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Step one in commercialisation of smallholder agriculture is participation, which is significantly influenced by yield; the second step is selling at remunerative sources (market centres), which is influenced by these three variables (yield, FBOs and price).
Thus, a strong relationship is expected between tsetse abundance and the remaining wet areas represented by these three variables.
The total amount of variation in LoT explained by these three variables was 29%.
For observed heterozygosity, ~16% of the variation was explained by these three variables plus the mean number of covering reads.
Because underlying factors such as age, comorbidity, and setting of care lead to variation in the costs of treating CAP, the data were stratified by these three variables.
In multiple regression analysis, 31% of the f-IGFBP1 variation at baseline could be accounted for by these three variables (P<0.001).
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Baseline age, experimental group, and baseline physiological falls risk were statistically controlled by entering these three variables into the regression model first.
The model was able to classify 70% of the adolescents correctly (i.e. being assigned to the clinical or the control group) by using these three variables.
In models of the diagnosis-to-death interval restricted by any one of these three variables, HRs were as follows: for blood lead, 0.9; for patella lead, 0.3 0.5; and for tibia lead, 0.2 0.4.
Finally, BMI, NT-proBNP and HDL cholesterol were introduced in the univariate ANOVA models as covariates; this choice was supported by the fact that these three variables have proven to be the strongest predictors of AD, as previously reported by us [ 19] and others [ 3, 13].
The between-trial variance for this model was very small, indicating that most between-study variability was explained by these four variables.
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