From Phenotyping to Phenomics: Present and Future Approaches in Grape Trait Analysis to Inform Grape Gene Function

  • Lance Cadle-DavidsonEmail author
  • Jason Londo
  • Dani Martinez
  • Surya Sapkota
  • Ben Gutierrez
Part of the Compendium of Plant Genomes book series (CPG)


Phenotyping in grapevines is the assessment of qualitative and quantitative traits including growth, development, tolerance, resistance, architecture, physiology, chemistry, ecology, and yield. Traditionally, phenotyping techniques relied on measurement of visual, chemical, physiological, or other characteristics by experts, often at low-throughput. The use of standardized OIV or phenological descriptors and scales to phenotype grapevine traits has provided a good foundation for international adoption of phenotyping standards and cross-comparison of results. However, many of these descriptors are subjective, fail to capture complete trait variation, or may not be relevant to some studies. Phenomics, the future of phenotyping, brings opportunities and challenges in increased throughput, objectivity, precision, dynamic measures, and integration that demand new approaches for standardization, data management, and analysis. Here, with a focus on large-scale genetic studies, such as QTL mapping, we describe current phenotyping approaches and their limitations and introduce some future opportunities in phenomics, including the promotion of FAIR data principles of Findability, Accessibility, Interoperability, and Reusability.


  1. Adamchuk VI, Hummel JW, Morgan MT, Upadhyaya SK (2004) On-the-go soil sensors for precision agriculture. Comput Electron Agric 44:71–91CrossRefGoogle Scholar
  2. Alaimo S, Marceca GP, Giugno R et al (2017) Current knowledge and computational techniques for grapevine meta-omics analysis. Front Plant Sci 8:2241PubMedCrossRefPubMedCentralGoogle Scholar
  3. Alaniz S, Armengol J, García-Jiménez J et al (2009) A multiplex PCR system for the specific detection of Cylindrocarpon liriodendri, C. macrodidymum, and C. pauciseptatum from Grapevine. Plant Dis 93:821–825PubMedCrossRefPubMedCentralGoogle Scholar
  4. Anastasiou E, Balafoutis A, Darra N et al (2018) Satellite and proximal sensing to estimate the yield and quality of table grapes. Collect FAO Agric 8:94Google Scholar
  5. Aquino A, Barrio I, Diago M-P et al (2018) vitisBerry: an Android-smartphone application to early evaluate the number of grapevine berries by means of image analysis. Comput Electron Agric 148:19–28CrossRefGoogle Scholar
  6. Atkinson JA, Pound MP, Bennett MJ, Wells DM (2018) Uncovering the hidden half of plants using new advances in root phenotyping. Curr Opin Biotechnol 55:1–8PubMedCrossRefPubMedCentralGoogle Scholar
  7. Auat Cheein F, Steiner G, Perez Paina G, Carelli R (2011) Optimized EIF-SLAM algorithm for precision agriculture mapping based on stems detection. Comput Electron Agric 78:195–207CrossRefGoogle Scholar
  8. Ban Y, Mitani N, Sato A et al (2016) Genetic dissection of quantitative trait loci for berry traits in interspecific hybrid grape (Vitis labruscana × Vitis vinifera). Euphytica 211:295–310CrossRefGoogle Scholar
  9. Barba P, Cadle-Davidson L, Harriman J et al (2014) Grapevine powdery mildew resistance and susceptibility loci identified on a high-resolution SNP map. Theor Appl Genet 127:73–84PubMedCrossRefPubMedCentralGoogle Scholar
  10. Barba P, Lillis J, Luce RS et al (2018) Two dominant loci determine resistance to Phomopsis cane lesions in F1 families of hybrid grapevines. Theor Appl Genet 131:1173–1189PubMedPubMedCentralCrossRefGoogle Scholar
  11. Barrios-Masias FH, Knipfer T, McElrone AJ (2015) Differential responses of grapevine rootstocks to water stress are associated with adjustments in fine root hydraulic physiology and suberization. J Exp Bot 66:6069–6078PubMedPubMedCentralCrossRefGoogle Scholar
  12. Bates T, Dresser J, Eckstrom R, et al (2018) Variable-rate mechanical crop adjustment for crop load balance in “Concord” vineyards. In: 2018 IoT vertical and topical summit on agriculture—Tuscany (IOT Tuscany), pp 1–4Google Scholar
  13. Bautista-Ortín AB, Martínez-Hernández A, Ruiz-García Y et al (2016) Anthocyanins influence tannin–cell wall interactions. Food Chem 206:239–248PubMedCrossRefGoogle Scholar
  14. Bellvert J, Zarco-Tejada PJ, Girona J, Fereres E (2014) Mapping crop water stress index in a “Pinot-noir” vineyard: comparing ground measurements with thermal remote sensing imagery from an unmanned aerial vehicle. Precis Agric 15:361–376CrossRefGoogle Scholar
  15. Benheim D, Rochfort S, Ezernieks V et al (2011) Early detection of grape phylloxera (Daktulosphaira vitifoliae Fitch) infestation through identification of chemical biomarkers. Acta Hortic 904:17–24 Google Scholar
  16. Billet K, Houillé B, Dugé de Bernonville T et al (2018) Field-based metabolomics of Vitis vinifera L. stems provides new insights for genotype discrimination and polyphenol metabolism structuring. Front Plant Sci 9:798PubMedPubMedCentralCrossRefGoogle Scholar
  17. Bindon KA, Madani SH, Pendleton P et al (2014) Factors affecting skin tannin extractability in ripening grapes. J Agric Food Chem 62:1130–1141PubMedCrossRefGoogle Scholar
  18. Blanco-Ulate B, Amrine KCH, Collins TS et al (2015) Developmental and metabolic plasticity of white-skinned grape berries in response to Botrytis cinerea during noble rot. Plant Physiol 169:2422–2443PubMedPubMedCentralGoogle Scholar
  19. Blasi P, Blanc S, Wiedemann-Merdinoglu S et al (2011) Construction of a reference linkage map of Vitis amurensis and genetic mapping of Rpv8, a locus conferring resistance to grapevine downy mildew. Theor Appl Genet 123:43–53PubMedCrossRefGoogle Scholar
  20. Blein-Nicolas M, Albertin W, Valot B et al (2013) Yeast proteome variations reveal different adaptive responses to grape must fermentation. Mol Biol Evol 30:1368–1383PubMedCrossRefGoogle Scholar
  21. Boonham N, Kreuze J, Winter S et al (2014) Methods in virus diagnostics: from ELISA to next generation sequencing. Virus Res 186:20–31PubMedCrossRefGoogle Scholar
  22. Boso S, Gago P, Alonso-Villaverde V et al (2016) Density and size of stomata in the leaves of different hybrids (Vitis sp.) and Vitis vinifera varieties. Vitis. Scholar
  23. Brewer MT, Cadle-Davidson L, Cortesi P et al (2011) Identification and structure of the mating-type locus and development of PCR-based markers for mating type in powdery mildew fungi. Fungal Genet Biol 48:704–713PubMedCrossRefGoogle Scholar
  24. Bronson K, Knezevic I (2016) Big Data in food and agriculture. Big Data Soc 3:2053951716648174CrossRefGoogle Scholar
  25. Cadle-Davidson L (2008) Monitoring pathogenesis of natural Botrytis cinerea infections in developing grape berries. Am J Enol Vitic 59:387–395Google Scholar
  26. Cadle-Davidson L, Gadoury D, Fresnedo-Ramírez J et al (2016) Lessons from a phenotyping center revealed by the genome-guided mapping of powdery mildew resistance loci. Phytopathology 106:1159–1169PubMedCrossRefGoogle Scholar
  27. Caffarra A, Eccel E (2011) Projecting the impacts of climate change on the phenology of grapevine in a mountain area: effects of climate change on grape phenology. Aust J Grape Wine Res 17:52–61CrossRefGoogle Scholar
  28. Callen ST, Klein LL, Miller AJ (2016) Climatic niche characterization of 13 North American Vitis species. Am J Enol Vitic 67:339–349CrossRefGoogle Scholar
  29. Castro AJ, Carapito C, Zorn N et al (2005) Proteomic analysis of grapevine (Vitis vinifera L.) tissues subjected to herbicide stress. J Exp Bot 56:2783–2795PubMedCrossRefGoogle Scholar
  30. Cevallos-Cevallos JM, Reyes-De-Corcuera JI, Etxeberria E et al (2009) Metabolomic analysis in food science: a review. Trends Food Sci Technol 20:557–566CrossRefGoogle Scholar
  31. Chaïb J, Torregrosa L, Mackenzie D et al (2010) The grape microvine—a model system for rapid forward and reverse genetics of grapevines: Grape microvines. Plant J 62:1083–1092 Google Scholar
  32. Chitarrini G, Soini E, Riccadonna S et al (2017) Identification of biomarkers for defense response to Plasmopara viticola in a resistant grape variety. Front Plant Sci 8:1524PubMedPubMedCentralCrossRefGoogle Scholar
  33. Chuine I, Yiou P, Viovy N et al (2004) Historical phenology: grape ripening as a past climate indicator. Nature 432:289–290PubMedCrossRefPubMedCentralGoogle Scholar
  34. Comas LH, Becker SR, Cruz VMV et al (2013) Root traits contributing to plant productivity under drought. Front Plant Sci 4:442PubMedPubMedCentralCrossRefGoogle Scholar
  35. Coombe BG (1995) Growth stages of the grapevine: adoption of a system for identifying grapevine growth stages. Aust J Grape Wine Res 1:104–110CrossRefGoogle Scholar
  36. Coppens F, Wuyts N, Inzé D, Dhondt S (2017) Unlocking the potential of plant phenotyping data through integration and data-driven approaches. Curr Opin Syst Biol 4:58–63CrossRefGoogle Scholar
  37. Correa J, Mamani M, Muñoz-Espinoza C et al (2014) Heritability and identification of QTLs and underlying candidate genes associated with the architecture of the grapevine cluster (Vitis vinifera L.). Theor Appl Genet 127:1143–1162PubMedCrossRefPubMedCentralGoogle Scholar
  38. Costa JM, Ortuño MF, Lopes CM, Chaves MM (2012) Grapevine varieties exhibiting differences in stomatal response to water deficit. Funct Plant Biol 39:179–189CrossRefGoogle Scholar
  39. Coupel-Ledru A, Lebon É, Christophe A et al (2014) Genetic variation in a grapevine progeny (Vitis vinifera L. cvs Grenache × Syrah) reveals inconsistencies between maintenance of daytime leaf water potential and response of transpiration rate under drought. J Exp Bot 65:6205–6218PubMedPubMedCentralCrossRefGoogle Scholar
  40. Coupel-Ledru A, Lebon E, Christophe A et al (2016) Reduced nighttime transpiration is a relevant breeding target for high water-use efficiency in grapevine. Proc Natl Acad Sci USA 113:8963–8968PubMedCrossRefPubMedCentralGoogle Scholar
  41. Crupi P, Bergamini C, Perniola R et al (2015) A chemometric approach to identify the grape cultivar employed to produce nutraceutical fruit juice. Eur Food Res Technol 241:487–496CrossRefGoogle Scholar
  42. Czemmel S, Stracke R, Weisshaar B et al (2009) The grapevine R2R3-MYB transcription factor VvMYBF1 regulates flavonol synthesis in developing grape berries. Plant Physiol 151:1513–1530PubMedPubMedCentralCrossRefGoogle Scholar
  43. Dalbó MA, Ye GN, Weeden NF et al (2000) A gene controlling sex in grapevines placed on a molecular marker-based genetic map. Genome 43:333–340PubMedCrossRefPubMedCentralGoogle Scholar
  44. Dami IE, Li S, Zhang Y (2016) Evaluation of primary bud freezing tolerance of twenty-three winegrape cultivars new to the Eastern United States. Am J Enol Vitic 67:139–145CrossRefGoogle Scholar
  45. De Rosso M, Tonidandel L, Larcher R et al (2014) Identification of new flavonols in hybrid grapes by combined liquid chromatography–mass spectrometry approaches. Food Chem 163:244–251PubMedCrossRefPubMedCentralGoogle Scholar
  46. Delaunois B, Colby T, Belloy N et al (2013) Large-scale proteomic analysis of the grapevine leaf apoplastic fluid reveals mainly stress-related proteins and cell wall modifying enzymes. BMC Plant Biol 13:24PubMedPubMedCentralCrossRefGoogle Scholar
  47. Delsart C, Ghidossi R, Poupot C et al (2012) Enhanced extraction of phenolic compounds from Merlot grapes by pulsed electric field treatment. Am J Enol Vitic 63:205–211CrossRefGoogle Scholar
  48. Di Carli M, Zamboni A, Pè ME et al (2011) Two-dimensional differential in gel electrophoresis (2D-DIGE) analysis of grape berry proteome during postharvest withering. J Proteome Res 10:429–446PubMedCrossRefPubMedCentralGoogle Scholar
  49. Dokoozlian NK (1999) Chilling temperature and duration interact on the Budbreak of ‘Perlette’ grapevine cuttings. HortScience 34:1–3CrossRefGoogle Scholar
  50. Doligez A, Bouquet A, Danglot Y et al (2002) Genetic mapping of grapevine (Vitis vinifera L.) applied to the detection of QTLs for seedlessness and berry weight. Theor Appl Genet 105:780–795PubMedCrossRefPubMedCentralGoogle Scholar
  51. Doligez A, Adam-Blondon AF, Cipriani G et al (2006) An integrated SSR map of grapevine based on five mapping populations. Theor Appl Genet 113:369–382PubMedCrossRefPubMedCentralGoogle Scholar
  52. Donoso A, Valenzuela S (2018) In-field molecular diagnosis of plant pathogens: recent trends and future perspectives. Plant Pathol 67:1451–1461CrossRefGoogle Scholar
  53. Dorj U-O, Lee M, Yun S-S (2017) An yield estimation in citrus orchards via fruit detection and counting using image processing. Comput Electron Agric 140:103–112CrossRefGoogle Scholar
  54. Dubiela CR, Fajardo TVM, Souto ER et al (2013) Simultaneous detection of Brazilian isolates of grapevine viruses by TaqMan real-time RT-PCR. Trop Plant Pathol 38:158–165CrossRefGoogle Scholar
  55. Duchene E (2016) How can grapevine genetics contribute to the adaptation to climate change? OENO One. Scholar
  56. Duursma RA, Blackman CJ, Lopéz R et al (2018) On the minimum leaf conductance: its role in models of plant water use, and ecological and environmental controls. New Phytol. Scholar
  57. Ershadi A, Karimi R, Mahdei KN (2015) Freezing tolerance and its relationship with soluble carbohydrates, proline and water content in 12 grapevine cultivars. Acta Physiol Plant 38:2CrossRefGoogle Scholar
  58. Failmezger H, Lempe J, Khadem N et al (2018) MowJoe: a method for automated-high throughput dissected leaf phenotyping. Plant Methods 14:27PubMedPubMedCentralCrossRefGoogle Scholar
  59. Fechter I, Hausmann L, Zyprian E et al (2014) QTL analysis of flowering time and ripening traits suggests an impact of a genomic region on linkage group 1 in Vitis. Theor Appl Genet 127:1857–1872PubMedCrossRefPubMedCentralGoogle Scholar
  60. Fennell AY, Schlauch KA, Gouthu S et al (2015) Short day transcriptomic programming during induction of dormancy in grapevine. Front Plant Sci 6:834PubMedPubMedCentralCrossRefGoogle Scholar
  61. Ferguson JC, Tarara JM, Mills LJ et al (2011) Dynamic thermal time model of cold hardiness for dormant grapevine buds. Ann Bot 107:389–396PubMedPubMedCentralCrossRefGoogle Scholar
  62. Ferguson JC, Moyer MM, Mills LJ et al (2014) Modeling dormant bud cold hardiness and budbreak in twenty-three Vitis genotypes reveals variation by region of origin. Am J Enol Vitic 65:59–71CrossRefGoogle Scholar
  63. Fila G, Di Lena B, Gardiman M et al (2012) Calibration and validation of grapevine budburst models using growth-room experiments as data source. Agric For Meteorol 160:69–79CrossRefGoogle Scholar
  64. Fischer BM, Salakhutdinov I, Akkurt M et al (2004) Quantitative trait locus analysis of fungal disease resistance factors on a molecular map of grapevine. Theor Appl Genet 108:501–515PubMedCrossRefPubMedCentralGoogle Scholar
  65. Flexas J, Galmés J, Gallé A et al (2010) Improving water use efficiency in grapevines: potential physiological targets for biotechnological improvement. Aust J Grape Wine Res 16:106–121CrossRefGoogle Scholar
  66. Font D, Pallejà T, Tresanchez M et al (2014a) A proposal for automatic fruit harvesting by combining a low cost stereovision camera and a robotic arm. Sensors 14:11557–11579PubMedCrossRefPubMedCentralGoogle Scholar
  67. Font D, Pallejà T, Tresanchez M et al (2014b) Counting red grapes in vineyards by detecting specular spherical reflection peaks in RGB images obtained at night with artificial illumination. Comput Electron Agric 108:105–111CrossRefGoogle Scholar
  68. Frenkel O, Portillo I, Brewer MT et al (2012) Development of microsatellite markers from the transcriptome of Erysiphe necator for analysing population structure in North America and Europe: polymorphic markers from the Erysiphe necator transcriptome. Plant Pathol 61:106–119CrossRefGoogle Scholar
  69. Fuller MP, Telli G (1999) An investigation of the frost hardiness of grapevine (Vitis vinifera) during bud break. Ann Appl Biol 135:589–595CrossRefGoogle Scholar
  70. Furbank RT (2009) Foreword: plant phenomics: from gene to form and function. Funct Plant Biol 36:v–viCrossRefGoogle Scholar
  71. Furbank RT, Tester M (2011) Phenomics–technologies to relieve the phenotyping bottleneck. Trends Plant Sci 16:635–644PubMedCrossRefPubMedCentralGoogle Scholar
  72. Gadoury DM (2015) Climate, asynchronous phenology, ontogenic resistance, and the risk of disease in deciduous fruit crops. IOBC-WPRS Bull 110:15–24Google Scholar
  73. Gale EJ, Moyer MM (2017) Cold hardiness of Vitis vinifera roots. Am J Enol Vitic 68:468–477CrossRefGoogle Scholar
  74. García de Cortázar-Atauri I, Duchêne E, Destrac-Irvine A et al (2017) Grapevine phenology in France: from past observations to future evolutions in the context of climate change. OENO One 51:115CrossRefGoogle Scholar
  75. Garris A, Clark L, Owens C et al (2009) Mapping of photoperiod-induced growth cessation in the wild grape Vitis riparia. J Am Soc Hortic Sci 134:261–272CrossRefGoogle Scholar
  76. George IS, Pascovici D, Mirzaei M, Haynes PA (2015) Quantitative proteomic analysis of cabernet sauvignon grape cells exposed to thermal stresses reveals alterations in sugar and phenylpropanoid metabolism. Proteomics 15:3048–3060PubMedCrossRefPubMedCentralGoogle Scholar
  77. George IS, Fennell AY, Haynes PA (2018) Shotgun proteomic analysis of photoperiod regulated dormancy induction in grapevine. J Proteom 187:13–24CrossRefGoogle Scholar
  78. Ghan R, Van Sluyter SC, Hochberg U et al (2015) Five omic technologies are concordant in differentiating the biochemical characteristics of the berries of five grapevine (Vitis vinifera L.) cultivars. BMC Genom 16:946CrossRefGoogle Scholar
  79. Granier C, Aguirrezabal L, Chenu K et al (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 Phytol 169:623–635PubMedCrossRefPubMedCentralGoogle Scholar
  80. Greer DH, Weedon MM (2013) The impact of high temperatures on Vitis vinifera cv. Semillon grapevine performance and berry ripening. Front Plant Sci 4:491PubMedPubMedCentralCrossRefGoogle Scholar
  81. Grimplet J, Wheatley MD, Jouira HB et al (2009) Proteomic and selected metabolite analysis of grape berry tissues under well-watered and water-deficit stress conditions. Proteomics 9:2503–2528PubMedPubMedCentralCrossRefGoogle Scholar
  82. Hall ME, Loeb GM, Cadle-Davidson L et al (2018) Grape sour rot: a four-way interaction involving the host, yeast, acetic acid bacteria, and insects. Phytopathology. Scholar
  83. Hemming J, Ruizendaal J, Hofstee JW, van Henten EJ (2014) Fruit detectability analysis for different camera positions in sweet-pepper. Sensors 14:6032–6044PubMedCrossRefPubMedCentralGoogle Scholar
  84. Henderson SW, Baumann U, Blackmore DH et al (2014) Shoot chloride exclusion and salt tolerance in grapevine is associated with differential ion transporter expression in roots. BMC Plant Biol 14:273PubMedPubMedCentralCrossRefGoogle Scholar
  85. Henderson SW, Dunlevy JD, Wu Y et al (2017) Functional differences in transport properties of natural HKT1;1 variants influence shoot Na + exclusion in grapevine rootstocks. New Phytol. Scholar
  86. Hoffmann S, Di Gaspero G, Kovács L et al (2008) Resistance to Erysiphe necator in the grapevine “Kishmish vatkana” is controlled by a single locus through restriction of hyphal growth. Theor Appl Genet 116:427–438PubMedCrossRefPubMedCentralGoogle Scholar
  87. Hopper DW, Ghan R, Cramer GR (2014) A rapid dehydration leaf assay reveals stomatal response differences in grapevine genotypes. Hortic Res 1:2PubMedPubMedCentralCrossRefGoogle Scholar
  88. Hou L, Zhang G, Zhao F et al (2018) VvBAP1 is involved in cold tolerance in Vitis vinifera L. Front Plant Sci 9:726PubMedPubMedCentralCrossRefGoogle Scholar
  89. Houel C, Chatbanyong R, Doligez A et al (2015) Identification of stable QTLs for vegetative and reproductive traits in the microvine (Vitis vinifera L.) using the 18 K Infinium chip. BMC Plant Biol 15:205PubMedPubMedCentralCrossRefGoogle Scholar
  90. Houle D, Govindaraju DR, Omholt S (2010) Phenomics: the next challenge. Nat Rev Genet 11:855–866PubMedCrossRefPubMedCentralGoogle Scholar
  91. Huang Y-F, Bertrand Y, Guiraud J-L et al (2013) Expression QTL mapping in grapevine—revisiting the genetic determinism of grape skin colour. Plant Sci 207:18–24PubMedCrossRefPubMedCentralGoogle Scholar
  92. Huang Y-F, Vialet S, Guiraud J-L et al (2014) A negative MYB regulator of proanthocyanidin accumulation, identified through expression quantitative locus mapping in the grape berry. New Phytol 201:795–809PubMedCrossRefPubMedCentralGoogle Scholar
  93. Ihlow A, Schweizer P, Seiffert U (2008) A high-throughput screening system for barley/powdery mildew interactions based on automated analysis of light micrographs. BMC Plant Biol 8:6PubMedPubMedCentralCrossRefGoogle Scholar
  94. Jaillon O, Aury J-M, Noel B et al (2007) The grapevine genome sequence suggests ancestral hexaploidization in major angiosperm phyla. Nature 449:463–467PubMedPubMedCentralCrossRefGoogle Scholar
  95. Jastrzembski JA, Bee MY, Sacks GL (2017) Trace-level volatile quantitation by direct analysis in real time mass spectrometry following headspace extraction: optimization and validation in grapes. J Agric Food Chem 65:9353–9359PubMedCrossRefPubMedCentralGoogle Scholar
  96. Jellouli N, Ben Jouira H, Skouri H et al (2008) Proteomic analysis of Tunisian grapevine cultivar Razegui under salt stress. J Plant Physiol 165:471–481PubMedPubMedCentralCrossRefGoogle Scholar
  97. Jones GV, White MA, Cooper OR, Storchmann K (2005) Climate change and global wine quality. Clim Change 73:319–343CrossRefGoogle Scholar
  98. Jorge TF, Rodrigues JA, Caldana C et al (2016) Mass spectrometry-based plant metabolomics: metabolite responses to abiotic stress. Mass Spectrom Rev 35:620–649PubMedCrossRefPubMedCentralGoogle Scholar
  99. Kambiranda D, Katam R, Basha SM, Siebert S (2014) iTRAQ-based quantitative proteomics of developing and ripening muscadine grape berry. J Proteome Res 13:555–569PubMedCrossRefPubMedCentralGoogle Scholar
  100. Katam R, Chibanguza K, Latinwo LM, Smith D (2015) Proteome biomarkers in xylem reveal pierce’s disease tolerance in grape. J Proteom Bioinform 8:217–224Google Scholar
  101. Kicherer A, Herzog K, Pflanz M et al (2015) An automated field phenotyping pipeline for application in grapevine research. Sensors 15:4823–4836PubMedCrossRefGoogle Scholar
  102. Kicherer A, Herzog K, Bendel N et al (2017a) Phenoliner: a new field phenotyping platform for grapevine research. Sensors 17:1625. Scholar
  103. Kicherer A, Klodt M, Sharifzadeh S et al (2017b) Automatic image-based determination of pruning mass as a determinant for yield potential in grapevine management and breeding: image-based automated estimation of pruning mass. Aust J Grape Wine Res 23:120–124CrossRefGoogle Scholar
  104. Koch B, Oehl F (2018) Climate change favors grapevine production in temperate zones. AS 09:247–263CrossRefGoogle Scholar
  105. Koyama K, Kamigakiuchi H, Iwashita K et al (2017) Polyphenolic diversity and characterization in the red-purple berries of East Asian wild Vitis species. Phytochemistry 134:78–86PubMedCrossRefGoogle Scholar
  106. Kuska M, Wahabzada M, Leucker M et al (2015) Hyperspectral phenotyping on the microscopic scale: towards automated characterization of plant–pathogen interactions. Plant Methods 11:28PubMedPubMedCentralCrossRefGoogle Scholar
  107. Kustas WP, Anderson MC, Alfieri JG et al (2018) The grape remote sensing atmospheric profile and evapotranspiration experiment (GRAPEX). Bull Am Meteorol Soc. Scholar
  108. Lahogue F, This P, Bouquet A (1998) Identification of a codominant scar marker linked to the seedlessness character in grapevine. Theor Appl Genet 97:950–959CrossRefGoogle Scholar
  109. Lavoie-Lamoureux A, Sacco D, Risse P-A, Lovisolo C (2017) Factors influencing stomatal conductance in response to water availability in grapevine: a meta-analysis. Physiol Plant 159:468–482PubMedCrossRefPubMedCentralGoogle Scholar
  110. Leolini L, Moriondo M, Fila G et al (2018) Late spring frost impacts on future grapevine distribution in Europe. Field Crops Res 222:197–208CrossRefGoogle Scholar
  111. Liang Z, Yang Y, Cheng L, Zhong G-Y (2012) Polyphenolic composition and content in the ripe berries of wild Vitis species. Food Chem 132:730–738CrossRefGoogle Scholar
  112. Lindblom J, Lundström C, Ljung M, Jonsson A (2017) Promoting sustainable intensification in precision agriculture: review of decision support systems development and strategies. Precis Agric 18:309–331CrossRefGoogle Scholar
  113. Lindén L, Palonen P, Lindén M (2000) Relating freeze-induced electrolyte leakage measurements to lethal temperature in red raspberry. J Am Soc Hortic Sci 125:429–435CrossRefGoogle Scholar
  114. Liu G-T, Wang J-F, Cramer G et al (2012) Transcriptomic analysis of grape (Vitis vinifera L.) leaves during and after recovery from heat stress. BMC Plant Biol 12:174PubMedPubMedCentralCrossRefGoogle Scholar
  115. Liu G-T, Ma L, Duan W et al (2014) Differential proteomic analysis of grapevine leaves by iTRAQ reveals responses to heat stress and subsequent recovery. BMC Plant Biol 14:110PubMedPubMedCentralCrossRefGoogle Scholar
  116. Londo JP, Johnson LM (2014) Variation in the chilling requirement and budburst rate of wild Vitis species. Environ Exp Bot 106:138–147CrossRefGoogle Scholar
  117. Londo JP, Kovaleski AP (2017) Characterization of wild North American grapevine cold hardiness using differential thermal analysis. Am J Enol Vitic 68:203–212CrossRefGoogle Scholar
  118. Londo JP, Kovaleski AP, Lillis JA (2018) Divergence in the transcriptional landscape between low temperature and freeze shock in cultivated grapevine (Vitis vinifera). Hortic Res 5:10PubMedPubMedCentralCrossRefGoogle Scholar
  119. Lovisolo C, Tramontini S (2010) Methods for assessment of hydraulic conductance and embolism extent in grapevine organs. In: Delrot S, Medrano H, Or E, Bavaresco L, Grando S (eds) Methodologies and results in grapevine research. Springer, Dordrecht, pp 71–85 CrossRefGoogle Scholar
  120. Lowenberg-DeBoer J, Boehlje M (1996) Revolution, evolution or dead-end: economic perspectives on precision agriculture. In: Robert PC, Rust RH and Larson WE (eds) Precision agriculture. American Society of Agronomy, Crop Science Society of America, Soil Science Society of America, Madison, pp 923–944Google Scholar
  121. Luedeling E (2012) Climate change impacts on winter chill for temperate fruit and nut production: a review. Sci Hortic 144:218–229CrossRefGoogle Scholar
  122. Marks VD, van der Merwe GK, van Vuuren HJJ (2003) Transcriptional profiling of wine yeast in fermenting grape juice: regulatory effect of diammonium phosphate. FEMS Yeast Res 3:269–287PubMedCrossRefGoogle Scholar
  123. Marti G, Schnee S, Andrey Y et al (2014) Study of leaf metabolome modifications induced by UV-C radiations in representative Vitis, Cissus and Cannabis species by LC–MS based metabolomics and antioxidant assays. Molecules 19:14004–14021PubMedPubMedCentralCrossRefGoogle Scholar
  124. Martínez-Esteso MJ, Sellés-Marchart S, Lijavetzky D et al (2011) A DIGE-based quantitative proteomic analysis of grape berry flesh development and ripening reveals key events in sugar and organic acid metabolism. J Exp Bot 62:2521–2569PubMedCrossRefGoogle Scholar
  125. McCartney HA, Foster SJ, Fraaije BA, Ward E (2003) Molecular diagnostics for fungal plant pathogens. Pest Manag Sci 59:129–142PubMedCrossRefGoogle Scholar
  126. Medrano H, Tomás M, Martorell S, Flexas J, Hernández E, Rosselló J, Pou A, Escalona JM, Bota J (2015) From leaf to whole-plant water use efficiency (WUE) in complex canopies: limitations of leaf WUE as a selection target. Crop J 3(3):220–228CrossRefGoogle Scholar
  127. Mehta SS, Burks TF (2014) Vision-based control of robotic manipulator for citrus harvesting. Comput Electron Agric 102:146–158CrossRefGoogle Scholar
  128. Mills LJ, Ferguson JC, Keller M (2006) Cold-hardiness evaluation of grapevine buds and cane tissues. Am J Enol Vitic 57:194–200Google Scholar
  129. Minsavage GV, Thompson CM, Hopkins DL et al (1994) Development of a polymerase chain reaction protocol for detection of Xylella fastidiosa in plant tissue. Phytopathology 84:456–461CrossRefGoogle Scholar
  130. Mira de Orduña R (2010) Climate change associated effects on grape and wine quality and production. Food Res Int 43:1844–1855CrossRefGoogle Scholar
  131. Moorehead SJ, Wellington CK, Gilmore BJ, Vallespi C (2012) Automating orchards: a system of autonomous tractors for orchard maintenance. In: Proceedings of the IEEE international conference of intelligent robots and systems; workshop on agricultural robotsGoogle Scholar
  132. Morin X, Améglio T, Ahas R et al (2007) Variation in cold hardiness and carbohydrate concentration from dormancy induction to bud burst among provenances of three European oak species. Tree Physiol 27:817–825PubMedCrossRefGoogle Scholar
  133. Mosedale JR, Wilson RJ, Maclean IMD (2015) Climate change and crop exposure to adverse weather: changes to frost risk and grapevine flowering conditions. PLoS ONE 10:e0141218PubMedPubMedCentralCrossRefGoogle Scholar
  134. Mozell MR, Thach L (2014) The impact of climate change on the global wine industry: challenges & solutions. Wine Econ Policy 3:81–89CrossRefGoogle Scholar
  135. Negrel L, Halter D, Wiedemann-Merdinoglu S et al (2018) Identification of lipid markers of Plasmopara viticola infection in grapevine using a non-targeted metabolomic approach. Front Plant Sci 9:360PubMedPubMedCentralCrossRefGoogle Scholar
  136. Negri AS, Prinsi B, Rossoni M et al (2008) Proteome changes in the skin of the grape cultivar Barbera among different stages of ripening. BMC Genom 9:378CrossRefGoogle Scholar
  137. OIV (2018) OIV descriptor list for grape varieties and Vitis species. In: The International Organization of Vine and Wine, 2nd edn.
  138. Pagay V, Santiago M, Sessoms DA et al (2014) A microtensiometer capable of measuring water potentials below −10 MPa. Lab Chip 14:2806–2817PubMedCrossRefGoogle Scholar
  139. Pagter M, Williams M (2011) Frost dehardening and rehardening of Hydrangea macrophylla stems and buds. HortScience 46:1121–1126CrossRefGoogle Scholar
  140. Palmieri MC, Perazzolli M, Matafora V et al (2012) Proteomic analysis of grapevine resistance induced by Trichoderma harzianum T39 reveals specific defence pathways activated against downy mildew. J Exp Bot 63:6237–6251PubMedPubMedCentralCrossRefGoogle Scholar
  141. Pap D, Riaz S, Dry IB et al (2016) Identification of two novel powdery mildew resistance loci, Ren6 and Ren7, from the wild Chinese grape species Vitis piasezkii. BMC Plant Biol 16:170PubMedPubMedCentralCrossRefGoogle Scholar
  142. Parpinello GP, Nunziatini G, Rombolà AD et al (2013) Relationship between sensory and NIR spectroscopy in consumer preference of table grape (cv Italia). Postharvest Biol Technol 83:47–53CrossRefGoogle Scholar
  143. Payne AB, Walsh KB, Subedi PP, Jarvis D (2013) Estimation of mango crop yield using image analysis—segmentation method. Comput Electron Agric 91:57–64CrossRefGoogle Scholar
  144. Pellegrini E, Campanella A, Paolocci M et al (2015) Functional leaf traits and diurnal dynamics of photosynthetic parameters predict the behavior of grapevine varieties towards ozone. PLoS ONE 10:e0135056PubMedPubMedCentralCrossRefGoogle Scholar
  145. Pendergrass SA, Verma A, Okula A et al (2015) Phenome-wide association studies: embracing complexity for discovery. Hum Hered 79:111–123PubMedCrossRefGoogle Scholar
  146. Picariello G, Ferranti P, Garro G et al (2014) Profiling of anthocyanins for the taxonomic assessment of ancient purebred V. vinifera red grape varieties. Food Chem 146:15–22PubMedCrossRefGoogle Scholar
  147. Pinasseau L, Vallverdú-Queralt A, Verbaere A et al (2017) Cultivar diversity of grape skin polyphenol composition and changes in response to drought investigated by LC–MS based metabolomics. Front Plant Sci 8:1826PubMedPubMedCentralCrossRefGoogle Scholar
  148. Pinelli P, Romani A, Fierini E, Agati G (2018) Prediction models for assessing anthocyanins in grape berries by fluorescence sensors: dependence on cultivar, site and growing season. Food Chem 244:213–223PubMedCrossRefGoogle Scholar
  149. Poland JA, Nelson RJ (2011) In the eye of the beholder: the effect of rater variability and different rating scales on QTL mapping. Phytopathology 101(2):290–298PubMedCrossRefGoogle Scholar
  150. Pou A, Medrano H, Tomàs M et al (2012) Anisohydric behaviour in grapevines results in better performance under moderate water stress and recovery than isohydric behaviour. Plant Soil 359:335–349CrossRefGoogle Scholar
  151. Povero G, Papale M, Gesualdo L et al (2010) Identification of grapevine cultivar biomarkers using surface-enhanced laser desorption and ionization (SELDI-TOF-MS). Am J Enol Vitic 61:492–497CrossRefGoogle Scholar
  152. Rahaman MM, Chen D, Gillani Z et al (2015) Advanced phenotyping and phenotype data analysis for the study of plant growth and development. Front Plant Sci 6:619PubMedPubMedCentralCrossRefGoogle Scholar
  153. Rossdeutsch L, Edwards E, Cookson SJ et al (2016) ABA-mediated responses to water deficit separate grapevine genotypes by their genetic background. BMC Plant Biol 16:91PubMedPubMedCentralCrossRefGoogle Scholar
  154. Saito S, Suzuki S, Takayanagi T (2009) Nested PCR-RFLP is a high-speed method to detect fungicide-resistant Botrytis cinerea at an early growth stage of grapes. Pest Manag Sci 65:197–204PubMedCrossRefPubMedCentralGoogle Scholar
  155. Salazar Parra C, Aguirreolea J, Sánchez-Díaz M et al (2010) Effects of climate change scenarios on Tempranillo grapevine (Vitis vinifera L.) ripening: response to a combination of elevated CO2 and temperature, and moderate drought. Plant Soil 337:179–191CrossRefGoogle Scholar
  156. Salazar-Parra C, Aranjuelo I, Pascual I et al (2015) Carbon balance, partitioning and photosynthetic acclimation in fruit-bearing grapevine (Vitis vinifera L. cv. Tempranillo) grown under simulated climate change (elevated CO2, elevated temperature and moderate drought) scenarios in temperature gradient greenhouses. J Plant Physiol 174:97–109PubMedCrossRefPubMedCentralGoogle Scholar
  157. Sankaran S, Mishra A, Ehsani R, Davis C (2010) A review of advanced techniques for detecting plant diseases. Comput Electron Agric 72:1–13CrossRefGoogle Scholar
  158. Santesteban LG, Guillaume S, Royo JB, Tisseyre B (2013) Are precision agriculture tools and methods relevant at the whole-vineyard scale? Precis Agric 14:2–17CrossRefGoogle Scholar
  159. Schoedl K, Schuhmacher R, Forneck A (2013) Correlating physiological parameters with biomarkers for UV-B stress indicators in leaves of grapevine cultivars Pinot noir and Riesling. J Agric Sci 151:189–200CrossRefGoogle Scholar
  160. Schueuermann C, Steel CC, Blackman JW et al (2019) A GC–MS untargeted metabolomics approach for the classification of chemical differences in grape juices based on fungal pathogen. Food Chem 270:375–384PubMedCrossRefPubMedCentralGoogle Scholar
  161. Schultz H (2000) Climate change and viticulture: a European perspective on climatology, carbon dioxide and UV-B effects. Aust J Grape Wine Res 6:2–12CrossRefGoogle Scholar
  162. Serra I, Strever A, Myburgh PA, Deloire A (2014) Review: the interaction between rootstocks and cultivars (Vitis vinifera L.) to enhance drought tolerance in grapevine: Rootstocks to enhance drought tolerance in grapevine. Aust J Grape Wine Res 20:1–14CrossRefGoogle Scholar
  163. Shavrukov YN, Dry IB, Thomas MR (2004) Inflorescence and bunch architecture development in Vitis vinifera L. Aust J Grape Wine Res 10:116–124CrossRefGoogle Scholar
  164. Shellie K, Kovaleski AP, Londo JP (2018) Water deficit severity during berry development alters timing of dormancy transitions in wine grape cultivar Malbec. Sci Hortic 232:226–230CrossRefGoogle Scholar
  165. Sherwood RT, Berg CC, Hoover MR, Zeiders KE (1983) Illusions in visual assessment of Stagonospora leaf spot of orchardgrass. Phytopathology 73:173–177CrossRefGoogle Scholar
  166. Singh A, Ganapathysubramanian B, Singh AK, Sarkar S (2016) Machine learning for high-throughput stress phenotyping in plants. Trends Plant Sci 21:110–124PubMedCrossRefGoogle Scholar
  167. Smart DR, Schwass E, Lakso A, Morano L (2006) Grapevine rooting patterns: a comprehensive analysis and a review. Am J Enol Vitic 57:89–104Google Scholar
  168. Sommer S, Cohen S (2018) Comparison of different extraction methods to predict anthocyanin concentration and color characteristics of red wines. Fermentation 4:39CrossRefGoogle Scholar
  169. Sonka ST (2016) Big data: fueling the next evolution of agricultural innovation. J Innov Manag 4:114–136CrossRefGoogle Scholar
  170. Spagnolo A, Magnin-Robert M, Alayi TD et al (2012) Physiological changes in green stems of Vitis vinifera L. cv. Chardonnay in response to esca proper and apoplexy revealed by proteomic and transcriptomic analyses. J Proteome Res 11:461–475PubMedCrossRefPubMedCentralGoogle Scholar
  171. Springer LF, Sacks GL (2014) Protein-precipitable tannin in wines from Vitis vinifera and interspecific hybrid grapes (Vitis ssp.): differences in concentration, extractability, and cell wall binding. J Agric Food Chem 62:7515–7523PubMedCrossRefPubMedCentralGoogle Scholar
  172. Springer LF, Sherwood RW, Sacks GL (2016) Pathogenesis-related proteins limit the retention of condensed tannin additions to red wines. J Agric Food Chem 64:1309–1317PubMedCrossRefPubMedCentralGoogle Scholar
  173. Sucu S, Yağcı A, Yıldırım K (2018) Changes in morphological, physiological traits and enzyme activity of grafted and ungrafted grapevine rootstocks under drought stress. Erwerbs-Obstbau 60:127–136CrossRefGoogle Scholar
  174. Sun R-Z, Cheng G, Li Q et al (2017) Light-induced variation in phenolic compounds in Cabernet Sauvignon grapes (Vitis vinifera L) involves extensive transcriptome reprogramming of biosynthetic enzymes, transcription factors, and phytohormonal regulators. Front Plant Sci 8:547PubMedPubMedCentralCrossRefGoogle Scholar
  175. Sweetman C, Sadras VO, Hancock RD et al (2014) Metabolic effects of elevated temperature on organic acid degradation in ripening Vitis vinifera fruit. J Exp Bot 65:5975–5988PubMedPubMedCentralCrossRefGoogle Scholar
  176. Tang X, Wang Y, Han J et al (2018) Separation, purification of anthocyanin and vitis linn polysaccharide from grape juice by the two-step extraction and dialysis. J Food Process Preserv 42:e13344CrossRefGoogle Scholar
  177. Tattersall EAR, Grimplet J, DeLuc L et al (2007) Transcript abundance profiles reveal larger and more complex responses of grapevine to chilling compared to osmotic and salinity stress. Funct Integr Genom 7:317–333CrossRefGoogle Scholar
  178. Taylor JA, Link K, Taft T et al (2017) A protocol to map vine size in commercial single high-wire trellis vineyards using “off-the-shelf” proximal canopy-sensing systems. Catal Discov Pract 1:35–47CrossRefGoogle Scholar
  179. Teh SL, Fresnedo-Ramírez J, Clark MD et al (2017) Genetic dissection of powdery mildew resistance in interspecific half-sib grapevine families using SNP-based maps. Mol Breed 37:1PubMedCrossRefPubMedCentralGoogle Scholar
  180. Tello J, Ibáñez J (2018) What do we know about grapevine bunch compactness? A state-of-the-art review: review on bunch compactness. Aust J Grape Wine Res 24:6–23CrossRefGoogle Scholar
  181. Tello J, Torres-Pérez R, Grimplet J et al (2015) Polymorphisms and minihaplotypes in the VvNAC26 gene associate with berry size variation in grapevine. BMC Plant Biol 15:253PubMedPubMedCentralCrossRefGoogle Scholar
  182. Tello J, Cubero S, Blasco J et al (2016) Application of 2D and 3D image technologies to characterise morphological attributes of grapevine clusters. J Sci Food Agric 96:4575–4583PubMedCrossRefPubMedCentralGoogle Scholar
  183. Tester M, Langridge P (2010) Breeding technologies to increase crop production in a changing world. Science 327:818–822PubMedCrossRefPubMedCentralGoogle Scholar
  184. Tomás M, Medrano H, Escalona JM et al (2014) Variability of water use efficiency in grapevines. Environ Exp Bot 103:148–157CrossRefGoogle Scholar
  185. Tomasi D, Jones GV, Giust M et al (2011) Grapevine phenology and climate change: relationships and trends in the Veneto region of Italy for 1964–2009. Am J Enol Vitic 62:329–339CrossRefGoogle Scholar
  186. Torregrosa L, Bigard A, Doligez A et al (2017) Developmental, molecular and genetic studies on grapevine response to temperature open breeding strategies for adaptation to warming. OENO One 51:155CrossRefGoogle Scholar
  187. Toumi I, Gargouri M, Nouairi I et al (2008) Water stress induced changes in the leaf lipid composition of four grapevine genotypes with different drought tolerance. Biol Plant 52:161–164CrossRefGoogle Scholar
  188. Väinölä A, McNamara S, Pellett H (1997) Stem and flower bud hardiness of deciduous azaleas. J Environ Hortic 15:45–50 Google Scholar
  189. Vega A, Gutiérrez RA, Peña-Neira A et al (2011) Compatible GLRaV-3 viral infections affect berry ripening decreasing sugar accumulation and anthocyanin biosynthesis in Vitis vinifera. Plant Mol Biol 77:261–274PubMedPubMedCentralCrossRefGoogle Scholar
  190. Velasco R, Zharkikh A, Troggio M et al (2007) A high quality draft consensus sequence of the genome of a heterozygous grapevine variety. PLoS ONE 2:e1326PubMedPubMedCentralCrossRefGoogle Scholar
  191. Versari A, Laurie VF, Ricci A et al (2014) Progress in authentication, typification and traceability of grapes and wines by chemometric approaches. Food Res Int 60:2–18CrossRefGoogle Scholar
  192. Vivin P, Lebon É, Dai Z et al (2017) Combining ecophysiological models and genetic analysis: a promising way to dissect complex adaptive traits in grapevine. OENO One 51:181–189CrossRefGoogle Scholar
  193. Wang C, Han J, Shangguan L et al (2014) Depiction of grapevine phenology by gene expression information and a test of its workability in guiding fertilization. Plant Mol Biol Rep 32:1070–1084CrossRefGoogle Scholar
  194. Wang Y, He Y-N, Chen W-K et al (2018) Effects of cluster thinning on vine photosynthesis, berry ripeness and flavonoid composition of Cabernet Sauvignon. Food Chem 248:101–110PubMedCrossRefPubMedCentralGoogle Scholar
  195. Ward E, Foster SJ, Fraaije BA, Mccartney HA (2004) Plant pathogen diagnostics: immunological and nucleic acid-based approaches. Ann Appl Biol 145:1–16CrossRefGoogle Scholar
  196. Webb LB, Whetton PH, Barlow EWR (2007) Modelled impact of future climate change on the phenology of winegrapes in Australia. Aust J Grape Wine Res 13:165–175CrossRefGoogle Scholar
  197. Webb LB, Whetton PH, Barlow EWR (2008) Climate change and winegrape quality in Australia. Clim Res 36:99–111CrossRefGoogle Scholar
  198. Wilkinson MD, Dumontier M, Aalbersberg IJJ et al (2016) The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3:160018CrossRefGoogle Scholar
  199. Wolkovich EM, Burge DO, Walker MA, Nicholas KA (2017) Phenological diversity provides opportunities for climate change adaptation in winegrapes. J Ecol 105:905–912CrossRefGoogle Scholar
  200. Xin H, Zhu W, Wang L et al (2013) Genome wide transcriptional profile analysis of Vitis amurensis and Vitis vinifera in response to cold stress. PLoS ONE 8:e58740PubMedPubMedCentralCrossRefGoogle Scholar
  201. Xu H, Liu G, Liu G et al (2014a) Comparison of investigation methods of heat injury in grapevine (Vitis) and assessment to heat tolerance in different cultivars and species. BMC Plant Biol 14:156PubMedPubMedCentralCrossRefGoogle Scholar
  202. Xu W, Li R, Zhang N et al (2014b) Transcriptome profiling of Vitis amurensis, an extremely cold-tolerant Chinese wild Vitis species, reveals candidate genes and events that potentially connected to cold stress. Plant Mol Biol 86:527–541PubMedCrossRefPubMedCentralGoogle Scholar
  203. Yang L, Lin H, Takahashi Y et al (2011) Proteomic analysis of grapevine stem in response to Xylella fastidiosa inoculation. Physiol Mol Plant Pathol 75:90–99CrossRefGoogle Scholar
  204. Yıldırım K, Yağcı A, Sucu S, Tunç S (2018) Responses of grapevine rootstocks to drought through altered root system architecture and root transcriptomic regulations. Plant Physiol Biochem 127:256–268PubMedCrossRefPubMedCentralGoogle Scholar
  205. Yuan X, Wu Z, Li H et al (2014) Biochemical and proteomic analysis of “Kyoho” grape (Vitis labruscana) berries during cold storage. Postharvest Biol Technol 88:79–87CrossRefGoogle Scholar
  206. Zamboni A, Di Carli M, Guzzo F et al (2010) Identification of putative stage-specific grapevine berry biomarkers and omics data integration into networks. Plant Physiol 154:1439–1459PubMedPubMedCentralCrossRefGoogle Scholar
  207. Zapata D, Salazar-Gutierrez M, Chaves B et al (2017) Predicting key phenological stages for 17 grapevine cultivars (Vitis vinifera L.). Am J Enol Vitic 68:60–72CrossRefGoogle Scholar
  208. Zarraonaindia I, Owens SM, Weisenhorn P et al (2015) The soil microbiome influences grapevine-associated microbiota. MBio 6:e02527-14. Scholar
  209. Zendler D, Schneider P, Töpfer R, Zyprian E (2017) Fine mapping of Ren3 reveals two loci mediating hypersensitive response against Erysiphe necator in grapevine. Euphytica 213:68CrossRefGoogle Scholar
  210. Zhang C, Kovacs JM (2012) The application of small unmanned aerial systems for precision agriculture: a review. Precis Agric 13:693–712CrossRefGoogle Scholar
  211. Zhao YH, Guo YS, Lin H et al (2015) Quantitative trait locus analysis of grape weight and soluble solid content. Genet Mol Res 14:9872–9881PubMedCrossRefGoogle Scholar
  212. Zhao C, Lee WS, He D (2016) Immature green citrus detection based on colour feature and sum of absolute transformed difference (SATD) using colour images in the citrus grove. Comput Electron Agric 124:243–253CrossRefGoogle Scholar
  213. Zyprian E, Ochßner I, Schwander F et al (2016) Quantitative trait loci affecting pathogen resistance and ripening of grapevines. Mol Genet Genom 291:1573–1594CrossRefGoogle Scholar

Copyright information

© This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2019

Authors and Affiliations

  • Lance Cadle-Davidson
    • 1
    Email author
  • Jason Londo
    • 1
  • Dani Martinez
    • 2
  • Surya Sapkota
    • 2
  • Ben Gutierrez
    • 1
  1. 1.USDA-ARS Plant Genetic Resources UnitGenevaUSA
  2. 2.School of Integrative Plant SciencesGenevaUSA

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