sarunaite et al., 2013) et la composition (Fried et al., 2008 ; Poggio, 2005 ; Gunton et al., 2011 ; Perronne et al., 2015) de la communauté d'adventices du pois sont différentes de celles d'autres cultures comme les céréales ou le colza. En effet, une céréale d'hiver présente généralement une abondance d'adventices plus réduite si elle fait suite à une culture de pois de printemps que si elle fait suite à une autre culture d'hiver (Stevenson et van Kessel, 1996 ; Chauvel et al., 2001 ; Colbach et al., 2010). Néanmoins, les mécanismes écologiques déterminant l'assemblage des communautés d'adventices au sein de la culture de pois et des cultures suivantes dans la rotation, ainsi que leurs conséquences en termes de compétition entre ces cultures et les adventices, sont encore mal connus.
Une approche centrée sur les traits fonctionnels de la culture et des adventices associées semble être un moyen approprié afin d'appréhender ces relations de compétition (Gaba et al., 2014).

Ainsi, la mesure des traits fonctionnels de la culture, mais également des adventices, pourraient permettre de comprendre les mécanismes à l'œuvre dans la constitution des communautés d'adventices au sein d'une rotation impliquant des cultures de pois et colza comme précédents d'une culture de blé.

Dans le troisième chapitre, nous avons évalué, en utilisant une partie des données collectées sur la même expérimentation au champ que pour les communautés microbiennes, les effets du pois sur la composition et la diversité fonctionnelle de sa propre communauté d'adventices en comparaison de celle du colza, ainsi que sur celle du blé lui succédant dans la rotation. Cette approche est complétée par une estimation de la relation compétitive culture-adventice au-travers d'une approche de quantification du recouvrement de leurs niches écologiques respectives basée sur des mesures de traits fonctionnels aériens. Ces résultats sont présentés sous la forme d'un article en préparation pour Journal of Applied Ecology.

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Response of microbial communities to crop species and interannual
climatic variability at field scale

Nicolas Romillac<sup>a,b</sup>, Sophie Slezack-Deschaumes<sup>a,b</sup>, Bernard Amiaud<sup>c,d</sup>, Séverine Piutti<sup>a,b</sup>

Mis en forme pour PLOS ONE

<footnote><sup>a</sup> Université de Lorraine, Laboratoire Agronomie et Environnement, UMR 1121, TSA 40602, F-54518 Vandœuvre
cedex, France</footnote>

<footnote><sup>b</sup> INRA, Laboratoire Agronomie et Environnement, UMR 1121, TSA 40602, F-54518 Vandœuvre cedex, France</footnote>

<footnote><sup>c</sup> Université de Lorraine, UMR 1137, Ecologie et Ecophysiologie Forestière, F-54500 Vandoeuvre lès Nancy,
France</footnote>

<footnote><sup>d</sup> INRA, UMR 1137, Ecologie et Ecophysiologie Forestière, F-54280 Champenoux, France</footnote>
**Abstract**

The abundance and activity of soil microbial communities are influenced by several environmental filters which modify soil parameters such as moisture, pH or organic and mineral nutrients availability. Among those filters, the influence of climate has often been demonstrated at a regional or global scale but rarely at a local scale and taking account interannual variability. The crops species constitute another filter likely to strongly act at a local scale. In order to determine the influence of interannual climatic variability and different crop species on microbial communities, we determined microbial abundances, enzymatic activities (arylsulfatase and protease activities) and N and S mineralization in field over 3 years and under 3 different crops differing in their N and S requirements, amount and composition of rhizodeposits and root litter and cycle length (oilseed rape (*Brassica napus*), barley (*Hordeum vulgare*) and pea (*Pisum sativum*)). Microbial abundances determined by molecular techniques varied according to the crop species, with a higher abundance of fungi, bacteria and proteolytic bacteria under oilseed rape but a higher bacteria/fungi ratio under pea. Abundances of culturable bacteria and enzymatic activities did not vary according to crop. However, all the microbial variables were more influenced by the meteorology than by the crop species, microbial abundances being more influenced by precipitations and microbial activities by temperature. The precipitations and temperature effects on microbial variables depended of the crop species, with a stronger link between temperature and enzymatic activities under oilseed rape than under other crops. N and S net mineralization variation were explained by protease activity, arylsulfatase activity and abundance of arylsulfatase bacteria. As enzymatic activities were strongly responding to meteorological factors in our study, we suppose that the influence of meteorology over pass the influence of crops for N and S mineralization.

**Key words:** protease, arylsulfatase, nitrogen mineralization, sulfur mineralization, interannual climatic variation, *Pisum sativum*, *Brassica napus*, *Hordeum vulgare*

**Introduction**

Soil microbial communities decompose and mineralize organic matter through the production of different enzymes, leading to the release of plant available nutrients. Factors shaping the structure of soil microbial communities are considered to be environmental filters. Among soil properties, bacterial community composition and diversity are shaped at relatively large scales by pH (Lauber et al., 2008) whereas the extracellular enzymes responsible for hydrolyzing the labile fraction of soil organic matter are determined by substrate availability, soil pH and the stoichiometry of microbial nutrient demand
(Sinsabaugh et al., 2008). Climate conditions such as precipitation regime and mean temperature constitute another environmental filter. Precipitations influence the biomass, composition and activity of litter decomposer communities in particular in forest ecosystems (Fierer et al., 2003). Soil moisture generally has a greater impact on soil enzymatic activities than temperature variation (Henry, 2013). Nevertheless, in the case of nitrogen, the potential enzyme activities may be decoupled from moisture availability when substrates are limited (Geisseler et al., 2011). As previously shown for soil properties, mean annual precipitation is a good predictor of litter decomposition rate at global or regional scales but not at local scale suggesting that biotic filters such as plant species and phenology have a greater influence. Throughout their development, plants influence microbial communities by modifying their abundance, composition and enzymatic activities (Hu et al., 2002; Knauff et al., 2003; Dinesh et al., 2004; Mougel et al., 2006; Gschwendtner et al., 2010; Xuan et al., 2012; Legay et al., 2014; Bell et al., 2015). The plant influence on the microbial communities depends on the considered scale with a stronger effect of plants on microbes inhabiting the rhizosphere than the whole soil (Hargreaves et al., 2015). In perennial cropping systems, plants induce changes in the physiological capacity of the microbial community by modifying the activity associated with soil N cycling without increasing microbial biomass (Hargreaves and Hofmockel, 2014).

These microbial shifts in turn may alter the rates of soil C, N and S mineralization (Van der Krift and Berendse, 2001; Hu et al., 2002; Parkin et al., 2002; Gan et al., 2010; Zhu et al., 2014).

In ecosystems, seasonal changes in enzyme activity evidenced in different studies (e.g. Watanabe and Hayano, 1995; Sardans et al., 2008) suggest a great dependence of C and N dynamics on short-term substrate supply from crop roots. As soil microbes are heterotrophs and given their stoichiometric requirements, the availability of C and mineral nutrients has to be considered (Fierer et al., 2009; Sinsabaugh et al., 2008) because microbial communities induce enzymes synthesis either to respond to nutrient limitation and/or to energy requirement. Among enzymes involved in microbial nutrition, proteases, which hydrolyze proteins to peptides and amino acids, and arylsulfatases, which hydrolyze sulfate esters to sulfate, are thought to play a key role for N and S mineralization at ecosystem level. Proteases are microbial enzymes whose activity is positively correlated to N gross mineralization (Zaman et al., 1999; Mishra et al., 2005; Yuan et al., 2011) and that are considered to realize the limiting step of N mineralization (Jan et al., 2009). The synthesis of proteases is rather induced by the microbial need for C (Sims and Wander, 2002; Geisseler and Horwath, 2008), except in soils with strong mineral N limitation (Geisseler and Horwath, 2008). Among the microbial enzymes implicated in S cycling, arylsulfatase (ARS) is believed to be involved in mineralization of sulfate esters in soils (Tabatabai and Bremner, 1970). Dedourge et al. (2004) evidenced a positive correlation between S immobilization and ARS activity in soil without any changes in S microbial biomass suggesting a regulation of the synthesis
of ARS by the substrate and/or the sulfate limiting conditions (Cregut et al., 2013). Arylsulfatase activity (Legay et al., 2014) and protease activity (Geisseler and Horwath, 2008) may be influenced by plants which can modify the availability of organic and mineral nutrients in soil through root uptake and rhizodeposition (comprising root exudation and root litter production; Wichern et al., 2008) (Haynes and Francis, 1993; Inselsbacher et al., 2010; Bell et al., 2015).

The amount and composition of plant rhizodeposition and nutrient uptake is crop specific. This is especially true for crops belonging to different botanical families such as oilseed rape (Brassicaceae), wheat or barley (Poaceae) and pea (Fabaceae), which differ in terms of phenology length, biomass production and N and S requirements. While pea due to biological nitrogen fixation can be almost independent from soil for N nutrition in N poor soils, oilseed rape (Chamorro et al., 2002; Berry et al., 2010; Kessel et al., 2012) and barley (Delogu et al., 1998; Rutkowska et al., 2014; Eckersten et al., 2010) uptake from soil high N amounts ranging from 50 to 200 kgN ha⁻¹. Concerning the sulfur, crops S requirements increase from 10-15 kgS ha⁻¹ for barley to 50-80 kgS ha⁻¹ for oilseed rape (Walker and Booth, 1992). Fabaceae species require also a large amount of S (20 to 50 kg S ha⁻¹; Taureau et al., 1987), probably because of their high protein content and because S is essential for biological nitrogen fixation (Varin et al., 2010). Oilseed rape and barley produce a higher amount of rhizodeposit than pea. The C rhizodeposition decrease in the following way: spring barley (530 to 880 kgC ha⁻¹; Swinnen 1994) > spring oilseed rape (348 kgC ha⁻¹; Gan et al., 2009) > spring pea (132 to 145 kgC ha⁻¹; Gan et al., 2009). Nevertheless, for pea, due to biological nitrogen fixation, N rhizodeposition reach on average 57 mg N plant⁻¹ (Wichern et al., 2008), a value closed to those found for oilseed rape (59 mgN plant⁻¹; Arcand et al., 2013). In comparison, N rhizodeposition of barley is about 17 mg N plant⁻¹; Jensen, 1996). As a consequence, pea rhizodeposits, calculated by isotopic methods, have C:N as low as 17 (Wichern et al., 2007a,b) which is probably lower than those of oilseed rape and barley. Despite no study to our knowledge measured the rhizodeposits C:N of oilseed rape and barley, the C:N of water-soluble contents are 9.6 and 11.3 respectively for rape and barley roots (Vong et al., 2007). No data to our knowledge exist on S rhizodeposition or root C:S for those three crops even if C:S of water-soluble contents are 26 and 38 respectively for rape and barley roots (Vong et al., 2007) suggesting that oilseed rape rhizodeposits have probably a lower C:S than barley rhizodeposits.

Plant rhizodeposition and nutrient uptake, as well as their impact on microbial communities and soil processes, have been evaluated both in microcosms and in field conditions, but field experiments are often complicated by high seasonal and interannual variability of microbial variables (Schjonning et al., 2007; Soon et al., 2007; Yin et al., 2010; Vaughn et al., 2010; Liu et al., 2010; Reardon et al., 2014).
Seasonal variability in temperature and precipitations may directly influence microbial communities both in terms of abundance, structure and activity and indirectly through the regulation of nutrient resources. Different studies focusing on these aspects were dedicated to forest (Kaiser et al., 2010; Richard et al., 2011; Burke 2015) or grassland ecosystems (Lipson et al., 1999). Whatever the ecosystem, the links between the seasonal or interannual evolution of soil microbial community composition and microbial functions related to nutrient cycles were little discussed in the literature. Waldrop and Firestone (2006) which showed significant effect of both season and year stated that soil moisture may be the principal factor controlling microbial community composition and function in Mediterranean climate. Among the possible drivers of the interannual variability of soil microbial communities in grasslands are included plant growth, soil moisture, and ambient temperature (Gutknecht et al. 2012). In agricultural soils, Lauber et al. (2013) evidenced that over a 7-month period soil moisture and temperature were two predictors of community composition and that a large proportion of these seasonal variations were probably explained by the C availability through the regulation of C-inputs (i.e. root exudates and plant litter). Soil moisture and temperature are important determinants of soil microbial abundance (Franzluebbers et al., 2001; Castro et al., 2010; Serna-Chavez et al., 2013) and microbial enzymatic activities (Mancinelli et al., 2013; Sardans and Penuelas, 2005; Fraser et al., 2013; Vanegas et al., 2013). Likewise, climatic conditions affect plant growth with subsequent consequences on rhizodeposition and nutrient uptake (Bassirirad, 2000; Jones et al., 2004; Mahieu et al., 2009; Canarini and Dijkstra, 2015). As a consequence, crop species and climatic conditions could have interactive effects on microbial abundances and activities (Classen et al., 2015). At field level, the relative importance of climatic conditions and crop characteristics (in terms of duration of development and nutrient requirements) as determinants of abundance and enzymatic activities of microbial communities remain still unresolved. Particularly, no study to our knowledge compared the influence of climatic conditions on microbial communities implicated in protease and arylsulfatase activity under different winter and spring crops. In order to assess the effect of three crop species with different rhizodeposition and nutrient uptake abilities, namely winter oilseed rape, winter barley and spring pea, in interaction with climatic conditions, on abundance and activities of microbial communities involved in N and S mineralization, we used data collected from a rotation experiment over 3 years. Microbial abundance was evaluated by molecular and culture-dependent techniques, targeting total communities and specific functional groups. Microbial activities involved in N and S mineralization were evaluated by protease and arylsulfatase activities.

We hypothesized (1) that microbial abundances will be higher under plants with high C rhizodeposition (i.e. oilseed rape and barley) but that bacteria will be favored over fungi under plants with low root C/N (i.e. pea), (2) that enzymatic activities (i.e. protease and arylsulfatase activities) will be higher under plants with the highest requirements for N and S, (3) that climate conditions will more strongly
influence microbial abundances and activities than crop species and that this effect will be stronger
under spring crops than winter crops, and finally (4) that the influence of crop species and climate on
microbial abundances and activities can results in modifications of N and S mineralization.

**Material and Methods**

*Ethic statement*

No specific permit was required to perform the culture trial. Permission was provided by the
landowner (Bouzule experimental farm).

*Site description and soil sampling*

The soil samples were collected in an experimental field experimental trial located in the Lorraine
region of Northeastern France (Champenoux, 48° 73' N, 6° 31' E). The mean annual rainfall was 734
mm and the mean annual air temperature was 9,9 °C over the experiment period. Precipitation and
temperature data were recorded every month at the meteorological station of the Institut National de
la Recherche Agronomique (INRA Champenoux, located 2 km from the experimental field). Monthly
temperature and precipitations are summarized in Table 1. The soil is a vertic (clayic) cambisol (FAO-
WRB classification), containing 62% clay, 35% silt and 3% sand. Soil characteristics over the entire
experimental site were analyzed before setting up the different plots. The physico-chemical properties
are detailed in Table 2. The field, previously cultivated with a maize-maize-spring barley rotation
system, was shown to be non-homogeneous for the two following soil parameters measured: pH and
CaO. To avoid plot-to-plot variations, the experimental design consisted of 4 blocks, arranged
perpendicular to a pH and CaO gradient (Table 2). Each block (100x72m) was divided in 8 plots of 50x18
m. Each plot corresponds to a different culture belonging either to a 3- or 5-year crop rotation systems.
The 3 year crop rotation system consists in a winter crop succession oilseed rape (Brassica napus L.) –
wheat (Triticum aestivum L.) – barley (Hordeum vulgare L.). The 5 year crop rotation system
characterized by the introduction of spring pea consists in the following winter succession: oilseed
rape – wheat – barley-spring pea (Pisum sativum L.) – wheat. On each plot of a block, the rotation
began at a different phase in order to allow each of rotations crops to be present each year in each
block (randomized “phases differences” design; Yates, 1954), allowing each of the cropping phases of
the two rotations to be present each year in each block. All plots were deep-ploughed every year
before crop establishment. Management practices were in accordance to standard recommendations
for fertilization. Oilseed rape was systematically fertilized with mineral nitrogen and sulfur, barley was
systematically fertilized with nitrogen, while pea plots did not receive
<table><caption>**Table 1** Average monthly temperature and monthly precipitations amounts during the three cropping cycles</caption><thead><tr><th rowspan="2"></th><th colspan="3">Temperature (°C)</th><th colspan="3">Precipitations (mm)</th></tr><tr><th>2010-2011</th><th>2011-2012</th><th>2012-2013</th><th>2010-2011</th><th>2011-2012</th><th>2012-2013</th></tr></thead><tbody><tr><td>August</td><td>17.0</td><td>18.2</td><td>19.4</td><td>105.8</td><td>98.2</td><td>39.0</td></tr><tr><td>September</td><td>13.0</td><td>16.0</td><td>14.2</td><td>94.4</td><td>29.6</td><td>51.6</td></tr><tr><td>October</td><td>8.9</td><td>10.0</td><td>10.0</td><td>41.6</td><td>51.6</td><td>59.2</td></tr><tr><td>November</td><td>5.8</td><td>6.1</td><td>5.9</td><td>84.2</td><td>8.4</td><td>82.6</td></tr><tr><td>December</td><td>-1.4</td><td>4.9</td><td>3.7</td><td>103.6</td><td>115.4</td><td>99.6</td></tr><tr><td>January</td><td>2.4</td><td>3.4</td><td>1.1</td><td>39.2</td><td>67.4</td><td>35.4</td></tr><tr><td>February</td><td>3.2</td><td>-1.8</td><td>-0.2</td><td>17.4</td><td>8.6</td><td>49.6</td></tr><tr><td>March</td><td>7.3</td><td>8.3</td><td>2.7</td><td>12.4</td><td>16.2</td><td>28.0</td></tr><tr><td>April</td><td>12.6</td><td>8.6</td><td>9.1</td><td>13.8</td><td>49.0</td><td>103.2</td></tr><tr><td>May</td><td>14.6</td><td>14.4</td><td>10.7</td><td>30.2</td><td>126.0</td><td>120.6</td></tr><tr><td>June</td><td>16.9</td><td>16.0</td><td>16.1</td><td>66</td><td>60.4</td><td>60.8</td></tr><tr><td>July</td><td>15.9</td><td>17.2</td><td>20.4</td><td>76.2</td><td>73.6</td><td>51.2</td></tr></tbody></table>

N or S fertilization. Cereals straw was exported. Pesticides were applied when necessary according to the regional alert thresholds.

Soil samples were taken in the plots cultivated with rapeseed, barley and peas. For each plot, soil samples were collected from the plough layer (0-20 cm) in July 2011 and in May and July 2012 and 2013. In May, phenological stages corresponded respectively to flowering, stem elongation and leaves initiation for rapeseed, barley and pea. In July, all crops were close to maturity stage. The sampling area was identified by GPS coordinates in order to limit the spatial variability of samples between years. Soil samples were sieved at 5 mm, thoroughly homogenized and stored at 4 °C for microbial analysis. A sub-sample of soil was frozen at -20 °C for molecular analysis. A total of 20 soil samples corresponding to 5 plots per block were collected at each sampling date.
<table><caption>**Table 2** Site characteristics per block</caption><thead><tr><th>A)</th><th>Bloc 1</th><th>Bloc 2</th><th>Bloc 3</th><th>Bloc 4</th></tr></thead><tbody><tr><td>Clay (%)</td><td>60</td><td>65</td><td>61</td><td>59</td></tr><tr><td>Silt (%)</td><td>36</td><td>33</td><td>34</td><td>35</td></tr><tr><td>Sand (%)</td><td>4</td><td>3</td><td>5</td><td>6</td></tr><tr><td>pH H<sub>2</sub>O</td><td>7.21</td><td>7.38</td><td>7.41</td><td>8.04</td></tr><tr><td>Cation-exchange capacity (meq/100g)</td><td>26.20</td><td>26.10</td><td>26.00</td><td>24.60</td></tr><tr><td>Organic matter (%)</td><td>5.26</td><td>5.84</td><td>5.20</td><td>4.73</td></tr><tr><td>Organic C (g/kg)</td><td>30.4</td><td>33.8</td><td>30.1</td><td>27.4</td></tr><tr><td>Total N (g/kg)</td><td>3.36</td><td>3.64</td><td>3.59</td><td>2.88</td></tr><tr><td>Total S (mg/kg)</td><td>707</td><td>763</td><td>715</td><td>664</td></tr><tr><td>CaO (ppm)</td><td>8001</td><td>8625</td><td>9849</td><td>12848</td></tr><tr><td>C/N</td><td>9.03</td><td>9.27</td><td>8.38</td><td>9.52</td></tr><tr><td>C/S</td><td>43.0</td><td>44.2</td><td>42.1</td><td>41.2</td></tr></tbody></table>

## Soil protease and arylsulfatase activities

Potential protease activity was determined according to Ladd and Butler (1972). Briefly, 1g of fresh soil was incubated for 4 h at 45 °C with 2.5 ml of 0.1 M Tris-HCl buffer (pH 8) and 2.5 mL 2% sodium caseinate solution. Addition of 5 mL 10% trichloroacetic acid terminated the enzymatic reaction. For each sample, a control assay was performed with 1 g of fresh soil incubated 4 h at 45 °C in 2.5 ml of 0.1 M Tris-HCl buffer (pH 8). Addition of 5 mL 10% trichloroacetic acid terminated the enzymatic reaction. Then, the substrate (sodium caseinate) was added. After centrifugation at 16,000 g for 2 min, 0.5 mL of supernatant was mixed with 0.75 mL 1.4M sodium carbonate and 0.25ml 16.5% Folin-Ciocalteu reagent during 5 min. The concentration of tyrosine released was determined colorimetrically at 680 nm using a Cary® 300 spectrophotometer (Varian, Palo Alto, USA). Protease activity was expressed as the amount of tyrosine produced per hour per gram of dry soil.

Potential arylsulfatase activity was determined according to Tabatabai and Bremner (1970). Briefly, 1g of fresh soil was incubated for 1 h at 37 °C with 4 mL of 0.5 M sodium acetate buffer (pH 5.8), 0.25 mL toluene and 1mL of 25 mM p-nitrophenyl sulphate. Addition of 1 mL 0.5 M CaCl₂ and 4 mL 0.5 M NaOH terminated the enzymatic reaction. For each sample, a control assay was performed with 1 g of fresh soil incubated one hour at 37°C in 4 ml of 0.5 M acetate buffer (pH 5.8) and 0.25 ml toluene. After filtration, 5 mL of NaOH 0.5 M was added to stop the enzymatic reaction. Then, the
substrate (p-nitrophenyl-sulfate) was added. After filtration on Whatman 2V filter paper (Whatman, Maidstone, United Kingdom), the concentration of p-nitrophenol released was determined colorimetrically at 400 nm using a Cary® 300 spectrophotometer (Varian, Palo Alto, USA). Arylsulfatase activity was expressed as the amount of p-nitrophenol produced per hour per gram of dry soil.

## Quantification of 16S rDNA, 18S rDNA and apr genes

Genomic DNA was extracted from each soil samples using Fast DNA® SPIN Kit for Soil (MP Biomedical, Santa Anna, USA) according to the manufacturer's instructions. The DNA concentration in the extracts was diluted to 5 ng µl⁻¹ to be used as template for the amplification by PCR of different molecular markers. Evaluation of the total bacterial, the total fungal and the protease communities' abundances were performed using the 16S rDNA, the 18S rDNA and the *apr* (Bach et al. 2002; Lee et al. 1996; Prévost-Bourré et al. 2011) as molecular markers, respectively. The quantification of 16S rDNA gene was performed with the 341F and the 534R primers (Lopez-Gutierrez et al. 2004) in a 10 µL reaction volume containing 10 ng of template DNA, 1x Power SYBR® Green PCR Master Mix (Applied Biosystems, Foster City, USA), 1 µM of each primer and 250 ng T4Gene 32 (MP Biomedicals, Strasbourg, France). The PCR conditions were: 95 °C for 10 min, 35 cycles of 15 s at 95 °C (denaturation), 30s at 60 °C (annealing), 30 s at 72 °C (extension). The quantification of 18S rDNA gene was performed with the FR1 and the FF390 primers (Prévost-Bouré et al. 2011) in a 20 µL reaction volume containing 10 ng of template DNA, 1x Power SYBR® Green PCR Master Mix (Applied Biosystems, Foster City, USA), 1.25 µM of each primer and 100 ng T4Gene 32 (MP Biomedicals, Strasbourg, France). The PCR conditions were: 95 °C for 10 min, 40 cycles of 15 s at 95 °C (denaturation), 30s at 50 °C (annealing), 60 s at 72 °C (extension). The abundance of *apr* genes was determined using *apr* I and *apr* II primers (Bach et al. 2002) in a 20 µL reaction volume containing 10 ng of template DNA, 1x Power SYBR® Green PCR Master Mix (Applied Biosystems, Foster City, USA), 1.75 µM of each primer and 150 ng T4Gene 32 (MP Biomedicals, Strasbourg, France). The PCR conditions were: 95 °C for 10 min, 40 cycles of 20 s at 95°C, 30 s at 53°C, 60 s at 72°C (Gschwendtner et al. 2010). PCR were carried out in a StepOne Plus™ thermocycler (Applied Biosystems, Foster City, USA) and quantification was based on the increasing fluorescence of the SYBR Green during amplification. For each sample, three replicates were done on different PCR plates. Standard curves were obtained using serial dilutions of linearized PCR8®/GW/TOPO® plasmids (Life Technologies, Carlsbad, USA), containing the targeted DNA sequences obtained from soil bacterial or fungal strains. Specificity of the amplified products was checked by melt curves analysis and by electrophoresis of the qPCR products on a 1.5 % (w/v) agarose gel.
## Quantification of total culturable bacteria and culturable bacteria possessing ARS or proteolytic activities

Ten grams of each soil sample was mixed with 100 ml phosphate buffer (8 g NaCl, 1.44 g Na2HPO4, 0.24 g KH2PO4/l, pH 7.2). Soil suspensions were serially diluted. The dilutions ($10^{-3}$ to $10^{-5}$) were used for the determination of the number of culturable cells as colony forming units (CFU) by spread-plating serial dilutions onto tryptone soy agar 10% (Biokar Diagnostics, Pantin, France). Three replicates for each dilution were prepared. The agar plates were incubated in the dark at 28 °C for 10 days.

The abundance of culturable proteolytic bacteria was determined by plating the soil suspension on a calcium caseinate-agar $30.7 g L^{-1}$ solid growth medium (Sigma-Aldrich, Saint-Louis, USA). Colonies surrounded by a clear halo, expressing protease activity, were counted after 10 days incubation at 28°C. All bacterial abundances were expressed as CFU per gram of dry soil.

The abundance of ARS bacteria was estimated by plate counting (three replicates for each dilution from $10^{-2}$ to $10^{-4}$) on M9-X-sulf medium (Cregut et al. 2009). X-sulf is a chromogenic substrate that reports the bacterial hydrolysis of this sulfate ester with the appearance of a blue colour (Wyss 1989). The blue colonies (expressing ARS activity) were counted after 21 days of incubation at 28 °C.

## Sulfur and nitrogen mineralization kinetics

For field soil samples collected in July, incubation on 10 g of equivalent dry soil was performed in the dark at 20°C with replicates corresponding to 5 incubation sampling dates (0, 3, 7, 14, 28 days). The soil was moistened to 80% of the water-holding capacity (WHC). Microbial communities were placed under non limiting conditions for N and S by addition of 0.7 mg N-KNO3 and 0.2 mg S-Na2SO4. At each sampling date (0, 3, 7, 14, 28 days), part of the soil from each pot (5g) was removed for moisture determination after drying at 105°C for 24 h. The other part (5g) was shaken with 25 ml 0.016 M KH2PO4 in an end-over-end shaker for 30 min (Walker and Doornenbal, 1972). The suspension was filtered on a Whatman 2V and refiltered through a 0.45 µm cellulose nitrate filter (Millipore). KH2PO4 extracts were frozen at -20°C until the determination of the soil mineral S (SO42-) and N (NO3-) contents by ionic chromatography.

## Statistical analysis

The general structure of the data was assessed with a Principal Component Analysis (PCA), realized with the FactoMineR package v1.28 (Lê et al., 2008), with the crop species and the sampling time as illustrative factors. The effect of crop on the median of the examined variables was analyzed using a Friedman paired test with the crop as a factor. Data were paired according to the block and the
sampling time. The means were grouped using a post-hoc pairwise multiple comparison Nemenyi test, realized with the PMCMR package v1.1 (Pohlert, 2014). The relationship between the climatic variables (explanatory) and the microbial variables (response) was analyzed using a redundancy analysis (RDA), a constrained ordination procedure based on the principal component analysis that allows assessments of the relationship between a set of explanatory variables and a set of response variables. Only the variables measured at all sampling time were included in the RDA. Prior to the RDA, the set of explanatory variables underwent forward selection using the ordistep function (Oksanen et al., 2013) to select the most significant variables and avoid multicollinearity (Legendre and Legendre, 1998). The RDAs and variation partitioning were performed using the vegan 2.0-9 package (Oksanen, 2013).

The adjusted R² values were calculated using Ezekiel's formula (Ezekiel, 1930). The significance of the RDA and variation partitioning results was assessed using a Monte-Carlo permutation test with 1,000 permutations. R 3.0.0 software (R Core Team 2013) was used for all the statistical analyses.

## Results

### Effect of crop species on soil microbial abundances and activities

For the five sampling dates, the abundance of total soil microbial communities was followed for the different crop modalities (Fig 1). No significant crop effect was evidenced on the culturable part of total bacterial community while the copy numbers of 16S rDNA and 18S rDNA were significantly different according to the crop species. The 16S rDNA copy numbers per gram of dry soil were highest under R/W (2.38 10^8) and lowest under P/B (1.84 10^8) ($\chi^2$ = 9.8, p < 0.05). The 18S rDNA copy numbers were specifically higher in oilseed rape treatment (3.02 10^7 and 2.57 10^7 respectively for R/W and R/B) compared to B/W (1.60 10^7) and P/B (1.46 10^7) ($\chi^2$ = 10.70, p<0.01). The 16S rDNA:18S rDNA was also highest in the oilseed rape treatments (respectively 7.7 and 9 for R/W and R/B) compared to the other crop species (16.7 under B/W and 14.3 under P/B) ($\chi^2$ = 11.02, p<0.01). The densities of culturable bacteria possessing arylsulfatase and protease activities were not significantly different in soil samples whatever the crop species at the same manner of soil enzymatic activities (protease and arylsulfatase). The copy number of *apr* gene was highest under R/W (1.76 10^7) and B/W (2.00 10^7) and lowest under P/B (1.35 10^7) ($\chi^2$ = 10.44, p < 0.05) but the relative *apr* to 16S rDNA ratio (mean value 0.10) was not different between the crop species.
<figure><img src="image_41.png" /><figcaption>**Fig. 1** Boxplots of microbial activities and abundances of N and S net mineralization according to crop. R/W: oilseed rape after wheat, R/B: oilseed rape after barley, B/W: barley after wheat, P/B: pea after barley. Values with different letters are significantly different (p < 0.05). Boxplots indicate median, interquartiles (25-75%), maximum, minimum and outliers.</figcaption></figure>
### *Effect of sampling date and climatic conditions on soil microbial abundances and activities*

The sampling date had a significant effect on every single microbial variable, with strong amplitude (variations from 5 to 17 times between the highest and the lowest value, depending on the variable) (Fig. 2). Overall, most of the variables varied between years but only the density of culturable bacteria with protease activity and soil arylsulfatase activity varied between May and July of the same year. The density of total culturable bacteria was lower in May and July 2013 (mean value 4.78 10^7) compared other sampling dates (1.80 10^8). The number of 16S rDNA copies was higher in May 2012 (3.19 10^8), lower in July 2011 (1.41 10^8) and intermediate at the other sampling dates while the number of 18S rDNA copies was higher in May 2012 (4.98 10^7) but lower in May and July 2013 (6.41 10^6). As a consequence, the 16S rDNA:18S rDNA was higher in May and July 2013 (36) compared to other sampling dates (16). The densities of culturable bacteria possessing protease activity was lower in May and July 2013 (1.84 10^7) compared other sampling dates (7.35 10^7), while the abundance of culturable bacteria possessing arylsulfatase activity was higher in July 2013 (3.01 10^7) than at any other sampling date (7.01 10^6). The copy number of *apr* gene was higher in May and July 2013 (3.47 10^7) than at other sampling dates (4.68 10^6) and the *apr* to 16S rDNA ratio followed the same trend (0.21 in May and July 2013, 0.02 at other sampling dates). Soil protease activity was lower in May 2013 (54.6) and higher in July 2012 (202) while arylsulfatase activity was higher in July 2011 (416) and July 2013 (272) than at other sampling dates (142).

### *Relative importance of crop species and sampling date for the microbial abundances and activities*

Multivariate regression trees (MRTs) were used to evaluate the relative importance of crop species and sampling date to explain the changes in microbial abundances and activities. The MRT analysis explained the relationship of microbial variables (Fig. 3a microbial abundance and fig. 3b microbial activities) and biotic (crop species) and abiotic factors (sampling date) in a visualized tree. The tree cross-validated error was 0.72 and 0.33 for microbial abundances and activities, respectively, i.e. 18% and 77% of the variability were explained by the trees. The sampling date was more important than the crop species both for the microbial abundances and activities, as highlighted by the primary split and by the importance of the sampling date for the MRTs construction (65% for the MRT analysis of microbial abundances and 100% for the MRT analysis of enzymatic activities). In the first tree (Fig. 3a), the variables of microbial abundance were first split by sampling date (first split error 0.83). Data from 2011 and 2013 were placed into one group and data from 2012 were placed into a separate group. The first group was split further according the sampling year which appeared more influential than the sampling month (i.e. May or July) for microbial abundances as observed previously in Fig. 2. Crop species appeared only at the third node in the MRT, segregating R/W from the other crops in July 2011.
<figure><img src="image_42.png" /><figcaption>**Fig. 2** Boxplots of microbial activities and abundances of N and S net mineralization according to sampling date. Values with different letters are significantly different (p < 0.05). Boxplots indicate median, interquartiles (25-75%), maximum, minimum and outliers.</figcaption></figure>
In the MRT analysis of microbial activities, crop factor never appeared while the sampling season appeared as important as the sampling year as shown by the first split (first split error 0.83) which separated May 2012, May 2013 and July 2012 samples from July 2011 and 2013 samples (Fig. 3b). The two MRT analyses evidenced that each microbial variable was more influenced by the sampling date (reflecting seasonal and interannual variability) than by the crop species, as shown by the high relative importance of this factor for the construction of univariate regression tree (Table 3). Among microbial abundance variables, the density of culturable proteolytic bacteria was almost equally influenced by sampling date and by crop species. This largely explained the influence of crop

<table><caption>**Table 3** Importance of sampling date and crop species for the construction of an univariate regression tree with the microbial abundances and activities as response variables.</caption><thead><tr><th>Variable</th><th>Relative error</th><th>Sampling date importance (%)</th><th>Crop importance (%)</th></tr></thead><tbody><tr><td>Protease activity</td><td>0.51</td><td>100</td><td>0</td></tr><tr><td>Arylsulfatase activity</td><td>0.25</td><td>100</td><td>0</td></tr><tr><td>Total bacteria</td><td>0.71</td><td>96</td><td>4</td></tr><tr><td>Proteolytic bacteria</td><td>0.52</td><td>56</td><td>44</td></tr><tr><td>Arylsulfatase bacteria</td><td>0.21</td><td>92</td><td>8</td></tr><tr><td>apr</td><td>0.30</td><td>100</td><td>0</td></tr><tr><td>16S rDNA</td><td>0.72</td><td>87</td><td>13</td></tr><tr><td>18S rDNA</td><td>0.37</td><td>78</td><td>22</td></tr></tbody></table>

species on the variables related to microbial abundance in July 2011, due to the high abundance of proteolytic bacteria under R/W compared to the other crop species as seen on the third split on MRT (Fig. 3a). Furthermore microbial variables were not all affected to the same degree by the sampling date and the crop species as shown by the diverse relative errors (i.e., the fraction of variance not explained) of the univariate regression trees (Table 3).

Indeed, arylsulfatase activity, culturable arylsulfatase bacteria, *apr* genes and 18S rDNA genes were particularly influenced by sampling date and crop species, with relative errors as low as 0.21. On the other hand, total bacteria and 16S rDNA were only slightly influenced by sampling date and crop species, with relative errors of 0.71 and 0.72, respectively, while culturable proteolytic bacteria and protease seemed intermediary with relative errors of 0.52 and 0.51, respectively.
In order to understand the changes of microbial abundances and activities according to the sampling date, we performed redundancy analyses (RDA), using as explanatory variables the average temperature and cumulative precipitation over different time scales (averaged or summed over the last 7, 30, 60, 90, 120, 150, 180, 210 or 240 days before sampling or since plant sowing). In order to analyse possible interactions between meteorological factors and crop species, RDAs were performed first for all crops and then independently for each crop (Fig. 3).

<figure><img src="image_43.png" /><figcaption>**Fig. 3** Multivariate regression tree of the microbial abundances (A) and activities (B) explained by sampling date (Date) and crop species (Crop). Variables at each node indicate the most influential factor for predicting microbial variables. R/W: oilseed rape after wheat, R/B: oilseed rape after barley, B/W: barley after wheat, P/B: pea after barley. Histograms at each leaves indicates the value of each variable and the numbers below indicate the number of soil samples (n). CV Error: cross validated error. SE: standard error of the CV Error.</figcaption></figure>

The RDAs including all crops showed that meteorological variables better explained the variation of microbial activities (arylsulfatase and protease) than those of microbial abundances ($r^2$= 0.61 and 0.36, respectively) (Fig. 3). Microbial abundances were most likely influenced by the cumulative precipitations while microbial activities were most likely influenced by the average temperature. Among microbial abundance variables, *apr* genes and culturable arylsulfatase bacteria were positively correlated to cumulative precipitations, while total culturable bacteria, culturable proteolytic bacteria, the copy numbers of 16S rDNA and 18S rDNA genes were negatively correlated with them (Table 4). Arylsulfatase and protease activities were both positively correlated to average temperature, but protease activity was also negatively correlated to cumulative precipitation (Table 4).

Overall, the effect of meteorological variables on microbial abundances and activities tended to be different under oilseed rape compared to barley and pea crops. The explanatory power of the RDAs
was similar between the three crops for microbial abundances but was two times higher under oilseed rape compared to barley and pea crops for microbial activities (0.67 against 0.38 and 0.41). This strong explanatory power was partly due to protease activity which exhibited correlations with more explanatory variables under oilseed rape compared to the other two crops (Table 4). The timescale of meteorological factors influencing microbial abundances was very similar under barley and pea (cumulative precipitations over 60 and 120 days), but microbial abundances under oilseed rape were influenced by cumulative precipitations over longer periods (i.e. 180 and 240 days). Microbial activities were influenced, in the case of pea, by average temperature over the shortest period (i.e. 7 and 30 days). For oilseed rape crop these activities were influenced by average temperature over the longest periods (i.e. 30 days, 180 days and since crop sowing) while microbial activities in barley treatment evidenced an intermediate position (cumulative precipitations over 30 days and average temperature over 90 days) between oilseed rape and pea.

### *Effect of microbial abundances and activities on net N and S mineralization*

Net N and S and mineralization measured in lab conditions over 28 days incubation were influenced by the sampling year but only net N mineralization was influenced by crop species, being higher under R/W than under B/W (Fig. 5). The link between the microbial variables and net N and S mineralization was assessed by a RDA with the microbial activities and abundances as explanatory variables (Fig. 6). Net N and S mineralization was best explained by the two enzymatic activities, i.e. arylsulfatase and protease activities, and by the abundance of culturable arylsulfatase bacteria. Both N and S net mineralization were negatively correlated with arylsulfatase activity (r²=0.31, p<0.001 and 0.38, p<0.001 respectively), while S net mineralization was slightly positively correlated with the abundance of arylsulfatase bacteria (r²=0.10, p<0.05) and N net mineralization slightly correlated with the protease activity (r²=0.13, p<0.01).

## Discussion

### *Plant species effect on microbial abundances*

In accordance with our first hypothesis, microbial abundances were influenced by the crop species. Both bacteria and fungi estimated by molecular methods (16S rDNA, 18S rDNA) were more abundant under winter oilseed rape than under spring pea. The mean values under barley appeared intermediate between those measured under oilseed rape and pea. Spring pea, because of a short growing period, could produce a lower root biomass compared to winter crops like barley or oilseed rape and provide in turn limited carbon input to soil. Great differences in root biomass production and
<figure><img src="image_44.png" /><figcaption>**Fig. 4** Redundancy analysis correlation biplots of microbial abundances and enzymatic activities in relation to the meteorological factors. Blue lines are meteorological factors (explanatory variables), red lines are microbial variables (dependent variables). The set of explanatory variables is composed of the mean temperature and the precipitations cumulation over different time scales, from 7 to 240 days. Only significant explanatory variables selected through a forward procedure are represented. The RDAs were significant at *P* < 0.01 (**) or *P* < 0.001 (***). CFUtot: abundance of total culturable bacteria; CFUprot: abundance of culturable bacteria with protease activity; CFUars: abundance of culturable bacteria with arylsulfatase activity</figcaption></figure>
<table><caption>**Table 4** Correlation matrices between meteorological variables and microbial abundances and activities under oilseed rape, barley and dry pea.</caption><thead><tr><th colspan="4">All crops</th><th colspan="4">Oilseed rape</th></tr><tr><th></th><th>Precipitations<br>60d</th><th>Precipitations<br>120d</th><th>Temperature<br>90d</th><th>Temperature<br>since sowing</th><th>Precipitations<br>180d</th><th>Precipitations<br>240d</th><th></th><th></th></tr></thead><tbody><tr><td>Total bacteria</td><td>-0.22*</td><td>-0.28**</td><td>n.s.</td><td>n.s.</td><td></td><td></td><td></td><td></td></tr><tr><td>Proteolytic<br>bacteria</td><td>-0.27*</td><td>-0.35***</td><td>0.24*</td><td>0.25*</td><td></td><td></td><td></td><td></td></tr><tr><td>Arylsulfatase<br>bacteria</td><td>0.42***</td><td>0.65***</td><td>n.s.</td><td>n.s.</td><td></td><td></td><td></td><td></td></tr><tr><td>apr</td><td>0.35**</td><td>0.62***</td><td>n.s.</td><td>n.s.</td><td></td><td></td><td></td><td></td></tr><tr><td>16S rDNA</td><td>n.s.</td><td>-0.27*</td><td>-0.25*</td><td>n.s.</td><td></td><td></td><td></td><td></td></tr><tr><td>18S rDNA</td><td>-0.63***</td><td>-0.66***</td><td>n.s.</td><td>n.s.</td><td></td><td></td><td></td><td></td></tr><tr><th></th><th>Temperature<br>180d</th><th>Temperature<br>240d</th><th>Precipitations<br>60d</th><th></th><th>Temperature<br>30d</th><th>Temperature<br>180d</th><th>Temperature<br>since sowing</th><th></th></tr><tr><td>Protease<br>activity</td><td>0.37***</td><td>0.58***</td><td>-0.30**</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Arylsulfatase<br>activity</td><td>0.59***</td><td>0.40***</td><td>-0.28**</td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

<table><thead><tr><th colspan="4">Barley</th><th colspan="4">Pea</th></tr><tr><th></th><th>Precipitations<br>7d</th><th>Precipitations<br>60d</th><th>Precipitations<br>120d</th><th></th><th>Precipitations<br>60d</th><th>Precipitations<br>120d</th><th></th><th></th></tr></thead><tbody><tr><td>Total<br>bacteria</td><td>n.s.</td><td>n.s.</td><td>n.s.</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Proteolytic<br>bacteria</td><td>0.39*</td><td>n.s.</td><td>-0.36*</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Arylsulfatase<br>bacteria</td><td>0.39*</td><td>0.38*</td><td>0.64***</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>apr</td><td>n.s.</td><td>n.s.</td><td>0.62***</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>16S rDNA</td><td>n.s.</td><td>-0.50</td><td>-0.53</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>18S rDNA</td><td>-0.45**</td><td>-0.64***</td><td>-0.65***</td><td></td><td></td><td></td><td></td><td></td></tr><tr><th></th><th>Temperature<br>90d</th><th>Precipitations<br>30d</th><th></th><th></th><th>Temperature<br>7d</th><th>Temperature<br>30d</th><th></th><th></th></tr><tr><td>Protease<br>activity</td><td>n.s.</td><td>-0.59***</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Arylsulfatase<br>activity</td><td>0.54**</td><td>n.s.</td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

shoot to root ratio between crop species have been reported in controlled conditions with barley and oilseed rape differing significantly in root dry weights 62 days after emergence (Zagal, 1994). In field conditions, the comparison of total root length revealed mean values about 9.8 km m⁻³ for winter wheat and 12.4 for oilseed rape km m⁻³ (White et al., 2015). The proportion and the distribution
patterns of fine roots which are of great importance in both the uptake of nutrients and carbon rhizodeposition are different between oilseed rape and pea crops (Liu et al., 2010). Composition and amounts of rhizodeposits vary depending on various environmental factors and plant specific factors (e.g. species or growth stage). As the abundance of soil microorganisms is often positively correlated to availability of soluble organic compounds (Wardle, 1998; Dequiedt et al., 2011; Mrkonjic Fuka et al., 2008), the lower abundances in soil cultivated with spring pea could be linked to differences of rhizodeposits. Oilseed rape and barley produce high C rhizodeposition (Swinnen, 1994; Gan et al., 2009), whereas pea releases lower amount of C (Gan et al., 2009) but more readily mineralizable N rhizodeposits (Mayer et al., 2003). The relatively lower C/N ratio of pea

<figure><img src="image_45.png" /><figcaption>**Fig. 5** Boxplots of microbial activities and abundances of N and S net mineralization according to crop (A and B) and sampling date (C and D). R/W: oilseed rape after wheat, R/B: oilseed rape after barley, B/W: barley after wheat, P/B: pea after barley. Values with different letters are significantly different (*p* < 0.05). Boxplots indicate median, interquartiles (25-75%), maximum, minimum and outliers.</figcaption></figure>

roots and rhizodeposits compared to barley (Wichern, 2007a, b) did not influence the 18S rDNA:16S rDNA ratio. However, oilseed rape with an intermediate root C:N between barley and pea (Jensen, 1996; Gan et al., 2011), favored fungi over bacteria compared to the two others crops. Using FAME technique, Solaiman et al. (2007) found a greater abundance of bacteria under oilseed rape than under wheat at flowering stage but not at maturity. The species belonging to the Brassicaceae family produce biocidal compounds (e.g. Isothiocyanates) (Brown and Morra, 1997) such as PEITC that has been detected in the rhizosphere of canola and may influence the microbial community structure
(Rumberger and Marschner 2003, 2004). However, in our study, as soil sampling occurred after flowering, the dominance of fungi could be explained by a higher turnover of fine roots in oilseed rape.

<figure><img src="image_46.png" /><figcaption>**Fig. 6** Redundancy analysis correlation biplots of net N and S mineralization over 28 days incubation in relation to the microbial abundances and enzymatic activities. Blue lines are microbial abundances and enzymatic activities (explanatory variables), red lines are net N and S mineralization (dependent variables). Only significant explanatory variables selected through a forward procedure are represented. The RDA was significant at *P* < 0.001 (***)</figcaption></figure>

The densities of total culturable bacteria did not vary according to the crop species. The discrepancy between the abundance of total bacteria assessed by culture-dependent and molecular methods was not surprising (Leckie et al., 2004; Calbrix et al., 2007). Culturable bacteria are more likely to be copiotrophic bacteria with high growth rates (Davis et al., 2011) while molecular methods determine the abundance of active, dormant and possibly dead bacteria. Furthermore, the number of 16S rDNA copy per cell can vary from 1 to 15 (Klappenbach et al., 2000). The density of culturable ARS producing bacteria was not significantly influenced by crop species. Cregut et al. (2009) observed a higher abundance of this ARS bacterial community under field-grown oilseed rape compared to barley crop. In the latter study, field experiment was conducted on a calcareous soil (a typic rendolls) and rhizosphere soil defined as the root adhering soil was considered. The effect of oilseed rape on ARS bacterial community could be restricted to the rhizosphere area and thus could not be evidenced in our conditions as we sampled global soil (i.e., bulk and rhizosphere soil). No significant plant effect was evidenced on protease bacterial community assessed by culturable dependent approach while the
copy numbers of *apr* genes was lower under pea compared to oilseed rape and barley. The higher *apr* abundance under winter crops could be attributed to a higher root turnover, as root litter could be rich in protein (Trinsoutrot et al., 2001; Rossato et al., 2002), a substrate which could constitute a source of C and energy for this functional group.

Considering all the abundance variables, there was no difference between R/W and R/B suggesting no significant effect of the preceding crop. Preceding crop may alter the microbial community due to differences in terms of crop residues amount and composition. In our study, the two preceding crops are cereals which residues have similar C:N (Cookson et al., 1998). Although barley decompose faster than wheat (Summerell and Burgess, 1989; Cookson et al., 1998; Curtin et al., 2008) due to lower lignin content, this preceding effect was certainly difficult to evidence considering that the soil sampling occurred between 9 to 11 months after residues incorporation.

*Plant species effect on microbial activities*

Contrary to our second hypothesis, we observed no effect of the crop species on protease and arylsulfatase activity. This was unexpected as a higher arylsulfatase activity has often been observed in oilseed rape rhizosphere compared to wheat or barley rhizosphere (Dedourge et al., 2003; Knauff et al., 2003; Vong et al., 2007; Cregut et al., 2009). As already discussed, the soil considered in our study corresponded to global soil and not to rhizosphere soil. In these conditions, the effect of the plant species on this activity could be more difficult to detect. In soils, different pools of mineralizing enzymes coexist, with differences in terms of regulation. The synthesis of some enzymes could be regulated by C (either rhizodeposits or crop residues), thus creating variable nutrient-limiting conditions, depending on the quality of the C sources (Moorhead et al., 2012). In soil, ARS activity is more highly regulated by the S needs of microorganisms for sustaining growth in response to C additions than by the ester-sulfate presence in crop residues (Piutti et al., 2015). By contrast, some mineralizing enzymes in soil such as protease can be regulated by the presence of substrate (Geisseler and Horwath, 2009b; Allison et al., 2011). In our conditions the growth of crops differing in terms of both rhizodeposition characteristics (e.g. amount and composition) and nitrogen and sulfur requirement revealed no specific change in protease and arylsulfatase activities. This lack of response may be related on one hand to the stabilization of the extracellular part of these enzymes, which constitute most of the protease activity (Vranova et al., 2013) and about half of the arylsulfatase activity (Klose and Tabatabai, 1999), by the high clay constituent of our soil. In clay soils proteolytic activity is not correlated with the copy numbers of genes (*npr* and *sub*) encoding for extracellular
proteases probably due to the stabilization of these enzymes (Fuka et al., 2008a). On the other hand, the availability of the substrates and the nutrient limiting conditions as perceived by microorganisms can be strongly impacted by other factors such as temperature and moisture content of the soil (Redfield, 2002; Allison et al., 2011). As enzymatic activities were strongly responding to meteorological factors in our study, we suppose that the influence of meteorology over passed the influence of crops.

Influence of meteorological conditions on the microbial abundances and activities

According to our third hypothesis, multivariate regression trees (MRTs) revealed that microbial variables were more influenced by sampling dates than crop species. Interestingly, microbial abundances seemed more influenced by interannual variations than microbial activities which are more influenced by seasonal variations. While the strong influence of climate, especially precipitations, on microbial biomass has been already demonstrated at a regional scale, when comparing soils from different climatic zones (Drenovsky et al., 2010; Brockett et al., 2012), our study evidenced this influence at the local scale when comparing three years. The correlations observed in the RDAs between cumulative precipitations, average temperature and microbial variables (e.g. abundances and activities) suggested that the meteorological variables explained a part of the temporal variation. For all crops, cumulative precipitations constituted the principal factor controlling the microbial abundances, average temperature being secondary. At a global scale, mean annual precipitation was recognized as the first driver of microbial biomass (Serna-Chavez et al., 2013). Despite a lack of studies following microbial abundances through molecular (16S rDNA and 18S rDNA) or culture techniques, a strong link was often observed between soil moisture or precipitations and microbial biomass. Contrary to our results, in most of the cases the correlation between microbial biomass and precipitations was positive (Sandor et al., 2011; Zhang et al., 2013; Liu et al., 2010; Serna-Chavez et al., 2013; Chen et al., 2012) but at least one study found a negative correlation (Gray et al., 2011). The negative correlation between microbial abundances and moisture could be attributed to an increase of grazing by nematodes in wetter soils (Gray et al., 2011) or to anoxia (Reichardt et al., 2001; Drenovsky et al., 2010). Indeed, the particularly high cumulative precipitations during the spring period in 2012 and 2013 (Table 1) have probably increased the water saturation of clay soil in surface. The positive correlation between the density of arylsulfatase bacterial community and cumulative precipitations is in accordance with the results already obtained under oilseed rape grown on the same soil (Goux et al., 2012).
The variability of protease and arylsulfatase activities is better explained by average temperature than by cumulative precipitations with both activities positively correlated to average temperature and negatively correlated with cumulative precipitations. Protease, as well as arylsulfatase activity, has often been found influenced by precipitations and soil moisture (Mancinelli et al., 2013; Sardans and Penuelas, 2005; Gispert et al., 2013; Dilly et al., 2007; Fekete et al., 2012; Vanegas et al., 2013; Brockett et al., 2012). The observations of a positive correlation between temperature and protease (Fraser et al., 2013) and arylsulfatase activities (Kang and Freeman, 1999) are scarce. The determinants of this relation remained unclear but increasing temperature could induce many changes related to (i) rhizodeposition (Uselman et al., 2000), (ii) microbial communities in terms of abundance and structure (Barcenas-Moreno et al., 2009; Castro et al., 2010; Gray et al., 2011), particularly the fungal/bacterial ratio (Zhang et al., 2005), (iii) microbial biomass turnover (Joergensen et al., 1990; Wu et al., 2010), (iv) enzyme synthesis rate (due to modification of substrate diffusion or products consumption; Zhang et al., 2013). Despite we cannot conclude on the origin of the temperature effect, we could partly attribute it to changes in microbial abundances which are weakly positively correlated to temperatures. The better correlation of average temperature with arylsulfatase activity compared to protease activity is related to the close relation existing between arylsulfatase activity and soil organic carbon (Deng et Tabatabai, 1997) and therefore microbial biomass.

The comparison of RDAs obtained for each crop evidenced that the relationship between cumulative precipitations and microbial abundances seemed to be little affected by the crop species judging by the similarity of the determination coefficients of RDA for the three crops (Fig. 4). The trend was different considering the relationship between average temperature and microbial activities with higher temperature effect under oilseed rape (R² = 0.67) compared to barley (R² = 0.38) or pea (R² = 0.41). Under oilseed rape microbial abundances were influenced by cumulative precipitations over longer timescales (180 and 240 days before the sampling date) than under barley and pea (60 and 120 days before the sampling date). Furthermore, microbial activities were influenced by average temperature calculated over different timescales: shorter under pea (7 and 30 days), intermediate under barley (90 days) and larger under oilseed rape (180 days and since sowing, i.e. more than 260 days). Such a discrepancy in the meteorology influence on enzymatic activities under plants of differing cycle length has not been reported before, to our knowledge. In the case of oilseed rape, the dependence of microbial communities on cumulative precipitations and average temperature over longer timescales could thus be due to modification of either the plant carbon allocation (Chen et al., 2007) and/or of fine root distribution patterns (Quijano et al., 2013), causing lasting changes in the soil conditions such as C and nutrients availability.
Influence of crop species and meteorology conditions on N and S mineralization

The influence of crop species and climate on microbial abundances and activities resulted in variations of N and S net mineralization. Among the measured microbial variables, net mineralization fluxes of N and S were best explained by protease and arylsulfatase activities and by the abundance of culturable bacteria possessing arylsulfatase activity. This result is in accordance with the studies observing a positive correlation between protease activity and gross N mineralization (Zaman et al., 1999; Mishra et al., 2005) and net N mineralization (Shindo, 1992). It is also in accordance with the observation by Jan et al. (2009) that protease activity is the limiting step of N mineralization in grasslands soils. If protease activity appears to be a key N cycle enzyme in arable soils as well as grassland soils, the abundances of microbial functional groups (revealed by cultural dependent or independent techniques) are not correlated with this activity. Arylsulfatase activity is often used as an indicator microorganisms' nutritional status (Goodwin et al., 2000; Puglisi et al., 2006), as arylsulfatases realize one of the last steps of S mineralization and are directly linked to the microbial biomass. The explanatory power of arylsulfatase activity is thus confirmed by our study. S net mineralization was negatively correlated with arylsulfatase activity. Arylsulfatase has often been found positively correlated to net S mineralization (Riffaldi et al., 2006; Balota et al., 2014) but also to gross S immobilization (Dedourge et al., 2003, 2004; Vong et al., 2003, 2008). Thus an increase in arylsulfatase activity may induce a net release of sulfates only if there are non-limiting sulfate conditions for microbial growth. The abundance of culturable bacteria possessing arylsulfatase activity is slightly but positively correlated with S mineralization, but not correlated with arylsulfatase activity. This absence of correlation between arylsulfatase activity and abundance of arylsulfatase bacteria is surprising as positive correlations between these two variables have previously been observed but in rhizosphere soils (Cregut et al., 2009; Slezack-Deschaumes et al., 2012). The lack of correlation was probably due to a too slight influence of labile carbon on microbial biomass.

Additionally, arylsulfatase activity appeared to be a good predictor of net N mineralization. This could be easily explained by the often observed positive correlation of arylsulfatase activity with organic carbon or microbial biomass (Gajda et al., 2013; Klose et al., 1999; Ajwa et al., 1999; Dedourge et al., 2003; Vong et al., 2003; Dedourge et al., 2004) and other microbial enzymatic activities involved in organic matter decomposition (Gajda et al., 2013; Ajwa et al., 1999; Perucci et al., 1984; Deng and Tabatabai, 1997).

Despite the ability of crop species to modify mineralization fluxes (Van Der Krift and Berendse, 2001; Hu et al., 2002; Vong et al., 2007; Zhu et al., 2014), the three variables explaining N and S mineralization did not vary according to crop species but were strongly influenced by the meteorology. As a