Mohammad Reza Sayadi; Mehdi Ghorbani; Mohammad Jafari; Hamidreza Keshtkar; Leila Avazpour
Abstract
The objective of this paper is to identify the factors affecting the medicinal plant supply chain in the Nadushan region using a Glaser approach. The research method is applied in terms of purpose and qualitative in terms of method based on grounded theory and Glaser approach (emerging approach); and ...
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The objective of this paper is to identify the factors affecting the medicinal plant supply chain in the Nadushan region using a Glaser approach. The research method is applied in terms of purpose and qualitative in terms of method based on grounded theory and Glaser approach (emerging approach); and it is exploratory based on the nature of the data and the use of inductive philosophy. The study population consisted of experienced local people and managers and experts in the field of the medicinal plant supply chain with more than five years of experience. Participants were selected using purposeful sampling and theoretical judgment. The data collection method was fieldwork, and the data collection tool was in-depth and structured interviews with 30 participants, including native farmers (15), researchers and experts (10), and intermediaries (5) in the field. The grounded theory approach was used to analyze the data and identify the key factors affecting the supply chain. The results identified 9 selective codes and 41 core codes. The factors affecting the supply chain include climate and weather, the region's high potential for medicinal plant cultivation, initial budget and capital, storage conditions, institutional support, policy, medicinal plant production and harvesting management, medicinal plant processing management, and the use of healthy practices in productivity. Therefore, ensuring a sustainable and efficient supply chain is crucial for maintaining the quality, availability, and affordability of medicinal plants.
Mehrdad Norouzi; Hamidreza Keshtkar; Seyed Ebrahim Sadeghi; Mohammad Javan Nikkhah; Esmaeil Alizadeh; Jalil Alavi
Abstract
The vast land of Iran, with its diverse climate and geographical location, is known as a suitable habitat for different types of thyme. Thyme (Thymus spp.), a plant of great nutritional and medicinal value, holds a special place among pasture plants. Consequently, investigating the factors that threaten ...
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The vast land of Iran, with its diverse climate and geographical location, is known as a suitable habitat for different types of thyme. Thyme (Thymus spp.), a plant of great nutritional and medicinal value, holds a special place among pasture plants. Consequently, investigating the factors that threaten the survival of this plant is important. Two species of thyme, T. kotschyanus (Boiss. & Hohen) and T. pubescens (Boiss. & Kotschy ex Čelak) have significant habitats in the pastures of Alborz province. In this research, pests and diseases of these two species were investigated in 14 different stations in Alborz province. The samples were collected at three time points (May, July and September) and the collected insects were taken to the laboratory for identification. The results indicated the presence of six types of pests on thyme species: Aphis serpylli, Haplothrips reuteri, Tetranychus urticae, Aeolothrips mongolicus, a mite of the genus Bryobia, and also Cuscuta spp. as a parasitic plant. Alternaria leaf spot was also detected on thyme leaves. The results of this research are reported for the first time It can be used by regional managers and stakeholders and help in the protection of Thymus species in the rangelands of Alborz province.
Hamidreza Keshtkar; Hassan Yeganeh; Omid Kavoosi
Abstract
Ferula gummosa is one of the rare and valuable species in Iran's rangelands, which is exploited by local stakeholders due to its high economic value. Protecting this species can help maintain the biodiversity and stability of mountainous areas. This study was conducted to compare the performance of six ...
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Ferula gummosa is one of the rare and valuable species in Iran's rangelands, which is exploited by local stakeholders due to its high economic value. Protecting this species can help maintain the biodiversity and stability of mountainous areas. This study was conducted to compare the performance of six predictive models: Artificial Neural Networks, Random Forest, Classification Tree Analysis, Surface Range Envelope, Generalized Boosting Machines, and Generalized Linear Models. To evaluate the interactions between topographic factors and other variables, two environmental datasets were quantified and used for model calibration. The first dataset includes eleven factors covering topographic, climatic, edaphic, and remote sensing variables. Meanwhile, the second dataset contains six factors, focusing on climatic, edaphic, and remote-sensing variables. Model accuracy was evaluated using the True Skill Statistic (TSS), the area under the curve of the Receiver Operating Characteristics (ROC), and the Accuracy Index. The evaluation indices indicate that the Generalized Boosting Machine (GBM) model predicted the ecological niche of F. gummosa more accurately than the other methods. Additionally, the results showed that removing topographical variables reduced the model accuracy by 11 to 25%. The slope, NDVI, wetness, and soil groups were found to be the most important factors in mapping potentially suitable habitats for the target plant. According to the results obtained from the GBM model, approximately 45% of the Ghorkhoud area is in excellent condition. This knowledge can aid in the selection of predictors for practical Species Distribution Model (SDM) applications and provide information on which modeling techniques are most useful for a group of species.
Marziyeh Haji Mohammadi; Aliakbar Nazari Samani; Arash Zare Garizi; Hamidreza Keshtkar; Mahmood Arabkhedri; Amir Sadoddin
Abstract
The SWAT model is widely used to simulate watersheds and evaluate the impact of conservation watershed management practices. In this model, the simulation of the watershed processes is based on hydrological response units (HRUs) which are created by overlaying land use /land cover, soil and slope maps. ...
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The SWAT model is widely used to simulate watersheds and evaluate the impact of conservation watershed management practices. In this model, the simulation of the watershed processes is based on hydrological response units (HRUs) which are created by overlaying land use /land cover, soil and slope maps. Meanwhile, in the HRUs definition steps, these units become conceptual and lose their spatial location and continuously. This approach is a useful and often inevitable way to simulate large and heterogeneous watersheds in terms of computational efficiency. However, if the aim is spatializing and evaluating the effectiveness of management methods on runoff, sediment and other pollutants in medium to small basins, it is necessary to know the exact location of HRUs. The purpose of conducting this study was present a new approach to defining spatial and independent HRUs and compare the simulation results based on this method with the standard form of the model. In the new approach, independent and spatial HRUs are defined through pre-processing procedures in GIS and uniquely named soil units. The model results of both approaches were very similar and no significant difference was observed in the model outputs in Taleghan watershed. The Nash-Sutcliffe coefficient of the simulated runoff and sediment at the outlet with the standard approach was 0.75 and 0.64, respectively. While, it was obtained 0.74 and 0.61, respectively for the new approach. The definition of spatial HRUs by applying the proposed method provides more tangible and practical outputs, which is more beneficial for identifying the critical areas as well as locating conservation practices compared to the conceptual HRUs approach.
Serveh Darvand; Hassan Khosravi; Hamidreza Keshtkar; Gholamreza Zehtabian; Omid Rahmati
Abstract
The purpose of this study was to compare machine learning models including Support Vector Machine, Classification and Regression Tree, Random Forest, and Multivariate Discriminate Analysis to prioritize susceptible areas to dust production. To determine the dust days, hourly meteorological data of Alborz ...
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The purpose of this study was to compare machine learning models including Support Vector Machine, Classification and Regression Tree, Random Forest, and Multivariate Discriminate Analysis to prioritize susceptible areas to dust production. To determine the dust days, hourly meteorological data of Alborz and Qazvin provinces and satellite images of the same days for the period 2000 to 2019 were used. 420 dust collection points were identified and the map of their distribution was prepared. The maps of factors affecting the occurrence of dust, including landuse map, soil orders map, slope map, slope aspect map, elevation map, vegetation map, topographic surface moisture, topographic surface ratio, and geology mam were prepared. Using the mentioned models, the impact of each of the effective factors of dust was determined and prioritization maps of dust harvesting areas were prepared. Models were evaluated using the ROC curve. According to the results, the elevation factor is more important in all models than the other parameters used in the model. The modeling results also showed that the Random Forest )RF( and Multivariate Discriminate Analysis (MDA) models had the highest values of accuracy (0.96), precision (0.94), Probability of Detection (POD) (0.98), and False Alarm Ratio (FAR) (0.051) compared to the others. The performance of the RF and MDA models is better than the other models, followed by the Support Vector Machine (SVM) and Classification and Regression Tree (CART) models, respectively. Also, in evaluating the models using Receiver Operating Characteristic (ROC), the RF model was selected as the best model.
