Hassan Yeganeh; Fatemeh Mobarhan; Fatemeh Mehdi Gholian; Sadegh Atashi
Abstract
Prickly pear plays a very important role in arid and semi-arid ecosystems due to its high ability to withstand adverse weather conditions and soil protection. This study aims to investigate some morphological and phytochemical characteristics of different parts of the Opuntia stricta plant in the Neka ...
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Prickly pear plays a very important role in arid and semi-arid ecosystems due to its high ability to withstand adverse weather conditions and soil protection. This study aims to investigate some morphological and phytochemical characteristics of different parts of the Opuntia stricta plant in the Neka and Anbaralum regions. The sampling of different organs was done randomly in 2019. Some characteristics of the plant include fruit and cladode size, vitamin C and acidity, measurement of pectin from skin and fruit flesh, measurement of chlorophyll and carotenoid, anthocyanin of skin and flesh, measurement of soluble solids (TSS) from plant organs using calipers. , spectrophotometer, and refractometer were measured. The results showed that the amount of dry weight of the seed, the amount of electrical conductivity (EC), the number of bases, the number of fruit, and the number of cladodes are significant at the level of 5 and 1%, and the amount of dry weight of the seed in Neka region (1.71 grams) is more than Anbar Alum. 1.3) is warm. Also, the amount of electrical conductivity (EC) in the Neka area (3.78 decisiemens/cm) was measured higher than in Anbaralum (6.34 decisiemens/cm). On the other hand, the number of stems, number of fruits, and number of cladodes per unit area were measured in the Neka region (1.7, 51, 111.16) more than Anbaralum (1.33, 11.03, 36.4). It can be said that climatic conditions have an effect on its growth parameters and fruit production, and the most important factor is access to water.
Ziba Maghsodi; Hamid Reza Matinfar; Seyed Roohollah Mousavi
Abstract
The scale of environmental variables is one of the most important features to consider when selecting data. The aim of this study is to improve the accuracy of digital mapping by selecting the optimal scale for predicting six soil properties, For this purpose, 100 surface soil samples (0-30 cm depth) ...
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The scale of environmental variables is one of the most important features to consider when selecting data. The aim of this study is to improve the accuracy of digital mapping by selecting the optimal scale for predicting six soil properties, For this purpose, 100 surface soil samples (0-30 cm depth) were collected based on a random sampling pattern. Environmental variables related to topography and remote sensing were extracted from the digital elevation model (DEM) and Landsat-8 satellite. The optimal environmental variables were selected using the recursive feature elimination method in the Silakhor Plain region. Soil property modeling was conducted using machine learning models such as random forest (RF), Support Vector Regression (SVR), Cubist (CB), and hybrid modeling. The modeling results showed that the RF model performed best for predicting CCE, pH, sand, and silt with R² values of 0.64, 0.65, 0.59, and 0.70, respectively. Additionally, the SVR model showed the highest accuracy for predicting SOC with an R² of 0.62, while the CB model had the highest accuracy for predicting clay with an R² of 0.66. The most suitable cell sizes for CCE, pH, sand, and silt were identified as 30*30 m, for SOC as 60*60m, and for clay as 90*90m. The most important environmental variables for SOC, pH, silt, sand, and clay were valley depth, differential vegetation index, and modified vegetation index, respectively. Overall, the results indicated that in the study areas, the use of intermediate scales (cell sizes of 30 to 90 m) led to higher accuracy in predicting soil properties. This is because using larger cell sizes introduces noise that hinders accuracy.
Farzaneh Parsaie; Ahmad Farrokhian Firouzi; Masoud Davari; Ruhollah Taghizadeh-Mehrjardi
Abstract
Surface soil saturated hydraulic conductivity (Ks), as one of the most important physical properties of soil, plays a key role in the distribution of water and nutrients within the soil environment and holds particular significance in water and soil resource management. This study aimed to digitally ...
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Surface soil saturated hydraulic conductivity (Ks), as one of the most important physical properties of soil, plays a key role in the distribution of water and nutrients within the soil environment and holds particular significance in water and soil resource management. This study aimed to digitally model Ks using machine learning approaches in the Kilanah watershed, located in Kurdistan Province, covering an area of 12,000 hectares. Three machine learning algorithms, including Gradient Boosted Decision Tree (XGBoost), Random Forest (RF), and k-Nearest Neighbors (k-NN), were utilized, incorporating various environmental variables derived from the digital elevation model and Sentinel-2 satellite imagery. These variables included distance from the drainage channel, valley depth, relative slope position, channel base level, brightness index, wind effect index, Normalized Difference Vegetation Index (NDVI), Band 12, greenness index, and surface curvature. Additionally, soil parameters such as organic matter, lime content, bulk density, geometric mean particle diameter, soil texture, and near-soil spectroscopic data (Latent Variable) within the wavelength range of 400–2450 nm were used as proxies for pedogenic factors to model saturated hydraulic conductivity. The results indicated that the XGBoost model exhibited the highest accuracy for predicting Ks, with an R² value of 0.65 and an nRMSE of 0.25, outperforming the other models. Spectral data, topographic variables, and soil parameters, as model inputs, played a significant role in predicting the spatial variability of Ks. The XGBoost model was able to provide highly accurate predictions. The results demonstrated that topographic, physical, and spectral variables influence Ks; organic matter, soil texture, and topographic indices such as slope and relative position had the most substantial impact. The generated maps can be utilized for water and soil resource management and hydrological models.
Maryam Osat; Shahrokh Fatehi; Javad Seyed Mohamadi
Abstract
Optimal utilization of production factors is considered one of the fundamental pillars of sustainable development. The increasing pressure on soil resources due to the population growth, industrial development, and the limitation of agricultural lands makes the optimal and sustainable use of soil essential. ...
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Optimal utilization of production factors is considered one of the fundamental pillars of sustainable development. The increasing pressure on soil resources due to the population growth, industrial development, and the limitation of agricultural lands makes the optimal and sustainable use of soil essential. Kurdistan Province, as one of the agricultural hub, is suitable for the development of orchard. The aim of this research is to determine the land suitability for the development of walnut, almond, apple, pear, grape, and cherry orchards in 3,000 hectares of the downstream lands of the Azad Dam to establish the optimal planting patterns. This study seeks to establish optimal planting patterns using the FAO standard method for land suitability assessment. Climatic characteristics, slope, flooding, drainage, texture/structure, coarse fragment, soil depth, CaCO3, pH, EC and organic carbon were examined in this study. Results show that in the relatively suitable class (S2), almond occupy the largest area with 561.4 hectares; in the critical suitability class (S3), walnut cover 679.4 hectares; and in the unsuitable class (N), pear account for 1,275.1 hectares representing the most significant area. This crops have no limitations in terms of salinity and alkalinity. The minimum and maximum climatic limitations correspond to almond and pear, respectively. The most important limiting factors for these crops include slope, soil depth, and texture. In steep land with deep soil, following soil conservation principles can enhance effective utilization and improve the land's suitability class. In steep lands of the region, if there is appropriate soil depth and the principles of soil conservation are implemented, it is possible to reduce the limitations of slope and improve land suitability classes.
Seyed Masoud Soleimanpour; Omid Rahmati; Samad Shadfar; Maryam Enayati
Abstract
Field measurements of soil loss due to gully erosion are very time-consuming and costly, so direct measurement of gully erosion at large scales is a time-consuming, costly, and labor-intensive process. For this purpose, the present study attempted to accomplish this by modeling soil loss due to gully ...
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Field measurements of soil loss due to gully erosion are very time-consuming and costly, so direct measurement of gully erosion at large scales is a time-consuming, costly, and labor-intensive process. For this purpose, the present study attempted to accomplish this by modeling soil loss due to gully erosion using random forest and support vector machine learning models and evaluating their efficiency in the Mahurmilati watershed located in the southwest of Fars province. Field measurements of dimensional parameters of 70 gullies were conducted over four years (2021 to 2024). In the modeling process, 15 environmental factors were considered as independent variables and the rate of soil loss in ditches as the dependent variable, and modeling was performed with a cross-validation approach. The accuracy of the models was evaluated using quantitative criteria such as root mean square error (RMSE), coefficient of determination (R2), root mean square error (RSR), and correlation coefficient (d). The rate of soil loss in gullies during the study period was 15300.94 tons. The results of the model prediction accuracy evaluation showed that the random forest model has better performance than the support vector machine model in terms of evaluation criteria and was introduced as the superior model for predicting the rate of soil loss due to gully erosion. The findings showed that "modeling" can provide valuable services to water and soil conservation management in saving time and money. For this purpose, it is suggested that the use of artificial intelligence-based models and machine learning structures be given more attention in future research.
Reza Erfanzadeh; Fatemeh-al-Sadat Hosseini; Seyed Homzeh Hosseini Kahnuj
Abstract
In this study, the effect of the canopy of Pteropyrum aucheri on the soil seed bank was investigated in southern Kerman province, Sehchah village rangelands. By studying the seed bank at two depths of 0-5 and 5-10 cm under and outside the canopy of this shrub species, the potential of the soil seed bank ...
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In this study, the effect of the canopy of Pteropyrum aucheri on the soil seed bank was investigated in southern Kerman province, Sehchah village rangelands. By studying the seed bank at two depths of 0-5 and 5-10 cm under and outside the canopy of this shrub species, the potential of the soil seed bank in preserving and recovering of the valuable medicinal species Pergularia tomentosa was evaluated. For this purpose, 15 individuals of the P. aucheri were selected in which P. tomentosa could also be observed under the canopy of P. aucheri. Soil sampling was carried out from under and outside the canopy from both depths, totally of 60 samples. Under the canopy (and also outside the canopy), 10 soil augers were taken. The soil samples were transferred to the greenhouse in Faculty of Natural Resources, Tarbiat Modares University, and the species present in the soil were identified using the seed germination method. In the greenhouse cultivation, a total of 2164 seeds germinated. Of these, 1486 seeds belonged to the under-canopy (1156 seeds at the upper depth and 330 seeds at the lower depth) and 678 seeds belonged to the outside of the canopy (341 seeds at the upper depth and 337 seeds at the lower depth). The density of the seed bank at the upper depth was significantly higher in the under-canopy than outside the canopy. This study showed that the valuable species of P. tomentosa not only had a seed bank under the canopy but also did not form a canopy outside the canopy of P. aucheri. Therefore, for the conservation and restoration of this species, it is not possible to rely on soil seed bank.
Ahmad Zare; Sayed Amir Moosavi
Abstract
An experiment with 10 constant temperatures (5, 10, 15, 20, 25, 30, 35, 40, and 45°C), was conducted in 2019 at the Agricultural Sciences and Natural Resources University of Khuzestan based on completely randomized design (CRD) with 8 replications to investigate germination and determine the cardinal ...
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An experiment with 10 constant temperatures (5, 10, 15, 20, 25, 30, 35, 40, and 45°C), was conducted in 2019 at the Agricultural Sciences and Natural Resources University of Khuzestan based on completely randomized design (CRD) with 8 replications to investigate germination and determine the cardinal temperature of Physorhynchus chamaerapistrum (Boiss.) Boiss. The highest seed germination was 86% at 25°C. Germination percentages at temperatures of 10, 15, 20, 30, 35, and 40°C were 77%, 83%, 85%, 85%, 34%, and 13%, respectively. No germination was observed at 5 and 45°C. The relationship between imbibition time and cumulative germination percentage was described by a three-parameter sigmoid equation. According to this equation, the results indicated that the shortest time required to reach 50% germination (T50) was observed at 25 and 30°C (33 and 34 hours), and the longest at 10 and 40°C (106 and 141 hours). T50 for temperatures of 15, 20, and 35°C was predicted to be 76, 37, and 51 hours, respectively. According to the dent-like model, the base temperature, lower optimal temperature, upper optimal temperature, and ceiling temperature were predicted to be 16.5, 22.91, 29.52, and 43.45°C, respectively. The results of this study showed that this plant could have the highest germination percentage from November to January, considering the growth conditions. Therefore, during these months, plans for managing this rangeland plant (seed dispersal and rangeland restoration) and issues related to grazing management should be considered.
Zahra Heidari; Pejman Tahmasebi; Ali Asghar Naghipour
Abstract
Assessing rangeland health is of great importance due to its impact on ecosystem sustainability. This study investigated the relationship between evaluation criteria of rangeland health and functional diversity indices in part of the Sabzkouh Protected Area (Chaharmahal Va Bakhtiari Province, Iran). ...
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Assessing rangeland health is of great importance due to its impact on ecosystem sustainability. This study investigated the relationship between evaluation criteria of rangeland health and functional diversity indices in part of the Sabzkouh Protected Area (Chaharmahal Va Bakhtiari Province, Iran). For this purpose, four rangeland sites with different grazing intensities (reference, light, moderate, and heavy grazing) were selected. In each site, two representative areas were sampled using three 30×30 meter macroplots. Plant functional traits, including plant height, various leaf traits, mean crown diameter, and growth form, were measured. Functional diversity indices, including Functional Richness, Functional Evenness, Functional Dispersion, Rao’s Quadratic entropy and growth forms, were calculated using the FD package in R 4.4.2. Additionally, three evaluation criteria of rangeland health, including soil/site stability, hydrologic functions, and biotic integrity, were evaluated. The data were analyzed using ANOVA, correlation, and univariate regression tests, employing the aov and duncan.test functions and the R packages ggcorrplot, dplyr, reshape2, ggpubr, and ggpmisc for 21 data pairs. Results revealed that soil/site stability, hydrologic function, and biotic integrity had significant positive relationships with certain functional traits such as leaf dry weight (R² = 0.6), leaf area (R² = 0.5), leaf length (R² = 0.5), perennial forb (R² = 0.3), and perennial grass (R² = 0.5). Conversely, negative correlations were observed with other indices including functional divergence (R² = 0.5), functional dispersion (P < 0.05), mean crown diameter (P < 0.05), annual grass (P < 0.05), and shrub (R² = 0.5). These findings emphasize the importance of incorporating quantitative biodiversity indices alongside qualitative assessments to improve rangeland management and develop a quantitative evaluation protocol.
