Hostname: page-component-78c5997874-dh8gc Total loading time: 0 Render date: 2024-11-19T21:55:41.087Z Has data issue: false hasContentIssue false
  • English
  • Français

Selection of alfalfa cultivars adapted for tropical environments with repeated measures using PROC MIXED of SAS® System

Published online by Cambridge University Press:  29 July 2009

G. M. L. de Assis ,
A. C. Ruggieri ,
M. E. Z. Mercadante ,
G. M. F. de Camargo  and
J. M. Carneiro Júnior
G. M. L. de Assis*
Affiliation:
Brazilian Agricultural Research Corporation (Embrapa), Agroforestry Research Centre of Acre State (Embrapa Acre), Rodovia BR 364, km 14, C. P. 321, CEP 69908-970, Rio Branco, AC, Brazil
A. C. Ruggieri
Affiliation:
São Paulo State University (Unesp), Jaboticabal, SP, Brazil
M. E. Z. Mercadante
Affiliation:
Animal Science Institute, Sertãozinho, SP, Brazil
G. M. F. de Camargo
Affiliation:
São Paulo State University (Unesp), Jaboticabal, SP, Brazil
J. M. Carneiro Júnior
Affiliation:
Brazilian Agricultural Research Corporation (Embrapa), Agroforestry Research Centre of Acre State (Embrapa Acre), Rodovia BR 364, km 14, C. P. 321, CEP 69908-970, Rio Branco, AC, Brazil
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Although alfalfa (Medicago sativa L.) is a leguminous herbage widely used in temperate regions as animal feed, there is not much research in tropical regions to develop cultivars adapted to these environmental conditions. The utilization of adapted cultivars with adequate management practices is important to improve productivity, quality and persistence of cultivated pastures. The objectives of this study were to verify the genetic variability among alfalfa cultivars and to rank them using mixed model methodology. A total of 35 alfalfa cultivars were evaluated in the rainy and dry seasons, from 1996 to 2000, in plots of 2.8 m2 in Sertãozinho, São Paulo, Brazil. The experimental design was a randomized complete block with three replications. Longitudinal data of dry matter yield were analyzed using PROC MIXED of SAS® System. Several covariance structures were tested and the spherical spatial structure was selected. The results show that the genetic variability was statistically significant only for the dry season. Moreover, the interaction among cultivars and harvests variance was highly significant for both seasons. The empirical best linear unbiased predictions of cultivar effects were obtained, allowing for the selection of the superior cultivars MH 15, 5715, SW 8210, Rio, High, 5888, Monarca, Victoria, Florida 77 and Falcon. Crioula, the most common cultivar in Brazil, showed low forage potential in Sertãozinho. Results indicate potential for use of more productive cultivars of alfalfa to produce animal feed in tropical environments.

Keywords

covariance structuregenetic evaluationherbage productionlongitudinal dataMedicago sativaPROC MIXED
Type
Research Article
Information
Plant Genetic Resources , Volume 8 , Issue 1 , April 2010 , pp. 55 - 62
Copyright
Copyright © NIAB 2009

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

Assis, GML, Valentim, JF, Carneiro Júnior, JM, Azevedo, JMA and Ferreira, AS (2008) Selection of forage peanut genotypes for ground cover and aerial biomass production during establishment period using mixed model methodology. Brazilian Journal of Animal Science 37(11): 1905–1911.Google Scholar
Duarte, JB and Vencovsky, R (2001) Estimação e predição por modelo linear misto com ênfase na ordenação de médias de tratamentos genéticos. Scientia Agricola 58: 109–117.Google Scholar
Furlani, RCM, Moraes, MLT, Resende, MDV, Furlani Júnior, E, Gonçalves, PS, Valério Filho, WV and Paiva, JR (2005) Estimation of variance components and prediction of breeding values in rubber tree breeding using the REML/BLUP procedure. Genetics and Molecular Biology 28: 271–276.Google Scholar
Henderson, CR (1973) Sire evaluation and genetic trends. Proceedings of Animal Breeding Genetic Symposium in Honor of Dr. J.L. Lush. Champaign, IL: ASAS/ADSA, pp. 10–28.Google Scholar
Holland, JB (2006) Estimating genotypic correlations and their standard errors using multivariate restricted maximum likelihood estimation with SAS Proc Mixed. Crop Science 46: 642–654.CrossRefGoogle Scholar
Huynh, H and Feldt, LS (1970) Conditions under which mean square rations in repeated measure designs have exact F-distributions. Journal of American Statistical Association 65: 1582–1589.CrossRefGoogle Scholar
Littell, RC, Milliken, GA, Stroup, WW and Wolfinger, RD (1996) SAS® system for mixed models. Cary, NC: SAS Institute Inc., p. 633.Google Scholar
Littell, RC, Henry, PR and Ammerman, CB (1998) Statistical analysis of repeated measures data using SAS procedures. Journal of Animal Science 76: 1216–1231.Google Scholar
Liu, BH, Knapp, SJ and Birkes, D (1997) Sampling distributions, biases, variances and confidence intervals for genetic correlations. Theorical and Applied Genetics 94: 8–19.Google Scholar
Monteiro, ALG, Costa, C and Silveira, AC (1998) Dry matter production and seasonal distribution and chemical composition of alfalfa cultivates (Medicago sativa L.). Brazilian Journal of Animal Science 27: 868–874.Google Scholar
Patterson, HD and Thompson, R (1971) Recovery of inter-block information when block size are unequal. Biometrics 58: 545–554.CrossRefGoogle Scholar
Resende, MDV (2002) Genética biométrica e estatística no melhoramento de plantas perenes. Brasília: Embrapa Informação Tecnológica, p. 975.Google Scholar
Resende, MDV (2004) Métodos estatísticos ótimos na análise de experimentos de campo. Colombo: Embrapa Florestas, p. 65.Google Scholar
Resende, MDV and Duarte, JB (2007) Precisão e controle de qualidade em experimentos de avaliação de cultivares. Pesquisa Agropecuária Tropical 37(3): 182–194.Google Scholar
Resende, MDV, Resende, RMS, Jank, L and Valle, CB (2008) Experimentação e análise estatística no melhoramento de forrageiras. In: Resende, MS, Valle, CB and Jank, L (eds) Melhoramento de Forrageiras Tropicais. Campo Grande, MS: Embrapa Gado de Corte, pp. 195–293.Google Scholar
Smith, A, Cullis, B and Gilmour, A (2001) The analysis of crop variety evaluation data in Australia. Australian and New Zealand Journal of Statistics 43: 129–145.CrossRefGoogle Scholar
Snedecor, GW and Cochran, W (1989) Statistical Methods. 8th ed. Ames, IA: Iowa State University Press, p. 491.Google Scholar
Souza-Sobrinho, F, Ledo, FJS, Pereira, AV, Botrel, MA, Evangelista, AR and Viana, MCM (2004) Estimates of repeatability for alfalfa dry matter production. Ciência Rural 34: 531–537.CrossRefGoogle Scholar
Steel, RGD, Torrie, JH and Dickey, DA (1997) Principles and procedures of statistics: a biometrical approach. New York: McGraw Hill Companies, p. 666.Google Scholar
Verbeke, G and Molenberghs, G (2000) Linear Mixed Models for Longitudinal Data. Springer Series in Statistics. New York: Springer, p. 568.Google Scholar
Welham, S, Cullis, B, Gogel, B, Gilmour, A and Thompson, R (2004) Prediction in linear mixed models. Australian and New Zealand Journal of Statistics 46: 325–347.Google Scholar