The home field advantage of modern plant breeding
Autoři:
Patrick M. Ewing aff001; Bryan C. Runck aff002; Thomas Y. J. Kono aff003; Michael B. Kantar aff004
Působiště autorů:
Department of Crop, Soil, and Microbial Sciences, Michigan State University, East Lansing, MI, United States of America
aff001; GEMS Agroinformatics Initiative, University of Minnesota, Minneapolis, MN, United States of America
aff002; Minnesota Supercomputing Institute, Minneapolis, MN, United States of America
aff003; Department of Tropical Plant and Soil Science, University of Hawaii at Manoa, Honolulu, HI, United States of America
aff004
Vyšlo v časopise:
PLoS ONE 14(12)
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pone.0227079
Souhrn
Since the mid-20th century, crop breeding has driven unprecedented yield gains. Breeders generally select for broadly- and reliably-performing varieties that display little genotype-by-environment interaction (GxE). In contrast, ecological theory predicts that across environments that vary spatially or temporally, the most productive population will be a mixture of narrowly adapted specialists. We quantified patterns of broad and narrow adaptation in modern, commercial maize (Zea mays L.) hybrids planted across 216 site-years, from 1999–2018, for the University of Illinois yield trials. We found that location was the dominant source of yield variation (44.5%), and yearly weather was the smallest (1.7%), which suggested a benefit for reliable performance in narrow biophysical environments. Varieties displayed a large “home field advantage” when growing in the location of best performance relative to other varieties. Home field advantage accounted for 19% of GxE and provided a yield increase of 1.01 ± 0.04 Mg ∙ ha-1 (7.6% relative to mean yield), yet was both smaller than predicted by a null model and unchanged across time. This counterfactual suggests that commercial breeding programs have missed an opportunity to further increase yields by leveraging local adaptation. Public breeding programs may pursue this opportunity by releasing specialist varieties that perform reliably in narrow environments. As seed sources are increasingly privatized and consolidated, this alternate strategy may compliment private breeding to support global food security.
Klíčová slova:
Maize – Crops – Permutation – Plant breeding – Agronomy – Crop genetics – Illinois
Zdroje
1. Finlay KW, Wilkinson GN. The analysis of adaptation in a plant-breeding programme. Australian journal of agricultural research. 1963;14(6):742–54.
2. Gauch H, Zobel RW. Identifying mega-environments and targeting genotypes. Crop science. 1997;37(2):311–26.
3. Darwin CR. The Variation of Animals and Plants Under Domestication, 1st edn., 2 vols. New York: Orange Judd & Co. 1868.
4. De Candolle A. origin of cultivated plants. 207 pp. Appleton Inc., New York. 1890.
5. Levins R. Theory of fitness in a heterogeneous environment. I. The fitness set and adaptive function. The American Naturalist. 1962 Nov 1;96(891):361–73.
6. IPCC. (2014). Ch. 11: Agriculture, forestry and other land use (AFOLU) (No. AR 5). Intergovernmental Panel on Climate Change.
7. Thrall PH, Oakeshott JG, Fitt G, Southerton S, Burdon JJ, Sheppard A et al. Evolution in agriculture: the application of evolutionary approaches to the management of biotic interactions in agro‐ecosystems. Evolutionary Applications. 2011; 4(2):200–15. doi: 10.1111/j.1752-4571.2010.00179.x 25567968
8. Sustainable-Intensification-Agriculture, FAO, 5 June 2019, retrieved from: www.fao.org/policy-support/policy-themes/sustainable-intensification-agriculture/en/.
9. Geyer CJ, Wagenius S, Shaw RG. Aster models for life history analysis. Biometrika 2007; 94: 415–426.
10. Zeven AC, de Wet JMU. Dictionary of cultivated plants and their regions of diversity: excluding most ornamentals, forest trees, and lower plants. Wagenigen, The Netherlands: CAPD 1982
11. Newton A. C., Akar T., Baresel J. P., Bebeli P. J., Bettencourt E., Bladenopoulos K. V., et al. Cereal landraces for sustainable agriculture. In Sustainable Agriculture Volume 2 (pp. 147–186). 2011 Springer, Dordrecht.
12. Mercer K, Martínez‐Vásquez Á, Perales HR. Asymmetrical local adaptation of maize landraces along an altitudinal gradient. Evolutionary Applications. 2008;1(3):489–500. doi: 10.1111/j.1752-4571.2008.00038.x 25567730
13. Perales RH, Brush SB, Qualset CO. Landraces of maize in Central Mexico: an altitudinal transect. Economic Botany. 2003; 57(1):7–20.
14. Pray CE, Fuglie KO. Agricultural research by the private sector. Annu. Rev. Resour. Econ. 2015;7(1):399–424.
15. Holt RD. On the evolutionary ecology of species’ ranges. Evolutionary ecology research. 2003;5(2):159–78.
16. Zhang F, Cui Z, Fan M, Zhang W, Chen X, Jiang R. Integrated soil–crop system management: reducing environmental risk while increasing crop productivity and improving nutrient use efficiency in China. Journal of Environmental Quality. 2011; 40(4):1051–7. doi: 10.2134/jeq2010.0292 21712573
17. MacArthur RH. Patterns of species diversity. Biological reviews. 1965;40(4):510–33.
18. MacArthur R, Levins R. The limiting similarity, convergence, and divergence of coexisting species. The American Naturalist. 1967;101(921):377–85.
19. Endler JA, May RM. Natural selection in the wild. Princeton University Press; 1986 Apr 21.
20. Jannink JL, Lorenz AJ, Iwata H. Genomic selection in plant breeding: from theory to practice. Briefings in functional genomics. 2010; 9(2):166–77. doi: 10.1093/bfgp/elq001 20156985
21. Bernardo R. Bandwagons I, too, have known. Theoretical and applied genetics. 2016; 129(12):2323–32. doi: 10.1007/s00122-016-2772-5 27681088
22. Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology: A Journal of the Royal Meteorological Society. 2005;25(15):1965–78.
23. Hengl T, de Jesus JM, Heuvelink GB, Gonzalez MR, Kilibarda M, Blagotić A, Shangguan W, et al. SoilGrids250m: Global gridded soil information based on machine learning. PLoS one. 2017;12(2):e0169748. doi: 10.1371/journal.pone.0169748 28207752
24. Team RC. R: a language and environment for statistical computing, v. 3.4. 1.[WWW document] URL https://www.R-project.org. 2018.
25. Wickham H. Springer; New York: 2009. Ggplot2: elegant graphics for data analysis. 2009.
26. Merkle EC, Rosseel Y. blavaan: Bayesian structural equation models via parameter expansion. JOURNAL OF STATISTICAL SOFTWARE. 2018;85(4):1–30.
27. Merkle EC, Wang T. Bayesian latent variable models for the analysis of experimental psychology data. Psychonomic bulletin & review. 2018;25(1):256–70.
28. Blanquart F, Kaltz O, Nuismer SL, Gandon S. A practical guide to measuring local adaptation. Ecology letters. 2013;16(9):1195–205. doi: 10.1111/ele.12150 23848550
29. Fox J, Weisberg S. Car: companion to applied regression Available at: http://CRAN.R-project.org/package=car Accessed. 2011 Apr;20.
30. Koenker R, Portnoy S, Ng PT, Zeileis A, Grosjean P, Ripley BD. Package ‘quantreg’.
31. Beavis WD, Grant D, Albertsen M, Fincher R. Quantitative trait loci for plant height in four maize populations and their associations with qualitative genetic loci. Theoretical and Applied Genetics. 1991;83(2):141–5. doi: 10.1007/BF00226242 24202349
32. Bongaarts J. Human population growth and the demographic transition. Philosophical Transactions of the Royal Society B: Biological Sciences. 2009;364(1532):2985–90.
33. Khoury CK, Achicanoy HA, Bjorkman AD, Navarro-Racines C, Guarino L, Flores-Palacios X, et al. Origins of food crops connect countries worldwide. Proceedings of the Royal Society B: Biological Sciences. 2016;283(1832):20160792
34. Troyer AF. Breeding widely adapted, popular maize hybrids. Euphytica. 1996;92(1–2):163–74.
35. Evenson RE, Gollin D. Assessing the impact of the Green Revolution, 1960 to 2000. Science 2003, 300(5620), 758–762. doi: 10.1126/science.1078710 12730592
36. Clausen J, Keck DD, Hiesey WM. Experimental studies on the nature of species. III. Environresponses of climatic races of Achillea. Experimental studies on the nature of species. III. Environresponses of climatic races of Achillea. 1948(Publ. No. 581).
37. Brandenburg JT, Mary-Huard T, Rigaill G, Hearne SJ, Corti H, Joets J, et al. Independent introductions and admixtures have contributed to adaptation of European maize and its American counterparts. PLoS genetics. 2017;13(3):e1006666. doi: 10.1371/journal.pgen.1006666 28301472
38. Falconer DS, Mackay TF, Frankham R. Introduction to quantitative genet‑ics: trends in genetics. Harlow: Longman Frankel. 1996.
39. Zamir D. Improving plant breeding with exotic genetic libraries. Nature reviews genetics. 2001;2(12):983. doi: 10.1038/35103590 11733751
40. Cobb JN, Juma RU, Biswas PS, Arbelaez JD, Rutkoski J, Atlin G, et al. Enhancing the rate of genetic gain in public-sector plant breeding programs: lessons from the breeder’s equation. Theoretical and Applied Genetics. 2019;132(3):627–45. doi: 10.1007/s00122-019-03317-0 30824972
41. Xu Y. Envirotyping for deciphering environmental impacts on crop plants. Theoretical and Applied Genetics. 2016;129(4):653–73. doi: 10.1007/s00122-016-2691-5 26932121
42. Bahram M, Hildebrand F, Forslund SK, Anderson JL, Soudzilovskaia NA, Bodegom PM, et al. Structure and function of the global topsoil microbiome. Nature. 2018;560(7717):233. doi: 10.1038/s41586-018-0386-6 30069051
43. Walters WA, Jin Z, Youngblut N, Wallace JG, Sutter J, Zhang W et al. Large-scale replicated field study of maize rhizosphere identifies heritable microbes. Proceedings of the National Academy of Sciences. 2018;115(28):7368–73.
44. Gauch HG. A simple protocol for AMMI analysis of yield trials. Crop Science. 2013;53(5):1860–9.
45. Mzuku M, Khosla R, Reich R, Inman D, Smith F, MacDonald L. Spatial variability of measured soil properties across site-specific management zones. Soil Science Society of America Journal 2005, 69: 1572.
46. Castañeda-Álvarez NP, Khoury CK, Achicanoy HA, Bernau V, Dempewolf H, Eastwood RJ, et al. Global conservation priorities for crop wild relatives. Nature plants. 2016 Apr;2(4):16022.
47. Williams A, Hunter MC, Kammerer M, Kane DA, Jordan NR, Mortensen DA, et al. Soil Water Holding Capacity Mitigates Downside Risk and Volatility in US Rainfed Maize: Time to Invest in Soil Organic Matter? ( Gonzalez-Andujar JL, Ed.). PLOS ONE 2016 11: e0160974. doi: 10.1371/journal.pone.0160974 27560666
48. Haefele SM, Kato Y, Singh S. 2016. Climate ready rice: augmenting drought tolerance with best management practices. Field Crops Research, 2016. 190, 60–69.
Článok vyšiel v časopise
PLOS One
2019 Číslo 12
- Metamizol jako analgetikum první volby: kdy, pro koho, jak a proč?
- Nejasný stín na plicích – kazuistika
- Masturbační chování žen v ČR − dotazníková studie
- Úspěšná resuscitativní thorakotomie v přednemocniční neodkladné péči
- Fixní kombinace paracetamol/kodein nabízí synergické analgetické účinky
Najčítanejšie v tomto čísle
- Methylsulfonylmethane increases osteogenesis and regulates the mineralization of the matrix by transglutaminase 2 in SHED cells
- Oregano powder reduces Streptococcus and increases SCFA concentration in a mixed bacterial culture assay
- The characteristic of patulous eustachian tube patients diagnosed by the JOS diagnostic criteria
- Parametric CAD modeling for open source scientific hardware: Comparing OpenSCAD and FreeCAD Python scripts