# The impact of the rice production system (irrigated vs lowland) on root-associated microbiome from farmer's fields in western Burkina Faso

Mariam Barro, Issa Wonni, Marie Simonin, Abalo Itolou Kassankogno, Agnieszka Klonowska, Lionel Moulin, Gilles Béna, Irénée Somda, Caroline Brunel, Charlotte Tollenaere

► To cite this version:

Mariam Barro, Issa Wonni, Marie Simonin, Abalo Itolou Kassankogno, Agnieszka Klonowska, et al. The impact of the rice production system (irrigated vs lowland) on root-associated microbiome from farmer's fields in western Burkina Faso. FEMS Microbiology Ecology, 2022, 98 (9), 10.1093/fem-sec/fiac085. hal-03773237

HAL Id: hal-03773237

https://hal.inrae.fr/hal-03773237

Submitted on 23 Nov 2023

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
1 The impact of the rice production system (irrigated vs lowland) on root-associated microbiome from farmer's fields in western Burkina Faso

3

4 Mariam Barro<sup>1,2,3</sup>, Issa Wonni<sup>2</sup>, Marie Simonin<sup>1,4</sup>, Abalo Itolou Kassankogno<sup>2</sup>, Agnieszka

5 Klonowska<sup>1,2</sup>, Lionel Moulin<sup>1</sup>, Gilles Béna<sup>1</sup>, Irénée Somda<sup>3</sup>, Caroline Brunel<sup>1,5§</sup>, Charlotte

6 Tollenaere<sup>1§*</sup>

7

8 <sup>1</sup> PHIM Plant Health Institute, Univ Montpellier, IRD, CIRAD, INRAE, Institut Agro,

9 Montpellier, France

10 <sup>2</sup> INERA, Institut de l'Environnement et de Recherches Agricoles du Burkina Faso,

11 Laboratoire de Phytopathologie, Bobo-Dioulasso, Burkina Faso

12 <sup>3</sup> Univ Nazi Boni, Institut du Développement rural, Laboratoire des Systèmes naturels,

13 Agrosystèmes et Ingénierie de l'Environnement (SyNAIE), Bobo-Dioulasso, Burkina Faso

14 <sup>4</sup> Present address: Univ Angers, Institut Agro, INRAE, IRHS, SFR QUASAV, F-49000

15 Angers, France

16 <sup>5</sup> Present address: CIRAD, UPR HortSys, F-34398, Montpellier, France

17 <sup>§</sup> equal contribution

18 * Corresponding author, E-mail: charlotte.tollenaere@ird.fr

19
## 20 Abstract

As a consequence of its potential applications for food safety, there is a growing interest in rice root-associated microbial communities, but some systems remain understudied. Here, we compare the assemblage of root-associated microbiota in rice sampled in 19 small farmer's fields from irrigated and rainfed lowlands in western Burkina Faso, using an amplicon metabarcoding approach 16S (Prokaryotes, three plant sample per field) and ITS (fungi, one sample per field). In addition to the expected structure according to the root compartment (root vs. rhizosphere) and geographical zones, we show that the rice production system is a major driver of microbiome structure, both for prokaryotes and fungi. In irrigated systems, we found a higher diversity of prokaryotic communities from rhizosphere and more complex co-occurrence networks, compared to rainfed lowlands. Core taxa were different between the two systems, and indicator species were identified: mostly within *Bacillaceae* and *Bradyrhizobiaceae* families in rainfed lowlands, and within *Burkholderiaceae* and *Moraxellaceae* in irrigated areas. Finally, phylotypes assigned to putative phytobeneficial and pathogen species were found. Mycorrhizal fungi *Glomeromycetes* abundance was higher in rainfed lowlands. Our results highlight deep microbiome differences induced by contrasted rice production systems that should consequently be considered for potential microbial engineering applications.

## 38 Key words

Irrigated rice; Metabarcoding; *Oryza sativa*; Rainfed lowlands; Rice production system; Root associated micro-organisms; West Africa.
41 Introduction

42 Soil and rhizosphere host megadiverse and dynamic communities of microorganisms that are 43 crucial to the plants they associate with. Their role is particularly recognized for crops as the 44 below-ground microbiota supply plants with nutrients and provide protection against 45 pathogens (Singh et al. 2020; Chialva et al. 2021). Recent research suggests that root- 46 associated microbes can improve plant tolerance to environmental stressors (Chialva et al. 47 2020), and modify phenology (Lu et al. 2018) and morphological traits (Senthil Kumar et al. 48 Cultivated plants and their associated microbial communities are thus increasingly 49 studied jointly, as holobionts (a concept reviewed by Vandenkoornhuyse et al. in 2015), 50 because a deeper understanding of their interaction might help to develop microbial 51 engineering applications for modern sustainable agricultural systems (Chialva et al. 2021). 52 While much progress has been made, the mechanisms that control root-associated 53 microbiome assembly (i.e., structure, composition and dynamics) remain difficult to 54 disentangle (Brunel et al. 2020). Vertical transmission of the microorganisms (i.e., across 55 plant generation) exists as well as the horizontal transmission (i.e., recruitment from the soil 56 “seed bank”), with variable contributions of the seed and soil to the seedling microbiome 57 (Rochefort et al. 2021; Walsh et al. 2021). Beyond the environmental drivers known to shape 58 bulk soil communities (e.g., climate, soil properties, agricultural practices; see Vieira et al. 59 2020), the influence of the cultivated plant in structuring communities is more and more 60 documented and now referred as the extended root phenotype (see de la Fuente Cantó et al. 61 2020). Indeed, while the root-associated micro-habitats (i.e., rhizosphere, rhizoplane, 62 endosphere) modulate the intensity of assembly processes (Beattie, 2018), the plant identity 63 (e.g., species, genotype, age, etc.) plays a major role in recruiting specific microbial taxa 64 shared across multiple environmental conditions (Schweitzer et al. 2008).

65 Rice is the most important food crop in the world, grown in variable climatic conditions and 66 representing the staple food of more than half of the world's population, mostly in Asia, 67 Africa and Latin America (Pandey et al. 2010). The major species cultivated worldwide is 68 *Oryza sativa* L. (known as 'Asian rice'). This is also true in Africa, where a second rice 69 species was domesticated (*O. glaberrima*, referred to as 'African rice'), but is much less 70 grown because of lower yield (Linares, 2002). Given its importance for food security, and the 71 impact of microbiota on plant productivity, it is not surprising that many recent studies used 72 metabarcoding approaches to describe the microbiome of *O. sativa* (Kim & Lee, 2020),
particularly its root-associated microbial communities (reviewed by Ding et al. in 2019). Rice has the particularity to be cultivated in flooded paddy soils over most growth stages, so that the rhizosphere is located in an oxic-anoxic interface (Ding et al. 2019). Microbial communities inhabiting rice roots are distinct from those found in other crops, with for example an enrichment in *Deltaproteobacteria*, *Euryarchaeota*, *Chytridiomycota* (Ding et al. 2019). On the other hand, like for other crops, their structuring is driven both by the host plant (in terms of root compartment / microhabitats, plant genotype) and its environment (geographical zone, bioclimate, soil properties, agricultural practices; Ding et al. 2019). In terms of microhabitats, differences between rhizosphere and endosphere compartments are clear (see e.g. Edwards et al. 2015; Guo et al. 2021) and recent evidence shows differences in microbiota composition between root types and along root axes (Kawasaki et al. 2021). An effect of host genotype (subspecies/cultivars) were evidenced in some cases (Edwards et al. 2015; Alonso et al. 2020), but it is generally weak compared to other factors, or even absent (Edwards et al. 2018; Guo et al. 2021). Environmental factors, such as the geographical zone and agricultural practices, are important drivers of microbiota structuring as well. For example, Edwards et al. (2015) evidenced an effect of geographical location and cultivation practices, namely organic vs conventional farming. Other environmental factors, such as drought stress (Santos-Medellín et al. 2017), water management (Chialva et al. 2020), phosphorus (Long & Yao, 2020), were also shown to affect rice root-associated microbiota. In addition, bacterial and archaeal communities evolve during the vegetative phase of plant growth, then shift and stabilize compositionally at the transition to reproductive growth at flowering stage (Edwards et al. 2018). Vertical transmission through seeds seems quite weak or even absent (Guo et al. 2021). However, the rice microbiome has been poorly explored in African context in spite of (1) the importance to investigate a diversity of geographical areas (as a consequence of the effect of geographical zone, see above) and document the diversity of cultural practices, and (2) the growing importance of rice in Africa (recent surge in rice consumption with 8% yearly increase from 2009 to 2019, Soullier et al. 2020). An exception is the recent work by Kanasugi et al. (2020) that evidenced an effect of the region in structuring of rice microbiome described in six tropic savanna regions in Ghana. More generally, there is a lack of knowledge concerning crop-associated microbiota in the African continent, that results in a biased view of the microbial world associated with crops due to worldwide sampling repartition (Brunel et al. 2020; Hughes et al. 2021), and it is of particular concern due to the potential of microbial engineering for future agriculture.
Rice is grown around the world in a diversity of rice-growing systems, three of them being the most important (Rao *et al.* 2017). First, irrigated lowlands, with full water control, produce 75% of the global rice production. Second, rainfed lowlands (including flood prone), represents around 19% of the world's rice production. Finally, rainfed upland rice, only possible under high rainfall, results in 4% of the global total rice production. In Burkina Faso, irrigated rice represents small areas (costly infrastructures representing less than 30% of harvested areas; CountrySTAT, 2020), but produces more than half of the national rice production (MAHRH, 2011). On the other hand, rainfed lowlands represent the majority of rice growing surfaces (67% between 1984 and 2009), but only 42% of the production as a consequence of lower yields compared to irrigated areas (MAHRH, 2011). Irrigated areas (IR) and rainfed lowlands (RL) host different agricultural practices in West Africa (Nonvide *et al.* 2018). In western Burkina Faso in particular, these contrasted practices have been documented, showing that the possibility to grow rice twice a year was restricted to irrigated areas, while direct sowing was performed only in rainfed lowlands, and that mineral fertilization was more frequent in irrigated areas (Barro *et al.* 2021). In terms of the host plant genetic diversity however, a study led in six locations from western Burkina Faso revealed few differences between irrigated areas and rainfed lowlands in general, except in one of the study sites, the rainfed lowland of Karfiguela, highly differentiated from the other five sites (Barro *et al.* 2021).

This study aims at describing rice root-associated microbial communities in farmer's fields from western Burkina Faso. More specifically, we investigate whether rice roots from two contrasted rice growing systems (irrigated and rainfed lowland areas) host different microbial communities. To this aim, we collected rice root and rhizosphere samples in farmer's fields from three geographical zones, each consisting of an irrigated rice-growing area and neighboring rainfed lowlands. A previous study performed in the same study sites documented more intensive agricultural practices in irrigated areas, compared to rainfed lowlands (Barro *et al.* 2021). Considering the effect of intensification on belowground biodiversity and microbial network complexity (Banerjee *et al.* 2019; Tamburini *et al.* 2020), we hypothesize an effect of the rice growing system on root-associated microbial communities diversity and complexity. If true, this may have consequences on plant health (Wei *et al.* 2015), particularly in this system where rice diseases were shown to circulate at higher levels in irrigated areas compared to rainfed lowlands (Barro *et al.* 2021).
138

139

# Material and methods

140

## Study sites in western Burkina Faso

141

142

143

144

145

146

147

The study sites are located in three geographic zones in western Burkina Faso, with maximum distance between each zone about 90 kilometers (Fig. 1a). Each zone comprises one irrigated area and the neighboring rainfed lowland, with maximum distance between the two rice growing systems of each zone being 7 kilometers (Fig. 1b). The climate consequently do not differ between rice growing systems within each zone, but average precipitation during the rice growing season (July to early December) differ between the three geographical zones (WorldClim 2 data; Fick & Hijmans, 2017; see Fig. S1).

148

149

150

151

152

These six sites were studied from 2016 to 2019, with the characterization of agricultural practices and the follow-up of major rice diseases symptoms (Barro et al. 2021; see further details on the methodology and raw data at: https://doi.org/10.23708/8FDWIE). Rice genotyping data on samples collected in 2018 in these six sites are also available (see Barro et al. 2021).

153

## Rice root sampling

154

155

156

157

158

159

160

161

162

163

164

Within the six sites, we investigated a total of 19 fields, with three fields per site, except in Bama, the largest irrigated perimeter studied, where four fields were sampled (Fig. 1b). Each field studied corresponds to a square of approximately 25 meters on a side. Root and rhizosphere sampling was performed at rice maturation, between October, 16th and December 3rd 2018. We chose this developmental stage based on a previous study showing that the rice microbiome composition evolves during the growing season until a 'mature' microbiome at the flowering stage (Edwards et al. 2018). Within each field, we sampled three plants located on the square diagonal, with at least 5 meters distance. Sampling was performed with gloves and scissors (ethanol sanitized between two sampling) and involved nodal, bases and seminal roots (all sampled together). Roots were roughly shaken to remove non-adherent soil, and placed in 50 mL sterile tubes containing sterile Phosphate Buffered Saline (PBS) solution for
165 a rapid (15s) rinse and then stored in another 50 mL sterile PBS-containing tube. We placed
166 the tubes in a cooler and then at 4°C when back to the laboratory, on the same day.

167 Soil physicochemical properties: data acquisition and analysis

168 The three geographical zones studied (Fig. 1a) are characterized by Lixisols (soils with
169 subsurface accumulation of low activity clays and high base saturation) according to the
170 Harmonized World Soil Database (HWSD) map (FAO/IIASA/ISRIC/ISSCAS/JRC, 2012).

171 Soil sampling was performed on the same day as rice root sampling, and in three locations
172 nearby sampled plants, using a 10 cm depth auger. Back from the field, sampled soil was
173 dried in the shade at room temperature and stored until analysis.

174 INERA/GRN service performed the analyses of soil samples according to a standardized
175 methodology. Briefly, soil physical properties were assessed by soil particle size distribution
176 following Bouyoucos (1962), pH was estimated according to AFNOR (1981), total organic
177 carbon with the Walkley & Black (1934)'s method, total concentrations of nitrogen with
178 Kjeldhal method Hillebrand et al. (1953), and finally phosphorus and potassium content as
179 described respectively in Novozansky et al. (1983) and Walinga et al. (1989). Then, cation
180 exchange capacity (CEC), a measure of fertility, nutrient retention capacity, and the capacity
181 to protect groundwater from cation contamination, was estimated, as well as sorptive
182 bioaccessibility extraction (SBE), that relates to the environmental mobility, partitioning and
183 toxicity of soil pollutants, following Metson (1956). Soil data are publicly available on the
184 IRD Dataverse: https://doi.org/10.23708/LZ8A5B.

185 Rice root conditioning, DNA extraction and sequencing

186 Less than 24h after sampling, root samples (rice roots including rhizosphere) stored at 4°C
187 were processed. In order to separate the different root compartments, the tubes were vortexed
188 vigorously one minute and then, roots were removed from the PBS solution using sterile
189 forceps. The remaining PBS solution was considered as the 'rhizosphere' compartment. Roots
190 were then surface-sterilized with 70% alcohol (30s), 1% bleach (30s) and finally rinsed three
191 times in sterile water. We considered these surface-sterilized roots as the 'root' compartment,
192 which comprises both endosphere microorganisms as well as persistent DNA from the
rhizoplane. DNA extraction from the rhizosphere and root samples were performed on the same day as the process of compartment separation.

For DNA extractions, 0.25g of root samples (crushed in liquid nitrogen beforehand) and 0.25g of rhizosphere were extracted using the DNeasy PowerSoil Kit (Qiagen, Hilden, Germany), following manufacturer's recommendations. DNA quality and quantity was verified using a NanoDrop ND-1000 spectrophotometer. PCR amplification, library and MiSeq Illumina sequencing were performed by Macrogen (Seoul, South Korea) using primers 341F (16S V3F, 5'-CCTACGGGNGGCWGCAG-3') and 785R (16S V4R, 5'-GACTACHVGGGTATCTAATCC-3') to amplify the V3-V4 regions of the 16S rRNA gene, and using primers ITS1f: CTTGGTCATTTAGAGGAAGTAA and ITS2: GCTGCGTTCTTCATCGATGC to amplify the internal transcribed spacer 1 (ITS1) region. We specifically focused on the factors structuring prokaryotic communities (16S sequencing), but were also interested to see whether similar tendencies hold for fungal communities (ITS sequencing). We consequently chose the following approach: sequencing was performed for each sampled plant (19 fields * 3 plants = 57 samples per compartment) for 16S sequencing and for a composite sample (3 plant samples were pooled to result in one sample per field, so that the total number of samples per compartment is 19) for the ITS marker. Negative controls (three for 16S and one for ITS) were sequenced to remove potential contaminants.

Sequence data are retrievable from NCBI (National Center for Biotechnology Information) under the Bioproject ID: PRJNA763095.

## Bioinformatic analyses of obtained sequences

All bioinformatics and statistical analyses were performed in R software v 3.6.3 (R core Team, 2018) and the package ggplot2 was used for the visualization.

Raw sequences were processed using a custom script from the *dada2* pipeline, which is designed to resolve exact biological sequences (ASVs for Amplicon Sequence Variants) from Illumina sequence data without sequence clustering (Callahan *et al.* 2016). Raw sequences were first demultiplexed by comparing index reads with a key, and paired sequences were trimmed. Sequences were dereplicated, and the unique sequence pairs were denoised using the *dada* function. Primers and adapters were screened and removed using a custom script with *cutadapt* (Martin, 2011). Next, paired-end sequences were merged, and chimeras were
removed. Contaminants were identified and removed using negative controls and the *decontam* package. Rarefaction curves were drawn for each sample, using the *rarecurve* function of the *vegan* R package, and the rarefaction plateau was reached for all samples (Fig. S2). To account for differences in sequencing depths, samples were rarefied to 4236 and 7828 for 16S and ITS, respectively. Taxonomy assignments were determined against the UNITE 2021 (Abarenkov *et al.* 2021) and the SILVA SSU r138 (Quast *et al.* 2013) taxonomic databases for ITS and 16S, respectively using the *idtaxa* function from the *decipher* R package. Mitochondria and chloroplast sequences were then removed. ASVs not seen more than once in at least 2% of the samples were removed. We then obtained 2 116 969 (8260 ASVs) final sequences for 16S and 172 719 (566 ASVs) sequences for ITS. A figure showing the phyla relative abundances was constructed and presented according to the rice-growing system, site and root compartment.

Indices of α-diversity (observed richness and Shannon diversity index) were calculated using the *estimate_richness* function from the *phyloseq* package (McMurdie & Holmes, 2013). Members of the core microbiota were identified for 16S and ITS communities (including both rhizosphere and roots compartments) in each rice grown system using the prevalence threshold of 60%.

For 16S dataset only, we inferred co-occurrence networks using the *SpiecEasi* pipeline (Kurtz *et al.* 2015), independently for each rice growing system: rainfed lowland and irrigated areas. Networks were calculated for ASVs present in more than 15% of the samples using the method 'mb' and setting the lambda.min.ratio to 1e-3 and nlambda to 50. We identified hub taxa, i.e. the potential keystone of the microbial network belonging to the most connected ASVs, based on their node parameters (method developed by Berry & Widder, 2014): a low betweenness centrality (lower quantile, < 0.9), and a high closeness centrality (higher quantile, > 0.75), transitivity (higher quantile, > 0.25) and degree (higher quantile, > 0.75). The node and network parameters were determined using the R package *igraph* (Csardi & Nepusz, 2006) and *qgraph* (Epskamp *et al.* 2012). Complete networks were further described by calculating the number of nodes and hubs, the network mean degree, mean closeness and betweenness centralities, the total number of edges, and the positive to negative edges ratio. Core taxa (prevalent in more than 60% of the samples) were also identified for each system.

The taxonomic affiliation of all ASVs was refined using nucleotide basic local alignment search tool (BLASTn) analyses on NCBI nr database. We screened the table of blast best hits
of all 16S and ITS ASVs in order to search the genus or species names of a number of pathogen species listed a priori (Table S6), based on the reference book *Compendium of Rice Diseases* (Cartwright *et al.* 2018). We also searched within blast best hits for ITS ASVs assigned to the *Glomeromycetes* class, as well as 16S and ITS ASV assigned to a species name's including "oryz", as this may correspond to a specific interaction (pathogen or beneficial) with rice.

## 261 Statistical analyses

We first analyzed soil physico-chemical parameters. To this purpose, PERMANOVA were performed on soil physical properties (texture, i.e. relative amount of sand, silt, and clay) on the one hand, and soil chemical properties (7 variables: pH, total carbon, total nitrogen, total phosphorus, total potassium, SBE, CEC) on the other hand. For both models, we included as explanatory factors the ‘geographical zone’ and ‘rice-growing system’, as well as their interaction, using *adonis2* function from the *vegan* R package (Oksanen *et al.* 2007), with 999 permutations. Post hoc tests were done using the *pairwiseAdonis* function (https://github.com/pmartinezarizu/pairwiseAdonis). In addition, we performed Kruskal-Wallis non-parametric tests on each soil variable independently, testing for an effect of the geographical zone or the rice growing system, using the *kruskal_test* function from the *rstatix* package. Dunn tests (*dunn_test* function) were then performed in case of significant effect evidenced to identify statistically differing groups.

For microbial communities, PERMANOVA were used to test for significant effects of root compartments, geographical zones and rice growing systems on microbial β-diversity, based on a Bray-Curtis dissimilarity matrix. Sites differences were further tested using the *pairwise.adonis* function of the same *vegan* package. The graphical representation of β-diversity was based on Non-metric Multi-Dimensional Scaling (NMDS, *metaMDS* function). The effect of edaphic variables (i.e., pH, total organic carbon, total phosphorus, total nitrogen, total potassium, CEC and SBE) in structuring β-diversity was tested using the *envfit* function (9999 permutations) in R package *vegan*. Structuring soil properties were thus fitted onto the ordination space.

We tested for an effect of the rice growing system on obtained indices of alpha diversity (Shannon diversity index and observed richness). To this purpose, we performed non-parametric statistical tests (*kruskal_test* function from the library *rstatix*) independently for
each kingdom (prokaryotes and fungi respectively) and each compartment (rhizosphere and root associated). In addition, for 16S data only (because of insufficient sample size for ITS), we also tested for an effect of the specific site, using Kruskal-Wallis test, and then performed post hoc tests using *dunn_test* function.

Then, we identified particular soil taxa that were associated with lowlands or irrigated systems using indicator analyses with the function *multipatt* implemented in the *indicspecies* package (De Cáceres *et al.* 2010). The algorithm determines both fidelity and consistency to a system.

Finally, we analyzed the repartition of ASVs assigned to the class *Glomeromycetes* (arbuscular mycorrhizal fungi, AMFs). The summed abundance of all ASVs which best blast hit is within the *Glomeromycetes* class was modeled by generalized linear mixed models (GLMMs), with Poisson distribution, with the package *lme4* (Bates *et al.* 2015), followed by Type III ANOVA with the package *car* (Fox & Weisberg, 2019). The compartment and the rice growing system were included as fixed effects and the geographical zone as a random effect. In addition, differential repartition analysis was performed using DESeq2 (Love *et al.* 2014) to identify *Glomeromycetes* ASVs showing significant enrichment in one of the two rice growing systems.

R code used to perform the analyses and generate the figures are available upon request.

## 305 Results

### 306 Structure of rice soil properties and rice root associated microbial communities

PERMANOVA analysis performed on the Bray-Curtis distance matrix of soil characteristics to describe the overall soil properties, highlighted no significant influence of the rice growing system, but a differentiation according to the geographical zone, for both physical (F = 6.420, r² = 0.448, *p* = 0.006, Fig. 1b) and chemical (F = 4.121, r² = 0.346, *p* = 0.026) soil parameters. More precisely, we found no effect of the rice growing system but a significant effect of the geographical zone on the clay and sand contents, as well as total Phosphorus, total Potassium,
SBE and CEC (Table S1). Posthoc tests revealed that the geographic zones that differed statistically were the same for the six above-mentioned variables, namely Banzon and Karfiguela zones (Fig. 1c).

To determine whether the geographical zones (Karfiguela, Bama or Banzon), the rice-growing systems (irrigated vs. rainfed lowlands), or their interactions, structured root-associated or rhizosphere microbial communities, we performed PERMANOVA analysis on the Bray-Curtis distance matrix of 16S and ITS ASVs, respectively (Table S2, Fig. S2). As different structures were revealed between root and rhizosphere communities of both prokaryotes (F= 6.863, r² = 0.052, p < 0.001) and fungi (F= 3.753, r² = 0.080, p < 0.001), we further subsetted both datasets to observe the relative influence of the rice-growing systems and the geographical zones in shaping root and rhizosphere communities separately (Table 1, Fig. 2).

In the rhizosphere, prokaryotic communities were mainly structured by the rice growing system (F=5.096, r² = 0.079, p < 0.001, Table 1), the geographical zone (F=2.118, r² = 0.069, p < 0.001) and the interaction between rice growing system and zone (F=1.877, r² = 0.058, p < 0.001). Posthoc tests revealed that most of the pairs of sites (i.e., irrigated and lowland from the same geographical zone) were significantly different, but interestingly revealed no significant difference between communities originating from irrigated systems, whereas all communities from rainfed lowland sites exhibited distinct structures (Table S3). Fungal communities of the rhizosphere were also mostly structured by the rice growing system (F=2.452, r² = 0.115, p < 0.001, Table 1). The geographical zone (F=1.555, r² = 0.146, p = 0.007) and the interaction between rice growing system and the geographical zone (F=1.335, r² = 0.126, p = 0.036) were also driving the rhizosphere fungal microbiome. The low number of samples did not allow to detect, if any, statistically significant differences between sites in communities' structures for ITS (Table S3).

As observed for rhizosphere communities, the root-associated prokaryotic communities were mainly shaped by the rice growing system (F=5.155, r² = 0.079, p < 0.001, Table 1), the interaction between rice growing system and the geographical zone (F=2.451, r² = 0.075, p = 0.002) and the geographical zone (F=2.247, r² = 0.069, p < 0.001). Most of the pairs of sites (from the same geographical zone) were significantly different. No significant difference was detected between communities originating from irrigated systems, whereas all communities from rainfed lowland sites exhibited distinct structures (Table S3). Root-associated fungal communities were also mostly influenced by the rice growing system (F=2.289, r² = 0.111, p < 0.001).
345 = 0.002, Table 1), and by the interaction between rice growing system and the geographical
346 zone (F=1.457, r² =0.141, p = 0.013). The effect of the geographical zone on root-associated
347 fungal communities was not evidenced (F=1.206, r² = 0.117, p =0.123). As for rhizosphere,
348 posthoc tests on root-associated fungal communities were all non-significant (Table S3).

349 The influence of soil chemical parameters on microbial community structure is reported in
350 Fig. 2 as arrows and in Table S4. We noticed that the prokaryotic communities of both
351 rhizosphere and roots were affected by the same three parameters: SBE (r² =0,482, p < 0,001
352 for rhizosphere, and r² = 0,175, p = 0,006 for roots), CEC (r² =0,314, p < 0,001 for
353 rhizosphere, and r² = 0,204, p = 0,003 for roots) and total phosphorus (r² = 0,132, p = 0,023
354 for rhizosphere, and r² = 0,179, p = 0,004 for roots). For fungi, although various parameters
355 were marginally significant in each compartment (Table S4), we only detected a significant
356 effect of total nitrogen on rhizosphere communities (r² =0,320, p = 0,043).

357 Composition of rice root microbiomes and comparison of alpha-diversity

358 While 16S data were assigned at the genus level for 64% of ASVs, only 34% of ITS ASVs
359 could be assigned (see assignations at the phyla level in Fig. S4). Assignations at the phylum
360 level were obtained for all (100%) 16S ASVs, but only for 62% for ITS ASVs (see Fig. S4).
361 Assigned prokaryotic taxa represent 17 phyla, most abundant ones being Proteobacteria,
362 Firmicutes, Mixoccoccota and Acidobacteriota. For ITS, seven phyla were found, with the
363 most abundant ones being Ascomycota followed by Basicomycota.

364 We tested the effect of the rice growing system on the diversity indices (alpha-diversity). No
365 effect could be evidenced on the fungal (Shannon: H = 0.026, p =0.87 for the rhizosphere, and
366 H = 0.107, p =0.74 for root-associated communities), or root-associated prokaryote diversities
367 (Shannon: H = 1.05 p = 0.306). On the other hand, the rice growing system had a significant
368 effect on the prokaryote diversity of the rhizosphere (Shannon: H = 11.6, p <0.001), with a
369 higher Shannon diversity index in irrigated areas (5.03 ± 0.13), compared to rainfed lowlands
370 (4.39 ± 0.12) and a higher observed richness (275.6 ± 26.4) in irrigated areas compared to
371 rainfed lowlands (143.7 ± 17.4) (Fig. 3). We noticed however an opposite pattern for fungal
372 communities of the rhizosphere with higher observed richness in rainfed lowlands, compared
373 to irrigated areas (Fig. 3).
This effect of the rice growing system on 16S rhizosphere data was also clearly observed when plotting the diversity indices by site (Fig. S5). In addition, we found that the specific site had an effect on the prokaryotic communities of the rhizosphere, and also, but to a lesser extent, in roots (Fig. S5 and Table S5). In the rhizosphere, the highest diversity was found in the irrigated perimeter of Karfiguela, and to a lesser extent in the irrigated area of Bama. A particularly low diversity was found in the rainfed lowland of Karfiguela zone, and to a lesser extent in the rainfed lowland of Banzon. Conversely, we noticed a slightly higher diversity in fungal root associated communities in the rainfed lowland of Karfiguela (Fig. S5).

## Core microbiome and co-occurrence networks in the two rice-growing systems

ASVs belonging to the core microbiome of lowland vs. irrigated rice were respectively identified with a prevalence threshold set to 60%. For 16S, we identified 26 core ASVs associated with the irrigated systems, and two core ASVs in lowlands (Fig. 4). Among the core taxa in irrigated areas, the vast majority of phylotypes (25/26) belonged to the *Burkholderiaceae* family, with 24 assigned to *Ralstonia pickettii* and one to *Paraburkholderia kururiensis*. One of the core ASVs is common to both irrigated area and rainfed lowlands systems. Its best blast hit corresponds to *Bradyrhizobium tropiciagri* (Bradyrhizobiaceae) with a 99.5% sequence similarity. Another core phylotype in rainfed lowlands is assigned to the same species with 99.3% sequence similarity.

For ITS, we identified 5 core ASVs in the irrigated systems, compared to 11 core ASVs associated with the lowlands, 4 of them being common to both rice growing systems (Fig. 4).

Then, we compared the prokaryotic co-occurrence networks in each rice growing system respectively (Table 2). We identified 15 hub ASVs in the irrigated systems and 20 in rainfed lowlands. We found a higher edge number in irrigated compared to rainfed lowlands: 1720 positive and 269 negative resulting in 2029 total edges in irrigated areas, while only 1163 positive and 85 negative resulting in 1248 total edges were found in rainfed lowlands. Finally, the network computed from irrigated areas had higher connectivity compared to the one from rainfed lowlands (9.8 vs 7.9 node mean degrees, respectively).

None of the identified hub taxa were also core in any of the two rice growing systems. Only one ASV was identified as a hub in both irrigated and rainfed lowland systems, assigned to *Enterobacter mori* (*Enterobacteriaceae*). Hub taxa in irrigated areas (15 ASVs) were assigned
404 to 8 different species from 5 families, while hub taxa in rainfed lowland (20 ASVs) only
405 corresponded to 4 species from 2 families (Table 2).

406 Indicator taxa of the two rice growing systems

407 For 16S data, we found 128 indicator taxa in irrigated areas, including ASVs from eight
408 bacterial families, most of them assigned to *Acinetobacter*, *Ralstonia*, *Aeromonas*,
409 *Comamonas*, *Clostridium* and *Enterobacter* (Table 3). On the other hand, only 63 were
410 identified in rainfed lowlands, most of them within the Bacillaceae family, including ASVs
411 assigned to *Exiguobacterium* and *Priestia*, and Bradyrhizobiaceae family, genus
412 *Bradyrhizobium* (Table 3). The ASV assigned to *Paraburkholderia kururiensis*
413 (Burkholderiaceae) revealed as indicator in irrigated areas (Table 3) was also a core taxa in
414 irrigated areas. Also, among the 24 indicator ASVs in irrigated areas assigned to *Ralstonia*
415 *pickettii* (Burkholderiaceae), 16 were also core in irrigated areas. In addition, four indicator
416 ASVs in irrigated areas were also hubs in this system: two assigned to *Aeromonas hydrophilai*
417 (Aeromonadaceae), another assigned to *Enterobacter cloacae* (Enterobacteriaceae), and
418 finally one corresponding to *Acinetobacter soli* (Moraxellaceae). Three ASVs assigned to
419 *Priestia flexa* (Bacillaceae) were hubs in rainfed lowlands.

420 For ITS data, we found 16 indicator taxa in irrigated areas, and 27 in rainfed lowlands (Table
421 3). Indicator taxa in irrigated areas were assigned to seven classes: *Agaricomycetes*,
422 *Chytridiomycetes*, *Dothideomycetes*, *Geoglossomycetes*, *Leotiomycetes*, *Sordariomycetes*, and
423 *Ustilaginomycetes*. Indicator taxa in rainfed lowlands were assigned to nine classes:
424 *Agaricomycetes*, *Chytridiomycetes*, *Dothideomycetes*, *Glomeromycetes*, *Saccharomycetes*
425 *Schizosaccharomycetes*, *Sordariomycetes*, *Tremellomycetes*, *Ustilaginomycetes*. One ITS
426 ASV identified as indicator taxa in irrigated, with best hit *Pulveroboletus sinensis*
427 (Agaricomycetes), was also core in this rice growing system, and two indicator taxa in rainfed
428 lowlands were also core in this system: one assigned to *Pseudobaeospora wipapatiae*
429 (Agaricomycetes) and the other to *Paraphaeosphaeria michotii* (Dothideomycetes).

430 Putative pathogen or phytobeneficial taxa

431 First, responses for an ‘oryz’ query within assignation and blast, found matching records only
432 in the 16S dataset. A total of 200 ASVs included ‘oryz’ in their names, from 21 different
433 genera, none of these species corresponded to pathogens from Table S6. Among them,
putative beneficial taxa were found, particularly the following: *Azospirillum oryzae*,
*Novosphingobium oryzae*, *Paenibacillus oryzae*, and *Rhizobium oryzae*, *R. rhizoryzae* and *R.
straminoryzae*.

Next, we made a subset of the ITS dataset for ASVs assigned to the *Glomeromycetes* class (total of 14 ASVs). AMF summed abundance was affected by the compartment ($\chi^2$= 101.22, *p*<0.001) and by the rice growing system ($\chi^2$ = 951.12, *p*<0.001), with higher abundances in rhizosphere compartment and in rainfed lowlands (Fig. 5). In addition, differential abundance testing between rice growing systems detected an ASV assigned to *Racocetra crispa* as preferentially found in rainfed lowlands (L2FC = 24.59; *p*<0.001). We also noticed that another *Glomeromycetes* (*Dentiscutata savannicola*) was identified as indicator taxa in rainfed lowland environments (Table 3).

We then screened the list of all assigned ASVs for a set of pathogen species defined *a priori*
(see the list in the Table S6). For Prokaryotes (16S data), a number of ASVs corresponded to
the genera of pathogens, but only *Burkholderia glumae* (two ASVs), *Acidovorax avenae* (four
ASVs) and *Dickeya chrysanthemi* (six ASVs) were identified at the species level. These 12
ASVs identified at the species level were however only found in one sample. The
*Xanthomonas* genus was found, but with no assignation to *X. oryzae* (instead, assignation to
*X. theicola* which is phylogenetically closed to the rice associated *X. sontii* (Bansal *et al.*
2020). A similar situation was observed for the genera *Pseudomonas*, *Pantoea*, and
*Sphingomonas*. The same analysis of putative pathogens for ITS revealed the presence of the
following ten genera: *Alternaria*, *Bipolaris*, *Ceratobasidium*, *Curvularia*, *Fusarium*,
*Helminthosporium*, *Microdochium*, *Rhizoctonia*, *Sarocladium*. We notice that one ASV
whose best blast hit was *Curvularia chonburiensis* was core in both irrigated and rainfed
lowlands (Fig. 4).

Discussion

This study aimed at describing the rice root-associated microbiome by comparing contrasted
rice growing systems in farmer's fields in Burkina Faso. We found that the rice growing
system was a structuring factor for rice root-associated microbiomes, and that the diversity of
prokaryotic community from the rhizosphere was higher in irrigated areas compared to
rainfed lowland. In addition, we identified a number of phylotypes with potential key roles
464 (hub, core, indicators) in the two contrasted systems, as well as putative phytobeneficial and
465 pathogen species. Although the results on fungi (ITS region) must be taken with caution due
466 to a smaller sample size and the poor representation of obtained sequences in available
467 databases, this study shed light on some drivers of assemblage of rice root associated
468 microbial communities in a sparsely documented African system.

469 The structuring of microbial diversity is affected by the rice growing system

470 Although Edwards *et al.* (2018) showed that the root-associated microbiome of distant field
471 sites converge in similarity during the growing season, our study performed at the maturity
472 stage of rice still evidenced some drivers of rice root-associated microbial communities
473 structure. First, as for most rice microbiome studies, we found an effect of the compartment /
474 micro-habitat (Edwards *et al.* 2015; Santos-Medellín *et al.* 2017; Guo *et al.* 2021; Kawasaki *et
475 al.* 2021), and the geographical zone (Edwards *et al.* 2015; Kanasugi *et al.* 2020) on the beta-
476 diversity of rice root-associated microbiome. In addition, our study shows that the contrasted
477 rice-growing systems, namely irrigated perimeters vs rainfed lowlands, harbor contrasted rice
478 root-associated microbial communities, both for prokaryotic and fungal communities, and for
479 rhizosphere and root compartment. We notice that the soil physicochemical properties weakly
480 differ between irrigated areas and rainfed lowland, the soil composition was instead mostly
481 affected by the geographical zones. Consequently, we evidence a structuring effect of the rice-
482 growing system that was only slightly related to contrasted soil physicochemical properties.
483 Our results are in line with a previous study comparing microbiomes from two contrasted
484 water management conditions (upland vs lowland rice) in controlled settings (a field
485 experiment in northern Italy), which showed differentiation in microbial communities,
486 particularly for root microbiome, and to a lesser extent in soil samples (Chialva *et al.* 2020).

487 Only a few other studies compared variable water management agricultural systems. Cui *et al.*
488 (2019) showed that irrigation water quality affected bacterial community alpha and beta
diversity in maize, with pH and available phosphorus being the major factors shaping
489 microbiome soil composition. Mavrodi *et al.* (2018) evidenced a slight effect of the three
490 seasons of irrigation on the overall diversity within the rhizosphere microbiome in wheat, but
491 significant differences in the relative abundances of specific taxa.
492

493 In our study, some of the soil physicochemical parameters affected rice root-associated
494 microbial communities. In particular, CEC and SBE, that reflect soil exchange capacity and
bioaccessibility, were the most important soil parameters for the structure of both rhizosphere and root prokaryotic communities. These parameters are not commonly measured, nor identified as important, in other studies of the root-associated microbiome. Our results argue for including them in soil chemical characterization, to investigate whether their impact in microbiome structure is general or not. In addition, phosphorus content significantly structured the prokaryotic communities, both in rhizosphere and roots. Such an effect of phosphorus is known for the rice root associated microbiome (Long & Yao, 2020). On the other hand, the soil chemical parameter evidenced in this study to structure fungal rhizosphere communities was the total nitrogen (N). This is in accordance with a study by Chen et al. (2019) showing that nitrogen input drives changes in the microbial root-associated community structure in wheat. Moreover, Wang & Huang (2021) showed the effect of optimized N application on fungal community structure from paddy soils. Kanasugi et al. (2020) also evidenced an effect of soil nitrate on rice root fungal communities in Ghana.

## 508 Effect of rice growing system on alpha-diversity and network topology

We found a higher taxonomic diversity in irrigated areas, compared to rainfed lowlands, for prokaryotic communities of the rhizosphere. Our findings differ from Chialva *et al.* (2020)'s results, where the 16S diversity was similar in lowland and upland rice. Chialva *et al.* (2020) also show significantly higher ITS diversity in lowland rice compared to upland, which may relate to the tendency (not significant, maybe as a consequence of the low sample size for ITS) observed here in the rhizosphere.

It is important to note that our sampling was performed in farmer's fields, while most results, including those of Chialva *et al.* (2020), were obtained in field trials, potentially explaining the differences. Indeed, in our study, various factors, such as rice cultivars, fertilization regime and rotation, exhibit large variability. However, rice genetic diversity was shown to be comparable in irrigated areas and rainfed lowlands (Barro *et al.* 2021), therefore the effect of rice growing system on alpha-diversity of microbiomes could not be attributed to result from difference in terms of genetic diversity of the host plant.

Considering the irrigated areas as systems with more intensive agricultural practices, compared to rainfed lowland, we expected an opposite pattern of microbiome diversity. Indeed, agricultural intensification was shown to reduce microbial network complexity and the abundance of keystone taxa in roots (Banerjee *et al.* 2019). In addition, the fertilization
regime is known to have strong impact on root-associated microbiota (Ding et al. 2019; Xiong et al. 2021). Various studies showed that organic fertilization enhances microbial diversity (Liu et al. 2020). For example, recommended fertilization preserved belowground microbial populations, compared to the fertilization mostly used ('conventional fertilization') that depressed bacterial diversity, in experiments performed in China (Ullah et al. 2020). We considered the irrigated areas as more intensified systems, compared to rainfed lowland, particularly because only irrigated areas allow growing rice twice a year, and because only rainfed lowland sites presented fields with no mineral fertilization at all (Barro et al. 2021). We noticed however that organic fertilization remained rare, and its frequency was not drastically affected by the rice growing system. Finally, transplantation was always performed in irrigated areas, while direct sowing was the most common practice in rainfed lowlands.

On the other hand, paddy soils studied in western Burkina Faso (all over the six sites) are particularly poor if compared for example to a study of more than 8 000 soils in Hunan Province (Duan et al. 2020), where average organic carbon was 1.972%, compared to 0.922% in our study, total nitrogen was 0.191%, higher than 0.072%, and total phosphorus was 0.71g.kg⁻¹, compared to 0.24g.kg⁻¹. The studies previously cited evidencing fertilization effects, were performed in soils with higher carbon and nitrogen contents (see for example Ullah et al. 2020, where minimum average organic carbon was 2% and total nitrogen 0.1%). The effect of fertilization on microbial diversity may actually depend on various aspects, including the soil type. Notably, a positive relationship was found between rice fertilization and soil bacterial richness and diversity in a 19-years inorganic fertilization assay in a reddish paddy soil in southern China (Huang et al. 2019); while Wang & Huang (2021) showed an effect of the fertilization on paddy soils microbial community composition but no effect on the diversity. In poor soil systems such as in this study, fertilization input may actually increase microbial diversity.

Finally, a complementary hypothesis could be the higher fragmentation of rainfed lowlands compared to irrigated areas. Indeed, irrigated sites correspond to larger areas cultivated in rice, possibly with two rice seasons per year, so that rice fields are likely to be more connected to each other than in rainfed lowlands. Higher connectivity generally leads to higher biodiversity (Fletcher et al. 2016). The principles of metacommunity theory could also be applied to micro-organisms, with reduction in host habitats and fragmentation potentially
557 increasing extinction rates (Mony et al. 2020), but as our study misses an explicit
558 characterization of the rice landscape structure, this hypothesis could not be formally tested.

559 Distant rainfed lowlands differ more than distant irrigated perimeters

560 Our results showed that the prokaryotic communities in the rice rhizosphere and roots from
561 the three irrigated sites do not differ significantly from each other. On the other hand, the
562 same analysis revealed significant differentiation between the three rainfed lowland study
563 sites (in all three cases for rhizosphere and two out of three comparisons in roots). Also, we
564 found very few core phylotypes in rainfed lowland, with only two core ASVs for 16S, what
565 reinforces the above-mentioned observation. These results are likely driven by a higher
566 heterogeneity between rainfed sites, in terms of water control, agricultural practices or rice
567 genotypes.

568 Indeed, in irrigated rice, the farmer has the potential to control irrigation water during the
569 whole growing season. On the other hand, irrigation in rainfed lowland is dependent on
570 precipitations that differ between the three geographical zone sampled within the rice growing
571 season. In addition, we showed a high heterogeneity of agricultural practices in rainfed
572 lowlands: for example, legume rotation was common in the rainfed lowland of Bama zone,
573 but rare or absent in the two other rainfed lowland sites, and organic fertilization was more
574 frequent in the rainfed lowland of Karfiguela zone, than in the other sites (Barro et al. 2021).
575 Finally, in terms of rice genetics, a high rice genetic differentiation was found between the
576 rainfed lowland site of Karfiguela zone and the five sites: a distinct genetic group O. sativa
577 Aus, and other distinct landraces were found in this peculiar site, compared to the five others
578 where only O. sativa indica was grown (Barro et al. 2021). These specificities of the rainfed
579 lowland from Karfiguela zone, in terms of rice grown and agricultural practices, may also
580 drive its specific patterns of alpha diversity, with a particularly low prokaryote diversity (in
581 rhizosphere and also, in a lesser extent, in roots), and a tendency for higher fungal diversity in
582 roots.

583 Our sampling size was much lower for ITS and this likely explains the absence of such a
584 pattern, with no significant differences obtained between pairs of sites. Alternatively, the
585 pattern may be different for fungal diversity, as suggested by the higher number of core taxa
586 in rainfed lowlands than in irrigated areas.
588 Identification of core microbiota and hub phylotypes

589 The prevalent taxa, indicator taxa and hubs may be considered as having an important
590 ecological role in microbiome assembly and ecosystem functions (Banerjee et al. 2018). In
591 this study, we identified the core prokaryote and fungal microbiota in both irrigated and
592 rainfed lowland environments. While four fungal taxa were found to be cores in both systems,
593 only one bacterial core taxa was shared between the two rice growing systems: assigned to
594 Bradyrhizobium tropiciagri, a nitrogen-fixing symbiont isolated from tropical forage legumes
595 (Delamuta et al. 2015), known as rice root endophytes capable of fixing N₂ (Chaintreuil et al.
596 2000; Ding et al. 2019). In addition, the core taxa in irrigated areas likely includes
597 Paraburkholderia kururiensis, a bacterium with potential phytobeneficial properties
598 (bioremediation, biofertilization and biocontrol of pathogens; Dias et al. 2019). Various ASVs
599 identified as core in irrigated areas were assigned to Ralstonia pickettii, an ubiquitous
600 Betaproteobacteria found in water and soil, and capable to thrive in low nutrient
601 (oligotrophic) conditions (Ryan et al. 2007). Isolated from the plant rhizosphere, R. pickettii
602 injected in tomato stem could reduce bacterial wilt disease caused by its congeneric pathogen
603 R. solanacearum, and could consequently be considered as potential biocontrol agent (Wei et
604 al. 2013). Noteworthy, this bacterium is also described as human emerging pathogen, causing
605 nosocomial infections (Ryan et al. 2006).

606 One ASVs assigned to Enterobacter mori (Enterobacteriaceae) was identified as hub of the
607 16S-based co-occurrence networks both in irrigated areas and rainfed lowland site. On the
608 other hand, most of the bacterial taxa identified as hubs differed between irrigated areas and
609 rainfed lowlands reflecting a highly contrasted structuring of bacterial communities in the two
610 rice growing systems.

611 Characterization of indicator taxa in each rice growing system and contrasted
612 repartition of AMFs

613 We identified indicator taxa for each rice growing system, most of them being in irrigated for
614 procaryotes (128, vs only 63 in rainfed lowlands) while the opposite was found for fungi (27
615 in rainfed lowlands vs only 16 in irrigated areas). For prokaryotes, five taxa identified as
616 indicator species in irrigated areas were also core or hub: the previously mentioned
617 *Paraburkholderia kururiensis* and *Ralstonia pickettii* as well as *Aeromonas hydrophila*,
618 *Enterobacter cloacae* and *Acinetobacter soli*. In rainfed lowlands, it was the case for *Priestia*
619 *flexa*. In particular, we notice that *Acinetobacter soli* was identified as potent phosphorus
620 solubilizer in rice and consequently promising for plant growth promotion (Rasul *et al.* 2019).

621 For fungi, we identified as both indicator and core taxa: *Pulveroboletus sinensis* in irrigated
622 areas, as well as both *Pseudobaeospora wipapatiae* and *Paraphaeosphaeria michotii* in
623 rainfed lowlands. Eight ASVs assigned to *Chytridiomycetes* were identified as indicator
624 species in rainfed lowland systems. Members of this fungal division of aquatic fungi (Barr,
625 2001) were found in the rhizosphere compartment in our study. They are known as
626 particularly abundant in microbial communities associated with rice roots, compared to other
627 crops (Ding *et al.* 2019) and were preferentially associated to lowland conditions compared to
628 upland (Chialva *et al.* 2020).

629 We identified a few potentially beneficial taxa that could be investigated further. In particular,
630 AMFs of the class *Glomeromycetes* were found preferentially in rainfed lowlands, with one
631 ASV, *Racocetra crispa*, enriched in rainfed lowland system compared to irrigated areas, and
632 one ASV, *Dentiscutata savannicola*, identified as indicator in rainfed system. This was
633 expected considering the lower frequency of mineral fertilization in rainfed lowlands
634 compared to irrigated areas. Indeed, AMF colonization was shown to be affected by farming
635 regimes: the rice roots cultivated in the conventional agrosystem (N and P fertilization and
636 pesticides) or under permanent flooding showed no AMF colonization, while the rice plants
637 grown with organic conditions showed typical mycorrhization patterns (Lumini *et al.* 2011).

Towards the identification of putative pathogens and the study of interactions
between microbiome and diseases

640 Various pathogen species are suspected from the sequence variants identified in this study. In
641 particular, the 16S dataset contained ASVs assigned to *Burkholderia glumae*, *Acidovorax
642 avenae* and *Dickeya chrysanthemi*. All three remained rare, with only one sample containing
643 each of these sequences. The presence of *B. glumae* was described in Burkina Faso (but with
644 no molecular data; Ouedraogo *et al.* 2004), and targeted detection performed in two sites
645 failed to detect *B. glumae* and *A. avenae* (Bangratz *et al.* 2020). For fungal pathogens, we
646 found ASVs assigned to ten genera comprising rice pathogens, including *Bipolaris*,
647 *Curvularia*, *Fusarium* and *Rhizoctonia*. It has to be noted that the major rice pathogens
causing foliar diseases, namely *Pyricularia oryzae* and *Xanthomonas oryzae*, known from symptom observations to be present in the study sites (Barro *et al.* 2021), were not detected in this root-associated metabarcoding data. Some of these putative fungal pathogens are frequent, particularly one, whose best blast hit is *Curvularia chonburiensis*, identified as core taxa in both irrigated perimeters and rainfed lowlands. Various *Curvularia* species are known to be pathogenic in rice, potentially causing contrasted symptoms (Gao *et al.* 2012; Majeed *et al.* 2015), and their widespread repartition evidenced here argues for more work in plant pathology to better understand the interactions between *Curvularia* and rice.

The literature shows that higher microbiome diversity may be associated with a lower infection rate (see for example Rutten *et al.* 2021). Our results for rice from western Burkina Faso are somehow opposite as irrigated perimeters harbor more diverse prokaryotic communities of rhizosphere compared to rainfed lowlands (this study), but also higher prevalence of major rice diseases, particularly bacterial leaf streak and the fungal rice blast disease, based on the observation of foliar symptoms (Barro *et al.* 2021). On the other hand, for fungal communities, the pattern may actually be opposite; a tendency for higher diversity in rainfed lowlands is observed but low sample size prevents from obtaining significant results. Also, some diseases, such as the viral yellow mottle disease (Barro *et al.* 2021), are not affected by the rice growing system but by the specific site. The relationship between the diversity of root-associated microbiome and diseases is complex and remains to be studied in more details, including under controlled conditions.

More generally, scientific interest in the relationship between root-associated microbiota and plant diseases is growing (Vannier *et al.* 2019; Trivedi *et al.* 2020). In rice, various studies evidenced an inhibition of disease development by root-associated micro-organisms (see Yasmin *et al.* 2016 for bacterial diseases; and Spence *et al.* 2014; Law *et al.* 2017 for rice blast). On the other hand, disease was shown to affect root-associated microbiomes, with for example the effect of *Magnaporthe grisea* inoculation on microbial endosphere diversity (Tian *et al.* 2021). A promising avenue of research is consequently to investigate the relationship between root-associated microbiota and rice diseases in the particular rice-growing systems of Burkina Faso.
678 **Perspectives**

679 We are only at the beginning of understanding the complexity of rice root microbial
680 communities, especially for rice cultivation in Africa. The originality, but also a limitation of
681 our study, lies in the fact that the samples were collected in farmer fields, and it globally
682 compares the two contrasting rice production systems that differ in various management
683 practices, so that it could not tease apart the specific effect of each individual factor (water
684 management, variety, fertilization, pesticides, etc). More investigations are now required to
685 decipher each structuring factor at a smaller scale: in particular between fields within each
686 site, where the rice cultivar and specific agricultural practices are likely to play a significant
687 role (Delitte et al. 2021).

688 Describing rice microbiota through metabarcoding is a first mandatory step that needs to be
689 combined with culturomics for a greater accuracy and a deeper description, in particular in
690 such systems where some taxa are poorly described in taxonomic databases. Experimental
691 work in an integrative approach is also required to move on towards microbiota management
692 methodologies. Such microbiota-based strategies could contribute to improving rice health
693 and productivity (Sessitsch & Mitter, 2015), while preserving human health. They are
694 consequently an important component of the toolbox of science-based strategies to achieve
695 zero-hunger in Africa.

696

697 **Acknowledgments**

698 This work was performed thanks to the facilities of the “International joint Laboratory LMI
699 PathoBios: Observatory of plant pathogens in West Africa: biodiversity and biosafety”
700 (www.pathobios.com; twitter.com/PathoBios), and conducted within E-SPACE project
701 (www6.inrae.fr/e-space/), led by Claire Neema. We are very grateful to Abdoul Kader
702 Guigma, Yacouba Kone, Moumouni Traoré and Arzouma Diasso for their contributions to the
703 fieldwork in Burkina Faso. We thank the rice farmers from Badala, Bama, Senzon, Banzon,
704 Tengrela and Karfiguela for their kind collaboration. We thank Corinne Vacher and Alain
705 Sarniguet for constructive discussions at earlier stage of the project. The manuscript benefited
706 from helpful comments by Philippe Roumagnac, who also contributed to the initial design of
707 the study. Finally, we dedicate this manuscript to the late Christian Vernière for his kind
708 feedback at different stages of this project.

709

710

## Funding

711 This work was publicly funded through ANR (the French National Research Agency) under
712 «Investissements d'avenir» programme with the reference ANR-10-LABX-001-01 Labex
713 Agro (E-Space and RiPaBIOME projects), coordinated by Agropolis Fondation under the
714 frame of I-SITE MUSE (ANR-16-IDEX-006) and by the CGIAR Research Program on Rice
715 Agri-food Systems (RICE).

716

717

## Reference cited

718 Abarenkov, Kessy, Zirk A, Piirmann T, Pöhönen R, Ivanov F, Nilsson R, Henrik & Köljalg
719 U. UNITE general FASTA release for Fungi 2. 2021; Version 10.05.2021. UNITE
720 Community.

721 AFNOR. Détermination du pH (Association Française de Normalisation). 1981 ; NF ISO
722 10390. 339-348. AFNOR qualité des sols. Paris.

723 Alonso P, Blondin L, Gladieux P, et al. Heterogeneity of the rice microbial community of the
724 Chinese centuries-old Honghe Hani rice terraces system. *Environmental Microbiology* 2020;
725 **22**: 3429-3445.

726 Banerjee S, Schlaeppi K & van der Heijden MGA. Keystone taxa as drivers of microbiome
727 structure and functioning. *Nature Reviews Microbiology* 2018; **16**: 567-576.

728 Banerjee S, Walder F, Büchi L, Meyer M, Held AY, Gattinger A, Keller T, Charles R & van
729 der Heijden MGA. Agricultural intensification reduces microbial network complexity and the
730 abundance of keystone taxa in roots. *The ISME Journal* 2019; **13**: 1722-1736.

731 Bangratz M, Wonni I, Kini K, et al. Design of a new multiplex PCR assay for rice pathogenic
732 bacteria detection and its application to infer disease incidence and detect co-infection in rice
733 fields in Burkina Faso. *PLOS ONE* 2020; **15**: e0232115.

734 Barr DJS. *Chytridiomycota. Systematics and Evolution: Part A*, (McLaughlin DJ, McLaughlin
735 EG & Lemke PA, eds.), 2001; 93-112. Springer Berlin Heidelberg, Berlin, Heidelberg.

736 Barro M, Kassankogno AI, Wonni I, Sérémé D, Somda I, Kaboré HK, Béna G, Brugidou C,
737 Tharreau D & Tollenaere C. Spatiotemporal Survey of Multiple Rice Diseases in Irrigated
738 Areas Compared to Rainfed Lowlands in the Western Burkina Faso. *Plant Disease* 2021; **105**:

739 3889-3899.

740 Barro M, Konate KA, Wonni I, et al. Assessment of Genetic Diversity of Rice in Registered

741 Cultivars and Farmers' Fields in Burkina Faso. *Crops* 2021; **1**: 129-140.

742 Bates D, Mächler M, Bolker B & Walker S. Fitting Linear Mixed-Effects Models Using lme4.

743 *Journal of Statistical Software* 2015; **67**: 1 - 48.

744 Beattie GA. Metabolic coupling on roots. *Nature Microbiology* 2018; **3**: 396-397.

745 Berry D & Widder S. Deciphering microbial interactions and detecting keystone species with

746 co-occurrence networks. *Frontiers in Microbiology* 2014; **5**: 219.

747 Bouyoucos GJ. Hydrometer Method Improved for Making Particle Size Analysis of Soils.

748 *Agronomy Journal* 1962; **54**: 464-465.

749 Brunel C, Pouteau R, Dawson W, Pester M, Ramirez KS & van Kleunen M. Towards

750 Unraveling Macroecological Patterns in Rhizosphere Microbiomes. *Trends in Plant Science*

751 2020; **25**: 1017-1029.

752 Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA & Holmes SP. DADA2:

753 High-resolution sample inference from Illumina amplicon data. *Nature Methods* 2016; **13**:

754 581-583.

755 Cartwright RD, Groth DE, Wamishe YA, Greer CA, Calvert LA, Cruz CMV, Verdier V &

756 Way MO. *Compendium of Rice Diseases and Pests, Second Edition*. 2018; The American

757 Phytopathological Society.

758 Chaintreuil C, Giraud E, Prin Y, Lorquin J, Bâ A, Gillis M, de Lajudie P & Dreyfus B.

759 Photosynthetic Bradyrhizobium Are Natural Endophytes of the African Wild Rice *Oryza*

760 *breviligulata*. *Applied and Environmental Microbiology* 2000; **66**: 5437-5447.

761 Chen S, Waghmode TR, Sun R, Kuramae EE, Hu C & Liu B. Root-associated microbiomes

762 of wheat under the combined effect of plant development and nitrogen fertilization.

763 *Microbiome* 2019; **7**: 136.

764 Chialva M, Lanfranco L & Bonfante P. The plant microbiota: composition, functions, and

765 engineering. *Current Opinion in Biotechnology* 2021; **73**: 135-142.

766 Chialva M, Ghignone S, Cozzi P, Lazzari B, Bonfante P, Abbruscato P & Lumini E. Water

767 management and phenology influence the root-associated rice field microbiota. *FEMS*

768 *Microbiology Ecology* 2020; **96**.

769 CountrySTAT. Faadn Annuaire statistique agricole 1993-2021: Superficie des céréales. (Faso

770 CB, ed.) 2020.

771 Csardi G & Nepusz T. The igraph software package for complex network research. 1695.

772 2006

773 Cui E, Fan X, Li Z, Liu Y, Neal AL, Hu C & Gao F. Variations in soil and plant-microbiome

774 composition with different quality irrigation waters and biochar supplementation. *Applied Soil*

775 *Ecology* 2019; **142**: 99-109.

776 De Cáceres M, Legendre P & Moretti M. Improving indicator species analysis by combining

777 groups of sites. *Oikos* 2010; **119**: 1674-1684.

778 de la Fuente Cantó C, Simonin M, King E, Moulin L, Bennett MJ, Castrillo G & Laplaze L.

779 An extended root phenotype: the rhizosphere, its formation and impacts on plant fitness. *The*

780 *Plant Journal* 2020; **103**: 951-964.
781 Delamuta JRM, Ribeiro RA, Ormeño-Orrillo E, Parma MM, Melo IS, Martínez-Romero E & Hungria M. *Bradyrhizobium tropiciagri* sp. nov. and *Bradyrhizobium embrapense* sp. nov., nitrogen-fixing symbionts of tropical forage legumes. *International Journal of Systematic and Evolutionary Microbiology* 2015; **65**: 4424-4433.

785 Delitte M, Caulier S, Bragard C & Desoignies N. Plant Microbiota Beyond Farming Practices: A Review. *Frontiers in Sustainable Food Systems* 2021; **5**: 66.

787 Dias GM, de Sousa Pires A, Grilo VS, Castro MR, de Figueiredo Vilela L & Neves BC. Comparative genomics of *Paraburkholderia kururiensis* and its potential in bioremediation, biofertilization, and biocontrol of plant pathogens. *Microbiology Open* 2019; **8**: e00801-790 e00801.

791 Ding L-J, Cui H-L, Nie S-A, Long X-E, Duan G-L & Zhu Y-G. Microbiomes inhabiting rice roots and rhizosphere. *FEMS Microbiology Ecology* 2019; **95**.

793 Duan L, Li Z, Xie H, Li Z, Zhang L & Zhou Q. Large-scale spatial variability of eight soil chemical properties within paddy fields. *CATENA* 2020; **188**: 104350.

795 Edwards J, Johnson C, Santos-Medellin C, Lurie E, Podishetty NK, Bhatnagar S, Eisen JA & Sundaresan V. Structure, variation, and assembly of the root-associated microbiomes of rice. *Proc Natl Acad Sci USA* 2015; **112**: E911-E920.

798 Edwards JA, Santos-Medellín CM, Liechty ZS, Nguyen B, Lurie E, Eason S, Phillips G & Sundaresan V. Compositional shifts in root-associated bacterial and archaeal microbiota track the plant life cycle in field-grown rice. *PLOS Biology* 2018; **16**: e2003862.

801 Epskamp S, Cramer AOJ, Waldorp LJ, Schmittmann VD & Borsboom D. qgraph: Network Visualizations of Relationships in Psychometric Data. *J. Stat. Software* 2012; **48**: 1-18.

803 FAO/IIASA/ISRIC/ISSCAS/JRC Harmonized World Soil Database (version 1.2). FAO and IIASA, Rome, Italy and Laxenburg, Austria. 2012.

805 Fick SE & Hijmans RJ. WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. *International Journal of Climatology* 2017; **37**: 4302-4315.

807 Fletcher RJ, Burrell NS, Reichert BE, Vasudev D & Austin JD. Divergent Perspectives on Landscape Connectivity Reveal Consistent Effects from Genes to Communities. *Current Landscape Ecology Reports* 2016; **1**: 67-79.

810 Fox J & Weisberg S. *An R Companion to Applied Regression*. Sage, Thousand Oaks CA. 2019.

812 Gao BD, Huang W & Xia H. A New Rice Disease, Black Sheath Spot, Caused by *Curvularia fallax* in China. *Plant Disease* 2012; **96**: 1224-1224.

814 Guo J, Ling N, Li Y, Li K, Ning H, Shen Q, Guo S & Vandenkoornhuyse P. Seed-borne, endospheric and rhizospheric core microbiota as predictors of plant functional traits across rice cultivars are dominated by deterministic processes. *New Phytologist* 2021; **230**: 2047-2060.

818 Hillebrand WF, Lundel GEF, Bright HA & Hoffman JI. *Applied inorganic analysis*. 2nd edition. New York, USA. 1953.

820 Huang Q, Wang J, Wang C & Wang Q. The 19-years inorganic fertilization increased bacterial diversity and altered bacterial community composition and potential functions in a paddy soil. *Applied Soil Ecology* 2019; **144**: 60-67.
823 Hughes AC, Orr MC, Ma K, Costello MJ, Waller J, Provoost P, Yang Q, Zhu C & Qiao H.

824 Sampling biases shape our view of the natural world. *Ecography* 2021; **44**: 1259-1269.

825 Kanasugi M, Sarkodee-Addo E, Ansong Omari R, Mohammad Golam Dastogeer K, Fujii Y,

826 Oppong Abebrese S, Bam R, Asuming-Brempong S & Okazaki S. Exploring Rice Root

827 Microbiome; The Variation, Specialization and Interaction of Bacteria and Fungi In Six

828 Tropic Savanna Regions in Ghana. *Sustainability* 2020; **12**: 5835.

829 Kawasaki A, Dennis PG, Forstner C, Raghavendra AKH, Richardson AE, Watt M, Mathesius

830 U, Gilliham M & Ryan PR. The microbiomes on the roots of wheat (*Triticum aestivum* L.)

831 and rice (*Oryza sativa* L.) exhibit significant differences in structure between root types and

832 along root axes. *Functional Plant Biology*. 2021; **48(9)**:871-888.

833 Kim H & Lee Y-H. The Rice Microbiome: A Model Platform for Crop Holobiome.

834 *Phytobiomes Journal* 2020; **4**: 5-18.

835 Kurtz ZD, Müller CL, Miraldi ER, Littman DR, Blaser MJ & Bonneau RA. Sparse and

836 Compositionally Robust Inference of Microbial Ecological Networks. *PLOS Computational*

837 *Biology* 2015; **11**: e1004226.

838 Law JWF, Ser HL, Khan TM, Chuah LH, Pusparajah P, Chan KG, Goh BH & Lee LH. The

839 Potential of *Streptomyces* as Biocontrol Agents against the Rice Blast Fungus, *Magnaporthe*

840 *oryzae* (*Pyricularia oryzae*). *Frontiers in Microbiology* 2017; **8**.

841 Linares OF. African rice (*Oryza glaberrima*): History and future potential. *Proc Natl Acad Sci*

842 USA 2002; **99**: 16360.

843 Liu Z, Guo Q, Feng Z, Liu Z, Li H, Sun Y, Liu C & Lai H. Long-term organic fertilization

844 improves the productivity of kiwifruit (*Actinidia chinensis* Planch.) through increasing

845 rhizosphere microbial diversity and network complexity. *Applied Soil Ecology* 2020; **147**:

846 103426.

847 Long X-E & Yao H. Phosphorus Input Alters the Assembly of Rice (*Oryza sativa* L.) Root-

848 Associated Communities. *Microbial Ecology* 2020; **79**: 357-366.

849 Love M, Huber W & Anders S. Moderated estimation of fold change and dispersion for RNA-

850 seq data with DESeq2. *Genome Biology* 2014; **15**: 550.

851 Lu T, Ke M, Lavoie M, et al. Rhizosphere microorganisms can influence the timing of plant

852 flowering. *Microbiome* 2018; **6**: 231.

853 Lumini E, Vallino M, Alguacil MM, Romani M & Bianciotto V. Different farming and water

854 regimes in Italian rice fields affect arbuscular mycorrhizal fungal soil communities.

855 *Ecological Applications* 2011; **21**: 1696-1707.

856 MAHRH. Burkina Faso : National Rice Development Strategy (NRDS). 2011.

857 Majeed RA, Shahid AA, Ashfaq M, Saleem MZ & Haider MS. First Report of *Curvularia*

858 *lunata* Causing Brown Leaf Spots of Rice in Punjab, Pakistan. *Plant Disease* 2015; **100**: 219-

859 219.

860 Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads.

861 *EMBnetjournal* 2011; **17(1)**: 10-12.

862 Mavrodi DV, Mavrodi OV, Elbourne LDH, Tetu S, Bonsall RF, Parejko J, Yang M, Paulsen

863 IT, Weller DM & Thomashow LS. Long-Term Irrigation Affects the Dynamics and Activity

864 of the Wheat Rhizosphere Microbiome. *Frontiers in Plant Science* 2018; **9**:345.
865 McMurdie PJ & Holmes S. phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. *PLOS ONE* 2013; **8**: e61217.

867 Metson AJ Methods of chemical analysis for soil survey samples. 1956; N Z Soil Bur Bull n° 12.

869 Mony C, Vandenkoornhuyse P, Bohannan BJM, Peay K & Leibold MA A Landscape of Opportunities for Microbial Ecology Research. *Frontiers in Microbiology* 2020; **11**.

871 Nonvide GMA, Sarpong DB, Kwadzo GTM, Anim-Somuah H & Amoussouga Gero F. Farmers' perceptions of irrigation and constraints on rice production in Benin: a stakeholder-consultation approach. *International Journal of Water Resources Development* 2018; **34**: 1001-1021.

875 Novozansky IV, Houba JG, Vaneck R & Van Vark W. A novel digestion technic for multi-element analysis. *Commun Sail Sei Plant Anal* 1983; 239-249.

877 Oksanen J, Blanchet FG, Friendly, et al. The vegan package. *Community Ecol Package* 2007.

878 Ouedraogo S, Somda I, Borro F & Sere Y. Detection and characterization of plant pathogenic bacteria transmitted by seed rice in Burkina Faso. *Agronomie Africaine* 2004; **6**: 9-17.

880 Pandey S, Byerlee D & Dawe D. *Rice in the global economy: strategic research and policy issues for food security*. Los Banos (Philippines). 2010.

882 Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J & Glöckner FO. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. *Nucleic Acids Research* 2013; **41**: D590-D596.

885 R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. 2018; Vienna, Austria. URL https://www.R-project.org/.

887 Rao A, Wani S, Ramesha M & Ladha J. Chapter 8 Rice Production Systems. *Rice Production Worldwide*, (Chauhan B, Jabran K & Mahajan G, eds.), Springer International Publishing AG 2017. 2017.

890 Rasul M, Yasmin S, Suleman M, Zaheer A, Reitz T, Tarkka MT, Islam E & Mirza MS. Glucose dehydrogenase gene containing phosphobacteria for biofortification of Phosphorus with growth promotion of rice. *Microbiological Research* 2019; **223-225**: 1-12.

893 Rochefort A, Simonin M, Marais C, Guillerm-Erckelboudt A-Y, Barret M, Sarniguet A & Fodor A. Transmission of Seed and Soil Microbiota to Seedling. *mSystems* 2021;**6**: e00446-00421.

896 Rutten G, Hönig L, Schwaß R, Braun U, Saadani M, Schuldt A, Michalski SG & Bruelheide H. More diverse tree communities promote foliar fungal pathogen diversity, but decrease infestation rates per tree species, in a subtropical biodiversity experiment. *Journal of Ecology* 2021; **109**: 2068-2080.

900 Ryan MP, Pembroke JT & Adley CC. *Ralstonia pickettii*: a persistent Gram-negative nosocomial infectious organism. *Journal of Hospital Infection* 2006; **62**: 278-284.

902 Ryan MP, Pembroke JT & Adley CC. *Ralstonia pickettii* in environmental biotechnology: potential and applications. *Journal of Applied Microbiology* 2007; **103**: 754-764.

904 Santos-Medellín C, Edwards J, Liechty Z, Nguyen B, Sundaresan V & Ausubel Frederick M. Drought Stress Results in a Compartment-Specific Restructuring of the Rice Root-Associated Microbiomes. *mBio* 2017; **8**: e00764-00717.
907 Schweitzer JA, Bailey JK, Fischer DG, LeRoy CJ, Lonsdorf EV, Whitham TG & Hart SC.
908 Plant-Soil-Microorganism Interactions: Heritable Relationship between Plant Genotype and
909 Associated Soil Microorganisms. *Ecology* 2008; **89**: 773-781.

910 Senthil Kumar CM, Jacob TK, Devasahayam S, Thomas S & Geethu C. Multifarious plant growth promotion by an entomopathogenic fungus *Lecanicillium psalliotae*. *Microbiological Research* 2018; **207**: 153-160.

913 Sessitsch A & Mitter B. 21st century agriculture: integration of plant microbiomes for improved crop production and food security. *Microbial biotechnology* 2015; **8**: 32-33.

915 Singh BK, Trivedi P, Egidi E, Macdonald CA & Delgado-Baquerizo M. Crop microbiome and sustainable agriculture. *Nature Reviews Microbiology* 2020; **18**: 601-602.

917 Soullier G, Demont M, Arouna A, Lancon F & del Villar PM. The state of rice value chain upgrading in West Africa. *Global Food Security-Agriculture Policy Economics and Environment* 2020; **25**.

920 Spence C, Alff E, Johnson C, Ramos C, Donofrio N, Sundaresan V & Bais H. Natural rice rhizospheric microbes suppress rice blast infections. *BMC Plant Biology* 2014; **14**.

922 Tamburini G, Bommarco R, Wanger TC, Kremen C, van der Heijden MGA, Liebman M & Hallin S. Agricultural diversification promotes multiple ecosystem services without compromising yield. *Science Advances* 2020; **6**: eaba1715.

925 Tian D, Chen Z, Lin Y, Liang T, Chen Z, Guo X, Wang F & Wang Z. The Interaction between Rice Genotype and *Magnaporthe oryzae* Regulates the Assembly of Rice Root-Associated Microbiota. *Rice* 2021; **14**: 40.

928 Trivedi P, Leach JE, Tringe SG, Sa T & Singh BK. Plant-microbiome interactions: from community assembly to plant health. *Nature Reviews Microbiology* 2020; **18**: 607-621.

930 Ullah S, He P, Ai C, Zhao S, Ding W, Song D, Zhang J, Huang S, Abbas T & Zhou W. How Do Soil Bacterial Diversity and Community Composition Respond under Recommended and Conventional Nitrogen Fertilization Regimes? *Microorganisms* 2020; **8**: 1193.

933 Vandenkoornhuyse P, Quaiser A, Duhamel M, Le Van A & Dufresne A. The importance of the microbiome of the plant holobiont. *New Phytologist* 2015; **206**: 1196-1206.

935 Vannier N, Agler M & Hacquard S. Microbiota-mediated disease resistance in plants. *PLOS Pathogens* 2019; **15**: e1007740.

937 Vieira S, Sikorski J, Dietz S, Herz K, Schrumpf M, Bruelheide H, Scheel D, Friedrich MW & Overmann J. Drivers of the composition of active rhizosphere bacterial communities in temperate grasslands. *The ISME Journal* 2020; **14**: 463-475.

940 Walinga J, Van Vark W, Houba VJ & Van Der Lee JJ. Plant analysis procedures. *Soil and plant analysis*, (Wageningen & University A, eds.), 1989; 7: 197-200. Wageningen: Landbouwuniversiteit, 1986-1995.

943 Walkley A & Black IA. An examination method of the detjareff and a proposed modification of the chromic acid titration method. *Soil science* 1934; **37**: 29-38.

945 Walsh CM, Becker-Uncapher I, Carlson M & Fierer N. Variable influences of soil and seed-associated bacterial communities on the assembly of seedling microbiomes. *The ISME Journal* 2021; **15**: 2748-2762.

948 Wang L & Huang D. Soil microbial community composition in a paddy field with different fertilization managements. *Canadian Journal of Microbiology* 2021; **67**: 864-874.
950 Wei Z, Huang J, Tan S, Mei X, Shen Q & Xu Y. The congeneric strain *Ralstonia pickettii*
951 QL-A6 of *Ralstonia solanacearum* as an effective biocontrol agent for bacterial wilt of
952 tomato. *Biological Control* 2013; **65**: 278-285.

953 Wei Z, Yang T, Friman V-P, Xu Y, Shen Q & Jousset A. Trophic network architecture of
954 root-associated bacterial communities determines pathogen invasion and plant health. *Nature*
955 *Communications* 2015; **6**: 8413.

956 Xiong QQ, Hu JL, Wei HY, Zhang HC & Zhu JY. Relationship between Plant Roots,
957 Rhizosphere Microorganisms, and Nitrogen and Its Special Focus on Rice. *Agriculture* 2021;
958 **11**.

959 Yasmin S, Zaka A, Imran A, Zahid MA, Yousaf S, Rasul G, Arif M & Mirza MS. Plant
960 Growth Promotion and Suppression of Bacterial Leaf Blight in Rice by Inoculated Bacteria.
961 *PLOS ONE* 2016; **11**: e0160688.

962
963 **Figure 1** Location of the study sites and soil physico-chemical properties

964 Location of the study sites in western Burkina Faso on the soil Harmonized Word Soil Database (HWSD) map of West Africa (https://webarchive.iiasa.ac.at/Research/LUC/External-World-soil-database/HTML/). The three geographical zones studied are in Lixisols (LX: soils with subsurface accumulation of low activity clays and high base saturation)

968 Location of the field studied within each geographical zones. Soil texture, are indicated for each of the 19 studied fields, with pie charts representing relative proportions of sand (in yellow), silt (in green), and clay (in blue). Soil chemical properties estimated in each geographical zones, with colors representing the rice growing system (irrigated areas in blue and rainfed lowlands in red). Each point corresponds to one field studied. Averages over the 19 studied fields are indicated for each parameter, as well as the results of statistical tests for the geographical zone effect.

<figure><img src="image_2.png" /></figure>
**Figure 2** NMDS ordination showing the three factors identified as drivers of the structuration of rice root microbial communities: the color of points represent the rice growing system (irrigated vs rainfed lowland), while the shape shows the geographical zone (Banzon, Karfiguela and Bama).

On the left side are presented the analyses based on 16S rRNA gene reflecting Prokaryote communities, where one point corresponds to one plant. On the right side are shown the analyses based on ITS reflecting fungal communities, where one point corresponds to one field. The root compartment is presented on the upper side of the figure while the rhizosphere data appear on the bootom side. Only the soil physicochemical parameter that revealed as having a significant effect (see Supplementary Table S4) are represented with arrows: cation exchange capacity (CEC), Sorptive Bioaccessibility Extraction (SBE), total concentrations of phosphorus (Total P), total concentrations of nitrogen (Total N), total organic carbon (Organic C).

<figure><img src="image_3.png" /></figure>
<caption>**Figure 3** Comparison of root-associated microbiota α-diversity in contrasted rice-growing systems: irrigated (in blue) vs rainfed lowland (in red).</caption>

Observed richness and Shannon indices are reported for each sample (i.e. one plant for 16S and one field for ITS), as violin diagram for each rice growing system. The left side of the figure presents the results obtained for 16S microbiome, while the right side shows the results obtained for ITS analysis. On top are shown the results of the rhizosphere compartment and on the bottom are the results obtained for the root associated compartment.

<figure><img src="image_4.png" /></figure>
**Figure 4** Venn diagram representing the core sequence variants for each rice growing system : irrigated vs rainfed lowlands. A. For prokaryotes B. For fungi

<figure><img src="image_5.png" /></figure>

**Figure 5** Spatial repartition of summed abundance of 14 ITS ASVs assigned to the class *Glomeromycetes* in the two rice root microbiome compartments (rhizosphere and roots), and in each rice growing system (irrigated perimeters vs rainfed lowlands).

The shape of the point corresponds to each geographic zone.

<figure><img src="image_6.png" /></figure>
**Table 1**

Results of PERMANOVA analysis performed independently for rhizosphere compartment and root compartment, for 16S and ITS microbiome data.

<table><thead><tr><th rowspan="2"></th><th colspan="5">Prokaryotes 16S</th><th colspan="5">Fungi ITS</th></tr><tr><th>Df</th><th>SumOfSqs</th><th>R2</th><th>F</th><th>Pr(&gt;F)</th><th>Df</th><th>SumOfSqs</th><th>R2</th><th>F</th><th>Pr(&gt;F)</th></tr></thead><tbody><tr><td>Rice growing system</td><td>1</td><td>1.890</td><td>0.079</td><td>5.096</td><td>0.001</td><td>1</td><td>0.687</td><td>0.115</td><td>2,452</td><td>0.001</td></tr><tr><td>Geographical zone</td><td>2</td><td>1.646</td><td>0.069</td><td>2.218</td><td>0.001</td><td>2</td><td>0.872</td><td>0.146</td><td>1,555</td><td>0.007</td></tr><tr><td>Rice growing system</td><td>2</td><td>1.393</td><td>0.058</td><td>1.877</td><td>0.001</td><td>2</td><td>0.748</td><td>0.126</td><td>1,335</td><td>0.036</td></tr><tr><td>* Geographical zone</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Residual</td><td>51</td><td>18.919</td><td>0.793</td><td>NA</td><td>NA</td><td>13</td><td>3,644</td><td>0.612</td><td>NA</td><td>NA</td></tr><tr><td>Total</td><td>56</td><td>23.848</td><td>1.000</td><td>NA</td><td>NA</td><td>18</td><td>5,951</td><td>1,000</td><td>NA</td><td>NA</td></tr></tbody></table>
<table><tbody><tr><td rowspan="10">Root</td><td>Rice growing system</td><td>1</td><td>1.614</td><td>0.079</td><td>5.155</td><td>0.001</td><td>1</td><td>0.846</td><td>0.111</td><td>2,289</td><td>0.002</td></tr><tr><td>Geographical zone</td><td>2</td><td>1.407</td><td>0.069</td><td>2.247</td><td>0.001</td><td>2</td><td>0.891</td><td>0.117</td><td>1,206</td><td>0.123</td></tr><tr><td>Rice growing system</td><td>2</td><td>1.535</td><td>0.075</td><td>2.451</td><td>0.002</td><td>2</td><td>1,076</td><td>0.141</td><td>1,457</td><td>0.013</td></tr><tr><td>* Geographical zone</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Residual</td><td>51</td><td>15.966</td><td>0.778</td><td>NA</td><td>NA</td><td>13</td><td>4,803</td><td>0.631</td><td>NA</td><td>NA</td></tr><tr><td>Total</td><td>56</td><td>20.522</td><td>1.000</td><td>NA</td><td>NA</td><td>18</td><td>7,616</td><td>1,000</td><td>NA</td><td>NA</td></tr></tbody></table>
**Table 2**

Properties of co-occurrence networks of Prokaryote taxa in rhizosphere and root-associated samples from irrigated areas in the one hand, and rainfed lowlands in the other hand.

<table><thead><tr><th></th><th>Irrigated areas</th><th>Rainfed lowlands</th></tr></thead><tbody><tr><td>Number of nodes</td><td>414</td><td>313</td></tr><tr><td>Degree</td><td>9.802 ± 2.913</td><td>7.974 ± 2.619</td></tr><tr><td>Closeness</td><td>0.00076 ± 0.00006</td><td>0.00093 ± 0.00009</td></tr><tr><td>Betweenness</td><td>450.271 ± 336.298</td><td>389.978 ± 370.222</td></tr><tr><td>Total edges</td><td>2029</td><td>1248</td></tr><tr><td>(Positive + Negative edges)</td><td>(1760 + 269)</td><td>(1163 + 85)</td></tr><tr><td>Number of hubs</td><td>15</td><td>20</td></tr><tr><td>Hub's families</td><td>Hub species (Number of ASV)</td><td></td></tr><tr><td>Aeromonadaceae</td><td>Aeromonas hydrophila (2)</td><td></td></tr><tr><td>Bacillaceae</td><td>Bacillus zanthoxyli (6)<br>Neobacillus cucumis (5)<br>Priestia flexa (4)</td><td></td></tr><tr><td>Bradyrhizobiaceae</td><td>Bradyrhizobium oligotrophicum (1)</td><td></td></tr><tr><td>Enterobacteriaceae</td><td>Enterobacter cloacae (2)<br>Enterobacter hormaechei (2)<br>Enterobacter mori (5)</td><td>Enterobacter mori (5)</td></tr><tr><td>Moraxellaceae</td><td>Acinetobacter modestus (1)<br>Acinetobacter soli (1)</td><td></td></tr><tr><td>Weeksellaceae</td><td>Elizabethkingia anophelis (1)</td><td></td></tr></tbody></table>
<caption>**Table 3**</caption>

List of species assignation and number of sequence variants (ASVs) identified as indicator taxa in irrigated and rainfed lowland environment.
The species in bold were also found as potential hub or core taxa.

<table><thead><tr><th>Kingdom</th><th>Family (Prokaryotes) / Class (Fungi)</th><th>Irrigated</th><th>Lowlands</th></tr></thead><tbody><tr><td rowspan="10">Prokaryotes</td><td>Acidobacteriaceae</td><td></td><td>Occallatibacter savannae (2)</td></tr><tr><td>Aeromonadaceae</td><td><i>Aeromonas hydrophila</i> (7)</td><td></td></tr><tr><td>Bacillaceae</td><td></td><td>Bacillus zanthoxyli (1)<br><b>Priestia flexa</b> (10)<br>Exiguobacterium acetylicum (25)<br>Exiguobacterium indicum (19)</td></tr><tr><td>Bradyrhizobiaceae</td><td></td><td>Bradyrhizobium tropiciagri (6)</td></tr><tr><td>Burkholderiaceae</td><td><b>Paraburkholderia kururiensis</b> (1)<br><b>Ralstonia pickettii</b> (24)</td><td></td></tr><tr><td>Clostridiaceae</td><td>Clostridium beijerinckii (10)<br>Clostridium huakuui (9)</td><td></td></tr><tr><td>Comamonadaceae</td><td>Comamonas testosteroni (19)</td><td></td></tr><tr><td>Enterobacteriaceae</td><td><b>Enterobacter cloacae</b> (7)<br>Enterobacter mori (1)</td><td></td></tr><tr><td>Moraxellaceae</td><td>Acinetobacter modestus (1)<br><b>Acinetobacter soli</b> (46)</td><td></td></tr><tr><td>Pseudomonadaceae</td><td>Pseudomonas glareae (2)</td><td></td></tr><tr><td>Weeksellaceae</td><td>Elizabethkingia anophelis (1)</td><td></td></tr><tr><td></td><td><b>Number of ASVs</b></td><td><b>128</b></td><td><b>63</b></td></tr><tr><td rowspan="10">Fungi</td><td rowspan="3">Agaricomycetes</td><td>Corneriella bambusarum (1)</td><td>Cortinarius violaceomaculatus (1)</td></tr><tr><td>Pseudosperma notodryinum (1)</td><td></td></tr><tr><td><b>Pulveroboletus sinensis</b> (1)</td><td><b>Pseudobaeospora wipapatiae</b> (1)</td></tr><tr><td rowspan="3">Chytridiomycetes</td><td>Lobulomyces poculatus (1)</td><td>Clydaea vesicula (6)<br>Lobulomyces poculatus (1)<br>Operculomyces laminatus (1)</td></tr><tr><td>Aureoconidiella foliicola (2)</td><td>Bambusicola splendida (1)</td></tr><tr><td>Bambusicola didymospora (1)<br>Gordomyces mucovaginatus (1)</td><td>Muyocopron laterale (1)<br>Neocamarosporium salsolae (1)</td></tr><tr><td rowspan="4">Dothideomycetes</td><td></td><td><b>Paraphaeosphaeria michotii</b> (1)<br>Poaceascoma filiforme (1)<br>Stemphylium botryosum (2)<br>Valsaria rudis (1)</td></tr><tr><td>Geoglossomycetes</td><td>Hemileucoglossum pusillum (1)</td><td></td></tr><tr><td>Glomeromycetes</td><td></td><td>Dentiscutata savannicola (1)</td></tr><tr><td></td><td></td><td></td></tr></tbody></table>
Leotiomycetes Curviclavula anemophila (2)

Saccharomycetes [Candida] boidinii (1)

Schizosaccharomycetes Schizosaccharomyces cryophilus (1)

Sordariomycetes Achroiostachys aurantispora (1) Diatrypella vulgaris (1)

Acremonium persicinum (1) Tristratiperidium microsporum (1)

Idriellomyces eucalypti (1)

Ophiostoma pityokteinis (1)

Tremellomycetes Hannaella surugaensis (1)

Papiliotrema zeae (1)

Ustilaginomycetes Violaceomyces palustris (1) Cintractiella scirpodendri (1)

Farysia acheniorum (1)

Number of ASVs 16

27
**Figure S1:** Monthly precipitation (on the left) and average temperature (on the right) over 1970-2000 period, for each of the six study sites in Western Burkina Faso during the rice growing season (July to November).

WorldClim 2 data (Fick & Hijmans, 2017): https://worldclim.org/data/worldclim21.html

<figure><img src="image_7.png" /></figure>

**Figure S2:** Rarefaction curves (plot of the number of ASVs obtained against the number of analysed reads) for each analyzed samples. On the left, are shown 16S metabarcoding data, representing prokaryotic communities. On the right are presented the ITS metabarcoding data, representing fungal communities.

<figure><img src="image_8.png" /></figure>
**Figure S3:** NMDS ordination showing the three factors identified as drivers of the structuration of rice root microbial communities: the color of points represent the rice growing system (irrigated vs rainfed lowland), while the shape shows the compartments (rhizosphere vs roots) and the geographical zone (Banzon, Karfiguela and Bama).

a. Analysis based on 16S rRNA gene reflecting Prokaryote communities. One point corresponds to one plant.

b. Analysis based on ITS reflecting fungal communities. One point corresponds to one field.

<figure><img src="image_9.png" /></figure>
<figure><img src="image_10.png" /><figcaption>**Figure S4:** Prokaryote (16S) and fungi (ITS) taxonomic diversity obtained for each study site and each compartment</figcaption></figure>
**Figure S5:** Comparison of root-associated microbiota α-diversity in the six study sites: for Prokaryotes (16S data) on the left and for fungi (ITS data) on the right.

Data obtained for the rhizosphere compartment are presented on top and roots data are on the bottom of the figure. The study sites from irrigated areas are represented in blue, while the ones from rainfed lowlands appears in red.

<figure><img src="image_11.png" /></figure>
**Table S1**

Non-parametric tests (Wilcoxon tests) on the soil physico-chemical parameters

<table><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="2">Rice growing system</th><th colspan="2">Geographical zone</th></tr><tr><th>W</th><th>P-value</th><th>W</th><th>P-value</th></tr></thead><tbody><tr><td rowspan="3">Physical parameters</td><td>Clay %</td><td>43,000</td><td>0,902</td><td>9,765</td><td><strong>0,008</strong></td></tr><tr><td>Silt %</td><td>41,000</td><td>0,775</td><td>2,831</td><td>0,243</td></tr><tr><td>Sand %</td><td>49,000</td><td>0,775</td><td>8,382</td><td><strong>0,015</strong></td></tr><tr><td rowspan="7">Chemical parameters</td><td>pH water</td><td>23,500</td><td>0,086</td><td>3,456</td><td>0,178</td></tr><tr><td>Organic C</td><td>27,000</td><td>0,156</td><td>3,574</td><td>0,168</td></tr><tr><td>Total N</td><td>29,000</td><td>0,205</td><td>2,952</td><td>0,229</td></tr><tr><td>Total P</td><td>31,500</td><td>0,288</td><td>7,577</td><td><strong>0,023</strong></td></tr><tr><td>Total K</td><td>60,000</td><td>0,243</td><td>6,063</td><td><strong>0,048</strong></td></tr><tr><td>SBE</td><td>24,000</td><td>0,094</td><td>8,282</td><td><strong>0,016</strong></td></tr><tr><td>CEC</td><td>27,000</td><td>0,153</td><td>7,577</td><td><strong>0,023</strong></td></tr></tbody></table>
**Table S2**

Results of permanova analysis on 16S and ITS microbiome data (all dataset for each of the two markers)

<table><thead><tr><th></th><th colspan="5">Prokaryotes 16S</th><th colspan="5">Fungi ITS</th></tr><tr><th></th><th>Df</th><th>SumOfSqs</th><th>R2</th><th>F</th><th>Pr(&gt;F)</th><th>Df</th><th>SumOfSqs</th><th>R2</th><th>F</th><th>Pr(&gt;F)</th></tr></thead><tbody><tr><td>Root compartment</td><td>1</td><td>2.418</td><td>0.052</td><td>6.863</td><td>0.001</td><td>1</td><td>1,187</td><td>0,080</td><td>3,753</td><td>0.001</td></tr><tr><td>Rice growing system</td><td>1</td><td>2.256</td><td>0.048</td><td>6.402</td><td>0.001</td><td>1</td><td>1,178</td><td>0,080</td><td>3,725</td><td>0.001</td></tr><tr><td>Geographical zone</td><td>2</td><td>2.258</td><td>0.048</td><td>3.205</td><td>0.001</td><td>2</td><td>1,278</td><td>0,087</td><td>2,020</td><td>0.001</td></tr><tr><td>Rice growing system</td><td>2</td><td>2.155</td><td>0.046</td><td>3.059</td><td>0.001</td><td>2</td><td>1,306</td><td>0,089</td><td>2,065</td><td>0.001</td></tr><tr><td>* Geographical zone</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Residual</td><td>107</td><td>37.700</td><td>0.806</td><td>NA</td><td>NA</td><td>31</td><td>9,804</td><td>0,665</td><td>NA</td><td>NA</td></tr><tr><td>Total</td><td>113</td><td>46.788</td><td>1.000</td><td>NA</td><td>NA</td><td>37</td><td>14,754</td><td>1.000</td><td>NA</td><td>NA</td></tr></tbody></table>
<caption>**Table S3**</caption>

Results of Posthoc tests realized after permanova analysis on 16S and ITS microbiome data, and each compartment independently.

<table><thead><tr><th rowspan="3">Type of comparison</th><th rowspan="3">Pair of sites</th><th colspan="6">16S Prokaryotes</th><th colspan="6">ITS Fungi</th></tr><tr><th colspan="3">Rhizosphere</th><th colspan="3">Roots</th><th colspan="3">Rhizosphere</th><th colspan="3">Roots</th></tr><tr><th>F</th><th>R2</th><th>p adj</th><th>F</th><th>R2</th><th>p adj</th><th>F</th><th>R2</th><th>p adj</th><th>F</th><th>R2</th><th>p adj</th></tr></thead><tbody><tr><td rowspan="4">Within-zone, between rice systems</td><td>BZ-IR vs BZ-RL</td><td>2,265</td><td>0,124</td><td><strong>0,015</strong></td><td>3,667</td><td>0,186</td><td><strong>0,045</strong></td><td>1,608</td><td>0,287</td><td>1,000</td><td>1,575</td><td>0,283</td><td>1,000</td></tr><tr><td>KA-IR vs KA-RL</td><td>3,225</td><td>0,168</td><td><strong>0,015</strong></td><td>2,982</td><td>0,157</td><td><strong>0,015</strong></td><td>1,799</td><td>0,310</td><td>1,000</td><td>2,108</td><td>0,345</td><td>1,000</td></tr><tr><td>BM-IR vs BM-RL</td><td>3,342</td><td>0,150</td><td><strong>0,045</strong></td><td>3,343</td><td>0,150</td><td><strong>0,015</strong></td><td>1,730</td><td>0,257</td><td>0,960</td><td>1,605</td><td>0,243</td><td>0,855</td></tr><tr><td>BM-IR vs BZ-IR</td><td>1,717</td><td>0,083</td><td>0,120</td><td>1,749</td><td>0,084</td><td>0,600</td><td>1,177</td><td>0,191</td><td>1,000</td><td>1,003</td><td>0,167</td><td>1,000</td></tr><tr><td rowspan="3">Between zone, within irrigated sites</td><td>BM-IR vs KA-IR</td><td>1,693</td><td>0,082</td><td>0,360</td><td>1,658</td><td>0,080</td><td>0,285</td><td>1,679</td><td>0,251</td><td>0,435</td><td>1,364</td><td>0,214</td><td>1,000</td></tr><tr><td>BZ-IR vs KA-IR</td><td>1,758</td><td>0,099</td><td>0,330</td><td>2,075</td><td>0,115</td><td>0,195</td><td>1,289</td><td>0,244</td><td>1,000</td><td>1,510</td><td>0,274</td><td>1,000</td></tr><tr><td>BM-RL vs BZ-RL</td><td>2,299</td><td>0,126</td><td><strong>0,015</strong></td><td>1,688</td><td>0,095</td><td>1,000</td><td>1,327</td><td>0,249</td><td>1,000</td><td>1,004</td><td>0,201</td><td>1,000</td></tr><tr><td rowspan="3">Between zone, within rainfed lowland sites</td><td>BM-RL vs KA-RL</td><td>3,075</td><td>0,161</td><td><strong>0,015</strong></td><td>3,768</td><td>0,191</td><td><strong>0,015</strong></td><td>1,952</td><td>0,328</td><td>1,000</td><td>1,915</td><td>0,324</td><td>1,000</td></tr><tr><td>BZ-RL vs KA-RL</td><td>1,728</td><td>0,097</td><td><strong>0,015</strong></td><td>3,022</td><td>0,159</td><td><strong>0,015</strong></td><td>1,293</td><td>0,244</td><td>1,000</td><td>1,355</td><td>0,253</td><td>1,000</td></tr><tr><td>BM-RL vs BZ-IR</td><td>2,536</td><td>0,137</td><td><strong>0,030</strong></td><td>3,480</td><td>0,179</td><td><strong>0,045</strong></td><td>2,209</td><td>0,356</td><td>1,000</td><td>1,736</td><td>0,303</td><td>1,000</td></tr><tr><td rowspan="3">Others</td><td>BM-RL vs KA-IR</td><td>3,891</td><td>0,196</td><td><strong>0,015</strong></td><td>2,623</td><td>0,141</td><td>0,105</td><td>2,092</td><td>0,343</td><td>1,000</td><td>1,825</td><td>0,313</td><td>1,000</td></tr><tr><td>BM-IR vs BZ-RL</td><td>3,091</td><td>0,140</td><td><strong>0,015</strong></td><td>3,245</td><td>0,146</td><td><strong>0,015</strong></td><td>1,767</td><td>0,261</td><td>0,375</td><td>1,254</td><td>0,200</td><td>1,000</td></tr></tbody></table>
<table><tr><td>BM-IR vs KA-RL</td><td>3,866</td><td>0,169</td><td><strong>0,015</strong></td><td>3,934</td><td>0,172</td><td><strong>0,015</strong></td><td>1,743</td><td>0,259</td><td>0,360</td><td>1,769</td><td>0,261</td><td>0,420</td></tr><tr><td>BZ-IR vs KA-RL</td><td>2,811</td><td>0,149</td><td><strong>0,015</strong></td><td>4,113</td><td>0,205</td><td><strong>0,015</strong></td><td>1,457</td><td>0,267</td><td>1,000</td><td>2,031</td><td>0,337</td><td>1,000</td></tr><tr><td>KA-IR vs BZ-RL</td><td>2,704</td><td>0,145</td><td><strong>0,030</strong></td><td>2,458</td><td>0,133</td><td><strong>0,015</strong></td><td>1,804</td><td>0,311</td><td>1,000</td><td>1,219</td><td>0,234</td><td>1,000</td></tr></table>
**Table S4**

Results of the statistical analyses testing for the effect of soil chemical parameters on microbiome communities (each compartment analyzed separately).

<table><thead><tr><th rowspan="3">Soil<br>chemical<br>parameter</th><th colspan="4">Prokaryotes 16S</th><th colspan="4">Fungi ITS</th></tr><tr><th colspan="2">Rhizosphere</th><th colspan="2">Roots</th><th colspan="2">Rhizosphere</th><th colspan="2">Roots</th></tr><tr><th>r<sup>2</sup></th><th>p-value</th><th>r<sup>2</sup></th><th>p-value</th><th>r<sup>2</sup></th><th>p-value</th><th>r<sup>2</sup></th><th>p-value</th></tr></thead><tbody><tr><td>pH water</td><td>0,001</td><td>0,985</td><td>0,024</td><td>0,519</td><td>0,272</td><td>0,081</td><td>0,201</td><td>0,166</td></tr><tr><td>Organic C</td><td>0,089</td><td>0,079</td><td>0,076</td><td>0,122</td><td>0,293</td><td>0,061</td><td>0,266</td><td>0,085</td></tr><tr><td>Total N</td><td>0,086</td><td>0,089</td><td>0,059</td><td>0,198</td><td>0,320</td><td><strong>0,043</strong></td><td>0,273</td><td>0,079</td></tr><tr><td>Total P</td><td>0,132</td><td><strong>0,023</strong></td><td>0,179</td><td><strong>0,004</strong></td><td>0,181</td><td>0,205</td><td>0,303</td><td>0,055</td></tr><tr><td>Total K</td><td>0,037</td><td>0,372</td><td>0,066</td><td>0,159</td><td>0,017</td><td>0,866</td><td>0,029</td><td>0,792</td></tr><tr><td>SBE</td><td>0,482</td><td><strong>0,000</strong></td><td>0,175</td><td><strong>0,006</strong></td><td>0,106</td><td>0,416</td><td>0,265</td><td>0,083</td></tr><tr><td>CEC</td><td>0,314</td><td><strong>0,000</strong></td><td>0,204</td><td><strong>0,003</strong></td><td>0,028</td><td>0,814</td><td>0,161</td><td>0,244</td></tr></tbody></table>
<table><caption>**Table S5** Results of Posthoc tests on the effect of the particular site on alpha diversity indices (Shannon diversity index and observed richness) for 16S microbiome data only and for each compartment independently.</caption><thead><tr><th rowspan="3">Type of comparison</th><th rowspan="3">Pair of sites</th><th colspan="6">Rhizosphere</th><th colspan="6">Roots</th></tr><tr><th colspan="3">Shannon diversity index</th><th colspan="3">Observed richness</th><th colspan="3">Shannon diversity index</th><th colspan="3">Observed richness</th></tr><tr><th>statistic</th><th>p</th><th>p.adj</th><th>statistic</th><th>p</th><th>p.adj</th><th>statistic</th><th>p</th><th>p.adj</th><th>statistic</th><th>p</th><th>p.adj</th></tr></thead><tbody><tr><td rowspan="2">Within-zone, between rice systems</td><td>BZ-IR vs BZ-RL</td><td>-1,803</td><td>0,071</td><td>1,000</td><td>-1,725</td><td>0,084</td><td>1,000</td><td>1,164</td><td>0,244</td><td>1,000</td><td>0,263</td><td>0,793</td><td>1,000</td></tr><tr><td>KA-IR vs KA-RL</td><td>-3,110</td><td>0,002</td><td><b>0,028</b></td><td>-3,352</td><td>0,001</td><td><b>0,012</b></td><td>-2,826</td><td>0,005</td><td>0,071</td><td>-3,742</td><td>0,000</td><td><b>0,003</b></td></tr><tr><td rowspan="2">Between zone, within irrigated sites</td><td>BM-IR vs BM-RL</td><td>0,721</td><td>0,471</td><td>1,000</td><td>0,786</td><td>0,432</td><td>1,000</td><td>-0,053</td><td>0,958</td><td>1,000</td><td>-0,478</td><td>0,632</td><td>1,000</td></tr><tr><td>BM-IR vs BZ-IR</td><td>-1,556</td><td>0,120</td><td>1,000</td><td>-1,416</td><td>0,157</td><td>1,000</td><td>-1,844</td><td>0,065</td><td>0,977</td><td>-0,660</td><td>0,509</td><td>1,000</td></tr><tr><td rowspan="2">Between zone, within rainfed lowland sites</td><td>BM-IR vs KA-IR</td><td>-0,144</td><td>0,885</td><td>1,000</td><td>-0,201</td><td>0,841</td><td>1,000</td><td>0,615</td><td>0,539</td><td>1,000</td><td>1,199</td><td>0,230</td><td>1,000</td></tr><tr><td>BZ-IR vs KA-IR</td><td>1,321</td><td>0,187</td><td>1,000</td><td>1,136</td><td>0,256</td><td>1,000</td><td>2,300</td><td>0,021</td><td>0,321</td><td>1,740</td><td>0,082</td><td>1,000</td></tr><tr><td rowspan="2">Others</td><td>BM-RL vs BZ-RL</td><td>-2,584</td><td>0,010</td><td>0,146</td><td>-2,315</td><td>0,021</td><td>0,309</td><td>-0,611</td><td>0,541</td><td>1,000</td><td>-0,802</td><td>0,422</td><td>1,000</td></tr><tr><td>BM-RL vs KA-RL</td><td>-2,570</td><td>0,010</td><td>0,152</td><td>-2,805</td><td>0,005</td><td>0,076</td><td>-2,300</td><td>0,021</td><td>0,321</td><td>-3,068</td><td>0,002</td><td><b>0,032</b></td></tr><tr><td rowspan="2">Others</td><td>BZ-RL vs KA-RL</td><td>0,014</td><td>0,989</td><td>1,000</td><td>-0,490</td><td>0,624</td><td>1,000</td><td>-1,690</td><td>0,091</td><td>1,000</td><td>-2,265</td><td>0,024</td><td>0,353</td></tr><tr><td>BM-RL vs BZ-IR</td><td>-0,781</td><td>0,435</td><td>1,000</td><td>-0,589</td><td>0,556</td><td>1,000</td><td>-1,775</td><td>0,076</td><td>1,000</td><td>-1,065</td><td>0,287</td><td>1,000</td></tr><tr><td rowspan="2">Others</td><td>BM-RL vs KA-IR</td><td>0,540</td><td>0,589</td><td>1,000</td><td>0,547</td><td>0,585</td><td>1,000</td><td>0,525</td><td>0,599</td><td>1,000</td><td>0,675</td><td>0,500</td><td>1,000</td></tr><tr><td>BM-IR vs BZ-RL</td><td>-3,484</td><td>0,000</td><td><b>0,007</b></td><td>-3,260</td><td>0,001</td><td><b>0,017</b></td><td>-0,600</td><td>0,549</td><td>1,000</td><td>-0,380</td><td>0,704</td><td>1,000</td></tr><tr><td rowspan="2">Others</td><td>BM-IR vs KA-RL</td><td>-3,469</td><td>0,001</td><td><b>0,008</b></td><td>-3,784</td><td>0,000</td><td><b>0,002</b></td><td>-2,406</td><td>0,016</td><td>0,242</td><td>-2,801</td><td>0,005</td><td>0,076</td></tr><tr><td>BZ-IR vs KA-RL</td><td>-1,789</td><td>0,074</td><td>1,000</td><td>-2,215</td><td>0,027</td><td>0,401</td><td>-0,525</td><td>0,599</td><td>1,000</td><td>-2,002</td><td>0,045</td><td>0,679</td></tr><tr><td rowspan="2">Others</td><td>KA-IR vs BZ-RL</td><td>-3,124</td><td>0,002</td><td><b>0,027</b></td><td>-2,862</td><td>0,004</td><td>0,063</td><td>-1,136</td><td>0,256</td><td>1,000</td><td>-1,477</td><td>0,140</td><td>1,000</td></tr></tbody></table>
# 1 Table S6

## 2 List of the pathogen species searched for within the microbiome data

<table><thead><tr><th>Kingdom</th><th>Pathogen species</th><th>Known rice diseases</th></tr></thead><tbody><tr><td rowspan="10">Bacteria</td><td>Xanthomonas oryzae</td><td>Bacterial Leaf Blight (BLB) and Bacterial Leaf Streak (BLS)</td></tr><tr><td>Pseudomonas fuscovaginae</td><td>Sheath brown rot, Manchado de grano</td></tr><tr><td>Pseudomonas syringae</td><td>Bacterial brown stripe, Sheat rot, Glume blotch, Grain rot, Halo blight</td></tr><tr><td>Acidovorax avenae subsp. avenae</td><td>Bacterial brown stripe</td></tr><tr><td>Burkholderia glumae</td><td>Bacterial panicle blight / Grain rot / Seedling rot</td></tr><tr><td>Burkholderia gladioli</td><td>Bacterial panicle blight / Grain rot</td></tr><tr><td>Burkholderia plantarii</td><td>Seedling blight</td></tr><tr><td>Erwinia spp</td><td>Brown stripe</td></tr><tr><td>Erwinia herbicola</td><td>Black rot</td></tr><tr><td>Dickeya chrysanthemi</td><td>Culm and root disease</td></tr><tr><td>Pantoea ananatis, P. stewartii, P. agglomerans</td><td rowspan="2">Bacterial Leaf Blight ?</td></tr><tr><td>Sphingomonas</td></tr><tr><td rowspan="10">Fungi</td><td>Pyricularia oryzae (syn. Magnaporthe oryzae)</td><td>Rice blast</td></tr><tr><td>Bipolaris oryzae (syn Helminthosporium oryzae, syn Cochliobolus miyabeanus, Syn Drechslera oryzae)</td><td rowspan="3">Brown spot</td></tr><tr><td>Bipolaris spp</td></tr><tr><td>Exserohilum rostratum</td></tr><tr><td>Curvularia spp</td><td>Black kernel</td></tr><tr><td>Microdochium albescens (syn Rhynchosporium oryzae, Gerlachia oryzae, Grophosphaerella albescens, Metasphaeria albescens, Micronectriella pavgii, Monographella albescens)</td><td>Leaf Scald</td></tr><tr><td>Cercospora janseana (syn C. oryzae, Sphaerulina oryzina, Napicladium janseanum, Passalora janseana)</td><td>Narrow brown leaf spot</td></tr><tr><td>Fusarium fujikuroi (syn Giberella fujikuroi)</td><td>Bakanae</td></tr><tr><td>Fusarium graminearum (syn Giberella zeae)</td><td>Scab</td></tr><tr><td>Fusarium spp.</td><td rowspan="3">Water mold</td></tr><tr><td>Achlya spp.</td></tr><tr><td>Pythium spp.</td></tr><tr><td>Rhizoctonia solani (syn Thanatephorus cucumeris)</td><td>Sheath blight</td></tr></tbody></table>
<table><tr><td>Sarocladium oryzae (syn Acrocylindrium oryzae)</td><td>Sheath rot</td></tr><tr><td>Ustilaginoidea virens</td><td>False smut</td></tr><tr><td>Epicoccum sorghinum (syn Phoma sorghina, Phyllosticta oryzina)</td><td>Glume blight</td></tr><tr><td>Epicoccum spp.</td><td>Red blotch of Grains</td></tr><tr><td>Trichoniella padwickii (syn Alternaria padwickii, Trichoconis padwickii)</td><td>Stackburn</td></tr><tr><td>Nakataea oryzae (syn Leptosphaeria salvinii, Vakrabeeja sigmoidea)</td><td>Stem rot</td></tr><tr><td>Athelia rolfsii (syn Sclerotium rolfsii)</td><td>Seedling blight</td></tr><tr><td>Eballistra oryzae (syn Entyloma oryzae)</td><td>Leaf smut</td></tr><tr><td>Drechslera gigantea</td><td>Eyespot</td></tr><tr><td>Sclerophthora macrospora (syn Sclerospora macrospora)</td><td>Downy mildew</td></tr><tr><td>Ramularia oryzae (syn Mycovellosiella oryzae)</td><td>White leaf streak</td></tr><tr><td>Ascochyta oryzae (syn Phomopsis oryzae-sativa)</td><td>Collar rot</td></tr><tr><td>Waitea circinata (syn Rhizoctonia oryzae)</td><td>Sheath Spot</td></tr><tr><td>Ceratobasidium setariae (syn Rhizoctonia oryzae-sativa)</td><td>Aggregate Sheat Spot</td></tr><tr><td>Calonectria morganii (syn Cylindrocladium scoparium)</td><td>Sheath Net Blotch</td></tr><tr><td>Gaeumannomyces graminis</td><td>Crown Sheat Rot</td></tr><tr><td>Myrothecium verrucaria</td><td>Myrothecium blotch</td></tr><tr><td>Pyrenochaeta acicula (syn P. oryzae)</td><td>Sheat Blotch</td></tr><tr><td>Globisporangium spinosum (syn Pythium spinosum)</td><td>Root rot</td></tr><tr><td>Tilletia spp.</td><td>Kernel smut</td></tr><tr><td>Balansia oryzae (syn Ephelis oryzae)</td><td>Udbatta</td></tr><tr><td>Nicrospora spp.</td><td>Minute Leaf and grain spot</td></tr></table>

3

4
<figure><img src="image_12.png" /><figcaption>a</figcaption></figure>

bioRxiv preprint doi: https://doi.org/10.1101/2022.03.29.486073; this version posted March 29, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

<figure><img src="image_13.png" /><figcaption>b</figcaption></figure>

Banzon (BZ)

<figure><img src="image_14.png" /></figure>

Karfiguela (KA)

<figure><img src="image_15.png" /></figure>

Bama (BM)

<figure><img src="image_16.png" /><figcaption>c</figcaption></figure>

pH water
5.716

Organic C
0.922 %

Total N
0.0716 %

Total P
237.1 mg/kg

Total K
1443.6 mg/kg

SBE
4.218 Cmol/Kg

CEC
10.658 Cmol/Kg

Geographical zone
16S Prokaryotes

<figure><img src="image_17.png" /></figure>

bioRxiv preprint doi: https://doi.org/10.1101/2022.03.29.436073; this version posted March 29, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.

Rhizosphere

<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /></figure>

<figure><img src="image_20.png" /></figure>

Root

<figure><img src="image_21.png" /></figure>
<figure><img src="image_22.png" /></figure>

Rhizosphere

Chi² = 0.027
P = 0.87

Chi² = 3.54
P = 0.060

<figure><img src="image_23.png" /></figure>

Root

Chi² = 0.107
P = 0.744

Chi² = 1.13
P = 0.288

<figure><img src="image_24.png" /></figure>
a. Prokaryotes (16S)

Irrigated

• *Paraburkholderia kururiensis*

• *Ralstonia pickettii*

<figure><img src="image_25.png" /></figure>

Rainfed Lowland

• *Bradyrhizobium tropiciagri*

Both

• *Bradyrhizobium tropiciagri*

b. Fungi (ITS)

Irrigated

• *Pulveroboletus sinensis*

<figure><img src="image_26.png" /></figure>

Both

• *Curvularia chonburiensis*

• *Nigrospora magnoliae*

• *Quixadomyces hongheensis*

• *Staurosphaeria rhamnicola*

Rainfed Lowland

• *Cladosporium chasmanthicola*

• *Operculomyces laminatus*

• *Paraphaeosphaeria michotii*

• *Pseudobaeospora wipapatiae*

• *Pseudothielavia arxii*

• *Sympodiella acicola*
Abundance Glomeromycetes

Rhizosphere

Root

bioRxiv preprint doi: https://doi.org/10.1101/2022.03.29.486073; this version posted March 29, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

**Geographical zone**

Bama (BM)

Banzon (BZ)

Karfiguela (KA)

**Rice growing system**

Rainfed lowland (RL)

Irrigated (IR)

<figure><img src="image_27.png" /></figure>

Rice system