Volume 26, 2019
Sunflower and climate change / Tournesol et changement climatique
Article Number 9
Number of page(s) 7
Published online 21 February 2019
  • All JN, Boerma HR, Todd JW. 1989. Screening soybean genotypes in the greenhouse for resistance to insects. Crop Sci 29(5): 1156–1159. [CrossRef] [Google Scholar]
  • Badouin H, et al. 2017. The sunflower genome provides insights into oil metabolism, flowering and asterid evolution. Nature 546(7656): 148–152. [CrossRef] [PubMed] [Google Scholar]
  • Bailey-Serres J, Lee SC, Brinton E. 2012. Waterproofing crops: Effective flooding survival strategies. Plant Physiol 160(4): 1698–1709. [CrossRef] [Google Scholar]
  • Baute GJ, et al. 2016. Genome-wide genotyping-by-sequencing data provide a high-resolution view of wild helianthus diversity, genetic structure, and interspecies gene flow. Am J Bot 103(12): 2170–2177. [CrossRef] [PubMed] [Google Scholar]
  • Bebber DP, Holmes T, Gurr SJ. 2014. The global spread of crop pests and pathogens. Global Ecol Biogeogr 23: 1398–1407. [CrossRef] [Google Scholar]
  • Blonder B, 2017. Hypervolume concepts in niche- and trait-based ecology. Ecography (August) 41: 1–13. [Google Scholar]
  • Bowsher AW, et al. 2016. Fine root tradeoffs between nitrogen concentration and xylem vessel traits preclude unified whole-plant resource strategies in Helianthus. Ecol Evol 6(4): 1016–1031. [CrossRef] [PubMed] [Google Scholar]
  • Burke JM, Rieseberg LH. 2003. Fitness effects of transgenic disease resistance in sunflowers. Science 300(5623): 1250. [CrossRef] [PubMed] [Google Scholar]
  • CGIAR, 2018. Climate analogues. Available from: [Google Scholar]
  • Chapin FS, Autumn K, Pugnaire F. 1993. Evolution of suites of traits in response to environmental stress. Am Nat 142(December 2013): S78–S92. [CrossRef] [Google Scholar]
  • Debaeke P, et al. 2017. Sunflower crop and climate change: Vulnerability, adaptation, and mitigation potential from case-studies in Europe. Ocl 24(1): D102. [Google Scholar]
  • Dempewolf H, et al. 2014. Adapting agriculture to climate change: A global initiative to collect, conserve, and use crop wild relatives. Agroecol Sustain Food Syst 38(4): 369–377. [CrossRef] [Google Scholar]
  • Dorrell DG, Huang HC. 1978. Influence of Sclerotinia wilt on seed yield and quality of sunflower wilted at different stages of development. Crop Sci 18(1): 974–976. [Google Scholar]
  • Dray S, et al. 2017. ade4: Analysis of ecological data: Exploratory and euclidean methods in environmental sciences. [Google Scholar]
  • FAO. 2015. FAOSTAT, Rome: [Google Scholar]
  • Graham DM. 2017. A walk on the wild side. Lab Anim 46(11): 423–427. [Google Scholar]
  • Gulya TJ, Vick BA, Nelson BD. 1989. Sclerotinia head rot of sunflower in North Dakota: 1986 incidence, effect on yield and oil components, and sources of resistance. Plant Dis 73(6): 504–507. [Google Scholar]
  • Harlan JR, de Wet JMJ. 1971. Toward a rational classification of cultivated plants. Int Assoc Plant Taxon (IAPT) 20(4): 509–517. [Google Scholar]
  • Hijmans RJ, 2016. raster: Geographic data analysis and modeling. [Google Scholar]
  • Hijmans RJ, et al. 2005. Very high resolution interpolated climate surfaces for global land areas. Int J Clim 25(15): 1965–1978. [Google Scholar]
  • IPCC. 2014. Climate change synthesis report. Contribution of working groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Geneva: IPCC. [Google Scholar]
  • Kalyar T, et al. 2014. Handling sunflower (Helianthus annuus L.) populations under heat stress. Arch Agron Soil Sci 60(5): 655–672. [CrossRef] [Google Scholar]
  • Kane NC, Rieseberg LH. 2007. Selective sweeps reveal candidate genes for adaptation to drought and salt tolerance in common sunflower, Helianthus annuus. Genetics 175(4): 1823–1834. [CrossRef] [PubMed] [Google Scholar]
  • Kantar MB, et al. 2015. Ecogeography and utility to plant breeding of the crop wild relatives of sunflower (Helianthus annuus L.). Front Plant Sci 6(October): 1–11. [Google Scholar]
  • Karasov TL, et al. 2017. Mechanisms to mitigate the trade-off between growth and defense. Plant Cell 29(4): 666–680. [Google Scholar]
  • Khoury CK, et al. 2016. Origins of food crops connect countries worldwide. Proc Royal Soc B: Biol Sci 283(1832): 1–9. [CrossRef] [Google Scholar]
  • Koziol EK, et al. 2012. Reduced drought tolerance during domestication and the evolution of weediness results from tolerance-growth trade-offs. Evolution 66: 3803–3814. [PubMed] [Google Scholar]
  • Luedders VD, Dickerson WA. 1977. Resistance of selected soybean genotypes and segregating populations to cabbage looper feeding. Crop Sci 17(3): 395–397. [Google Scholar]
  • Mandel JR, et al. 2011. Genetic diversity and population structure in cultivated sunflower and a comparison to its wild progenitor, Helianthus annuus L. Theor Appl Genet 123(5): 693–704. [CrossRef] [PubMed] [Google Scholar]
  • Mariotte P, et al. 2018. Plant-soil feedback: Bridging natural and agricultural sciences. Trends Ecol Evol 33: 129–142. [CrossRef] [PubMed] [Google Scholar]
  • Mason CM, Donovan LA. 2015. Evolution of the leaf economics spectrum in herbs: Evidence from environmental divergences in leaf physiology across Helianthus (Asteraceae). Evolution 69(10): 2705–2720. [PubMed] [Google Scholar]
  • Mason CM, et al. 2016. Macroevolution of leaf defenses and secondary metabolites across the genus Helianthus. New Phytol 209(4): 1720–1733. [CrossRef] [PubMed] [Google Scholar]
  • Mayrose M, et al. 2011. Increased growth in sunflower correlates with reduced defences and altered gene expression in response to biotic and abiotic stress. Mol Ecol 20(22): 4683–4694. [CrossRef] [PubMed] [Google Scholar]
  • Monfreda C, Ramankutty N, Foley JA. 2008. Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000. Global Biogeochem Cycles 22: 1–19. [Google Scholar]
  • Pebesma E, Bivand R. 2017. sp: Classes and methods for spatial data. [Google Scholar]
  • Pugh TAM, et al. 2016. Climate analogues suggest limited potential for intensification of production on current croplands under climate change. Nat Commun 7: 1–8. [Google Scholar]
  • R Core Team. 2017. R: A language and environment for statistical computing. [Google Scholar]
  • Ramankutty N, et al. 2008. Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000. Global Biogeochem Cycles 22(February 2007): 1–19. [Google Scholar]
  • Reich PB, et al. 2003. The evolution of plant functional variation: Traits, spectra, and strategies. Int J Plant Sci 164(S3): S143–S164. [Google Scholar]
  • Seiler GJ. 2007. Wild annual Helianthus anomalus and H. deserticola for improving oil content and quality in sunflower. Ind Crops Prod 25(1): 95–100. [Google Scholar]
  • Seiler GJ, Qi LL, Marek LF. 2017. Utilization of sunflower crop wild relatives for cultivated sunflower improvement. Crop Sci 57(3): 1083–1101. [Google Scholar]
  • Smedegaard-Petersen V, Tolstrup K. 1985. The limiting effect of disease resistance on yield. Annu Rev Phytopathol 23(1): 475–490. [Google Scholar]
  • Stephens JD, et al. 2015. Species tree estimation of diploid Helianthus (Asteraceae) using target enrichment. Am J Bot 102(6): 910–920. [CrossRef] [PubMed] [Google Scholar]
  • Turner KG, Hufbauer RA, Rieseberg LH. 2014. Rapid evolution of an invasive weed. New Phytologist 202(1): 309–21. [CrossRef] [Google Scholar]
  • USDA. 2017. USDA crop composition database. [Google Scholar]
  • Vear F, Grezes-Besset B. 2010. Progress in breeding sunflowers for resistance to Sclerotinia. Proceedings of the International Symposium: Sunflower breeding on resistance to diseases. France: International Sunflower Association, p. 30. [Google Scholar]
  • Wickham H, et al. 2018. ggplot2: Create elegant data visualisations using the grammar of graphics. Available from: [Google Scholar]
  • Yu G, Lam TTY. 2018. ggtree: An R package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Available from: [Google Scholar]

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