
Hiltner, L. Über neuere Erfahrungen und Probleme auf dem Gebiet der Bodenbakteriologie und unter besonderer Berücksichtigung der Gründüngung und Brache. Arb. Dtsch. Landwirtsch. Ges. 98, 59–78 (1904).
Berendsen, R. L., Pieterse, C. M. J. & Bakker, P. A. H. M. The rhizosphere microbiome and plant health. Trends Plant Sci. 17, 478–486 (2012).
Rolfe, S. A., Griffiths, J. & Ton, J. Crying out for help with root exudates: adaptive mechanisms by which stressed plants assemble health-promoting soil microbiomes. Curr. Opin. Microbiol. 49, 73–82 (2019).
Teixeira, P. J. P., Colaianni, N. R., Fitzpatrick, C. R. & Dangl, J. L. Beyond pathogens: microbiota interactions with the plant immune system. Curr. Opin. Microbiol. 49, 7–17 (2019).
Haney, C. H., Samuel, B. S., Bush, J. & Ausubel, F. M. Associations with rhizosphere bacteria can confer an adaptive advantage to plants. Nat. Plants 1, 15051 (2015).
Goossens, P. et al. Obligate biotroph downy mildew consistently induces near-identical protective microbiomes in Arabidopsis thaliana. Nat. Microbiol. 8, 2349–2364 (2023).
Raaijmakers, J. M., Paulitz, T. C., Steinberg, C., Alabouvette, C. & Moënne-Loccoz, Y. The rhizosphere: a playground and battlefield for soilborne pathogens and beneficial microorganisms. Plant Soil 321, 341–361 (2009).
Thomas, G. & Sansonetti, G. New Light on a Hidden Treasure: International Year of the Potato 2008, an End-of-Year Review (Food and Agriculture Organization of the United Nations, 2009).
Devaux, A., Kromann, P. & Ortiz, O. Potatoes for sustainable global food security. Potato Res. 57, 185–199 (2014).
Zarzyńska, K., Boguszewska-Mańkowska, D., Feledyn-Szewczyk, B. & Jończyk, K. The vigor of seed potatoes from organic and conventional systems. Agriculture 12, 1764 (2022).
Struik, P. C. The canon of potato science: 40. physiological age of seed tubers. Potato Res. 50, 375–377 (2007).
Zou, C. et al. Using sprouting behaviour to quantify physiological ageing of seed tubers of potato (Solanum tuberosum L.). Environ. Exp. Bot. 219, 105648 (2024).
Bak, G.-R. et al. The potato rhizosphere microbiota correlated to the yield of three different regions in Korea. Sci. Rep. 14, 4536 (2024).
Song, Y. et al. Seed tuber imprinting shapes the next-generation potato microbiome. Environ. Microbiome 19, 12 (2024).
Kurm, V., Mendes, O., Gros, J. & van der Wolf, J. Potato tuber origin and microbial composition determines resistance against soft rot Pectobacteriaceae. Eur. J. Plant Pathol. 168, 383–399 (2024).
Shi, W. et al. The occurrence of potato common scab correlates with the community composition and function of the geocaulosphere soil microbiome. Microbiome 7, 14 (2019).
Arseneault, T., Goyer, C. & Filion, M. Biocontrol of potato common scab is associated with high Pseudomonas fluorescens LBUM223 populations and phenazine-1-carboxylic acid biosynthetic transcript accumulation in the potato geocaulosphere. Phytopathology 106, 963–970 (2016).
Petrushin, I. S., Filinova, N. V. & Gutnik, D. I. Potato microbiome: relationship with environmental factors and approaches for microbiome modulation. Int. J. Mol. Sci. 25, 750 (2024).
Fiers, M. et al. Potato soil-borne diseases. A review. Agron. Sustain. Dev. 32, 93–132 (2012).
Van der Wolf, J. M. & De Boer, S. H. in Potato Biology and Biotechnology (eds. Vreugdenhil, D. et al.) 595–617 (Elsevier Science, 2007).
Bakker, P. A. H. M., Bakker, A. W., Marugg, J. D., Weisbeek, P. J. & Schippers, B. Bioassay for studying the role of siderophores in potato growth stimulation by Pseudomonas spp in short potato rotations. Soil Biol. Biochem. 19, 443–449 (1987).
Buchholz, F., Antonielli, L., Kostić, T., Sessitsch, A. & Mitter, B. The bacterial community in potato is recruited from soil and partly inherited across generations. PLoS ONE 14, e0223691 (2019).
Delventhal, K., Busby, P. E. & Frost, K. Tare soil alters the composition of the developing potato rhizosphere microbiome. Phytobiomes J. 7, 91–99 (2023).
Deng, Z., Zhang, J., Li, J. & Zhang, X. Application of deep learning in plant-microbiota association analysis. Front. Genet. 12, 697090 (2021).
Emmenegger, B. et al. Identifying microbiota community patterns important for plant protection using synthetic communities and machine learning. Nat. Commun. 14, 7983 (2023).
Yuan, J. et al. Predicting disease occurrence with high accuracy based on soil macroecological patterns of Fusarium wilt. ISME J. 14, 2936–2950 (2020).
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).
Oudah, M. & Henschel, A. Taxonomy-aware feature engineering for microbiome classification. BMC Bioinf. 19, 227 (2018).
Atza, E. & Budko, N. High-throughput analysis of potato vitality. In Progress in Industrial Mathematics at ECMI 2021 (eds Ehrhardt, M. & Günther, M.) 273–279 (Springer, 2022).
Lottmann, J., Heuer, H., Smalla, K. & Berg, G. Beneficial bacteria in underground organs of potato (Solanum tuberosum L.). In Proc. 7th International Verticillium Congress (eds Tjamos, E. C. et al.) 264–268 (1997).
Clulow, S. A., Stewart, H. E., Dashwood, E. P. & Wastie, R. L. Tuber surface microorganisms influence the susceptibility of potato tubers to late blight. Ann. Appl. Biol. 126, 33–43 (1995).
Aliche, E. B. et al. Drought response in field grown potatoes and the interactions between canopy growth and yield. Agric. Water Manag. 206, 20–30 (2018).
Zhou, Z., Plauborg, F., Parsons, D. & Andersen, M. N. Potato canopy growth, yield and soil water dynamics under different irrigation systems. Agric. Water Manag. 202, 9–18 (2018).
Haverkort, A. J. & Bicamumpaka, M. Correlation between intercepted radiation and yield of potato crops infested by Phytophthora infestans in central Africa. Neth. J. Plant Pathol. 92, 239–247 (1986).
de Jesus Colwell, F. et al. Development and validation of methodology for estimating potato canopy structure for field crop phenotyping and improved breeding. Front. Plant Sci. 12, 612843 (2021).
Rasche, F. et al. Impact of transgenic potatoes expressing anti‐bacterial agents on bacterial endophytes is comparable with the effects of plant genotype, soil type and pathogen infection. J. Appl. Ecol. 43, 555–566 (2006).
Manter, D. K., Delgado, J. A., Holm, D. G. & Stong, R. A. Pyrosequencing reveals a highly diverse and cultivar-specific bacterial endophyte community in potato roots. Microb. Ecol. 60, 157–166 (2010).
Fierer, N. Embracing the unknown: disentangling the complexities of the soil microbiome. Nat. Rev. Microbiol. 15, 579–590 (2017).
Benitez, M.-S., Osborne, S. L. & Lehman, R. M. Previous crop and rotation history effects on maize seedling health and associated rhizosphere microbiome. Sci. Rep. 7, 15709 (2017).
Hartmann, M., Frey, B., Mayer, J., Mäder, P. & Widmer, F. Distinct soil microbial diversity under long-term organic and conventional farming. ISME J. 9, 1177–1194 (2015).
Lutz, S. et al. Soil microbiome indicators can predict crop growth response to large-scale inoculation with arbuscular mycorrhizal fungi. Nat. Microbiol. 8, 2277–2289 (2023).
Zuno-Floriano, F. G. et al. Effect of Acinetobacter sp. on metalaxyl degradation and metabolite profile of potato seedlings (Solanum tuberosum L.) alpha variety. PLoS ONE 7, e31221 (2012).
Schlatter, D., Kinkel, L., Thomashow, L., Weller, D. & Paulitz, T. Disease suppressive soils: new insights from the soil microbiome. Phytopathology 107, 1284–1297 (2017).
Bowers, J. H., Kinkel, L. L. & Jones, R. K. Influence of disease-suppressive strains of Streptomyces on the native Streptomyces community in soil as determined by the analysis of cellular fatty acids. Can. J. Microbiol. 42, 27–37 (1996).
Liu, D., Anderson, N. A. & Kinkel, L. L. Biological control of potato scab in the field with antagonistic Streptomyces scabies. Phytopathology 85, 827–831 (1995).
Wanner, L. A. High proportions of nonpathogenic Streptomyces are associated with common scab-resistant potato lines and less severe disease. Can. J. Microbiol. 53, 1062–1075 (2007).
Friedman, J. H. Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001).
Hernández Medina, R. et al. Machine learning and deep learning applications in microbiome research. ISME Commun. 2, 98 (2022).
Pasolli, E., Truong, D. T., Malik, F., Waldron, L. & Segata, N. Machine learning meta-analysis of large metagenomic datasets: tools and biological insights. PLoS Comput. Biol. 12, e1004977 (2016).
Ditzler, G., Morrison, J. C., Lan, Y. & Rosen, G. L. Fizzy: feature subset selection for metagenomics. BMC Bioinform. 16, 358 (2015).
Zhou, Y. & Gallins, P. A review and tutorial of machine learning methods for microbiome host trait prediction. Front. Genet. 10, 579 (2019).
Jones, J. D. & Dangl, J. L. The plant immune system. Nature 444, 323–329 (2006).
Wintermans, P. C. A., Bakker, P. A. H. M. & Pieterse, C. M. J. Natural genetic variation in Arabidopsis for responsiveness to plant growth-promoting rhizobacteria. Plant Mol. Biol. 90, 623–634 (2016).
Rodríguez-Álvarez, M. X., Boer, M. P., van Eeuwijk, F. A. & Eilers, P. H. C. Correcting for spatial heterogeneity in plant breeding experiments with P-splines. Spat. Stat. 23, 52–71 (2018).
Atza, E. & Budko, N. Data underlying the publication: Seed tuber microbiome is a predictor of next-season potato vigor. 4TU.ResearchData https://doi.org/10.4121/21892a06-078a-4600-8386-1abe46f42271 (2024).
Song, Y., Jongekrijg, C. D., Manders, E. J. H. H. & de Rooil, P. Flight-to-vitality project microbiome sequencing protocols. Zenodo https://doi.org/10.5281/zenodo.10955437 (2024).
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).
Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).
Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, 590–596 (2013).
Abarenkov, K. et al. The UNITE database for molecular identification of fungi–recent updates and future perspectives. N. Phytol. 186, 281–285 (2010).
Breiman, L. Manual on Setting Up, Using, and Understanding Random Forests v3.1 (Statistics Department Univ. California Berkeley, 2002).
Friedman, J. H. & Popescu, B. E. Predictive learning via rule ensembles. Ann. Appl Stat. 2, 916–954 (2008).