Hostname: page-component-8448b6f56d-t5pn6 Total loading time: 0 Render date: 2024-04-24T06:52:57.171Z Has data issue: false hasContentIssue false
  • English
  • Français

Morphological diversity in a barley composite cross-derived population evolved under low-input conditions and its relationship with molecular diversity: indications for breeding

Published online by Cambridge University Press:  21 January 2016

L. RAGGI ,
V. NEGRI  and
S. CECCARELLI
L. RAGGI*
Affiliation:
Dipartimento di Scienze Agrarie Alimentari e Ambientali, Università degli Studi di Perugia, Borgo XX Giugno 74, 06121 Perugia, Italy
V. NEGRI
Affiliation:
Dipartimento di Scienze Agrarie Alimentari e Ambientali, Università degli Studi di Perugia, Borgo XX Giugno 74, 06121 Perugia, Italy
S. CECCARELLI
Affiliation:
Via delle Begonie 2, 63100 Ascoli Piceno, Italy
*
*To whom all correspondence should be addressed. Email: lorenzo.raggi@gmail.com
Get access Rights & Permissions [Opens in a new window]

Summary

In order to reduce the environmental impact of agriculture and improve the resilience and sustainability of our food systems, there is an increasing interest in shifting from the present agricultural systems, which are characterized by high external inputs, to low-input productive systems characterized by high resilience and sustainability. Purposely developed varieties are needed for the latter. With the rapid disappearance of landraces, heterogeneous populations such as composite cross populations (CCPs) or line mixtures, developed through evolutionary plant breeding, could be the ideal source of breeding material for the development of new cultivars for low-input productive systems. Parental lines of CCPs should be selected among old breeding lines, varieties or landraces because the specific characteristics required for low-input or organic farming systems might have been lost during selection of modern varieties. In the current scenario of renewed interest in evolutionary plant breeding, the evolution of diversity in heterogeneous populations needs to be better investigated to maximize the advantages that can be obtained by their utilization.

The present paper reports on the analysis of 88 barley plants chosen randomly from a CCP, namely AUT DBA (where AUT indicates autumn sowing and DBA is the acronym of the former Department) that was multiplied for 13 years under a low-input management system without any conscious human selection, aiming to investigate the morphological diversity still existing in the population and its potential value as source of breeding material for low-input/organic agriculture and understanding the traits that contributed to the adaptive success of certain groups of individuals.

Eighteen plant and spike morphological traits were analysed using bi-dimensional spatial analysis, cluster analysis, non-parametric tests and multivariate approaches. Low lodging and loose smut damage were observed in the CCP where several individuals were superior to the best control for at least one of the four yield-related traits, namely spike weight, number of seeds per spike, weight of seeds in a spike and grain weight. Three morphological clusters were identified using cluster analysis. Clusters 2 and 3 grouped the largest number of CCP individuals which, compared with those in cluster 1, were characterized mainly by heavier spikes with higher seed number, taller culms and early flowering. Interestingly, the plant architecture of all the controls was different from that of the most frequent genotypes in the CCP, showing that low-input systems may require a plant architecture different from the one usually considered as the most suitable for high-input systems. Taking advantage of results from Raggi et al. (2015), phenotypic data were also analysed according to individual genetic group assignment. Results suggest that plant height at the beginning of stem elongation, and days to heading, together with traits related to culm and leaf morphology, could have played a significant role in determining the success of plants from genetic group D, which is the group most represented in the CCP.

According to the wide range of morphological diversity existing in the AUT DBA and the high percentage of lines that show favourable combinations of different traits, this population could be a useful gene-pool from which to select lines for breeding activities. Even though further use of the CCP for breeding purposes may be limited by its possible evolution, there are different ways of manipulating the CCP to counteract the undesirable changes without great economic and/or technical efforts. The high number of multi-locus genotypes and the evolutionary responses observed in AUT DBA show that the prediction that phenotypic micro-evolution in natural systems may be limited by low genetic variances in harsh environments and low selection pressure in good environments is not necessarily true for low-input systems.

Type
Crops and Soils Research Papers
Information
The Journal of Agricultural Science , Volume 154 , Issue 6 , August 2016 , pp. 943 - 959
Check for updates
Copyright
Copyright © Cambridge University Press 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Allard, R. W. & Hansche, P. E. (1964). Some parameters of population variability and their implications in plant breeding. In Advances in Agronomy vol. 16 (Ed. Norman, A. G.), pp. 281–326. London, UK: Academic Press Inc.Google Scholar
Blijenburg, J. G. & Sneep, J. (1975). Natural selection in a mixture of eight barley varieties, grown in six successive years. 1. Competition between the varieties. Euphytica 24, 305–315.CrossRefGoogle Scholar
Brancourt-Hulmel, M., Heumez, E., Pluchard, P., Beghin, D., Depatureaux, C., Giraud, A. & le Gouis, J. (2005). Indirect versus direct selection of winter wheat for low-input or high-input levels. Crop Science 45, 1427–1431.Google Scholar
Ceccarelli, S. (1994). Specific adaptation and breeding for marginal conditions. Euphytica 77, 205–219.Google Scholar
Ceccarelli, S. (1996). Adaptation to low/high input cultivation. Euphytica 92, 203–214.Google Scholar
Ceccarelli, S. (2014). Drought. In Plant Genetic Resources and Climate Change (Eds Jackson, M., Ford-Lloyd, B. V. & Parry, M. L.), pp. 221–235. London, UK: CABI Climate Change Series.Google Scholar
Ceccarelli, S. (2015). Efficiency of plant breeding. Crop Science 55, 87–97.Google Scholar
Denison, R. F., Kiers, E. T. & West, S. A. (2003). Darwinian agriculture: when can humans find solutions beyond the reach of natural selection? The Quarterly Review of Biology 78, 145–168.Google Scholar
Early, H. L. & Qualset, C. O. (1971). Complementary competition in cultivated barley (Hordeum vulgare L.). Euphytica 20, 400–409.Google Scholar
Enjalbert, J., Dawson, J. C., Paillard, S., Rhoné, B., Rousselle, Y., Thomas, M. & Goldringer, I. (2011). Dynamic management of crop diversity: from an experimental approach to on-farm conservation. Comptes Rendus Biologies 334, 458–468.CrossRefGoogle ScholarPubMed
Gabriel, K. R. (1971). The biplot graphic display of matrices with application to principal component analysis. Biometrika 58, 453–467.Google Scholar
Goldringer, I., Prouin, C., Rousset, M., Galic, N. & Bonnin, I. (2006). Rapid differentiation of experimental populations of wheat for heading time in response to local climatic conditions. Annals of Botany 98, 805–817.Google Scholar
Hammer, Ø., Harper, D. A. T. & Ryan, P. D. (2001). PAST: paleontological statistics software package for education and data analysis. Palaeontologia Electronica 4, 1–9.Google Scholar
Harlan, H. V. & Martini, M. L. (1938). The effect of natural selection in a mixture of barley varieties. Journal of Agricultural Research 57, 189–199.Google Scholar
Hensleigh, P. F., Blake, T. K. & Welty, L. E. (1992). Natural selection on winter barley composite cross XXVI affects winter survival and associated traits. Crop Science 32, 57–62.CrossRefGoogle Scholar
Horneburg, B. & Becker, H. C. (2008). Crop adaptation in on-farm management by natural and conscious selection: a case study with lentil. Crop Science 48, 203–212.Google Scholar
Jain, S. K. & Qualset, C. (1975). New development in the evaluation and theory of bulk populations. In Barley Genetics III: Proceedings of the Third International Barley Genetics Symposium (Ed. Gaul, H.), pp. 739–749. Munich, Germany: Verlag Karl Thiemig.Google Scholar
Jalata, Z. (2011). GGE-biplot analysis of multi-environment yield trials of barley (Hordeium vulgare L.) genotypes in southeastern Ethiopia highlands. International Journal of Plant Breeding and Genetics 5, 59–75.CrossRefGoogle Scholar
Jennings, P. R. & Aquino, R. C. (1968). Studies on competition in rice. III. The mechanism of competition among phenotypes. Evolution 22, 529–542.Google Scholar
Komatsuda, T., Pourkheirandish, M., He, C., Azhaguvel, P., Kanamori, H., Perovic, D., Stein, N., Graner, A., Wicker, T., Tagiri, A., Lundqvist, U., Fujimura, T., Matsuoka, M., Matsumoto, T. & Yano, M. (2007). Six-rowed barley originated from a mutation in a homeodomain-leucine zipper I-class homeobox gene. Proceedings of the National Academy of Sciences of the United States of America 104, 1424–1429.Google Scholar
Krzanowski, W. J. (1988). Principles of Multivariate Analysis: A User's Perspective. Oxford, UK: Oxford University Press.Google Scholar
Lammerts van Bueren, E. T., Struik, P. C. & Jacobsen, E. (2002). Ecological concepts in organic farming and their consequences for an organic crop ideotype. NJAS – Wageningen Journal of Life Sciences 50, 1–26.CrossRefGoogle Scholar
Lammerts van Bueren, E. T., van Soest, L. J. M., de Groot, E. C., Boukema, I. W. & Osman, A. M. (2005). Broadening the genetic base of onion to develop better-adapted varieties for organic farming systems. Euphytica 146, 125–132.CrossRefGoogle Scholar
Lammerts van Bueren, E. T., Jones, S. S., Tamm, L., Murphy, K. M., Myers, J. R., Leifert, C. & Messmer, M. M. (2011). The need to breed crop varieties suitable for organic farming, using wheat, tomato and broccoli as examples: a review. NJAS – Wageningen Journal of Life Sciences 58, 193–205.Google Scholar
Mak, C. & Harvey, B. L. (1982). Exploitable genetic variation in a composite bulk population of barley. Euphytica 31, 85–92.CrossRefGoogle Scholar
Murphy, K. M., Campbell, K. G., Lyon, S. R. & Jones, S. S. (2007). Evidence of varietal adaptation to organic farming systems. Field Crops Research 102, 172–177.Google Scholar
Negri, V. & Petti, R. (1995). Fonti germoplasma non adattato per la selezione di varietà adatte all'agricoltura biologica: il lavoro dell'Istituto di Miglioramento Genetico Vegetale di Perugia sull'orzo. In Atti del Convegno “Agricoltura biologica in Italia: aspetti tecnici, economici e normativi” (Eds Santucci, F. M. & Zanoli, R.), pp. 185–196. Ancona, Italy: Centro Stampa Consiglio Regionale delle Marche.Google Scholar
Nelson, G. C., Rosegrant, M. W., Koo, J., Robertson, R., Sulser, T., Zhu, T., Ringler, C., Msangi, S., Palazzo, A., Batka, M., Magalhaes, M., Valmonte-Santos, R., Ewing, M. & Lee, D. (2009). Climate Change: Impact on Agriculture and Costs of Adaptation. Washington, DC: International Food Policy Research Institute.Google Scholar
Nevo, E., Fu, Y.-B., Pavlicek, T., Khalifa, S., Tavasi, M. & Beiles, A. (2012). Evolution of wild cereals during 28 years of global warming in Israel. Proceedings of the National Academy of Sciences of the United States of America 109, 3412–3415.CrossRefGoogle ScholarPubMed
Patel, J. D., Reinbergs, E., Mather, D. E., Choo, T. M. & Sterling, J. D. E. (1987). Natural selection in a doubled-haploid mixture and a composite cross of barley. Crop Science 27, 474–479.CrossRefGoogle Scholar
Payne, R., Harding, S. A., Murray, D. A., Soutar, D. M., Baird, D. B., Glaser, A. I., Welham, S. J., Gilmour, A. R., Thompson, R. & Webster, R. (2011). The Guide to GenStat® Release 14. Part 2: Statistics. Hemel Hempstead, UK: VSN International.Google Scholar
Phillips, S. L. & Wolfe, M. S. (2005). Evolutionary plant breeding for low input systems. The Journal of Agricultural Science, Cambridge 143, 245–254.Google Scholar
R Development Core Team (2013). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. Available from: http://www.R-project.org Google Scholar
Raggi, L., Ceccarelli, S. & Negri, V. (2015). Evolution of a barley composite cross derived population: an insight gained by molecular markers. The Journal of Agricultural Science, Cambridge. doi: http://dx.doi.org/10.1017/S0021859614001269 Google Scholar
Raquin, A-L., Brabant, P., Rhoné, B., Balfourier, F., Leroy, P. & Goldringer, I. (2008). Soft selective sweep near a gene that increases plant height in wheat. Molecular Ecology 17, 741–756.Google Scholar
Rollins, J. A., Drosse, B., Mulki, M. A., Grando, S., Baum, M., Singh, M., Ceccarelli, S. & von Korff, M. (2013). Variation at the vernalisation genes Vrn-H1 and Vrn-H2 determines growth and yield stability in barley (Hordeum vulgare) grown under dryland conditions in Syria. Theoretical and Applied Genetics 126, 2803–2824.Google Scholar
Sandfær, J. (1970). An analysis of the competition between some barley varieties. Riso Report 230, 1–113.Google Scholar
Sherwood, S. & Fu, Q. (2014). Climate change. A drier future? Science 343, 737–739.Google Scholar
Sinebo, W., Gretzmacher, R. & Edelbauer, A. (2002). Environment of selection for grain yield in low fertilizer input barley. Field Crops Research 74, 151–162.Google Scholar
Singh, M., Ceccarelli, S. & Grando, S. (1997). Precision of the genotypic correlation estimated from variety trials conducted in incomplete block designs. Theoretical and Applied Genetics 95, 1044–1048.Google Scholar
Singh, M., Malhotra, R. S., Ceccarelli, S., Sarker, A., Grando, S. & Erskine, W. (2003). Spatial variability models to improve dryland field trials. Experimental Agriculture 39, 151–160.Google Scholar
Smithson, J. B. & Lenné, J. M. (1996). Varietal mixtures: a viable strategy for sustainable productivity in subsistence agriculture. Annals of Applied Biology 128, 127–158.Google Scholar
Torricelli, R., Ciancaleoni, S. & Negri, V. (2014). Performance and stability of homogeneous and heterogeneous broccoli (Brassica oleracea L. var. italica Plenck) varieties in organic and low-input conditions. Euphytica 199, 385–395.Google Scholar
Wilson, A. J., Pemberton, J. M., Pilkington, J. G., Coltman, D. W., Mifsud, D. V., Clutton-Brock, T. H. & Kruuk, L. E. B. (2006). Environmental coupling of selection and heritability limits evolution. PLoS Biology 4, e216. doi: 10.1371/journal.pbio.0040216 CrossRefGoogle ScholarPubMed
Wolfe, M. S., Baresel, J. P., Desclaux, D., Goldringer, I., Hoad, S., Kovacs, G., Löschenberger, F., Miedaner, T., Østergård, H. & Lammerts van Bueren, E. T. (2008). Developments in breeding cereals for organic agriculture. Euphytica 163, 323–346.Google Scholar
Zadoks, J. C., Chang, T. T. & Konzak, C. F. (1974). A decimal code for the growth stages of cereals. Weed Research 14, 415–421.CrossRefGoogle Scholar
Supplementary material: File

Raggi supplementary material

Table S1

Download Raggi supplementary material(File)
File 19.2 KB