New technologies for phenotyping

Phenomics: How Next-Generation Phenotyping is Revolutionizing Plant Breeding

Volumn , Issue , 2015, Pages 1-14

  Araus, José Luis ^a   Elazab, Abdelhalim ^a   Vergara, Omar ^a   Cabrera Bosquet, Llorenç ^b   Serret, Maria Dolors ^a   Zaman Allah, Mainassara ^c   Cairns, Jill E ^c  

^a UNIVERSITY OF BARCELONA   (Spain)

^b UMR 759 LEPSE   (France)

^c CIMMYT   (Zimbabwe)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84944560528     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1007/978-3-319-13677-6_1     Document Type: Chapter

References (61)

1. Araus JL, Slafer GA, Reynolds MP, Royo C (2002) Plant breeding and water stress in C3 cereals: what to breed for? Ann Bot 89:925-940.
2. Araus JL, Slafer GA, Royo C, Serret MD (2008) Breeding for yield potential and stress adaptation in cereals. Crit Rev Plant Sci 27:1-36.
3. Araus JL, Cabrera-Bosquet L, Sánchez C (2010) Is heterosis in maize mediated through better water use? New Phytol 187:392-406.
4. Araus JL, Serret MD, Edmeades GO (2012) Phenotyping maize for adaptation to drought. Front Physiol 3:305.
5. Araus JL, Cabrera-Bosquet L, Serret MD, Bort J, Nieto-Taladriz MT (2013) Comparative performance of δ13C, δ18O and δ15N for phenotyping durum wheat adaptation to a dryland environment. Funct Plant Biol 40:595-608.
6. Araus JL, Cairns J (2014) Field high-throughput phenotyping-the new crop breeding frontier. Trends Plant Sci 19:52-61.
7. Barbour MM, Fischer RH, Sayre KD, Farquhar GD (2000) Oxygen isotope ratio of leaf and grain material correlates with stomatal conductance and grain yield in irrigated wheat. Aust J Plant Physiol 27:625-637.
8. Busemeyer L, Mentrup D, Möller K, Wunder E, Alheit K, Hahn V, Maurer HP, Reif JC, Würschum T, Müller J, Rahe F, Ruckelshausen A (2013) BreedVision-a multi-sensor platform for non-destructive field-based phenotyping in plant breeding. Sensors 13:2830-2847.
9. Cabrera-Bosquet L, Sánchez C, Araus JL (2009a) How yield relates to ash content, Δ13C and Δ18O in maize grown under different water regimes. Ann Bot 104:1207-1216.
10. Cabrera-Bosquet L, Sanchez C, Araus JL (2009b) Oxygen isotope enrichment (Δ18O) reflects yield potential and drought resistance in maize. Plant Cell Environ 32:1487-1499.
11. Cabrera-Bosquet L, Albrizio R, Nogués S, Araus JL (2011a) Dual Δ13C/δ18O response to water and nitrogen availability and its relationship with yield in field-grown durum wheat. Plant Cell Environ 34:418-433.
12. Cabrera-Bosquet L, Sánchez C, Rosales A, Palacios-Rojas N, Araus JL (2011b) NIRS-assessment of δ18O, nitrogen and ash content for improved yield potential and drought adaptation in maize. J Agric Food Chem 59:467-474.
13. Cabrera-Bosquet L, Crossa J, von Zitzewitz J, Serret MD, Araus JL (2012) High-throughput phenotyping and genomic selection: the frontiers of crop breeding converge. J Integr Plant Biol 54:312-320.
14. Cairns JE, Impa SM, O’Toole JC, Jagadish SVK, Price AH (2011) Influence of the soil physical environment on drought stress and its implications for drought research. Field Crop Res 121:303-310.
15. Cairns JE, Sanchez C, Vargas M, Ordoñez RA, Araus JL (2012) Dissecting maize productivity: ideotypes associated with grain yield under drought stress and well-watered conditions. J Integr Plant Biol 54:1007-1020.
16. Cairns J, Hellin J, Sonder K, Araus JL, MacRobert JF, Thierfelder C, Prasanna BP (2013) Adapting maize to climate change in sub-saharan Africa. Food Secur 5:345-360.
17. 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. Ann Appl Biol 150:227-236.
18. Casadesús J, Villegas D (2014) Conventional digital cameras as a tool for assessing leaf area index and biomass for cereal breeding. J Integr Plant Biol 56:7-14.
19. 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.
20. Comar A, Burger P, Benoit de Solan C, Baret F, Daumard F, Hanocq J-F (2012) A semi-automatic system for high throughput phenotyping wheat cultivars in-field conditions: description and first results. Funct Plant Biol 39:914-924.
21. Condon AG, Richards RA, Rebetkke GJ, Farquhar GD (2002) Improving intrinsic water-use efficiency and crop yield. Crop Sci 42:122-131.
22. Condon AG, Richards RA, Rebetkke GJ, Farquhar GD (2004) Breeding for high water-use efficiency. J Exp Bot 55:2447-2460.
23. Cooper M, Messina CD, Podlich D, Totir LR, Baumgarten A, Hausmann NJ, Wright D, Graham G (2014) Predicting the future of plant breeding: complementing empirical evaluation with genetic prediction. Crop Pasture Sci 65:311-336.
24. 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.
25. Farquhar GD (1983) On the nature of carbon isotope discrimination in C4 species. Aust J Plant Physiol 10:205-226.
26. Farquhar GD, Cernusak LA, Barnes B (2007) Heavy water fractionation during transpiration. Plant Physiol 143:11-18.
27. Ferrio JP, Bertran E, Nachit M, Royo C, Araus JL (2001) Near infrared reflectance spectroscopy as a new surrogate analysis for Δ13C in mature kernels of durum wheat. Aust J Agric Res 52:809-816.
28. Gebbers R, Adamchuk VI (2010) Precision agriculture and food security. Science 327:828-831.
29. Gresset SI, Westermeier P, Rademacher S, Ouzunova M, Presterl T, Westhoff P, Schön CC (2014) Stable carbon isotope discrimination is under genetic control in the C4 species maize with several genomic regions influencing trait expression. Plant Physiol 164(1):131-143. doi: 10. 1104/pp.113.224816. Epub 2013 Nov 26
30. Gutierrez M, Reynolds MP, Klatt AR (2010) Association of water spectral indices with plant and soil water relations in contrasting wheat genotypes. J Exp Bot 12:3291-3303.
31. Hacisalihoglu G, Larbi B, Settles AM (2010) Near-infrared reflectance spectroscopy predicts protein, starch, and seed weight in intact seeds of common bean (Phaseolus vulgaris L.). J Agric Food Chem 58:702-706.
32. Hawkins E, Fricker TE, Challinor AJ, Ferro CAT, Ho CK, Osborne TM (2013) Increasing influence of heat stress on French maize yields from the 1960s to the 2030s. Glob Change Biol 19:937-947.
33. Henderson S, Caemmerer S, Farquhar G (1992) Short-term measurements of carbon isotope discrimination in several C4 species. Funct Plant Biol 19:263-285.
34. Kleinebecker T, Schmidt SR, Fritz C, Smolders AJP, Hölze N (2009) Prediction of δ13C and δ15N in plant tissues with near-infrared reflectance spectroscopy. New Phytol 184:732-739.
35. Leitner D, Meunier F, Bodner G, Javaux M, Schnepf A (2014) Impact of contrasted maize root traits at flowering on water stress tolerance-a simulation study. Field Crops Res 165:125-137.
36. Lobell DB, Schlenker W, Costa-Roberts J (2011a) Climate trends and global crop production since 1980. Science 333:616-620.
37. Lobell DB, Bänziger M, Magorokosho C, Vivek B (2011b) Nonlinear heat effects on African maize as evidenced by historical yield trials. Nat Glob Change 1:42-45.
38. Lopes MS, Araus JL, van Heerden PDR, Foyer CH (2011) Enhancing drought tolerance in C4 crops. J Exp Bot 62:3135-3153.
39. Masuka B, Araus JL, Sonder K, Das B, Cairns JE (2012) Deciphering the code: successful abiotic stress phenotyping for molecular breeding. J Integr Plant Biol 54:238-249.
40. Mir RR, Zaman-Allah M, Sreenivasulu N, Trethowan R, Varshney RK (2012) Integrated genomics, physiology and breeding approaches for improving drought tolerance in crops. Theor Appl Genet 125:625-645.
41. Montes JM, Melchinger AE, Reif JC (2007) Novel throughput phenotyping platforms in plant genetic studies. Trends Plant Sci 12:433-436.
42. Montes JM, Technow F, Dhillon B, Mauch F, Melchinger A (2011) High-throughput nondestructive biomass determination during early plant development in maize under field conditions. Field Crops Res 121:268-273.
43. Nguy-Robertson A, Gitelson AA, Peng Y, Viña A, Arkebauer T, Rundquist D (2012) Green leaf area index estimation in maize and soybean: combining vegetation indices to achieve maximal sensitivity. Agron J 104:1336-1347.
44. Passioura JB (1977) Grain yield, harvest index, and water use of wheat. J Aust I Agr Sci 43:117-120.
45. Passioura JB (2006) The perils of pot experiments. Funct Plant Biol 33:1075-1079.
46. Poorter H, Bühler J, van Dusschoten D, Climent J, Postma JA (2012) Pot size matters: a meta-analysis of the effects of rooting volume on plant growth. Funct Plant Biol 39:839-850.
47. Prasanna BP, Araus JL, Crossa J, Cairns JE, Palacios N, Das B, Magotokosho C (2013) High-throughput and precision phenotyping for cereal breeding programs. In: Gupta PK, Varshney RK (eds) Cereal genomics II. Springer, Dordrecht, pp 341-374 (Chapter 13).
48. Rebetzke GJ, Condon AG, Farquhar GD, Appels R, Richards RA (2008) Quantitative trait loci for carbon isotope discrimination are repeatable across environments and wheat mapping populations. Theor Appl Genet 118:123-137.
49. Rebetzke GJ, Chenu K, Biddulph B, Moeller C, Deery DM, Rattey AR, Bennett D, Barrett-Lennard EG, Mayer JE (2013) A multisite managed environment facility for targeted trait and germplasm phenotyping. Funct Plant Biol 40:1-13.
50. Reynolds MP, Pask AJD, Mullan DM (eds) (2012) Physiological breeding I: interdisciplinary approaches to improve crop adaptation. CIMMYT, Mexico
51. Romano G, Zia S, Spreer W, Cairns J, Araus JL, Müller J (2011) Use of thermography for high throughput phenotyping of tropical maize adaptation in water stress. Comput Electron Agric 79:67-74.
52. Rorie RL, Purcell LC, Karcher DE, King CA (2011) The assessment of leaf nitrogen in corn from digital images. Crop Sci 51:2174-2180.
53. Svensgaard J, Roitsch Y, Christensen S (2014) Development of a mobile multispectral imaging platform for precise field phenotyping. Agronomy 4:322-336.
54. Trachsel S, Kaeppler SM, Brown KM, Lynch JP (2011) Shovelomics: high throughput phenotyping of maize (Zea mays L.) root architecture in the field. Plant Soil 341:75-87.
55. Weber VS, Araus JL, Cairns JE, Sanchez C, Melchinger AE, Orsini E (2012) Prediction of grain yield using reflectance spectra of canopy and leaves in maize plants grown under different water regimes. Field Crop Res 128:82-90.
56. Weiss U, Biber P (2011) Plant detection and mapping for agricultural robots using a 3D LIDAR sensor. Robot Auton Syst 59:266-273.
57. 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 Res 133:101-112.
58. Whitmore AP, Whalley WR (2009) Physical effects of soil drying on roots and crop growth. J Exp Bot 60:2845-2857.
59. Winterhalter L, Mistele B, Jampatong S, Schmidhalter U (2011) High throughput phenotyping of canopy water mass and canopy temperature in well-watered and drought stressed tropical maize hybrids in the vegetative stage. Eur J Agron 35:22-32.
60. Yousfi S, Serret MD, Márquez AJ, Voltas J, Araus JL (2012) Combined use of δ13C, δ18O and δ15N tracks nitrogen metabolism and genotypic adaptation of durum wheat to salinity and water deficit. New Phytol 194:230-244.
61. Zia S, Romano G, Spreer W, Sanchez C, Cairns J, Araus JL, Müller J (2013) Infrared thermal imaging as a rapid tool for identifying water stress tolerant maize genotypes of different phenology. J Agron Crop Sci 199:75-84.