Please wait a minute...
Journal of Integrative Agriculture  2020, Vol. 19 Issue (10): 2429-2438    DOI: 10.1016/S2095-3119(20)63154-9
Crop Science Advanced Online Publication | Current Issue | Archive | Adv Search |
High-throughput phenotyping identifies plant growth differences under well-watered and drought treatments
Seth TOLLEY1, Yang Yang2, Mohsen MoHAMMADI1
 
1 Department of Agronomy, Purdue University, West Lafayette, IN 47907, United States
2 College of Agriculture, Department of Agronomy, Purdue University, West Lafayette, IN 47907, United States
Download:  PDF in ScienceDirect  
Export:  BibTeX | EndNote (RIS)      
Abstract  
The ability to screen larger populations with fewer replicates and non-destructive measurements is one advantage of high-throughput phenotyping (HTP) over traditional phenotyping techniques.  In this study, two wheat accessions were grown in a controlled-environment with a moderate drought imposed from stem elongation to post-anthesis.  Red-green-blue (RGB) imaging was performed on 17 of the 22 d following the start of drought imposition.  Destructive measurements from all plants were performed at the conclusion of the experiment.  The effect of line was significant for shoot dry matter, spike dry matter, root dry matter, and tiller number, while the water treatment was significant on shoot dry matter and root dry matter.  The temporal, non-destructive nature of HTP allowed the drought treatment to be significantly differentiated from the well-watered treatment after 6 d in a line from Argentina and 9 d in a line from Chile.  This difference of 3 d indicated an increased degree of drought tolerance in the line from Chile.  Furthermore, HTP from the final day of imaging accurately predicted reference plant height (r=1), shoot dry matter (r=0.95) and tiller number (r=0.91).  This experiment illustrates the potential of HTP and its use in modeling plant growth and development.
 
Keywords:  high-throughput phenotyping        drought        controlled-environment        wheat  
Received: 25 July 2019   Accepted: 08 September 2020
Fund: Financial support was from the College of Agriculture of Purdue University to Mohsen Mohammadi, USDA (1013073). We would like to thank Institute for Plant Sciences and College of Agriculture for facilitating controlled environment phenotyping research.
Corresponding Authors:  Correspondence Mohsen Mohammadi, Tel: +1-765-4966885, E-mail: mohamm20@purdue.edu   

Cite this article: 

Seth TOLLEY, Yang Yang, Mohsen MOHAMMADI. 2020. High-throughput phenotyping identifies plant growth differences under well-watered and drought treatments. Journal of Integrative Agriculture, 19(10): 2429-2438.

raus J, Kefauver S, Zaman-Allah M, Olsen M, Cairns J. 2018. Shifting the limits in wheat research and breeding using a fully annotated reference genome. Science, 23, 451–466.
Chen D, Neumann K, Friedel S, Kilian B, Chen M, Altmann T, Klukas C. 2014. Dissecting the phenotypic components of crop plant growth and drought responses based on highthroughput image analysis. The Plant Cell, 26, 4636–4655.
Chenu K, Porter J, Martre P, Basso B, Chapman S, Ewert F, Bindi M, Asseng S. 2017. Contribution of crop models to adaptation in wheat. Trends in Plant Science, 22, 472–490.
Consultative Group for International Agricultural Research (CGIAR). 2018. Translating high-throughput phenotyping into genetic gain. Trends in Plant Science, 23, 451–466.
Dai A. 2011. Drought under global warming: A review. Wiley Interdisciplinary Reviews: Climate Change, 2, 45–65.
Fahlgren N, Feldman M, Gehan M, Wilson M, Shyu C, Bryant D, Hill S, McEntree C, Warnasooriya S, Kumar I, Ficor T, Turnipseed S, Gilbert K, Brutnell T, Carrington J, Mockler T, Baxter I. 2015. A versatile phenotyping system and analytics platform reveals diverse temporal responses to water availability in setaria. Molecular Plant, 8, 1520–1535.
FAO (Food and Agriculture Organization). 2009. The challenge. Rome, Italy. [2019-02-21]. http://www.fao.org/fileadmin/templates/wsfs/docs/Issues_papers/HLEF2050_Global_Agriculture.pdf
FAO (Food and Agriculture Organization). 2017. FAOSTAT. [2019-02-21]. http://www.fao.org/faostat/en/#data/QC
Ge Y, Bai G, Stoerger V, Schnable J. 2016. Temporal dynamics of maize plant growth, water use, and leaf water content using automated high throughput RGB and hyperspectral imaging. Computers and Electronics in Agriculture, 127, 625–632.
Ghanem M, Marrou H, Sinclair T. 2015. Physiological phenotyping of plants for crop improvement. Trends in Plant Science, 20, 139–144.
International Wheat Genome Sequencing Consortium. 2018. Shifting the limits in wheat research and breeding using a fully annotated reference genome. Science, 361, 1–13.
Ley T. 2003. Visual crop moisture stress symptoms. [2019-02-20]. http://sis.prosser.wsu.edu
Lollato R, DeWolf E, Knapp M. 2013. Drought conditions across most of Kansas starts to affect the wheat crop. [2019-02-21]. https://webapp.agron.ksu.edu/agr_social/m_eu_article.throck?article_id=890
Lopes M S, Reynolds M P. 2012. Stay-green in spring wheat can be determined by spectral reflectance measurements (normalized difference vegetation index) independently from phenology. Journal of Experimental Botany, 63, 3789–3798.
Maqbool M, Ali A, ul Haq T, Majeed M, Lee D. 2015. Response of spring wheat (Triticum aestivum L.) to induced water stress at critical growth stages. Sarhad Journal of Agriculture, 31, 53–58.
Nezhadahmadi A, Prodhan Z, Faruq G. 2013. Drought tolerance in wheat. The Scientific World Journal, 2013, 1–12.
Ogren E, Oquist G. 1985. Effects of drought on photosynthesis, chlorophyll fluorescence and photoinhibition susceptibility in intact willow leaves. Planta, 166, 380–388.
Palta J A, Chen X, Milroy S, Rebetzke G, Dreccer M, Wat M. 2011. Large root systems: Are they useful in adapting wheat to dry environments? Functional Plant Biology, 38, 347–354.
del Pozo A, Yanez A, Matus I, Tapia G, Castillo D, Sanchez-Jardon L, Araus J. 2016. Physiological traits associated with wheat yield potential and performance under water-stress in a Mediterranean environment. Frontiers in Plant Science, 7, 1–13.
R Core Team. 2014. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.
Ray D, Ramankutty N, Mueller N, West P, Foley J. 2012. Recent patterns of crop yield growth and stagnation. Nature Communications, 3, 1–7.
Shakoor N, Lee S, Mockler T. 2017. High throughput phenotyping to accelerate crop breeding and monitoring of diseases in the field. Current Opinion in Plant Biology, 38, 184–192.
Venables W N, Ripley B D. 2002. Modern Applied Statistics with S. 4th ed. Springer, New York.
Yang W, Guo Z, Huang C, Duan L, Chen G, Jiang N, Fang W, Feng H, Xie W, Lian X, Wang G, Luo Q, Zhang Q, Liu Q, Xiong L. 2014. Combining high-throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice. Nature Communications, 5, doi: 101038/ncomms6087
[1] ZHAO Lai-bin, XIE Die, HUANG Lei, ZHANG Shu-jie, LUO Jiang-tao, JIANG Bo, NING Shun-zong, ZHANG Lian-quan, YUAN Zhong-wei, WANG Ji-rui, ZHENG You-liang, LIU Deng-cai, HAO Ming. Integrating the physical and genetic map of bread wheat facilitates the detection of chromosomal rearrangements[J]. >Journal of Integrative Agriculture, 2021, 20(9): 2333-2342.
[2] LI Si-nan, CHEN Wen, MA Xin-yao, TIAN Xia-xia, LIU Yao, HUANG Li-li, KANG Zhen-sheng, ZHAO Jie. Identification of eight Berberis species from the Yunnan-Guizhou plateau as aecial hosts for Puccinia striiformis f. sp. tritici, the wheat stripe rust pathogen[J]. >Journal of Integrative Agriculture, 2021, 20(6): 1563-1569.
[3] LIU Yang, LI Yu-xiang, LI Yi-xiang, TIAN Zhong-wei, HU Jin-ling, Steve ADKINS, DAI Ting-bo. Changes of oxidative metabolism in the roots of wheat (Triticum aestivum L.) seedlings in response to elevated ammonium concentrations[J]. >Journal of Integrative Agriculture, 2021, 20(5): 1216-1228.
[4] LIU Hang, TANG Hua-ping, LUO Wei, MU Yang, JIANG Qian-tao, LIU Ya-xi, CHEN Guo-yue, WANG Ji-rui, ZHENG Zhi, QI Peng-fei, JIANG Yun-feng, CUI Fa, SONG Yin-ming, YAN Gui-jun, WEI Yuming, LAN Xiu-jin, ZHENG You-liang, MA Jian. Genetic dissection of wheat uppermost-internode diameter and its association with agronomic traits in five recombinant inbred line populations at various field environments[J]. >Journal of Integrative Agriculture, 2021, 20(11): 2849-2861.
[5] LIU Da-zhong, YANG Fei-fei, LIU Sheng-ping. Estimating wheat fractional vegetation cover using a density peak k-means algorithm based on hyperspectral image data[J]. >Journal of Integrative Agriculture, 2021, 20(11): 2880-2891.
[6] XIAO Jing-xiu, ZHU Ying-an, BAI Wen-lian, LIU Zhen-yang, TANG Li, ZHENG Yi. Yield performance and optimal nitrogen and phosphorus application rates in wheat and faba bean intercropping[J]. >Journal of Integrative Agriculture, 2021, 20(11): 3012-3025.
[7] ZHANG Li, CHU Qing-quan, JIANG Yu-lin, CHEN Fu, LEI Yong-deng. Impacts of climate change on drought risk of winter wheat in the North China Plain[J]. >Journal of Integrative Agriculture, 2021, 20(10): 2601-2612.
[8] Hamid NAWAZ, Nazim HUSSAIN, Niaz AHMED, Haseeb-ur-REHMAN, Javaiz ALAM. Efficiency of seed bio-priming technique for healthy mungbean productivity under terminal drought stress[J]. >Journal of Integrative Agriculture, 2021, 20(1): 87-99.
[9] LI Peng-cheng, YANG Xiao-yi, WANG Hou-miao, PAN Ting, YANG Ji-yuan, WANG Yun-yun, XU Yang, YANG Ze-feng, XU Chen-wu. Metabolic responses to combined water deficit and salt stress in maize primary roots[J]. >Journal of Integrative Agriculture, 2021, 20(1): 109-119.
[10] JIA Teng-jiao, LI Jing-jing, WANG Li-feng, CAO Yan-yong, MA Juan, WANG Hao, ZHANG Deng-feng, LI Hui-yong. Evaluation of drought tolerance in ZmVPP1-overexpressing transgenic inbred maize lines and their hybrids[J]. >Journal of Integrative Agriculture, 2020, 19(9): 2177-2187.
[11] ZHAO Fu-nian, ZHOU Shuang-xi, WANG Run-yuan, ZHANG Kai, WANG He-ling, YU Qiang. Quantifying key model parameters for wheat leaf gas exchange under different environmental conditions[J]. >Journal of Integrative Agriculture, 2020, 19(9): 2188-2205.
[12] LIU Rui-xuan, WU Fang-kun, YI Xin, LIN Yu, WANG Zhi-qiang, LIU Shi-hang, DENG Mei, MA Jian, WEI Yu-ming, ZHENG You-liang, LIU Ya-xi. Quantitative trait loci analysis for root traits in synthetic hexaploid wheat under drought stress conditions[J]. >Journal of Integrative Agriculture, 2020, 19(8): 1947-1960.
[13] HONG Ye, ZHANG Guo-ping. The influence of drought stress on malt quality traits of the wild and cultivated barleys[J]. >Journal of Integrative Agriculture, 2020, 19(8): 2009-2015.
[14] PAN Li-jun, LU Lin, LIU Yu-ping, WEN Sheng-xian, ZHANG Zeng-yan. The M43 domain-containing metalloprotease RcMEP1 in Rhizoctonia cerealis is a pathogenicity factor during the fungus infection to wheat[J]. >Journal of Integrative Agriculture, 2020, 19(8): 2044-2055.
[15] ZHOU Chun-yun, XIONG Hong-chun, LI Yu-ting, GUO Hui-jun, XIE Yong-dun, ZHAO Lin-shu, GU Jiayu, ZHAO Shi-rong, DING Yu-ping, SONG Xi-yun, LIU Lu-xiang. Genetic analysis and QTL mapping of a novel reduced height gene in common wheat (Triticum aestivum L.)[J]. >Journal of Integrative Agriculture, 2020, 19(7): 1721-1730.
No Suggested Reading articles found!