Hebron is located between longitudes 35˚05' East and latitudes 31˚32' North, rises from 700 - 1000 meters above sea level, and cover an area of 74.102 km². The geographic location of Hebron plays a major role in affecting the features of its climate and the biodiversity.

Bioclimate indicators are directly related to plant physiological processes determining productivity [5] - [12] [25] , and there have been several more recent classifications using bioclimate indicators to model terrestrial ecosystem distributions [26] , they are now mainly used in modeling the impacts of climate change on vegetation [27] [28] , and bioclimate classification of the earth [29] .

However, data were used from the meteorological station of Hebron for the years 1993 to 2014, (Figure 1) and for the same years for production of plant (Table 1). The study analyzed the relationship between bioclimate, climate factors (independent variables) and apricot yield (dependent variable) by using time series data belong to the climate variables such as mean monthly temperature (T), precipitation (P), soil water reserve (R), and deficit water (Df) during the time period of 1993-2014; and bioclimate factors as annual ombrothermic index (Io), simple continentality index (Ic), and compensated thermicity index (It/Itc), these factors were obtained according methodology of Salvador Rivas-Martinez [29] [30] [31] [32] [33] , nevertheless we used the following formulas to calculate the values of the various index Rivas Martínez [29] [30] , annual

Yield: Kg. dunum; T: Mean monthly temperature; P: precipitation; R: Soil water reserve; Df: Deficit water; Io: Annual ombrothermic index; Ic: Simple continentality index; and It/Itc: Compensated thermicity index.

ombrothermic index, Io = Pp/Tp; bimonthly summer ombrothermic index, Is2 = P July + August/T July + August; trimonthly summer ombrothermic index, Is3 = P June + July + August/T June + July + August; and continentality index, Ic = Tmax − Tmin; thermicity index, or where applicable compensated thermicity index, It/Itc = (T + M + m) 10. Pp = positive precipitation and Tp = positive temperature (in this case equivalent to annual precipitation and average annual temperature divided by 12, as all the months have an average temperature above 0˚); P = precipitation of the months indicated; T = average temperature of the months indicated; Tmax = maximum temperature of the averages of the warmest month of the year; Tmin = minimum temperature of the averages of the coldest month of the year; T = average annual temperature; M = average of the maximum temperature of the coldest month of the year; and m = average of the minimum temperature of the coldest month of the year.

Moreover, in this study, the Shapiro-Wilk and Jarque-Bera normality tests were applied [34] [35] [36] [37] , and the p-value was obtained for the seven variables. We applied analysis of variance (ANOVA) linear regression analysis to each of the eight variables (independent and dependent variables), the three bioclimatic variables (Io, Ic and It/Itc) and the four remaining physical variables as (climate factors), and the dependent variable (apricot production)to obtain the Pearson’s correlation matrix and the principal component analysis (PCA) were subsequently applied and find the correlation between variables and factors and to determine the influence of independent variables on production, and these statistical analyses were done using the XLSTAT software.

The correlation coefficient is always between −1 and +1, the closer the correlation is to +/−1, the closer to a perfect linear relationship in analysis, therefore if the correlation coefficient is equal (−1.0 to −0.7 a strong negative association, −0.7 to −0.3 weak negative association, −0.3 to +0.3 little or no association, +0.3 to +0.7 weak positive association, and +0.7 to +1.0 strong positive association), and the p-value was obtained with statistically significant (P = 0.05). However a correlation is a number between −1 and +1 that measures the degree of association between two variables, and Pearson correlation was developed by Karl Pearson from a related idea introduced by Francis Galton in the 1880s [38] [39] and the formula for the Pearson correlation is computed as:

r = ∑ i = 1 n ( X i − X ¯ ) ( Y i − Y ¯ ) ( n − 1 ) S X S Y (1)

According of the analysis in (Table 2) shows the Pearson’s correlation matrix between the variables studied, precipitation (0.974), annual ombrothermic index (0.919) and simple continentality index (0.890) were strong positive correlated to yield and growth activates of plant, a positive value for the correlation implies a positive association between the factors previous (P, Io and Ic) and the plant, whereas mean monthly temperature, deficit water, soil water reserve, and compensated thermicity index were negatively correlated between different variables, a negative value for the correlation implies a negative or inverse association between the factors of (T, R, Df and It/Itc) and the plant activities as growth, yield, sustainability, and a high correlation negatively was observed with mean monthly temperature (−0.953), deficit water (−0.705), soil water reserve (−0.300) and compensated thermicity index (−0.579). In the other hand, the correlation coefficient is a strong negative association or significance in case of the mean monthly temperature (−0.959) because correlation coefficient value is closed to −1 and weak negative association with the factors of deficit water, soil water reserve and compensated thermicity index.

Principal component analysis (PCA) is a useful statistical technique that has found application in fields such as face recognition and image compression, and is a common technique for finding patterns in data of high dimension, it covers standard deviation, covariance.

In this study, we applied correlation coefficient between variables and factors, the correlation coefficient is constrained for fall in the range ±1. A value of +1 tells us that the points (xi, yi) define a straight line with a positive slope. A value of −1 tells us that the points (xi, yi) define a straight line with a negative slope. A value of 0 shows that there is no dependence of y on x or vice versa (no correlation), and the p-value was obtained with statistically significant (P = 0.05). Moreover, in the (Table 3) shows the factor 1 was correlated by climate factors as precipitation (0.972) and bioclimate factors as annual omrothermic index (0.950) and simple continentality index (0.936), except compensated thermicity index is a negatively (0.492), and these factors were located at the left of axes F1 with accounts variance (70.10%) as in (Figure 2). Factor 2 is a high correlated by

soil water reserve (0.972), and bioclimate factors as annual omrothermic index (0.310) and simple continentality index (0.235) and compensated thermicity index (0.841), with a large proportion of the variance explained by axes F1 and F2 (93.50%) (Figure 2), but the rest of factors were correlated negatively because the tendency in the straight line of the variables was negative and below the y-axis, therefore this confirms that these variables have a negative impact on the production of apricot. Factor 3 is a high correlated by precipitation, deficit water, annual ombrothermic index, simple continentality index and compensated thermicity index, while negatively correlated by soil water reserve and mean monthly temperature and these factors were located at the right of axes F2 with accounts variance (29.90%).

Moreover, we observed that the variables that located in the area of the x-axis and above y-axis was a slope straight line for these variables positive meaning that it has a positive effect on the production of apricot, unlike variables that have occurred under the y-axis was negative, F1 is a positively affected by precipitation, simple continentality index and annual ombrothermic index on plant yield and a high correlated.

Analysis of variance (ANOVA), with a standard coefficients (95% confidence interval), applied to each of the apricot production, with the seven independent variable factors (T, P, R, Df, Io, Ic and It/Itc), reveals significant differences, in case of the bioclimate factors as simple thermicity index with the value (0.896) (Figure 3(a)) because the histograms were positive and regression coefficient positively R² (0.803) (Figure 3(b)), and climate factors as precipitation with the value (0.984) (Figure 3(c)) and regression coefficient R² positively (0.903), whereas there are no significant differences between the apricot yield and the reset of climate, bioclimate factors as compensated thermicity index (−0.579), because the histograms were a negativeand regression coefficient R² (0.335)and climate factor as deficit water (−0.659) and regression coefficient R² (0.483) (Figures 3(d)-(g)).