BLUP’s to Quantify Yield Gain under Wheat Coordinated System for Northern Hills Zone by Factor Analytic Approach

DOI: 10.18805/IJARe.A-5365    | Article Id: A-5365 | Page : 495-500
Citation :- BLUP’s to Quantify Yield Gain under Wheat Coordinated System for Northern Hills Zone by Factor Analytic Approach.Indian Journal Of Agricultural Research.2020.(54):495-500
Ajay Verma, Ravish Chatrath, G.P. Singh verma.dwr@gmail.com
Address : ICAR-Indian Institute of Wheat and Barley Research, Karnal-132 001, Haryana, India.
Submitted Date : 14-08-2019
Accepted Date : 25-11-2019

Abstract

Trend in linear manner has been observed for wheat production under irrigated timely and late sown along with rainfed timely sown trials of Northern Hills Zone of country. Production elevated to the level of 53, 30 and 36q/ha for irrigated timely, late sown and rainfed timely sown trials. By the end of considered period 0.81, 0.61 and 2.06 quintal per hectare could be added in subsequent trials. Low values of R2 for irrigated timely and late sown trials suggested marginal increase in linear fashion in production values. More over consistent improvement observed in rainfed timely sown trials as justified by highly significant value of R2.

Keywords

BLUP FA Fixed and random effects Mixed model REML

References

  1. Baretta D., Nardino, M., Carvalho, I.R., Pelegrin, A.J., Ferrari, M., Szareski, V.J., (2017). Estimates of genetic parameters and genotypic values prediction in maize landrace populations by REML/BLUP Procedure. Genetics and Molecular Research. 16: 1-14.
  2. Borges V, Ferreira PV, Soares L, Santos GM, Santos AMM. (2010). Sweet potato clone selection by REML/BLUP procedure. Acta Sci Agron. 32(4):643–649
  3. Burgueño J., J. Crossa, P.L. Cornelius and R.C. Yang. (2008). Using factor analytic models for joining environments and genotypes without crossover genotype x environment interac­tion. Crop Sci, 48:1291–1305. 
  4. Crespo-Herrera, L.A., J. Crossa, J. Huerta-Espino, E. Autrique, S. Mondal, G. Velu,. et al (2017). Genetic yield gains in CIM­MYT’s international Elite Spring Wheat Yield Trials by modeling the genotype x environment interaction. Crop Sci. 57:789–801. 
  5. de Pelegrin, A.J., Carvalho, I.R., Nunes, A.C.P., Demari, G.H., Szareski, V.J., et al (2017). Adaptability, stability and multivariate selection by mixed Models. American Journal of Plant Sciences. 8: 3324.
  6. Eileen Azevedo Santos, Alexandre Pio Viana, Josie Cloviane de Oliveira Freitas, et al (2015). Genotype selection by REML/BLUP methodology in a segregating population from an interspecific Passiflora spp. Crossing. Euphytica. 204:1–11.
  7. Gustavo H.F. Oliveira, Camila B. Amaral, Flávia A.M. Silva, Sophia M.F. Dutra, et al (2016). Mixed models and multivariate analysis for selection of superior maize genotypes. Chilean Journal of Agricultural Research 76:427-431.
  8. Mohan D., Tiwari, V. and Gupta R. K. (2017). Progression in yield and value addition of Indian bread wheat – An Analysis. Indian J. Genet. 77(1): 16-24.
  9. Mendes F.F.; Guimarães, L.J.M.; Souza, J.C.; Guimarães, P.E.O.; Pacheco, C.A.P.; et al (2012). Adaptability and stability of maize varieties using mixed models methodology. Crop Breeding and Applied Biotechnology. 12: 111-117.
  10. Olivoto T., Nardino, M., Carvalho, I.R., Follmann, D.N., Ferrari, M., Szareski, V.J., et al (2017). REML/BLUP and sequential path analysis in estimating genotypic values and inter relationships among simple maize grain yield-related traits. Genetics and Molecular Research. 16: 1-10.
  11. Piepho HP, Möhring J, Melchinger AE and Büchse A. (2008). BLUP for phenotypic selection in plant breeding and variety testing. Euphytica. 161: 209-228.
  12. Piepho H.P. (1998). Empirical best linear unbiased prediction in cul­tivar trials using factor analytic variance–covariance structures. Theor. Appl. Genet. 97:195–201. 
  13. Smith A., A. Ganesalingam, H. Kuchel and B.R. Cullis. (2015). Factor analytic mixed models for the provision of grower infor­mation from national crop variety testing programs. Theor. Appl. Genet. 128:55–72. 

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