Multivariate statistical models that summarize initial large data sets into few uncorrelated components,
without substantial loss of information, have proven potentially suitable for a better understanding
of soil condition and its functioning. Principal component analysis has been used by several authors
for the identification of soil qualities (Bredja et al., 2000; Andrews et al., 2002; Cox et al., 2003). Yemefack
et al. (2006) identified soil pH, organic carbon, available P, and bulk density as the minimum data
set that can be used to evaluate SQs in shifting cultivation systems. In the present study, we intend i)
to identify causal factors of different maize (Zea mays L.) growth using controlled soil media and ii) to
statistically model SQs, their spatial variation, and their relationships to dry matter production under
increasing P supply.