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Showing 2 results for Correlation Coefficients

G. Saeidi,
Volume 5, Issue 4 (1-2002)
Abstract

Flax (Linum usitatissimum L.) is an oilseed and widely adapted crop. Oil of regular genotypes of flax is used in industry because of its unique fatty acid profile. New, mutant genotypes of flax have oils similar to sunflower oil which can be used as edible oil. This experiment was conducted to investigate the genetic variation of agronomic traits and productivity in different edible and industrial oil genotypes of flax in Isfahan. The genotypes were evaluated in augmented design.

Based on the results, the average numbers of seedling/m2 in edible and industrial oil genotypes were 178 and 367 with variation coefficients of 70 and 10%, respectively. Maturity also showed variation and varied between 89 to 116 days in edible oil genotypes and between 89 to 128 days in industrial oil genotypes. Plant height varied between 57 to 86 and 49 to 73 cm in edible and industrial oil genotypes, respectively. Seed yield also had considerable variations and varied between 429 to 2651 and 779 to 2389 kg/ha with variation coefficients of 35 and 25 in edible and industrial oil genotypes, respectively. Seed yield per plant showed a high and positive correlation with basal branches (r=0.77**) and bolls per plant (r=0.93**), but high and negative correlation with stand (r=-0.66**). Regression analysis revealed that approximately 96% of the variation in seed yield per plant was attributed to variation in bolls per plant, seeds per boll and seed weight and they were determined as the major yield components, respectively. Bolls per plant was the most important yield component and contributed to 87% of the variation for seed yield per plant.


S. Ghorbani, R. Moddress,
Volume 23, Issue 3 (12-2019)
Abstract

The purpose of this study was to model the relationship between the frequency of dust storms and climatic variables in desert areas of Iran. For this purpose, climatic data of temperature (maximum and minimum), rainfall, wind speed (maximum and minimum), and their relationship with the number of days with dust recorded in 25 meteorological stations (statistical period since their inception until 2014) in summer using Pearson correlation coefficient and linear regression method multivariate was analyzed in SPSS software. Also, due to regional analysis, correlation coefficient between climatic variables and frequency of drought storms in desert areas of Iran, the mapping of these coefficients was prepared by method of Inverse distance weighting (IDW) in Arc GIS software. The results showed that the stations in the south and southwest of the study area have the highest dust incidence in the summer season. So that Zabul station with (3892 days) has the most frequent occurrence of dust storms. In most stations, there was a significant relationship between the frequency of dust storms and the variables of average wind speed and maximum wind speed. The highest correlation coefficient of the mean wind speed was related to the station of the Chabahar Konarak with correlation coefficient of 0.710 and Iranshahr station with a correlation coefficient of 0.65, showed the highest correlation with maximum wind speed. The maximum temperature variable at Qom station with a correlation coefficient of 0.398 shows a significant and positive relationship. Iranshahr station has a correlation coefficient of -0.620 with a mean temperature and Minab station has a correlation coefficient of -0.446 with maximum temperature. The results of temperature correlation with the frequency of dust storms indicate that ground low pressure is effective in creating the phenomena in the warm course of the year. Most stations have inverse correlation with precipitation. The highest correlation coefficients between precipitation and dust events were observed at -0.208 and -0.185 at east of Isfahan and Torbat Heidariyeh stations, respectively. Multivariate regression modelling between dust and climatic variables in summer also shows that the most important parameter in dust events are average wind speed, maximum wind speed and average temperature. Regression models show that, at the best condition, climate variables explain only 70% of the variation of dust frequency.


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