Investigating the Effect of Climate Change on the Quality of White Seedless Grapes (Case Study: Golmakan Agricultural Weather Station)

The purpose of this articleCheck it outThe effects are wider on temperature and rainfall parameters AlsoIf any of these islamic parameters qualifyGrapesWhite seed in Golmkan area of Khorasan Razavi. One of the major effects of climate change is its impact on the quality of agricultural products. Grapes are also one of the strategic agricultural horticultural products.And the semi-arid climate of Iran is highly vulnerable to future climate change, so it is an appropriate and low-cost alternative to this type of study to achieve grape crop responses to climate change by developing modeling techniques.Golmakan, a city of Golbahar section of Chenaran city in Razavi Khorasan province, 45 kilometers from Mashhad, covers an area of 2000 thousand hectares and is 1176 meters in height at 36, 29 and 59.17 meters in height. According to the Cold Desert dry climate, the climate is also classified as Cold Dry by climate.

Currently the customer is producing equipment for the three-phase circulation converter of the Air-Ocean Circuit.What are the rules (provided by mathematical relations).Headcm3 model, designed and studied in the country by Ngelis and the Hadley Center Institute.And 360 days suitable for RCP8.5 and RCP4.5 scenarios.Daily temperature and precipitation values based on Hadcm3 models in baseline and future under RCP4.5, RCP8.5 scenarios. in a more complete way, the DownscalingWith the use of the change factor approach for Golmakan region, were scaled And using station data, scaled Downscaling to baseline quality by Perceptron Neural Network in A2019 Content for the Future.Multilayer neural network approach was used in this study. Multilayer neural networks have three input, middle and output segments. The middle layer corresponds to constraints. The first layer is removed from the middle section, extra data or out of the dataset; in the second layer, the data is normalized using operational definition.After defining variables and intermediate layers, the quality data is input to the neural network through the input layer and after performing the middle layer corrections, using the function definition, they exit the neural network output layer.This output consists of two parts: the first part teaching (or learning) and the second part exam. In this study, 70% of the data were used in the training section and 30% in the default test section. Pearson correlation test and Spearman and Chi-square tests were used to assess the relationship between each of these variables.In both sections, there was a significant relationship between the quality variables and the quantitative variables.Therefore, descriptive tests were used to assess the relationship between each of these variables.These tests included the Pearson correlation test and Spearman and Chi-square tests.After knowing the positive relationship between grape quality variables and basic meteorological data, the simulation process continues.

Climate models increasing temperature and decreasing rainfIn the future under RCP4.5, RCP8.5 scenarios Shows the ratio to the baseline period.The maximum temperature increased by 3.9 and 4.7 ° C, respectively, while the minimum temperature increased by 3.8 and 4.4 ° C, respectively, and the rainfall decreased by 0.3 and 0.8%, respectively.Pearson test showed a significant relationship between the variables.That is, each of the independent variables included min, max temperature, and precipitation Each of the dependent variables represented tree age, sugar content, panicle weight, panicle size, fruit length, fruit width, acidity, pH and TSS. Both scenarios represented panicle weight, panicle size, fruit length, fruit width, sugar, HP, TSS, Brix, acidity and weight of berries. It is predicted to decrease.In RCP8.5, the change rate is greater than in RCP4.5. Features: Juice color, meat color, fruit flavor, warehousing, marketability and transportation are unchanged in two scenarios.T-test for most variables: pH, sugar, acidity, panicle weight, fruit length and panicle length were significant in two scenarios.In the variables of berry weight and fruit width in two scenarios 4.5 and 8.5, cluster size of scenario 8.5 and length in berry scenario 4.5 were not significant.

The results show that as a result of rising temperatures and decreasing rainfall in the future climate, some components of grape quality will change in the future.Such studies provide the opportunity for agricultural managers and practitioners in the relevant agencies to take appropriate measures, such as the proper location of gardens, to determine appropriate and appropriate future climate patterns to mitigate the potential adverse effects of new methods and practices. Provide adaptation to new and changing climate conditions.