Bayesian Credible Intervals for Maize Grain Yields of the Maintenance Varieties Evaluated in Sudan

Authors

  • Siraj Osman Omer Experimental Design and Analysis Unit, Agricultural Research Corporation (ARC), P.O. Box 126, Wad, Medani
  • Mohammed Salah Abdalla Cereal Research Center, Agricultural Research Corporation (ARC), P.O. Box 126, Wad, Medain
  • Ibrahim Nuraldin Alzain Nile Sun Company, Wad, Medain
  • Abdelmoniea Dafaalla Nile Sun Company, Wad, Medain

DOI:

https://doi.org/10.3126/ijasbt.v5i3.18303

Keywords:

Bayesian inference, maize, maintenance varieties, block design

Abstract

Improved of maintenance crop varieties developed at agricultural research corporation, Wad median, Sudan is intended for seed foundation which is recently established. This study was undertaken to establish statistical investigation using Bayesian estimation for credible interval or posterior interval as a Bayesian strategy for a maintenance variety. Data on grain yield (kg/ha) on a maize crop variety were used. Bayesian posterior information can be annoying to investigate but are important in maintenance varieties that foundational claims are used to make general recommendations for practice. Half normal informative priors set were used. The heritability of yield (varieties)) was (h = 0.75). Predicted posterior means of varieties were shown with a Bayesian interval for scientific inference in the maintenance maize grain yield. Bayesian approach is useful for reducing uncertainty on decisions based on economic evaluation of new maize varieties in Sudan, the use of credible intervals for grain yield allow for early decisions.

Int. J. Appl. Sci. Biotechnol. Vol 5(3): 390-396

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Published

2017-09-27

How to Cite

Omer, S. O., Abdalla, M. S., Alzain, I. N., & Dafaalla, A. (2017). Bayesian Credible Intervals for Maize Grain Yields of the Maintenance Varieties Evaluated in Sudan. International Journal of Applied Sciences and Biotechnology, 5(3), 390–396. https://doi.org/10.3126/ijasbt.v5i3.18303

Issue

Section

Research Articles: Biological Sciences