Hartman, K. et al. Cropping practices manipulate abundance patterns of root and soil microbiome members paving the way to smart farming. Microbiome 6, 14 (2018).
Google Scholar
Berzsenyi, Z., Győrffy, B. & Lap, D. Effect of crop rotation and fertilisation on maize and wheat yields and yield stability in a long-term experiment. Eur. J. Agron. 13, 225–244. https://doi.org/10.1016/S1161-0301(00)00076-9 (2000).
Google Scholar
Körschens, M. The importance of long-term field experiments for soil science and environmental research: a review. Plant Soil Environ. 52, 1–8 (2006).
Zuber, S. M., Behnke, G., Nafziger, E. & Villamil, M. B. Crop rotation and tillage effects on soil physical and chemical properties in Illinois. Agron. J. 107, 971–978. https://doi.org/10.2134/agronj14.0465 (2015).
Google Scholar
Stott, D. E. Recommended Soil Health Indicators and Associated Laboratory Procedures. National Soil Health Specialist, Soil Health Division, U.S. Department of Agriculture (USDA), Natural Resources Conservation Service (NRCS), Washington, D.C. (2019).
Barrios, E. Soil biota, ecosystem services and land productivity. Ecol. Econ. 64, 269–285. https://doi.org/10.1016/j.ecolecon.2007.03.004 (2007).
Google Scholar
Garbeva, P., van Veen, J. A. & van Elsas, J. D. Microbial diversity in soil: selection of microbial populations by plant and soil type and implications for disease suppressiveness. Annu. Rev. Phytopathol. 42, 243–270. https://doi.org/10.1146/annurev.phyto.42.012604.135455 (2004).
Google Scholar
Hatfield, J., Prueger, J. & Kustas, W. Spatial and temporal variation of energy and carbon fluxes in central Iowa. Agron. J. 99, 285–296. https://doi.org/10.2134/agronj2005.0116S (2007).
Google Scholar
Lehman, R. M. et al. Understanding and enhancing soil biological health: the solution for reversing soil degradation. Sustainability 7, 988–1027. https://doi.org/10.3390/su7010988 (2015).
Google Scholar
Weil, R. R., Islam, K. R., Stine, M. A., Gruver, J. B. & Samson-Liebig, S. E. Estimating active carbon for soil quality assessment: a simplified method for laboratory and field use. Am. J. Alter. Agric. 18, 3–17. https://doi.org/10.1079/AJAA200228 (2003).
Google Scholar
Idowu, O. et al. Farmer-oriented assessment of soil quality using field, laboratory, and VNIR spectroscopy methods. Int. J. Plant-Soil Relatsh. 307, 243–253. https://doi.org/10.1007/s11104-007-9521-0 (2008).
Google Scholar
Gunapala, N. & Scow, K. M. Dynamics of soil microbial biomass and activity in conventional and organic farming systems. Soil Biol. Biochem. 30, 805–816. https://doi.org/10.1016/S0038-0717(97)00162-4 (1998).
Google Scholar
Plaza-Bonilla, D., Álvaro-Fuentes, J. & Cantero-Martínez, C. Identifying soil organic carbon fractions sensitive to agricultural management practices. Soil Tillage Res. 139, 19–22. https://doi.org/10.1016/j.still.2014.01.006 (2014).
Google Scholar
Mirsky, S., Lanyon, L. & Needelman, B. Evaluating soil management using particulate and chemically labile soil organic matter fractions. Soil Sci. Soc. Am. J. 72, 180–185. https://doi.org/10.2136/sssaj2005.0279 (2008).
Google Scholar
Wright, S. F. & Upadhyaya, A. Extraction of an abundant and unusual protein from soil and comparison with hyphal protein of arbuscular mycorrhizal fungi. Soil Sci. 161, 575–586 (1996).
Google Scholar
Jan, M. T., Roberts, P., Tonheim, S. K. & Jones, D. L. Protein breakdown represents a major bottleneck in nitrogen cycling in grassland soils. Soil Biol. Biochem. 41, 2272–2282. https://doi.org/10.1016/j.soilbio.2009.08.013 (2009).
Google Scholar
Nannipieri, P. & Eldor, P. The chemical and functional characterization of soil N and its biotic components. Soil Biol. Biochem. 41, 2357–2369. https://doi.org/10.1016/j.soilbio.2009.07.013 (2009).
Google Scholar
Weintraub, M. N. & Schimel, J. P. Seasonal protein dynamics in Alaskan arctic tundra soils. Soil Biol. Biochem. 37, 1469–1475. https://doi.org/10.1016/j.soilbio.2005.01.005 (2005).
Google Scholar
Ros, G. H., Temminghoff, E. J. M. & Hoffland, E. Nitrogen mineralization: a review and meta-analysis of the predictive value of soil tests. Eur. J. Soil Sci. 62, 162–173. https://doi.org/10.1111/j.1365-2389.2010.01318.x (2011).
Google Scholar
Ros, G. H., Hanegraaf, M. C., Hoffland, E. & van Riemsdijk, W. H. Predicting soil N mineralization: relevance of organic matter fractions and soil properties. Soil Biol. Biochem. 43, 1714–1722. https://doi.org/10.1016/j.soilbio.2011.04.017 (2011).
Google Scholar
Chang, E.-H., Chung, R.-S. & Tsai, Y.-H. Effect of different application rates of organic fertilizer on soil enzyme activity and microbial population. Soil Sci. Plant Nutr. 53, 132–140. https://doi.org/10.1111/j.1747-0765.2007.00122.x (2007).
Google Scholar
Liebig, M., Carpenter-Boggs, L., Johnson, J. M. F., Wright, S. & Barbour, N. Cropping system effects on soil biological characteristics in the Great Plains. Renew. Agric. Food Syst. 21, 36–48. https://doi.org/10.1079/RAF2005124 (2006).
Google Scholar
Wright, S. & Upadhyaya, A. Comparison of N-linked oligosaccharides of glomalin from arbuscular mycorrhizal fungi and soils by capillary electrophoresis. Soil Biol. Biochem. 30, 1853–1857 (1998).
Google Scholar
Wright, S. F. & Upadhyaya, A. A survey of soils for aggregate stability and glomalin, a glycoprotein produced by hyphae of arbuscular mycorrhizal fungi. Plant Soil 198, 97–107 (1998).
Google Scholar
Lovelock, C. E., Wright, S. F., Clark, D. A. & Ruess, R. W. Soil stocks of glomalin produced by arbuscular mycorrhizal fungi across a tropical rain forest landscape. J. Ecol. 92, 278–287. https://doi.org/10.1111/j.0022-0477.2004.00855.x (2004).
Google Scholar
Emran, M., Gispert, M. & Pardini, G. Patterns of soil organic carbon, glomalin and structural stability in abandoned Mediterranean terraced lands. Eur. J. Soil Sci. 63, 637–649. https://doi.org/10.1111/j.1365-2389.2012.01493.x (2012).
Google Scholar
Nichols, K. A. & Millar, J. Glomalin and soil aggregation under six management systems in the Northern Great Plains, USA. Open J. Soil Sci. 03(08), 5. https://doi.org/10.4236/ojss.2013.38043 (2013).
Google Scholar
Rillig, M., Ramsey, P., Morris, S. & Paul, E. Glomalin, an arbuscular-mycorrhizal fungal soil protein, responds to land-use change. Int. J. Plant-Soil Relatsh. 253, 293–299. https://doi.org/10.1023/A:1024807820579 (2003).
Google Scholar
Klose, S. & Tabatabai, M. A. Response of phosphomonoesterases in soils to chloroform fumigation. J. Plant Nutr. Soil Sci. 165, 429–434. https://doi.org/10.1002/1522-2624(200208)165:4%3c429::AID-JPLN429%3e3.0.CO;2-S (2002).
Google Scholar
Wang, X.-C. & Lu, Q. Beta-glucosidase activity in paddy soils of the Taihu Lake Region, China. Pedosphere 16, 118–124. https://doi.org/10.1016/S1002-0160(06)60033-7 (2006).
Google Scholar
Wilson, D. B. Microbial diversity of cellulose hydrolysis. Curr. Opin. Microbiol. 14, 259–263. https://doi.org/10.1016/j.mib.2011.04.004 (2011).
Google Scholar
Shewale, J. G. β-Glucosidase: Its role in cellulase synthesis and hydrolysis of cellulose. Int. J. Biochem. 14, 435–443. https://doi.org/10.1016/0020-711X(82)90109-4 (1982).
Google Scholar
Acosta-Martínez, V., Reicher, Z., Bischoff, M. & Turco, R. F. The role of tree leaf mulch and nitrogen fertilizer on turfgrass soil quality. Biol. Fertil. Soils 29, 55–61. https://doi.org/10.1007/s003740050524 (1999).
Google Scholar
Krogh, K. et al. Characterization and kinetic analysis of a thermostable GH3 β-glucosidase from Penicillium brasilianum. Appl. Microbiol. Biotechnol. 86, 143–154. https://doi.org/10.1007/s00253-009-2181-7 (2010).
Google Scholar
Chen, M. et al. Isolation and characterization of a β-glucosidase from Penicillium decumbens and improving hydrolysis of corncob residue by using it as cellulase supplementation. Enzyme Microb. Technol. 46, 444–449. https://doi.org/10.1016/j.enzmictec.2010.01.008 (2010).
Google Scholar
Günata, Z. & Vallier, M.-J. Production of a highly glucose-tolerant extracellular β-glucosidase by three Aspergillus strains. Biotechnol. Lett. 21, 219–223. https://doi.org/10.1023/A:1005407710806 (1999).
Google Scholar
Riou, C., Salmon, J.-M., Vallier, M.-J., Gunata, Z. & Barre, P. Purification, characterization, and substrate specificity of a novel highly glucose-tolerant beta -glucosidase from Aspergillus oryzae. Appl. Environ. Microbiol. 64, 3607 (1998).
Google Scholar
Tsukada, T., Igarashi, K., Yoshida, M. & Samejima, M. Molecular cloning and characterization of two intracellular β-glucosidases belonging to glycoside hydrolase family 1 from the basidiomycete Phanerochaete chrysosporium. Appl. Microbiol. Biotechnol. 73, 807–814. https://doi.org/10.1007/s00253-006-0526-z (2006).
Google Scholar
Yang, S., Wang, L., Yan, Q., Jiang, Z. & Li, L. Hydrolysis of soybean isoflavone glycosides by a thermostable β-glucosidase from Paecilomyces thermophila. Food Chem. 115, 1247–1252. https://doi.org/10.1016/j.foodchem.2009.01.038 (2009).
Google Scholar
Arévalo Villena, M., Úbeda Iranzo, J. F., Gundllapalli, S. B., Cordero Otero, R. R. & Briones Pérez, A. I. Characterization of an exocellular β-glucosidase from Debaryomyces pseudopolymorphus. Enzyme Microb. Technol. 39, 229–234. https://doi.org/10.1016/j.enzmictec.2005.10.018 (2006).
Google Scholar
Amouri, B. & Gargouri, A. Characterization of a novel β-glucosidase from a Stachybotrys strain. Biochem. Eng. J. 32, 191–197. https://doi.org/10.1016/j.bej.2006.09.022 (2006).
Google Scholar
Okamoto, K., Sugita, Y., Nishikori, N., Nitta, Y. & Yanase, H. Characterization of two acidic β-glucosidases and ethanol fermentation in the brown rot fungus Fomitopsis palustris. Enzyme Microb. Technol. 48, 359–364. https://doi.org/10.1016/j.enzmictec.2010.12.012 (2011).
Google Scholar
Singhania, R. R., Patel, A. K., Sukumaran, R. K., Larroche, C. & Pandey, A. Role and significance of beta-glucosidases in the hydrolysis of cellulose for bioethanol production. Biores. Technol. 127, 500–507. https://doi.org/10.1016/j.biortech.2012.09.012 (2013).
Google Scholar
Okamoto, K., Nakano, H., Yatake, T., Kiso, T. & Kitahata, S. Purification and some properties of a β-glucosidase from Flavobacterium johnsonae. Biosci. Biotechnol. Biochem. 64, 333–340. https://doi.org/10.1271/bbb.64.333 (2000).
Google Scholar
Spano, G. et al. A β-glucosidase gene isolated from wine Lactobacillus plantarum is regulated by abiotic stresses. J. Appl. Microbiol. 98, 855–861. https://doi.org/10.1111/j.1365-2672.2004.02521.x (2005).
Google Scholar
Mendes, R. et al. Deciphering the rhizosphere microbiome for disease-suppressive bacteria. Science 332, 1097–1100. https://doi.org/10.1126/science.1203980 (2011).
Google Scholar
Crookston, R., Kurle, J., Copeland, P. J., Ford, J. H. & Lueschen, W. E. Rotational cropping sequence affects yield of corn and soybean. Agron. J. 83, 108–113 (1991).
Google Scholar
Meese, B. G., Carter, P. R., Oplinger, E. S. & Pendleton, J. W. Corn/soybean rotation effect as influenced by tillage, nitrogen, and hybrid/cultivar. J. Prod. Agric. 4, 74–80 (1991).
Google Scholar
Kelley, K. W., Long, J. H. & Todd, T. C. Long-term crop rotations affect soybean yield, seed weight, and soil chemical properties. Field Crop Res. 83, 41–50. https://doi.org/10.1016/S0378-4290(03)00055-8 (2003).
Google Scholar
Mourtzinis, S. et al. Corn and soybean yield response to tillage, rotation, and nematicide seed treatment. Crop Sci. 57, 1704–1712. https://doi.org/10.2135/cropsci2016.09.0792 (2017).
Google Scholar
Farmaha, B. S. et al. Rotation impact on on-farm yield and input-use efficiency in high-yield irrigated maize-soybean systems. Agron. J. 108, 2313–2321. https://doi.org/10.2134/agronj2016.01.0046 (2016).
Google Scholar
Crookston, R. K. & Kurle, J. E. Corn residue effect on the yield of corn and soybean grown in rotation. Agron. J. 81, 229–232. https://doi.org/10.2134/agronj1989.00021962008100020018x (1989).
Google Scholar
Whiting, K. R. & Crookston, R. K. Host-specific pathogens do not account for the corn-soybean rotation effect. Crop Sci. 33, 539–543. https://doi.org/10.2135/cropsci1993.0011183X003300030024x (1993).
Google Scholar
Copeland, P. J., Allmaras, R. R., Crookston, R. K. & Nelson, W. W. Corn-soybean rotation effects on soil water depletion. Agron. J. 85, 203–210. https://doi.org/10.2134/agronj1993.00021962008500020008x (1993).
Google Scholar
Li, J. et al. Soil-plant indices help explain legume response to crop rotation in a semiarid environment. Front. Plant Sci. https://doi.org/10.3389/fpls.2018.01488 (2018).
Google Scholar
Nickel, S. E., Crookston, R. K. & Russelle, M. P. Root growth and distribution are affected by corn-soybean cropping sequence. Agron. J. 87, 895–902. https://doi.org/10.2134/agronj1995.00021962008700050020x (1995).
Google Scholar
Bennett, A. J., Bending, G. D., Chandler, D., Hilton, S. & Mills, P. Meeting the demand for crop production: the challenge of yield decline in crops grown in short rotations. Biol. Rev. 87, 52–71. https://doi.org/10.1111/j.1469-185X.2011.00184.x (2012).
Google Scholar
Johnson, N., Copeland, P. J., Crookston, R. & Pfleger, F. L. Mycorrhizae: possible explanation for yield decline with continuous corn and soybean. Agron. J. 84, 387–390 (1992).
Google Scholar
Chen, S., Porter, P. M., Reese, C. D. & Stienstra, W. C. Crop sequence effects on soybean cyst nematode and soybean and corn yields this research was supported by Minnesota soybean producers check-off funding through Minnesota research and promotion council and Minnesota agric. exp. stn.. Crop Sci. 41, 1843–1849. https://doi.org/10.2135/cropsci2001.1843 (2001).
Google Scholar
Grabau, Z. J. & Chen, S. Determining the role of plant-parasitic nematodes in the corn-soybean crop rotation yield effect using nematicide application: II. Soybean. Agron. J. 108, 1168–1179. https://doi.org/10.2134/agronj2015.0432 (2016).
Google Scholar
Hoss, M., Behnke, G., Davis, A., Nafziger, E. & Villamil, M. B. Short corn rotations do not improve soil quality. Compared with corn monocultures. Agron. J. 110, 1274–1288. https://doi.org/10.2134/agronj2017.11.0633 (2018).
Google Scholar
Plaza, C., Courtier-Murias, D., Fernández, J. M., Polo, A. & Simpson, A. J. Physical, chemical, and biochemical mechanisms of soil organic matter stabilization under conservation tillage systems: a central role for microbes and microbial by-products in C sequestration. Soil Biol. Biochem. 57, 124–134. https://doi.org/10.1016/j.soilbio.2012.07.026 (2013).
Google Scholar
Tardy, V. et al. Shifts in microbial diversity through land use intensity as drivers of carbon mineralization in soil. Soil Biol. Biochem. 90, 204–213. https://doi.org/10.1016/j.soilbio.2015.08.010 (2015).
Google Scholar
Tiedje, J. M., Asuming-Brempong, S., Nüsslein, K., Marsh, T. L. & Flynn, S. J. Opening the black box of soil microbial diversity. Appl. Soil. Ecol. 13, 109–122. https://doi.org/10.1016/S0929-1393(99)00026-8 (1999).
Google Scholar
Hussain, S., Ghaffar, A. & Aslam, M. Biological-control of macrophomina-phaseolina charcoal rot of sunflower and mung bean. J. Phytopathol. 130, 157–160. https://doi.org/10.1111/j.1439-0434.1990.tb01163.x (1990).
Google Scholar
Khan, A. N. et al. Molecular identification and genetic characterization of Macrophomina phaseolina strains causing pathogenicity on sunflower and chickpea. Front. Microbiol. 8, 1309. https://doi.org/10.3389/fmicb.2017.01309 (2017).
Google Scholar
Ramezani, M. et al. Soybean charcoal rot disease fungus Macrophomina phaseolina in Mississippi produces the phytotoxin (-)-botryodiplodin but no detectable phaseolinone. J. Nat. Prod. 70, 128–129. https://doi.org/10.1021/np060480t (2007).
Google Scholar
Smith, L.J., Datnoff, L.E., Pernezny, K. & Schlub, R.L. Phylogenetic and pathogenic characterization of Corynespora cassiicola isolates. In II International Symposium on Tomato Diseases 808, 51–56 (2007)
Deon, M. et al. Characterization of a cassiicolin-encoding gene from Corynespora cassiicola, pathogen of rubber tree (Hevea brasiliensis). Plant Sci. 185–186, 227–237. https://doi.org/10.1016/j.plantsci.2011.10.017 (2012).
Google Scholar
Videira, S. I. R. et al. Mycosphaerellaceae: chaos or clarity?. Stud. Mycol. 87, 257–421 (2017).
Google Scholar
Wijayawardene, N. N. et al. Outline of ascomycota: 2017. Fungal Divers. 88, 167–263. https://doi.org/10.1007/s13225-018-0394-8 (2018).
Google Scholar
Olofsson, J., Ericson, L., Torp, M., Stark, S. & Baxter, R. Carbon balance of Arctic tundra under increased snow cover mediated by a plant pathogen. Nat. Clim. Change 1, 220–223. https://doi.org/10.1038/Nclimate1142 (2011).
Google Scholar
Wells, L. D. & McManus, P. S. A photographic diagnostic guide for identification of the principal cranberry fruit rot pathogens. Plant Health Prog. https://doi.org/10.1094/php-2013-0729-01-dg (2013).
Google Scholar
Yeager, C. M. et al. Polysaccharide degradation capability of actinomycetales soil isolates from a semiarid grassland of the Colorado Plateau. Appl. Environ. Microbiol. https://doi.org/10.1128/aem.03020-16 (2017).
Google Scholar
Loria, R., Bukhalid, R. A., Fry, B. A. & King, R. R. Plant pathogenicity in the genus Streptomyces. Plant Dis. 81, 836–846. https://doi.org/10.1094/Pdis.1997.81.8.836 (1997).
Google Scholar
Li, Y., Liu, J., Diaz-Cruz, G., Cheng, Z. & Bignell, D. R. D. Virulence mechanisms of plant-pathogenic Streptomyces species: an updated review. Microbiology 165, 1025–1040. https://doi.org/10.1099/mic.0.000818 (2019).
Google Scholar
Abdalla, M. H. Solubilization of rock phosphates by rhizobium and bradyrhizobium. Folia Microbiol. 39, 53–56. https://doi.org/10.1007/Bf02814530 (1994).
Google Scholar
Bargaz, A., Lyamlouli, K., Chtouki, M., Zeroual, Y. & Dhiba, D. Soil microbial resources for improving fertilizers efficiency in an integrated plant nutrient management system. Front Microbiol. https://doi.org/10.3389/fmicb.2018.01606 (2018).
Google Scholar
Moebius-Clune, B. N. Comprehensive Assessment of Soil Health: The Cornell Framework Manual (Cornell University, 2016).
Deng, S. P. & Tabatabai, M. A. Cellulase activity of soils. Soil Biol. Biochem. 26, 1347–1354. https://doi.org/10.1016/0038-0717(94)90216-X (1994).
Google Scholar
Riesenfeld, C. S., Goodman, R. M. & Handelsman, J. Uncultured soil bacteria are a reservoir of new antibiotic resistance genes. Environ. Microbiol. 6, 981–989 (2004).
Google Scholar
Gohl, D. et al. Systematic improvement of amplicon marker gene methods for increased accuracy in microbiome studies. Nat. Biotechnol. 34, 942–949. https://doi.org/10.1038/nbt.3601 (2016).
Google Scholar
R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing (Vienna, Austria, 2020).
Bates, D., Machler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Google Scholar
Bolyen, E. et al. Qiime 2: reproducible, interactive, scalable, and extensible microbiome data science. Report No. 2167–9843, (PeerJ Preprints, 2018).
Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583. https://doi.org/10.1038/nmeth.3869 (2016).
Google Scholar
Mandal, S. et al. Analysis of composition of microbiomes: a novel method for studying microbial composition. Microb. Ecol. Health Dis. https://doi.org/10.3402/mehd.v26.27663 (2015).
Google Scholar
Morton, J. T. et al. Balance trees reveal microbial niche differentiation. mSystems https://doi.org/10.1128/mSystems.00162-16 (2017).
Google Scholar
Nilsson, R. H. et al. The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications. Nucleic Acids Res. 47, D259–D264. https://doi.org/10.1093/nar/gky1022 (2019).
Google Scholar
Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590-596. https://doi.org/10.1093/nar/gks1219 (2013).
Google Scholar
Pedregosa, F. et al. Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
Google Scholar
Kruskal, W. H. & Wallis, W. A. Use of ranks in one-criterion variance analysis. J. Am. Stat. Assoc. 47, 583–621. https://doi.org/10.1080/01621459.1952.10483441 (1952).
Google Scholar
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