Caracterización del metagenoma de la comunidad microbiana edáfica asociada a un cultivo de arroz (oryza sativa) bajo un esquema agronómico de manejo de agricultura por ambientes

ilustraciones, diagramas, fotografías a color

Autores:
Saavedra Correa, Juan David
Tipo de recurso:
Fecha de publicación:
2023
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
eng
OAI Identifier:
oai:repositorio.unal.edu.co:unal/84610
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/84610
https://repositorio.unal.edu.co/
Palabra clave:
570 - Biología::577 - Ecología
Microbiología agrícola
Ecología Microbiana
Arroz
Agricultura sostenible
Rice
Sustainable agriculture
Rice soil metagenomics
Bulk soil Microbiomes
Rhizosphere microbiomes
Amplicon sequencing
Shotgun sequencing
Metagenómica del suelo de arroz
Microbiomas de suelo de soporte
Microbiomas de rizosfera
Secuenciacion shotgun
Rights
openAccess
License
Atribución-SinDerivadas 4.0 Internacional
id UNACIONAL2_7d449dfe86843923c453674f0c1462e9
oai_identifier_str oai:repositorio.unal.edu.co:unal/84610
network_acronym_str UNACIONAL2
network_name_str Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv Caracterización del metagenoma de la comunidad microbiana edáfica asociada a un cultivo de arroz (oryza sativa) bajo un esquema agronómico de manejo de agricultura por ambientes
dc.title.translated.eng.fl_str_mv Metagenomic characterization of the edaphic microbial community associated with a rice crop (Oryza sativa) under an agronomic scheme of agriculture management by management zones
title Caracterización del metagenoma de la comunidad microbiana edáfica asociada a un cultivo de arroz (oryza sativa) bajo un esquema agronómico de manejo de agricultura por ambientes
spellingShingle Caracterización del metagenoma de la comunidad microbiana edáfica asociada a un cultivo de arroz (oryza sativa) bajo un esquema agronómico de manejo de agricultura por ambientes
570 - Biología::577 - Ecología
Microbiología agrícola
Ecología Microbiana
Arroz
Agricultura sostenible
Rice
Sustainable agriculture
Rice soil metagenomics
Bulk soil Microbiomes
Rhizosphere microbiomes
Amplicon sequencing
Shotgun sequencing
Metagenómica del suelo de arroz
Microbiomas de suelo de soporte
Microbiomas de rizosfera
Secuenciacion shotgun
title_short Caracterización del metagenoma de la comunidad microbiana edáfica asociada a un cultivo de arroz (oryza sativa) bajo un esquema agronómico de manejo de agricultura por ambientes
title_full Caracterización del metagenoma de la comunidad microbiana edáfica asociada a un cultivo de arroz (oryza sativa) bajo un esquema agronómico de manejo de agricultura por ambientes
title_fullStr Caracterización del metagenoma de la comunidad microbiana edáfica asociada a un cultivo de arroz (oryza sativa) bajo un esquema agronómico de manejo de agricultura por ambientes
title_full_unstemmed Caracterización del metagenoma de la comunidad microbiana edáfica asociada a un cultivo de arroz (oryza sativa) bajo un esquema agronómico de manejo de agricultura por ambientes
title_sort Caracterización del metagenoma de la comunidad microbiana edáfica asociada a un cultivo de arroz (oryza sativa) bajo un esquema agronómico de manejo de agricultura por ambientes
dc.creator.fl_str_mv Saavedra Correa, Juan David
dc.contributor.advisor.none.fl_str_mv Gonzalez Sayer, Sandra Milena
Aristizabal Gutierrez, Fabio Ancizar
dc.contributor.author.none.fl_str_mv Saavedra Correa, Juan David
dc.contributor.researchgroup.spa.fl_str_mv Bioprocesos y Bioprospección
dc.contributor.orcid.spa.fl_str_mv Saavedra, Juan David [0000-0003-1527-0428]
dc.contributor.cvlac.spa.fl_str_mv https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000074382
dc.subject.ddc.spa.fl_str_mv 570 - Biología::577 - Ecología
Microbiología agrícola
Ecología Microbiana
topic 570 - Biología::577 - Ecología
Microbiología agrícola
Ecología Microbiana
Arroz
Agricultura sostenible
Rice
Sustainable agriculture
Rice soil metagenomics
Bulk soil Microbiomes
Rhizosphere microbiomes
Amplicon sequencing
Shotgun sequencing
Metagenómica del suelo de arroz
Microbiomas de suelo de soporte
Microbiomas de rizosfera
Secuenciacion shotgun
dc.subject.lemb.spa.fl_str_mv Arroz
Agricultura sostenible
dc.subject.lemb.eng.fl_str_mv Rice
Sustainable agriculture
dc.subject.proposal.eng.fl_str_mv Rice soil metagenomics
Bulk soil Microbiomes
Rhizosphere microbiomes
Amplicon sequencing
Shotgun sequencing
dc.subject.proposal.spa.fl_str_mv Metagenómica del suelo de arroz
Microbiomas de suelo de soporte
Microbiomas de rizosfera
Secuenciacion shotgun
description ilustraciones, diagramas, fotografías a color
publishDate 2023
dc.date.accessioned.none.fl_str_mv 2023-08-29T14:56:13Z
dc.date.available.none.fl_str_mv 2023-08-29T14:56:13Z
dc.date.issued.none.fl_str_mv 2023-06-05
dc.type.spa.fl_str_mv Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv Text
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/TM
status_str acceptedVersion
dc.identifier.uri.none.fl_str_mv https://repositorio.unal.edu.co/handle/unal/84610
dc.identifier.instname.spa.fl_str_mv Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv https://repositorio.unal.edu.co/
url https://repositorio.unal.edu.co/handle/unal/84610
https://repositorio.unal.edu.co/
identifier_str_mv Universidad Nacional de Colombia
Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv Andrew, J., & Edwards, A. (2011). Structure, Variation, and Dynamics of the RootAssociated Microbiota of the Crop Plant Rice.
Baldrian, P. (2019). The known and the unknown in soil microbial ecology. In FEMS Microbiology Ecology (Vol. 95). Oxford University Press. https://doi.org/10.1093/femsec/fiz005
Berg, G., Rybakova, D., Fischer, D., Cernava, T., Vergès, M. C. C., Charles, T., Chen, X., Cocolin, L., Eversole, K., Corral, G. H., Kazou, M., Kinkel, L., Lange, L., Lima, N., Loy, A., Macklin, J. A., Maguin, E., Mauchline, T., McClure, R., … Schloter, M. (2020). Microbiome definition re-visited: Old concepts and new challenges. In Microbiome (Vol. 8). BioMed Central Ltd. https://doi.org/10.1186/s40168-020- 00875-0
Bolyen, E., Rideout, J. R., Dillon, M. R., Bokulich, N. A., Abnet, C. C., Al-Ghalith, G. A., Alexander, H., Alm, E. J., Arumugam, M., Asnicar, F., Bai, Y., Bisanz, J. E., Bittinger, K., Brejnrod, A., Brislawn, C. J., Brown, C. T., Callahan, B. J., CaraballoRodríguez, A. M., Chase, J., … Caporaso, J. G. (2019). Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nature Biotechnology, 37(8), 852–857. https://doi.org/10.1038/s41587-019-0209-9
Breunig, F. M., Galvão, L. S., Dalagnol, R., Dauve, C. E., Parraga, A., Santi, A. L., Della Flora, D. P., & Chen, S. (2020). Delineation of management zones in agricultural fields using cover–crop biomass estimates from PlanetScope data. International Journal of Applied Earth Observation and Geoinformation, 85, 102004.
Carreño, J. del P. (2019). Evaluación de la diversidad taxonómica y funcional de la comunidad microbiana relacionada con el ciclo del nitrógeno en suelos de cultivo de arroz con diferentes manejos del tamo. Universidad Nacional de Colombia.
Chandra, R. (2021). Soil Biodiversity and Community Composition for Ecosystem Services. In A. Rakshit, S. K. Singh, P. C. Abhilash, & A. Biswas (Eds.), Soil Science: Fundamentals to Recent Advances (pp. 69–84). Springer. https://doi.org/10.1007/978-981-16-0917-6_5
Chang, H. X., Haudenshield, J. S., Bowen, C. R., & Hartman, G. L. (2017). Metagenomewide association study and machine learning prediction of bulk soil microbiome and crop productivity. Frontiers in Microbiology, 8(APR). https://doi.org/10.3389/fmicb.2017.00519
Chauhan, B. S., Jabran, K., & Mahajan, G. (Eds.). (2017). Rice Production Worldwide (1st ed. 2017). Springer International Publishing : Imprint: Springer. https://doi.org/10.1007/978-3-319-47516-5
Chen, S., Du, T., Wang, S., Parsons, D., Wu, D., Guo, X., & Li, D. (2021). Quantifying the effects of spatial-temporal variability of soil properties on crop growth in management zones within an irrigated maize field in Northwest China. Agricultural Water Management, 244, 106535. https://doi.org/10.1016/j.agwat.2020.106535
DANE. (2014). Censo Nacional Agropecuario 2014. In Departamento Administrativo Nacional de Estadística (DANE). https://www.dane.gov.co/files/images/foros/forode-entrega-de-resultados-y-cierre-3-censo-nacional-agropecuario/CNATomo2- Resultados.pdf
DANE. (2021). Encuesta Nacional de Arroz Mecanizado (ENAM) Primer semestre de 2021. https://fedearroz.s3.amazonaws.com/media/documents/comunicado_ENAM_Isem 21_2_XnnAwff.pdf
De Gannes, V., Eudoxie, G., Bekele, I., & Hickey, W. J. (2015). Relations of microbiome characteristics to edaphic properties of tropical soils from Trinidad. Frontiers in Microbiology, 6(SEP), 1045. https://doi.org/10.3389/fmicb.2015.01045
Ding, L.-J., Cui, H., Nie, S., Long, X., Duan, G., & Zhu, Y.-G. (2019). Microbiomes inhabiting rice roots and rhizosphere. FEMS Microbiology Ecology. https://doi.org/10.1093/femsec/fiz040
Doerge, T. (2005). Management Zone Concepts. South Dakota State University, 1. http://www.ipni.net/publication/ssmg.nsf/0/C0D052F04A53E0BF852579E500761A E3/$FILE/SSMG-02.pdf
Doni, F., Suhaimi, N. S. M., Mispan, M. S., Fathurrahman, F., Marzuki, B. M., Kusmoro, J., & Uphoff, N. (2022). Microbial Contributions for Rice Production: From Conventional Crop Management to the Use of ‘Omics’ Technologies. International Journal of Molecular Sciences, 23(2), 737. https://doi.org/10.3390/ijms23020737
Fahad, S., Adnan, M., Noor, M., Arif, M., Alam, M., Khan, I. A., Ullah, H., Wahid, F., Mian, I. A., Jamal, Y., Basir, A., Hassan, S., Saud, S., Amanullah, Riaz, M., Wu, C., Khan, M. A., & Wang, D. (2018). Major constraints for global rice production. In Advances in Rice Research for Abiotic Stress Tolerance (pp. 1–22). Elsevier. https://doi.org/10.1016/B978-0-12-814332-2.00001-0
Fahad, S., Adnan, M., Noor, M., Arif, M., Alam, M., Khan, I. A., Ullah, H., Wahid, F., Mian, I. A., Jamal, Y., Basir, A., Hassan, S., Saud, S., Amanullah, Riaz, M., Wu, C., Khan, M. A., & Wang, D. (2018). Major constraints for global rice production. In Advances in Rice Research for Abiotic Stress Tolerance (pp. 1–22). Elsevier. https://doi.org/10.1016/B978-0-12-814332-2.00001-0
FEDEARROZ. (2018). Adopción Masiva De Tecnologia AMTEC AMTEC FEDEARROZ. Revista Arroz, 22–34.
FEDEARROZ. (2020). Federación Nacional de Arroceros, Estadísticas Arroceras en Colombia. In Area, Producción y Rendimientos. http://www.fedearroz.com.co/new/apr_public.php
Garcés-Varón, G., & Medina-Rubio, J. (2018). LA FISIOLOGIA DEL CULTIVO DEL ARROZ EN EL PROGRAMA AMTEC. Fedearroz, 1(2).
Garrido-Cardenas, J. A., & Manzano-Agugliaro, F. (2017). The metagenomics worldwide research. In Current Genetics (Vol. 63). Springer Verlag. https://doi.org/10.1007/s00294-017-0693-8
Gebbers, R., & Adamchuk, V. I. (2010). Precision agriculture and food security. In Science (Vol. 327). https://doi.org/10.1126/science.1183899
Harwood, C. & Buckley, M. (2008). A golden age for microbial ecology. In Nature Reviews Microbiology (Vol. 6). Nature Publishing Group. https://doi.org/10.1038/nrmicro1957
Illumina. (2015). Technology Spotlight: Illumina ® Sequencing. IRRI. (1993). Rice Researcn in a time of change. International Rice Research Institute, 1(1).
Jansson, J. (2013). Encyclopedia of Metagenomics: Soil Metagenomics. In Encyclopedia of Metagenomics (Vol. 2). Springer New York. https://doi.org/10.1007/978-1-4614- 6418-1
Jing, J., Cong, W.-F., & Bezemer, T. M. (2022). Legacies at work: Plant–soil–microbiome interactions underpinning agricultural sustainability. Trends in Plant Science, 27(8), 781–792. https://doi.org/10.1016/j.tplants.2022.05.007
Justo, C., & Scianca, ; Carlos. (2011). Agricultura Por Ambientes. Estrategias De Manejo De Maiz En Suelos Con Diferentes Aptitud Productiva. EEA INTA GENERAL VILLEGAS, 1.
Kim, H., & Lee, Y.-H. (2020). The Rice Microbiome: A Model Platform for Crop Holobiome. Phytobiomes Journal • 2020 •, 4, 5–18. https://doi.org/10.1094/PBIOMES-07-19-0035-RVW
Kutílek, M., & Nielsen, D. R. (2017). Soil The Skin of the Planet Earth (1st ed.). Springer Books.
Lopes, R. (2013). Towards a sustainable rice culture: The role of microbiota [PhD Thesis]. Universidade do Porto
Lukac, M., Grenni, P., & Gamboni, M. (2017). Soil Biological Communities and Ecosystem Resilience. In Soil Biological Communities and Ecosystem Resilience (Vol. 1). Springer International Publishing. https://doi.org/10.1007/978-3-319-63336-7
Marchesi, J. R., & Ravel, J. (2015). The vocabulary of microbiome research: A proposal. Microbiome, 3(1), 1–3. https://doi.org/10.1186/s40168-015-0094-5
Moharana, P. C., Jena, R. K., Pradhan, U. K., Nogiya, M., Tailor, B. L., Singh, R. S., & Singh, S. K. (2020). Geostatistical and fuzzy clustering approach for delineation of site-specific management zones and yield-limiting factors in irrigated hot arid environment of India. Precision Agriculture, 21(2), 426–448. https://doi.org/10.1007/s11119-019-09671-9
Nannipieri, P., Ascher, J., Ceccherini, M. T., Petramellara, G., Giancarlo, R., & Schloter, M. (2020). Beyond microbial diversity for predicting soil functions: A mini review. Pedosphere, 30(1), 5–17. https://doi.org/10.1016/S1002-0160(19)60824-6
Alteio, L. V., Séneca, J., Canarini, A., Angel, R., Jansa, J., Guseva, K., Kaiser, C., Richter, A., & Schmidt, H. (2021). A critical perspective on interpreting amplicon sequencing data in soil ecological research. Soil Biology and Biochemistry, 160, 108357. https://doi.org/10.1016/j.soilbio.2021.108357
Auer, L., Mariadassou, M., O’Donohue, M., Klopp, C., & Hernandez-Raquet, G. (2017). Analysis of large 16S rRNA Illumina data sets: Impact of singleton read filtering on microbial community description. Molecular Ecology Resources, 17(6), e122– e132. https://doi.org/10.1111/1755-0998.12700
Banerjee, S., & van der Heijden, M. G. A. (2022). Soil microbiomes and one health. Nature Reviews Microbiology, 1–15. https://doi.org/10.1038/s41579-022-00779-w
Bokulich, N. A., Kaehler, B. D., Rideout, J. R., Dillon, M., Bolyen, E., Knight, R., Huttley, G. A., & Gregory Caporaso, J. (2018). Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome, 6(1), 90. https://doi.org/10.1186/s40168-018-0470-z
Brooks, J. P., Edwards, D. J., Harwich, M. D., Rivera, M. C., Fettweis, J. M., Serrano, M. G., Reris, R. A., Sheth, N. U., Huang, B., Girerd, P., Strauss, J. F., Jefferson, K. K., Buck, G. A., & Vaginal Microbiome Consortium (additional members). (2015). The truth about metagenomics: Quantifying and counteracting bias in 16S rRNA studies. BMC Microbiology, 15(1), 66. https://doi.org/10.1186/s12866-015- 0351-6
Bruno, F., Marinella, M., & Santamaria, M. (2015). e-DNA Meta-Barcoding: From NGS Raw Data to Taxonomic Profiling. In E. Picardi (Ed.), RNA Bioinformatics (pp. 257– 278). Springer. https://doi.org/10.1007/978-1-4939-2291-8_16
Bukin, Y. S., Galachyants, Y. P., Morozov, I. V., Bukin, S. V., Zakharenko, A. S., & Zemskaya, T. I. (2019). The effect of 16S rRNA region choice on bacterial community metabarcoding results. Scientific Data, 6(1), Article 1. https://doi.org/10.1038/sdata.2019.7
Callahan, B. J., McMurdie, P. J., Rosen, M. J., Han, A. W., Johnson, A. J. A., & Holmes, S. P. (2016). DADA2: High resolution sample inference from Illumina amplicon data. Nature Methods, 13(7), 581–583. https://doi.org/10.1038/nmeth.3869
Dubey, A., Malla, M. A., Khan, F., Chowdhary, K., Yadav, S., Kumar, A., Sharma, S., Khare, P. K., & Khan, M. L. (2019). Soil microbiome: A key player for conservation of soil health under changing climate. Biodiversity and Conservation, 28(8), 2405– 2429. https://doi.org/10.1007/s10531-019-01760-5
Gołębiewski, M., & Tretyn, A. (2020). Generating amplicon reads for microbial community assessment with next-generation sequencing. Journal of Applied Microbiology, 128(2), 330–354. https://doi.org/10.1111/jam.14380
Haas, B. J., Gevers, D., Earl, A. M., Feldgarden, M., Ward, D. V., Giannoukos, G., Ciulla, D., Tabbaa, D., Highlander, S. K., Sodergren, E., Methé, B., DeSantis, T. Z., Human Microbiome Consortium, Petrosino, J. F., Knight, R., & Birren, B. W. (2011). Chimeric 16S rRNA sequence formation and detection in Sanger and 454- pyrosequenced PCR amplicons. Genome Research, 21(3), 494–504. https://doi.org/10.1101/gr.112730.110
Hall, M., & Beiko, R. G. (2018). 16S rRNA Gene Analysis with QIIME2. In R. G. Beiko, W. Hsiao, & J. Parkinson (Eds.), Microbiome Analysis: Methods and Protocols (pp. 113–129). Springer. https://doi.org/10.1007/978-1-4939-8728-3_8
Highlander, S. (2013). Mock Community Analysis. In K. E. Nelson (Ed.), Encyclopedia of Metagenomics (pp. 1–7). Springer. https://doi.org/10.1007/978-1-4614-6418- 1_54-1
Karstens, L., Asquith, M., Davin, S., Fair, D., Gregory, W., Wolfe, A., Braun, J., & Mcweeney, S. (2018). Controlling for contaminants in low biomass 16S rRNA gene sequencing experiments. https://doi.org/10.1101/329854
Knauth, S., Schmidt, H., & Tippkötter, R. (2013). Comparison of commercial kits for the extraction of DNA from paddy soils. Letters in Applied Microbiology, 56(3), 222– 228. https://doi.org/10.1111/lam.12038
Knight, R., Vrbanac, A., Taylor, B. C., Aksenov, A., Callewaert, C., Debelius, J., Gonzalez, A., Kosciolek, T., McCall, L.-I., McDonald, D., Melnik, A. V., Morton, J. T., Navas, J., Quinn, R. A., Sanders, J. G., Swafford, A. D., Thompson, L. R., Tripathi, A., Xu, Z. Z., ... Dorrestein, P. C. (2018). Best practices for analysing microbiomes. Nature Reviews Microbiology, 16(7), Article 7. https://doi.org/10.1038/s41579-018-0029-9
Li, S., Deng, Y., Wang, Z., Zhang, Z., Kong, X., Zhou, W., Yi, Y., & Qu, Y. (2020). Exploring the accuracy of amplicon-based internal transcribed spacer markers for a fungal community. Molecular Ecology Resources, 20(1), 170–184. https://doi.org/10.1111/1755-0998.13097
Liu, M., Clarke, L. J., Baker, S. C., Jordan, G. J., & Burridge, C. P. (2020). A practical guide to DNA metabarcoding for entomological ecologists. Ecological Entomology, 45(3), 373–385. https://doi.org/10.1111/een.12831
Liu, Qin, Y., Chen, T., Lu, M., Qian, X., Guo, X., & Bai, Y. (2021). A practical guide to amplicon and metagenomic analysis of microbiome data. Protein & Cell, 12(5), 315–330. https://doi.org/10.1007/s13238-020-00724-8
Mayday, M., Khan, L., Chow, E. D., Zinter, M. S., & DeRisi, J. L. (2019). High- Throughput Library Pooling for NGS. https://www.protocols.io/view/high- throughput-library-pooling-for-ngs-tcdeis6
Nilsson, R. H., Anslan, S., Bahram, M., Wurzbacher, C., Baldrian, P., & Tedersoo, L. (2019). Mycobiome diversity: High-throughput sequencing and identification of fungi. Nature Reviews Microbiology, 17(2), Article 2. https://doi.org/10.1038/s41579-018-0116-y
Pecundo, M. H., Chang, A. C. G., Chen, T., dela Cruz, T. E. E., Ren, H., & Li, N. (2021). Full-Length 16S rRNA and ITS Gene Sequencing Revealed Rich Microbial Flora in Roots of Cycas spp. In China. Evolutionary Bioinformatics, 17, 1176934321989713. https://doi.org/10.1177/1176934321989713
Pollock, J., Glendinning, L., Wisedchanwet, T., & Watson, M. (2018). The Madness of Microbiome: Attempting To Find Consensus “Best Practice” for 16S Microbiome Studies. Applied and Environmental Microbiology, 84(7), e02627-17. https://doi.org/10.1128/AEM.02627-17
Prasad, S., Malav, L. C., Choudhary, J., Kannojiya, S., Kundu, M., Kumar, S., & Yadav, A. N. (2021). Soil Microbiomes for Healthy Nutrient Recycling. In A. N. Yadav, J. Singh, C. Singh, & N. Yadav (Eds.), Current Trends in Microbial Biotechnology for Sustainable Agriculture (pp. 1–21). Springer. https://doi.org/10.1007/978-981-15- 6949-4_1
Prodan, A., Tremaroli, V., Brolin, H., Zwinderman, A. H., Nieuwdorp, M., & Levin, E. (2020). Comparing bioinformatic pipelines for microbial 16S rRNA amplicon sequencing. PLOS ONE, 15(1), e0227434. https://doi.org/10.1371/journal.pone.0227434
Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., Peplies, J., & Glöckner, F. O. (2013). The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Research, 41(Database issue), D590-596. https://doi.org/10.1093/nar/gks1219
Schloss, P. D. (2020). Reintroducing mothur: 10 Years Later. Applied and Environmental Microbiology, 86(2), e02343-19. https://doi.org/10.1128/AEM.02343-19
Semenov, M. V. (2021). Metabarcoding and Metagenomics in Soil Ecology Research: Achievements, Challenges, and Prospects. Biology Bulletin Reviews, 11(1), 40– 53. https://doi.org/10.1134/S2079086421010084
Starke, R., Pylro, V. S., & Morais, D. K. (2021). 16S rRNA Gene Copy Number Normalization Does Not Provide More Reliable Conclusions in Metataxonomic Surveys. Microbial Ecology, 81(2), 535–539. https://doi.org/10.1007/s00248-020- 01586-7
Sze, M. A., & Schloss, P. D. (2019). The Impact of DNA Polymerase and Number of Rounds of Amplification in PCR on 16S rRNA Gene Sequence Data. MSphere, 4(3), e00163-19. https://doi.org/10.1128/mSphere.00163-19
Yang, R.-H., Su, J.-H., Shang, J.-J., Wu, Y.-Y., Li, Y., Bao, D.-P., & Yao, Y.-J. (2018). Evaluation of the ribosomal DNA internal transcribed spacer (ITS), specifically ITS1 and ITS2, for the analysis of fungal diversity by deep sequencing. PLOS ONE, 13(10), 1–17. https://doi.org/10.1371/journal.pone.0206428
Amanullah, D., & Khalid, S. (2020). Agronomy: Climate Change (Vol. 1). IntechOpen. https://doi.org/10.5772/intechopen.78102
Arunrat, N., Pumijumnong, N., & Hatano, R. (2017). Practices sustaining soil organic matter and rice yield in a tropical monsoon region. Soil Science and Plant Nutrition, 1-14. https://doi.org/10.1080/00380768.2017.1323546
Arunrat, N., Sansupa, C., Kongsurakan, P., Sereenonchai, S., & Hatano, R. (2022). Soil Microbial Diversity and Community Composition in Rice-Fish Co-Culture and Rice Monoculture Farming System. Biology, 11(8), 1242. https://doi.org/10.3390/biology11081242
Atique-ur-Rehman, Farooq, M., Rashid, A., Nadeem, F., Stuerz, S., Asch, F., Bell, R. W., & Siddique, K. H. M. (2018). Boron nutrition of rice in different production systems. A review. Agronomy for Sustainable Development, 38(3), 25. https://doi.org/10.1007/s13593-018-0504-8
Azadi, A., Baghernejad, M., Gholami, A., & Shakeri, S. (2021). Forms and distribution pattern of soil Fe (Iron) and Mn (Manganese) oxides due to long-term rice cultivation in fars Province Southern Iran. Communications in Soil Science and Plant Analysis, 52(16), 1894-1911. https://doi.org/10.1080/00103624.2021.1900226
Barillot, C. D. C., Sarde, C.-O., Bert, V., Tarnaud, E., & Cochet, N. (2013). A standardized method for the sampling of rhizosphere and rhizoplan soil bacteria associated to a herbaceous root system. Annals of Microbiology, 63(2), 471-476. https://doi.org/10.1007/s13213-012-0491-y
Benson, D. A., Cavanaugh, M., Clark, K., Karsch-Mizrachi, I., Lipman, D. J., Ostell, J., & Sayers, E. W. (2013). GenBank. Nucleic Acids Research, 41(Database issue), D36- D42. https://doi.org/10.1093/nar/gks1195
Biswas, R., & Sarkar, A. (2018). ‘Omics’ Tools in Soil Microbiology: The State of the Art. En T. K. Adhya, B. Lal, B. Mohapatra, D. Paul, & S. Das (Eds.), Advances in Soil Microbiology: Recent Trends and Future Prospects: Volume 1: Soil-Microbe Interaction (pp. 35-64). Springer. https://doi.org/10.1007/978-981-10-6178-3_3
Caulfield, M. E., Fonte, S. J., Groot, J. C. J., Vanek, S. J., Sherwood, S., Oyarzun, P., Borja, R. M., Dumble, S., & Tittonell, P. (2020). Agroecosystem patterns and land management co-develop through environment, management, and land-use interactions. Ecosphere, 11(4), e03113. https://doi.org/10.1002/ecs2.3113
Chaparro, J. M., Badri, D. V., & Vivanco, J. M. (2014). Rhizosphere microbiome assemblage is affected by plant development. The ISME Journal, 8(4), 790-803. https://doi.org/10.1038/ismej.2013.196
Chen, L., Zhao, D., Han, G., Yang, F., Gong, Z., Song, X., Li, D., & Zhang, G. (2022). Iron loss of paddy soil in China and its environmental implications. Science China Earth Sciences, 65(7), 1277-1291. https://doi.org/10.1007/s11430-021-9936-6
Chialva, M., Ghignone, S., Cozzi, P., Lazzari, B., Bonfante, P., Abbruscato, P., & Lumini, E. (2020). Water management and phenology influence the root-associated rice field microbiota. FEMS microbiology ecology, 96. https://doi.org/10.1093/femsec/fiaa146
Cox, M. P., Peterson, D. A., & Biggs, P. J. (2010). SolexaQA: At-a-glance quality assessment of Illumina second-generation sequencing data. BMC Bioinformatics, 11(1), 485. https://doi.org/10.1186/1471-2105-11-485
De Gruyter, J., Weedon, J. T., Bazot, S., Dauwe, S., Fernandez-Garberí, P.-R., Geisen, S., De La Motte, L. G., Heinesch, B., Janssens, I. A., Leblans, N., Manise, T., Ogaya, R., Löfvenius, M. O., Peñuelas, J., Sigurdsson, B. D., Vincent, G., & Verbruggen, E. (2020). Patterns of local, intercontinental and interseasonal variation of soil bacterial and eukaryotic microbial communities. FEMS Microbiology Ecology, 96(3), fiaa018. https://doi.org/10.1093/femsec/fiaa018
Devi, R., Kaur, T., Kour, D., Yadav, A., Yadav, A. N., Suman, A., ... & Saxena, A. K. (2022). Minerals solubilizing and mobilizing microbiomes: A sustainable approach for managing minerals’ deficiency in agricultural soil. Journal of Applied Microbiology, 133(3), 1245-1272.
Ding, L.-J., Cui, H., Nie, S., Long, X., Duan, G., & Zhu, Y.-G. (2019). Microbiomes inhabiting rice roots and rhizosphere. FEMS microbiology ecology. https://doi.org/10.1093/femsec/fiz040
Dong, H., Sun, H., Jiang, L., Ma, D., & Fan, S. (2022). Characteristics of root-associated bacterial community and nitrogen biochemical properties of two Japonica rice cultivars with different yields. Food and Energy Security, 11(1), e357. https://doi.org/10.1002/fes3.357
Doni, F., Suhaimi, N. S. M., Mispan, M. S., Fathurrahman, F., Marzuki, B. M., Kusmoro, J., & Uphoff, N. (2022). Microbial Contributions for Rice Production: From Conventional Crop Management to the Use of ‘Omics’ Technologies. International Journal of Molecular Sciences, 23(2), Art. 2. https://doi.org/10.3390/ijms23020737
Dou, F., Soriano, J., Tabien, R. E., & Chen, K. (2016). Soil texture and cultivar effects on rice (Oryza sativa, L.) grain yield, yield components and water productivity in three water regimes. PLOS ONE, 11(3), e0150549. https://doi.org/10.1371/journal.pone.0150549
FEDEARROZ. (2018). Adopción Masiva De Tecnologia AMTEC AMTEC FEDEARROZ. Revista Arroz, 22-34.
Garlapati, D., Charankumar, B., Ramu, K., Madeswaran, P., & Ramana Murthy, M. V. (2019). A review on the applications and recent advances in environmental DNA (eDNA) metagenomics. Reviews in Environmental Science and Bio/Technology, 18(3), 389-411. https://doi.org/10.1007/s11157-019-09501-4
Gliński, J., Horabik, J., & Lipiec, J. (Eds.). (2011). Cation Exchange Capacity. En Encyclopedia of Agrophysics (pp. 110-110). Springer Netherlands. https://doi.org/10.1007/978-90-481-3585-1_550
Guo, X., Liu, J., Xu, L., Sun, F., Ma, Y., Yin, D., ... & Lv, Y. (2022). Combined organic and inorganic fertilization can enhance dry direct-seeded rice yield by improving soil fungal community and structure. Agronomy, 12(5), 1213.
Hartmann, M., & Six, J. (2022). Soil structure and microbiome functions in agroecosystems. Nature Reviews Earth & Environment, 1-15. https://doi.org/10.1038/s43017-022- 00366-w
He, H., Li, W., Yu, R., & Ye, Z. (2017). Illumina-Based Analysis of Bulk and Rhizosphere Soil Bacterial Communities in Paddy Fields Under Mixed Heavy Metal Contamination. Pedosphere, 27(3), 569-578. https://doi.org/10.1016/S1002- 0160(17)60352-7
Hu, H. W., Zhang, L. M., Yuan, C. L., & He, J. Z. (2013). Contrasting Euryarchaeota communities between upland and paddy soils exhibited similar pH-impacted biogeographic patterns. Soil Biology and Biochemistry, 64, 18-27.
Jensen, L. J., Julien, P., Kuhn, M., von Mering, C., Muller, J., Doerks, T., & Bork, P. (2008). eggNOG: Automated construction and annotation of orthologous groups of genes. Nucleic Acids Research, 36(Database issue), D250-254. https://doi.org/10.1093/nar/gkm796
Kalam, S., Basu, A., Ahmad, I., Sayyed, R. Z., El-Enshasy, H. A., Dailin, D. J., & Suriani, N. L. (2020). Recent Understanding of Soil Acidobacteria and Their Ecological Significance: A Critical Review. Frontiers in Microbiology, 11. https://www.frontiersin.org/articles/10.3389/fmicb.2020.580024
Kanehisa, M., & Goto, S. (2000). KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Research, 28(1), 27-30. https://doi.org/10.1093/nar/28.1.27
Keegan, K. P., Glass, E. M., & Meyer, F. (2016). MG-RAST, a Metagenomics Service for Analysis of Microbial Community Structure and Function. En F. Martin & S. Uroz (Eds.), Microbial Environmental Genomics (MEG) (pp. 207-233). Springer. https://doi.org/10.1007/978-1-4939-3369-3_13
Kendzior, J., Warren raffa, D., & Bogdanski, A. (2022). The soil microbiome: A game changer for food and agriculture : Executive summary for policymakers and researchers. FAO. https://doi.org/10.4060/cc0717en
Li, S., Li, G., Huang, X., Chen, Y., Lv, C., Bai, L., Zhang, K., He, H., & Dai, J. (2023). Cultivar-specific response of rhizosphere bacterial community to uptake of cadmium and mineral elements in rice (Oryza sativa L.). Ecotoxicology and Environmental Safety, 249, 114403. https://doi.org/10.1016/j.ecoenv.2022.114403
Lopes, L. D., Wang, P., Futrell, S. L., & Schachtman, D. P. (2022). Sugars and Jasmonic Acid Concentration in Root Exudates Affect Maize Rhizosphere Bacterial Communities. Applied and Environmental Microbiology, 88(18), e0097122. https://doi.org/10.1128/aem.00971-22
Lyu, D., & Smith, D. L. (2022). The root signals in rhizosphere inter-organismal communications. Frontiers in Plant Science, 13. https://www.frontiersin.org/articles/10.3389/fpls.2022.1064058
Mahender, A., Swamy, B. P. M., Anandan, A., & Ali, J. (2019). Tolerance of iron-deficient and -toxic soil conditions in rice. Plants, 8(2), 31. https://doi.org/10.3390/plants8020031
Magrane, M. & UniProt Consortium. (2011). UniProt Knowledgebase: A hub of integrated protein data. Database: The Journal of Biological Databases and Curation, 2011, bar009. https://doi.org/10.1093/database/bar009
Mathesius, U., & Costa, S. R. (2021). Plant signals differentially affect rhizosphere nematode populations. Journal of Experimental Botany, 72(10), 3496-3499. https://doi.org/10.1093/jxb/erab149
Meyer, F., Paarmann, D., D’Souza, M., Olson, R., Glass, E., Kubal, M., Paczian, T., Rodriguez, A., Stevens, R., Wilke, A., Wilkening, J., & Edwards, R. (2008). The metagenomics RAST server – a public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinformatics, 9(1), 386. https://doi.org/10.1186/1471-2105-9-386
Mhete, M., Eze, P. N., Rahube, T. O., & Akinyemi, F. O. (2020). Soil properties influence bacterial abundance and diversity under different land-use regimes in semi-arid environments. Scientific African, 7, e00246. https://doi.org/10.1016/j.sciaf.2019.e00246
Mustafa, G., Hayat, N., & Alotaibi, B. A. (2023). Chapter fifteen—How and why to prevent over fertilization to get sustainable crop production. En T. Aftab & K. R. Hakeem (Eds.), Sustainable Plant Nutrition (pp. 339-354). Academic Press. https://doi.org/10.1016/B978-0-443-18675-2.00019-5
Naveed, M., Herath, L., Moldrup, P., Arthur, E., Nicolaisen, M., Norgaard, T., Ferré, T. P. A., & de Jonge, L. W. (2016). Spatial variability of microbial richness and diversity and relationships with soil organic carbon, texture and structure across an agricultural field. Applied Soil Ecology, 103, 44-55. https://doi.org/10.1016/j.apsoil.2016.03.004
Nguyen, B. T., Phan, B. T., Nguyen, T. X., Nguyen, V. N., Van Tran, T., & Bach, Q.-V. (2020). Contrastive nutrient leaching from two differently textured paddy soils as influenced by biochar addition. Journal of Soils and Sediments, 20(1), 297-307. https://doi.org/10.1007/s11368-019-02366-8
Nuccio, E. E., Starr, E., Karaoz, U., Brodie, E. L., Zhou, J., Tringe, S. G., Malmstrom, R. R., Woyke, T., Banfield, J. F., Firestone, M. K., & Pett-Ridge, J. (2020). Niche differentiation is spatially and temporally regulated in the rhizosphere. The ISME Journal, 14(4), 999-1014. https://doi.org/10.1038/s41396-019-0582-x
O’Brien, S., Gibbons, S., Owens, S., Hampton-Marcell, J., Johnston, E., Jastrow, J., Jack, G., Meyer, F., & Antonopoulos, D. (2016). Spatial scale drives patterns in soil bacterial diversity. Environmental microbiology, 18. https://doi.org/10.1111/1462- 2920.13231
Oksanen, J., Blanchet, F. G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., Minchin, P. R., O’Hara, R. B., Simpson, G. L., Solymos, P., Stevens, M. H. H., Szoecs, E., & Wagner, H. (2019). Vegan: Community ecology package. http://CRAN.R- project.org/package=vegan
Osman, K. T. (2013). Plant Nutrients and Soil Fertility Management. In K. T. Osman (Ed.), Soils: Principles, Properties and Management (pp. 129–159). Springer Netherlands. https://doi.org/10.1007/978-94-007-5663-2_10
Otero-Jiménez, V., Carreño-Carreño, J. del P., Barreto-Hernandez, E., van Elsas, J. D., & Uribe-Vélez, D. (2021). Impact of rice straw management strategies on rice rhizosphere microbiomes. Applied Soil Ecology, 167, 104036. https://doi.org/10.1016/j.apsoil.2021.104036
Overbeek, R., Olson, R., Pusch, G. D., Olsen, G. J., Davis, J. J., Disz, T., Edwards, R. A., Gerdes, S., Parrello, B., Shukla, M., Vonstein, V., Wattam, A. R., Xia, F., & Stevens, R. (2014). The SEED and the Rapid Annotation of microbial genomes using Subsystems Technology (RAST). Nucleic Acids Research, 42(Database issue), D206-214. https://doi.org/10.1093/nar/gkt1226
Pausch, J., & Kuzyakov, Y. (2018). Carbon input by roots into the soil: Quantification of rhizodeposition from root to ecosystem scale. Global Change Biology, 24(1), 1-12. https://doi.org/10.1111/gcb.13850
Phongchanmixay, S., Bounyavong, B., Khanthavong, P., Khanthavong, T., Ikeura, H., Matsumoto, N., & Kawamura, K. (2019). Rice plant growth and nutrient leaching under different patterns of split chemical fertilization on sandy soil using a pot. Paddy and Water Environment, 17(2), 91-99. https://doi.org/10.1007/s10333-019- 00701-w
Quince, C., Walker, A. W., Simpson, J. T., Loman, N. J., & Segata, N. (2017). Shotgun metagenomics, from sampling to analysis. Nature Biotechnology, 35(9), Art. 9. https://doi.org/10.1038/nbt.3935
R Core Team. (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www. R-project. org/.
Schroth, G., & Sinclair, F. L. (2003). Trees, Crops, and Soil Fertility: Concepts and Research Methods. CABI.
Speirs, L. B. M., Rice, D. T. F., Petrovski, S., & Seviour, R. J. (2019). The Phylogeny, Biodiversity, and Ecology of the Chloroflexi in Activated Sludge. Frontiers in Microbiology, 10. https://www.frontiersin.org/articles/10.3389/fmicb.2019.02015
Sun, W., Xiao, E., Pu, Z., Krumins, V., Dong, Y., Li, B., & Hu, M. (2017). Paddy soil microbial communities driven by environment-and microbe-microbe interactions: A case study of elevation-resolved microbial communities in a rice terrace. https://doi.org/10.1016/j.scitotenv.2017.08.275
Wang, Q., Liang, A., Chen, X., Zhang, S., Zhang, Y., McLaughlin, N. B., ... & Jia, S. (2021). The impact of cropping system, tillage and season on shaping soil fungal community in a long-term field trial. European Journal of Soil Biology, 102, 103253.
Wang, W., Luo, X., Chen, Y., Ye, X., Wang, H., Cao, Z., Ran, W., & Cui, Z. (2019). Succession of Composition and Function of Soil Bacterial Communities During Key Rice Growth Stages. Frontiers in Microbiology, 10, 421. https://doi.org/10.3389/fmicb.2019.00421
Wang, X., He, T., Gen, S., Zhang, X.-Q., Wang, X., Jiang, D., Li, C., Li, C., Wang, J., Zhang, W., & Li, C. (2020). Soil properties and agricultural practices shape microbial communities in flooded and rainfed croplands. Applied Soil Ecology, 147, 103449. https://doi.org/10.1016/j.apsoil.2019.103449
Wang, L., & Huang, D. (2021). Soil ammonia-oxidizing archaea in a paddy field with different irrigation and fertilization managements. Scientific Reports, 11(1), 1-11.
Wei, T., & Simko, V. (2021). R package «corrplot»: Visualization of a Correlation Matrix. (Version 0.92). (Version 0.92). https://cran.r- project.org/web/packages/corrplot/citation.html
Wilke, A., Bischof, J., Gerlach, W., Glass, E., Harrison, T., Keegan, K. P., Paczian, T., Trimble, W. L., Bagchi, S., Grama, A., Chaterji, S., & Meyer, F. (2016). The MG- RAST metagenomics database and portal in 2015. Nucleic Acids Research, 44(D1), D590-D594. https://doi.org/10.1093/nar/gkv1322
Yadav, A. N. (2021). Soil Microbiomes for Sustainable Agriculture: Functional Annotation. Springer Nature.
Yuan, C. L., Zhang, L. M., Wang, J. T., Hu, H. W., Shen, J. P., Cao, P., & He, J. Z. (2019). Distributions and environmental drivers of archaea and bacteria in paddy soils. Journal of Soils and Sediments, 19, 23-37.
Xu, C., Li, Y., Hu, X., Zang, Q., Zhuang, H., & Huang, L. (2022). The influence of organic and conventional cultivation patterns on physicochemical property, enzyme activity and microbial community characteristics of paddy soil. Agriculture, 12(1), 121.
Zhang, Q., Li, Y., Xing, J., Brookes, P. C., & Xu, J. (2019). Soil available phosphorus content drives the spatial distribution of archaeal communities along elevation in acidic terrace paddy soils. Science of the Total Environment, 658, 723-731.
Zhong, Y., Hu, J., Xia, Q., Zhang, S., Li, X., Pan, X., Zhao, R., Wang, R., Yan, W., Shangguan, Z., Hu, F., Yang, C., & Wang, W. (2020). Soil microbial mechanisms promoting ultrahigh rice yield. Soil Biology and Biochemistry, 143, 107741. https://doi.org/10.1016/j.soilbio.2020.107741
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.license.spa.fl_str_mv Atribución-SinDerivadas 4.0 Internacional
dc.rights.uri.spa.fl_str_mv http://creativecommons.org/licenses/by-nd/4.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv Atribución-SinDerivadas 4.0 Internacional
http://creativecommons.org/licenses/by-nd/4.0/
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.extent.spa.fl_str_mv 130 páginas
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv Bogotá - Ciencias - Maestría en Ciencias - Microbiología
dc.publisher.faculty.spa.fl_str_mv Facultad de Ciencias
dc.publisher.place.spa.fl_str_mv Bogotá, Colombia
dc.publisher.branch.spa.fl_str_mv Universidad Nacional de Colombia - Sede Bogotá
institution Universidad Nacional de Colombia
bitstream.url.fl_str_mv https://repositorio.unal.edu.co/bitstream/unal/84610/1/license.txt
https://repositorio.unal.edu.co/bitstream/unal/84610/2/Juan_Saavedra_Rice_Microbiomes_THESIS_MSc.pdf
https://repositorio.unal.edu.co/bitstream/unal/84610/3/Juan_Saavedra_Rice_Microbiomes_THESIS_MSc.pdf.jpg
bitstream.checksum.fl_str_mv eb34b1cf90b7e1103fc9dfd26be24b4a
52b81b252f299e8af41026407a5cf683
848f703c2cd5399fa9ab58a1088b61cc
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
repository.name.fl_str_mv Repositorio Institucional Universidad Nacional de Colombia
repository.mail.fl_str_mv repositorio_nal@unal.edu.co
_version_ 1814089442899525632
spelling Atribución-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Gonzalez Sayer, Sandra Milena3593b682e7c51b52ccc3853f2df6738fAristizabal Gutierrez, Fabio Ancizar50cb08feda89e1535b0cf798399c1a3cSaavedra Correa, Juan David6fb7c9a118b93d93d383ea8d1e04238eBioprocesos y BioprospecciónSaavedra, Juan David [0000-0003-1527-0428]https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=00000743822023-08-29T14:56:13Z2023-08-29T14:56:13Z2023-06-05https://repositorio.unal.edu.co/handle/unal/84610Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramas, fotografías a colorDada la importancia del cultivo del arroz en Colombia, se han utilizado muchas estrategias para incrementar el rendimiento por hectárea, las cuales buscan incentivar directa e indirectamente la promoción de servicios ecosistémicos como el ciclaje de nutrientes, siendo el microbioma del suelo un factor clave en la modulación de muchos nutrientes presentes en el suelo, con un efecto en la productividad de las plantas. Se propuso como objetivo de este trabajo de investigación, estudiar los microbiomas edáficos de un campo comercial de arroz y su relación con las propiedades físico-químicas del suelo. Para ello, se tomaron muestras de suelo de soporte y rizosférico en un lote de arroz de 33 hectáreas, previamente caracterizadas según el historial de datos de rendimiento, en tres zonas de manejo (rendimiento alto, rendimiento medio y rendimiento bajo). Las muestras de suelo se tomaron antes de la siembra del cultivo y después de la última fertilización química; Además, se realizaron análisis fisicoquímicos del suelo y extracción de ADN. Inicialmente se planteó una estrategia para el estudio de microbiomas a través de 16s rRNA y amplicones ITS, sin embargo, los resultados obtenidos con esta metodología no fueron lo suficientemente confiables, por lo que se tomó la decisión de realizar el análisis desde la perspectiva de la metagenómica, para su posterior secuenciación por “shotgun” y análisis de metagenoma. Las comunidades microbianas del campo de arroz reportaron una baja diversidad en general, se encontró que las muestras estaban dominadas por los filos Proteobacteria, Acidobacteria y Actinobacteria. Aunque hubo variaciones en la composición y estructura de los microbiomas del suelo de soportea lo largo del tiempo y entre los microbiomas asociados con las tres zonas de manejo, no se encontraron diferencias significativas. Se encontró que la diversidad, la composición y la función predicha de los microbiomas de la rizosfera eran significativamente diferentes de los microbiomas del suelo de soporte. Además, se identificó que estos suelos tenían un pH particularmente ácido, y también se pudo detectar que la materia orgánica incidía en la diversidad de los microbiomas, asi como las prácticas de manejo. (Texto tomado de la fuente)Given the importance of rice cultivation in Colombia, many strategies have been used to increase yield per hectare, which seek to directly and indirectly encourage the promotion of ecosystem services such as nutrient cycling, with the soil microbiome being a key factor in the modulation of many nutrients present in the soil and having an effect on plant productivity, it was proposed as the objective of this research, to study the edaphic microbiomes of a commercial field of rice and its relationship with the physico-chemical properties of the soil. For this, bulk and rhizosphere soil samples were taken in a 33-hectare rice plot, previously characterized according to the yield data history, in three management zones (high, medium, and low yield). The soil samples were taken before planting the crop and seventy days after plants germination; moreover, physicochemical analyzes of the soil and DNA extraction were performed. Initially, a strategy for the study of microbiomes through 16s rRNA and ITS amplicons was proposed, however, the results obtained with this methodology were not reliable enough, for which the decision was made to carry out the analysis from the perspective of metagenomics, for subsequent shotgun sequencing and metagenome analysis. The microbial communities from the rice field reported a low diversity in general, the samples were found to be dominated by the phyla Proteobacteria, Acidobacteria, and Actinobacteria. Even though, there were variations in the composition and structure of the bulk soil microbiomes across time and between the microbiomes associated with the three management zones, no significant differences were discovered. The diversity, composition, and predicted function of rhizosphere microbiomes were found to be significantly different from the bulk soil microbiomes. Moreover, it was identified that these soils had a particularly acid pH, and it was also possible to detect that organic matter, as well as management practices had an impact on the diversity of the microbiomes.FEDEARROZ - FNAMaestríaMagíster en Ciencias - Microbiología130 páginasapplication/pdfengUniversidad Nacional de ColombiaBogotá - Ciencias - Maestría en Ciencias - MicrobiologíaFacultad de CienciasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá570 - Biología::577 - EcologíaMicrobiología agrícolaEcología MicrobianaArrozAgricultura sostenibleRiceSustainable agricultureRice soil metagenomicsBulk soil MicrobiomesRhizosphere microbiomesAmplicon sequencingShotgun sequencingMetagenómica del suelo de arrozMicrobiomas de suelo de soporteMicrobiomas de rizosferaSecuenciacion shotgunCaracterización del metagenoma de la comunidad microbiana edáfica asociada a un cultivo de arroz (oryza sativa) bajo un esquema agronómico de manejo de agricultura por ambientesMetagenomic characterization of the edaphic microbial community associated with a rice crop (Oryza sativa) under an agronomic scheme of agriculture management by management zonesTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAndrew, J., & Edwards, A. (2011). Structure, Variation, and Dynamics of the RootAssociated Microbiota of the Crop Plant Rice.Baldrian, P. (2019). The known and the unknown in soil microbial ecology. In FEMS Microbiology Ecology (Vol. 95). Oxford University Press. https://doi.org/10.1093/femsec/fiz005Berg, G., Rybakova, D., Fischer, D., Cernava, T., Vergès, M. C. C., Charles, T., Chen, X., Cocolin, L., Eversole, K., Corral, G. H., Kazou, M., Kinkel, L., Lange, L., Lima, N., Loy, A., Macklin, J. A., Maguin, E., Mauchline, T., McClure, R., … Schloter, M. (2020). Microbiome definition re-visited: Old concepts and new challenges. In Microbiome (Vol. 8). BioMed Central Ltd. https://doi.org/10.1186/s40168-020- 00875-0Bolyen, E., Rideout, J. R., Dillon, M. R., Bokulich, N. A., Abnet, C. C., Al-Ghalith, G. A., Alexander, H., Alm, E. J., Arumugam, M., Asnicar, F., Bai, Y., Bisanz, J. E., Bittinger, K., Brejnrod, A., Brislawn, C. J., Brown, C. T., Callahan, B. J., CaraballoRodríguez, A. M., Chase, J., … Caporaso, J. G. (2019). Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nature Biotechnology, 37(8), 852–857. https://doi.org/10.1038/s41587-019-0209-9Breunig, F. M., Galvão, L. S., Dalagnol, R., Dauve, C. E., Parraga, A., Santi, A. L., Della Flora, D. P., & Chen, S. (2020). Delineation of management zones in agricultural fields using cover–crop biomass estimates from PlanetScope data. International Journal of Applied Earth Observation and Geoinformation, 85, 102004.Carreño, J. del P. (2019). Evaluación de la diversidad taxonómica y funcional de la comunidad microbiana relacionada con el ciclo del nitrógeno en suelos de cultivo de arroz con diferentes manejos del tamo. Universidad Nacional de Colombia.Chandra, R. (2021). Soil Biodiversity and Community Composition for Ecosystem Services. In A. Rakshit, S. K. Singh, P. C. Abhilash, & A. Biswas (Eds.), Soil Science: Fundamentals to Recent Advances (pp. 69–84). Springer. https://doi.org/10.1007/978-981-16-0917-6_5Chang, H. X., Haudenshield, J. S., Bowen, C. R., & Hartman, G. L. (2017). Metagenomewide association study and machine learning prediction of bulk soil microbiome and crop productivity. Frontiers in Microbiology, 8(APR). https://doi.org/10.3389/fmicb.2017.00519Chauhan, B. S., Jabran, K., & Mahajan, G. (Eds.). (2017). Rice Production Worldwide (1st ed. 2017). Springer International Publishing : Imprint: Springer. https://doi.org/10.1007/978-3-319-47516-5Chen, S., Du, T., Wang, S., Parsons, D., Wu, D., Guo, X., & Li, D. (2021). Quantifying the effects of spatial-temporal variability of soil properties on crop growth in management zones within an irrigated maize field in Northwest China. Agricultural Water Management, 244, 106535. https://doi.org/10.1016/j.agwat.2020.106535DANE. (2014). Censo Nacional Agropecuario 2014. In Departamento Administrativo Nacional de Estadística (DANE). https://www.dane.gov.co/files/images/foros/forode-entrega-de-resultados-y-cierre-3-censo-nacional-agropecuario/CNATomo2- Resultados.pdfDANE. (2021). Encuesta Nacional de Arroz Mecanizado (ENAM) Primer semestre de 2021. https://fedearroz.s3.amazonaws.com/media/documents/comunicado_ENAM_Isem 21_2_XnnAwff.pdfDe Gannes, V., Eudoxie, G., Bekele, I., & Hickey, W. J. (2015). Relations of microbiome characteristics to edaphic properties of tropical soils from Trinidad. Frontiers in Microbiology, 6(SEP), 1045. https://doi.org/10.3389/fmicb.2015.01045Ding, L.-J., Cui, H., Nie, S., Long, X., Duan, G., & Zhu, Y.-G. (2019). Microbiomes inhabiting rice roots and rhizosphere. FEMS Microbiology Ecology. https://doi.org/10.1093/femsec/fiz040Doerge, T. (2005). Management Zone Concepts. South Dakota State University, 1. http://www.ipni.net/publication/ssmg.nsf/0/C0D052F04A53E0BF852579E500761A E3/$FILE/SSMG-02.pdfDoni, F., Suhaimi, N. S. M., Mispan, M. S., Fathurrahman, F., Marzuki, B. M., Kusmoro, J., & Uphoff, N. (2022). Microbial Contributions for Rice Production: From Conventional Crop Management to the Use of ‘Omics’ Technologies. International Journal of Molecular Sciences, 23(2), 737. https://doi.org/10.3390/ijms23020737Fahad, S., Adnan, M., Noor, M., Arif, M., Alam, M., Khan, I. A., Ullah, H., Wahid, F., Mian, I. A., Jamal, Y., Basir, A., Hassan, S., Saud, S., Amanullah, Riaz, M., Wu, C., Khan, M. A., & Wang, D. (2018). Major constraints for global rice production. In Advances in Rice Research for Abiotic Stress Tolerance (pp. 1–22). Elsevier. https://doi.org/10.1016/B978-0-12-814332-2.00001-0Fahad, S., Adnan, M., Noor, M., Arif, M., Alam, M., Khan, I. A., Ullah, H., Wahid, F., Mian, I. A., Jamal, Y., Basir, A., Hassan, S., Saud, S., Amanullah, Riaz, M., Wu, C., Khan, M. A., & Wang, D. (2018). Major constraints for global rice production. In Advances in Rice Research for Abiotic Stress Tolerance (pp. 1–22). Elsevier. https://doi.org/10.1016/B978-0-12-814332-2.00001-0FEDEARROZ. (2018). Adopción Masiva De Tecnologia AMTEC AMTEC FEDEARROZ. Revista Arroz, 22–34.FEDEARROZ. (2020). Federación Nacional de Arroceros, Estadísticas Arroceras en Colombia. In Area, Producción y Rendimientos. http://www.fedearroz.com.co/new/apr_public.phpGarcés-Varón, G., & Medina-Rubio, J. (2018). LA FISIOLOGIA DEL CULTIVO DEL ARROZ EN EL PROGRAMA AMTEC. Fedearroz, 1(2).Garrido-Cardenas, J. A., & Manzano-Agugliaro, F. (2017). The metagenomics worldwide research. In Current Genetics (Vol. 63). Springer Verlag. https://doi.org/10.1007/s00294-017-0693-8Gebbers, R., & Adamchuk, V. I. (2010). Precision agriculture and food security. In Science (Vol. 327). https://doi.org/10.1126/science.1183899Harwood, C. & Buckley, M. (2008). A golden age for microbial ecology. In Nature Reviews Microbiology (Vol. 6). Nature Publishing Group. https://doi.org/10.1038/nrmicro1957Illumina. (2015). Technology Spotlight: Illumina ® Sequencing. IRRI. (1993). Rice Researcn in a time of change. International Rice Research Institute, 1(1).Jansson, J. (2013). Encyclopedia of Metagenomics: Soil Metagenomics. In Encyclopedia of Metagenomics (Vol. 2). Springer New York. https://doi.org/10.1007/978-1-4614- 6418-1Jing, J., Cong, W.-F., & Bezemer, T. M. (2022). Legacies at work: Plant–soil–microbiome interactions underpinning agricultural sustainability. Trends in Plant Science, 27(8), 781–792. https://doi.org/10.1016/j.tplants.2022.05.007Justo, C., & Scianca, ; Carlos. (2011). Agricultura Por Ambientes. Estrategias De Manejo De Maiz En Suelos Con Diferentes Aptitud Productiva. EEA INTA GENERAL VILLEGAS, 1.Kim, H., & Lee, Y.-H. (2020). The Rice Microbiome: A Model Platform for Crop Holobiome. Phytobiomes Journal • 2020 •, 4, 5–18. https://doi.org/10.1094/PBIOMES-07-19-0035-RVWKutílek, M., & Nielsen, D. R. (2017). Soil The Skin of the Planet Earth (1st ed.). Springer Books.Lopes, R. (2013). Towards a sustainable rice culture: The role of microbiota [PhD Thesis]. Universidade do PortoLukac, M., Grenni, P., & Gamboni, M. (2017). Soil Biological Communities and Ecosystem Resilience. In Soil Biological Communities and Ecosystem Resilience (Vol. 1). Springer International Publishing. https://doi.org/10.1007/978-3-319-63336-7Marchesi, J. R., & Ravel, J. (2015). The vocabulary of microbiome research: A proposal. Microbiome, 3(1), 1–3. https://doi.org/10.1186/s40168-015-0094-5Moharana, P. C., Jena, R. K., Pradhan, U. K., Nogiya, M., Tailor, B. L., Singh, R. S., & Singh, S. K. (2020). Geostatistical and fuzzy clustering approach for delineation of site-specific management zones and yield-limiting factors in irrigated hot arid environment of India. Precision Agriculture, 21(2), 426–448. https://doi.org/10.1007/s11119-019-09671-9Nannipieri, P., Ascher, J., Ceccherini, M. T., Petramellara, G., Giancarlo, R., & Schloter, M. (2020). Beyond microbial diversity for predicting soil functions: A mini review. Pedosphere, 30(1), 5–17. https://doi.org/10.1016/S1002-0160(19)60824-6Alteio, L. V., Séneca, J., Canarini, A., Angel, R., Jansa, J., Guseva, K., Kaiser, C., Richter, A., & Schmidt, H. (2021). A critical perspective on interpreting amplicon sequencing data in soil ecological research. Soil Biology and Biochemistry, 160, 108357. https://doi.org/10.1016/j.soilbio.2021.108357Auer, L., Mariadassou, M., O’Donohue, M., Klopp, C., & Hernandez-Raquet, G. (2017). Analysis of large 16S rRNA Illumina data sets: Impact of singleton read filtering on microbial community description. Molecular Ecology Resources, 17(6), e122– e132. https://doi.org/10.1111/1755-0998.12700Banerjee, S., & van der Heijden, M. G. A. (2022). Soil microbiomes and one health. Nature Reviews Microbiology, 1–15. https://doi.org/10.1038/s41579-022-00779-wBokulich, N. A., Kaehler, B. D., Rideout, J. R., Dillon, M., Bolyen, E., Knight, R., Huttley, G. A., & Gregory Caporaso, J. (2018). Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome, 6(1), 90. https://doi.org/10.1186/s40168-018-0470-zBrooks, J. P., Edwards, D. J., Harwich, M. D., Rivera, M. C., Fettweis, J. M., Serrano, M. G., Reris, R. A., Sheth, N. U., Huang, B., Girerd, P., Strauss, J. F., Jefferson, K. K., Buck, G. A., & Vaginal Microbiome Consortium (additional members). (2015). The truth about metagenomics: Quantifying and counteracting bias in 16S rRNA studies. BMC Microbiology, 15(1), 66. https://doi.org/10.1186/s12866-015- 0351-6Bruno, F., Marinella, M., & Santamaria, M. (2015). e-DNA Meta-Barcoding: From NGS Raw Data to Taxonomic Profiling. In E. Picardi (Ed.), RNA Bioinformatics (pp. 257– 278). Springer. https://doi.org/10.1007/978-1-4939-2291-8_16Bukin, Y. S., Galachyants, Y. P., Morozov, I. V., Bukin, S. V., Zakharenko, A. S., & Zemskaya, T. I. (2019). The effect of 16S rRNA region choice on bacterial community metabarcoding results. Scientific Data, 6(1), Article 1. https://doi.org/10.1038/sdata.2019.7Callahan, B. J., McMurdie, P. J., Rosen, M. J., Han, A. W., Johnson, A. J. A., & Holmes, S. P. (2016). DADA2: High resolution sample inference from Illumina amplicon data. Nature Methods, 13(7), 581–583. https://doi.org/10.1038/nmeth.3869Dubey, A., Malla, M. A., Khan, F., Chowdhary, K., Yadav, S., Kumar, A., Sharma, S., Khare, P. K., & Khan, M. L. (2019). Soil microbiome: A key player for conservation of soil health under changing climate. Biodiversity and Conservation, 28(8), 2405– 2429. https://doi.org/10.1007/s10531-019-01760-5Gołębiewski, M., & Tretyn, A. (2020). Generating amplicon reads for microbial community assessment with next-generation sequencing. Journal of Applied Microbiology, 128(2), 330–354. https://doi.org/10.1111/jam.14380Haas, B. J., Gevers, D., Earl, A. M., Feldgarden, M., Ward, D. V., Giannoukos, G., Ciulla, D., Tabbaa, D., Highlander, S. K., Sodergren, E., Methé, B., DeSantis, T. Z., Human Microbiome Consortium, Petrosino, J. F., Knight, R., & Birren, B. W. (2011). Chimeric 16S rRNA sequence formation and detection in Sanger and 454- pyrosequenced PCR amplicons. Genome Research, 21(3), 494–504. https://doi.org/10.1101/gr.112730.110Hall, M., & Beiko, R. G. (2018). 16S rRNA Gene Analysis with QIIME2. In R. G. Beiko, W. Hsiao, & J. Parkinson (Eds.), Microbiome Analysis: Methods and Protocols (pp. 113–129). Springer. https://doi.org/10.1007/978-1-4939-8728-3_8Highlander, S. (2013). Mock Community Analysis. In K. E. Nelson (Ed.), Encyclopedia of Metagenomics (pp. 1–7). Springer. https://doi.org/10.1007/978-1-4614-6418- 1_54-1Karstens, L., Asquith, M., Davin, S., Fair, D., Gregory, W., Wolfe, A., Braun, J., & Mcweeney, S. (2018). Controlling for contaminants in low biomass 16S rRNA gene sequencing experiments. https://doi.org/10.1101/329854Knauth, S., Schmidt, H., & Tippkötter, R. (2013). Comparison of commercial kits for the extraction of DNA from paddy soils. Letters in Applied Microbiology, 56(3), 222– 228. https://doi.org/10.1111/lam.12038Knight, R., Vrbanac, A., Taylor, B. C., Aksenov, A., Callewaert, C., Debelius, J., Gonzalez, A., Kosciolek, T., McCall, L.-I., McDonald, D., Melnik, A. V., Morton, J. T., Navas, J., Quinn, R. A., Sanders, J. G., Swafford, A. D., Thompson, L. R., Tripathi, A., Xu, Z. Z., ... Dorrestein, P. C. (2018). Best practices for analysing microbiomes. Nature Reviews Microbiology, 16(7), Article 7. https://doi.org/10.1038/s41579-018-0029-9Li, S., Deng, Y., Wang, Z., Zhang, Z., Kong, X., Zhou, W., Yi, Y., & Qu, Y. (2020). Exploring the accuracy of amplicon-based internal transcribed spacer markers for a fungal community. Molecular Ecology Resources, 20(1), 170–184. https://doi.org/10.1111/1755-0998.13097Liu, M., Clarke, L. J., Baker, S. C., Jordan, G. J., & Burridge, C. P. (2020). A practical guide to DNA metabarcoding for entomological ecologists. Ecological Entomology, 45(3), 373–385. https://doi.org/10.1111/een.12831Liu, Qin, Y., Chen, T., Lu, M., Qian, X., Guo, X., & Bai, Y. (2021). A practical guide to amplicon and metagenomic analysis of microbiome data. Protein & Cell, 12(5), 315–330. https://doi.org/10.1007/s13238-020-00724-8Mayday, M., Khan, L., Chow, E. D., Zinter, M. S., & DeRisi, J. L. (2019). High- Throughput Library Pooling for NGS. https://www.protocols.io/view/high- throughput-library-pooling-for-ngs-tcdeis6Nilsson, R. H., Anslan, S., Bahram, M., Wurzbacher, C., Baldrian, P., & Tedersoo, L. (2019). Mycobiome diversity: High-throughput sequencing and identification of fungi. Nature Reviews Microbiology, 17(2), Article 2. https://doi.org/10.1038/s41579-018-0116-yPecundo, M. H., Chang, A. C. G., Chen, T., dela Cruz, T. E. E., Ren, H., & Li, N. (2021). Full-Length 16S rRNA and ITS Gene Sequencing Revealed Rich Microbial Flora in Roots of Cycas spp. In China. Evolutionary Bioinformatics, 17, 1176934321989713. https://doi.org/10.1177/1176934321989713Pollock, J., Glendinning, L., Wisedchanwet, T., & Watson, M. (2018). The Madness of Microbiome: Attempting To Find Consensus “Best Practice” for 16S Microbiome Studies. Applied and Environmental Microbiology, 84(7), e02627-17. https://doi.org/10.1128/AEM.02627-17Prasad, S., Malav, L. C., Choudhary, J., Kannojiya, S., Kundu, M., Kumar, S., & Yadav, A. N. (2021). Soil Microbiomes for Healthy Nutrient Recycling. In A. N. Yadav, J. Singh, C. Singh, & N. Yadav (Eds.), Current Trends in Microbial Biotechnology for Sustainable Agriculture (pp. 1–21). Springer. https://doi.org/10.1007/978-981-15- 6949-4_1Prodan, A., Tremaroli, V., Brolin, H., Zwinderman, A. H., Nieuwdorp, M., & Levin, E. (2020). Comparing bioinformatic pipelines for microbial 16S rRNA amplicon sequencing. PLOS ONE, 15(1), e0227434. https://doi.org/10.1371/journal.pone.0227434Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., Peplies, J., & Glöckner, F. O. (2013). The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Research, 41(Database issue), D590-596. https://doi.org/10.1093/nar/gks1219Schloss, P. D. (2020). Reintroducing mothur: 10 Years Later. Applied and Environmental Microbiology, 86(2), e02343-19. https://doi.org/10.1128/AEM.02343-19Semenov, M. V. (2021). Metabarcoding and Metagenomics in Soil Ecology Research: Achievements, Challenges, and Prospects. Biology Bulletin Reviews, 11(1), 40– 53. https://doi.org/10.1134/S2079086421010084Starke, R., Pylro, V. S., & Morais, D. K. (2021). 16S rRNA Gene Copy Number Normalization Does Not Provide More Reliable Conclusions in Metataxonomic Surveys. Microbial Ecology, 81(2), 535–539. https://doi.org/10.1007/s00248-020- 01586-7Sze, M. A., & Schloss, P. D. (2019). The Impact of DNA Polymerase and Number of Rounds of Amplification in PCR on 16S rRNA Gene Sequence Data. MSphere, 4(3), e00163-19. https://doi.org/10.1128/mSphere.00163-19Yang, R.-H., Su, J.-H., Shang, J.-J., Wu, Y.-Y., Li, Y., Bao, D.-P., & Yao, Y.-J. (2018). Evaluation of the ribosomal DNA internal transcribed spacer (ITS), specifically ITS1 and ITS2, for the analysis of fungal diversity by deep sequencing. PLOS ONE, 13(10), 1–17. https://doi.org/10.1371/journal.pone.0206428Amanullah, D., & Khalid, S. (2020). Agronomy: Climate Change (Vol. 1). IntechOpen. https://doi.org/10.5772/intechopen.78102Arunrat, N., Pumijumnong, N., & Hatano, R. (2017). Practices sustaining soil organic matter and rice yield in a tropical monsoon region. Soil Science and Plant Nutrition, 1-14. https://doi.org/10.1080/00380768.2017.1323546Arunrat, N., Sansupa, C., Kongsurakan, P., Sereenonchai, S., & Hatano, R. (2022). Soil Microbial Diversity and Community Composition in Rice-Fish Co-Culture and Rice Monoculture Farming System. Biology, 11(8), 1242. https://doi.org/10.3390/biology11081242Atique-ur-Rehman, Farooq, M., Rashid, A., Nadeem, F., Stuerz, S., Asch, F., Bell, R. W., & Siddique, K. H. M. (2018). Boron nutrition of rice in different production systems. A review. Agronomy for Sustainable Development, 38(3), 25. https://doi.org/10.1007/s13593-018-0504-8Azadi, A., Baghernejad, M., Gholami, A., & Shakeri, S. (2021). Forms and distribution pattern of soil Fe (Iron) and Mn (Manganese) oxides due to long-term rice cultivation in fars Province Southern Iran. Communications in Soil Science and Plant Analysis, 52(16), 1894-1911. https://doi.org/10.1080/00103624.2021.1900226Barillot, C. D. C., Sarde, C.-O., Bert, V., Tarnaud, E., & Cochet, N. (2013). A standardized method for the sampling of rhizosphere and rhizoplan soil bacteria associated to a herbaceous root system. Annals of Microbiology, 63(2), 471-476. https://doi.org/10.1007/s13213-012-0491-yBenson, D. A., Cavanaugh, M., Clark, K., Karsch-Mizrachi, I., Lipman, D. J., Ostell, J., & Sayers, E. W. (2013). GenBank. Nucleic Acids Research, 41(Database issue), D36- D42. https://doi.org/10.1093/nar/gks1195Biswas, R., & Sarkar, A. (2018). ‘Omics’ Tools in Soil Microbiology: The State of the Art. En T. K. Adhya, B. Lal, B. Mohapatra, D. Paul, & S. Das (Eds.), Advances in Soil Microbiology: Recent Trends and Future Prospects: Volume 1: Soil-Microbe Interaction (pp. 35-64). Springer. https://doi.org/10.1007/978-981-10-6178-3_3Caulfield, M. E., Fonte, S. J., Groot, J. C. J., Vanek, S. J., Sherwood, S., Oyarzun, P., Borja, R. M., Dumble, S., & Tittonell, P. (2020). Agroecosystem patterns and land management co-develop through environment, management, and land-use interactions. Ecosphere, 11(4), e03113. https://doi.org/10.1002/ecs2.3113Chaparro, J. M., Badri, D. V., & Vivanco, J. M. (2014). Rhizosphere microbiome assemblage is affected by plant development. The ISME Journal, 8(4), 790-803. https://doi.org/10.1038/ismej.2013.196Chen, L., Zhao, D., Han, G., Yang, F., Gong, Z., Song, X., Li, D., & Zhang, G. (2022). Iron loss of paddy soil in China and its environmental implications. Science China Earth Sciences, 65(7), 1277-1291. https://doi.org/10.1007/s11430-021-9936-6Chialva, M., Ghignone, S., Cozzi, P., Lazzari, B., Bonfante, P., Abbruscato, P., & Lumini, E. (2020). Water management and phenology influence the root-associated rice field microbiota. FEMS microbiology ecology, 96. https://doi.org/10.1093/femsec/fiaa146Cox, M. P., Peterson, D. A., & Biggs, P. J. (2010). SolexaQA: At-a-glance quality assessment of Illumina second-generation sequencing data. BMC Bioinformatics, 11(1), 485. https://doi.org/10.1186/1471-2105-11-485De Gruyter, J., Weedon, J. T., Bazot, S., Dauwe, S., Fernandez-Garberí, P.-R., Geisen, S., De La Motte, L. G., Heinesch, B., Janssens, I. A., Leblans, N., Manise, T., Ogaya, R., Löfvenius, M. O., Peñuelas, J., Sigurdsson, B. D., Vincent, G., & Verbruggen, E. (2020). Patterns of local, intercontinental and interseasonal variation of soil bacterial and eukaryotic microbial communities. FEMS Microbiology Ecology, 96(3), fiaa018. https://doi.org/10.1093/femsec/fiaa018Devi, R., Kaur, T., Kour, D., Yadav, A., Yadav, A. N., Suman, A., ... & Saxena, A. K. (2022). Minerals solubilizing and mobilizing microbiomes: A sustainable approach for managing minerals’ deficiency in agricultural soil. Journal of Applied Microbiology, 133(3), 1245-1272.Ding, L.-J., Cui, H., Nie, S., Long, X., Duan, G., & Zhu, Y.-G. (2019). Microbiomes inhabiting rice roots and rhizosphere. FEMS microbiology ecology. https://doi.org/10.1093/femsec/fiz040Dong, H., Sun, H., Jiang, L., Ma, D., & Fan, S. (2022). Characteristics of root-associated bacterial community and nitrogen biochemical properties of two Japonica rice cultivars with different yields. Food and Energy Security, 11(1), e357. https://doi.org/10.1002/fes3.357Doni, F., Suhaimi, N. S. M., Mispan, M. S., Fathurrahman, F., Marzuki, B. M., Kusmoro, J., & Uphoff, N. (2022). Microbial Contributions for Rice Production: From Conventional Crop Management to the Use of ‘Omics’ Technologies. International Journal of Molecular Sciences, 23(2), Art. 2. https://doi.org/10.3390/ijms23020737Dou, F., Soriano, J., Tabien, R. E., & Chen, K. (2016). Soil texture and cultivar effects on rice (Oryza sativa, L.) grain yield, yield components and water productivity in three water regimes. PLOS ONE, 11(3), e0150549. https://doi.org/10.1371/journal.pone.0150549FEDEARROZ. (2018). Adopción Masiva De Tecnologia AMTEC AMTEC FEDEARROZ. Revista Arroz, 22-34.Garlapati, D., Charankumar, B., Ramu, K., Madeswaran, P., & Ramana Murthy, M. V. (2019). A review on the applications and recent advances in environmental DNA (eDNA) metagenomics. Reviews in Environmental Science and Bio/Technology, 18(3), 389-411. https://doi.org/10.1007/s11157-019-09501-4Gliński, J., Horabik, J., & Lipiec, J. (Eds.). (2011). Cation Exchange Capacity. En Encyclopedia of Agrophysics (pp. 110-110). Springer Netherlands. https://doi.org/10.1007/978-90-481-3585-1_550Guo, X., Liu, J., Xu, L., Sun, F., Ma, Y., Yin, D., ... & Lv, Y. (2022). Combined organic and inorganic fertilization can enhance dry direct-seeded rice yield by improving soil fungal community and structure. Agronomy, 12(5), 1213.Hartmann, M., & Six, J. (2022). Soil structure and microbiome functions in agroecosystems. Nature Reviews Earth & Environment, 1-15. https://doi.org/10.1038/s43017-022- 00366-wHe, H., Li, W., Yu, R., & Ye, Z. (2017). Illumina-Based Analysis of Bulk and Rhizosphere Soil Bacterial Communities in Paddy Fields Under Mixed Heavy Metal Contamination. Pedosphere, 27(3), 569-578. https://doi.org/10.1016/S1002- 0160(17)60352-7Hu, H. W., Zhang, L. M., Yuan, C. L., & He, J. Z. (2013). Contrasting Euryarchaeota communities between upland and paddy soils exhibited similar pH-impacted biogeographic patterns. Soil Biology and Biochemistry, 64, 18-27.Jensen, L. J., Julien, P., Kuhn, M., von Mering, C., Muller, J., Doerks, T., & Bork, P. (2008). eggNOG: Automated construction and annotation of orthologous groups of genes. Nucleic Acids Research, 36(Database issue), D250-254. https://doi.org/10.1093/nar/gkm796Kalam, S., Basu, A., Ahmad, I., Sayyed, R. Z., El-Enshasy, H. A., Dailin, D. J., & Suriani, N. L. (2020). Recent Understanding of Soil Acidobacteria and Their Ecological Significance: A Critical Review. Frontiers in Microbiology, 11. https://www.frontiersin.org/articles/10.3389/fmicb.2020.580024Kanehisa, M., & Goto, S. (2000). KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Research, 28(1), 27-30. https://doi.org/10.1093/nar/28.1.27Keegan, K. P., Glass, E. M., & Meyer, F. (2016). MG-RAST, a Metagenomics Service for Analysis of Microbial Community Structure and Function. En F. Martin & S. Uroz (Eds.), Microbial Environmental Genomics (MEG) (pp. 207-233). Springer. https://doi.org/10.1007/978-1-4939-3369-3_13Kendzior, J., Warren raffa, D., & Bogdanski, A. (2022). The soil microbiome: A game changer for food and agriculture : Executive summary for policymakers and researchers. FAO. https://doi.org/10.4060/cc0717enLi, S., Li, G., Huang, X., Chen, Y., Lv, C., Bai, L., Zhang, K., He, H., & Dai, J. (2023). Cultivar-specific response of rhizosphere bacterial community to uptake of cadmium and mineral elements in rice (Oryza sativa L.). Ecotoxicology and Environmental Safety, 249, 114403. https://doi.org/10.1016/j.ecoenv.2022.114403Lopes, L. D., Wang, P., Futrell, S. L., & Schachtman, D. P. (2022). Sugars and Jasmonic Acid Concentration in Root Exudates Affect Maize Rhizosphere Bacterial Communities. Applied and Environmental Microbiology, 88(18), e0097122. https://doi.org/10.1128/aem.00971-22Lyu, D., & Smith, D. L. (2022). The root signals in rhizosphere inter-organismal communications. Frontiers in Plant Science, 13. https://www.frontiersin.org/articles/10.3389/fpls.2022.1064058Mahender, A., Swamy, B. P. M., Anandan, A., & Ali, J. (2019). Tolerance of iron-deficient and -toxic soil conditions in rice. Plants, 8(2), 31. https://doi.org/10.3390/plants8020031Magrane, M. & UniProt Consortium. (2011). UniProt Knowledgebase: A hub of integrated protein data. Database: The Journal of Biological Databases and Curation, 2011, bar009. https://doi.org/10.1093/database/bar009Mathesius, U., & Costa, S. R. (2021). Plant signals differentially affect rhizosphere nematode populations. Journal of Experimental Botany, 72(10), 3496-3499. https://doi.org/10.1093/jxb/erab149Meyer, F., Paarmann, D., D’Souza, M., Olson, R., Glass, E., Kubal, M., Paczian, T., Rodriguez, A., Stevens, R., Wilke, A., Wilkening, J., & Edwards, R. (2008). The metagenomics RAST server – a public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinformatics, 9(1), 386. https://doi.org/10.1186/1471-2105-9-386Mhete, M., Eze, P. N., Rahube, T. O., & Akinyemi, F. O. (2020). Soil properties influence bacterial abundance and diversity under different land-use regimes in semi-arid environments. Scientific African, 7, e00246. https://doi.org/10.1016/j.sciaf.2019.e00246Mustafa, G., Hayat, N., & Alotaibi, B. A. (2023). Chapter fifteen—How and why to prevent over fertilization to get sustainable crop production. En T. Aftab & K. R. Hakeem (Eds.), Sustainable Plant Nutrition (pp. 339-354). Academic Press. https://doi.org/10.1016/B978-0-443-18675-2.00019-5Naveed, M., Herath, L., Moldrup, P., Arthur, E., Nicolaisen, M., Norgaard, T., Ferré, T. P. A., & de Jonge, L. W. (2016). Spatial variability of microbial richness and diversity and relationships with soil organic carbon, texture and structure across an agricultural field. Applied Soil Ecology, 103, 44-55. https://doi.org/10.1016/j.apsoil.2016.03.004Nguyen, B. T., Phan, B. T., Nguyen, T. X., Nguyen, V. N., Van Tran, T., & Bach, Q.-V. (2020). Contrastive nutrient leaching from two differently textured paddy soils as influenced by biochar addition. Journal of Soils and Sediments, 20(1), 297-307. https://doi.org/10.1007/s11368-019-02366-8Nuccio, E. E., Starr, E., Karaoz, U., Brodie, E. L., Zhou, J., Tringe, S. G., Malmstrom, R. R., Woyke, T., Banfield, J. F., Firestone, M. K., & Pett-Ridge, J. (2020). Niche differentiation is spatially and temporally regulated in the rhizosphere. The ISME Journal, 14(4), 999-1014. https://doi.org/10.1038/s41396-019-0582-xO’Brien, S., Gibbons, S., Owens, S., Hampton-Marcell, J., Johnston, E., Jastrow, J., Jack, G., Meyer, F., & Antonopoulos, D. (2016). Spatial scale drives patterns in soil bacterial diversity. Environmental microbiology, 18. https://doi.org/10.1111/1462- 2920.13231Oksanen, J., Blanchet, F. G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., Minchin, P. R., O’Hara, R. B., Simpson, G. L., Solymos, P., Stevens, M. H. H., Szoecs, E., & Wagner, H. (2019). Vegan: Community ecology package. http://CRAN.R- project.org/package=veganOsman, K. T. (2013). Plant Nutrients and Soil Fertility Management. In K. T. Osman (Ed.), Soils: Principles, Properties and Management (pp. 129–159). Springer Netherlands. https://doi.org/10.1007/978-94-007-5663-2_10Otero-Jiménez, V., Carreño-Carreño, J. del P., Barreto-Hernandez, E., van Elsas, J. D., & Uribe-Vélez, D. (2021). Impact of rice straw management strategies on rice rhizosphere microbiomes. Applied Soil Ecology, 167, 104036. https://doi.org/10.1016/j.apsoil.2021.104036Overbeek, R., Olson, R., Pusch, G. D., Olsen, G. J., Davis, J. J., Disz, T., Edwards, R. A., Gerdes, S., Parrello, B., Shukla, M., Vonstein, V., Wattam, A. R., Xia, F., & Stevens, R. (2014). The SEED and the Rapid Annotation of microbial genomes using Subsystems Technology (RAST). Nucleic Acids Research, 42(Database issue), D206-214. https://doi.org/10.1093/nar/gkt1226Pausch, J., & Kuzyakov, Y. (2018). Carbon input by roots into the soil: Quantification of rhizodeposition from root to ecosystem scale. Global Change Biology, 24(1), 1-12. https://doi.org/10.1111/gcb.13850Phongchanmixay, S., Bounyavong, B., Khanthavong, P., Khanthavong, T., Ikeura, H., Matsumoto, N., & Kawamura, K. (2019). Rice plant growth and nutrient leaching under different patterns of split chemical fertilization on sandy soil using a pot. Paddy and Water Environment, 17(2), 91-99. https://doi.org/10.1007/s10333-019- 00701-wQuince, C., Walker, A. W., Simpson, J. T., Loman, N. J., & Segata, N. (2017). Shotgun metagenomics, from sampling to analysis. Nature Biotechnology, 35(9), Art. 9. https://doi.org/10.1038/nbt.3935R Core Team. (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www. R-project. org/.Schroth, G., & Sinclair, F. L. (2003). Trees, Crops, and Soil Fertility: Concepts and Research Methods. CABI.Speirs, L. B. M., Rice, D. T. F., Petrovski, S., & Seviour, R. J. (2019). The Phylogeny, Biodiversity, and Ecology of the Chloroflexi in Activated Sludge. Frontiers in Microbiology, 10. https://www.frontiersin.org/articles/10.3389/fmicb.2019.02015Sun, W., Xiao, E., Pu, Z., Krumins, V., Dong, Y., Li, B., & Hu, M. (2017). Paddy soil microbial communities driven by environment-and microbe-microbe interactions: A case study of elevation-resolved microbial communities in a rice terrace. https://doi.org/10.1016/j.scitotenv.2017.08.275Wang, Q., Liang, A., Chen, X., Zhang, S., Zhang, Y., McLaughlin, N. B., ... & Jia, S. (2021). The impact of cropping system, tillage and season on shaping soil fungal community in a long-term field trial. European Journal of Soil Biology, 102, 103253.Wang, W., Luo, X., Chen, Y., Ye, X., Wang, H., Cao, Z., Ran, W., & Cui, Z. (2019). Succession of Composition and Function of Soil Bacterial Communities During Key Rice Growth Stages. Frontiers in Microbiology, 10, 421. https://doi.org/10.3389/fmicb.2019.00421Wang, X., He, T., Gen, S., Zhang, X.-Q., Wang, X., Jiang, D., Li, C., Li, C., Wang, J., Zhang, W., & Li, C. (2020). Soil properties and agricultural practices shape microbial communities in flooded and rainfed croplands. Applied Soil Ecology, 147, 103449. https://doi.org/10.1016/j.apsoil.2019.103449Wang, L., & Huang, D. (2021). Soil ammonia-oxidizing archaea in a paddy field with different irrigation and fertilization managements. Scientific Reports, 11(1), 1-11.Wei, T., & Simko, V. (2021). R package «corrplot»: Visualization of a Correlation Matrix. (Version 0.92). (Version 0.92). https://cran.r- project.org/web/packages/corrplot/citation.htmlWilke, A., Bischof, J., Gerlach, W., Glass, E., Harrison, T., Keegan, K. P., Paczian, T., Trimble, W. L., Bagchi, S., Grama, A., Chaterji, S., & Meyer, F. (2016). The MG- RAST metagenomics database and portal in 2015. Nucleic Acids Research, 44(D1), D590-D594. https://doi.org/10.1093/nar/gkv1322Yadav, A. N. (2021). Soil Microbiomes for Sustainable Agriculture: Functional Annotation. Springer Nature.Yuan, C. L., Zhang, L. M., Wang, J. T., Hu, H. W., Shen, J. P., Cao, P., & He, J. Z. (2019). Distributions and environmental drivers of archaea and bacteria in paddy soils. Journal of Soils and Sediments, 19, 23-37.Xu, C., Li, Y., Hu, X., Zang, Q., Zhuang, H., & Huang, L. (2022). The influence of organic and conventional cultivation patterns on physicochemical property, enzyme activity and microbial community characteristics of paddy soil. Agriculture, 12(1), 121.Zhang, Q., Li, Y., Xing, J., Brookes, P. C., & Xu, J. (2019). Soil available phosphorus content drives the spatial distribution of archaeal communities along elevation in acidic terrace paddy soils. Science of the Total Environment, 658, 723-731.Zhong, Y., Hu, J., Xia, Q., Zhang, S., Li, X., Pan, X., Zhao, R., Wang, R., Yan, W., Shangguan, Z., Hu, F., Yang, C., & Wang, W. (2020). Soil microbial mechanisms promoting ultrahigh rice yield. Soil Biology and Biochemistry, 143, 107741. https://doi.org/10.1016/j.soilbio.2020.107741EXPLORACIÓN DE LA INTERACCIÓN DE LO FÍSICO, COMPONENTES QUÍMICOS Y BIOLÓGICOS DEL SUELO EN LA PRODUCTIVIDAD DEL CULTIVO DE ARROZ EN AGRICULTURA POR AMBIENTESUniversidad Nacional de ColombiaEstudiantesLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/84610/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINALJuan_Saavedra_Rice_Microbiomes_THESIS_MSc.pdfJuan_Saavedra_Rice_Microbiomes_THESIS_MSc.pdfTesis de Maestría en Ciencias - Microbiologíaapplication/pdf3543431https://repositorio.unal.edu.co/bitstream/unal/84610/2/Juan_Saavedra_Rice_Microbiomes_THESIS_MSc.pdf52b81b252f299e8af41026407a5cf683MD52THUMBNAILJuan_Saavedra_Rice_Microbiomes_THESIS_MSc.pdf.jpgJuan_Saavedra_Rice_Microbiomes_THESIS_MSc.pdf.jpgGenerated Thumbnailimage/jpeg4913https://repositorio.unal.edu.co/bitstream/unal/84610/3/Juan_Saavedra_Rice_Microbiomes_THESIS_MSc.pdf.jpg848f703c2cd5399fa9ab58a1088b61ccMD53unal/84610oai:repositorio.unal.edu.co:unal/846102024-08-12 23:11:37.333Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.coUEFSVEUgMS4gVMOJUk1JTk9TIERFIExBIExJQ0VOQ0lBIFBBUkEgUFVCTElDQUNJw5NOIERFIE9CUkFTIEVOIEVMIFJFUE9TSVRPUklPIElOU1RJVFVDSU9OQUwgVU5BTC4KCkxvcyBhdXRvcmVzIHkvbyB0aXR1bGFyZXMgZGUgbG9zIGRlcmVjaG9zIHBhdHJpbW9uaWFsZXMgZGUgYXV0b3IsIGNvbmZpZXJlbiBhIGxhIFVuaXZlcnNpZGFkIE5hY2lvbmFsIGRlIENvbG9tYmlhIHVuYSBsaWNlbmNpYSBubyBleGNsdXNpdmEsIGxpbWl0YWRhIHkgZ3JhdHVpdGEgc29icmUgbGEgb2JyYSBxdWUgc2UgaW50ZWdyYSBlbiBlbCBSZXBvc2l0b3JpbyBJbnN0aXR1Y2lvbmFsLCBiYWpvIGxvcyBzaWd1aWVudGVzIHTDqXJtaW5vczoKCgphKQlMb3MgYXV0b3JlcyB5L28gbG9zIHRpdHVsYXJlcyBkZSBsb3MgZGVyZWNob3MgcGF0cmltb25pYWxlcyBkZSBhdXRvciBzb2JyZSBsYSBvYnJhIGNvbmZpZXJlbiBhIGxhIFVuaXZlcnNpZGFkIE5hY2lvbmFsIGRlIENvbG9tYmlhIHVuYSBsaWNlbmNpYSBubyBleGNsdXNpdmEgcGFyYSByZWFsaXphciBsb3Mgc2lndWllbnRlcyBhY3RvcyBzb2JyZSBsYSBvYnJhOiBpKSByZXByb2R1Y2lyIGxhIG9icmEgZGUgbWFuZXJhIGRpZ2l0YWwsIHBlcm1hbmVudGUgbyB0ZW1wb3JhbCwgaW5jbHV5ZW5kbyBlbCBhbG1hY2VuYW1pZW50byBlbGVjdHLDs25pY28sIGFzw60gY29tbyBjb252ZXJ0aXIgZWwgZG9jdW1lbnRvIGVuIGVsIGN1YWwgc2UgZW5jdWVudHJhIGNvbnRlbmlkYSBsYSBvYnJhIGEgY3VhbHF1aWVyIG1lZGlvIG8gZm9ybWF0byBleGlzdGVudGUgYSBsYSBmZWNoYSBkZSBsYSBzdXNjcmlwY2nDs24gZGUgbGEgcHJlc2VudGUgbGljZW5jaWEsIHkgaWkpIGNvbXVuaWNhciBhbCBww7pibGljbyBsYSBvYnJhIHBvciBjdWFscXVpZXIgbWVkaW8gbyBwcm9jZWRpbWllbnRvLCBlbiBtZWRpb3MgYWzDoW1icmljb3MgbyBpbmFsw6FtYnJpY29zLCBpbmNsdXllbmRvIGxhIHB1ZXN0YSBhIGRpc3Bvc2ljacOzbiBlbiBhY2Nlc28gYWJpZXJ0by4gQWRpY2lvbmFsIGEgbG8gYW50ZXJpb3IsIGVsIGF1dG9yIHkvbyB0aXR1bGFyIGF1dG9yaXphIGEgbGEgVW5pdmVyc2lkYWQgTmFjaW9uYWwgZGUgQ29sb21iaWEgcGFyYSBxdWUsIGVuIGxhIHJlcHJvZHVjY2nDs24geSBjb211bmljYWNpw7NuIGFsIHDDumJsaWNvIHF1ZSBsYSBVbml2ZXJzaWRhZCByZWFsaWNlIHNvYnJlIGxhIG9icmEsIGhhZ2EgbWVuY2nDs24gZGUgbWFuZXJhIGV4cHJlc2EgYWwgdGlwbyBkZSBsaWNlbmNpYSBDcmVhdGl2ZSBDb21tb25zIGJham8gbGEgY3VhbCBlbCBhdXRvciB5L28gdGl0dWxhciBkZXNlYSBvZnJlY2VyIHN1IG9icmEgYSBsb3MgdGVyY2Vyb3MgcXVlIGFjY2VkYW4gYSBkaWNoYSBvYnJhIGEgdHJhdsOpcyBkZWwgUmVwb3NpdG9yaW8gSW5zdGl0dWNpb25hbCwgY3VhbmRvIHNlYSBlbCBjYXNvLiBFbCBhdXRvciB5L28gdGl0dWxhciBkZSBsb3MgZGVyZWNob3MgcGF0cmltb25pYWxlcyBkZSBhdXRvciBwb2Ryw6EgZGFyIHBvciB0ZXJtaW5hZGEgbGEgcHJlc2VudGUgbGljZW5jaWEgbWVkaWFudGUgc29saWNpdHVkIGVsZXZhZGEgYSBsYSBEaXJlY2Npw7NuIE5hY2lvbmFsIGRlIEJpYmxpb3RlY2FzIGRlIGxhIFVuaXZlcnNpZGFkIE5hY2lvbmFsIGRlIENvbG9tYmlhLiAKCmIpIAlMb3MgYXV0b3JlcyB5L28gdGl0dWxhcmVzIGRlIGxvcyBkZXJlY2hvcyBwYXRyaW1vbmlhbGVzIGRlIGF1dG9yIHNvYnJlIGxhIG9icmEgY29uZmllcmVuIGxhIGxpY2VuY2lhIHNlw7FhbGFkYSBlbiBlbCBsaXRlcmFsIGEpIGRlbCBwcmVzZW50ZSBkb2N1bWVudG8gcG9yIGVsIHRpZW1wbyBkZSBwcm90ZWNjacOzbiBkZSBsYSBvYnJhIGVuIHRvZG9zIGxvcyBwYcOtc2VzIGRlbCBtdW5kbywgZXN0byBlcywgc2luIGxpbWl0YWNpw7NuIHRlcnJpdG9yaWFsIGFsZ3VuYS4KCmMpCUxvcyBhdXRvcmVzIHkvbyB0aXR1bGFyZXMgZGUgZGVyZWNob3MgcGF0cmltb25pYWxlcyBkZSBhdXRvciBtYW5pZmllc3RhbiBlc3RhciBkZSBhY3VlcmRvIGNvbiBxdWUgbGEgcHJlc2VudGUgbGljZW5jaWEgc2Ugb3RvcmdhIGEgdMOtdHVsbyBncmF0dWl0bywgcG9yIGxvIHRhbnRvLCByZW51bmNpYW4gYSByZWNpYmlyIGN1YWxxdWllciByZXRyaWJ1Y2nDs24gZWNvbsOzbWljYSBvIGVtb2x1bWVudG8gYWxndW5vIHBvciBsYSBwdWJsaWNhY2nDs24sIGRpc3RyaWJ1Y2nDs24sIGNvbXVuaWNhY2nDs24gcMO6YmxpY2EgeSBjdWFscXVpZXIgb3RybyB1c28gcXVlIHNlIGhhZ2EgZW4gbG9zIHTDqXJtaW5vcyBkZSBsYSBwcmVzZW50ZSBsaWNlbmNpYSB5IGRlIGxhIGxpY2VuY2lhIENyZWF0aXZlIENvbW1vbnMgY29uIHF1ZSBzZSBwdWJsaWNhLgoKZCkJUXVpZW5lcyBmaXJtYW4gZWwgcHJlc2VudGUgZG9jdW1lbnRvIGRlY2xhcmFuIHF1ZSBwYXJhIGxhIGNyZWFjacOzbiBkZSBsYSBvYnJhLCBubyBzZSBoYW4gdnVsbmVyYWRvIGxvcyBkZXJlY2hvcyBkZSBwcm9waWVkYWQgaW50ZWxlY3R1YWwsIGluZHVzdHJpYWwsIG1vcmFsZXMgeSBwYXRyaW1vbmlhbGVzIGRlIHRlcmNlcm9zLiBEZSBvdHJhIHBhcnRlLCAgcmVjb25vY2VuIHF1ZSBsYSBVbml2ZXJzaWRhZCBOYWNpb25hbCBkZSBDb2xvbWJpYSBhY3TDumEgY29tbyB1biB0ZXJjZXJvIGRlIGJ1ZW5hIGZlIHkgc2UgZW5jdWVudHJhIGV4ZW50YSBkZSBjdWxwYSBlbiBjYXNvIGRlIHByZXNlbnRhcnNlIGFsZ8O6biB0aXBvIGRlIHJlY2xhbWFjacOzbiBlbiBtYXRlcmlhIGRlIGRlcmVjaG9zIGRlIGF1dG9yIG8gcHJvcGllZGFkIGludGVsZWN0dWFsIGVuIGdlbmVyYWwuIFBvciBsbyB0YW50bywgbG9zIGZpcm1hbnRlcyAgYWNlcHRhbiBxdWUgY29tbyB0aXR1bGFyZXMgw7puaWNvcyBkZSBsb3MgZGVyZWNob3MgcGF0cmltb25pYWxlcyBkZSBhdXRvciwgYXN1bWlyw6FuIHRvZGEgbGEgcmVzcG9uc2FiaWxpZGFkIGNpdmlsLCBhZG1pbmlzdHJhdGl2YSB5L28gcGVuYWwgcXVlIHB1ZWRhIGRlcml2YXJzZSBkZSBsYSBwdWJsaWNhY2nDs24gZGUgbGEgb2JyYS4gIAoKZikJQXV0b3JpemFuIGEgbGEgVW5pdmVyc2lkYWQgTmFjaW9uYWwgZGUgQ29sb21iaWEgaW5jbHVpciBsYSBvYnJhIGVuIGxvcyBhZ3JlZ2Fkb3JlcyBkZSBjb250ZW5pZG9zLCBidXNjYWRvcmVzIGFjYWTDqW1pY29zLCBtZXRhYnVzY2Fkb3Jlcywgw61uZGljZXMgeSBkZW3DoXMgbWVkaW9zIHF1ZSBzZSBlc3RpbWVuIG5lY2VzYXJpb3MgcGFyYSBwcm9tb3ZlciBlbCBhY2Nlc28geSBjb25zdWx0YSBkZSBsYSBtaXNtYS4gCgpnKQlFbiBlbCBjYXNvIGRlIGxhcyB0ZXNpcyBjcmVhZGFzIHBhcmEgb3B0YXIgZG9ibGUgdGl0dWxhY2nDs24sIGxvcyBmaXJtYW50ZXMgc2Vyw6FuIGxvcyByZXNwb25zYWJsZXMgZGUgY29tdW5pY2FyIGEgbGFzIGluc3RpdHVjaW9uZXMgbmFjaW9uYWxlcyBvIGV4dHJhbmplcmFzIGVuIGNvbnZlbmlvLCBsYXMgbGljZW5jaWFzIGRlIGFjY2VzbyBhYmllcnRvIENyZWF0aXZlIENvbW1vbnMgeSBhdXRvcml6YWNpb25lcyBhc2lnbmFkYXMgYSBzdSBvYnJhIHBhcmEgbGEgcHVibGljYWNpw7NuIGVuIGVsIFJlcG9zaXRvcmlvIEluc3RpdHVjaW9uYWwgVU5BTCBkZSBhY3VlcmRvIGNvbiBsYXMgZGlyZWN0cmljZXMgZGUgbGEgUG9sw610aWNhIEdlbmVyYWwgZGUgbGEgQmlibGlvdGVjYSBEaWdpdGFsLgoKCmgpCVNlIGF1dG9yaXphIGEgbGEgVW5pdmVyc2lkYWQgTmFjaW9uYWwgZGUgQ29sb21iaWEgY29tbyByZXNwb25zYWJsZSBkZWwgdHJhdGFtaWVudG8gZGUgZGF0b3MgcGVyc29uYWxlcywgZGUgYWN1ZXJkbyBjb24gbGEgbGV5IDE1ODEgZGUgMjAxMiBlbnRlbmRpZW5kbyBxdWUgc2UgZW5jdWVudHJhbiBiYWpvIG1lZGlkYXMgcXVlIGdhcmFudGl6YW4gbGEgc2VndXJpZGFkLCBjb25maWRlbmNpYWxpZGFkIGUgaW50ZWdyaWRhZCwgeSBzdSB0cmF0YW1pZW50byB0aWVuZSB1bmEgZmluYWxpZGFkIGhpc3TDs3JpY2EsIGVzdGFkw61zdGljYSBvIGNpZW50w61maWNhIHNlZ8O6biBsbyBkaXNwdWVzdG8gZW4gbGEgUG9sw610aWNhIGRlIFRyYXRhbWllbnRvIGRlIERhdG9zIFBlcnNvbmFsZXMuCgoKClBBUlRFIDIuIEFVVE9SSVpBQ0nDk04gUEFSQSBQVUJMSUNBUiBZIFBFUk1JVElSIExBIENPTlNVTFRBIFkgVVNPIERFIE9CUkFTIEVOIEVMIFJFUE9TSVRPUklPIElOU1RJVFVDSU9OQUwgVU5BTC4KClNlIGF1dG9yaXphIGxhIHB1YmxpY2FjacOzbiBlbGVjdHLDs25pY2EsIGNvbnN1bHRhIHkgdXNvIGRlIGxhIG9icmEgcG9yIHBhcnRlIGRlIGxhIFVuaXZlcnNpZGFkIE5hY2lvbmFsIGRlIENvbG9tYmlhIHkgZGUgc3VzIHVzdWFyaW9zIGRlIGxhIHNpZ3VpZW50ZSBtYW5lcmE6CgphLglDb25jZWRvIGxpY2VuY2lhIGVuIGxvcyB0w6lybWlub3Mgc2XDsWFsYWRvcyBlbiBsYSBwYXJ0ZSAxIGRlbCBwcmVzZW50ZSBkb2N1bWVudG8sIGNvbiBlbCBvYmpldGl2byBkZSBxdWUgbGEgb2JyYSBlbnRyZWdhZGEgc2VhIHB1YmxpY2FkYSBlbiBlbCBSZXBvc2l0b3JpbyBJbnN0aXR1Y2lvbmFsIGRlIGxhIFVuaXZlcnNpZGFkIE5hY2lvbmFsIGRlIENvbG9tYmlhIHkgcHVlc3RhIGEgZGlzcG9zaWNpw7NuIGVuIGFjY2VzbyBhYmllcnRvIHBhcmEgc3UgY29uc3VsdGEgcG9yIGxvcyB1c3VhcmlvcyBkZSBsYSBVbml2ZXJzaWRhZCBOYWNpb25hbCBkZSBDb2xvbWJpYSAgYSB0cmF2w6lzIGRlIGludGVybmV0LgoKCgpQQVJURSAzIEFVVE9SSVpBQ0nDk04gREUgVFJBVEFNSUVOVE8gREUgREFUT1MgUEVSU09OQUxFUy4KCkxhIFVuaXZlcnNpZGFkIE5hY2lvbmFsIGRlIENvbG9tYmlhLCBjb21vIHJlc3BvbnNhYmxlIGRlbCBUcmF0YW1pZW50byBkZSBEYXRvcyBQZXJzb25hbGVzLCBpbmZvcm1hIHF1ZSBsb3MgZGF0b3MgZGUgY2Fyw6FjdGVyIHBlcnNvbmFsIHJlY29sZWN0YWRvcyBtZWRpYW50ZSBlc3RlIGZvcm11bGFyaW8sIHNlIGVuY3VlbnRyYW4gYmFqbyBtZWRpZGFzIHF1ZSBnYXJhbnRpemFuIGxhIHNlZ3VyaWRhZCwgY29uZmlkZW5jaWFsaWRhZCBlIGludGVncmlkYWQgeSBzdSB0cmF0YW1pZW50byBzZSByZWFsaXphIGRlIGFjdWVyZG8gYWwgY3VtcGxpbWllbnRvIG5vcm1hdGl2byBkZSBsYSBMZXkgMTU4MSBkZSAyMDEyIHkgZGUgbGEgUG9sw610aWNhIGRlIFRyYXRhbWllbnRvIGRlIERhdG9zIFBlcnNvbmFsZXMgZGUgbGEgVW5pdmVyc2lkYWQgTmFjaW9uYWwgZGUgQ29sb21iaWEuIFB1ZWRlIGVqZXJjZXIgc3VzIGRlcmVjaG9zIGNvbW8gdGl0dWxhciBhIGNvbm9jZXIsIGFjdHVhbGl6YXIsIHJlY3RpZmljYXIgeSByZXZvY2FyIGxhcyBhdXRvcml6YWNpb25lcyBkYWRhcyBhIGxhcyBmaW5hbGlkYWRlcyBhcGxpY2FibGVzIGEgdHJhdsOpcyBkZSBsb3MgY2FuYWxlcyBkaXNwdWVzdG9zIHkgZGlzcG9uaWJsZXMgZW4gd3d3LnVuYWwuZWR1LmNvIG8gZS1tYWlsOiBwcm90ZWNkYXRvc19uYUB1bmFsLmVkdS5jbyIKClRlbmllbmRvIGVuIGN1ZW50YSBsbyBhbnRlcmlvciwgYXV0b3Jpem8gZGUgbWFuZXJhIHZvbHVudGFyaWEsIHByZXZpYSwgZXhwbMOtY2l0YSwgaW5mb3JtYWRhIGUgaW5lcXXDrXZvY2EgYSBsYSBVbml2ZXJzaWRhZCBOYWNpb25hbCBkZSBDb2xvbWJpYSBhIHRyYXRhciBsb3MgZGF0b3MgcGVyc29uYWxlcyBkZSBhY3VlcmRvIGNvbiBsYXMgZmluYWxpZGFkZXMgZXNwZWPDrWZpY2FzIHBhcmEgZWwgZGVzYXJyb2xsbyB5IGVqZXJjaWNpbyBkZSBsYXMgZnVuY2lvbmVzIG1pc2lvbmFsZXMgZGUgZG9jZW5jaWEsIGludmVzdGlnYWNpw7NuIHkgZXh0ZW5zacOzbiwgYXPDrSBjb21vIGxhcyByZWxhY2lvbmVzIGFjYWTDqW1pY2FzLCBsYWJvcmFsZXMsIGNvbnRyYWN0dWFsZXMgeSB0b2RhcyBsYXMgZGVtw6FzIHJlbGFjaW9uYWRhcyBjb24gZWwgb2JqZXRvIHNvY2lhbCBkZSBsYSBVbml2ZXJzaWRhZC4gCgo=