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Die Steigerung der landwirtschaftlichen Produktion in Afrika nach dem Muster der Grünen Revolution in Asien führt aufgrund anderer Umweltbedingugnen zu weiterer Erosion und Desertifikation. (DSE)

Multiple ANOVAs applied to simultaneous multimodality measures often result in: (1) excessive alpha error, (2) loss of experimental power, and (3) loss of information regarding interdependence of the dependent variables. The appropriate solution, MANOVA, is not designed to extract maximum information from data. Two multivariate post-comparison techniques are presented to respond to this void: (1) Univariate F Tests which evaluate the impact of significant independent variables on each dependent measure and (2) Modified Discriminant Analysis which isolates the impact of each level of the significant independent variable on the aggregate dependent measures, while transformation of normalized weights of the characteristic vector into standardized weights determines the relative sensitivity of the dependent variables to main effects. Recommended post-comparison techniques are described and applied to representative research. An efficient computer package is proposed to facilitate computation of MANOVA, univariate F tests, characteristic roots, corresponding linear discriminant functions, and standardized discriminant weights.

The use of Analysis of Variance (ANOVA) for data characterized by lower order trends is inefficient and wasteful of statistical power. Significance of an effect may be diluted because ANOVA is responsive to higher order components in the data which are not likely to be significant. Polynomial Regression Analysis (PRA) is recommended for precise detection of significance and determination of the nature of the relationship.