Revisiting Application of Statistics in Agricultural Research in Sub-Saharan Africa: Entry Points for Improvement
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Date
2019
Authors
Journal Title
Journal ISSN
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Publisher
African Crop Science Journal
Abstract
The importance of statistics in empowering the agricultural research process and sharpening
interventions cannot be over-emphasized. Undocumented evidence points to misconceptions, misuse
or underuse of statistics among agricultural researchers in sub-Saharan Africa (SSA); pointing to the
possibility that the subject has been part of the causes the unfulfilled targets in the agricultural sector
in the region. The objective of this study was to analyse and document weaknesses in statistical
practice in agricultural research, with a view to identifying entry points for strengthening the
performance of the sector for SSA to be able to achieve its set goals. A desk study involving 165
research articles published in the African Crop Science Journal over the period of 17 years (2000 to
2017) was conducted through a rigorous SWOT analysis for issues related to the use of statistics in
the implementation of agricultural research in SSA. A checklist consisting of key elements related to
study design; data collection, analysis and exploitation; and presentation, was used to guide the
interrogation. Findings indicated that researchers generally made explicit description of treatment
structures that fairly matched the study objectives and hypotheses (in the few cases where they were
stated), with a few weaknesses in the description of factorial treatment structure. The Randomised
Complete Block Design was most commonly used among the designs, with 3-4 replicates. However,
there was hardly any justification for its use, as the blocking factors were never mentioned and thus
their role in determining the precision of the results was difficult to determine. Analysis of Variance
was the main method for data analysis, followed by correlations. The F-test and the associated Pvalues
were the basis for decisions on treatment differences. Most researchers had problems with
presentation and interpretation of P-values and significance level. Post adhoc tests mostly used the
Least Significant Difference (LSD) for pairwise mean comparisons, with little consideration for the
treatment structure, the number of treatments and the nature (qualitative or quantitative). Generally,
estimates of treatment means were presented together with various measures of precision, in both
tables and graphical forms. In several cases, LSD was used or misused interchangeably with standard
error (SE) or standard error of difference (SED). Several statistical software were used for data analysis
and presentation, with the main ones being SAS, Genstat and MSTAT-C. Key entry points for
improvement heavily lie in human and infrastructural resource capacity improvement, most specifically
in (i) periodic review of university and other tertiary institutions’ curricula to provide sufficient time allocation, physical space and relevant infrastructure for true hands on practice; (ii) more effective
utilization of the few statisticians available in the region, (iii) short term staff in-service retooling
courses, (iv) sustained statistical service units wherever necessary, and (v) provision for periodic
interactive statistician-researcher platforms (such as conferences and workshops) for sharing notes
on challenges and achievements during implementation of their research programmes.
Description
Keywords
Experimental design, P-values, SWOT analysis
Citation
Odong, T. L., Tenywa, J. S., & Nabasirye, M. (2019). Revisiting application of statistics in agricultural research in Sub-Saharan Africa: entry points for improvement. African Crop Science Journal, 27(3), 529-544. https://dx.doi.org/10.4314/acsj.v27i3.14