جستجوی مقالات مرتبط با کلیدواژه « simulation » در نشریات گروه « کشاورزی »

Climate change is one of the biggest challenges that humanity has faced in the 21st century, because it can have severe effects on water resources, agriculture, energy, and even human bioclimate. The purpose of this study is to investigate the changes of climatic parameters in four sites of Plour, Rineh, Kangarchal and Poshtkoh in Mazandaran province using general atmospheric circulation models. For this purpose, the data of IPSL-CM5A-MR and CSIRO-MK36 models under RCP4/5 and RCP8/5 scenarios have been scaled with LARS WG statistical micro-rotator to evaluate the impact of climate change. After carrying out calibration, verification and modeling of data in two stations (Abali and Kiasar), the effectiveness of the models in terms of the amount of compliance of the observed data with the simulated value using the RMSE index, R2, MAE was evaluated. The obtained results showed that IPSL-CM5A-MR and CSIRO-MK36 models obtain acceptable results. Considering that in Abali station, the amount of rainfall in the base period is 542.08 mm under RCP4.5 and RCP8.5 scenarios in both IPSL-CM5A-MR and CSIRO-MK36 models in the future between (2021-2060) will increase by 0.91-0.97 percent. The average maximum temperature will increase by 1.7-2.2 centigrade and minimum temperature by 1.56-1.88 centigrade compared to the base period. Accordingly, the amount of rainfall in the base period in Kiasar station is 528.46 mm, which under the scenarios of RCP4.5 and RCP8.5 in both models in the future between the years (2021-2060) is 96. 0-87% will increase. The average maximum temperature will increase by 1.72-18.2 centigrade and the minimum temperature will increase by 1.62-1.82 centigrade compared to the base period.

To Estimate Parameters and Evaluation of Cropsyst Model to Predict the Growth and Yield of Two Wheat Cultivars in Northeast Khuzestan, an field experiment was conducted in the form of randomized complete block design in a research farm in Ramhormoz with three replications. Factors were included irrigation regime at two levels (full irrigation and stop irrigation after flowering), cultivation date (six dates including: October 30, November 15, November 30, December 15, December 30, and January 15) and two varieties of wheat. (Chamran 2 and Sirvan). Based on the results, the critical day length, the ceiling day length, the number of basic vernalization days, the minimum growth rate in the absence of vernalization, and the saturation point of vernalization for Chamran 2 variety are equal to 14, 8, zero, and 0.9, respectively. and 24, and for the number of Sirvan, it was 13.8, 7.8, zero, 0.93 and 25, respectively. The results obtained from the simulation of flowering and physiological ripening stages based on RMSE, d, CRM and EF statistics showed that the Cropsyst model simulated the mentioned stages with good accuracy for different figures. The simulation results of dry matter of aerial organs for different figures showed that this model was able to simulate 95 to 98 percent of this parameter in different treatments of planting date and irrigation. The error value of the model based on the RMSE statistic for the dry matter of aerial organs in Chamran 2 and Sirvan cultivars was 627 and 351 kg per hectare, respectively.

This study was conducted in order to evaluate the SWAP model to simulating yield and water productivity of potato under deficit irrigation conditions in the main potato cultivation regions of Razavi Khorasan province, including Fariman, Ghoochan, and Golmkan in 2019. First, the model performance to simulate the leaf area index and potato yield was evaluated based on the measurements taken from the fields of three locations. Then, using the evaluated model, the potato yield and water productivity were simulated under different irrigation and deficit irrigation scenarios, including irrigation based on farmer management, and 100, 90, 80, 70, 60 and 50% of potato water requirement. The results showed that the SAWP model had good performance in simulating potato leaf area index (R2>0.89 and RMSE<0.73), yield (R2=0.96 and RMSE=5.908 kg ha-1), and water productivity (R2=0.93 and RMSE=0.5 kg m-3). Compared to full irrigation, by reducing irrigation water amount to 50% of the water requirement, the potato yield decreased. The amount of yield decrease for each one percent decrease in the amount of irrigation water in Ghoochan, Fariman, and Golmkan was 255.97, 120.19 and 78.62 kg ha-1, respectively. The potato water also increased by using deficit irrigation up to 50% of the water requirement in Golmkan and Fariman and up to 90% of the water requirement in Ghoochan. In general, by adopting the deficit irrigation at the level of 80% of the water requirement in Golmakan and Fariman and 90% of the water requirement in Ghoochan, it is possible to improve the water productivity compared to full irrigation while maintaining the optimum yield.

Drying is one of the oldest methods of food preservation. To increase the efficiency of heat and mass transfer while maintaining product quality, the study of the drying process is crucial scientifically and meticulously. It is possible to conduct experimental tests, trial and error, in the drying process. However, this approach consumes time and cost, with a significant amount of energy resources. By harnessing available software and leveraging technological advancement to develop a general model for drying food under varying initial conditions, the drying process can be significantly optimized.

This study was conducted with the aim of simulating heat and mass transfer during Refractance window drying for aloe vera gel. Comsol Multiphysics version 5.6 is a three-dimensional model used to solve heat and mass transfer equations. For this purpose, the differential equations of heat and mass transfer were solved simultaneously and interdependently. The above model considered various initial conditions: water temperature of 60, 70, 80, and 90℃, and aloe vera gel thickness of 5 and 10 mm. The initial humidity and temperature of the aloe vera is uniform. The initial temperature is 4℃ and the initial humidity of the fresh aloe vera sample is 110 gwater/gdry matter. Heat is supplied only by hot water from the bottom surface of the product.

The drying time was needed to reduce the moisture content of aloe vera gel from 110 to 0.1 gwater/gdry matter during Refractance window drying. Aloe vera gel with a thickness of 5 mm dried in 120, 100, 70, and 50 minutes at water temperatures of 60, 70, 80, and 90℃, respectively. For a 10 mm thick layer of aloe vera gel, the drying time was 240, 190, 150, and 120 minutes, for water temperatures of 60 to 90℃, respectively. These results demonstrate the importance of both the water temperature and thickness on the drying time. Furthermore, the drying rate of aloe vera gel increased as the water temperature increased from 60 to 90℃, the drying rates were 0.915, 1.099, 1.57, and 2.198 gwater/min for 5 mm thickness and 0.457, 0.578, 0.732, and 0.915 gwater/min for 10 mm thick layer of aloe vera gel, respectively.

Based on the simulation results, the optimal model is with a water temperature of 90℃ and an aloe vera gel thickness of 5 mm. Overall, the modeling results are consistent with the results of experimental data.

Evaporation, the process by which water molecules escape a surface after absorbing sufficient energy to overcome vapor pressure, is a major contributor to water scarcity, especially in arid and semi-arid regions where heat readily facilitates this escape. Accurately estimating evaporation losses is crucial for effective water resource management, crop water demand prediction, and irrigation scheduling. Machine learning (ML) has emerged as a powerful tool for tackling the complex and stochastic nature of environmental problems. ML models excel at identifying relationships between predictor variables and outcomes (predictands), often surpassing traditional methods. However, their performance can vary depending on input factors and climatic conditions. Recently, hybrid techniques that combine multiple models have gained traction in climate and hydrology studies. These techniques leverage the strengths of different approaches within a single algorithm, potentially capturing more complex patterns in data series. This research will explore the potential of various individual ML models and propose a novel hybrid approach for estimating pan evaporation in Sistan and Baluchistan Province.

This study investigates pan evaporation simulation and prediction in Sistan and Baluchistan Province, Iran. Synoptic station data (1980-2019) served as model inputs, while pan evaporation measurements from these stations provided the observed values. In this research, in the approach of individual performance of data mining models, eight data mining models were used to simulate and predict evaporation from the pan. In addition to the individual performance approach, the combined VEDL approach was used to provide a hybrid model (a combination of the mentioned eight individual models of deep learning). In this hybrid approach to regression issues, the estimators of all models are averaged to obtain an estimate for a set called vote regressors (VRs). There are two approaches to awarding votes: average voting (AV) and weighted voting (WV). In the case of AV, the weights are equivalent and equal1. A disadvantage of AV is that all of the models in the ensemble are accepted as equally effective; however, this situation is very unlikely, especially if different machine learning algorithms are used. WV specifies a weight coefficient for each ensemble member. The weight can be a floating-point number between Zero and one, in which case the sum is equal to one, or an integer starting at one denoting the number of votes given to the corresponding ensemble member. the weight of each model was selected based on the accuracy of the model's performance using the evaluation criteria obtained from the training implementation section of individual models. the model’s performance was assessed using statistical measures, including R2, RMSE, MAE, and Taylor diagram.

The results showed that all the models had very good results in both the training and testing stages. All models exhibited excellent performance during training and testing. The Artificial Neural Network (ANN) achieved the highest accuracy in both phases at the Zahedan station (R² = 0.89, RMSE = 45.95 in training; R² = 0.96, RMSE = 44.18 in validation). It emerged as the best model for monthly pan evaporation prediction at this station. Other models also performed well, with the Support Vector Machine (SVM) and Random Forest (RF) models achieving R² values of 0.89 and 0.88 in training, respectively. Notably, the BART model ranked second in validation (R² = 0.96). The Tree Model (TM) had the lowest accuracy (R² = 0.84 and 0.93 in training and validation, respectively). Across all stations, ANN, SVM, and RF consistently delivered the best results in both training and testing. In the test phase, the SVM model outperformed others in Khash, Iranshahr, and Chabahar stations (R² = 0.94, 0.96, and 0.94, respectively). At the Saravan station, the RF model achieved the highest R² (0.94) during testing. To develop a hybrid data mining model, the Voting Ensemble for Deep Learning (VEDL) technique was employed with weighted voting in the training stage. The combined model significantly improved upon the best individual model. RMSE decreased from 45.95 to 33.1, R² increased from 0.89 to 0.94, and MAE improved from 32.92 to 23.9. Evaluation using the Taylor diagram further confirmed the superior performance of the VEDL model compared to the individual ANN model.

The results showed that among all the models, ANN, SVM, and RF models had the best performance in the two stages of training and verification. In the validation stage, the SVM model with R2 values equal to 0.94, 0.96, and 0.94 performed best in the Khash, Iranshahr, and Chabahar stations. At the Saravan station, in the Sensji validity stage, the RF model with an R2 value of 0.94 had the best performance among the models. The excellent performance of the models in the two stages of training and validation is another finding of the research, These results are consistent with the results of researchers who have expressed the appropriate efficiency of machine learning models in estimating evaporation/evaporation and transpiration in different climatic regions of Iran. The results of the combined model showed that the combined model improved the results compared to the best individual model so that the RMSE values increased from 45.95 to 33.1, the R2 values increased from 0.89 to 0.94, and the MAE value improved from 32.92 to 23.9. The use of the VEDL approach to estimate evaporation from the pan was a new approach that has not been used in past studies. Therefore, according to the results of this research, the proposed deep sensing model is proposed to estimate the evaporation of arid and semi-arid areas for water resources management and agricultural planning.

In order to simulate yield, leaf area, and nitrogen content in rice plant by using the ORYZA2000 model under different nitrogen fertilizer amounts and various splitting approaches, a factorial experiment based on a randomized complete block design with three replications was carried out at a research farm located in the Mazandaran Province (Babol) during 2017-18.

The first factor comprised four nitrogen fertilizer rates (50, 90, 130, and 170 Kg n ha−1, supplied as urea) and the second factor comprised various fertilizer splitting appraoches (T1: 70% basal+ 30% in tillering stage; T2: 33.33% basal+ 33.33% in tillering stage+ 33.33% in panicle initiation stage; T3: 25% basal+ 50% in tillering stage+ 25% in panicle initiation stage; T4: 25% basal+ 25% in tillering stage+ 50% in panicle initiation stage).

 The results demonstrated that the model successfully simulated grain yield, biomass, grain nitrogen, and total nitrogen with RMSEn of four, four, nine, and six percent in calibration, and five, four, four, and eight percent in validation, respectively. The mean square error for the leaf area index was 0.5 and 0.4 in the model’s calibration and validation, respectively.

Based on the results obtained, the ORYZA2000 model can be used as a suitbale simulation model for selecting the best strategy for enhancing rice yield under different nitrogen fertilizer management in the rice-rich regions of northern Iran.

Many human civilizations have collapsed due to the climate. Historical and geographical evidence demonstrates that the majority of significant and stable civilizations were established in specific geographical-climatic areas with suitable biological conditions. Human civilizations have faced serious damages and collapses due to unfavorable climatic conditions. The existence of most human civilizations can be attributed to climatic conditions, with historical and geographic evidence, especially the special environmental condition of the world in the current situation. Countries that deal with biological, economic, social, and even political issues may not give special importance to this issue. The climate conditions around the world are constantly changing, and there will be severe problems in different dimensions in the not-too-distant future. Their existence could even be threatened by economic problems they will face. The world's population increase and energy demand in today's industrial world also result in damage to the planet's climate and atmosphere, as well as the effects of natural disasters. The human burden has not been spared and has been affected by many fundamental changes, of which the phenomenon of climate change is one of the clear examples. The most important indicators of climate change are temperature and precipitation. The change of each of these factors causes climate variability, which also has consequences on people's lives. The increase in greenhouse gases and suspended particles in the atmosphere, along with variations in the earth's surface, are among the most apparent consequences, and the damage caused by the world's industrial activities and human societies in the past two centuries. Different economic sectors, such as agriculture, industry, tourism, water, and health, are affected by this phenomenon. Based on what has been stated, it can be concluded that climate change will definitely occur in Iran. The agricultural sector will face significant challenges due to the occurrence of such things, given the climatic situation, hydrological characteristics, and limitations of the country's agricultural sector. It has created a unique climatic situation in Iran due to its location. The high-pressure rule in the subtropical zone causes the total amount of precipitation in Iran to be low.

The LARS-WG model is a model that is utilized for exponential micro-scale GCM models. This model is a great choice for generating random weather data, which can be used to generate rainfall, radiation, and maximum and minimum daily temperatures at a station for both current and future climate. In order to model meteorological variables, the LARS-WG model uses a complex statistical distribution. Modeling is based on the length of dry and wet periods, daily precipitation and radiation series, and semi-empirical distribution. Climatic parameters such as minimum temperature, maximum temperature, precipitation, and solar radiation are inputs for the LARS-WG model, which are all in the daily time frame. In this model, temperature is not taken into account when modeling radiation, and the sundial can be utilized instead. From the semi-empirical distribution of the rainfall for the month in question, and without taking into account the wet series or the amount of precipitation from the previous day, the amount of precipitation for one day is calculated. In this model, the temperature is estimated using Fourier series. The daily minimum and maximum temperatures are modeled as random processes using the average and standard deviation of the daily standards, which depend on whether the day is wet or dry. The mean and standard deviation of the seasonal temperature are simulated using the third-order Fourier series. Residue values are determined by subtracting the average values from the observed values, and the minimum and maximum data are utilized in time autocorrelation analysis. Minimum temperature, maximum temperature, precipitation, and radiation are the outputs of this model. LARS-WG model data generation involves three stages: calibrating, evaluating, and creating meteorological data. In the next step, using LARS-WG models under RCP 2.6, 4.5, and 8.5 scenarios and the micro-scale exponential LARS-WG generating model of Jiroft City climate changes during the planting period of each crop in the periods of 2011-2045, 2065-2046, 2066-2079 and 2080-2099. In the final stage, a positive mathematical programming model was utilized to investigate the impact of climate change scenarios on Jiroft City's planting patterns using predicted climate change results. The cultivation pattern of Jiroft city today and the predicted effects of climate change parameters during the periods 2011-2045, 2046-2065, 2066-2079, and 2080-2099 are examined in this section. The current cultivation pattern will be discussed in the previous step. A positive mathematical programming model and GAMS25 software have been employed in this regard. Separate information about the area under cultivation, production, and performance of selected agricultural products, as well as the consumption of inputs, for the agricultural year 2021-2022 is presented in the table.

The results indicate that the performance of selected products is significantly affected by the climatic parameters of temperature and precipitation. Also, by applying the forecast of climate variability in the cultivation pattern model of all selected crops in the periods 2011-2045, 2046-2065, 2066-2079 and 2080-2099 based on the noses of the HadCM3 model are affected by different climate scenarios. The improvement of agricultural productivity and climate change are both negative effects of this phenomenon. The findings of this investigation can be advantageous for agricultural planning and economic development in Hamadan province.

The price of red meat has grown significantly in recent years, which has reduced the welfare of the consumers of this product, and its consumption has decreased greatly in the low-income groups of the society. The presence of many challenges in the beef value chain and the presence of many activists with different goals in this chain have made it complicated and difficult to analyze this chain. One of the best methods for designing complex systems, determining policies and solving their problems is system dynamics modeling.

In this study, after identifying the actors in the value chain, the relationships among the elements of this system were expressed in the form of causal loop diagram. Then, the types of the variables in the system were determined including stock, rate, auxiliary and parameters and the stock-flow diagram of this value chain was drawn and then was formulated. After validating the designed model using the behavior reproduction test, the current state of this value chain has been simulated.

In this basic model, by creating scenarios on key variables such as the exchange rate and the import tariff rate of inputs and red meat, it is possible to examine the impact of various government support policies on the price of beef and other important variables of this chain.SuggestionsTherefore, such a model that shows the dynamics of the relationships between the factors involved in the red meat value chain can be used as an operational dashboard for decision making for policy makers and planners of the country's agricultural sector.

Climate change is one of the most important issues in the world in the 21st century which affects various sectors of agriculture, forestry, water and financial markets, and has serious economic consequences (Reidsma et al., 2009). In recent years, the management of agricultural water consumption has always been considered as one of the important issues in water resources management. Koochaki and colleagues (Koochaki and Kamali, 2006) by evaluating the climatic indicators of Iran's agriculture showed that during the next 20 years, the average monthly temperature will increase in almost all regions of the country, and the increase in evaporation and transpiration is one of the most important consequences of this warming. Simulated climate parameters can be obtained through different general GCM atmospheric models. Due to the low spatial resolution of these models, its output should be downscaled using dynamic or statistical methods.

The LARS-WG model predicts meteorological variables for a period of time in the future by using a series of basic and fine-scale meteorological data, output of one of the GCM models. Research has shown that the LARS-WG model has the necessary accuracy for this task. Calculating the amount of evapotranspiration and yield of very complex plants are time-consuming and dependent on spending a lot of money and limited to the tests performed, the shortness of the test time and also the limitation in the number of scenarios that are checked by the test. Therefore, plant models are considered and evaluated by researchers. The AquaCrop model has demonstrated commendable accuracy in various regions of Iran and globally for forecasting plant growth, water consumption efficiency, and evapotranspiration requirements. These predictions hold significant potential for optimizing irrigation strategies across different agricultural settings. AquaCrop is one of the applied agricultural models that was obtained from the modification and revision of FAO publication No. 33 by prominent experts from all over the world. In this study, the values of green water footprint of winter wheat plant (Pishgam) were estimated in climatic conditions obtained from LARS-WG model and DKRZ database under scenarios 4.5 and 8.5 and at different planting dates (15 October, 1 November, 15 November, 30 November and 15 December), in the next 4 periods (2021-2040, 2041-2060, 2061-2080 and 2081-2100) and by Aquacrop model.

The results showed that if planting date is on October 15, in the climatic conditions obtained from the LARS-WG model and under scenarios 4.5 and 8.5, in all future periods, the footprint of green water will increase compared to its value in the base period, and if planting is the rest of the dates, in each of the next 4 periods, the average green water footprint will decrease compared to its value in the base period. The results obtained for the DKRZ database show that the green water footprint attained for the dates of cultivation and periods investigated in scenarios 4.5 and 8.5 does not have a particular trend. On the planting dates of October 15 and November 1 for the periods of 2061-2080 and 2081-2100, the green water footprint will decrease and on the other three dates (15 November, 30 November, and 1 November) for these periods, there will be an increasing trend. On 15 December, for the DKRZ database, in both scenarios defined for all periods, an increase in green water footprint compared to the base period is reported. However, in the period of 2081-2100 in scenario 8.5, a decrease compared to the base period will be observed. The highest amount of green water footprint in all these periods and models for the period 2041-2060 under the climatic conditions of the DKRZ database in scenario 4.5, if the planting date is 15 October, it is estimated that the amount of water consumed is equal to 4272 cubic meters per ton with a standard deviation of 5018 cubic meters per ton is predicted. The lowest footprint of green water for the period 2081-2100 under the climatic conditions obtained from the LARS-WG model in scenario 8.5, if the planting date is on 15 December, is reported to be 232 tons per hectare with a standard deviation of 52.3 tons per hectare.

Due to population growth and Iran's location in arid and semi-arid regions of the world, the need for water and food has increased and as a result, the pressure on water and soil resources will be more than before. On the other hand, the risk of drying up Lake Urmia, which causes environmental problems in the region, requires macro-water planning for the region and the use of optimal cultivation pattern to deal with water scarcity. Therefore, optimal use of water preserves water resources and increases the quality of products. Today more than ever, increasing the production of strategic crops such as wheat requires the proper use of water resources. The main source of food for the Iranian people is wheat and related products, and any action that increases the yield of wheat due to limited soil resources, especially water resources, is important and necessary at the same time. In recent years, significant advances have been made in modeling product growth and development using mechanical models. Plant growth models are increasingly used in the analysis of agricultural systems and simulate the plant's response to growth factors using mathematical equations. The AquaCrop model is one of the dynamic and user-friendly models developed by the FAO. The AquaCrop model receives information about farm, plant, soil, irrigation and climate, and ultimately predicts important parameters such as crop. Wheat yield simulation allows efficient management and better planning under various environmental inputs such as soil and water. To achieve higher accuracy and less model error, field parameters must be properly calibrated by the model to achieve proper performance. Also, calibration of the model, if not done correctly, causes a high error prediction by the model, which leads to incorrect management, water loss, plant drought and other cases. Therefore, using a model that has accurate and close prediction to the AquaCrop model and requires fewer input parameters is essential, which saves time, reduces costs and eliminates calibration errors. However, this model requires relatively large input parameters and is a time-consuming model in the presence of multiple scenarios.3

In recent years, smart models have been able to show high accuracy and become reliable models. Therefore, in the present study, to solve this problem and develop a model with less input data, using the ANN, SVR and SVR-FFA intelligent models and creating 440 scenarios in 2 farms, the performance of the AquaCrop model was compared.99WestW2 farm is located in Miandoab city and has a yield of 6.588 (ton ha-1) and WestW10 farm is located in Mahabad city and has a yield of 5.05 (ton ha-1).

The results of the model are performed using 5 evaluation criteria of Correlation coefficient, Root mean square error, Nash-Sutcliffe coefficient, Wilmot’s index of agreement and, Mean absolute percentage error. The results of this study showed that for both 99WestW2 and WestW10 farms, the SVR-FFA3 model could have the lowest error rate, so that for the yield index, the RMSE value for the mentioned farms was 0.033 and 0.069 (ton ha-1), respectively. The use of three models SVR, SVR-FFA, and ANN and their comparison with the AquaCrop model to predict wheat yield has been done for the first time in this study. The SVR model was able to show the highest accuracy after the SVR-FFA model. For 99WestW2 farm, it can reduce the error rate to 0.043 (ton ha-1) and for WestW10 farm to 0.077 (ton ha-1) and show good performance. The ANN model, after the SVR model, was able to show acceptable accuracy. The ANN model for 99WestW2 farm was able to reduce the error rate to 0.123 (ton ha-1) and for WestW10 farm to 0.094 (ton ha-1). Finally, the ANN model had a relatively higher error than the SVR-FFA and SVR models, respectively, and showed a relatively lower performance than the two models.

Finally, the intelligent SVR-FFA, SVR and ANN models, despite having the least number of inputs, were able to predict yield values in the shortest time and with the highest accuracy. However, the results showed that the lower the model inputs, the weaker the model prediction. For further studies, it is suggested that the ANN model be combined using the firefly algorithm (MLP-FFA) to increase the accuracy of the ANN model and make more accurate predictions of wheat yield.

The aim of this research is to investigate the effects of cover plants (grass) in improving the microclimate.Data collection methods include field analytical, and quantitative, with a comparative analysis method of proposed design options using simulation in ENVI-met software (4.4.6) and Ray-Man 3.1 in two different patterns at the same site for a period of 8 hours (10:00 to 18:00), which took place on June 30, 2020, as the hottest day of the year; The research was also validated by comparing the model's output parameter values with field measurements. The results indicate the optimal performance of the pattern of green cover(grass) compared to the absence of it in improving thermal comfort and reducing physiological temperature. Because it has caused an increase in relative humidity of 0.06% and a decrease in the environmental comfort index (PMV) by 0.005, as well as a decrease in the physiological equivalent temperature (PET) by 0.23 degrees Celsius, the hours of 11:00, 12:00, and 18:00 contain the best performance of vegetation (grass) in terms of enhancing micro-climate conditions, whereas the hours of 14:00, 15:00, and 16:00 include the worst performance in terms of effective micro-climate alterations. The average change in physiological temperature in the most optimal and worst hours of operation is equal to 4.68 degrees Celsius, and these changes in the relative humidity index are equal to 0.45%.

Agriculture is a cornerstone of many developing economies, providing food, income, and employment for millions of people. It is also projected to play a vital role in feeding a global population of 9.1 billion people by 2050. However, there are growing concerns about the environmental impact of agriculture, particularly in arid and semi-arid regions like Iran. Managing water and fertilizer usage in agriculture is crucial to ensuring food security and sustainability. However, conducting field experiments to assess the interaction of all factors involved is expensive and time-consuming. This research focuses on optimizing maize production in Kerman province, a region where maize is a major crop. The research is motivated by the need to improve resource management in Iran, where water and fertilizer resources are limited. The APSIM model is used to determine the best management scenario for maize production in Kerman province. APSIM is a crop growth simulation model that can be used to predict the impact of different management practices on crop yield, water use efficiency, and nutrient use efficiency. The use of APSIM in this research provides a cost-effective and time-efficient alternative to conducting extensive field experiments. The results of this research will contribute to the development of sustainable and efficient agricultural practices in Kerman province and similar regions. These regions are characterized by resource constraints, such as limited water and fertilizer availability. The research aimed to simulate the effect of management parameters (planting date and irrigation) on Crop yield and subsequently achieve the optimal management scenario.

The APSIM model was used for simulation in three regions of Bardsir (temperate to cold climate), Jiroft (hot and humid climate), and Orzuye (hot and dry climate). The model requires four series of data: climate, soil, management, and crop data. The required climate data (from 1998 to 2018) including daily maximum and minimum temperatures, length of sunny hours, and daily precipitation were collected and prepared from the synoptic weather stations of the three mentioned regions.The management data set for each of the study regions was prepared in the form of questionnaires and field research from experts of the Agricultural Jihad Organization, the Agricultural Research Center Organization, and prominent farmers in those regions. The crop data includes the plant genetic coefficients of the maize single cross hybrid 704, which were obtained from the calibration of the APSIM model. To optimize planting date and irrigation management in the studied areas, different planting and irrigation date treatments were investigated. In this research, planting date treatments included the conventional planting date of the region, 20 days before the conventional planting date (as early planting date), and 20 days after the conventional planting date (as late planting date). Irrigation treatments included the usual number of irrigations in the region (13 irrigations), less irrigation (11 irrigations), and more irrigation (15 irrigations).

Our results showed that the model successfully simulated maize phenology, especially maturity date, with high accuracy for all fertilizer amounts tested. The model performance in predicting biomass under different nitrogen treatments was also satisfactory, with a minimal difference between observed data and model results. The nRMSE of grain yield in the calibration stage was 11.2% and in the validation stage was 9%. The nRMSE for calibration of the biological yield of SC 704 was 14.8% and for validation was 13.9%. Also, the model was able to simulate phenology with very high accuracy (especially the days to maturity). Overall, the nRMSE of days to flowering was less than 10% and for the days to maturity was less than 5%. Late planting dates consistently showed better performance across regions and irrigation treatments, resulting in significantly increased grain yield compared to conventional and early planting dates. The highest seed yield was obtained with 15 times of irrigation, among the various irrigation treatments. Late planting combined with 15 times of irrigation yielded the best results in Kerman province, particularly in Bardsir, with a yield of 9300 kg ha-1. Optimal moisture and air conditions, along with the cultivation of a late-maturing variety, contributed to the higher seed yield. These findings are consistent with previous research that has confirmed the positive impact of late planting and extended ripening periods on maize yield.

Our results showed that the model simulates the growth and yield of single cross 704 corn in Kerman province well, even after 20 days of late planting. Long-term simulation experiments showed that maize grain yield varied depending on the region, with the highest yield in Bardsir (8317 kg ha-1) and the lowest yield in Jiroft (4735 kg ha-1). The optimum maize grain yield (8872.8 kg ha-1) was obtained by the interaction effect of late planting date and 15 times of irrigation.

SWAT model is a suitable tool for simulating hydrological processes. This model requires many inputs that often cannot be measured directly and is considered one of the main sources of uncertainty in these models. The recalibration process can reduce the uncertainty in the model results by adjusting and adapting these inputs. The researches showed that calibrating a hydrological model by using the common automatic CV calibrating algorithms will not provide proper accuracy in the prediction of hydrological variables during the validation period, so PSO algorithm was used to calibrate the SWAT model. Since there is no mathematical and logical rule to determine the best combination of PSO algorithm parameters and these combinations are selected based on trial and error and among many different combinations, therefore trial and error based methods are very time-consuming and sometimes impossible. In this research, Taguchi method was used to determine the best combination of PSO algorithm parameters.

In this research, the ability to use the SWAT model to simulate monthly runoff in the Javanmardi Watershed, one of the main sub-basins of the Lordegan Watershed with an area of 380 square kilometers, was investigated. In this study, the PSO algorithm parameters, including the number of simulations (A), the number of repetitions (B), the speed calculation weight (C) and the movement parameter (D), were defined in four levels. Then, these parameters were designed and implemented according to the experiments in the L16 orthogonal array (using the Taguchi experiments design method). The performance scale used to evaluate the algorithms was RPD (Relative Percentage Deviation). Considering the variable nature of the response in this study, the S/N index "the lower the better" was used to analyze the Taguchi test results. The selection of arrays and calculations were done in Minitab 16 software.

In the sensitivity analysis stage, which was performed before the model recalibration, among the 28 parameters studied in this research, the model showed sensitivity to the changes of 22 parameters, and they were identified as variables influencing the simulation of runoff in Javanmardi Watershed. The results showed that the parameter of the runoff curve number (CN) is the most important factor and the parameters of soil apparent density in the wet state (SOL_BD) and average water usable by the plant (SOL_AWC) are among the most important factors controlling the flow rate in the study basin, respectively. Based on the results simulated by the PSO algorithm, it was found that the SWAT model has an acceptable accuracy for estimating the monthly runoff in the study area. So, in the recalibration phase, the r-factor and p-factor indices were 1.23 and 0.88, respectively, and the explanatory and Nash-Sutcliffe coefficients were 0.77 and 0.75, respectively. In the validation stage, the r-factor and p-factor indexes were 1.31 and 0.84, respectively and the explanatory and Nash-Sutcliffe coefficients were 0.72 and 0.73, respectively. In this study, the best combination resulting from the application of Taguchi method for the parameters of the number of simulations, the number of repetitions, the speed calculation weight and the appropriate parameters in the PSO algorithm were determined as 40, 100, 0.2 and 0.15 respectively (A4B4C4D3).

The results show that the SWAT model has an acceptable accuracy for estimating the monthly runoff in the Jawanmardi Watershed, and the PSO method is an effective algorithm in calibrating and determining the uncertainty of the model in this basin, and the use of the Taguchi test design method is a suitable way to determine the best combination of PSO algorithm parameters is for researchers who use this method to optimize the SWAT model.

The concept of Natural capital has emerged as a crucial element in the economic and social surveys of modern societies. It plays a pivotal role in promoting sustainable rural livelihoods and development across all dimensions. The recognition of natural capital as a valuable asset has gained significant attention in recent years, as it provides a framework for understanding the economic value of natural resources and the services they provide. This concept has become increasingly relevant in the context of sustainable development, as it highlights the importance of preserving and managing natural resources for the benefit of present and future generations. Therefore, policymakers and stakeholders must recognize the significance of natural capital and incorporate it into their decision-making processes to ensure sustainable development. Livelihood capital, which comprises environmental, social, physical, human, and financial capital, is a fundamental component of people's livelihoods, particularly those who are impoverished. These capitals are the cornerstone of sustainable livelihoods and are essential factors for local poor communities. Understanding the livelihood status of households and their access to livelihood funds is crucial for advancing development goals in rural areas, particularly in developing countries. It is one of the most significant and influential platforms for achieving this objective. Upon reviewing previous literature, it is evident that the application of a system approach in analyzing sustainable rural livelihoods has not received adequate attention. Given that the discussion of sustainable rural livelihoods involves different capitals, each comprising interrelated variables, neglecting the interrelationships between these variables may not be effective in addressing the issues at hand. Furthermore, disregarding feedback loops and dynamic complexity in a system can lead to political resistance. Therefore, this study aims to investigate the role of natural capital in sustainable rural development using a systemic approach.

System dynamics is a method that has been used to study and investigate the relationships between separate systems that are at the same time closely connected and enjoy dynamics in behavior. Dynamic simulation is useful for modeling the behavior of a system and observing its reaction to various changes over time. One of the most important features of the dynamic system is considering an endogenous structure of the studied system to show the reaction of different elements of the system on each other and the feedback effects between them. This approach enables us to gain a deeper understanding of the system's behavior and identify the factors that influence it. This study aims to conduct an analysis and provide an explanation of the natural capital of sustainable rural livelihoods using a systems thinking approach. The first step involved identifying the key variables of the system through a questionnaire and expert interviews in the study area. Subsequently, a new questionnaire was designed, and 120 residents in Qarabagh city, Ghazni province in Afghanistan were interviewed using Cochran's formula and targeted sampling via the snowball technique. The assessment of the region's situation was conducted in terms of key environmental asset variables.

The study suggests that the availability of water and forest per capita is likely to decrease due to factors such as population growth, deforestation for agriculture, fuel and charcoal extraction, livestock grazing, and fire. Mismanagement of natural areas has led to their destruction and reduction. Efforts to stop and reduce this process have been insufficient and unscientific. Deforestation has irreversible consequences and will significantly impact the natural ecosystem and sustainable rural livelihoods in the region. Afghanistan is facing severe water problems due to frequent droughts and excessive extraction of surface and underground water. The country's water situation has reached a critical level, and it is essential to invest in different parts of the water area to protect the surface and underground water sources. The reduction of desired variables is related to the increase in population and the need for more water and food resources. The development of the agricultural sub-sector can lead to over-harvesting of water resources and destruction of the forest. Therefore, natural resource management policies should be given more attention to ensure sustainable rural livelihoods. Water demand management is a practical and quick step in controlling water resources. Demand-oriented management policies, such as setting a reasonable tariff for water and improving irrigation technology, should be put on the agenda. To preserve and protect forest resources, caretakers should be made aware of the issue and executive bodies should stay more coordinated. Other solutions include developing fodder cultivation for livestock in low-yielding rainfed areas, expanding research, organizing environmental organizations, implementing successful forest management plans, attracting private sector investment, continuing the implementation of forestry projects, and planting productive forest trees in the empty parts of the forest by the residents. Identifying the state of environmental (natural) capital helps planners design rural development strategies and sustainable livelihoods with the capacities of the region.

Natural capital is an essential predictive component that provides the basis for rural development. Therefore, effective measures should be taken to strengthen environmental capital, and attention should be paid to it in formal and informal curricula. It seems that the current policies in the study area in the field of environmental asset management are not in line with achieving the development of sustainable rural livelihood. The government should invest in the development of irrigation technology to support the plans that seek to save available water while helping the development of the agricultural.

An artificial neural network (ANN) is a powerful data-driven tool capable of learning the linear and nonlinear relationships governing different systems. However, determining the best-performing algorithm in terms of convergence speed and accuracy for a particular problem is still a fundamental challenge for users of artificial neural networks.

We investigated the most effective algorithm among widely used processes to simulate and estimate nonlinear water quality parameters. For this purpose, we constructed 42 models combining artificial neural network topology (single or multilayer) and training processes. The quality parameters’ data acquired at 107 wells throughout the aquifer of Qorveh-Dehgolan plain were used for training (data from 1996 to 2013) and to test (data from 2014 to 2016) each model.

The results showed that artificial neural networks with a hidden layer that benefits from the optimal number of neurons could simulate the aquifer behavior with high accuracy and in less time. Also, increasing the number of hidden layers while increasing the response accuracy increases the number of optimal network neurons and the duration of the problem analysis. Finally, artificial neural networks based on the Broyden-Fletcher-Goldfarb (BFG) method had the highest efficiency in simulating aquifer behavior, although the performance of the Levenberg Marquart (LM) method is very close to BFG. BFG is more efficient than LM due to its lower Mean Square Error and Standard Deviation (3.46 and 3.09, respectively).

The aim of the study is to evaluate the changes of mean temperature and precipitation parameters using IPCC Sixth Report models (CMIP6) under SSP scenarios (SSP1-2.6, SSP2-4.5 and SPP5-8.5) during the period of 2022-2100 in Kashan Plain. The mean temperature and precipitation data was obtained from 7 stations (Kashan, Kavir-e-Hosseinabad, Kamu, Ardestan, Alavi, Noushabad and Sensen) in Kashan plain considering the base period of 30 years (1984-2014). Also, 7 models were selected from the models of the sixth report (CMIP6). The post-processing of the output of the models was carried out using the linear ratio method. Nash-Sutcliffe indices (NSE), root mean square error (RMSE) and correlation coefficient (r) were used to determine the accuracy of the models. The annual trend of changes was investigated using Mann-Kendall test. Finally, the mean of IPSL-CM6A-LR and BCC-CSM2-MR models outputs was used to simulate mean temperature and precipitation changes in the future period. According to the results, in all of the studied stations, precipitation in the coming period will have a decreasing trend compared to the base period. The mean temperature will also increase in the future period compared to the base period. In Kavir-e-Hossein Abad, Ardestan, Noush Abad and Sen Sen stations, the intensity of temperature increase will be higher than Kashan, Kamu and Alavi. According to the predicted conditions, it is necessary to pay attention to comprehensive policies in the field of adapting to climate change in Kashan Plain.