African Journal of
Biotechnology

  • Abbreviation: Afr. J. Biotechnol.
  • Language: English
  • ISSN: 1684-5315
  • DOI: 10.5897/AJB
  • Start Year: 2002
  • Published Articles: 12278

Full Length Research Paper

Analyses of spatial variations of kenaf in experimental field

Dauda, T. O.1*, Asiribo, O. E.2, Adekeye, K. S.3 and Agbaje, G. A.1
1Institute of Agricultural Research and Training, Obafemi Awolowo University, PMB 5029, Moor Plantation, Ibadan, Nigeria. 2Statistics Department, University of Agriculture, PMB 2240, Abeokuta, Nigeria. 3Statistics Department, Redeemers’ University, Mowe, Ogun State, Nigeria.
Email: [email protected]

  •  Accepted: 01 October 2009
  •  Published: 08 March 2010

Abstract

Preliminary investigations of experimental field usually involve collection of soil samples at widely spaced locations which are patchily or globally at variant spatially. This study was carried out to evaluate spatial variations in experimental fields using a split plot experiment distributed in a completely randomized design at Ikenne and Ilora between June and September 2006 (test crop was kenaf). The preliminary descriptive statistics suggested the dependency of the stem girth and height on the spatial positions. The variance - covariance analyses matrices of the plots showed that stem girth and plant height were independently distributed and exhibited a non stationarity principle. The results also revealed that spatial autocorrelation exists in patches in the experimental fields while the entire plots showed random distributions because the autocorrelatons were neither dominated by negative nor positive correlation and more than 50% of these values falls within the range of ± 2/√n. From this study, a regionalized spatial variation is imminent in 625 m2 experimental plot despite the difference in the treatments. Spatial variations study was found necessary in any plot not more than an acre (250 m2) of land otherwise such variations should be treated as block or environmental effect(s).

 

Key words: Spatial variation, mean growth parameters, classical statistical analysis.