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Apricot (Prunus armeniaca L.) is one of the most important export crops in Hebron of Palestine. In this work, we analyzed the mean monthly temperature and precipitation using data from one weather station of the Palestine Meteoro-logical Department, recorded in the period from 1993-2014, with the same years of plant production (rain-fed) from the Palestinian Central Bureau of Statistics (PCBS). Statistical analysis included a bioclimatic analysis of Palestinian meteorological station for the period previous by using bioclimatic classification of the Earth of Rivas Martinez Salvador, with regard to simple continentality index, compensated thermicity index, annual ombrothermic index, water deficit and soil water reserve. In concluded, climate and bioclimatic factors play a key role in apricot production, when we analyzed of variance (ANOVA), with a standard coefficients (95% confidence interval), reveals significant differences, in case of the bioclimate factors as simple thermicity index and climate factors as precipitation, whereas there are no significant differences between the apricot yield and the reset of climate, bioclimate factors. However, the optimum of the plant production is achieved with values of simple thermicity index between (14-18), annual ombrothermic index (2.5-4.5), compensated thermicity index (250-450), precipitation more than 750 mm, mean monthly temperature (15.4℃ - 20℃), and inframediterranean, thermomediterranean and mesomediterranean of thermotype in Hebron of Palestine.

Apricot (Prunus armeniaca L.) is a tree that bears the fruit of several species in the genus Prunus (stone fruits). It is belong to the Rosaceae family, the native is somewhat uncertain, though almost certainly somewhere in Asia, it was known in Armenia during ancient times, an archaeological excavation at Garni in Armenia found apricot seeds in an Eneolithic-era site [

However recent studies have found the impact of bioclimate and climate factors on yield, harvesting time, planting and flowering of apricot [

Many studies have been shown in 2013, that Turkey, Italy, Iran, Uzbekistan, Algeria and others countries have a high production of apricots in the world [

The objective of the study is to find the relationship between the influence of bioclimatic, climatic factors on apricot production to contribute and increase the production of Apricot in Hebron, and to increase the Palestinian national economy and for the participation in the development of strategic agricultural policy.

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 km^{2}. 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 [

However, data were used from the meteorological station of Hebron for the years 1993 to 2014, (

Years | T | P | Df | R | It/Itc | Ic | Io | Production of apricot |
---|---|---|---|---|---|---|---|---|

1993-1998 | 16.2 | 601 | 650 | 430 | 298 | 18.1 | 2.9 | 305 |

1998-2002 | 16.4 | 595 | 608 | 420 | 311 | 17.6 | 3.3 | 380 |

2002-2006 | 16.5 | 596 | 570 | 455 | 350 | 17.3 | 3.1 | 340 |

2006-2014 | 16.8 | 595 | 611 | 411 | 400 | 17.7 | 3.0 | 350 |

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 [

However, we used the bioclimatic classification of earth to Salvador Rivas-Martinez to analyses of the climate factors and bioclimatic parameters (independent variables), Rivas Martinez methodology [

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 [

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

According of the analysis in (

Variables | T | P | Df | R | It/Itc | Ic | Io | Production of apricot |
---|---|---|---|---|---|---|---|---|

T | 1 | −0.955 | 0.638 | 0.174 | 0.401 | −0.980 | −0.964 | −0.953 |

P | −0.959 | 1 | −0.555 | −0.454 | −0.674 | 0.861 | 0.844 | 0.974 |

Df | 0.638 | −0.555 | 1 | −0.380 | 0.012 | −0.683 | −0.787 | −0.705 |

R | 0.174 | −0.454 | −0.380 | 1 | 0.915 | 0.004 | 0.083 | −0.300 |

Itc | 0.401 | −0.674 | 0.012 | 0.915 | 1 | −0.208 | −0.178 | −0.579 |

Ic | −0.980 | 0.861 | −0.683 | 0.004 | −0.208 | 1 | 0.988 | 0.890 |

Io | −0.964 | 0.844 | −0.787 | 0.083 | −0.178 | 0.988 | 1 | 0.919 |

Apricot production | −0.953 | 0.974 | −0.705 | −0.300 | −0.579 | 0.890 | 0.919 | 1 |

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.

Correlations Coefficient between Variables and FactorsIn 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 (

Variables | F1 | F2 | F3 |
---|---|---|---|

T | −0.982 | −0.056 | −0.170 |

P | 0.972 | −0.239 | 0.017 |

Df | −0.709 | −0.531 | 0.464 |

R | −0.227 | 0.972 | −0.053 |

Itc | −0.492 | 0.841 | 0.233 |

Ic | 0.936 | 0.235 | 0.239 |

Io | 0.950 | 0.310 | 0.105 |

Apricot production | 0.992 | −0.088 | −0.076 |

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%) (

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) (^{2} (0.803) (^{2} 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 R^{2} (0.335)and climate factor as deficit water (−0.659) and regression coefficient R^{2} (0.483) (Figures 3(d)-(g)).

The climate and bioclimatic factors were influenced on apricot production and economic of Hebron, when we analyzed of variance (ANOVA), with a standard coefficients (95% confidence interval), reveals significant differences, in case of the bioclimate factors as simple thermicity index and climate factors as precipitation, whereas there are no significant differences between the apricot yield and the reset of climate, bioclimate factors as compensated thermicity index and climate factor as deficit water.

The optimum of the plant production is achieved with values of simple thermicity index between (14 - 18), annual ombrothermic index (2.5 - 4.5), compensated thermicity index (250 - 450), precipitation more than 750 mm, mean

monthly temperature (15.4˚C - 20˚C), and inframediterranean, thermomediterranean and mesomediterranean of thermotype in Hebron of Palestine.

Ighbareyeh, J.M.H. and Carmona, E.C. (2017) Impact of Climate and Bioclimate Factors on Apricot (Prunus armeniaca L.) Yield to Increase Economy and Achieve Maintaining Food Security of Palestine. Open Access Library Journal, 4: e4119. https://doi.org/10.4236/oalib.1104119