Issue
OCL
Volume 26, 2019
Sunflower and climate change / Tournesol et changement climatique
Article Number 9
Number of page(s) 7
DOI https://doi.org/10.1051/ocl/2019003
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: https://ccafs.cgiar.org/tool-climate-analogue-tool#.W0kgji31kWo. [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. [CrossRef] [EDP Sciences] [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: http://faostat3.fao.org/. [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. [CrossRef] [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. [CrossRef] [PubMed] [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. [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. [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: https://cran.r-project.org/package=ggplot2. [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: https://guangchuangyu.github.io/software/ggtree. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.