Using multiple regression analysis, a

quadratic polynomial equation was constructed to predict JOMEs yield.

According to Figure 1B, the

quadratic polynomial model fitted best to the data, and maximum emergence speed index (ESI) was equal to 0.34 seedlings [day.sup.-1] for the cultivar Casado irrigated with 3.8 dS [m.sup.-1] water; to 2.8 seedlings [day.sup.-1] for the cultivar Meruinho irrigated with 1.98 dS [m.sup.-1] water; and to 4.32 seedlings [day.sup.-1] for the cultivar Ligeirinho irrigated with 1.63 dS [m.sup.-1] water.

However, a significant problem that remains is the following: letting an interphase property Mi(r) be approached by a

quadratic polynomial in the general form Ar2 + Br + C, there are three coefficients A;B;C needed to be found.

Design-Expert 8.0.6 Trial software was used to analyze the experimental data of response surface and the

quadratic polynomial equation was presented in

When n = 3, it states that the

quadratic polynomial passing through the three points ([x.sub.1], [y.sub.1]), ([x.sub.2], [y.sub.2]) and ([x.sub.3], [y.sub.3]) is

A

quadratic polynomial model was found the best for fitness to mean (y = a + b x GA + c x GA2).

The following models were used:

quadratic polynomial model, where [Y.sub.i] = [[beta].sub.0] + [[beta].sub.1]P[B.sub.i] + [[beta].sub.2]Lis[3.sub.i2] + [e.sub.i], where Y = is the dependent variable; [beta]0 = intercept; [beta]1 = linearcomponent parameter; and [beta]2 = quadratic-component parameter.

Good agreement between the experimental and predicted values verified the adequacy and the quality of fit of the

quadratic polynomial models.

xi.sub.1] + [e.sub.i]),

quadratic polynomial ([Y.sub.i] = [[beta].sub.0] + [[beta].sub.1][x.sub.i] + [[beta].sub.2][x.sup.

First, as shown in Table 1, the three

quadratic polynomial terms are jointly significant.

Therefore, we could adjust the motion parameters to obtain the coefficients of the

quadratic polynomial. Then, we can correct the range cell migration easily with a totally known expression of the RCM curve.

The exponential function, linear function, logarithmic function,

quadratic polynomial, and power functions were used to match the data, and the matching GFI (goodness of fit index) obtained from various functions are collected.