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Seed tuber microbiome can predict growth potential of potato varieties

Nature Microbiology volume 10, pages 28–40 (2025)Cite this article

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Abstract

Potato vigour, the growth potential of seed potatoes, is a key agronomic trait that varies significantly across production fields due to factors such as genetic background and environmental conditions. Seed tuber microbiomes are thought to influence plant health and crop performance, yet the precise relationships between microbiome composition and potato vigour remain unclear. Here we conducted microbiome sequencing on seed tuber eyes and heel ends from 6 potato varieties grown in 240 fields. By using time-resolved drone imaging of three trial fields in the next season to track crop development, we were able to link microbiome composition with potato vigour. We used microbiome data at varying taxonomic resolutions to build random forest predictive models and found that amplicon sequence variants provided the highest predictive accuracy for potato vigour. The model revealed variety-specific relationships between the seed tuber microbiome and next season’s crop vigour in independent trial fields. With a coefficient of determination value of 0.69 for the best-performing variety, the model accurately predicted vigour in seed tubers from fields not previously included in the analysis. Moreover, the model identified key microbial indicators of vigour from which a Streptomyces, an Acinetobacter and a Cellvibrio amplicon sequence variant stood out as the most important contributors to the model’s accuracy. This study shows that seed potato vigour can be reliably predicted based on the microbiota associated with seed tuber eyes, potentially guiding future microbiome-informed breeding strategies.

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Fig. 1: Variation in canopy development of potato plants from 6 varieties and 180 production fields.
Fig. 2: Community composition analysis of seed tuber microbiomes in the tuber eye compartment.
Fig. 3: Performance of RF models trained with tuber eye microbiome data at distinct taxonomic ranks and HFE features.
Fig. 4: Model performance on within-year and across-year test sets over all varieties.
Fig. 5: Model performance per variety on within-year and across-year test sets.
Fig. 6: The most predictive microorganisms selected by the PMI prediction model.

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Data availability

The raw sequence data generated from this study are available at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1091851/. Raw and spatial-corrected vigour data generated in this project and step-by-step protocol are available at https://data.4tu.nl/datasets/21892a06-078a-4600-8386-1abe46f42271. Source data are provided with this paper.

Code availability

The data and code used for modelling can be accessed through the following GitHub link: https://github.com/Yang-kf/Seed-tuber-microbiome-is-a-predictor-of-next-season-potato-vigor/tree/main.

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Acknowledgements

We acknowledge the valuable contributions of the Royal HZPC Group B.V. and Averis Seeds B.V. for providing the seed tuber material and supporting the field trials. Their collaboration was essential to the successful execution of this research. Special thanks are extended to F. Hofstra and M. ten Klooster from HZPC Holding B.V. for their contribution to the sample collection and J. Hopman from Averis Seeds B.V. for his valuable advice. We also thank C. Jongekrijg, E. Manders and E. de Kloe for excellent technical assistance in the laboratory. In addition, we acknowledge the funding support received from Europees Landbouwfonds voor Plattelandsontwikkeling (ELFPO) on the ‘Flight-to-vitality’ project. This work was also partly supported by the Dutch Research Council (NWO) through the Gravitation program MiCRop (grant number 024.004.014) and through project ‘Sequence-based POTato Microbiome tools for microbiome-optimized potatoes’ (project number 19769).

Author information

Authors and Affiliations

  1. Plant–Microbe Interactions, Institute of Environmental Biology, Department of Biology, Science4Life, Utrecht University, Utrecht, the Netherlands

    Yang Song, Juan J. Sánchez-Gil, Ronnie de Jonge, Peter G. H. de Rooij, David Kakembo, Peter A. H. M. Bakker, Corné M. J. Pieterse & Roeland L. Berendsen

  2. Numerical Analysis, Delft Institute of Applied Mathematics, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, the Netherlands

    Elisa Atza & Neil V. Budko

  3. HZPC Research B.V., Department of Plant Pathology, Metslawier, the Netherlands

    Doretta Akkermans

  4. AI Technology for Life, Department of Information and Computing Sciences, Science4Life, Utrecht University, Utrecht, the Netherlands

    Ronnie de Jonge

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Contributions

Y.S., D.A., E.A., N.V.B., R.d.J., P.A.H.M.B., C.M.J.P. and R.L.B. designed the experiments and approach. Y.S., E.A., J.J.S.G., N.V.B., P.A.H.M.B., C.M.J.P. and R.L.B. wrote the article. D.A. coordinated sampling collection and experimental field trials. Y.S. and P.G.H.d.R. carried out molecular analysis. E.A. and N.V.B. performed quantitative analysis of drone images and CSA data. Y.S., E.A., J.J.S.G., R.d.J. and D.K. performed data analysis.

Corresponding author

Correspondence to Roeland L. Berendsen.

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Competing interests

The authors declare that this study received funding from HZPC Research B.V. and Averis Seeds B.V. The funder had the following involvement in the study: study design, sample collection and the decision to submit it for publication. D.A. is a current employee of HZPC Research B.V. All other authors declare no competing interests.

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Nature Microbiology thanks Yang Bai, Alireza Pourreza, Detlef Weigel and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Experimental design for over 2 years.

a-b We collected seed tubers of 6 potato varieties from 30 fields per variety (180 fields in total) in the Netherlands in the autumn of 2018 (year 1) and 2019 (year 2). Tubers from these 180 fields per year were stored over winter and the tubers were planted in each of 3 trial fields in the next spring. c In both years, the trial fields were located near Montfrin (M) in France and near Kollumerwaard (K) and Veenklooster (V) in the Netherlands. In each of the trial fields, the seed tubers were planted in randomized block design with 4 replicate blocks of 24 tubers. We monitored the growth and development of the plants that emerged from these seed tubers using aerial images of the complete field with a drone-mounted camera. Of the 180 seedlots of year 1, we selected 60 seedlots from which we took 4 replicate samples for microbiome analysis. In year 2, the microbiomes were analyzed with 2 replicate samples of all 180 seedlots. Credit: icons in a,b, Flaticon.com.

Extended Data Fig. 2 From field images to potato vigor data.

a Exemplary ortho image of trial field M in year 2 obtained with drone-mounted camera. Plot boundaries of each seedlot are displayed in variety-specific colors. b Overview of raw canopy surface area (raw CSA) per plot in the trial field displayed as a heatmap. c Spatial trend of the trial field recovered with the SpaTS package and displayed as a heatmap. d Overview of spatially corrected raw CSA in the trial field as a heatmap. Average corrected seedlot CSA is shown in all replicate plots of the four seedlots.

Extended Data Fig. 3 Potato vigour of a seedlot is consistent across three trial fields.

a Scaled CSA for each of the 6 varieties and each of the 30 seedlots per variety in Field M, K and V in year 1. b Scaled CSA for each of the 6 varieties and each of the 30 seedlots per variety in Field M, K and V in year 2. Error bars signify the minimum and maximum values for a given seed lot per trial field. The CSA in each trial field, as estimated by the SpaTS package, is indicated with the field corresponding marker (see legend).

Extended Data Fig. 4 Microbiomes of replicate samples of the same seedlot cluster together in year 1.

PCoA ordination plot based on Bray-Curtis dissimilarities of bacterial communities of seed tuber eye compartment from year 1. Variety names are indicated in each panel with Challenger (a), Colomba (b), Festien (c), Innovator (d), Sagitta (e) and Seresta (f). Each data point represents a replication of one seedlot. Different colors represent different seedlots of a variety.

Extended Data Fig. 5 Seed tuber microbiomes in heel end compartments.

Principle coordinate analysis (PCoA) ordination plot based on Bray-Curtis dissimilarities of bacterial (a-c) and fungal (d-f) microbiomes. Symbols are colored by soil type (a,d), variety (b,e) and year (c,f) as indicated in the legend. Each data point represents a single replicate of a seedlot. Four replicate samples were analyzed for each of 60 seedlots in year 1 and two replicate samples for each of 180 seedlots in year 2. P (one-sided) and R2 in each PCoA are the result of PERMANOVA on soil type (a,d), variety (b,e) and year (c,f) as respective factors.

Extended Data Fig. 6 Scatter plots illustrating the Pearson correlation between the predicted and observed potato vigor in all fields and varieties.

a, Out-of-bag (OOB) model performance per variety. b, Model performance per variety on within-year test sets. c, Model performance per variety on across-year test sets. In all panels, values on the x-axis are predicted by a random forest model trained on microbiome data from year 2 and CSA from field M. The predicted potato vigor is based on the same microbiome data as was used for training the model (within-year testing set) or based on microbiome data from year 1 to which the model was naïve (across-year testing set). The 6 varieties are represented by different colors. Each symbol represents a prediction microbiome based on 1 eye compartment sample. Predicted and observed vigor are indicated by scaled CSA, which is scaled to the variety average in each trial field. The proportion of variance explained by the model is indicated by R2. Asterisks indicate significance level of *P < 0.05; ** P < 0.01; *** P < 0.001; **** P < 0.0001, two-sided. Error bands around the regression line represent 95% confidence interval.

Extended Data Fig. 7 General assessment of the relationship between the top 1% contributors to potato vigor.

a Bidimensional density plots showing scaled CSA values and normalized abundance of each of the top 1% contributing ASVs. ASV abundance is rescaled between 0 and 1 with respect to their minimum and maximum in order to show one single scale across ASVs. The clearest colors indicate areas that accumulate most of the data, and dark colors the areas where no data or few points are found. The line was fitted with a robust regression to outliers computed with the rlm() functions in the MASS R package, and the ρ values indicate Spearman’s ρ together with the significance level shown by asterisks (*P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001, two-sided). The percentages above and below the 0-line indicate the number of ASV occurrences in sample with vigor above and below the mean, respectively. b Partial contribution plots for the top 1% ASVs most predictive to potato vigor (scaled CSA) according to the RF model.

Extended Data Fig. 8 Heatmaps showing Spearman correlations of each of the top 1% contributing ASVs to potato vigor, their prevalence, and median abundance across samples.

The first column in every heatmap shows the computed value including all the data regardless of plant variety, and the rest of columns display those values calculated for individual potato varieties. The abundance of the fungal ASVs were shown as 1/10 of the original value to fit in the color scale. The significance level of Spearman’s ρ are shown by asterisks (*P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001).

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Song, Y., Atza, E., Sánchez-Gil, J.J. et al. Seed tuber microbiome can predict growth potential of potato varieties. Nat Microbiol 10, 28–40 (2025). https://doi.org/10.1038/s41564-024-01872-x

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