جستجوی مقالات مرتبط با کلیدواژه "geographic information system (gis)‌" در نشریات گروه "اکولوژی"

 Agriculture is one of the most important economic sectors of the country, and it plays a vital role in achieving sustainable development, which requires the use of scientific principles and methods and the recognition of environmental capabilities. In this regard, there is a close relationship between agricultural development and environmental resources. In recent years, the level of agricultural land has decreased, and this decrease in land suitable for agricultural production can be attributed to the lack of optimal land use, which has directly affected the ability to produce food on a large scale and seriously affected food security. However, in many cases, agricultural systems have low production, which is related to management issues. Also, the climatic capabilities of the regions are sometimes effective as a limiting factor in crop production. Based on this, it is very important to identify the capabilities of the land before the performance of various activities such as agriculture. This study was carried out with the aim of agroecological evaluation of lands in Behshahr County (Yane-Sar region) for lentil cultivation using spatial analysis of geographical information system (GIS) and analysis hierarchy process (AHP) in 2021-2022.

 This research was carried out in the agricultural lands of the Yane-sar district of Behshahr county in Mazandaran province from 2021 to 2022. In order to prepare digital maps of soil characteristics of agricultural lands, agricultural soil samples were taken after the harvest in the summer of 2021. In this research, climatic variables (minimum, maximum, average temperatures and annual precipitation), topography (slope, aspect, and elevation above sea level), and soil characteristics (texture, K, P, Cu, Mn, N, Fe, TNV, organic matter, EC, pH) were used. Thematic maps for each variable were created using various models in ArcGIS 10.8 and then overlaid with AHP weights. The final map was classified into four categories: high suitability, suitability, semi-suitability, and non-suitability.

 The results showed that the range of electrical conductivity (EC) in the soil of agricultural lands of the study area was between 0.84-2.63 dS m-1. According to its distribution, most of it is located in the northwest and southeast of the region, and its lowest value is located in the central and northern regions. Also, the results showed that the distribution of soil organic matter (OM) is between 0.35-7.22 percent. The central and southern areas of this region had the minimum percentage of organic matter. The results showed that the most suitable areas for lentil cultivation are located in the northern, central, and southern parts of the region. In general, two classes of high suitability and suitability comprise about 56.97% of the area. One of the main reasons for this result was the suitable soil fertility, rainfall, and topographical conditions. Zones with semi-suitability and non-suitability cultivation potential were also included in 43.03% of the region's area. The results showed that the northwest, parts of the centre, and parts of the southwest were located in the semi-suitability class for lentil cultivation. 

Conclusion :

 Considering various factors to determine the suitable lands for lentil cultivation, it was found that most of the agricultural lands in the studied region were in zones of high suitability for lentil cultivation. Also, in the semi-suitability and non-suitability classes, low nitrogen amount in parts of the centre, high humidity and rainfall in the west of the region, high phosphorus amounts in parts of the central and northwest regions, the inappropriate texture in some fields and low rainfall were determined as limiting factors for the growth of lentil.

In recent years, the heightened concentration of greenhouse gases has brought increased attention to the pressing issue of climate change. Therefore, monitoring climatic variables to prevent the adverse effects of climate change is more important than ever. In the pursuit of long-term climatic forecasting and the assessment of their evolving patterns, various international scientific societies have concentrated their efforts on understanding the extent of climate change and devising measures to counter its adverse effects. The development of general circulation models of the atmosphere (GCM) has been a significant stride in this direction. However, GCMs may lack precision in predicting minor changes at a local scale. To address this limitation, the utilization of downscaling models such as SDSM and Lars_WG (that were used here) becomes imperative. These models serve as essential tools for simulating the viability of cultivating agricultural species in the future, especially when considering localized impacts. Because climate change will probably change the conditions for growing canola, as one of the strategic and prominent crops in Iran, studying the effects of this worldwide event on the canola-grown fields in the future is needed. 

In this research, using temperature and precipitation forecasting models along with GIS functions and hierarchical analysis process (AHP), canola suitability classes for 2050 were determined in Mazandaran Province. For this, 37 meteorological and synoptic stations were involved, and climatic data (including temperatures and precipitation) were generated under three RCP scenarios (2.6, 4.5, 8.5). In this study, we utilized two general circulation models (Can-ESM2 and HadGEM2-ES) that had been recommended for application in the study area.

A comparison of the involved models showed that the SDSM model was superior in predicting temperature, while the Lars-WG model performed better in predicting precipitation. The results for the land suitability revealed that the changes in climatic variables in the future would lead to changing the suitability of agricultural lands for growing canola. The examination of temperature change maps in the investigated region revealed that both minimum and maximum temperature variables are poised to rise under climate change scenarios, with a more pronounced increase anticipated in maximum temperatures. The findings indicate that the projected temperature increase in the future will establish more conducive conditions for canola cultivation. Additionally, precipitation patterns exhibit an increase in both RCP 2.6 and 4 scenarios, with a more substantial rise in the RCP 2.6 scenario. Conversely, the RCP 8.5 scenario predicts a decline in precipitation levels. Top of Form
Also, considering the climate change scenarios, the spatial distribution and the area of each suitability class changed slightly, so the high-suitable class will extend under RCP2.6, especially toward the center parts of the study area. Under RCP 8.5 and RCP 4.5 scenarios, not only suitable lands were not considerable, but also the less suitable land extended to the southern and western parts of the study area.

The output of the land suitability maps showed that climate change would change the suitability of the studied agricultural lands in the future. Also, with the implementation of the climate change scenarios, the area and geographical distribution of detected classes will change. In general, in the optimistic scenario, the lands will be more appropriate for canola cultivation and will cover a wider area. In other scenarios, the conditions for canola cultivation in the lands of Mazandaran will be more unsuitable compared to the present, and the decrease in the level of suitability will be more evident in the western and southern lands of the province. Therefore, solutions such as the use of more compatible cultivars and changes in agricultural management in facing new conditions in these areas should be considered. The outcomes of these studies offer practical insights for shaping regional planting strategies, aiding decisions on crop inclusion or exclusion, and informing the overall design of agricultural patterns in the area

Despite the importance of mangrove forests, mismanagement and insufficient knowledge about the environmental factors affecting these ecosystems is the first step in their sustainable management. In this research we extracted the drainage structure of watersheds from the digital elevation model (DEM) by 30 meters cell size and the 81 basins on the coastline of Hormozgan province were identified and partitioned. Then 22 morphological variables for each watershed were calculated using computational methods and geographic information system software features. In the next step, the principal component analysis (PCA) used for dimension reduction and feature selection to investigate the correlation between the formation of mangrove forests and upland watersheds characteristics. Based on principal components analysis results six watershed variables identified as independent variables and entered to the regression model. The results showed 93.21% of the variance of the dependent variables explained by these six variables which including: equivalent circle diameter, time of concentration (Kirpich method), Horton coefficient, the maximum elevation and the main stream variables. The equivalent circle diameter was the first effective factor and had a direct relationship while the Kirpich time of Concentration and Gravelius or compactness coefficient were also considered as very important factors with an inverse effect on the presence and area of mangrove forests downstream. Generally, the results of this study indicated a strong systemic relationship between upstream watersheds and coastal and downstream mangrove ecosystems, and in the management and protection of these ecosystems, the characteristics of the upstream watershed should be considered.

With increasing population growth and increasing waste production, the need to determine the appropriate landfill for waste has increased. Therefore, the need for planning to determine the appropriate location for sanitary landfill is very important and menstrual. The use of Geographic Information System (GIS) tools and Analytic Hierarchy Process (AHP) are one of the most effective tools for selecting landfills. The purpose of this study is to locate the suitable landfill for Sahneh County. In this research, slope, distance from river, land use, distance from road, distance from fault have been used to determine the landfill. In order to locating the landfill GIS for extraction and necessary processing and from hierarchical analysis (AHP) used to measure criteria. The final weight in Analytic Hierarchy Process is given the highest score to the distance of the roads and the lowest score to the distance from the river. Based on the findings of this study, landfills in the study area can be classified into five categories. Very suitable areas for landfilling are> 20% slope, distance from roads 10000-20000 meters, distance River <6000 m, distance from fault <7000 m, land use of rocky outcrops with an area of 8139.79 hectares and 59.5% have a higher priority for landfilling in compliance with health and environmental standards and laws. Also, the analysis of the results of this study shows that areas designated for suitable landfill have highly desirable.

Potato (Solanum tuberosum L.) accounts for the largest share in production of food products after wheat, rice and corn, which plays an important role in the nutrition and food basket of the world. Currently, the issue of food security and supplying is very important in different parts of the world and thus prediction of its demand that is increasing. Also, growing population will intensify this issue too. A key strategy to overcome the nutritional challenge of the growing population of the world is eliminating the gap between the current achievement in farms and the yield which can be achieved by using the best cultivars compatible with the environment and the best water, soil and plant management methods.

In this study, potential yield of potato was estimated using the SSM-iCrop2. then the production and yield gap of potato were investigated with two methods of Global Yield Gap Atlas (GYGA) and Arc GIS software by interpolation method for Golestan province. For this aim, the information of potato cultivation management in the province level and the daily data of 23 synoptic weather station as well as their soil data were used. Meanwhile, potential yield gap analysis protocol (GYGA protocol) was used to identify the main weather stations named reference weather stations (RWSs) and climates where potato is planted in Golestan province. Potential yield of potato was estimated within the area covered by each RWS, and then scaled up to the province level. In order to implement the interpolation method in ArcGIS software, initially, potential yield of 23 stations in the Golestan province was estimated. Afterwards, the potential and actual yield in the whole province was estimated using Kriging and IDW methods, respectively.

According to the Agricultural Jihad Report, the actual yield of Golestan province (a ten-year average) was reported as 22 t.ha-1, while the actual yield calculated based on the interpolation method was estimated equal to 22 t.ha-1. However, the actual yield calculated using the global yield gap atlas (GYGA) method was estimated as 20 t.ha-1. Heigh yield of potato in the province was reported by farmers up to 55 tons, which was considered as potential in the province to be compared with GYGA and interpolation methods, so that in the GYGA method, 52 t.ha-1 and in the interpolation method, finding was 42 t.ha-1. yield gap and relative yield in the GYGA method (33 t.ha-1 and 38 percent), and interpolation (20 t.ha-1 and 52 percent) were obtained. Also, in GYGA method, the values ​​were estimated using only two stations, but in the interpolation method the province's yield was estimated using 23 stations and daily meteorological data. The GYGA protocol is a bottom-up approach used by Hochman et al. (2016) to assess the variation of national yield from climatic zoning to analyze similar agro-cluster groups. So far, several studies have been done using this protocol. Gobbett et al. (2017) showed that wheat lands in Australia are located in six key climatic regions. They selected 22 reference stations in this area and calculated the limited water potential yield using the APSIM model (for the years of 1996 to 2010).

The results showed that both Global yield gap Atlas (GYGA) and interpolation method had accurate estimation of actual and potential production and yield, but those values obtained using statistics and 23 weather stations in the interpolation method, and using just 2 weather station (Hashemabad and Gorgan stations) in the Global Atlas method. As previously mentioned, the GYGA method is designed to estimate the yield gap at the national level, which can calculate the potential yield, production, and yield gap in a wide range even with the least amount of information. One of the advantages of GYGA protocol is that the results obtained at any point in the world are comparable to other areas and cultivations of that particular product. Based on the comparison made in this study, it was concluded that the Global yield gap Atlas method should be used to estimate the yield of the country.

Population growth over the last decades and consequently the need for food, have led humans to dominate more cultivated land. Inevitably, these lands require water, which can be possible by controlling the rivers water by constructing dams. Finding suitable land for agricultural purposes in a way that suitable conditions for cultivation and also water required for it can be provided by various methods such as controlling surface water (dam) and underground water (well drilling, etc.) It has always been the attention of experts. In this study, the feasibility of suitable agricultural lands in the upstream of the Javeh dam has been investigated. Accordingly, by assuming pumping water with different heads from the pumping station in the dam reservoir, taking into account different factors (slope, head, distance and land use), the site selection has been carried out. Spatial analysis was performed using ArcGIS software. By overlapping the factor maps and weighting each one of them, the appropriate ranges are derived based on the defined factors. The results show that by increasing the head twofold, the area of the selected range is almost doubled, while by increasing the head fourfold , the area of the selected range increases about six times. It should be noted that in this study, verification of the selected areas has done by using satellite images.

Application of new techniques in precision farming for the determination of soil and plant spatial variability in such matters as soil nutrients, soil physical and chemical properties have received great attention. This study was carried out in order to map the spatial variability of essential plant and soil variables associated with olive yield, through generating maps that can be used in site-specific crop management (SSCM). For this purpose, two orchards with optimum and low yields were selected in the Tarom region of Zanjan province and 65 soil and plant samples were taken during 2014-2015 on a 49-m systematic sampling grid corresponding to a density of four soil samples per ha. All the samples were analyzed using appropriate laboratory methods. In addition, the percentage of fruit set in both years was calculated by counting the number of flowers and fruits. An interpolation map of each variable was created by the inverse distance weighted (IDW) method using ArcGIS. The results showed that nitrogen and potassium accounted for 94.6% of fruit set variations and silt accounted for 93.9%, according to the fitted models using the stepwise regression method. In addition, nitrogen and potassium accounted for 94.6% and silt for 93.9% of the fruit set variations. In aggregate, the main reasons for the differences in fruit set in the olive orchards were including nitrogen and potassium deficiency, high silt, deficiency in available phosphorous, high SAR, TNV and EC. Interpolation maps showed that the spatial distribution of fruit set in both orchards was inversely related to yield limiting variables.

Understanding how a weed community is affected by ecological، agronomic and management factors is one of the most effective ways to control and establish management programs for each crop. In order to conduct weed mapping and study the weed population indices of weeds in corn fields of Kermanshah Province، 48 corn fields in different cities based on planting area were selected for sampling during 2010. After sampling the weeds based on standard methods، the weed population indices based on equations were calculated. Using geographic coordinates of field and weed data، a map for each weed species using GIS was produced. To evaluate the similarity of different cities of the province for weed species diversity and evenness، the cluster analysis method was used. The results showed that the dominant broadleaf weed species of Kermanshah Province were redroot pigweed (Amarunthus retroflexus)، common lambsquarters (Chenopodium album)، field bindweed (Convolvulus arvensis)، Chinese lantern (Physalis alkekengi) and common purslane (Portulaca oleracea)، respectively. The dominant grass weeds of Kermanshah Province were green foxtail (Setaria viridis)، Johnson grass (Sorghum halepens) and barnyardgrass (Echinochloa crus-galli)، respectively. Phytosociological studies on weeds in corn fields showed that، according to the Shannon-Wiener diversity index، Kermanshah’s cities were grouped into three clusters. Comparisons performed using the t-test showed that the diversity of cities of the province also have significant differences. The results of the Simpson similarity index showed that Ghasreshirin and Guilan-e-Gharb had the most common species (SQ=0. 82)، while Kerend City along with Ghasreshirin and Guilan-e-Gharb had no common weed species with the lowest similarity. Based on similarities and differences derived from weeds of corn fields in different cities، population parameters for the management of weeds in corn fields were developed and implemented using the same recipes.