جستجوی مقالات مرتبط با کلیدواژه « آزمون کولموگروف-اسمیرنوف » در نشریات گروه « کشاورزی »

It is important to find a simple connection between physic-chemical parameters and indices due to complexity and difficulties of using some indices. This research was performed to determine the relationship between physicochemical parameters and water quality indices

In this research, dominant water type of Gamasyab and GharehSou rivers was first determined using radial diagram. Then, to classify the water quality of two rivers, 14 quality water indices together with national standards for drinking water 1053 and WHO were used. Linear regression was used to determine the relationships between quality indices and physicochemical parameters. Finally, for the accuracy of the results of linear regression, the obtained relationships were tested for two other water sources (river and Gorgan Bay).

Based on hydrochemical results, the predominant type of water in both rivers is Ca-HCO3. Based on diagrams and criteria related to drinking and agriculture, the water quality of both rivers is in the desired range. The results of Langelier, Ryznar, Larsson-Skold and Puckorius indices showed that water quality for the industrial sector was relatively corrosive. Also, the water quality at the study stations is suitable for livestock drinking. The results of linear regression between quality indices with physicochemical parameters of water and some ion ratios showed that the resulting relationships for calculating quality indices can be used for different water types from fresh to saline. Also, comparing the results of linear regression simulations with water quality indicators, indicates the high accuracy of the relationships in water classification for different uses.

Spatial pattern of trees in a stand is the outcome of different processes such as their interactions, seed dispersal, and environmental heterogeneity. In consequence, knowledge of spatial pattern of plant species is important for deeper understanding of different hypotheses in ecology. Considering the importance, unbiased recognition of spatial pattern of plant species using reliable techniques is essential in ecology. Summary statistics used in point pattern analysis to explore spatial pattern of plants are based on the null model of homogeneous Poisson process (Complete spatial randomness: CSR). So it seems necessary to investigate homogeneity of tree spatial distribution before analyzing their spatial pattern. Considering this issue, this study was aimed to evaluate the homogeneity of tree spatial distribution in a mixed mesquite stand in Khalij-Omanian vegetation zone using two methods. Moreover, it was also aimed to investigate the impact of ignoring the CSR null model on spatial pattern analysis of trees in the plot.
A 49-ha study plot (700 m × 700 m) was selected in a mixed mesquite stand in Barchah protected area in Hormozgand province. The spatial location of all tree and shrubs with height > 0.5 m was registered and their height and crown area were measured. Furthermore, another plot with similar number of plants and environmental conditions was simulated with homogeneous spatial distribution and clustered spatial pattern of plants. Two methods of Chi-squared test based on quadrat accompanied with Pearson residuals and Kolmogorov-Smirnov test were applied to explore homogeneity of tree spatial distribution. Moreover, homogeneous and inhomogeneous pair correlation functions were used to analyze spatial patterns in two plots.
The study plot was covered by 498 trees and shrubs including 149 mesquites, 248 umbrella thorns (Acacia tortilis), and 101 desert thorns (Lycium shawii). The results showed that Chi-squared test only with 4 × 4 quadrats could recognize homogeneity of the simulated plot. Mean of Pearson residuals also did not characterize the homogeneity difference of two plots. While Kolmogorov-Smirnov test explored the difference between tree spatial distribution of the real plot (p-value
In general, it was concluded that Kolmogorov-Smirnov test is a reliable method to assess homogeneity of plant spatial distribution in the study area. Moreover, it was revealed that efficiency of g(r) in spatial pattern recognition of plants was influenced by homogeneity of their spatial distribution and application of inappropriate form of g(r) resulted in biased spatial pattern analysis in the study site.