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Phenotypic diversity and relationships among Chilean Choclero maize (Zea mays L. mays) landraces

Published online by Cambridge University Press:  10 May 2016

Erika Salazar Open the ORCID record for Erika Salazar [Opens in a new window] ,
José Correa ,
María José Araya ,
Marco A. Méndez  and
Basilio Carrasco
Erika Salazar*
Affiliation:
Centro Regional de Investigación La Platina, Instituto de Investigaciones Agropecuarias, Av. Santa Rosa 11610, La Pintana, Santiago, Chile
José Correa
Affiliation:
Centro Regional de Investigación La Platina, Instituto de Investigaciones Agropecuarias, Av. Santa Rosa 11610, La Pintana, Santiago, Chile
María José Araya
Affiliation:
Centro Regional de Investigación La Platina, Instituto de Investigaciones Agropecuarias, Av. Santa Rosa 11610, La Pintana, Santiago, Chile
Marco A. Méndez
Affiliation:
Facultad de Ciencias, Universidad de Chile, Las Palmeras 3425, Ñuñoa, Santiago, Chile
Basilio Carrasco
Affiliation:
Facultad de Agronomía e Ingeniería Forestal, Pontificia Universidad Católica de Chile, Vicuña Mackenna 4860, Macul, Santiago, Chile
*
*Corresponding author. E-mail: esalazar@inia.cl
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Abstract

Choclero is a Chilean traditional floury maize, consumed as a vegetable, with large economic and cultural value due to its culinary properties that give unique characteristics to the traditional local cuisine. Market diversification demands new materials with different ear and kernel characteristics, which are at present not fulfilled by breeders due to lack of genetic diversity. At present, the Instituto de Investigaciones Agropecuarias has a Choclero germplasm collection composed of 96 accessions, which can supply this lack of diversity, or increase the gene pool. In the present study, 34 selected Chilean Choclero landraces were characterized for 41 agromorphological traits. Phenotypic evaluation in three environments representative of the core production area revealed significant genetic variability for most of the evaluated traits, leading to the identification of several promising accessions. The greater contribution of genotype in most phenological plant, ear and kernel traits suggest their potential usefulness for breeding purposes. Principal component analysis explained over 75% of the total variation for 29 quantitative agromorphological traits. Cluster analysis separated accessions into four major groups, differentiated mainly by plant phenology and ear trait. These findings indicate a number of useful traits at an intra-racial level and a wide range of phenotypic variation that provides a good source of diversity for use in the development of new Choclero varieties.

Keywords

Chilean landracesgenetic diversitygreen corntrait correlation
Type
Research Article
Information
Plant Genetic Resources , Volume 15 , Issue 5 , October 2017 , pp. 461 - 473
Copyright
Copyright © NIAB 2016 

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References

Acharjee, A, Kloosterman, B, de Vos, RC, Werij, JS, Bachem, CW, Visser, RG and Maliepaard, C (2011) Data integration and network reconstruction with ~omics data using Random Forest regression in potato. Analytica Chimica Acta 705: 56–63.CrossRefGoogle ScholarPubMed
Aljaro, A (1972) Efecto de un programa de selección recurrente y masal en una población de maíz Choclero (Zea mays L.). Thesis, Universidad Católica de Chile.Google Scholar
Badstue, LB, Bellon, M, Berthaud, J, Ramírez, A, Flores, D and Juárez, X (2007) The dynamics of farmer's maize seed supply practices in the Central Valleys of Oaxaca, Mexico. World Development 35: 1579–1593.CrossRefGoogle Scholar
Bates, D and Maechler, M (2009) lme4: Linear mixed-effects models using S4 classes. R package version 0.999375-32. Available at http://CRAN.R-project.org. Accessed March, 2012.Google Scholar
Beyene, Y, Bottha, AM and Myburg, AA (2005) A comparative study of molecular and morphological methods of describing genetic relationships in traditional Ethiopian highland maize. African Journal of Biotechnology 4: 586–595.Google Scholar
Breiman, L and Cutler, A (2004) Random forests. Berkeley. Available at http://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm. Accessed June 2014.Google Scholar
Campbell, MR, Anih, E, Conatser, C, Grau-Saavedra, B and Pollak, LM (2006) Development of a core subset of Chilean lowland subtropical and temperate maize (Zea mays L.) populations using near infrared transmittance spectroscopy. Plant Genetic Resources Newsletter 148: 1–9.Google Scholar
Camus-Kulandaivelu, L,Veyrieras, JB, Madur, D, Combes, V, Fourmann, M, Barraud, S, Dubreuil, P, Gouesnard, B, Manicacci, D and Charcosset, A (2006) Maize adaptation to temperate climate: relationship between population structure and polymorphism in the dwarf 8 gene. Genetics 172: 2449–2463.CrossRefGoogle Scholar
Castro, V (2000) Evaluación del comportamiento de cinco cultivares de maíz Choclero en una microcuenca de riego del secano de la VI región. Agroclima Hidango. Thesis, Universidad Santo Tomás.Google Scholar
Cox, J (1989) Choclero híbrido: avances en la investigación nacional. Chile Agrícola 14: 440–441.Google Scholar
Dieters, MJ, White, TL, Littell, RC and Hedge, GR (1995) Application of approximate variances of variance-components and their ratios in genetic tests. Theoretical and Applied Genetics 91: 15–24.Google Scholar
Dray, S and Dufour, AB (2007) The ade4 package: implementing the duality diagram for ecologists. Journal of Statistical Software 22: 1–20.Google Scholar
Everitt, B and Hothorn, T (2011) An Introduction to Applied Multivariate Analysis with R. Heidelberg: Springer. 273 pages. ISBN: 978-1-441-99649-7.Google Scholar
Faiguenbaum, H (2004) Cultivo del Choclo. El Campesino 135: 16–21.Google Scholar
Fuentes, ZJ and Ciudad, BC (1974) Contenido de proteína y lisina de algunos maíces Chocleros Chilenos (Zea mays). Agricultura Técnica 34: 36–38.Google Scholar
Galinat, WC (1996) Bl (Broadleaf), a genetic trait that may enhance yields by contributing to the canopy. MNL 70: 68.Google Scholar
Gouesnard, B, Dallard, J, Panouille, A and Boyat, A (1997) Classification of French maize populations based on morphological traits. Agronomie 17: 491–498.CrossRefGoogle Scholar
Gutierrez, L, Franco, J, Crossa, J and Abadie, T (2003) Comparing a preliminary racial classification with a numerical classification of the maize landraces of Uruguay. Crop Science 43: 718–727.Google Scholar
Hartings, H, Berardo, N, Mazzinelli, GF, Valoti, P, Verderio, A and Motto, M (2008) Assessment of genetic diversity and relationships among maize (Zea mays L.) Italian landraces by morphological traits and AFLP profiling. Theoretical and Applied Genetics 117: 831–84.Google Scholar
Hefner, JT, Spradley, MK and Anderson, B (2014) Ancestry assessment using random forest modeling. Journal of Forensic Science 59(3): 583–589.Google Scholar
Huang, H, Liu, Y and Marron, JS (2010) sigclust: Statistical Significance of Clustering. R package version: 1.0.0. Available at http://www.R-project.org. Accessed April 2014.Google Scholar
IBPGR (1991) Descriptors for Maize. Rome: International Maize and Wheat Improvement Center, Mexico City/International Board for Plant Genetic Resources, 88 p.Google Scholar
Jara, C (2005) Resultado parcial de un programa de mejoramiento genético en maíz Choclero (Zea mays L.). Thesis, Universidad Católica de Chile.Google Scholar
Jugenheimer, R (1959) Obtención de maíz híbrido y producción de semilla. Roma, Italia: FAO, 395 p.Google Scholar
Knežecić, J, Prodanović, S, Iwarsson, M and Minina, A (2010) Diversity of maize (Zea mays L.) landraces in Eastern Serbia: morphological and storage protein characterization. Maydica 55: 231–238.Google Scholar
Kuznetsova, A, Brockhoff, PB and Christensen, RHB (2013) lmer Test: Tests for random and fixed effects for linear mixed effect models (lmer objects of lme4 package). R package version 1.1-0. Available at http://CRAN.R-project.org/package=lmerTest. Accessed February, 2013.Google Scholar
López, G, Santacruz, A, Muñoz, A, Castillo, F, Córdova, L and Vaquera, H (2005) Caracterización morfológica de poblaciones nativas de maíz del Istmo de Tehuantepec, México. Interciencia 30: 284–290.Google Scholar
Louette, D (1999). Traditional management of seed and genetic diversity: What is a landrace? Genes in the field: on farm conservation of crop diversity. S-B. Brush, IPGRI, IDRC, Lewis.Google Scholar
Luchsinger, A and Camilo, F (2008) Sweet corn cultivars and their behavior with different sowing dates in the 6th region of Chile. IDESIA (Chile) 26: 45–52.Google Scholar
Magari, R and Kang, MS (1993) Genotype selection via a new yield-stability statistic in maize yield trials. Euphytica 70: 105–111.CrossRefGoogle Scholar
Mohammadi, SA and Prasanna, BM (2003) Analysis of genetic diversity in crop plants – salient statistical tools and considerations. Crop Science 43: 1235–1248.Google Scholar
Montecino, S (2004) Cocinas Mestizas de Chile. La Olla Deleitosa, Museo Chileno de Arte Precolombino. 132 p.Google Scholar
ODEPA (2013) Estadísticas Agrícolas. Available at http://www.odepa.cl/ Accessed December, 2013.Google Scholar
Paratori, O (1983) Recolección, clasificación y estudio de germoplasma chileno de maíz. Simiente 53: 33–38.Google Scholar
Paratori, O (1995a). Adaptación, clasificación y producción de semilla. In: Paratori, O and Altamirano, y.S. (eds) El cultivo del Maíz. Santiago: Instituto de Investigaciones Agropecuarias, pp. 13–19.Google Scholar
Paratori, O (1995b) Maíz para consumo tierno. In: Paratori, O and Altamirano, y.S. (eds). El cultivo del Maíz. Santiago: Instituto de Investigaciones Agropecuarias, pp. 13–19.Google Scholar
Paratori, O and Silva, F (1987) Evaluación y regeneración de germoplasma nativo de maíz. Informe Anual Etapa 1. Latin American Maize Project. Santiago. 79 p.Google Scholar
Paratori, O, Sbárbaro, R and Villegas, C (1990) Catálogo de recursos genéticos de maíz. Boletín Técnico N°165, Santiago, Chile: Instituto de Investigaciones Agropecuarias, 210 p.Google Scholar
Parker, I and Paratori, O (1965) Distribución geográfica, clasificación y estudio del maíz (Zea mays) en Chile. Agricultura técnica 25: 70–86.Google Scholar
Pollak, LM (2002) The history and success of the public-private project on germplasm enhancement of maize (GEM). Advances in Agronomy 78: 45–87.CrossRefGoogle Scholar
Prasanna, BM and Sharma, L (2005) The landraces of maize (Zea mays L.): diversity and utility. Indian Journal of Plant Genetic Resources 18: 155–168.Google Scholar
R Development Core Team (2011) R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing, ISBN 3-900051-07-0. http://www.R-project.org/. Accessed December, 2014.Google Scholar
Rincon, F, Johnson, B, Crossa, J and Taba, S (1997) Identifying sunsets of maize accessions by three-mode principal components analysis. Crop Science 37: 1936–1943.CrossRefGoogle Scholar
Ruiz de Galarreta, JI and Alvarez, A (2001) Morphological classification of maize landraces from northern Spain. Genetic Resources and Crop Evolution 48: 391–400.CrossRefGoogle Scholar
Salhuana, W and Pollak, LM (2006) Latin American Maize Project (LAMP) and Germplasm Enhancement of Maize (GEM) Project: generating useful breeding germplasm. Maydica 51: 339–355.Google Scholar
Salhuana, W, Pollak, LM, Ferrer, M, Paratori, O and Vivo, G (1998) Breeding potential of maize accessions from Argentina, Chile, USA, and Uruguay. Crop Science 38: 866–872.CrossRefGoogle Scholar
Santacruz-Varela, A, Widrlechner, MP, Ziegler, KE, Salvador, RJ, Millard, MJ and Bretting, PK (2004) Phylogenetic relationships among North American popcorns and their evolutionary links to Mexican and South American popcorns. Crop Science 44: 1456–1467.CrossRefGoogle Scholar
Sepúlveda, R (2000) Evaluación de seis variedades de maíz Choclero para consumo fresco. Thesis. Pontificia Universidad Católica de Chile.Google Scholar
Sillampää, M and Corander, J (2002) Model choice in gene mapping: what and why. Trends in Genetics 18: 301:307.Google Scholar
Timothy, D, Peña, B and Ramírez, R (1961) Races of maize in Chile. Washington, D.C., USA: National Academy of Sciences-National Research Council, Publication no. 847.Google Scholar
Uribe, JM, Cabrera, R, De la Fuente, A and Paneque, M (2012) Atlas bioclimático de Chile. Santiago: Universidad de Chile – CORFO – Ministerio de Bienes Nacionales.Google Scholar
Wehrens, R (2011) Chemometrics with R: Multivariate Data Analysis in the Natural Sciences and Life Sciences. Series: Use R!. Berlin Heidelberg: Springer-Verlag, 285p.Google Scholar
Wei, T (2013) corrplot: Visualization of a correlation matrix. R package version 0.72. Available at http://CRAN.R-project.org/package=corrplot Google Scholar
Wickham, H (2009) ggplot2: Elegant Graphics for Data Analysis. New York: Springer, 212 p.Google Scholar
Williams, MM (2014) Few crop traits accurately predict variables important to productivity of processing sweet corn. Field Crops Research 157: 20–26.Google Scholar
Yang, RC and Baker, RJ (1991) Genotype-environment interactions in two wheat crosses. Crop Science 31: 83–87.Google Scholar
Zerene, I (1985) Grado de asociación entre los caracteres de la panoja y el rendimiento del grano de maíz: (Zea mays L). Thesis. Universidad Austral de Chile.Google Scholar
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