فهرست مطالب مهدی نادی

The Hyrcanian forests in northern Iran exhibit diverse rainfall and air temperature patterns. Altitude above sea level is a crucial topographical factor that influences plant growth and development, prompting trees to adopt different morphological, anatomical, and physiological strategies for survival and growth at varying elevations. This study aims to understand the impact of altitude on radial growth and tree-ring width by examining the rate of radial growth of Oriental beech (Fagus orientalis Lipsky) across different age classes in three altitude zones: low, middle, and high altitudes of the Hyrcanian forests in Kordkuy and Asalem habitats.

t three selected sites, located at approximately 700, 1400, and 2000 meters above sea level, tree-ring cores were extracted from 62 beech trees using an increment borer. Following core extraction, the holes on the trees were completely sealed with grafting wax. The surface of each core was prepared using a sequence of 100-, 400-, and 1000-grit sandpaper. Tree-ring widths (TRWs) were measured using LINTAB 5 and TSAP software. Measurements were taken from one side of the core and then from the other side. The obtained TRW series were analyzed using statistical parameters such as sign test, correlation coefficient, and Student's t-test. After cross-dating, average TRW chronologies were prepared for each tree. This method was then used to match the trees within each site. TRW chronologies for each site were verified using statistical methods. A one-way analysis of variance (ANOVA) test was performed to compare TRW chronologies. Meteorological data such as rainfall and temperature were obtained from the nearest weather stations. Meteorological data for Kordkuy and Asalem were obtained from the Gorgan and Astara meteorological stations, respectively.

The findings demonstrated that the elevation gradient had a significant impact on the radial growth of beech trees. TRW in the examined habitats is influenced by elevation above sea level in trees aged 50 to 150 years. The average radial growth of these trees in the middle elevations of Asalem and the lower elevations of Kordkuy is substantially higher than in other altitude areas. The mean growth of trees aged 100-150 years in the middle elevations of Asalem was the highest, amounting to 3.98 mm. Statistical analysis revealed a significant difference between the TRW of trees aged 100-150 years in the highland of Kordkuy with a mean annual growth of 1.94 mm and the lowerland of Kordkuy with a mean annual growth of 2.97 mm. During this age period, no significant difference was observed between trees from the upperland of Kordkuy and the moderate altitudes of Kordkuy, but there was a statistical difference between trees from moderate altitudes and the lowerland. A comparison of trees aged 200-250 years revealed a statistically significant difference between Kordkuy and Asalem moderate altitudes. The mean radial growth of trees aged 300-350 years in the upperland and moderate altitudes of Asalem is 1.25 and 1.32 mm, respectively. Statistical analysis demonstrated that there is no statistically significant difference between the mean radial growth of trees aged 300-350 years in the upper and middle altitudes of Asalem and the lowerlands of Kordkuy.

This study demonstrated that radial growth and tree-ring width of beech trees exhibited notable variations along an east-west transect across the Hyrcanian forests. The observed variations in beech radial growth and tree-ring sensitivity along the elevational gradient highlight the ability of beech trees to develop adaptive functional traits in response to environmental changes.

Global warming cause changes in temperature variables and consequently increase in the occurrence of extreme events like as floods and droughts, result from extreme weather in most cases; Therefore, it is important to study and investigate the future changes of extreme values of meteorological and hydrological parameters, including temperature, more than the average or median value. Therefore, the purpose of this research is to investigate the effects of climate change on the trend of seasonal changes in extreme temperatures (very high and very low values) in coastal stations of Mazandaran province based on CMIP6 models and quantile regression method.

In this research, in order to investigate the trend of extreme values of temperature in Mazandaran province for the future periods, the output of the sixth generation of climate change models (Coupled Model Intercomparison Project phase 6, CMIP6) was used. For this purpose, minimum and maximum temperature of 4 main synoptic stations of the province, including Babolsar, Qarakhil, Ramsar and Nowshahr, and also the output of the NorESM2-MM climate model from the CMIP6 in near future (2026-2055) and far future (2071-2100) at three optimistic (SSP126), moderate (SSP245) and pessimistic (SSP585) scenarios was used. In order to downscaling climate model data at the studied meteorological stations, various methods in the CMhyd downscaling software were used, and the Temperature data output for the method that had higher accuracy, was selected to analysis of trend. In the next step, the quantile regression method was used to investigate the seasonal trend of temperature extreme values and the results were analyzed.

The results of downscaling using different methods have shown that the Variance Scaling method has the best performance among the available methods in CMhyd. In general, the results show a positive temperature anomaly (annual temperature increase compared to the base period in SSP126=1.3, SSP245=2.56, SSP585=3.2 oC) in Mazandaran province in all months of the year until the end of the 21st century. The intensity of the anomalies is higher in the warm months than in the cold ones. Under the optimistic scenario, the extreme values of the temperature variables in the spring and autumn seasons will decrease by a maximum of 1 degree per decade in the distant future. However, the average minimum temperature is increasing in all scenarios (including the optimistic scenario) and the decrease is only predicted for extreme values. But under the average and pessimistic scenarios, there will be an increase in the temperature extreme values in all seasons, so that its intensity will be greater for the pessimistic scenario and in the spring season (0.15 oC per year).

According to the results, it concluded that the significant increase in extreme temperature during the day and night, especially in the warm months of the year, will increase evapotranspiration, and along with the decrease in rainfall in the hot months, it will cause a decrease in water resources in different parts of Mazandaran province and pressure on the groundwater. Therefore, it is very important to formulate and implement appropriate management programs according to the needs of each region, in order to adapt to extreme temperatures and their adverse consequences.

Accurate spatial estimation of Precipitation and temperature is very important in hydrological models. Despite the development of automatic meteorological stations in recent years, obtaining reliable climate data in data-deficient areas is still a big challenge. Spatial estimation of climatic data is mainly done by geostatistical methods and satellite images. Nowadays, satellite products are widely accepted in the preparation of climatic maps. These products use satellite images to estimate temperature and rainfall data in points without data, and usually the provided data is accompanied by errors and needs to be recalibrated. It seems that the combination of covariates and satellite products can be effective in increasing the accuracy of climatic maps, especially in areas with complex topography such as Mazandaran province.

In this research, the accuracy of temperature and rainfall products of TRMM satellite was evaluated in Mazandaran province and the possibility of combining them with land features of latitude, longitude and elevation in the form of regression model was investigated. In this regard, the monthly rainfall data of 21 meteorological stations and 48 monthly and 4 annual images of TRMM products in 2014 and 2017 were used. The evaluation indicators are root mean square error (RMSE), mean bias error (MBE), mean absolute percent error (MAPE). Also, the annual temperature and rainfall maps of the province were drawn by satellite products and modified method.

The results showed that the TRMM products have a huge bias error, so that the amount of annual rainfall bias in some years reaches more than 180 mm per year. About the temperature products the underestimation error is more than 2 Celsius degrees. The correlation coefficients of land features and temperature and precipitation data in most of the months provided acceptable results and were significant in 95% confidence level. In general, the relationship between monthly temperature and, latitude and TRMM products was significantly positive in all the investigated months. in the case of altitude, the relationship was negative and strong. But the relationship between temperature and longitude was a little weaker than other covariates. Regarding the precipitation variable, satellite products have a positive and significant relationship in all the investigated months, and the altitude has a negative effect on precipitation data except in the spring months, but the latitude has a positive relationship in the cold months and in the warm months has almost a negative relationship, and no specific seasonal trend was found in the case of longitude. Also, the correlation coefficients of TRMM products with temperature and precipitation data was significant in 100 and 77% of the months, respectively. Investigating the possibility of combining the TRMM products with the latitude, longitude and altitude in a form of regression equation to estimate temperature and precipitation data showed that the hybrid method increased the accuracy of satellite productions, impressively and the error of rainfall and temperature products reduced by about 30% and 70%, respectively. But as expected, the spatial estimation error of precipitation data was higher than temperature in all investigated months. The annual rainfall maps of Mazandaran for the years 2014 and 2017 shows the higher accuracy of the hybrid methods compared to satellite products. So that it shows well the rainy area of the west coastline and also depicts the meridional and altitudinal gradients of precipitation in the Mazandaran province as well. Examining the annual isothermal maps showed that the drawn map with the correction method has a significant difference with the temperature product of the TRMM satellite and has well highlighted the ring of cold areas of Alborz Mountain range and the foothills of Damavand and Alam-Kouh peaks. Also, the modified map has correctly distinguished the temperate coasts of the southern Caspian Sea from the Middle Band and Upper Band regions. In addition, the higher temperature of eastern half of Mazandaran compared to the western half has shown well.

The results of the present research showed that the TRMM temperature and rainfall products alone do not have proper accuracy in the spatial estimation of climate data and have a large bias error, but their combination as a covariate, along with Longitude, latitude and altitude in a regression equation, improved the accuracy of temperature and rainfall maps, and can be used as a new post-processing method in modification of satellite products.

The purpose of this research is to evaluate the accuracy of GPM satellite precipitation products in Mazandaran province. In this regard, the accuracy of satellite precipitation products was compared with weighted interpolation methods of image distance and kriging. For this purpose, the data of 21 meteorological stations and 24 monthly images and 2 annual images of GPM satellite in 2016 and 2018 were used. The evaluation of the methods was done using the mean square error, skew error, and absolute mean error percentage indicators. Also, annual rainfall maps of the province for 2016 and 2018 were drawn and analyzed. The results showed that GPM satellite precipitation products have a high bias error, so that the amount of overestimation/underestimation of this satellite reaches more than 140 mm per year. Of course, the interpolation methods of kriging and weighted image of the distance also have a significant error and do not accurately distinguish between the low and high rainfall rings of the province. Investigating and evaluating the possibility of combining the precipitation products of the desired satellites with latitude and longitude variables and altitude in most of the 4-dimensional gradient equations also showed that the combined Grad-GPM method has significantly increased the accuracy of the spatial estimation of precipitation and the error of interpolation methods. And the satellite reduced the GPM by about 25 to 40 percent. In addition, the examination of precipitation maps also shows the increase in accuracy of combined methods compared to other methods.

Drought is a major threat to vegetation and can cause its decline. Due to natural and human factors, droughts are becoming more frequent and intense around the world. This research examines the trend and severity of droughts in the past and future using the standardized precipitation evapotranspiration index (SPEI) for a Juniper forest reserve in Keygooran, Lorestan Province, Iran. The SPEI index was calculated using daily precipitation and temperature data from the Aligudarz synoptic station for a 33-year period (1987-2019). The frequency and severity of droughts were assessed for the recent past (1987-2019) and the future (2030-2060) using the NorESM2-LM model under the SSP126, SSP370 and SSP585 scenarios. The results showed that the longest droughts occurred in 1990-1991, 1998-2000 and 2010-2012. Moreover, 2003, 2013, 2017 and 2018 were the years with the most severe droughts. In the future (2030-2060), drought periods will be projected to decrease under the SSP126 and SSP585 scenarios (6, 9 and 12 months) and increase under the SSP370 scenario (3, 9 and 12 months). The intensity and duration of extremely severe droughts will be projected to increase in the future despite the decrease in drought frequency based on SPEI. Therefore, the Juniper forest reserve will face more severe droughts in the future, which will affect the reproduction of juniper trees significantly.

One of the most important challenges of climate studies in Iran is the lack of access to long-term climate data. Tree climatology, by examining the effects of climate on the annual rings of trees, provides the possibility of reconstructing the temperature and precipitation of past periods. The main goal of this study is to reconstruct precipitation from the width of annual circles of juniper trees (Juniperus polycarpus K.Koch) in the Garkhod region, located in the northwest of Khorasan province in Iran. The samples were prepared from the juniper trees with a chronometer drill and after initial preparation, they were read by LINTAB device and their time agreement was checked with TSAPWIN software. The final chronology was prepared using ARSTAN software and based on reconstructing the precipitation in the period 1850-2020. The results showed that the widths of juniper annual circles were not affected by temperature fluctuations. Still, the total precipitation of January-May showed the maximum positive and significant correlation coefficient with the growth of these trees. Between the months of the year, February, March, April, and May had the most positive and significant correlation with the width of the annual circles. In general, during the period of 1850-2020, 8 years except for positive symbolic years and 9 years were reconstructed as negative years. The highest precipitation occurred in 1864 and the lowest in 1917. It can be said that the precipitation influences the growth of juniper trees in the region. In this way, suitable conditions for the activity of cambiums are provided with the increase in precipitation.

This study investigated the influence of land-use change on soil quality in the Alandan area of Sari, with five different types of land-uses, namely pure beech stand and destructed forest stand, ash plantation, pine plantation, and arable land. Soil samples were taken from a soil depth of 0-10cm, using six plots (20×20m) in each site, which were selected by random systematic sampling network (50×100 m). The different physical, chemical, and biological characteristics of soil were measured in the laboratory. To calculate the soil quality index, characteristics such as field capacity, bulk density, geometric mean diameter, calcium, magnesium, and phosphorus availability, total nitrogen, ammonium, nitrogen microbial biomass and urease, acid phosphatase, and invertase enzyme were selected as the minimum data set by principal component analysis. In this study, additive and weighted indices were used to evaluate soil quality. The results showed that the higher amount of both additive and weighted index of soil quality were observed in ash plantation (0.76, 0.25), and their lowest amount was found in pine plantation (0.58, 0.16) and arable land (0.54, 0.15), respectively. It should be noted that the weighted index of soil quality in ash plantations was significantly higher than in different pine plantations and arable land. Also, the results of this study confirm that rehabilitation of degraded forests using coniferous species, which has led to a decrease in soil quality, such as soil condition in agricultural land, is not recommended.

The study of temperature fluctuations is essential for agricultural planning and environmental studies. With global warming affecting various regions, access to long-term temperature data is becoming increasingly important. Dendroclimatology is a useful tool for long-term climate study, and in this study, the average habitat temperature was reconstructed after developing the chronology of Juniperus polycarpus trees in Keygoran site (Aligodarz, Lorestan). The correlation of annual tree rings with climatic variables of meteorological stations was investigated to reconstruct the average temperature in January-March for the period 1910-2014 for this region. The findings show a positive and significant correlation between the average January-March temperature and the growth of annual tree rings. The results show the occurrence of cold years in the first years of the 1910s to the first half of the 1960s. Warm periods have occurred since the second half of the 1960s, and the temperature trend has generally been increasing in recent years. An examination of the positive and negative pointer years also shows that most of the positive pointer years occurred in the second half of the twentieth century.

The scientists believe that human intervention has caused changes in climate conditions and these changes will be noteworthy in the future. Any significant change in climate variables can affect the milk production of industrial livestock farms.

In this research, the effect of temperature-humidity Index (THI) on milk production in Babolsar city was quantified, and then, using the SDSM model, the THI index was downscaled to the year 2100. Finally, the effect of climate change on milk production until the end of current century in three emission scenarios of RCP2.6, 4.5, and 8.5 were quantified.

The results showed that in the current situations, Babolsar station has been involved in temperature-humidity stress for 5 months of the year, which will increase to 7 months by the end of this century. Also, the results showed that in near future (before 2050) the stress condition is not much different from the base period, but after that, even in RCP2.6 scenario, the differences with the base period will increase significantly, and the most intensity of stress level will occur., As a result, on more days of the year, heavy livestocks will experience tempreture-humidity stress. Quantification of the effect of temperature-humidity stress on milk production showed that for increase of one unit of daily THI more than 70, the milk production of each animal decreases by 327 grams. The investigation of milk reduction trend in the future showed that until the year 2100, not only the milk production will decrease in more months of the year, moreover, the daily losses of livestock farmers also increase. so that in the stressful months, the daily milk reduction from 1 to 2 kg per animal in base period, will increase to 1.5 to 3.5 kg. So, the losses caused by climate change on milk producers will increase by at least 45 and at most more than 100 percent.

According to the results of this research, the stress level of emergency will occur in the last decades of this century, which will lead to a sharp decrease in milk production. Therefore, if adaptation policies to climate change are not considered in the future, there may be happens widespread deaths of heavy livestock especially in critical months of July and August, which is a serious alarm for livestock industry of the Mazandaran province.

Due to the negative effects of agricultural production activities on the environment, especially water and soil pollution, one of the most important decisions in the agricultural sector is the optimal allocation of resources. This decision should be in such a way that while maximizing the profit of farmers, it will result in less environmental effects. This action is often done by determining the optimal cropping pattern (CP). In this research, by quantifying the economic, social and environmental effects, a compatible CP with agricultural resources was presented by using a multi-objective planning model.

The social effects of different agricultural crops were calculated using various indicators such as social solidarity, social security, participation and quality of life through interviews with farmers. The environmental effects and economic efficiency of the CP were also considered through the concept of life cycle assessment (LCA) and gross margin per ha, respectively. Further, by calculating socio-economic and environmental indicators, the optimal CP was formulated by developing a multi-objective function based on maximizing profit, reducing water and fertilizer consumption, reducing negative environmental effects of production and improving social indicators. In order to solve the multiple programming model, the method of weighted LP-metric model was used. The information required in this study included information on the production pattern, consumption of inputs, price and yeild of major agricultural crops of Sari County.

The results showed that considering the social indicators, the least attention of the farmers was related to corn and onion, and the five priority crops were identified as wheat, cotton, lentils, rice and canola, respectively. The results of LCA showed that the cultivation of tobacco, canola and corn in this city have the most negative environmental effects. In the optimal CP by combining economic, social and environmental goals, alfalfa, cotton and corn were removed from the stydy area, and the cultivated area of cucumber and clover showed positive changes compared to the current pattern. Also, the cultivated area of the cereal decreaseed in the areaChanges in the cultivated area of barley were predicted more than wheat and rice. The total cultivated area reduced by 15%, resulting in 12.91% and 14.46% reduction in water and fertilizer consumption, respectively. In addition, the efficiency of the program in the studied area decreased by 12.97%.

The development of environmental goals in the implementation of CP programs requires that policymakers consider appropriate economic incentives for farmers. Therefore, policy makers should find suitable solutions to make the farmers to follow the proposed CP.

Precipitation is one of the basic elements of Hydrometeorological studies. Due to the limited number of rain-gauging stations and the lack of meteorological stations in highland area, the use of satellite rainfall products as an effective tool in predicting the regional spatial distribution of precipitation data has attracted the attention of researchers. But its disadvantage is the high estimation error of rainfall estimation, which requires accuracy evaluating before using. For this purpose, based on the rainfall data of 21 meteorological stations of Mazandaran province from 1998-2018, the performance of TRMM data and two interpolation methods of kriging and inverse distance weighting were investigated. The results showed that contrary to the proper correlation of TRMM data with observed data, this network has a high bias error. For example, in the case of annual rainfall data, the network bias error reached 70 mm, which is approximately 30% of the annual error. The modification coefficients of TRMM data were found between 0 and 2, that most of the coefficients were higher than one, which indicates the TRMM data in Mazandaran mostly has an underestimation error. The comparison of modified TRMM with the interpolation methods showed that using the modified network instead of interpolation methods in estimation of rainfall data reduces the mean square error of annual rainfall from 237 to 186 mm.

IGF-1 is a polypeptide hormone that plays a vital role in the development and regeneration of skeletal muscles.IGF-1 activates anabolic pathways such as PI3K and IRS-1/2 to activate a series of intracellular anabolic signals. The purpose of this study was to investigate the effect of 8 weeks resistance training on IGF1, PI3K and insulin sensitivity in neural muscle in type 2 diabetic rats.

In this experimental study, 14 male Wistar mice aged 8 to 10 weeks and induction of type 2 diabetes (type 2 diabetes was induced by intraperitoneal injection of nicotinamide and STZ in diabetic groups) and were divided into two groups of resistance training and control groups. The mice in the control group did not exercise during the study, while the resistance group performed 8 weeks of training for 5 sessions per week. Resistance exercises in each session were performed in the form of 10 repetitions with 90-second rest intervals in the form of climbing a 26-step ladder to a height of one meter with a vertical slope of 85%, and weight was applied to the base of the rat's tail. 48 hours after the last training session, blood samples were taken from the heart and neural muscle tissue was extracted. Real Time-PCR method was used to determine the genes and one-way ANOVA test was used to compare the variables. All statistical studies were done using SPSS version 16 software.

The results of this study showed that expression of IGF-1 and PI3K in the duodenal axis of diabetic rats in resistance training group was significantly higher than that of the control group (P = 0.001) (P = 0.11). Serum insulin sensitivity in diabetic rats was not significantly different in diabetic rats compared to the diabetic rats (P=0.778).

It seems that eight weeks of resistance training can be a strong stimulus on IGF-1 and PI3K gene expression and no change in insulin sensitivity in neural muscle in type 2 diabetic rats.

Saffron (Crocus sativus L) is an expensive and highly valuable plant recognized globally for its many medicinal properties and low water demand. Hence, the development of saffron farms in semiarid regions has received much attention as it increases water use efficiency (WUE) in such water-deficient areas. Considering the different factors involved in saffron growth and yield, the geographical information system (GIS) is the best and most cost-effective method to identify areas potentially favorable to saffron cultivation. The current study aimed at zoning the land area favorable to saffron cultivation in Kerman Province was conducted over the period 2017-2019. The required daily data were collected from 13 synoptic stations recorded during the years 1998 to 2016, which were subjected to the homogeneity test before the relevant GIS information layers including altitude, slope, frost, rainfall, absolute maximum and minimum temperatures, and probability of temperatures below 5º Celsius in November were prepared. Subsequently, the relevant variograms were drawn. Also, the slope map was extracted using the digital elevation model. Finally, weights were assigned to the above layers using the Analytical Hierarchy Process (AHP) technique, and the final zoning map was prepared in ArcMap. The map thus prepared revealed that the southern and eastern stretches of the province were not suitable for saffron cultivation while the more favorable areas were located at the foothills in Baft, Bardsir, Zarand, and Shahrbabak towns. Moreover, Kahnuj, Manojan, southern parts of Jiroft, eastern stretches of Bam, and southern stretches of Baft, accounting for around 59% of the total provincial land area, were recognized as unfavorable to saffron cultivation. The remaining lands of the total provincial area were classified as follows: 15% relatively suitable, 16% suitable, and about 10% highly suitable for saffron cultivation.

Closing the gap between actual yields which are currently obtained from the fields (actual field yields) and the maximum yields that can be achieved under favorable management conditions (attainable yield) is one of the most critical problems in crop production in Iran and the world. Quantifying the yield gap and identifying its primary causes for this purpose can be a key and promising strategy to increase production per unit area and to achieve food security. Meanwhile, considering the importance of oilseeds like soybean [Glycine max (L.) Merril] in the global economy and the need for edible oils and vegetable proteins, estimating and identifying the causes of the yield gap, while increasing production, it can improve land use and labor efficiency, and leads to costs saving and sustainable production. In this way, a survey study was conducted in 2019 to identify and determine the share of each of the managerial factors affecting the soybean yield gap in 301 farms in Mazandaran province.

In the present survey, CPA method was used to investigate the decision-making factors which limit soybean yield and to estimate its yield gap in 13 soybean cultivated cities of Mazandaran province. For this purpose, all information relevant to crop management, from seedbed preparation to harvesting, was gathered through interviews with 301 soybean producers. These management factors included items such as use or non-use of a plow, number of secondary tillage, planting method, field area, previous crop, the used cultivar, seed rate and its preparation site, inoculation/non-inoculation of seeds with N-fixing bacteria, type and the amount of fertilizers, type, amount, and number of herbicides, fungicides, and insecticides usages, number of irrigations and the amount of used water, irrigation type and harvesting methods. Field monitoring data included 81 quantitative and qualitative crop management variables that the relationship between all these variables and actual field yields was analyzed using stepwise regression in SAS (v9.1) software. In the end, the contribution of each limiting factor to the creation of a yield gap was determined using the resulting production function and management parameters values.

The findings of this study revealed that of the 81 studied management variables, the final production function with 10 independent variables was selected. These variables included the sari variety (J.K-695), previous crop (faba bean), use the row planter, number of secondary tillage, sulfur rate, application of herbicide, KCl, and (NH4)2HPO4 fertilizers, sprinkler irrigation system, and surface irrigation, which were identified as the main limiting factors for soybean yield in Mazandaran province. Based on the difference between the average actual yield recorded on farms (2464.97 kg ha-1) and the maximum attainable yield (6028.66 kg ha-1) predicted by the production function, the yield gap was 3563.70 kg ha-1, which is three factors including sulfur rate, previous crop (faba bean) and sprinkler irrigation system with the values of 873.87, 620.49 and 546.33 kg ha-1, respectively, had the highest share (24.52, 17.41 and 15.33%, respectively) in of the soybean yield gap.

According to the predicted attainable yield which derived from actual farm data it seems that adoption of proper crop managements such as using sulfur fertilizer resources appropriate to plant needs, an appropriate crop rotation and selection of plants suitable for rotation, providing facilities and modernization of agricultural machinery, and develop and promote new irrigation can reduce this gap (59%) and significantly improve soybean yield in Mazandaran province.

The present study was conducted for investigation the trend of soil carbon, total nitrogen and soil aggregate stability changes with increasing depth under different land uses in Alandan area–Sari. The soil samples were taken from soil depths 0-10, 10-20, 20-30, 30-40, 40-50 cm using coring (8 cm diameter) and auger method in each site systematic randomly (n=6). The soil texture, soil pH, the percentage of CaCO3, organic carbon, total nitrogen and geometric mean diameter (soil aggregate stability index) was measured in the laboratory. The result showed that, soil carbon and nitrogen were significantly (p<0.05) affected by soil depth and land use change. However, the geometric mean diameter was only affected by soil depth. The compared means showed that, the amount of carbon, nitrogen and soil aggregate stability index is significantly reduced with increasing soil depth. In the surface soil layers, the highest carbon (4.6%) and nitrogen (0.31%) were found in ash plantation and theirs lowest (2.5 % and 0.15% respectively) were observed in pine plantation. Also, soil organic carbon is significantly (p<0.05) increased with increasing geometric mean diameter.

Food security and sustainable agriculture are among the most challenging issues for the human being in the present age. These daunting challenges have arisen in the face of dramatic population growth as well as rising living standards, lifestyle, and, as a result, increased demand for a variety of foods. Due to limited water resources and arable lands, to achieve more production, it is necessary to increase the yield per unit area by using more materials (e.g., chemical fertilizers, pesticides, and fossil fuels) and energy. Obviously, in addition to the higher cost, it will lead to irreparable environmental consequences. In this regard, to achieve more sustainable production of agricultural products, there is a need to carefully evaluate the environmental status of these products in order to identify environmental hotspots to optimize them. Due to this, a study was conducted to assess the environmental damages to the soybean production system in different cities of Mazandaran province with a life cycle assessment (LCA) perspective. 

In the present survey, LCA approach was applied to evaluate the environmental impact and determine the most important hotspots of soybean produced in the cities scale of Mazandaran province (13 cities with soybean cultivation). LCA is a method that examines the environmental impacts associated with a product (or process) throughout its lifetime by accounting for resource consumption and pollutant emissions. For this purpose, the farm gate was considered the system boundary. Necessary data for LCA inventory were also obtained through face-to-face interviews with 303 farmers in 2019 and the EcoInvent 3.5 database in SimaPro 9.0.0.49 software. The potential environmental impacts were assessed by the IMPACT2002+ (v2.15) model in the software based on 1 ton of soybean seed produced as the functional unit (FU). Finally, impact assessment results of the soybean production system were presented in four environmental damage groups: human health, ecosystem quality, climate change, and resources. 

The findings revealed that soybean cultivation in the region and under the current conditions of inputs consumption has the highest impact on human health indicators that direct emissions from diesel fuel combustion and application of chemical fertilizers in the field play a major role in this damage category. Direct emissions from field operations claimed the highest share (>51%) in increasing global warming potential, which was mainly a result of CO2 emissions from burning diesel fuel. In addition, the findings of the present study showed that diesel fuel, followed by nitrogen fertilizer, had the most substantial role in the resources damage category. More generally, the total environmental damage for the production of 1 ton of soybean seed in surveyed cities ranged from 228.76 to 439.77 mPt, among which the cities of Galugah and Qaemshahr (228.76 and 261.18 mPt, respectively) had the least and Amol, and Sari (438.06 and 439.77 mPt, respectively) had the most value. To sum up, the LCA approach has been able to quantify the contribution of the soybean production system in causing environmental damage in the form of different damage categories by considering the amount of each of the inputs and their emissions based on a specific FU. 

In general, it can be concluded that diesel and chemical fertilizers inputs and direct emissions caused by their consumption on farms were the most substantial environmental hotspots. These factors, in turn, arise from the mismanagement of soybean farms in Mazandaran. Hence, it seems that efficient management practices by informing farmers and support of the authorities to provide appropriate facilities to farmers can be an important step toward reducing the environmental consequences and developing the cultivation of this valuable crop in the region. 

Acknowledgments

The authors would like to acknowledge the financial support of Tarbiat Modares University and the assistance of the esteemed officials of Sari Agricultural Sciences and Natural Resources University, Agriculture-Jihad Organization of Mazandaran province, and especially the honorable farmers of this province to collect data for this study.

Precipitation estimation and preparing of accurate rainfall maps in regions with no recorded climatic data is critical in hydrology and meteorology studies. This is more important especially in desert areas that do not have dense meteorological stations. The dense network of Aphrodite daily precipitation provides daily rainfall data at 0.25 * 0.25 degrees in Iran. But before using it must be evaluated and corrected.

The aim of this study was to investigate the accuracy of Aphrodite network precipitation data in four desert catchments of Iran from the Central Plateau catchment of Iran in monthly and annual time scales. For this purpose, long-term monthly and annual average data of 9 synoptic and 2 rain gauge weather stations of the Meteorological Organization were used. The climate of the stations varies from extra arid, arid and semi-arid. To validate the data at each time scale, the statistics of correlation coefficient, root mean square error and mean bias error were used. Then the network data were modified by coefficient of means ratio. Then, the accuracy of the modified data was compared with the two usual methods of kriging interpolation and weighted inverse distance.

The MBE showed that Aphrodite network data have a good correlation with station data, but it seems that rainfall network estimation is more than the actual data, which some correction coefficients is used to bias correction of rainfall data. The correction factors vary between 0.60 in low rainfall months to 0.94 in high rainfall months. Evaluation of the modified network in different months showed that in addition of solving network over estimation problem, the error was greatly reduced. as well as in the case of annual rainfall, the root mean square error was reduced from 71.25 to 15.19 mm, that indicating, increase network performance by five times after applying correction coefficients.

  Comparison of modified network with the interpolation methods showed that, the modified network is much more efficient than the two interpolation methods. Therefore, the use of obtained correction coefficients for Aphrodite network modification is recommended to improve the accuracy of the Aphrodite precipitation network.

Vegetation is very important in providing organic matter, regulating the carbon cycle and exchanging energy at the surface. In recent years, climate change and global warming have created frequent events such as floods, high temperatures and droughts that can damage terrestrial ecosystems. Weather fluctuation due to climate change directly affects vegetation growth, on the other hand, vegetation respond to climate change by regulating water, energy exchange, and carbon dioxide concentrations.

In this study, which was conducted to investigate the trend of vegetation changes in the study area during the period 2001-2018, 16-day combined time series data of MODIS-NDVI called MOD13Q1 with a spatial resolution of 250 meters was used. In this study in order to investigate the significance trend of vegetation cover, the non-parametric Mann-Kendall method was taken. Also the relationship between vegetation changes and altitude was investigated.

Of the total area of the study area, 52% of the area had a decreasing trend of vegetation and the rest showed an increasing trend of vegetation, although a significant decrease in vegetation at the level of 5 and 1% occurred in 36% and 32% of the area, respectively. Also, 31 and 26 percent of the study area had a significant increase in vegetation at the level of 5 and 1 percent. In the study of the relationship between Z-Kendall statistic and height, the results showed that with increasing the height of Z-Kendall statistic increases and the correlation coefficient of height with Z-statistic is about 0.62. It seems that significant positive trends in vegetation occur at higher altitudes and significant negative trends in vegetation occur at lower altitudes. 99% and altitudes of 670 and 840 were obtained for the negative trend of 95 and 99%. In other words, at altitudes above 2030 and 1860, the trend of vegetation changes is positive and at altitudes below 670 and 840 meters, the trend of vegetation changes is significantly decreasing.

The results of this study showed a significant trend of greening at altitudes of more than 2030 meters in the region. It seems that with the increase of temperature due to climate change at elevated area, suitable temperature conditions and increasing of growing season length is provided for crop growth at altitudes. This increase in vegetation was further observed in the east and northeast of the study area. Also, the significant decrease in vegetation in low altitude areas less than 670 meters can be due to increased water requirement of low altitude plants and the occurrence of temperature stresses in these areas, which are mostly in the eastern, southern and low altitudes of the study area. However, it seems that the area between these two altitudes have not had a significant trend in vegetation changes.

Rising greenhouse gases and the resulting increase temperature have disrupted the balance of the earth climate system. Combined increase in temperature and humidity above a certain threshold causes stress in livestock like dairy cows, which reduces milk production. To assess the effect of heat stress on livestock, a so-called temperature-humidity index (THI) is used. The aim of this study is to investigate the possible effects of climate change on THI in two stations located in north of Iran namely, Babolsar and Ramsar For this purpose, the projected values of THI under three climate change scenarios i.e. RCP2.6, RCP4.5, RCP8.5 were downscaled using SDSM weather generator. The results showed that there exist a significant positive trend in THI time series in both baseline (1961-2005) and future periods. According to results of this study, it is projected that number of days with heat stress would increase and more severe classes of THI will occur. The findings also indicated that the stress conditions in Babolsar have been more severe than Ramsar, such that, during the baseline period, Ramsar and Babolsar stations had 4 and 5 months of a year with heat stress , respectively, while it is projected that by the end of the century, these figures will increase to 6 and 7 months, respectively. Besides, the results showed that in the baseline period days with severe stress class (risk of mortality of dairy cattle) were not recorded in any of the studied stations, but for the far future period (2071-2100) in Ramsar and Babolsar stations, severe condition is projected to occur in two and six days of the year, respectively, which is a serious warning for the province's livestock industry.

Suitable prediction of climatic parameters is considerably important for adapting to climate change and mitigating vulnerabilities in local scales. In current research, for climate change assessment, the CanESM2 model in the 5th IPCC report and Statistical down scaling model (SDSM) was used under three scenarios: RCP2.6, RCP4.5 and RCP8.5. The input data were minimum and maximum temperature and solar radiation of Gharakheil synoptic station in Ghaemshahr and NCEP data (National Environmental Prediction Center) for the base period (1984-2005). Sunshine hours were converted to solar radiation using adjusted Angstrom formula for climate condition of the north of Iran. Future climate scenarios were then developed for early-future (2011–2040), medium-future (2041-2070) and late-future (2071-2100). The results showed that during these periods, solar radiation, maximum temperature and minimum temperature will increase by 4-9 %, 5-24% and 8-34% compared to the base period. The largest increases (5.30c and 4.20c for maximum and minimum temperatures respectively, and 1.3 Mj/m2 for solar radiation) will occur during late future period under RCP8.5 scenario. Since temperature and radiation are the main affecting factors for crop growth and yield simulation, the planning is essential in order to choose the appropriate planting date to adapt to climate change in the study area

The most common methods used to estimate and spatialise precipitation data are geostatistic methods and satellite images. Today, satellite rainfall products have become very popular in preparing rainfall maps. These products use satellite imagery to estimate precipitation in places with no observed data and are usually associated with a large error and require calibration. Interpolation methods also estimate precipitation data using recorded data. It seems that the combination of interpolation methods and satellite images can be effective in increasing the accuracy of rainfall maps, especially in areas with complex topography such as Mazandaran province

In this study, in order to evaluate different methods of estimating precipitation in Mazandaran province and combining satellite images with interpolation methods, precipitation data from 21 synoptic and rain gauge stations and 24 monthly images and 2 annual TRMM satellite images were used in 2012 and 2015 that the spatial resolution of this satellite product is 0.25 * 0.25 degrees. The studied interpolation methods included Kriging and Inverse Distance Weighting. Also the accuracy of rainfall products of TRMM satellite was investigated. In addition, to increase the accuracy of rainfall maps, multiple linear regression were used to combine satellite images with latitude, longitude and altitude covariate. The investigated methods were evaluated using the Root Mean Square Error and Mean Bias Error indices and regression analysis. Also the annual rainfall maps of the province for 2012 and 2015 were drawn and analysed.

In this study, 5 theoretical semivariogram models were fitted to the data, that the spherical and exponential models was selected as appropriate models. Also the coefficient of determination of selected variogram model and the ratio of structured part to total variation showed a relatively strong variography anlysis and the effective range of precipitation data wasobtained about 80 km. Correlation coefficients of covariates and precipitation in most months provided acceptable results and were significant in almost more than 50% of the studied months. As a result, the coefficients of determination of four-dimensional gradient regression equation also showed satisfactory values, so that the used covariates explained between 10 and more than 70% of the precipitation variations. evaluation of investigated methods showed that interpolation models and TRMM satellite network data are not efficient in estimation of precipitation in the province and the use of covariates in the gradient method could reduce the error estimation of rainfall data by 20 to 40 percent. Investigation of Bias error showed that TRMM precipitation network data, despite good correlation with observational data, has about 5 times more underestimation error than other interpolation methods, but the combination of TRMM network data with other covariates in the 4-dimensional gradient method has reduced the MBE to zero. Regression analysis of the studied methods showed a significant advantage of the 4-dimensional gradient method that the slope of this method is 3 times more than the geostatistical methods, which shows the performance of this method in detecting low and high rainfall rings in Mazandaran province.

The results showed the superiority of the 4-dimensional gradient method in spatial rainfall estimation of Mazandaran province and showed the role of covariates in increasing the accuracy of rainfall maps, that use of the selected method reduced the estimation error of geostatistical methods and TRMM network data by 30% and 40% respectively. The results of this study showed that the combination of satellite raifall products with interpolation methods will lead to more accurate estimation of precipitation in highlands and the points with no recorded rainfall data.

Precipitation estimation in areas without recorded meteorological data is of great importance in hydrological studies and flood forecasting. Due to the lack of high-altitude meteorological stations with long-term recorded data in Mazandaran province as well as stochastic attribute of rainfall data, using statistical methods based on covariates and comparison with geostatistical methods for interpolating monthly and annual rainfall data in this province is Inevitable. For this purpose, precipitation data from 21 meteorological stations over a 13-years recorded period (2004-2013) were used. In order to determination of appropriate interpolation method of rainfall data, six models including Ordinary Kriging, Cokriging, Inverse Distance Weighting, spline, three-dimensional linear gradient and regression-kriging were investigated. Evaluation of the methods was also performed on the basis of root mean square error, mean bias error and regression analysis. Variography analysis showed spherical and exponential models as the best theoretical semivariogram models. The results of error indices analysis showed the Spline model has the lowest efficiency and the three-dimensional linear gradient was found as the most appropriate interpolation model of rainfall data which in comparison with other models reduced the rainfall estimation error from 100 to 200 mm(about 40 to 60 percent). However, its accuracy is reduced in hot and humid months. Investication of rainfall maps illustrates the accuracy of covariate based interpolation methods in detecting low rainfall and high rainfall points of the province. So that high rainfall district is located on the west coast of the province and by moving east the amount of rainfall decrease. Due to the similarity of the rainfall map and the digital elevation model, it is noted that the amount of precipitation in valleys is very different from precipitation in the highlands of this province. The results of this study showed that in areas with complex topography, the use of covariates leads to a significant increase in the accuracy of rainfall maps.

Standard precipitation index (SPI) is the most widely drought monitoring index. However, this index only uses the gamma distribution function for fitting precipitation data and does not consider seasonal variations. The purpose of this study was to evaluate the efficiency of SPI in drought monitoring of arid and semi-arid regions of Iran and fix the related problems with this Index. Then the SPI was compared with its modified state (SPImod) over (1956–2010). The results showed that the generalized extreme value distribution function in more than 57% of the cases was the most appropriate probability distribution function of rainfall data. But the default distribution (Gamma) was selected only in 11% of months. Comparison of Kappa index showed that with increasing time window, the agreement between SPImod and SPI indexes increases. The amounts of one-month Kappa for studied stations was Tehran (0.31), Mashhad (0.33), Bushehr (0.32) and Khorram-Abad (0.26), while for nine-month the Kappa increased. Such that in Tehran (0.49) and in Mashhad, Bushehr and Khorramabad, respectively, with values (0.47), (0.56) and (0.45). Also, the results showed that the frequency and displacement of drought classes would be very variable in comparison to these two indices. As the displacement of normal, severe drought and severe, with a total of 259, 147 and 111 events in the time window-three and displacement of moderate drought, normal and moderate wet year, with a total of 68, 54 And 28 events in time window-nine were noticeable.

In most of hydrometeorological studies and water resources management, flood and drought forecasting, irrigation planning and climate change studies, access to rainfall data and especially its spatial distribution (precipitation map), are of particular importance. Different models are used for spatial interpolation of rainfall data that generally fall into two categories of statistical and geostatistical methods. In statistical methods, interpolation is based on linear and nonlinear regression between main variable and covariate, but in geostatistical methods such as Kriging, spatial correlation of the sampled points is take in to account. There are many problems with spatial estimation of rainfall data especially in complex topography. Using correlated covariates is one of the answers to overcoming this problem. The altitudinal range of Mazandaran province fluctuates between -61 to 5610 meters, which creates various climates in this province. Besides, presence of the Caspian Sea in the north and Alborz Mountains Range in the south of the province make further complicates in spatial rainfall estimation, especially in the impassable heights of the province, which are a major water resource for large rivers. Since the elevated meteorological station in Mazandaran province is located on 2134 m, no rainfall data is available in area between 2134 to 5610 meters. So, this study was aimed to compare four interpolation methods include inverse distance weighting, Kriging, Co-kriging, and three-dimensional linear gradient. Also in this study, the role of covariates in precipitation estimation was investigated. Spatial data interpolation methods are used to estimate a variable at a particular point from actual data measured at adjacent points. The Inverse Distance Weighting method obtains the unknown quantity and performs the interpolation, by weighting the data around the estimated point. The interpolation methods use a set of points with known values around points with unknown data to estimate their values. This method is based on a geographical law that each phenomenon is related to other phenomena, but more depended to the close phenomena. Kriging as an advanced interpolation method is suitable for data with locally defined trends. This method can interpolate with the least variance of estimation that its error rate depends on the variogram specification. Co-Kriging is a suitable method when a covariate is present in all parts of network. In 3D Linear Gradient method, it is assumed that there is a linear trend along the length, width, and height of the region. In this study, in order to evaluate different interpolation methods of rainfall (Inverse Distance Weighting, Kriging, Co-Kriging, 3D linear gradient) the data from 25 synoptic and rain gauge stations were used in Mazandaran province. Surveying the statistical period of stations (1991-1991), the data of 2012 used to select the best interpolation method. Since the height of stations in Mazandaran province varies between -2120 and -21 m, precipitation data is not available for altitudes above 2200 meters. Therefore, altitude variable was used as an auxiliary variable in this study to obtain the most accurate estimation of altitude rainfall. In this study GS + and Mini tab software was used to calculate the estimated values of the models, Arc GIS software to prepare maps and Excel software for other calculations. Root Mean Square Error and Mean Bias Error were used to select the best interpolation method in this study. In variographic analysis, 5 types of semivariable models (Spherical, Gaussian, exponential, Linear and Linear to Sill models) were fitted to the data. The coefficient of determination and the ratio of structural changes to total variations were used to select the best half-variance. The best-chosen model has closer amounts of the coefficient and ratio to the number one. P-value and correlation coefficient were used to select the best covariate. Due to the importance of auxiliary variables in spatializing precipitation data, the variables of latitude, longitude, and altitude were used for the three-dimensional gradient equation. In order to better understanding of the studied methods, the map of annual precipitation changes in the province was plotted with different methods; then comparing them, the best rainfall map was selected. In this study, in order to determine the best interpolation method for monthly and annual precipitation data of Mazandaran province, four interpolation methods (Inverse Distance Weighting, Kriging, Co-kriging and three-dimensional linear gradients) were compared. Examination of Root Mean Square Error and Mean Bias Error revealed that the best interpolation method for long-term monthly precipitation was the 3D linear gradient method. But, the problem with this method and the other investigated methods in this study was overestimation of precipitation in high and low estimation stations in coastal and plain areas of the province. The overestimation was occured due to the lack of the number of station above altitudes of 2000 m in Mazandaran province. Therefore, the estimation of precipitation in the province's highlands had error. The outcomes of the best semicircle model in this study showed that the best models (except for July with low coefficient of determination) were spherical and exponential models. The results also showed that in hot months the spatial structure of precipitation data became weaker. Also, the impact of precipitation data in this province is about 30 km. The outcomes of correlation between monthly and annual precipitation data with latitude, longitude and altitude revealed that the altitude parameter had a significant correlation with other auxiliary parameters (latitude and longitude), in all months except July. Also, the correlation of latitude and longitude variables with precipitation was significant in some months. Therefore, it can be concluded that altitude parameter was the best auxiliary variable among the others to estimate monthly and annual rainfall in Mazandaran province. Distribution graph of annual rainfall variations with altitude along with regression equation indicated relatively good fit of linear equation to altitude rainfall fluctuations. Based on the results, the importance of the role of latitude and longitude variables for spatial precipitation data was determined. Therefore, latitude, longitude, and altitude variables were used for the three-dimensional gradient equation. Survey of annual precipitation maps showed that the three-dimensional linear, Co-Kriging and gradient methods had the most reasonable estimation of the spatial variability of precipitation in the province. According to the rainfall-altitude diagram, the average rainfall in the province is reduced to 500 mm of annual rainfall per 1000 meters. From the annual precipitation map survey with the selected method, it can be seen that only the western coasts of the province experience more than 1000 mm of rainfall per year. The slope of rainfall variations with altitude in the west of the province is more than east and due to the complex topography of the west of the province the west coast has more rainfall than the western altitudes.

Drought study and forecasting requires accurate monitoring of this climate risk. Standard precipitation index (SPI) is the most common index in drought monitoring. But there are some shortcomings, such as the lack of seasonal changes, the problem of self-affiliation in the high time series, and the consideration of the gammachr('39')s distribution function for all-time series. In this research, to resolve the bugs of SPI, it was tried to first select the appropriate distribution of precipitation from 8 different distributions. Secondly, the modified standard precipitation index (SPImod) was used to resolve the seasonal variation problem. The SPImod and SPI indexes were compared in five stations from different parts of Iran with different temperature and humidity conditions during the 55-year period (1956-2010). The results of the most suitable probability distribution function of precipitation data showed that the gamma function was not one of the selected functions in any of the cases and that the General-pareto, Weibull, and Generalized extreme value functions were the most suitable fitted distribution functions at the stations under study. Comparison of SPI and SPImod correlation coefficient showed that increasing the time series, increases the correlation coefficients. In the wet areas, the two indexes are more similar to each other. Based on the findings, there was a significant difference between SPI and SPImod at all stations, which was less significant at time series than one, three, and six months at a much higher level than in the time series Over the last 12 months, the two indexes almost matched each other. The results also showed that the difference between these two indexes was more pronounced in dryer stations. SPI seems to monitor dryness, especially in dry areas rather than droughts, so the use of SPImod instead of SPI provides more precise results due to the elimination of seasonal effects of precipitation.