Prognosis of frosting occurrence almond orchards in Najafabad region

Introduction Nowadays one of the greatest difficulties in agricultural section is effect of Weather factors on agricultural crops. Purpose of this research is Prognosis of frosting occurrence almond orchards in Zayandeh Rud Basin. Freezing can damage on agriculture crops on the condition that it was prolonging and intense. Not only frost phenomenon and consequent damages of it in my country but also exist in world countries mostly. And mostly on time and accurate forecasting of occurrence time, by using confront methods with this Atmospheric phenomenon, can reduce damages of it. Though about frostbite many studies have been done but the majority of this research just this phenomenon from the perspective of the weather and more specifically the Productive synoptic patterns have checked. While at the agricultural scale Great view Often Useless results and it is necessary With the help of regional models, These predictions scale down On scales spatial and temporal (Downscaling). The purpose of this research is combining two phonological and meteorological models (WRF) In order to predict the phenomenon of spring frost in almond gardens of the region. Materials and Methods Case of study in this research is in almond orchards in Najafabad region and Zayandeh Rud Basin. Zayandeh Rud Basin and research area geographical coordinates has occurred at 50 degrees and 20 minute until 52 degrees and 24 minute eastern longitude and 31 degrees and 12 minute until 33 degrees and 42 minute northern latitude. Since the concept of cold damage in agriculture Regardless of product development stages (phenology) and only the temperature test has no practical value therefore, it was necessary first to estimate the model of Almond flowering stage develops in studied gardens to its results Combine with weather forecasting model (WRF). In this research, according to the phenology long-term statistics of almond trees in the Najafabad region, Extracted flowering dates were calculated based on Julian Day dates. Then the GDD matrix table (Table 1) is plotted. Integration of the WRF model with the model of phenology This table contains various temperature parameters, Next, the relationship between flowering history it is evaluated with the parameters mentioned. And the strongest relationship is chosen. Here the flowering is dependent variable and the other above mentioned is independent variable. The most relevant regression equation is determined with high correlation coefficient. Now according to the WRF model which it’s testing in the study area was performed by Nasr Esfahani et al and predicted model temperature was statistically significant, a quick warning can be made of the occurrence of frostbite in the coming days for the flowering date which is explained in the flowchart above. Results and Discussion According to the phenological data of Najafabad station, the flowering date for each year and based on Julian Day, in the statistical population of 10 years (2006-2017) was analyzed. The average flowering period is eighty days (80) days Juliusi. It should be noted that in 2010 the earliest date of flowering. The years 2007, 2011 and 2014 have the highest flowering time compared to the year. That flowering in 2007 twelve days, 2011 ten days, 2012 is fifteen days, 2014 is eight days and 2017 is six days later than the average. Among the existing parameters, between the flowering date and the number of days above the mean of the highest correlation of 0.945. This is a positive relationship and gives us that the number of days above the average accelerates the flowering of almonds. And the value of P-value Where X1 is the number of days above the average and X2 is the number of days below the average. Table 4 (Model Summary) is used to select the best model. Table 4 defines the coefficient for both models. It is noted that the modified coefficient of the second model is determined which has two independent variables, is 0.998 which is higher than the first-order correction coefficient of 0.88. It can be concluded that the second model is better than the first one. Table4: Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate Model 1 .945a .893 0.880 4.65497 Model 2 .999 b .998 .998 0.64924 Conclusion As we have seen, the linear multivariate regression equation was significant for predicting flowering history at 1% level. Now with the temperature prediction by the WRF model And calculating the number of days above the average and below the average the flowering date can be obtained and warns of frostbite in the presence of flowering Similar to this study, Prabha and Hoogenboom (2008) showed that Use the WRF Intermediate Scale for effective protection management to protect products and reduce frost damage, is a strategy. The results of this study are the feasibility and accuracy of the WRF model for radiation and radiant frost warning Indicates. Considering the daily temperature test output of the WRF model in the studied area which is very good in smooth areas it can be combined with two phenology models and the prediction of temperature by the WRF model, a quick warning of 48 hours of frostbite occurred in the gardens of the area with sufficient accuracy. Keywords: Phenology Models, WRF model, Flowering date, Temperature Forecast