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Tackling Nitrogen Use Efficiency in Cereal Crops Using High-Throughput Phenotyping

  • Nicholas John Sitlington Hansen
  • Darren Plett
  • Bettina Berger
  • Trevor Garnett
Chapter

Abstract

Nitrogen use efficiency involves a complex set of plant processes which are heavily influenced by the environment. This chapter explores a suite of new technologies which can be, and in some cases have been, brought to bear in order to categorize and improve the nitrogen use efficiency of cereals. A combination of high-throughput phenotyping, in controlled environments as well as the field, should enable scientists to better capitalize on the expanding genetic knowledge around the downstream pathways of NUE and make more progress in delivering high NUE crops. In this chapter, modern phenomics is explored, with a focus on those technologies which can give more insight into the determinants of yield and NUE.

Keywords

Nitrogen Nitrogen use efficiency NUE Phenomics Cereals Wheat 

Notes

Acknowledgements

Funding was received from the Australian Research Council (LP130101055, IH130200027), and the National Collaborative Research Infrastructure Strategy (NCRIS).

References

  1. Abiko T, Wakayama M, Kawakami A, Obara M, Kisaka H, Miwa T, Aoki N, Ohsugi R (2010) Changes in nitrogen assimilation, metabolism, and growth in transgenic rice plants expressing a fungal NADP (H)-dependent glutamate dehydrogenase (gdhA). Planta 232(2):299–311CrossRefPubMedGoogle Scholar
  2. Agrama HAS, Zakaria AG, Said FB, Tuinstra M (1999) Identification of quantitative trait loci for nitrogen use efficiency in maize. Mol Breeding 5(2):187–195CrossRefGoogle Scholar
  3. Al-Tamimi N, Brien C, Oakey H, Berger B, Saade S, Ho YS, Schmöckel SM, Tester M, Negrão S (2016) Salinity tolerance loci revealed in rice using high-throughput non-invasive phenotyping. Nature Commun 7:13342CrossRefGoogle Scholar
  4. An D, Su J, Liu Q, Zhu Y, Tong Y, Li J, Jing R, Li B, Li Z (2006) Mapping QTLs for nitrogen uptake in relation to the early growth of wheat (Triticum aestivum L.). Plant Soil 284(1–2):73–84CrossRefGoogle Scholar
  5. Andrade-Sanchez P, Gore MA, Heun JT, Thorp KR, Carmo-Silva AE, French AN, Salvucci ME, White JW (2013) Development and evaluation of a field-based high-throughput phenotyping platform. Funct Plant Biol 41(1):68–79CrossRefGoogle Scholar
  6. Araus JL, Cairns JE (2014) Field high-throughput phenotyping: the new crop breeding frontier. Trends Plant Sci 19(1):52–61CrossRefPubMedGoogle Scholar
  7. Babar MA, van Ginkel M, Klatt AR, Prasad B, Reynolds MP (2006) The potential of using spectral reflectance indices to estimate yield in wheat grown under reduced irrigation. Euphytica 150(1):155–172CrossRefGoogle Scholar
  8. Barraclough PB, Howarth JR, Jones J, Lopez-Bellido R, Parmar S, Shepherd CE, Hawkesford MJ (2010) Nitrogen efficiency of wheat: genotypic and environmental variation and prospects for improvement. Eur J Agron 33(1):1–11CrossRefGoogle Scholar
  9. Billiau K, Sprenger H, Schudoma C, Walther D, Köhl KI (2012) Data management pipeline for plant phenotyping in a multisite project. Funct Plant Biol 39(11):948–957CrossRefGoogle Scholar
  10. Borrell A, Hammer G, Van Oosterom E (2001) Stay-green: a consequence of the balance between supply and demand for nitrogen during grain filling? Ann Appl Biol 138(1):91–95CrossRefGoogle Scholar
  11. Brauer EK, Rochon A, Bi YM, Bozzo GG, Rothstein SJ, Shelp BJ (2011) Reappraisal of nitrogen use efficiency in rice overexpressing glutamine synthetase. Physiol Plant 141(4):361–372CrossRefPubMedGoogle Scholar
  12. Brien CJ, Berger B, Rabie H, Tester M (2013) Accounting for variation in designing greenhouse experiments with special reference to greenhouses containing plants on conveyor systems. Plant Methods 9(1):5CrossRefPubMedPubMedCentralGoogle Scholar
  13. Brown TB, Cheng R, Sirault XR, 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 18:73–79CrossRefPubMedGoogle Scholar
  14. Burger J, Geladi P (2006) Hyperspectral NIR imaging for calibration and prediction: a comparison between image and spectrometer data for studying organic and biological samples. Analyst 131(10):1152–1160CrossRefPubMedGoogle Scholar
  15. Burns IG (1980) Influence of the spatial distribution of nitrate and the uptake of N by plants: a review and a model for rooting depth. J Soil Sci 31:155–173CrossRefGoogle Scholar
  16. Cabrera-Bosquet L, Fournier C, Brichet N, Welcker C, Suard B, Tardieu F (2016) High-throughput estimation of incident light, light interception and radiation-use efficiency of thousands of plants in a phenotyping platform. New Phytol 212(1):269–281CrossRefPubMedGoogle Scholar
  17. Campbell MT, Du Q, Liu K, Brien CJ, Berger B, Zhang C, Walia H (2017) A comprehensive image-based phenomic analysis reveals the complex genetic architecture of shoot growth dynamics in rice (Oryza sativa). Plant Genome 10(2)Google Scholar
  18. Campbell MT, Knecht AC, Berger B, Brien CJ, Wang D, Walia H (2015) Integrating image-based phenomics and association analysis to dissect the genetic architecture of temporal salinity responses in rice. Plant Physiol 168(4):1476–1489CrossRefPubMedPubMedCentralGoogle Scholar
  19. Chapman S, Merz T, Chan A, Jackway P, Hrabar S, Dreccer M, Holland E, Zheng B, Ling T, Jimenez-Berni J (2014) Pheno-copter: a low-altitude, autonomous remote-sensing robotic helicopter for high-throughput field-based phenotyping. Agronomy 4Google Scholar
  20. 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 high-throughput image analysis. Plant Cell 26(12):4636–4655CrossRefPubMedPubMedCentralGoogle Scholar
  21. Cho Y-G, Kang H-J, Lee J-S, Lee Y-T, Lim S-J, Gauch H, Eun M-Y, McCouch SR (2007) Identification of quantitative trait loci in rice for yield, yield components, and agronomic traits across years and locations all rights reserved. no part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. permission for printing and for reprinting the material contained herein has been obtained by the publisher. Crop Sci 47(6):2403–2417CrossRefGoogle Scholar
  22. Cormier F, Foulkes J, Hirel B, Gouache D, Moenne-Loccoz Y, Le Gouis J (2016) Breeding for increased nitrogen-use efficiency: a review for wheat (T.aestivum L.). Plant Breed 135(3):255–278CrossRefGoogle Scholar
  23. Cormier F, Le Gouis J, Dubreuil P, Lafarge S, Praud S (2014) A genome-wide identification of chromosomal regions determining nitrogen use efficiency components in wheat (Triticum aestivum L.). Theor Appl Genet 127(12):2679–2693CrossRefPubMedGoogle Scholar
  24. Crain JL, Wei Y, Barker J, Thompson SM, Alderman PD, Reynolds M, Zhang N, Poland J (2016) Development and deployment of a portable field phenotyping platform. Crop Sci 56(3):965–975CrossRefGoogle Scholar
  25. Deery D, Jimenez-Berni J, Jones H, Sirault X, Furbank R (2014) Proximal remote sensing buggies and potential applications for field-based phenotyping. Agronomy 4(3):349CrossRefGoogle Scholar
  26. Dhugga KS, Waines J (1989) Analysis of nitrogen accumulation and use in bread and durum wheat. Crop Sci 29(5):1232–1239CrossRefGoogle Scholar
  27. Ding L, Wang KJ, Jiang GM, Biswas DK, Xu H, Li LF, Li YH (2005) Effects of nitrogen deficiency on photosynthetic traits of maize hybrids released in different years. Ann Bot 96(5):925–930CrossRefPubMedPubMedCentralGoogle Scholar
  28. Dueck T, van Ieperen W, Taulavuori K (2016) Light perception, signalling and plant responses to spectral quality and photoperiod in natural and horticultural environments. Environ Exp Bot 121:1–3CrossRefGoogle Scholar
  29. Ecarnot M, Compan F, Roumet P (2013) Assessing leaf nitrogen content and leaf mass per unit area of wheat in the field throughout plant cycle with a portable spectrometer. Field Crops Res 140:44–50CrossRefGoogle Scholar
  30. Echarte L, Rothstein S, Tollenaar M (2008) The response of leaf photosynthesis and dry matter accumulation to nitrogen supply in an older and a newer maize hybrid all rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Permission for printing and for reprinting the material contained herein has been obtained by the publisher. Crop Sci 48(2):656–665CrossRefGoogle Scholar
  31. Eitel JUH, Magney TS, Vierling LA, Brown TT, Huggins DR (2014) LiDAR based biomass and crop nitrogen estimates for rapid, non-destructive assessment of wheat nitrogen status. Field Crops Res 159:21–32CrossRefGoogle Scholar
  32. Evenson RE, Gollin D (2003) Assessing the impact of the green revolution, 1960 to 2000. Science 300(5620):758–762CrossRefPubMedGoogle Scholar
  33. Fageria NK, Baligar VC (2005) Enhancing nitrogen use efficiency in crop plants. In: Donald LS (ed) Advances in agronomy. Academic Press, pp. 97-185Google Scholar
  34. Fan X, Tang Z, Tan Y, Zhang Y, Luo B, Yang M, Lian X, Shen Q, Miller AJ, Xu G (2016) Overexpression of a pH-sensitive nitrate transporter in rice increases crop yields. Proc Natl Acad Sci 113(26):7118–7123CrossRefPubMedGoogle Scholar
  35. Fischer R, Wall P (1976) Wheat breeding in Mexico and yield increasesGoogle Scholar
  36. Fischer RA (2011) Wheat physiology: a review of recent developments. Crop Pasture Sci 62(2):95–114CrossRefGoogle Scholar
  37. Forde BG, Clarkson DT (1999) Nitrate and ammonium nutrition of plants: physiological and molecular perspectives. Adv Bot Res 30:1–90CrossRefGoogle Scholar
  38. Foulkes M, Sylvester-Bradley R, Scott R (1998) Evidence for differences between winter wheat cultivars in acquisition of soil mineral nitrogen and uptake and utilization of applied fertilizer nitrogen. J Agric Sci 130(01):29–44CrossRefGoogle Scholar
  39. Foulkes MJ, Hawkesford MJ, Barraclough PB, Holdsworth MJ, Kerr S, Kightley S, Shewry PR (2009) Identifying traits to improve the nitrogen economy of wheat: Recent advances and future prospects. Field Crops Res 114(3):329–342CrossRefGoogle Scholar
  40. Furbank RT, Tester M (2011) Phenomics–technologies to relieve the phenotyping bottleneck. Trends Plant Sci 16(12):635–644CrossRefPubMedGoogle Scholar
  41. Gallais A, Hirel B (2004) An approach to the genetics of nitrogen use efficiency in maize. J Exp Bot 55(396):295–306CrossRefPubMedGoogle Scholar
  42. Garnett T, Conn V, Kaiser BN (2009) Root based approaches to improving nitrogen use efficiency in plants. Plant Cell Environ 32(9):1272–1283CrossRefPubMedGoogle Scholar
  43. Garnett T, Conn V, Plett D, Conn S, Zanghellini J, Mackenzie N, Enju A, Francis K, Holtham L, Roessner U, Boughton B, Bacic A, Shirley N, Rafalski A, Dhugga K, Tester M, Kaiser BN (2013) The response of the maize nitrate transport system to nitrogen demand and supply across the lifecycle. New Phytol 198(1):82–94CrossRefPubMedGoogle Scholar
  44. Garnett T, Plett D, Heuer S, Okamoto M (2015) Genetic approaches to enhancing nitrogen-use efficiency (NUE) in cereals: challenges and future directions. Funct Plant Biol 42(10):921–941CrossRefGoogle Scholar
  45. Garnett T, Rebetzke G (2013) Improving crop nitrogen use in dryland farming. Improving water and nutrient-use efficiency in food production systems. Wiley. pp 123–144Google Scholar
  46. Golzarian M, Frick R, Rajendran K, Berger B, Roy S, Tester M, Lun D (2011) Accurate inference of shoot biomass from high-throughput images of cereal plants. Plant Methods 7Google Scholar
  47. Good AG, Johnson SJ, De Pauw M, Carroll RT, Savidov N, Vidmar J, Lu Z, Taylor G, Stroeher V (2007) Engineering nitrogen use efficiency with alanine aminotransferase. Botany 85(3):252–262Google Scholar
  48. Good AG, Shrawat AK, Muench DG (2004) Can less yield more? Is reducing nutrient input into the environment compatible with maintaining crop production? Trends Plant Sci 9(12):597–605CrossRefGoogle Scholar
  49. Gu R, Duan F, An X, Zhang F, von Wirén N, Yuan L (2013) Characterization of AMT-mediated high-affinity ammonium uptake in roots of maize (Zea mays L.). Plant Cell Physiol 54(9):1515–1524Google Scholar
  50. Han M, Okamoto M, Beatty PH, Rothstein SJ, Good AG (2015) The genetics of nitrogen use efficiency in crop plants. Annu Rev Genet 49:269–289CrossRefPubMedGoogle Scholar
  51. Hawkesford MJ (2017) Genetic variation in traits for nitrogen use efficiency in wheat. J Exp Bot 68(10):2627–2632CrossRefPubMedGoogle Scholar
  52. Heap JW, McKay AC (2009) Managing soil-borne crop diseases using precision agriculture in Australia. Crop Pasture Sci 60(9):824–833CrossRefGoogle Scholar
  53. Hogewoning SW, Trouwborst G, Maljaars H, Poorter H, van Ieperen W, Harbinson J (2010) Blue light dose–responses of leaf photosynthesis, morphology, and chemical composition of Cucumis sativus grown under different combinations of red and blue light. J Exp Bot 61(11):3107–3117CrossRefPubMedPubMedCentralGoogle Scholar
  54. Holman F, Riche A, Michalski A, Castle M, Wooster M, Hawkesford M (2016) High throughput field phenotyping of wheat plant height and growth rate in field plot trials using UAV based remote sensing. Remote Sens 8(12):1031CrossRefGoogle Scholar
  55. Honsdorf N, March TJ, Berger B, Tester M, Pillen K (2014) High-throughput phenotyping to detect drought tolerance QTL in wild barley introgression lines. PLoS ONE 9(5):e97047CrossRefPubMedPubMedCentralGoogle Scholar
  56. Howitt SM, Udvardi MK (2000) Structure, function and regulation of ammonium transporters in plants. Biochimica et Biophysica Acta (BBA). Biomembranes 1465(1):152–170CrossRefGoogle Scholar
  57. Kamprath EJ, Moll RH, Rodriguez N (1982) Effects of nitrogen fertilization and recurrent selection on performance of hybrid populations of corn. Agron J 74(6):955–958CrossRefGoogle Scholar
  58. Keeney DR (1982) Nitrogen management for maximum efficiency and minimum pollution. Nitrogen in agricultural soils. Madison, Wisconsin USA: American Society of Agronomy, pp 605–649Google Scholar
  59. Kokaly RF (2001) Investigating a physical basis for spectroscopic estimates of leaf nitrogen concentration. Remote Sens Environ 75(2):153–161CrossRefGoogle Scholar
  60. Krajewski P, Chen DJ, Cwiek H, van Dijk ADJ, Fiorani F, Kersey P, Klukas C, Lange M, Markiewicz A, Nap JP, van Oeveren J, Pommier C, Scholz U, van Schriek M, Usadel B, Weise S (2015) Towards recommendations for metadata and data handling in plant phenotyping. J Exp Bot 66(18):5417–5427CrossRefPubMedGoogle Scholar
  61. Ladha JK, Tirol-Padre A, Reddy CK, Cassman KG, Verma S, Powlson DS, van Kessel C, de B. Richter D, Chakraborty D, Pathak H (2016) Global nitrogen budgets in cereals: a 50-year assessment for maize, rice, and wheat production systems. Sci Rep 6:19355Google Scholar
  62. Le Gouis J, Béghin D, Heumez E, Pluchard P (2000) Genetic differences for nitrogen uptake and nitrogen utilisation efficiencies in winter wheat. Eur J Agron 12(3–4):163–173CrossRefGoogle Scholar
  63. Léran S, Varala K, Boyer J-C, Chiurazzi M, Crawford N, Daniel-Vedele F, David L, Dickstein R, Fernandez E, Forde B, Gassmann W, Geiger D, Gojon A, Gong J-M, Halkier BA, Harris JM, Hedrich R, Limami AM, Rentsch D, Seo M, Tsay Y-F, Zhang M, Coruzzi G, Lacombe B (2014) A unified nomenclature of nitrate transporter 1/peptide transporter family members in plants. Trends Plant Sci 19(1):5–9CrossRefPubMedGoogle Scholar
  64. Lin M, Huybers P (2012) Reckoning wheat yield trends. Environ Res Lett 7(2):024016CrossRefGoogle Scholar
  65. Lovett GM, Burns DA, Driscoll CT, Jenkins JC, Mitchell MJ, Rustad L, Shanley JB, Likens GE, Haeuber R (2007) Who needs environmental monitoring? Front Ecol Environ 5(5):253–260CrossRefGoogle Scholar
  66. Ludewig U, Neuhäuser B, Dynowski M (2007) Molecular mechanisms of ammonium transport and accumulation in plants. FEBS Lett 581(12):2301–2308CrossRefPubMedGoogle Scholar
  67. Martre P, Porter JR, Jamieson PD, Triboï E (2003) Modeling grain nitrogen accumulation and protein composition to understand the sink/source regulations of nitrogen remobilization for wheat. Plant Physiol 133(4):1959–1967CrossRefPubMedPubMedCentralGoogle Scholar
  68. Max JFJ, Schurr U, Tantau H-J, Mutwiwa UN, Hofmann T, Ulbrich A (2012) Greenhouse cover technology. horticultural reviews. Wiley, pp 259–396Google Scholar
  69. McAllister CH, Beatty PH, Good AG (2012) Engineering nitrogen use efficient crop plants: the current status. Plant Biotechnol J 10(9):1011–1025CrossRefPubMedGoogle Scholar
  70. Meng R, Saade S, Kurtek S, Berger B, Brien C, Pillen K, Tester M, Sun Y (2017) Growth curve registration for evaluating salinity tolerance in barley. Plant Methods 13(1):18CrossRefPubMedPubMedCentralGoogle Scholar
  71. Mickelson S, See D, Meyer FD, Garner JP, Foster CR, Blake TK, Fischer AM (2003) Mapping of QTL associated with nitrogen storage and remobilization in barley (Hordeum vulgare L.) leaves. J Exp Bot 54(383):801–812CrossRefPubMedGoogle Scholar
  72. Moll R, Kamprath E, Jackson W (1982) Analysis and interpretation of factors which contribute to efficiency of nitrogen utilization. Agron J 74(3):562–564CrossRefGoogle Scholar
  73. Muraya MM, Chu J, Zhao Y, Junker A, Klukas C, Reif JC, Altmann T (2017) Genetic variation of growth dynamics in maize (Zea mays L.) revealed through automated non-invasive phenotyping. Plant J 89(2):366–380CrossRefPubMedGoogle Scholar
  74. Neilson EH, Edwards A, Blomstedt C, Berger B, Møller BL, Gleadow R (2015) Utilization of a high-throughput shoot imaging system to examine the dynamic phenotypic responses of a C4 cereal crop plant to nitrogen and water deficiency over time. J Exp Bot eru526Google Scholar
  75. Ortiz-Monasterio R, Sayre K, Rajaram S, McMahon M (1997) Genetic progress in wheat yield and nitrogen use efficiency under four nitrogen rates. Crop Sci 37(3):898–904CrossRefGoogle Scholar
  76. Parent B, Shahinnia F, Maphosa L, Berger B, Rabie H, Chalmers K, Kovalchuk A, Langridge P, Fleury D (2015) Combining field performance with controlled environment plant imaging to identify the genetic control of growth and transpiration underlying yield response to water-deficit stress in wheat. J Exp Bot 66(18):5481–5492CrossRefPubMedPubMedCentralGoogle Scholar
  77. Passioura JB (2006) The perils of pot experiments. Funct Plant Biol 33(12):1075–1079CrossRefGoogle Scholar
  78. Peñuelas J, Filella I (1998) Visible and near-infrared reflectance techniques for diagnosing plant physiological status. Trends Plant Sci 3(4):151–156CrossRefGoogle Scholar
  79. Peoples M, Freney J, Mosier A, Bacon P (1995) Minimizing gaseous losses of nitrogen. Nitrogen fertilization in the environment, pp. 565–602Google Scholar
  80. Plett D, Toubia J, Garnett T, Tester M, Kaiser BN, Baumann U (2010) Dichotomy in the NRT  Gene Families of Dicots and Grass Species. PLoS ONE 5(12):e15289CrossRefPubMedPubMedCentralGoogle Scholar
  81. Poorter H, Fiorani F, Pieruschka R, Wojciechowski T, Putten WH, Kleyer M, Schurr U, Postma J (2016) Pampered inside, pestered outside? Differences and similarities between plants growing in controlled conditions and in the field. New Phytol 212(4):838–855CrossRefPubMedGoogle Scholar
  82. Quraishi UM, Abrouk M, Murat F, Pont C, Foucrier S, Desmaizieres G, Confolent C, Riviere N, Charmet G, Paux E (2011) Cross-genome map based dissection of a nitrogen use efficiency ortho-metaQTL in bread wheat unravels concerted cereal genome evolution. Plant J 65(5):745–756CrossRefPubMedGoogle Scholar
  83. Rajcan I, Tollenaar M (1999) Source: sink ratio and leaf senescence in maize: II. Nitrogen metabolism during grain filling. Field Crops Res 60(3):255–265CrossRefGoogle Scholar
  84. Raun WR, Johnson GV (1999) Improving nitrogen use efficiency for cereal production. Agron J 91(3):357–363CrossRefGoogle Scholar
  85. Ray DK, Mueller ND, West PC, Foley JA (2013) Yield trends are insufficient to double global crop production by 2050. PLoS ONE 8(6):e66428CrossRefPubMedPubMedCentralGoogle Scholar
  86. Ray DK, Ramankutty N, Mueller ND, West PC, Foley JA (2012) Recent patterns of crop yield growth and stagnation. 3:1293Google Scholar
  87. Rebetzke GJ, Chenu K, Biddulph B, Moeller C, Deery DM, Rattey AR, Bennett D, Barrett-Lennard EG, Mayer JE (2012) A multisite managed environment facility for targeted trait and germplasm phenotyping. Funct Plant Biol 40(1):1–13CrossRefGoogle Scholar
  88. Rebetzke GJ, Jimenez-Berni JA, Bovill WD, Deery DM, James RA (2016) High-throughput phenotyping technologies allow accurate selection of stay-green. J Exp Bot 67(17):4919–4924CrossRefPubMedPubMedCentralGoogle Scholar
  89. Sadras VO, Richards RA (2014) Improvement of crop yield in dry environments: benchmarks, levels of organisation and the role of nitrogen. J Exp Bot 65(8):1981–1995CrossRefPubMedGoogle Scholar
  90. Sankaran S, Khot LR, Carter AH (2015) Field-based crop phenotyping: multispectral aerial imaging for evaluation of winter wheat emergence and spring stand. Comput Electron Agric 118:372–379CrossRefGoogle Scholar
  91. Shaw R, Lark RM, Williams AP, Chadwick DR, Jones DL (2016) Characterising the within-field scale spatial variation of nitrogen in a grassland soil to inform the efficient design of in-situ nitrogen sensor networks for precision agriculture. Agr Ecosyst Environ 230:294–306CrossRefGoogle Scholar
  92. Sinclair TR (1998) Historical changes in harvest index and crop nitrogen accumulation. Crop Sci 38(3):638–643CrossRefGoogle Scholar
  93. Sun J, Shi S, Gong W, Yang J, Du L, Song S, Chen B, Zhang Z (2017) Evaluation of hyperspectral LiDAR for monitoring rice leaf nitrogen by comparison with multispectral LiDAR and passive spectrometer. 7:40362Google Scholar
  94. Sylvester-Bradley R, Kindred DR (2009) Analysing nitrogen responses of cereals to prioritize routes to the improvement of nitrogen use efficiency. J Exp Bot 60(7):1939–1951CrossRefPubMedGoogle Scholar
  95. Tanger P, Klassen S, Mojica JP, Lovell JT, Moyers BT, Baraoidan M, Naredo MEB, McNally KL, Poland J, Bush DR, Leung H, Leach JE, McKay JK (2017) Field-based high throughput phenotyping rapidly identifies genomic regions controlling yield components in rice. 7:42839Google Scholar
  96. Thomas H, Smart CM (1993) Crops that stay green1. Ann Appl Biol 123(1):193–219CrossRefGoogle Scholar
  97. Ugarte CC, Trupkin SA, Ghiglione H, Slafer G, Casal JJ (2010) Low red/far-red ratios delay spike and stem growth in wheat. J Exp Bot 61(11):3151–3162CrossRefPubMedPubMedCentralGoogle Scholar
  98. Van Herwaarden A, Farquhar G, Angus J, Richards R, Howe G (1998a) ‘Haying-off’, the negative grain yield response of dryland wheat to nitrogen fertiliser. I. Biomass, grain yield, and water use. Aust J Agric Res 49(7):1067–1081CrossRefGoogle Scholar
  99. van Herwaarden AF, Angus JF, Richards RA, Farquhar GD (1998b) ‘Haying-off’, the negative grain yield response of dryland wheat to nitrogen fertiliser—II. Carbohydrate and protein dynamics. Aust J Agric Res 49(7):1083–1093CrossRefGoogle Scholar
  100. Virlet N, Sabermanesh K, Sadeghi-Tehran P, Hawkesford MJ (2016) Field scanalyzer: an automated robotic field phenotyping platform for detailed crop monitoring. Funct Plant Biol 44(1):143–153CrossRefGoogle Scholar
  101. Watanabe K, Guo W, Arai K, Takanashi H, Kajiya-Kanegae H, Kobayashi M, Yano K, Tokunaga T, Fujiwara T, Tsutsumi N, Iwata H (2017) High-throughput phenotyping of sorghum plant height using an unmanned aerial vehicle and its application to genomic prediction modeling. Front Plant Sci 8(421)Google Scholar
  102. Wei D, Cui K, Ye G, Pan J, Xiang J, Huang J, Nie L (2012) QTL mapping for nitrogen-use efficiency and nitrogen-deficiency tolerance traits in rice. Plant Soil 359(1–2):281–295CrossRefGoogle Scholar
  103. White JW, Andrade-Sanchez P, Gore MA, Bronson KF, Coffelt TA, Conley MM, Feldmann KA, French AN, Heun JT, Hunsaker DJ (2012) Field-based phenomics for plant genetics research. Field Crops Res 133:101–112CrossRefGoogle Scholar
  104. Wolt JD (1994) Soil solution chemistry: applications to environmental science and agriculture. WileyGoogle Scholar
  105. Xu Y, Wang R, Tong Y, Zhao H, Xie Q, Liu D, Zhang A, Li B, Xu H, An D (2014) Mapping QTLs for yield and nitrogen-related traits in wheat: influence of nitrogen and phosphorus fertilization on QTL expression. Theor Appl Genet 127(1):59–72CrossRefPubMedGoogle Scholar
  106. Yang W, Guo Z, Huang C, Duan L, Chen G, Jiang N, Fang W, Feng H, Xie W, Lian X (2014) Combining high-throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice. Nat Commun 5Google Scholar
  107. Yendrek C, Tomaz T, Montes CM, Cao Y, Morse AM, Brown PJ, McIntyre L, Leakey A, Ainsworth E (2016) High-throughput phenotyping of maize leaf physiology and biochemistry using hyperspectral reflectance. Plant Physiol 01447–02016Google Scholar
  108. Zhang X, Huang C, Wu D, Qiao F, Li W, Duan L, Wang K, Xiao Y, Chen G, Liu Q, Xiong L, Yang W, Yan J (2017) High-throughput phenotyping and QTL mapping reveals the genetic architecture of maize plant growth. Plant Physiol 173(3):1554–1564CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Nicholas John Sitlington Hansen
    • 1
    • 2
  • Darren Plett
    • 1
  • Bettina Berger
    • 1
    • 2
  • Trevor Garnett
    • 1
    • 2
  1. 1.School of Agriculture, Food and WineWaite Research Institute, University of AdelaideAdelaideAustralia
  2. 2.The Plant Accelerator, Australian Plant Phenomics FacilityWaite Research Institute, University of AdelaideAdelaideAustralia

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