Image based phenotyping during winter: a powerful tool to assess wheat genetic variation in growth response to temperature
Christoph Grieder A , Andreas Hund A and Achim Walter A B
+ Author Affiliations
- Author Affiliations
A Institute of Agricultural Sciences, ETH Zurich, Universitätstrasse 2, 8092 Zurich, Switzerland.
B Corresponding author. Email: achim.walter@usys.ethz.ch
Functional Plant Biology 42(4) 387-396 https://doi.org/10.1071/FP14226
Submitted: 14 August 2014 Accepted: 16 December 2014 Published: 27 January 2015
Abstract
Having a strong effect on plant growth, temperature adaption has become a major breeding aim. Due to a lack of efficient methods, we developed an image-based approach to characterise genotypes for their temperature behaviour in the field. Twenty-nine winter wheat (Triticum aestivum L.) genotypes were continuously monitored at 3-day intervals on a plot basis during early growth from November to March using a modified digital camera. Canopy cover (CC) was determined by segmentation of leaves in calibrated images. Relative growth rates (RGR) of CC were then calculated for each measurement interval and related to the respective temperature. Also, classical traits used in plant breeding were assessed. Measurements of CC at single dates were highly repeatable with respect to genotype. For the tested range of temperatures (0−7°C), a linear relation between RGR and temperature was observed. Genotypes differed for base temperature and increase in RGR with rising temperature, these two traits showing a strong positive correlation with each other but being independent of CC at a single date. Our simple approach is suitable to screen large populations for differences in growth response to environmental stimuli. Furthermore, the derived parameters reveal additional information that cannot be assessed by usual measurements of static size.
Additional keywords: canopy development, dry matter accumulation, growth regulation, plant phenomics, temperature effects.
References
Amthor JS (1989) ‘Respiration and crop productivity.’ (Springer: New York)Butler DG, Cullis BR, Gilmour A, Gogel BJ (2007) ‘Analysis of mixed models for S language environments. ASReml-R reference manual. 2.0.’ (Queensland Department of Primary Industries and Fisheries and NSW Department of Primary Industries: Brisbane, Australia)
Cao W, Moss DN (1989) Temperature effect on leaf emergence and phyllochron in wheat and barley. Crop Science 29, 1018–1021.
| Temperature effect on leaf emergence and phyllochron in wheat and barley.Crossref | GoogleScholarGoogle Scholar |
Casadesús J, Villegas D (2014) Conventional digital cameras as a tool for assessing LAI and biomass for cereal breeding. Journal of Integrative Plant Biology 56, 7–14.
| Conventional digital cameras as a tool for assessing LAI and biomass for cereal breeding.Crossref | GoogleScholarGoogle Scholar | 24330531PubMed |
Casadesús J, Kaya Y, Bort J, Nachit MM, Araus JL, Amor S, Ferrazzano G, Maalouf F, Maccaferri M, Martos V, Ouabbou H, Villegas D (2007) Using vegetation indices derived from conventional digital cameras as selection criteria for wheat breeding in water-limited environments. Annals of Applied Biology 150, 227–236.
| Using vegetation indices derived from conventional digital cameras as selection criteria for wheat breeding in water-limited environments.Crossref | GoogleScholarGoogle 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? European Journal of Agronomy 42, 59–67.
| Resiliences to water deficit in a phenotyping platform and in the field: how related are they in maize?Crossref | GoogleScholarGoogle Scholar |
Coleman R, Gill G, Rebetzke G (2001) Identification of quantitative trait loci for traits conferring weed competitiveness in wheat (Triticum aestivum L.). Australian Journal of Agricultural Research 52, 1235–1246.
| Identification of quantitative trait loci for traits conferring weed competitiveness in wheat (Triticum aestivum L.).Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD38XltlOnuw%3D%3D&md5=819d53ad14fba3c2e3e661f667d2dba7CAS |
Condon AG, Richards RA, Rebetzke GJ, Farquhar GD (2004) Breeding for high water-use efficiency. Journal of Experimental Botany 55, 2447–2460.
| Breeding for high water-use efficiency.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2cXovVOisrk%3D&md5=3b097a32148dc8bcfa1265b6fceb63fdCAS | 15475373PubMed |
Dhondt S, Wuyts N, Inzé D (2013) Cell to whole-plant phenotyping: the best is yet to come. Trends in Plant Science 18, 428–439.
| Cell to whole-plant phenotyping: the best is yet to come.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3sXot1Cnu70%3D&md5=8d6a397341b5b552363e274f5a5874c9CAS | 23706697PubMed |
Drewry DT, Kumar P, Long SP (2014) Simultaneous improvement in productivity, water use, and albedo through crop structural modification. Global Change Biology 20, 1955–1967.
| Simultaneous improvement in productivity, water use, and albedo through crop structural modification.Crossref | GoogleScholarGoogle Scholar | 24700722PubMed |
Falconer DS, Mackay TF (1996) ‘Introduction to quantitative genetics.’ (Longman Group Limited: Harlow, UK)
Feng Y, Li X, Boersma L (1990) The Arrhenius equation as a model for explaining plant responses to temperature and water stresses. Annals of Botany 66, 237–244.
Fracheboud Y, Haldimann P, Leipner J, Stamp P (1999) Chlorophyll fluorescence as a selection tool for cold tolerance of photosynthesis in maize (Zea mays L.). Journal of Experimental Botany 50, 1533–1540.
| Chlorophyll fluorescence as a selection tool for cold tolerance of photosynthesis in maize (Zea mays L.).Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaK1MXmtlamtLY%3D&md5=76def79b6939063d2f987a90979a7658CAS |
Franklin K (2009) Light and temperature signal crosstalk in plant development. Current Opinion in Plant Biology 12, 63–68.
| Light and temperature signal crosstalk in plant development.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXntlyjtw%3D%3D&md5=69e8ce610832180b89742241774d165aCAS | 18951837PubMed |
Granier C, Aguirrezabal L, Chenu K, Cookson SJ, Dauzat M, Hamard P, Thioux JJ, Rolland G, Bouchier-Combaud S, Lebaudy A, Muller B, Simonneau T, Tardieu F (2006) PHENOPSIS, an automated platform for reproducible phenotyping of plant responses to soil water deficit in Arabidopsis thaliana permitted the identification of an accession with low sensitivity to soil water deficit. New Phytologist 169, 623–635.
| PHENOPSIS, an automated platform for reproducible phenotyping of plant responses to soil water deficit in Arabidopsis thaliana permitted the identification of an accession with low sensitivity to soil water deficit.Crossref | GoogleScholarGoogle Scholar | 16411964PubMed |
Hay RKM, Wilson GT (1982) Leaf appearance and extension in field-grown winter wheat plants: the importance of soil temperature during vegetative growth. Journal of Agricultural Science 99, 403–410.
| Leaf appearance and extension in field-grown winter wheat plants: the importance of soil temperature during vegetative growth.Crossref | GoogleScholarGoogle Scholar |
Hunt R (1982) ‘Plant growth curves: the functional approach to plant growth analysis.’ (Edward Arnold: London)
Karimi M, Siddique K (1991) Crop growth and relative growth rates of old and modern wheat cultivars. Crop and Pasture Science 42, 13–20.
| Crop growth and relative growth rates of old and modern wheat cultivars.Crossref | GoogleScholarGoogle Scholar |
Kipp S, Mistele B, Baresel P, Schmidhalter U (2014) High-throughput phenotyping early plant vigour of winter wheat. European Journal of Agronomy 52, 271–278.
| High-throughput phenotyping early plant vigour of winter wheat.Crossref | GoogleScholarGoogle Scholar |
Körner C (2008) Winter crop growth at low temperature may hold the answer for alpine treeline formation. Plant Ecology & Diversity 1, 3–11.
| Winter crop growth at low temperature may hold the answer for alpine treeline formation.Crossref | GoogleScholarGoogle Scholar |
Kurimoto K, Day D (2004) Effect of respiratory homeostasis on plant growth in cultivars of wheat and rice. Plant, Cell & Environment 27, 853–862.
| Effect of respiratory homeostasis on plant growth in cultivars of wheat and rice.Crossref | GoogleScholarGoogle Scholar |
Lambers H, Chappin FS, III, Pons TL (2008) ‘Plant physiological ecology.’ (Springer: New York)
Leister D, Varotto C, Pesaresi P, Niwergall A, Salamini F (1999) Large-scale evaluation of plant growth in Arabidopsis thaliana by non-invasive image analysis. Plant Physiology and Biochemistry 37, 671–678.
| Large-scale evaluation of plant growth in Arabidopsis thaliana by non-invasive image analysis.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaK1MXms1ahsL8%3D&md5=0abf4ec1787c73b17950001fc03f62d0CAS |
Liao M, Fillery IRP, Palta JA (2004) Early vigorous growth is a major factor influencing nitrogen uptake in wheat. Functional Plant Biology 31, 121–129.
| Early vigorous growth is a major factor influencing nitrogen uptake in wheat.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2cXhsl2gtrk%3D&md5=d8ea1fdc343e44d9e8a13f1e0d85bfe0CAS |
Malyshev AV, Henry HAL, Kreyling J (2014) Relative effects of temperature vs. photoperiod on growth and cold acclimation of northern and southern ecotypes of the grass Arrhenatherum elatius. Environmental and Experimental Botany 106, 189–196.
| Relative effects of temperature vs. photoperiod on growth and cold acclimation of northern and southern ecotypes of the grass Arrhenatherum elatius.Crossref | GoogleScholarGoogle Scholar |
Merz T, Chapman S (2011) Autonomous unmanned helicopter system for remote sensing missions in unknown environments. International archives of the photogrammetry, remote sensing and spatial information sciences. 38–1, 1–6.
Mielewczik M, Friedli M, Kirchgessner N, Walter A (2013) Diel leaf growth of soybean: a novel method to analyze two-dimensional leaf expansion in high temporal resolution based on a marker tracking approach (Martrack leaf). Plant Methods 9, 30
| Diel leaf growth of soybean: a novel method to analyze two-dimensional leaf expansion in high temporal resolution based on a marker tracking approach (Martrack leaf).Crossref | GoogleScholarGoogle Scholar | 23883317PubMed |
Mullan DJ, Reynolds MP (2010) Quantifying genetic effects of ground cover on soil water evaporation using digital imaging. Functional Plant Biology 37, 703–712.
| Quantifying genetic effects of ground cover on soil water evaporation using digital imaging.Crossref | GoogleScholarGoogle Scholar |
Murphy R, Underwood A, Jackson A (2009) Field-based remote sensing of intertidal epilithic chlorophyll: techniques using specialized and conventional digital cameras. Journal of Experimental Marine Biology and Ecology 380, 68–76.
| Field-based remote sensing of intertidal epilithic chlorophyll: techniques using specialized and conventional digital cameras.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXht1OgtLnP&md5=bad919be5e9992cbb2fd3b9fd7c21492CAS |
Nijland W, de Jong R, de Jong SM, Wulder MA, Bater CW, Coops NC (2014) Monitoring plant condition and phenology using infrared sensitive consumer grade digital cameras. Agricultural and Forest Meteorology 184, 98–106.
| Monitoring plant condition and phenology using infrared sensitive consumer grade digital cameras.Crossref | GoogleScholarGoogle Scholar |
Parent B, Tardieu F (2012) Temperature responses of developmental processes have not been affected by breeding in different ecological areas for 17 crop species. New Phytologist 194, 760–774.
| Temperature responses of developmental processes have not been affected by breeding in different ecological areas for 17 crop species.Crossref | GoogleScholarGoogle Scholar | 22390357PubMed |
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. Journal of Experimental Botany 61, 2057–2069.
| Modelling temperature-compensated physiological rates, based on the co-ordination of responses to temperature of developmental processes.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXmsVGnsrs%3D&md5=184e324e6d0b70c885da56727e957b40CAS | 20194927PubMed |
Pau G, Fuchs F, Skylar O, Boutros M, Huber W (2010) EBImage – an R package for image processing with applications to cellular phenotypes. Bioinformatics 26, 979–981.
| EBImage – an R package for image processing with applications to cellular phenotypes.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXjvFykur0%3D&md5=486b8d5d98ff92c11ad5672cbfd3f389CAS | 20338898PubMed |
Penfield S (2008) Temperature perception and signal transduction in plants. New Phytologist 179, 615–628.
| Temperature perception and signal transduction in plants.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1cXhtVKns7zP&md5=c1f4bce1c78f4401c3d423878cabd39cCAS | 18466219PubMed |
Polzehl J, Tabelow K (2007) Adaptive smoothing of digital images: the R package adimpro. Journal of Statistical Software 19, 1–17.
R Core Team (2014) ‘R: A language and environment for statistical computing.’ (R Foundation for statistical computing: Vienna, Austria)
Rajendran K, Tester M, Roy SJ (2009) Quantifying the three main components of salinity tolerance in cereals. Plant, Cell & Environment 32, 237–249.
| Quantifying the three main components of salinity tolerance in cereals.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXjsVKlsL4%3D&md5=d702ce520944ecd0ca520d02ca421625CAS |
Royo C, Aparicio N, Blanco R, Villegas D (2004) Leaf and green area development of durum wheat genotypes grown under Mediterranean conditions. European Journal of Agronomy 20, 419–430.
| Leaf and green area development of durum wheat genotypes grown under Mediterranean conditions.Crossref | GoogleScholarGoogle Scholar |
Sadok W, Naudin P, Boussuge B, Muller B, Welcker C, Tardieu F (2007) Leaf growth rate per unit thermal time follows QTL-dependent daily patterns in hundreds of maize lines under naturally fluctuating conditions. Plant, Cell & Environment 30, 135–146.
| Leaf growth rate per unit thermal time follows QTL-dependent daily patterns in hundreds of maize lines under naturally fluctuating conditions.Crossref | GoogleScholarGoogle Scholar |
Seefeldt S, Kidwell K, Waller J (2002) Base growth temperatures, germination rates and growth response of contemporary spring wheat (Triticum aestivum L.) cultivars from the US Pacific. Field Crops Research 75, 47–52.
| Base growth temperatures, germination rates and growth response of contemporary spring wheat (Triticum aestivum L.) cultivars from the US Pacific.Crossref | GoogleScholarGoogle Scholar |
Slafer G, Rawson H (1994) Sensitivity of wheat phasic development to major environmental factors: a re-examination of some assumptions made by physiologists and modellers. Functional Plant Biology 22, 393–426.
Slafer G, Rawson H (1995) Base and optimum temperatures vary with genotype and stage of development in wheat. Plant, Cell & Environment 18, 671–679.
| Base and optimum temperatures vary with genotype and stage of development in wheat.Crossref | GoogleScholarGoogle Scholar |
Smith AB, Thompson R, Cullis BR (2009) Embedded partially replicated designs for grain quality testing. XIV meeting of the biometrics in plant breeding section: book of abstracts. pp. 27–27. (Scottish Crop Research Institute: Dundee, Scotland)
Strigens A, Grieder C, Haussmann BIG, Melchinger AE (2012) Genetic variation among inbred lines and testcrosses of maize for early growth parameters and their relationship to final dry matter yield. Crop Science 52, 1084–1092.
| Genetic variation among inbred lines and testcrosses of maize for early growth parameters and their relationship to final dry matter yield.Crossref | GoogleScholarGoogle Scholar |
Valério I, Carvalho F, Oliveira A, Benin G, Queiroz de Souza V, de Almeida Machado A, Bertan I, Busato CC, da Silveira G, Fonseca DAR (2009) Seeding density in wheat genotypes as a function of tillering potential. Scientia Agricola 66, 28–39.
| Seeding density in wheat genotypes as a function of tillering potential.Crossref | GoogleScholarGoogle Scholar |
Van Delden A, Pecio A, Haverkort A (2000) Temperature response of early foliar expansion of potato and wheat. Annals of Botany 86, 355–369.
| Temperature response of early foliar expansion of potato and wheat.Crossref | GoogleScholarGoogle Scholar |
Walter A, Scharr H, Gilmer F, Zierer R, Nagel KA, Ernst M, Wiese A, Virnich O, Christ MM, Uhlig B, Jünger S, Schurr U (2007) Dynamics of seedling growth acclimation towards altered light conditions can be quantified via GROWSCREEN: a setup and procedure designed for rapid optical phenotyping of different plant species. New Phytologist 174, 447–455.
| Dynamics of seedling growth acclimation towards altered light conditions can be quantified via GROWSCREEN: a setup and procedure designed for rapid optical phenotyping of different plant species.Crossref | GoogleScholarGoogle Scholar | 17388907PubMed |
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 G (2012) Field-based phenomics for plant genetics research. Field Crops Research 133, 101–112.
| Field-based phenomics for plant genetics research.Crossref | GoogleScholarGoogle Scholar |
Yamori W, Noguchi K, Hikosaka K, Terashima I (2009) Cold-tolerant crop species have greater temperature homeostasis of leaf respiration and photosynthesis than cold-sensitive species. Plant & Cell Physiology 50, 203–215.
| Cold-tolerant crop species have greater temperature homeostasis of leaf respiration and photosynthesis than cold-sensitive species.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXhvF2ksrg%3D&md5=ed2da904b60400e7bd210c6ccb4d6c00CAS |
