A high value of r as +0.9 or - 0.9 only shows a strong association among the two variables but doesn't imply that there is a causal relationship that is change in one variable causes change in the other it is possible to find two variables which produce a high calculated r until now they don't have a causal relationship. This is termed as nonsense or spurious correlation for illustration high pass rates in QT in US and increased inflation in Asian countries.
Also note that a low correlation coefficient does not imply lack of relation among variables but lack of linear relationship among the variables for illustration there could exist a curvilinear relation.
A further problem in interpretation arises from the fact that the r value here measures the relationship between a single dependent variable and independent variable, where as a particular variable may be dependent on several independent variables for illustration crop yield may be dependent on fertilizer utilized, soil exhaustion season of the year, , soil acidity level, type of seed. Whether case multiple correlations should be utilized instead.