Korean Journal of Agricultural Science (농업과학연구)

Institute of Agricultural Science, CNU (충남대학교 농업과학연구소)
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Perspectives on high throughput phenotyping in developing countries

  • Chung, Yong Suk (Department of Crop Science, College of Life Sciences, Chungnam National University) ;
  • Kim, Ki-Seung (LG Chemical-FarmHannong Ltd.) ;
  • Kim, Changsoo (Department of Crop Science, College of Life Sciences, Chungnam National University)
  • Received : 2018.03.02
  • Accepted : 2018.07.16
  • Published : 2018.09.30
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Abstract

The demand for crop production is increasingly becoming steeper due to the rapid population growth. As a result, breeding cycles should be faster than ever before. However, the current breeding methods cannot meet this requirement because traditional phenotyping methods lag far behind even though genotyping methods have been drastically developed with the advent of next-generation sequencing technology over a short period of time. Consequently, phenotyping has become a bottleneck in large-scale genomics-based plant breeding studies. Recently, however, phenomics, a new discipline involving the characterization of a full set of phenotypes in a given species, has emerged as an alternative technology to come up with exponentially increasing genomic data in plant breeding programs. There are many advantages for using new technologies in phenomics. Yet, the necessity of diverse man power and huge funding for cutting-edge equipment prevent many researchers who are interested in this area from adopting this new technique in their research programs. Currently, only a limited number of groups mostly in developed countries have initiated phenomic studies using high throughput methods. In this short article, we describe the strategies to compete with those advanced groups using limited resources in developing countries, followed by a brief introduction of high throughput phenotyping.

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References

  1. Araus JL, Cairns JE. 2014. Field high-throughput phenotyping: The new crop breeding frontier. Trends in Plant Science 19:52-61. https://doi.org/10.1016/j.tplants.2013.09.008
  2. Barker J, Zhang N, Sharon J, Steeves R, Wang X, Wei Y, Poland J. 2016. Development of a field-based high-throughput mobile phenotyping platform. Computers and Electronics in Agriculture 122:74-85. https://doi.org/10.1016/j.compag.2016.01.017
  3. Cabrera-Bosquet L, Crossa J, von Zitzewitz J, Serret M, Araus L. 2012. High throughput phenotyping and genomic selection: The frontiers of crop breeding. Journal of Integrative Plant Biology 54:312-320. https://doi.org/10.1111/j.1744-7909.2012.01116.x
  4. Chung YS, Choi SC, Silva RR, Kang JW, Eom JH, Kim C. 2017. Case study: Estimation of sorghum biomass using digital image analysis with Canopeo. Biomass and Bioenergy 105:207-210. https://doi.org/10.1016/j.biombioe.2017.06.027
  5. Cobb JN, DeClerck G, Greenberg A, Clark R, McCouch S. 2013. Next-generation phenotyping: Requirements and strategies for enhancing our understanding of genotype-phenotype relationships and its relevance to crop improvement. Theoretical and Applied Genetics 126:867-887. https://doi.org/10.1007/s00122-013-2066-0
  6. Fahlgren N, Gehan MA, Baxter I. 2015. Lights, camera, action: High-throughput plant phenotyping is ready for a close-up. Current Opinion in Plant Biology 24:93-99. https://doi.org/10.1016/j.pbi.2015.02.006
  7. Ray DK, Mueller ND, West PC, Foley JA. 2013. Yield trends are insufficient to double global crop production by 2050. PloS One 8:e66428. https://doi.org/10.1371/journal.pone.0066428
  8. De Souza N. 2010. High-throughput phenotyping. Nature Methods 7:36-36.
  9. Furbank RT, Tester M. 2011. Phenomics-technologies to relieve the phenotyping bottleneck. Trends in Plant Science 16:635-644. https://doi.org/10.1016/j.tplants.2011.09.005
  10. Gehan MA, Kellogg EA. 2017. High-throughput phenotyping. American Journal of Botany 104:505-508. https://doi.org/10.3732/ajb.1700044
  11. Huang ZA, Jiang DA, Yang Y, Sun JW, Jin SH. 2004. Effects of nitrogen deficiency on gas exchange, chlorophyll fluorescence, and antioxidant enzymes in leaves of rice plants. Photosynthetica 42:357-64. https://doi.org/10.1023/B:PHOT.0000046153.08935.4c
  12. Ikeda M, Hirose Y, Takashi T, Shibata Y, Yamamura T, Komura T, Doi K, Ashikari M, Matsuoka M, Kitano H. 2010. Analysis of rice panicle traits and detection of QTLs using an image analyzing method. Breeding Science 60:55-64. https://doi.org/10.1270/jsbbs.60.55
  13. Kastberger G, Stachl R. 2003. Infrared imaging technology and biological applications. Behavior Research Methods, Instruments, and Computers 35:429-439. https://doi.org/10.3758/BF03195520
  14. Lu C, Zhang J. 2000. Photosynthetic $CO_2$ assimilation, chlorophyll fluorescence and photoinhibition as afected by nitrogen deficiency in maize plants. Plant Science 151:135-43. https://doi.org/10.1016/S0168-9452(99)00207-1
  15. Netto AT, Campostrini E, de Oliveira JG, Bressan-Smith RE. 2005. Photosynthetic pigments, nitrogen, chlorophyll a fluorescence and SPAD-502 readings in cofee leaves. Scientia Horticulturae 104:199-209. https://doi.org/10.1016/j.scienta.2004.08.013
  16. Montes J, Melchinger A, Reif J. 2007. Novel throughput phenotyping platforms in plant genetic studies. Trends in Plant Science 12:433-436. https://doi.org/10.1016/j.tplants.2007.08.006
  17. Prashar A, Yildiz J, McNicol JW, Bryan GJ, Jones HG. 2013. Infra-red thermography for high throughput field phenotyping in Solanum tuberosum. PLoS One 8:e65816. https://doi.org/10.1371/journal.pone.0065816
  18. Shangguan Z, Shao M, Dyckmans J. 2000. Effects of nitrogen nutrition and water deficit on net photosynthetic rate and chlorophyll fluorescence in winter wheat. Journal of Plant Physiology 156:46-51. https://doi.org/10.1016/S0176-1617(00)80271-0
  19. White JW, Andrade-Sanchez P, Gore MA, Bronson KF, Cofelt TA, Conley MM, Feldmann KA, French AN, Heun JT, Hunsaker DJ, Jenks MA. 2012. Field-based phenomics for plant genetics research. Field Crops Research 133:101-112. https://doi.org/10.1016/j.fcr.2012.04.003
  20. Tanger P, Klassen S, Mojica JP, Lovell JT, Moyers BT, Baraoidan M, Naredo ME, McNally KL, Poland J, Bush DR, Leung H. 2017. Field-based high throughput phenotyping rapidly identifies genomic regions controlling yield components in rice. Scientific Reports 7:42839. https://doi.org/10.1038/srep42839
  21. Yang WN, Xu XC, Duan LF, Luo QM, Chen SB, Zeng SQ, Liu Q. 2011. High-throughput measurement of rice tillers using a conveyor equipped with x-ray computed tomography. Review of Scientific Instruments 82:025102. https://doi.org/10.1063/1.3531980
  22. Zhang X, Huang C, Wu D, Qiao F, Li W, Duan L, Wang K, Xiao Y, Chen G, Liu Q, Xiong L. 2017. High-throughput phenotyping and QTL mapping reveals the genetic architecture of maize plant growth. Plant Physiology 173:1554-1564. https://doi.org/10.1104/pp.16.01516