Skip to main content
SpringerLink
Log in

Got All the Answers! What Were the Questions? Avoiding the Risk of “Phenomics” Slipping into a Technology Spree

  • Chapter
  • First Online:
High-Throughput Crop Phenotyping

Part of the book series: Concepts and Strategies in Plant Sciences ((CSPS))

Abstract

For many crops, the genomics revolution has given hope that breeding would become easier, faster, and more efficient. Relevant phenotyping is now the main bottleneck and new technologies provide opportunities for easier, faster, more sensitive, and more informative phenotyping. However, the phenotyping agenda must be driven by scientific questions rather than by a technological push, especially for complex constraints, such as drought. In this chapter, we provide a viewpoint on phenotyping and what it should take into account. Phenotyping is a full-fledge research effort, calling for a multidisciplinary effort between technology providers and several research disciplines, and which needs to address the issue of linking scales. Two phenotyping platforms are described; a lysimetric platform (LysiField) to assess the patterns of plant water use and relate these to grain yield, and an imaging platform (LeasyScan) to characterize crop canopy traits responsible for water savings. In both cases, the chapter discusses the thought process and the hypotheses around key traits for drought adaptation that were put in the development of these platforms. The chapter concludes with perspectives on the integration of high-throughput phenotyping (HTP) technology with breeding, starting with an analysis of the cost as a prerequisite to decide on its usage and adoption in breeding. It takes a few examples of current opportunities in the domain of imaging, trying to bring closer together what the technology can bring and what breeding pragmatically needs. In conclusion, while new technologies provide opportunities to make phenotyping easier, faster, better, cheaper, the risk of becoming the end that justifies the means can be avoided by driving the technology with research questions, made possible through a cross-discipline approach between genetics, breeding, modeling, engineering, physiology, and statistics.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Altangerel N, Walker JW, González PM, Bailey DW, Estell RE, O’Scully M. M (2017) Comparison of near infrared reflectance spectroscopy and raman spectroscopy for predicting botanical composition of cattle diets. Rangeland Ecol Manag 70:781–786

    Article  Google Scholar 

  • Araus JL, Cairns JE (2014) Field high-throughput phenotyping: the new crop breeding frontier. Trends Plant Sci 19:52–61

    Article  CAS  Google Scholar 

  • Belko N, Zaman-Allah M, Cisse N, Diop NN, Zombre G, Ehlers JD, Vadez V (2012) Lower soil moisture threshold for transpiration decline under water deficit correlates with lower canopy conductance and higher transpiration efficiency in drought-tolerant cowpea. Funct Plant Biol 39:306–322

    Article  Google Scholar 

  • Borrell AK, van Oosterom EJ, Mullet JE, George-Jaeggli B, Jordan DR, Klein PE, Hammer GL (2014) Stay-green alleles individually enhance grain yield in sorghum under drought by modifying canopy development and water uptake patterns. New Phytol 203:817–830

    Article  Google Scholar 

  • Brown TB, Cheng R, Sirault XRR, Rungrat T, Murray KD, Trtilek M, Furbank RT, Badger M, Pogson BJ, Borevitz JO (2014) TraitCapture: genomic and environment modelling of plant phenomic data. Curr Opin Plant Biol 18:73–79

    Google Scholar 

  • Buckler ES, Holland JB, Bradbury PJ, Acharya CB, Brown PJ, Browne C, Ersoz E, Flint-Garcia S, Garcia A, Glaubitz JC, Goodman MM, Harjes C, Guill K, Kroon DE, Larsson S, Lepak NK, Li H, Mitchell SE, Pressoir G, Peiffer JA, Oropeza Rosas M, Rocheford TR, Romay MC, Romero S, Salvo S, Sanchez Villeda H, da Silva HS, Sun Q, Tian F, Upadyayula N, Ware D, Yates H, Yu J, Zhang Z, Kresovich S, McMullen MD (2009) The genetic architecture of Maize flowering time. Science 325(5941):714–718. https://doi.org/10.1126/science.1174276

    Article  CAS  PubMed  Google Scholar 

  • Chapman S, Cooper M, Podlich D, Hammer G (2003) Evaluating plant breeding strategies by simulating gene action and dryland environment effects. Agron J 95:99–113

    Article  Google Scholar 

  • Chapuis R, Delluc C, Debeuf R, Tardieu F, Welcker C (2012) Resiliences to water deficit in a phenotyping platform and in the field: how related are they in maize? Eur J Agron 42:59–67

    Article  Google Scholar 

  • Chenu K, Chapman SC, Tardieu F, McLean G, Welcker C, Hammer GL (2009) Simulating the yield impacts of organ-level quantitative trait loci associated with drought response in maize: A “Gene-to-Phenotype” modeling approach. Genetics 183:1507–1523

    Article  Google Scholar 

  • 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. Theor Appl Genet 126:867–887

    Article  Google Scholar 

  • Cooper M, van Eeuwijk FA, Hammer GL, Podlich DW, Messina C (2009) Modeling QTL for complex traits: detection and context for plant breeding. Curr Opin Plant Biol 12:231–240

    Article  CAS  Google Scholar 

  • Deery D, Jimenez-Berni J, Jones H, Sirault X, Furbank R (2014) Proximal remote sensing buggies and potential applications for field-based phenotyping. Agronomy 5:349–379

    Article  Google Scholar 

  • Dornbusch T, Lorrain S, Kuznetsov D, Fortier A, Liechti R, Xenarios I, Fankhauser C (2012) Measuring the diurnal pattern of leaf hyponasty and growth in Arabidopsis—a novel phenotyping approach using laser scanning. Funct Plant Biol 39:860–869

    Article  Google Scholar 

  • Guo W, Zheng B, Potgieter AB, Diot J, Watanabe K, Noshita K, Jordan DR, Wang X, Watson J, Ninomiya S, Chapman SC (2018) Aerial imagery analysis—quantifying appearance and number of sorghum heads for applications in breeding and agronomy. Front Plant Sci. https://doi.org/10.3389/fpls.2018.01544

    Article  PubMed  PubMed Central  Google Scholar 

  • Hammer GL, van Oosterom E, McLean G, Chapman SC, Broad I, Harland P, Muchow RC (2010) Adapting APSIM to model the physiology and genetics of complex adaptive traits in field crops. J Exp Bot 61:2002–2185

    Article  Google Scholar 

  • Kar S, Tanaka R, Korbu LB, Kholova J, Iwata H, Durbha SS, Adinarayana J, Vadez V (2020a) Automated discretization of ‘transpiration restriction to increasing VPD’ features from outdoors high-throughput phenotyping data. Plant Method 16:140. https://doi.org/10.1186/s13007-020-00680-8

    Article  CAS  Google Scholar 

  • Kar S, Garin V, Kholová J, Vadez V, Durbha SS, Tanaka R, Iwata H, Urban MO, Adinarayana J (2020b) SpaTemHTP: a data analysis pipeline for efficient processing and utilization of temporal high-throughput phenotyping data. Front Plant Sci (In press)

    Google Scholar 

  • Kholova J, Hash CT, Kumar PL, Yadav RS, Kocova M, Vadez V (2010) Terminal drought-tolerant pearl millet Pennisetum glaucum (L.) R. Br. have high leaf ABA and limit transpiration at high vapour pressure deficit. J Exp Bot 61:1431–1440

    Article  CAS  Google Scholar 

  • Kholova J, Nepolean T, Hash CT, Supriya A, Rajaram V, Senthilvel S, Kakkera A, Yadav R, Vadez V (2012) Water saving traits co-map with a major terminal drought tolerance quantitative trait locus in pearl millet Pennisetum glaucum (L.) R. Br Molecular Breeding 30:1337–1353

    Article  Google Scholar 

  • Kholová J, Tharanya M, Kaliamoorthy S, Malayee S, Baddam R, Hammer GL, McLean G, Deshpande S, Hash CT, Craufurd PQ, Vadez V (2014) Modelling the effect of plant water use traits on yield and stay-green expression in sorghum. Funct Plant Biol 41(10–11):1019–1034

    Article  Google Scholar 

  • Korol AB, Ronin YI, Itskovich AM, Peng JH, Nevo E (2001) Enhanced efficiency of quantitative trait loci mapping analysis based on multivariate complexes of quantitative traits. Genetics 157:789–1803

    Article  Google Scholar 

  • Lynch JP, Brown KM (2012) New roots for agriculture: exploiting the root phenome. Philos Trans Royal Soc B-Biol Scie 367:1598–1604

    Article  Google Scholar 

  • Parent B, Turc O, Gibon Y, Stitt M, Tardieu F (2010) Modelling temperature-compensated physiological rates, based on the co-ordination of responses to temperature of developmental processes. J Exp Bot 61:2057–2069

    Article  CAS  Google Scholar 

  • Peak D, West JD, Messinger SM, Mott KA (2004) Evidence for complex, collective dynamics and emergent, distributed computation in plants. PNAS 101:918–922. https://www.pnas.org/cgi/doi/10.1073/pnas.0307811100

  • Poorter H, Niinemets U, Walter A, Fiorani F, Schurr U (2010) A method to construct dose-response curves for a wide range of environmental factors and plant traits by means of a meta-analysis of phenotypic data. J Exp Bot 61:2043–2055

    Article  CAS  Google Scholar 

  • Potgieter AB, Lobell DB, Hammer GL, Jordan DR, Davis P, Brider J (2016) Yield trends under varying environmental conditions for sorghum and wheat across Australia. Agric For Meteorol 228:276–285. https://doi.org/10.1016/J.AGRFORMET.2016.07.004

    Article  Google Scholar 

  • Ratnakumar P, Vadez V (2011) Groundnut (Arachis hypogaea) genotypes tolerant to intermittent drought maintain a high harvest index and have small leaf canopy under stress. Funct Plant Biol 38:1016–1023

    Article  CAS  Google Scholar 

  • Ratnakumar P, Vadez V, Nigam SN, Krishnamurthy L (2009) Assessment of transpiration efficiency in peanut (Arachis hypogaea L.) under drought using a lysimetric system. Plant Biol 11:124–130

    Article  CAS  Google Scholar 

  • Reynolds M, Kropff M, Crossa J, Koo J, Kruseman G, Molero Milan A, Rutkoski J, Schulthess U, Singh B, Sonder K, Tonnang H, Vadez V (2018) Role of modelling in international crop research: overview and some case studies. Agronomy 8:291. https://doi.org/10.3390/agronomy8120291

  • Schoppach R, Claverie E, Sadok W (2014) Genotype-dependent influence of night-time vapour pressure deficit on night-time transpiration and daytime gas exchange in wheat. Funct Plant Biol 41:963–971

    Article  CAS  Google Scholar 

  • Schuster I (2011) Marker-assisted selection for quantitative traits. CBAB 11:50–55

    Article  Google Scholar 

  • Sinclair TR, Seligman N (2000) Criteria for publishing papers on crop modeling. Field Crops Research 68:165–172

    Article  Google Scholar 

  • Soltani A, Ghassemi-Golezani K, Khooie FR, Moghaddam M (1999) A simple model for chickpea growth and yield. Field Crops Res 62:213–224

    Article  Google Scholar 

  • Vadez V (2014) Root hydraulics: the forgotten side of roots in drought adaptation. Field Crops Res 165:15–24

    Article  Google Scholar 

  • Vadez V, Soltani A, Sinclair TR (2012) Modelling possible benefits of root related traits to enhance terminal drought adaptation of chickpea. Field Crops Res 137:108–115

    Article  Google Scholar 

  • Vadez V, Kholova J, Yadav RS, Hash CT (2013a) Small temporal differences in water uptake among varieties of pearl millet (Pennisetum glaucum (L.) R. Br.) are critical for grain yield under terminal drought. Plant Soil 371:447–462

    Article  CAS  Google Scholar 

  • Vadez V, Kholova J, Zaman-Allah M, Belko N (2013b) Water: the most important ‘molecular’ component of water stress tolerance research. Funct Plant Biol 40:1310–1322

    Article  Google Scholar 

  • Vadez V, Kholova J, Medina S, Kakkera A, Anderberg H (2014) Transpiration efficiency: new insights into an old story. J Experimental Botany, eru040

    Google Scholar 

  • Vadez V, Rao S, Kholova J, Krishnamurthy L, Kashiwagi J, Ratnakumar P, Sharma K, Bhatnagar-Mathur P, Basu P (2008) Root research for drought tolerance in legumes: quo vadis. J Food Legumes 21:77–85

    Google Scholar 

  • Vadez V, Krishnamurthy L, Hash CT, Upadhyaya HD, Borrell AK (2011) Yield, transpiration efficiency, and water-use variations and their interrelationships in the sorghum reference collection. Crop Pasture Sci 62:645–655

    Article  Google Scholar 

  • Vadez V, Kholova J, Hummel G, Zhokhavets U, Gupta SK, Hash CT (2015) LeasyScan: a novel concept combining 3D imaging and lysimetry for highthroughput phenotyping of traits controlling plant water budget. J Exp Bot 66(18):5581–5593. https://doi.org/10.1093/jxb/erv251

  • Vadez V, Halilou O, Hissene HM, Sibiry-Traore P, Sinclair TR, Soltani A (2017) Mapping water stress incidence and intensity, optimal plant populations, and cultivar duration for african groundnut productivity enhancement. Front Plant Sci 8:432. https://doi.org/10.3389/fpls.2017.00432

    Article  PubMed  PubMed Central  Google Scholar 

  • Welcker C, Boussuge B, Bencivenni C, Ribaut JM, Tardieu F (2007) Are source and sink strengths genetically linked in maize plants subjected to water deficit? A QTL study of the responses of leaf growth and of anthesis silking interval to water deficit. J Exp Bot 58:339–349

    Article  CAS  Google Scholar 

  • Welcker C, Sadok W, Dignat G, Renault M, Salvi S, Charcosset A, Tardieu F (2011) A common genetic determinism for sensitivities to soil water deficit and evaporative demand: meta-analysis of quantitative trait loci and introgression lines of maize. Plant Physiol 157:718–729

    Article  CAS  Google Scholar 

  • White TA, Snow VO (2012) A modelling analysis to identify plant traits for enhanced water-use efficiency of pasture. Crop Pasture Sci 63:63–76

    Article  Google Scholar 

  • White JW, Andrade-Sanchez P, Gore MA, Bronson KF, Coffelt TA, Conley MM, Feldmann KA, French AN, Heun JT, Hunsaker DJ, Jenks MA, Kimball BA, Roth RL, Strand RJ, Thorp KR, Wall GW, Wang GY (2012) Field-based phenomics for plant genetics research. Field Crops Res 133:101–112

    Article  Google Scholar 

  • Zaman-Allah M, Jenkinson DM, Vadez V (2011a) Chickpea genotypes contrasting for seed yield under terminal drought stress in the field differ for traits related to the control of water use. Funct Plant Biol 38:270–281

    Article  Google Scholar 

  • Zaman-Allah M, Jenkinson DM, Vadez V (2011b) A conservative pattern of water use, rather than deep or profuse rooting, is critical for the terminal drought tolerance of chickpea. J Exp Bot 62:4239–4252

    Article  CAS  Google Scholar 

Download references

Acknowledgments

The authors are thankful for the funding from ICRISAT for the capital investment in the LeasyScan facility and to the Kirkhouse Trust fund for contributing to the acquisition of additional scales.

Author information

Authors and Affiliations

  1. Institut de Recherche pour le Développement (IRD), UMR DIADE, University of Montpellier, 911 Av Agropolis BP65401, 34394, Montpellier, France

    Vincent Vadez

  2. Crop Physiology Laboratory, ICRISAT, Greater Hyderabad, Patancheru, 502324, Telangana, India

    Jana Kholova

  3. Phenospex, Jan Campertstraat 11/NL-6416 SG, Heerlen, The Netherlands

    Grégoire Hummel & Uladzimir Zhokhavets

Authors
  1. Vincent Vadez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jana Kholova
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Grégoire Hummel
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Uladzimir Zhokhavets
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vincent Vadez .

Editor information

Editors and Affiliations

  1. University of Missouri, Columbia, MO, USA

    Jianfeng Zhou

  2. University of Missouri, Columbia, MO, USA

    Henry T. Nguyen

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Vadez, V., Kholova, J., Hummel, G., Zhokhavets, U. (2021). Got All the Answers! What Were the Questions? Avoiding the Risk of “Phenomics” Slipping into a Technology Spree. In: Zhou, J., Nguyen, H.T. (eds) High-Throughput Crop Phenotyping. Concepts and Strategies in Plant Sciences. Springer, Cham. https://doi.org/10.1007/978-3-030-73734-4_11

Download citation

Publish with us

Policies and ethics

Search

Navigation