Wybrane elementy nowoczesnych rozwiązań wpływające na skuteczność programów hodowlanych

Araus J. L., Cairns J. E. 2014. Field high-throughput phenotyping: the new crop breeding frontier. Trends in Plant Science 19, 1: 52 — 61.
Google Scholar

Arvidsson S., Pérez-Rodríguez P., Mueller-Roeber B. 2011. A growth phenotyping pipeline for Arabidopsis thaliana integrating image analysis and rosette area modeling for robust quantification of genotype effects. New Phytol. 191: 895 — 907.
Google Scholar

Bandillo N., Raghavan C., Muyco P. A., Lynn Sevilla M. A., Lobina I. T., Dilla-Ermita C. J., Tung C.-W., McCouch S., Thomson M., Mauleon R., Singh R. K., Gregorio G., Redoña E., Leung H. 2013. Multi-parent advanced generation inter-cross (MAGIC) populations in rice: progress and potential for genetics research and breeding. Rice 6: 11.
Google Scholar

Beavis W. D. 1998. Molecular dissection of complex traits. QTL analyses: power, precision, and accuracy. Eds A. H. Paterson. CRC Press, New York, pp. 145 — 162.
Google Scholar

Bednarek P. T., Orłowska R., Koebner R. M. D., Zimny J. 2007. Quantification of the tissue-culture induced variation in barley (Hordeum vulgare L.). BMC Plant Biology 7: 10.
Google Scholar

Carling J., Heller-Uszyńska K., Jaccoud D., Machado A., Hopper C., Xia L., Vippin C., Caig V., Uszyński G., Kilian A. 2015. DArTTM and DArTseqTM genome profiling for breeding, pre-breeding and population genetics applications. Contribution P0052, XXIII Plant and Animal Genome, San Diego, CA pp. 10 — 14.
Google Scholar

Darvishzadeh R., Atzberger C., Skidmore A. K. 2006. Hyperspectral vegetation indices for estimation of leaf area index. ISPRS Commission VII Mid-term Symposium "Remote Sensing: From Pixels to Processes", Enschede, the Netherlands, 8–11 May.
Google Scholar

Elshire R. J., Glaubitz J. C., Sun Q., Poland J. A., Kawamoto K., Buckler E. S., Mitchell S. E. 2011. A robust, simple Genotyping-by-Sequencing (GBS) approach for high diversity species. PLoS ONE 6, 5: e19379.
Google Scholar

Ferreira D. S., Pallone J. A. L., Poppi R. J. 2015. Direct analysis of the main chemical constituents in Chenopodium quinoa grain using Fourier transform near-infrared spectroscopy. Food Control 48: 91 — 95.
Google Scholar

Francis D. M., Merk H. L. 2012. Equation to estimate sample size required for QTL detection. Plant Breeding and Genomics. http://www.extension.org/pages/32355/equation-to-estimate-sample-size-required-for-qtl-detection#.VP7P9fmG__E.
Google Scholar

Gausman H. W. 1985. Plant leaf optical properties in visible and near-infrared light. Texas Tech Press; Lubbock, Texas, Graduate Studies No. 29.
Google Scholar

Gebbers R., Ehlert D., Adamek R. 2011. Rapid mapping of the leaf area index in agricultural crops. Agron. J. 103: 1532 — 1541.
Google Scholar

Golzarian M. R., Frick R. A., Rajendran K., Berger B., Roy S., Tester M., Lun D. S. 2011. Accurate inference of shoot biomass from high-throughput images of cereal plants. Plant Methods 7: 1 — 11.
Google Scholar

Hall D., Tegström C., Ingvarsson P. K. 2010. Using association mapping to dissect the genetic basis of complex traits in plants. Brief. Funct. Genomics 9, 2: 157 — 165.
Google Scholar

Hartmann A., Czauderna T., Hoffmann R., Stein N., Schreiber F. 2011. HTPheno: An image analysis pipeline for high-throughput plant phenotyping. BMC Bioinf. 12: 148.
Google Scholar

Hosoi F., Omasa K. 2009. Estimating vertical plant area density profile and growth parameters of a wheat canopy at different growth stages using three-dimensional portable lidar imaging. ISPRS J. Photogramm. Remote Sens. 64: 151 — 158.
Google Scholar

Huang B. E., George A. W., Forrest K. L., Kilian A., Hayden M. J., Morell M. K., Cavanagh C. R. 2012. A multiparent advanced generation inter-cross population for genetic analysis in wheat. Plant Bio. J. 10: 826 — 839.
Google Scholar

Huang C., Yang W., Duan L., Jiang N., Chen G., Xiong L., Liu Q. 2013. Rice panicle length measuring system based on dual-camera imaging. Comput. Electr. Agric. 98: 158 — 165.
Google Scholar

Huete A. R. 1988. A Soil-Adjusted Vegetation Index (SAVI). Remote Sensing Of Environment 25: 295 — 309.
Google Scholar

Jaccoud D. Peng K., Feinstein D., Kilian A., 2001. Diversity Arrays: a solid state technology for sequence information independent genotyping. Nucleic Acids Research 29, 4: 25.
Google Scholar

Jones H. G., Serraj R., Brian R. L., Xiong L., Wheaton A., Price A.H. 2009. Thermal infrared imaging of crop canopies for the remote diagnosis and quantification of plant responses to water stress in the field. Funct. Plant Biol. 36: 978 — 989.
Google Scholar

Kalaji M. H., Łoboda T. 2009. Fluorescencja chlorofilu w badaniach stanu fizjologicznego roślin. Wydawnictwo SGGW, Warszawa.
Google Scholar

Knipling, E. B. 1970. Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation. Remote Sens. Environ. 1: 155 — 159.
Google Scholar

Kover P. X., Valdar W., Trakalo J., Scarcelli N., Ehrenreich I. M., Purugganan M. D., Durrant C., Mott R. 2009. A multiparent advanced generation inter-cross to fine-map quantitative traits in Arabidopsis thaliana. PLoS Genet 5, 7: e1000551.
Google Scholar

Li L., Zhang Q., Huang D. 2014. A review of imaging techniques for plant phenotyping. Sensors 14: 20078 — 20111.
Google Scholar

Monneveux P., Jing R., Misra S. C. 2012. Phenotyping for drought adaptation in wheat using physiological traits. Frontiers in Physiology 3, 429: 1 — 12.
Google Scholar

Niedziela A., Bednarek P. T., Labudda M., Mańkowski D. R., Anioł A. 2014. Genetic mapping of a 7R Al tolerance QTL in triticale (x Triticosecale Wittmack). J. App. Genetics 55, 1: 1 — 14.
Google Scholar

Pearson R. L., Miller L. D. 1972. Remote mapping of standing crop biomass for estimation of the productivity of the short-grass. Prairie, Pawnee National Grassland, Colorado: 8th international symposium on remote sensing of environment. pp. 1357 — 1381.
Google Scholar

Poland J. A., Brown P. J., Sorrells M. E., Jannink J.-L. 2012. Development of high-density genetic maps for barley and wheat using a novel two-enzyme genotyping-by-sequencing approach. PLoS ONE 7, 2: e32253.
Google Scholar

Rafalski J. A. 2010. Association genetics in crop improvement. Current Opinion in Plant Biology, 13: 174 — 180.
Google Scholar

Ramoelo A., Dzikiti S., van Deventer H., Maherry A., Cho M. A., Gush M. 2015. Potential to monitor plant stress using remote sensing tools. J Arid Env. 113: 134 — 144.
Google Scholar

Rhodes D. H., Hoffmann L. Jr, Rooney W. L., Ramu P., Morris G. P., Kresovich S. 2014. Genome-wide association study of grain polyphenol concentrations in global sorghum (Sorghum bicolor (L.) Moench) germplasm. J Agric Food Chem. 62, 45: 10916 — 27.
Google Scholar

Richardson A. J., Wiegand C. L. 1977. Distinguishing vegetation from soil background information, Photogramm. Eng. Remote Sens. 43: 1541 — 1552.
Google Scholar

Semagn K., Bjørnstad Å., Ndjiondjop M. N. 2006. An overview of molecular marker methods for plants. Afr. J. Biotechnol. 5, 25: 2540 — 2568.
Google Scholar

Semagn K., Bjørnstad Å., Xu Y. 2010. The genetic dissection of quantitative traits in crops. Electron J Biotechnol. 13: 5, http://dx.doi.org/10.2225/vol13-issue5-fulltext-14.
Google Scholar

Srayeddin I., Doussan C. 2009. Estimation of the spatial variability of root water uptake of maize and sorghum at the field scale by electrical resistivity tomography. Plant Soil 319: 185 — 207.
Google Scholar

Vos P., Hogers R., Bleeker M., Rijans M., Van de Lee T., Hormes M., Frijters A., Pot J., Peleman J., Kuiper M., Zabeau M. 1995. AFLP: a new technique for DNA fingerprinting. Nucleic Acids Res. 23: 4407 — 4414.
Google Scholar

Wenzl P., Carling J., Kudrna D., Jaccoud D., Huttner E., Kleinhofs A., Kilian A. 2004. Diversity arrays technology (DArT) for whole-genome profiling of barley. Proc. Natl. Acad. Sci. USA 101: 9915 — 9920.
Google Scholar

White J. W., Andrade-Sanchez P., M. A. Gore, Bronson K. F., Coffelt T. A., Conley M. M., Feldmann K. A., French A. N., Heun J. T., Hunsaker D. J., Jenks M. A., Kimball B. A., Roth R. L., Strand R. J., Thorp K. R., Wall G. W., Wang G. 2012. Field-based phenomics for plant genetics research. Field Crops Research 133: 101 — 112.
Google Scholar

Xu S. 2003. Theoretical basis of the Beavis effect. Genetics 165: 2259 — 2268.
Google Scholar

Yu J.,. Holland J. B., McMullen M. D., Buckler E. S. 2008. Genetic design and statistical power of nested association mapping in maize. Genetics 178: 539 — 551.
Google Scholar