clustering

As one of the most influential cereals, wheat is among the most important food sources in Asian countries, including Iran, which has more cultivated area than other plant crops in Iran. Durum wheat is a type of tetraploid wheat that is particularly important for use in the food industry, especially pasta production. Durum wheat is a good source of dietary fiber, protein, and a wide range of vitamins and minerals such as iron, magnesium, and B vitamins, making it a healthy and nutritious food. This product also has a special economic importance for Iran and is a strategic product that has a significant impact on the country's agricultural economy. Based on the stability and productivity of plants in changing environmental conditions, plant breeders can develop products that are more flexible and have good stability even in the face of climate change and other environmental challenges. The upcoming study aims to investigate the durum wheat genotypes produced by Iranian breeders and their response to environmental changes to make it possible to introduce these genotypes as cultivars that can be planted by farmers.

To verify the feasibility of introducing new durum wheat varieties with high yields and stable performance in different environmental conditions, 18 genotypes of durum wheat along with two control varieties (Hana and Parsi) were studied in four crop years at the research station of the Agricultural Research and Training Center and The natural resources of Kermanshah Province (Islamabad West Agricultural Research Station) based on a completely randomized block design in three replications and four consecutive years from 2013 to 2016. After adjusting the data, composite variance analysis was performed considering year × genotype in relation to grain yield. Due to the significance of the genotype × environment interaction effect, the averages were compared for genotypes and the environment, as well as for their interaction, and stability analysis was calculated by univariate and multivariate methods. The univariate methods included environmental variance parameters (S2), coefficient of environmental changes related to all investigated environments (CV), Rick's equivalence (W2), Shukla's stability variance (Shukla-Var), regression coefficients based on the Eberhart-Russell model to analyze the genotype × environment interaction effect on the regression components (b), the standard error value or deviation from the regression line in the Eberhart-Russell model (Sd), and the explanatory coefficient value of the regression model in the Eberhart-Russell model (R2). Moreover, multivariate methods, including the AMMI method, the GGE method, and the heat mapping method, were used to analyze the stability of genotypes. SAS software was used for calculations related to composite analysis of variance (ANOVA) and mean comparison. Univariate stability calculations were done using the codes written by the authors in the matrix language of SAS software, which is known as Interactive Matrix Language. R software and the agricolae library were used for calculations related to multivariate methods in AMMI and GGE models. Heat mapping was also done in R software with the ggcorrplot library.

The composite ANOVA in this research showed the significance of the main effects of genotype and environment alone and the genotype × environment interaction effect in grain yield. Due to the significant genotype × environment interaction effect, the response of genotypes to different environments is different. Thus, analysis of stability was carried out by univariate and multivariate methods to allow for the introduction of new durum cultivars with high potential in terms of grain yield and production stability in different environments. The heat mapping results showed that this method could separate the three environmental groups and confirmed the average comparison results. The number of separated groups of genotypes based on experimental environments also included four different groups. The different stability methods used in this research also showed differences in relation to the stability and sensitivity of genotypes. Therefore, the ranking method was used based on different sustainability models. Accordingly, genotypes 3, 13, 14, and 16 produced high final yields and good stability (above average), and genotypes 18 and 19 showed high stability and good total yields based on all stability methods. The first four genotypes, with good stability, produced a higher average yield than both controls, but the next two genotypes, with high stability, showed a higher yield than the Hana variety and less than the Parsi variety.

Different methods of univariate stability, including environmental variance, coefficient of environmental change, Shokla variance, the regression sum of squares method, regression coefficient, the residual of the regression model, and explanatory coefficient, along with multivariate methods, including AMMI and GGE models as well as the heat mapping method were examined to estimate the stability and the response of 18 durum wheat genotypes along with two control varieties (Hana and Parsi). It was found that heat mapping had a good performance in assessing the response of the genotypes to environmental conditions and could separate three environmental groups, which confirmed the results of the average comparison. The number of separated groups of genotypes based on experimental environments included four different groups. Based on the stability analysis results, genotypes 14, 16, 13, and 3 produced high final yields and good stability (above average) based on all stability methods, and genotypes 19 and 18 showed high stability and good total yields (above average). The first four genotypes, with good stability, produced higher average yields than both controls, but the next two genotypes, with high stability, showed higher yields than the Hana variety and less than the Parsi variety. Finally, it is suggested that these six genotypes enter the regional research-promotion tests for further investigation so that the most suitable ones are finally introduced as a new variety of durum wheat.

The global warming trend has raised concerns about the state of water resources. In this study, to examine the impact of climate change on the precipitation pattern of Ilam Province, the outputs of 12 CMIP6 models were utilized. Considering the SSP1-2.6 and SSP5-8.5 climate change scenarios, precipitation variations in Ilam Province were analyzed up to the year 2050. After clustering the rain gauge stations (Cluster 1: South and East of the province, Cluster 2: North and West of the province) and evaluating the performance of CMIP6 models, the IPSL-CM6A-LR and ACCESS-CM2 models were selected as the best models for Cluster 1, while the IITM-ESM and BCC-CSM2-MR models were chosen for Cluster 2. The model outputs indicate that in the future period (2018-2027), for Cluster 1, under the SSP1-2.6 scenario, the average annual precipitation will decrease by 4.1% to 303.52 mm per year. Under the SSP5-8.5 scenario, this reduction will be 4.7%, bringing annual precipitation to 301.56 mm per year. For Cluster 2, under the SSP1-2.6 and SSP5-8.5 scenarios, the average annual precipitation will increase by 3% and 2.8%, respectively, rising from 431.10 mm per year to 444.04 mm per year (SSP1-2.6) and 443.31 mm per year (SSP5-8.5). By selecting the best probabilistic distribution for each rain gauge station, it was found that under both climate change scenarios and different return periods, the maximum 24-hour precipitation in most cases will decrease in the future period. Therefore, this highlights the importance of developing sustainable water supply strategies for the province.

Introduction and Goal:

The decreasing trend of precipitation in many regions of the country, including the northwestern provinces, has made it more necessary to investigate new methodologies to complete the databases of the country's watersheds. In this regard, understanding the changes in precipitation barycenters, as an emerging concept, that indicates the long-term spatial distribution of regional precipitation is especially important for the administration of watershed management issues such as reducing drought effect, flood control, and water resource conservation. To this end, the present study analyzes the precipitation barycenter and its relationship with the spatial distribution of the rain gauge network in Ardabil Province.

To calculate the precipitation amount, the statistics of 49 meteorological stations during the statistical period of 45 years (1971-2016) were used. Based on the principle of station extraction and considering the rationality of the lowest density and uniformity, five stations were determined as the minimum possible number. Then, densities of 10, 20, 30, and 40 stations were extracted randomly to compare the distribution of precipitation centroids with the density of the station network. The statistics above were used to implement the gravity center model, calculate the standard deviation ellipse in the ArcMap 10.8 software environment, and conventional correlation analysis on monthly, seasonal, and annual time scales using XLSTAT.

The results showed that in spring (April to June), the direction of movement of the precipitation barycenter was significantly different and the movement distance was 18.17 km. In summer (July to September), the precipitation barycenter mainly moved 20.18 km to the north. In autumn (October to December), the precipitation barycenter mainly moves 20.49 km to the south and has the longest movement distance among the seasons. In winter (December to March), the precipitation barycenter moves in different directions and has the smallest movement distance (8.35). The annual precipitation barycenter migrated mostly in the southeast direction. The maximum migration of barycenter for 1979 was towards the northwest with 111.78 km. Besides, the annual precipitation barycenter in Ardabil Province in the three decades of 1971-1981 migrated mostly to the northwest and in the other decades to the southeast. The stational network density was positively correlated with changes in the precipitation barycenter (CCA = 0.65 at a significance level of 5%).

Conclusion and Suggestion:

The highest conventional correlation coefficient of 0.80 was obtained for the density of 40 stations among different densities of 5, 10, 20, 30, 40, and 50, and the lowest coefficient of 0.04 was obtained for the minimum density of 5 stations. Based on the results of this study, changes in the movement of precipitation patterns that can be effective in drought and wet periods were identified. It is suggested that the relationship between climate change and elevation factors with spatial-temporal changes in the precipitation center of gravity be investigated.

Nowadays, due to the availability of large amounts of data, data analysis approaches have shown their potential to solve some problems in different economic sectors, for example, the concept of big data analysis has entered various disciplines, such as insurance, banking, agriculture, and environmental studies. Data analysis is performed using one of these three methods, i.e. regression analysis, clustering, and classification. To estimate the relationship between one or more independent variable(s) and a single dependent one, the regression analysis is used; a set of methods that allow the grouping of different agricultural objects is performed by clustering; and classification aims to categorize objects based on their properties, which are called predictors. Some categories of software tools are used for big data analysis such as image processing, machine learning, cloud-based platforms for large-scale information storing, analysis and computation, GIS systems, modeling and simulation, statistical tools, and time-series analysis. The R programming language, an open-source software, is a powerful platform to conduct big data analysis requiring machine learning processes, and statistical operations. This software also acts as a suitable tool for data visualization.

The aim of this research is the development of spatio-temporal maps of the value of agricultural crops (in five groups named cereals, legumes, industrial crops, vegetables, and fodder crops) produced in provinces of Iran using the tmap package of the RStudio software. The raw data used in this study was obtained from statistical tables presented by the Ministry of Agriculture Jihad statistics center about the value of different agronomy crops produced in 31 provinces from 2016 to 2020. Furthermore, in this study, some statistical methods were used to compare provinces based on the spatial-temporal deviations of agricultural crops produced from Iran’s official cultivation pattern. The clustering methods utilized herein were the K-means and K-medians methods of the partitioning clustering paradigm, and a hierarchical clustering method.

According to the results of this study, large deviations were recorded for Gilan, Alborz, and Yazd provinces followed by Bushehr, Tehran, and Qom provinces. Numerically, average deviations for the three leading provinces were 72.5%, 72%, and 70.95%, respectively. Furthermore, the average absolute deviations of crop yields in the cereals and vegetables categories from the official crop pattern were 5.9%, and 11.7% respectively; while similar measures for the legumes, fodder, and industrial crop categories were 30.2%, 36%, and 41.2% respectively. Moreover, clustering Iran provinces using the K-means and K-medians methods showed that by increasing the number of clusters, the results of these methods converge. Finally, from the practical vantage point, if the clustering curve contains clusters having central symmetry, by exchanging the cultivation patterns of these clusters, the yields of agronomy crops will be changed in the direction of matching the suggestions of the official cultivation pattern.

It is concluded that the existence of reliable input data of agricultural crops produced in provinces, the creation of spatial-temporal maps, and clustering provinces based on the deviations of crops produced in them from the official cultivation pattern helps main decision makers to obtain an appropriate view to establish suitable laws in compliance with matching the real production of agricultural crops with the suggestions of the cultivation pattern.

As one of the most important plants of the cereal family, wheat plays a very important role in world food security. The problem of food security in the world is facing a serious challenge due to severe climate changes and a lack of supplementary irrigation resources. Understanding the responses of plants to drought is the main part of the development of stress-resistant cultivars. The relative performance of genotypes under drought stress conditions and optimal conditions is one of the necessary and preliminary points in choosing the optimal genotypes for dry conditions. This study was conducted with the aim of identifying the relationships between indicators and determining the best indicators of drought tolerance, as well as selecting the best wheat lines suitable for cold and rainy regions for future breeding programs.

In order to identify the relationships between drought tolerance indices and determine the best index as well as select the best lines for future breeding programs, 144 wheat lines in the form of Alpha Lattice design in two replications and in two conditions of rain and supplementary irrigation in The rain research station of Zanjan Agricultural Research Center were investigated. Different tolerance and sensitivity indices including relative stress index (RSI), stress sensitivity index (SSI), stress resistance index (SRI), performance index (YI), performance stability index (YSI), relative reduction index (RR), Abiotic Stress Index (ATI), Tolerance Index (TOL), Mean Production Index (MP), Stress Tolerance Index (STI), Geometric Mean Productivity (GMP), Stress Sensitivity Percentage Index (SSPI), Harmonic Mean (HM) ), the index of product of stress and non-stress environment (SNPI) and yield loss ratio (PRR) were calculated.

The results of combined variance analysis showed that the effect of wet and rainy environment on the performance of lines was significant at the level of 1%. Also, the lines showed a significant difference in grain yield at the level of 1%. Drought stress decreased the grain yield of most lines in dry conditions. Line 82 had the highest yield in potential (2876 kg/ha) and rainfed (2000 kg/ha) conditions. Line 21 also had the lowest performance in two conditions (rainfed 825 and supplementary irrigation 1113 kg/ha respectively). Correlation results showed that grain yield in supplementary irrigation conditions had a positive and significant correlation with MP (0.56), GMP (0.56), HM (0.56), YI (0.55) and TOL (0.56) indicators. had 0.53). In the conditions of stress, MP (0.54), GMP (0.54), HM (0.54), and YI (0.53) indicators had the highest correlation with performance. Based on the first two selected components resulting from the decomposition into main components, which justified 91.35% of the changes, lines 90 and 102 were selected, which were also confirmed based on the three-dimensional diagram. The results of the cluster analysis of the lines also placed them in four groups, and lines such as 90, 131, 102 and 132 were also included in the group with high performance in both conditions. According to the results of this study, it is suggested to use the indicators of stress resistance, harmonic mean, geometric mean, and performance stability, which have the most significant positive correlation with performance in wet and dry conditions, in the selection of lines; Also, screening should be done based on the results of analysis into principal components and biplot and clustering using several indicators.

This study aimed at proposing suitable business and revenue models for businesses venturing into electronic commerce within the agricultural sector. The research methodology included an in-depth examination and classification of top global businesses in various sectors, with a focus on identifying the challenges and revenue models employed by them. The study utilized qualitative evaluation indices to assess the business models of top Iranian and foreign websites and applications. Data mining methods, clustering techniques, and MATLAB data mining tools were employed to classify businesses and compare them with global counterparts. The research identified and categorized the challenges and revenue models of top businesses across various sectors, with a specific focus on the agricultural domain. The study used valuable insights, presenting ten significant challenges within the agricultural sector, which are used for clustering businesses and proposing revenue models tailored to this specific industry. In addition, it introduced a model for businesses to identify suitable revenue models by recognizing the addressed challenges and their functional domain. The proposed model serves as a comprehensive guide for businesses seeking to establish a presence in electronic commerce within the agricultural sector. This study highlighted the potential for policymakers to update the identified challenges based on evolving environmental requirements. Moreover, by drawing insights from successful global businesses, the study suggested the possibility of updating clustering and revenue models to align with current industry trends and best practices.

According to experts, Iran is the largest producer of rose water in the world, but it has a weak position in the global market for the export of rose essence and its products. The majority of rose production in Iran is consumed domestically as well as internationally, as rose water, fresh and dried rose petals, while the profitability lies in the production and export of rose essence, perfumes, cosmetic and hygiene products. In this study, after calculating market share, market size, import continuity, export price, Herfindahl-Hirschman Index, and export price risk index, target countries were studied and analyzed using K-means clustering based on similarity with these indices. The required data for the research period of 2012-2021 were collected from the Islamic Republic of Iran Customs Administration (IRICA). One of the most important non-hierarchical clustering methods is the K-means method, which is considered as a basic method for many other clustering methods. According to this method, cluster centers are first obtained, which are actually the average points belonging to each cluster. Then, each data point is assigned to a cluster based on the cluster whose center has the smallest distance to that data point. The best clustering is one that maximizes the similarity between the cluster center and all cluster members and minimizes the similarity between cluster centers. In this study, the following variables were used for clustering:Market Share: According to the IRICA data, it includes those countries that have allocated more than one percent of Iran's exports share to themselves for rose and its products during the studied period (2012-2021).Market Size: Based on the IRICA data, this index represents the average amount of imports made by selected countries from Iran during the studied period.Market Growth: The average growth of imports during the studied period is considered as an indicator of market growth.Continuity of Imports: It is the continuous access to the desired market, and this indicator is measured by calculating the coefficient of variation (CV) of imports to that country.Market Structure: Market structure refers to the concentration of buyers and sellers, entry conditions, and the degree of homogeneity of goods. Market structure indicates whether the market is monopolistic or competitive, which can be determined by calculating concentration ratios such as the Concentration Ratio (CR) or the Herfindahl-Hirschman Index (HHI).Value of Exports: It is obtained by the ratio of the value of exports (in dollars) to the quantity of exports.Export Price Risk: The coefficient of variation (CV) of export prices over the years studied is considered as export price risk. During the studied period (2012-2021), Iran exported dried flowers and fresh rose petals to 45 countries around the world. The most important export markets for dried flowers and fresh rose petals during this period included Germany, Pakistan, the United Arab Emirates, Turkey, Vietnam, Kuwait, Iraq, and Spain. Iran exported rose water to approximately 60 countries from 2012 to 2021. The top ten major importers of rose water from Iran were found to be the United Arab Emirates, Kuwait, Bahrain, Oman, Qatar, Iraq, France, Saudi Arabia, China, and Afghanistan. The destination countries for rose water exports were categorized into five clusters. Similarly, based on the results, the target countries for exports were prioritized in 5 clusters. According to IRICA statistics, Iran exported rose essence to 17 countries worldwide during 2012-2021. The most important export markets for rose essence in terms of market size (average over the period) during the same period included Hong Kong, Australia, China, Germany, France, Canada, Spain, Bulgaria, Austria, and the United Arab Emirates. Germany was the largest importer of rose essence during the mentioned period. Based on the results, destination countries for the export of rose water essence were categorized into four clusters (priorities), with Japan and Canada in the first cluster, Hong Kong and China in the second cluster, Germany in the third cluster as well as with the highest number of countries including France, Russia, Spain, Bulgaria, Austria, and England in the fourth cluster. Based on the study results, the countries importing Damask Rose and Damask rosewater were grouped into five clusters each based on their similarity to the indicators mentioned. Furthermore, importing countries of rose essence from Iran were placed in four clusters. It is evident that any planning to maintain target markets, reduce export price risk, and sustain income and the export market share of Iran requires attention to consistent policies for the countries in each cluster. It is also worth-mentioning that export priority to target countries from the first cluster should be initiated and then, expanded to subsequent clusters. Finally, based on the research findings, it is recommended that the policy for the development of rose essence exports and related complementary industries be considered by policymakers.

Diverse plant genetic materials are potential treasures that are considered valuable support for plant breeding programs, because the plant breeding research is based on wide genetic diversity, and basically, without diversity, breeding and selection will have no meaning. This study was conducted to evaluate the diversity of different cotton landraces and identify the traits affecting their yield. 

In this study, 44 landraces of cotton were cultivated in the agricultural research station of Kashmir during the two years of 2018 and 2019. The traits of the number of open and closed bolls, crown diameter, crop yield, boll weight, number of monopodial and sympodial branches, plant height, and crop maturity were measured. Descriptive statistics, trait × genotype (GT) biplot analysis, factor analysis, and cluster analysis were used to investigate the objectives of this research. Statistical analyzes were performed based on the average of two-year data. In analyzing of descriptive statistics, JUMP software was used, SPSS 21 software was used for factor analysis and cluster analysis, and GEA-R software was used for GT biplot analysis. 

Based on the average of two-year data, the range of boll weight and the number of closed bolls were high, which indicates the diversity of the investigated genotypes for the mentioned traits. Comparison between genotypes based on multiple traits made it possible to distinguish different genotypic groups. Cluster analysis of studied traits classified the genotypes into four main groups. The first group obtained from cluster analysis was earlier, but had the lowest average plant height, the number of monopodial and sympodial branches, the number of open bolls, and crown diameter. On the other hand, the second and third groups had the lowest amount of earliness. The genotypes of the second group had the lowest boll weight, the number of shoot branches, and the crown diameter, and the third group had the highest plant height, the number of branches, and the number of open bolls. The genotypes of the forth group had the highest earliness and the number of closed bolls. In order to increase earliness and reduced the plant height, the selection of genotypes from the first group for breeding programs can be helpful in obtaining cultivars with greater tolerance to environmental stresses, including drought. It is also possible to use the genotypes of the third group with the highest number of open bolls to cross with the genotypes of the first group in order to create quality and early cultivars. 

 There was considerable genetic diversity among the studied landraces in terms of most of the evaluated traits, indicating high potential for improving these traits through targeted selection in the breeding programs. The boll weight was the most important factor affecting the yield of cotton, and the fiber component had a greater effect on the boll weight of some studied genotypes than the seed component.

Late spring cold stress in the early reproductive growth of wheat causes damage due to a sudden drop in temperature. The genetic diversity of plants determines their potential to increase efficiency and, as a result, their use in breeding programs. This study was conducted with the aim of evaluating population structure, diversity of wheat genotypes, and selection of superior genotypes under cold and normal stress conditions.

Sixty-seven bread wheat genotypes were evaluated through pot cultivation in the field under two normal conditions and late spring cold stress. The experiment followed a randomized complete block design with two replications. Stress was applied by transferring the pots to a growth room with a temperature of -3ºC at the beginning of the reproductive stage. Phenotypic traits, including performance and performance components, were measured. Genotyping was done using a single nucleotide polymorphism (SNP) array with 9K density. A total of 17,093 SNP markers were used in this study.

The polymorphic information content for SNP markers ranged from 0.03 to 0.38, with an average of 0.26. Population structure analysis grouped the genotypes into five subpopulations. Molecular variance analysis showed that 75% of the observed variance in the population was related to intra-population differences. The studied genotypes were divided into four groups based on SNP markers and five and six groups based on phenotypic traits under normal and stress conditions, respectively. The genotypes in the first group, which exhibited higher average traits than other groups under stress conditions, were identified as tolerant to late spring cold.

The results indicated that main spike weight, seed yield per plant, seed weight in main spike, and weight of 100 seeds were the most important factors for grouping under late spring cold stress conditions. The calculated molecular indices, including marker indices and diversity indices, demonstrated that the SNP markers used performed relatively well in calculating genetic diversity within the studied population. In cluster analysis based on the genotypic matrix, cold-tolerant genotypes formed separate groups, indicating different tolerance mechanisms among these genotypes. These findings provide useful information for wheat breeders regarding the selection of diverse genetic resources to improve cold tolerance.

Variety of cultivars is considered one of the important factors affecting performance. Therefore, accurate and correct identification of the genotypes of its seeds is very useful, in addition to being necessary in plant breeding programs; it is also very effective in preserving genetic reserves. The present research was conducted to investigate the cumin landraces in diversity terms of yield components and to select landraces with suitable potential for selectivity.

To investigate the phenological, morphological, and functional characteristics of native cumin stands, a study was conducted at the Targh research station in 2019-2018 on 30 native stands of cumin in the form of a randomized complete block design with three replications. The Characteristics of the number of days to 50 % flowering and at physiological and seed maturity, plant height, branch number, number of umbels per plant, number of umbellate per umbel, number of flowers in umbellate, biomass, and yield were measured.

A significant difference was observed between the landraces in all of the studied traits. Cover canopy (47-633 cm2), plant height (17.7-30 cm), branch number (6.3-62.3), number of umbels per plant (5.7-27.3), number of umbellate per umbel (3.17-5.33), number of flowers in umbellate (4.30-5.85), biomass (1.92-17.38 g), seed yield (0.129-6.72 g) per plant, were varied. Based on the results of the clustering, the landraces were placed in three separate groups. The Cluster grouping and comparison of the average of the groups with the total average showed that the landraces belonging to the second group, including Arak 6 (15316), Boshruyeh (38904), and Markazi 15 (14654) in morphological traits, yield components, and grain yield compared to the total average and other groups were the best. Cumin landraces belonging to the third group had a lower average than the total average in most traits. The results showed that among the landraces that showed the highest level of resistance to Fusarium disease, Ayask (C103) from the third group and Khash (22074) from the first group had a yield of 11 grams per plant. However, despite the high yield rate in the Ayask, due to the effectiveness of other traits in addition to performance, this landrace cannot be recommended for breeding programs.

Considerable variation was observed between landraces in terms of studied traits. The highest amount of grain yield was observed in the khash belonging to the first group and the Boshruyeh from the second group, and due to the relatively high resistance of these landraces to Fusarium disease. So, we proposed that as selected landraces for breeding programs.

Considering the role and importance of environmental factors in the distribution and spread of vegetation, as well as the existence of wind of 120 days in the Sistan region, and the necessity and importance of vegetation and flood spreading in the region, this research was done to classification and ordination of plant communities in Niatak flood spreading area, Zabol. Vegetation types were determined by Braun-Blanquet's physiognomy-floristic method and cluster analysis based on species composition and soil characteristics were used to classify vegetation. In each plant type, soil and vegetation sampling was done. In the key area of each plant type, 3 transects of 30 m were established, and during each transect, all surface features were measured. In addition, in each transect, soil samples were taken and physical and chemical properties were determined. The results of the preliminary studies obtained from the initial typology by the physiognomy-floristic method in 17 investigated types led to the identification of five plant communities, such as Ta. stricta, Ta. aphylla, Ha. ammodendron, Ha. persicum and Pr. stephaniana. In addition, the classification of vegetation by cluster analysis method based on species composition and soil properties was identified as five and three plant communities, respectively. The ordination results showed that the first axis and the second axis together with 64% of the variance of the distribution of plant communities are the most important axes. Sand percentage has the most positive correlation and absorbable phosphorus (Pav) has the most negative correlation with axis 1. Sodium (Na) and exchange sodium percentage (ESP) have the most positive correlation with axis 2. Redundancy analysis (RDA) biplot shows that characteristics, such as sand and fine sand percentage have a positive correlation with Ta. aphylla and Ha.ammodendron communities, and factors such as Pav, absorbable potassium (Kav), and cation exchange capacity (CEC) cause the distribution of Ta. stricta community in the region. Electrical conductivity (EC) and sodium absorption ratio (SAR) cause the presence of Ta. aphylla and Ha. ammodendron communities. There was a special relationship between different plant communities and soil characteristics.

Assessing precipitation alterations in a large area like Iran is required for the identification of those areas that are more vulnerable to changes in precipitation patterns, considering the fact that such changes may significantly influence water availability, agriculture, and other sectors that are dependent on water resources.  On the other hand, understanding the spatial variability of precipitation patterns can help develop purposive strategies, including drought or flood management in specific regions. Moreover, as severe weather events such as floods and droughts can devastate communities and their infrastructure, such an understanding can inform decisions made concerning disaster risk reduction efforts. Therefore, assessing precipitation variations is essential for the effective management of water resources and the reduction of disaster risks.
 

This study suggests a new method for analyzing precipitation properties in Iran, using a mixture of Maximal Overlap Discrete Wavelet Transform (MODWT) and Multiscale Entropy (MDE) techniques. This approach allows for a more detailed and accurate assessment of the spatial and temporal characteristics of precipitation properties, preparing the ground for the development of appropriate strategies for different regions in Iran. To this end, annual precipitation data collected from fifty Iranian synoptic stations for 1980-2020 were analyzed. Then, after classifying the precipitation data into different subseries, the concept of entropy was used to measure precipitation variability. Moreover, MDE values were used as input data for clustering purposes, followed by the calculation of internal evaluation criteria to be used for the determination of the optimal number of clusters and the most suitable clustering method calculated.
 

The variations and trends of the precipitation data can be identified through the analysis of partial coefficients D1-D4 and the approximation coefficient. Accordingly, while the smaller partial sub-categories indicate more rapid variations at higher frequencies, the greater partial coefficients show more moderate variations at lower frequencies. Moreover, the approximation coefficient reveals the slightest variations at low frequencies in annual time series. The study’s results suggested that northern and northwestern Iranian regions that are primarily characterized by rainy, cold, and in some cases semi-arid climates experienced the greatest variations in terms of annual precipitation. On the other hand, the eastern and southern parts of Iran, which are mostly dry areas, experienced more moderate variations in annual precipitation rates. Therefore, according to the results found in this study, it could be argued that northern and northwestern Iran enjoy more precipitation variability than other parts of the country. Furthermore, the D3 sub-category (eight years) was found to have the greatest variations in terms of MWE. On the other hand, based on the values of Sci, DBi, and CH index, the k-means clustering method performed better than SOM (Sci=0.41, DBi= 1.22, and CH= 14.58). Finally, fifty Iranian synoptic stations were categorized into four clusters based on the MWE index, with Chabahar, Shahrud, Abadan, and Zanjan being selected as core clusters.
 

The current study proposed a methodology for analyzing and zoning Iran’s annual precipitation based on the multiscale entropy method, considering the fact that the period and trend of the annual time series could be identified via the analysis of the precipitation series. Following the analysis of the collected data, this study used the multiscale entropy method to record precipitation variability in each synoptic station. On the other hand, the determination of SOM and k-means input data based on MWE values helped reduce the input data, leading to an increase in the accuracy of the zoning method and the selection of the homogenous clusters based on the proposed methodology. The results of the study indicated that generally, the K-means method offered more homogeneous areas than the SOM method. Moreover, the homogeneous distribution of annual precipitation variations obtained based on the K-means-derived clusters confirmed the positive performance of the methodology proposed in this study, which involves the identification of hydrologic uncertainty and temporal-spatial variations of precipitation in those stations related to a specific cluster. The methodology seeks to create distinct clusters of stations that share common features. This approach can provide insights into the spatial variability of hydrological processes and help improve water resource management by identifying areas susceptible to hydrological extremes such as floods or droughts. Some studies have already been conducted on the spatial clustering of precipitation stations in Iran using different methods, including the application of geographical proximity or precipitation rate as criteria for clustering. However, the method used in the current study involved clustering based on similarity in hydrological uncertainty and temporal-spatial complexity, which is consistent with the findings of Roshangar and Alizadeh (2019), and Roshangar et al. (2019). Identifying hydrologically homogeneous regions and their associated precipitation characteristics can significantly enhance the effective management of water resources in terms of adapting to climate change, preventing damage to water environments, and mitigating the impact of climate-related disasters. Therefore, the proposed methodology for spatial clustering of synoptic stations could be useful in managing water resources and all precipitation-related sectors and variables such as runoff and soil moisture. However, this study faced some limitations such as the small number of synoptic stations and the short length of the statistical period. Therefore, it is recommended that the methodology be applied to more stations at more varied time scales.

Identification of homologous hydrological groups is one of the fundamental topics in hydrology in both applied and research dimensions. One of the common methods to achieve homogeneous hydrological zones for estimating flood zones is the use of clustering methods. In this study, the using, evaluation and comparison of statistical methods and methods based on artificial intelligence for clustering Karkheh catchment have been investigated. SOM, K-meansand hierarchical clustering were used for the study area. In the following, the study of hydrological homogeneity of the obtained areas was evaluated using the linear torque method and the heterogeneity adjustment was evaluated using the methods proposed by Husking and Wallis. The results show that the study area can be converted into two clusters. The values ​​of homogeneity statistics for the first and second clusters were calculated to be 0.33 and 0.17, respectively, which indicates the homogeneity of each region. Due to the short statistical period in some stations, statistical shortcomings and the reliability of the results of regional frequency analysis, this method should be used in estimating floods in other catchments.

 The issue of employment and guiding the population of university graduates towards entrepreneurship has been and is one of the most challenging and favorite topics of researchers in this field. One of the basic questions in this field is why, in a relatively similar situation and context, some university graduates identify and operate entrepreneurial opportunities, while others do not have such ability? The answer to this multifaceted question can be investigated from various dimensions, one aspect of which, business intelligence indicators, has been considered in shaping the entrepreneurial future. 

 In the first step, by using the literature review and research background in the field of entrepreneurship and its relationship with business intelligence, 13 dimensions of business intelligence that can be effective in shaping the entrepreneurial future were identified. In the second step, quantitative methods in data mining (descriptive and predictive) were used to analyze the statistical population. To describe the research community, 401 students of Gorgan University of Agriculture and Natural Resources in 1401 were grouped and analyzed in three separate clusters using the Davis-Bouldin index. Also, artificial neural networks used to design a predictive model of students' entrepreneurial future.

 The results of the research showed that in the statistical population of the research, there is a significant difference between students in terms of having business intelligence so based on business intelligence indicators, students can be separated and differentiated into three clusters. The results in the modeling stage of predicting the entrepreneurial future of students using neural networks showed that business intelligence indicators have a high predictive ability and by using them, changes in the dependent variable can be predicted with an accuracy of 0.925. Also, the results of the sensitivity analysis of the importance of demographic variables confirmed the impact of these variables on shaping the entrepreneurial future.

 According to the results of the current research, it can be concluded that students are in a different situation from each other in terms of business intelligence indicators, which can play an important role in shaping their entrepreneurial future. Therefore, it is suggested that business intelligence indicators should be strengthened and improved through skill training courses, considering business intelligence indicators in the stages of student selection and selection, providing practical courses in the university, strengthening the relationship between industry and university, and introducing successful entrepreneurs to students.

Nitrate is one of the pollutants that affects groundwater in most aquifers of the country. In order to investigate the behavior of hydrogeochemical parameters of groundwater in the Ardabil plain aquifer, the results of qualitative data of 139 wells that were measured in both wet and dry seasons during the water year 2012-2011 were used. Using XLSTAT software, contamination was identified and samples were classified using principal factor analysis (PCA) and hierarchical clustering (HCA). Factor analysis method led to the extraction of five factors affecting groundwater quality with a total variance of 68.09 percent. Also, the results of factor analysis showed that the first, fourth and fifth factors are due to geogenic processes and the second and third factors are due to anthropogenic processes. The results of the hierarchical clustering method divided the samples into three groups, each group having two subsets. The set related to cluster one is spread in the northern, eastern, southern and parts of the center, west by mix(agriculture-garden), medium pasture and dry farming as well as urban areas. The set of the second cluster is often spread in the central, western and urban areas of the aquifer, with mix(agriculture-garden) and residential areas. The set of third cluster has been expanded to southwestern and urban areas. Generally, the existence of dissolution and ion exchange processes on the one hand, as well as leaching of fertilizers and incomplete development of the sewage system in the aquifer on the other hand has provided the groundwater pollution.

The absence or lack of improved seeds with high genetic variety has long been one of the most major obstacles to forest regeneration and growth. In addition to supplying replicable seeds and excellent seedlings, seed orchard creation is critical in providing enough genetic variety for future research. In a forest covering around 100,000 hectares, 18 maternal trees were discovered in three groups. The DNA was extracted using a modified CTAB technique, and the polymerase chain reaction was carried out with SSR markers. The allelic features of amplified markers, as well as genetic indices, were determined using GenALEx software, and the similarity matrix of Jaccard coefficients, as well as the clustering of 18 chosen trees, were performed using the UPGMA technique and NTSYS software. Fragment amplification was done in 10 SSR markers based on the findings of vertical electrophoresis of PCR products from 15 pairs of SSR markers. In all, 57 polymorphism alleles were discovered in 18 chosen ash for 10 SSR markers, with the FE3 marker having the most (ten polymorphic alleles) and the FE5 marker having the fewest (two polymorphic alleles). Each SSR marker had an average of 5.7 polymorphic alleles. The clustering of 18 distinct trees using the UPGMA algorithm revealed that the chosen trees were divided into three major clades. The P1, P2, P3, P4, P8, P9, P10, P15, P17, and ultimately P18 plants can be utilized to build a seed orchard based on the results.

Knowing the intensity and duration of rainfall can be useful in many environmental analyses, including the estimation of rain erosivity and soil erosion. There are various devices to record the intensity and duration of rainfall, but purchasing and maintaining them are costly and often requires an operator to take care of them. The present research deals with the feasibility of using the analysis of sound signals caused by the collision of droplets with surfaces and objects in nature to determine the intensity and duration of rainfall. For this purpose, in the laboratory of the Department of Soil Science, Faculty of Agriculture, University of Tabriz, in 2022, rain simulators were designed to produce rains of different intensities, then, the sound signals caused by the impact of raindrops with the metal tray that was placed under the rain were recorded and transferred to the computer for processing. Then, the frequency size of audio files was extracted in MATLAB software. The results showed that with the increase in rainfall intensity, the audio amplitude and frequency size of the audio signals increased. Then, the frequency measurements were automatically placed in two clusters in SPSS software using the two-stage clustering method. Then the mean and standard deviation of each cluster were calculated and according to the correlation of each with each other and with the intensity of rainfall, and in order to avoid the multi-collinearity phenomenon, only the average of the second cluster was used as the input of gene expression programming and linear regression models. In order to test the accuracy and correctness of the results obtained from the models, the coefficient of determination (R2), root mean square error (RMSE), geometric mean of error ratio (GMER), geometric standard deviation of error ratio (GSDER) statistics were used. The values of R2, RMSE (mm/h), GMER(mm/h) and GSDER (mm/h) for the gene expression programming model in the training series data were 0.97, 1.85, 1.11 and 1.09 respectively and for the validation series data were 0.96, 2.05, 1.14 and 1.12 respectively. While the values of the above criteria in the regression model were 0.94, 2.74, 1.25 and 1.34 respectively for the training series data and 0.92, 2.91, 1.28 and 1.37 respectively for the validation series data. The results of the above statistics indicate that the gene expression programming model is relatively more accurate than the regression and overestimation model, and the estimated data of the regression model is relatively more spread than the gene expression programming model.

The concern of rural development and the empowerment of rural communities is one of the most important issues of underdeveloped communities in the world today. Before this, it was believed that these communities can be saved from poverty and backwardness with some interventions and by preparing comprehensive plans and in an orderly manner and provide a sustainable livelihood for the rural residents; But today, it is clear that the inner power and capacity of rural communities is involved more than anything else in the possibility of realizing development and creating a sustainable livelihood. This article tries to classify the capacity and development potential of the villages of Malekshahi city based on socio-economic indicators. The research method is the survey and statistical population of households in the villages of Malekshahi city. The sample size is 580 samples determined by Cochran's formula. The sampling method is matched sampling and random selection of samples. The findings showed that the villages of Malekshahi have a much better situation in terms of social indicators such as cohesion, participation, trust, skill and education compared to economic indicators such as employment, access to credits, income and institutional structures. The results showed that in order to implement the empowerment program and provide sustainable livelihood, in addition to benefiting from the local social and human capital, it is necessary to take advantage of removing obstacles to access to financial credits and putting small and surplus funds into the cycle of rural communities.

Precipitation is one of the most significant climatic parameters; its distribution and values in different areas is the result of complex linear and nonlinear relationships between atmospheric elements-climatic processes and the spatial structure of the earth's surface environment. Classification of data and placing them in small and homogeneous zones can be effective in improving the understanding of these complex relationships and their results. In the present study, zoning and analyzing the distribution of rainfall in Iran concerning environmental factors was performed using the annual precipitation data of 3423 synoptic, climatological, and gauge stations in the country during the period from 1961 to 2015 and the altitude, slope, aspect, and station density data. After standardization and preparation of the data matrix, the optimal number of clusters was determined and the data set was entered into the neural-fuzzy network model (ANFIS-FCM). The results showed that the values of R2  and MAE  indices were 0.76 and 0.23, respectively which indicate the appropriate accuracy of the model. It was also found that in the four output zones of the model, environmental factors have a high impact on the spatial distribution of precipitation. In the first and third zones, the combination of high altitude and slope factors along with geographical proximity to precipitation systems has caused the average annual rainfall in these zones to be 318 and 181 mm, respectively. The mean annual rainfall has decreased to about 100 mm by the weakening of the role of environmental factors in the second and fourth clusters.