شبیه سازی

One of the most important problems in crop production is the difference between the actual yield of farmers and achievable yield, which is called the yield gap. Increasing yield in unit area is the key for suppling cerial needs in world. To achieve this aim, it is very important to identify yield limiting factors. This study was done during 2015-2019 to investigate the wheat yield potential in the Ramshir plain of Khuzestan province, for this propose the long-term climatic data, farm management data, physical and chemical properties of soil and plants data were measured in 100 farms and were registered, after the spatial interpolation of the data, the APSIM model was used to simulate the growth and evaluate the gape yield of wheat, in this conditions the yield potential was determined 7091 kg/ha. The results showed that the farmers have reached less than 40% of the achievable yield, which indicates the large gap in yield of this product. Choosing an inappropriate planting date for wheat cultivation with 31% of total yield gap is the main factor of yield reduction, which is due to the lack of access to suitable machinery and seeds for timely cultivation. Also the results demonstrated that the type of soil, water limitation, nitrogen fertilizer used, seed density in square meter and seed variety had 27, 18, 8 and1percent of total gape yield respectively. From the remaining 15% of share, we can mention improper timing of irrigation and fertilizing, improper use of basic fertilizers, poor mechanization and pests. It seems that wheat cultivation at the first opportunity after the relative decrease in temperature at beginning of the autumn season (November) and before cold weather that causes the plant go to sleep (Plant Dormancy), can improve the growth and yield of this crop.

The canola plant (Brassica napus L.), containing 40 to 44 percent oil, is considered one of the most important edible oilseeds and is the third most significant annual oilseed crop in the world after soybean and oil palm. The global demand for food is rapidly increasing due to the growing population. This demand is expected to rise by 60 percent by the year 2050, which poses a significant challenge, especially in the context of climate change. Human activities since the industrialization era have led to an increase in greenhouse gas emissions, which are expected to alter regional rainfall patterns and temperatures. The most critical feature of global climate change is the significant increase in temperature and uneven distribution of precipitation, which are limiting factors for sustainable development. The ultimate goal of assessing climate change risks is to identify adaptation strategies to achieve sustainable development in a specific region. Adaptation strategies vary depending on agricultural systems, regions, and climate change scenarios. The aim of this study is to examine adaptation strategies to enhance drought resistance in rapeseed plants concerning future climate conditions in the country.

The present study aims to predict the impact of climate change on the growth and development of rainfed canola in Iran using two general circulation models, HadGEM2-ES and IPSL-CM5A-MR, derived from the CMIP5 project under two emission scenarios, RCP4.5 and RCP8.5, as reported in the fifth assessment report of the IPCC for the future period from 2040 to 2069. The downscaling of climatic parameters generating weather data was conducted using climate scenario generation tools within the AgMIP project and implemented in R software. After simulating the future climate and producing the necessary parameters (minimum temperature, maximum temperature, precipitation, and solar radiation), the growth and development simulation of rainfed canola was carried out using the SSM-iCrop2 model under current and future climate conditions. Additionally, the results of the growth and development simulation of rainfed canola under future climatic conditions in Iran were evaluated with increased drought resistance.

The results indicated that the average temperature during the canola growing season in the future is expected to increase by an average of 2.3 degrees Celsius for the RCP4.5 emission scenario and by 3.1 degrees Celsius for the RCP8.5 scenario compared to current conditions. Additionally, the results showed that the distribution of precipitation among the growth seasons would vary between the two models. The simulation results under climate change for both RCP4.5 and RCP8.5 scenarios revealed that, with the increase in average temperature, the length of the growing season would decrease in both models studied. However, it is predicted that water productivity will increase under both emission scenarios. It is anticipated that the average yield of canola in the country in its main cultivation areas will increase by 5% and 8% under the RCP4.5 and RCP8.5 scenarios, respectively, compared to current conditions. By implementing adaptation strategies to enhance drought resistance, it is expected that under both RCP4.5 and RCP8.5 scenarios, the average yield changes will increase by 8% and 9%, respectively, compared to a future without adaptation strategies.

The results of this study indicate that, on average, the yield in most of the main canola cultivation areas in the country is expected to increase under both emission scenarios. By implementing adaptation strategies to enhance drought resistance in the future climate, it is predicted that the average yield will increase compared to a future without adaptation strategies.

Solar radiation is one of the main inputs to crop growth models to estimate growth and yield. This study was conducted with the aim of evaluating the impact of radiation estimation accuracy by different models on the accuracy of simulating wheat evapotranspiration and yield by CSM-CERES-Wheat model in Razavi Khorasan province. The efficiency of simple models including Angstrom-Prescott, Angstrom-Prescott suggested by FAO, Hargreaves-Samani, Hargreaves-Samani suggested by FAO, Kermani, Hunt and Power have been evaluated in this study. The results of the radiation estimation for different models in the calibration stage showed that Kermani, Angstrom-Prescott calibrated, Angstrom-Prescott models with FAO recommended coefficients, Power, Hunt and Hargreaves-Samani gave the closest estimates compared to the measured radiation in Mashhad, respectively. In the validation stage of the calibrated models, it is found that the Power, Kermani, Angstrom-Prescott models had the most accurate estimation of daily radiation compared to the measured radiation in the study areas, respectively. Furthermore, the lowest difference between simulated yield and evapotranspiration of wheat using the observed radiation data and the estimated radiation data is obtained from the models of Power, calibrated Angstrom-Prescott, Angstrom-Prescott with FAO suggested coefficients and finally the Kermani, respectively. Considering the accuracy and extensive spatial coverage of the radiation data in the Power model, it is recommended to use it as the best radiation estimation method for use in the crop models.

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.

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.

A new dimension in agricultural science has been opened up by the rapid development of computer-based information systems. Physiological processes of plant growth and development can be represented mathematically with a plant simulation model. Many studies have used simulation models to quantitatively analyze the effects of different environmental and management factors on the processes of agricultural plants due to the capability to simulate different processes. Since there is limited data on the ability of CERES-Maize to simulate maize under different nitrogen management treatments in Mazandaran, It took place in Qaemshahr city, and involved recalibration of the CERES-Maize model based on graphical representations and statistical analyses of the performance and physiological traits of this crop.

In order to simulate the physiological traits of maize using CERES-Maize model in different nitrogen treatments, an experiment was carried out in Qarakhil, a research farm of Mazandaran Agricultural Research Center, during 2017 and 2018 as randomized completely block design in four replications. The 10 treatments used in this research included: N1: not use of nitrogen (control), N2: use of 60 kg n ha-1 before planting, N3: use of 120 kg n ha-1 before planting, N4: use of 180 kg n ha-1 before planting, N5: Use of 60 kg n ha-1 in two stages (50% before planting + 50% in stage R1), N6: Use of 120 kg n ha-1 in two stages (50% before planting + 50% in R1), N7: use 180 kg n ha-1 in two stages (50% before planting + 50% in R1), N8: 60 kg n ha-1 in 3 stages (one third before planting + one third in stage R1 + one third in stage R3), N9: 120 kg n ha-1in 3 stages (one third before planting + one third in stage R1 + one third in stage R3), N10: 180 kg n ha-1in 3 stages ( One third was before planting + one third at R1 stage + one third at R3 stage).

It was shown that the model had an acceptable accuracy in simulating the yield, biomass and nitrogen level of a shoot at harvest time. It has also been shown that the trait model does not accurately simulate the leaf area index as well as other traits. According to linear regression analysis, R2 coefficients for the calibration and validation data of the model were 0.69 and 0.57, respectively,. Model validation showed a coefficient of explanation of 0.81, while calibration showed a coefficient of explanation of 0.79. Based on the two-year data, the simulated nitrogen value does not differ significantly from the measured value at 0.95 probability level. The model provided a 0.87 explanation coefficient for the difference between simulated and measured shoot nitrogen, thus indicating its accuracy in simulating total shoot nitrogen over the two-year period.

As a result of this research, the highest yield was achieved in the years 2017 and 2018 with 120 kg n ha-1 applied to the soil as 50% of the base + 50% in R1 stage. In view of the obtained results, the CERES-Maize model can be used as a suitable simulation model to find the optimal nitrogen fertilizer management strategy for maize. For generalizable results, this model can also be applied to interpreting climate data in the northern region of the country in terms of potential production, limitations, and reduction of long-term field experiments.

The crisis of water sources limitation has signified the necessity of irrigation management of canola crop unavoidable. This issue is especially critical in the context of climate change, which is accompanied by changes in precipitation amount and patterns as well astemperature flucatations. The time-consuming and costly nature of field research and the spatial and temporal dependence of the results have caused crop growth modelling to be considered as an effective and acceptable technique in decision support systems under climate change conditions. In this regard, the DSSAT program is one of the most successful programs available for simulating the growth and development of crops. In this study simulation program for plant growth and development was used to predict growth and seed yield of canola in Dehgolan in Kurdistan province of Iran.

This study consisted of two phases; field and simulation studies. Statistical Downscaling Model (SDSM) was used to simulate climate data and forecast climate changes in the future. For the future climate, the set of CanESM scenarios including; RCP2.6, RCP4.5 and RCP8.5 scenarios, presented by the Canadian Earth System Modeling Center, were used. Then, the changes in the growth and yield of canola under the mentioned scenarios were studied in the four periods of 2021-2040, 2041-2060, 2061-2080, and 2081-2100 using the CSM-CROPGRO-Canola model in the DSSAT program. In field studies, two field experiments were carried out to calibrate and validate the output of the model in the research farm of the Kurdistan University in Dehgolan 2016-2017 and 2017-2018 cropping seasons.

The results of validation for weather and growth models showed that the models had sufficient accuracy to simulate climate variables and also the growth of canola. The simulation results illustrated that the growth characteristics of canola were affected by the negative effects of climate change under all scenarios and different time periods, therefore, the plant traits in various climate change scenarios were low when compared to the baseline cropping season (2018-2019). In general, the results of the model showed that the maximum negative effects of climate change on canola growth was in the RCP8.5 scenario and the minimum in the RCP2.6 scenario. The adverse effects of climate change were accelerated by time, especially in the RCP8.5 scenario.

The results of this study showed that the future climate change will have a negative effect on the growth and yield of canola that depends on the scenario and time period, the seed yield will decrease from 0.3% to 37.58% as compared with the current conditions. Therefore, to mitigate climate change impact on agricultural ecosystems necessary management measures including development of suitable canola cultivars and agronomic mangament package should be taken into consideration for adaptation to the changing climate in future.

Agriculture is one of the most important economic activities highly dependent on climatic conditions more than any other factors. Climate change significantly affects crop production. Due to the limitation in production factors, inputs, climatic and geographic properties of different provinces, identification of the comparative advantage of crops is essential. Lack of knowledge about comparative advantage would lead to improper and non-optimal use of resources. Therefore, knowledge about comparative advantage in different regions is useful to plan and exploit the resources. The present study was conducted to identify the comparative advantage of some important rainfed winter crops.

In the agronomic section of this study, the yield of rainfed crops including wheat, barley, chickpea and lentil in Ardabil, Fars and Khorasan Razavi provinces were simulated using the SSM-iCrop2 model. Long-term meteorological data were used to decrease climatic fluctuations and more precise estimation. Economic evaluations were performed using policy analysis matrix (PAM), domestic resource cost (DRC) and social cost-benefit index (SCB) methods. Also, nominal protection coefficient (NPC), effective protection of crops (EPC) and nominal protection input (NPI) were measured. In the agronomic section of this study, the yield of each region was simulated according to the climatic conditions and using the SSM-iCrop2 model (to determine the production potential of each region). For this purpose, long-term weather data were used and simulation was done for a 30-year period from 1986 to 2015. Under the moderate production conditions (moderate agronomic management or unsuitable climatic conditions), 50 percent of the yield potential, and under the optimum production conditions (optimum agronomic management or good climatic conditions), 70 percent of the potential yield is attainable. Therefore, 50 and 70 percent of yield potential were simulated using the SSM-iCrop2 model and their effect on comparative advantage and social profit were investigated.

The results indicate that according to the DRC index, Ardabil in wheat, chickpea and lentil production, Khorasan Razavi in chickpea and lentil and Fars in chickpea production have a comparative advantage. Based on the SCB index, the production of wheat in Ardabil, chickpea in all three provinces and lentil in Ardabil and Khorasan Razavi provinces have social profit. According to the results, increase yield leads to improved comparative advantage and social profit so that all three provinces will have social profit in crop production (rainfed, wheat, barley, chickpea and lentil) provided that the farmers attain at least 50 percent of the yield potential (exploitable yield). According to the results of the simulation, Fars and Ardabil had the highest exploitable yield in wheat production, and regarding barley, chickpea and lentil, the highest exploitable yield was related to Ardabil province. Based on the actual yields, only Ardabil province had high yields and was superior to other provinces in terms of the economic indices.

Wheat, barley, chickpea and lentil crops had higher yield in Ardabil province due to the suitable climatic conditions of this region. One of the reasons for the advantage of crop production in Ardabil is its higher yield which has increased the income of growers compared with the other two provinces, and production of rainfed crops in this province has led to benefits.

Rice is one of the most important cereals and staple food of many people in the world. In order to identify the possibility of supplying food to the world's population, given the need for continued production in sustainable agriculture, it is necessary to correctly predict the yield of crops. For this purpose, modeling of growth stages and yield of rice based on meteorological statistics of Iran, was studied in Gorgan University of Agricultural Sciences and Natural Resources. The purpose of this study was to use the simple model SSM-iCrop2 to simulate rice growth and yield to investigate the effects of climatic factors, soil, agronomic management and to determine the genetic coefficients of rice in Iran. Due to the appropriate ability of the model in rice simulation, it can be used as a suitable tool for better planning and management of rice fields in the country. In this study, the SSM-iCrop2 model was used to simulate the potential yield. In this model, the amount of potential yield is calculated based on meteorological data, soil conditions, management and plant parameters. The model needs a series of inputs to run, which is made to perform the simulation of the collected model. The most important processes to be simulated in the model are plant phenology, leaf area changes, dry matter production and distribution, and soil water balance. For parameterization and evaluation of the model, the values of performance and day to maturity of the simulated were compared with those observed. For this purpose, a set of experimental data (data related to rice growth and production from published and unpublished articles and reports) was used in important areas under rice cultivation. According to the statistics of the Ministry of Agriculture, 2001-2016, the main areas of rice cultivation and production in Iran were identified. In this study, to compare the deviation of the simulated values from the observed squared error mean (RMSE), coefficient of variation (CV), correlation coefficient (r) and the deviation of the simulated results from line 1:1 with a range of 20% difference. Between the simulated and observed values was used to test the model results. In parameterization of SSM-iCrop2 model for rice, the comparison of observed and simulated days to maturity with RMSE, CV and r values of respectively 12 days, 11 percent and 0.61, respectively, and for grain yield of 56 g m-2, 21 percent and 0.80 indicated the accuracy of the used parameters. Furthermore, in evaluation the model, RMSE, CV and r values for days to maturity were 9 days, 10 percent and 0.95 and for grain yield were 43 g m-2, 14 percent and 0.77 and in simulation evapotranspiration were 44 mm, 9 percent and 0.79 respectively, which confirms the precision of the model simulation. Application of SSM-iCrop2 model is simple and acceptably precise simulation is possible with minimal parameters and inputs. The results of parameterization and evaluation of SSM-iCrop2 model, which was (RMSE), (r) and (CV), showed that this model includes phenological stages and grain yield in the history of different plantings in the climatic conditions of Iran simulates with great accuracy, which indicates the appropriate structure of the model in the simulation. Therefore, considering the appropriate accuracy of SSM-iCrop2 model in simulating rice phenology and yield, it can be used as a suitable tool to study cropping systems and interpret the results in different environmental and management conditions to plan and improve the management of rice fields in the country.

Predicting crop yield is an important factor for efficient planning and management. In the present study, a simple mathematical model is developed for predicting the yield of autumn-sown sugar beet in Khuzestan region. In this model, the logistic function was used, in which the root yield and white sugar yield is defined as a function of irrigation, rainfall and evaporation from the pan. In order to evaluate the model, data of two-year trials conducted under five treatments of drip irrigation water based on percentage of water requirement (25, 50, 75, 100 and 125%) at Safiabad Agricultural Research and Education and Natural Resources Center, Dezful, were used. Results showed that the model was able to estimate root yield and white sugar yield with good accuracy so that the normalized root mean squares error (NRMSE) for estimating root yield in the calibration and validation stage were 9.4 and 13.0%, respectively. Also, the value of NRMSE for estimating white sugar yield in both calibration and validation years was less than 10% and equal to 6.8 and 9.8%, respectively. The model is able to have good accuracy during the growing season and estimate the values of root yield and sugar yield during different days after planting (DAP) with an error of less than 20%. Considering that the model presented in the current study is an experimental model, it is recommended to first recalibrate the model, and then use it in regions with different climatic conditions.

Rice has a special role in the diet of Iranians. To meet the needs of the country, it is necessary to increase the production of this crop. Therefore, due to the limited cultivation area, increasing yield per unit area is a more appropriate method. One of the best methods to increase production is to reduce the yield gap. The present study was designed and conducted to estimate the rice yield gap in Iran in 2016. In this study, the climatic zones and reference weather stations of rice production in Iran were determined using the global yield gap atlas (GYGA) protocol. First, the actual yield of rice was extracted from the Iranian agricultural statistics. Then, potential yield in the main production areas of rice was estimated using the SSM-iCrop2 simulation model and management, soil and climatic data. Yield gap was calculated from the difference between the actual yield of farmers and potential yield. The actual yield of rice in the main climatic regions of this crop in Iran was estimated from 3.5 to 5.2 ton.ha-1 and the simulated potential yield from 6.3 to 9.5 ton.ha-1. Therefore, the yield gap varied from 2.1 to 5.1 ton.ha-1. The results of this study showed that if the yield gap of rice is reduced, its production can be increased from 2,440,000 tons to 4,103,000 tons and achieve the self-sufficiency in rice supply in the country.

In order to simulate transplantation, the parameters related to cotton seedling growth are firstly measured in a factorial experiment in a randomized complete block design at Gorgan University of agricultural sciences and natural resources within 2018. The parameters are then utilized in SSM-iCrop2 Model. In the simulation section, four seedling size based on the leaf area (namely 17, 22, 27, and 37 cm-2 per plant) are evaluated in 4 planting dates (15 June, 1 July, 15 July, and 30 July). Results show that in early planting date, seedling transplantation rushes the process of crop maturation for 43 to 49 days. However, this has had no significant effect on yield values (from 453 to 461 g/m2) and net water requirement (312 to 316 mm). The usual sowing date causes the crop to mature between 27 and 38 days (earlier vacant land), whereas seed sowing at this planting date impairs the subsequent crop cultivation. At this planting date, as in early planting, transplanting has no strong effect on the yield (from 444 to 452 g/m2) and water requirement (299 to 308 mm). In a late planting date, seedling transplanting with four seedling sizes between 1 and 5 days results in premature seed germination, even though seed cultivation impairs subsequent planting. At this planting date, transplanting has a noticeable effect on the yield (361 to 441 g/m2), but the amount of pure irrigation (271 to 300 mm) remains unaffected by transplanting.

The present research has been conducted to parameterize and evaluate the APSIM-Wheat model for winter wheat and to use the model for evaluation of climate change effects on grain yield. The model is used to simulate leaf area index, dry matter, and grain yield of Roshan cultivar winter wheat in Karaj and Khomein regions between 2014 and 2015, under four irrigation regimes and three levels of nitrogen fertilizers. The experiments have been conducted in both regions as split-plot in a randomized complete block design. Irrigation is considered as main plot and nitrogen as a sub-plot. Model parameterization is based on observational data from full irrigation and 200 kg/ha nitrogen treatment in both regions. Results from the model evaluation show that the simulated values closely predict the observed values so that the RMSE in both regions is less than 670 and 450 kg/ha for biomass and yield, respectively, being below 0.56 for leaf area index as well. Normalized RMSE in all treatments has been below 17.8%, with Wilmot's index being above 0.82. The evaluation of model shows high performance and its possible use in other studies. Results from climate change analysis show that increasing the temperature from 0 to 5 °C leads to a maximum reduction of 75% in grain yield and an increase in carbon dioxide concentration, resulting in a maximum increase of 24% and, if the temperature increase exceeds three degrees Celsius, the grain yield will decrease in all possible concentrations of carbon dioxide.

Nowadays, climatologists and researchers are interested in the long-term forecast of climatic variables to be informed about the extent of their variations and to take measures to mitigate the adverse effects of climate change. Accordingly, general circulation models (GCMs) of atmosphere have been developed to predict climatic parameters. LARS-WG is a model to downscale the output of GCMs. It was used in the present research to generate data of daily precipitation, radiation, and minimum and maximum temperature across the cotton cultivation climatic zones (Meteorological stations) in Iran. The objective of this study is to estimate climatic factors (rainfall, radiation and minimum and maximum temperature) in the future period in major climatic cotton cultivation areas in Iran, which may be used for optimized water resource preservation and management in these regions. Also, the 5th IPCC report was used in this study, in contrast with the majority of investigations that use the 4th report.

In this research, the important areas under cotton cultivation in Iran are the target area. The data of 23 synoptic stations were used in these areas. The required meteorological data recorded in the stations were daily rainfall, minimum temperature, maximum temperature and sunshine. The present study assessed the performance of five different GCM models in simulating the data of precipitation, radiation, minimum temperature, and maximum temperature in nine synoptic stations (SABZEVAR, GHOOCHAN, GHOM, HASHMABAD, LAR, BILESOWAR, EDAREGORGAN, HASANABADEDARAB, and MASHHAD) from 2011 to 2016, firstly. Finally, two GCMs (MIROC5 and GFDL-CM3) were selected for the research purpose based on the results of t-test, F-test, and the Kolmogorov-Smirnov non-parametric test. Then, the parameters were predicted by the selected GCM models for 20 years (2041-2060) under the emission scenarios of RCP4.5 and RCP8.5.

According to employing the scenarios of RCP4.5 and RCP8.5, the predicted solar radiation does not show a significant change in the future period versus the base period of 1981-2010 in all studied regions based on both GCM models. The highest change (0.249 MJ / m2 / day) is based on the MIROC5 model and the RCP8.5 scenario in climate zone 6102 (HASHMABAD station). The predictions revealed that the parameters of maximum and minimum temperature would be ascending for all the climatic regions over the future period. The highest variations in the average long-term annual minimum and maximum temperatures versus the base period would be 2.67 and 2.75°C happening in Climatic Region 6002 Includes stations (HASANABADEDARAB, KHAF, HAJI ABAD and GONBAD) over the 2041-2060 period under Scenario RCP8.5. For both scenarios RCP4.5 and RCP8.5, the highest temperature increase was observed in the climatic zones where located in south of Alborz and the east of Zagros mountain chains as well as the Central Iranian Dry Plateau and the lowest increasing was in the climatic regions located around the Caspian Sea, the Persian Gulf and the south of Iran. For the total annual precipitation over the future period (2041-2060), the highest amplification was predicted by MIROC5 to be 98.5 mm under Scenario RCP4.5 in Climatic Region 6202 (EDAREGORGAN station), and the greatest loss of precipitation was predicted by the same model to be -29.8 mm in Scenario RCP8.5 in Climatic Region 6102.

Given the prediction of rising average annual minimum and maximum temperature and the decline of precipitation over the future period (2041-2060) in the climatic regions leading to the arid plain of central and southern Iran, it can be concluded that the variations of meteorological parameters induced by climate change will be significant in the cotton-growing climatic regions of Iran over the future decades.

The present study was conducted to parameterize the SSM_iCrop model and evaluate the prediction of growth and development of faba bean in Gorgan climate condition. This study was carried out on faba bean cv."Barkat" as split-plot in randomized complete block design with four replications at Gorgan University of Agricultural Sciences and Natural Resources in 2015-2016. The experimental factors consisted of planting date (27 November, 25 December and 31 January) and plant density (5, 15, 25 and 35 plants/m2). The parameters of phonological stages, leaf expansion and senescence, production and distribution of dry matter and water balance were estimated using the present data experiment and other data. The results of model evaluation showed that, it can well predict, days to flowering (RMSE = 3.8 and CV =4.1), days to maturity (RMSE = 11.9 and CV= 8.1), node number on main stem (RMSE = 1.7 and CV = 10.0), leaf area index (RMSE =0.8 CV =28.8), biological yield (RMSE = 158.5 and CV =21.6) and seed yield (RMSE = 118.6 and CV = 24.7). Therefore, the SSM_iCrop model can be used to evaluate the agronomic management and analyze the growth and yield of faba bean in Gorgan conditions.

Transplanting or pot culture has been mentioned as a promising approach to increase crop yield and decrease irrigation water use in recent years. Using simulation models to assess pot culture is a valuable way in investigating the effect on yield, water consumption and other properties of crops such as soybean. For this purpose, several pot culture levels (seedling sizes) plus direct sowing were evaluated in soybean using SSM-iCrop2 for four different sowing dates in Gorgan, Iran. Seedling sizes were 13, 18, 24 and 29 cm2 per plant leaf area and at 200, 250, 300 and 350 °C cumulative temperature, respectively. Sowing dates were June 10 (early), June 25 (usual), July 14 (late) and July 27 (very late). The results of simulation showed that direct seed planting method at late sowing date (14 July) and all treatments at very late sowing date (27 July) are not feasible and cannot be recommended due to disturbance that cause in sowing of next crop in double cropping system of the region. Transplanting method at early (10 June), usual (25 June) and late planting dates resulted in earlier maturity. There was no significant difference between two methods regarding seed yield at early and usual planting dates, but at late planting date transplanting method resulted in higher yield compared to direct seed planting method. Seedling size had no significant effect on yield. In addition, there was no significant difference among sowing methods in all planting dates in terms of net irrigation requirement of soybean. It was concluded that transplanting is only preferred at late sowing date for its higher yield, but considering the higher production costs of this method compared to direct sowing, it is necessary to find some cost-effective measures before pot culture could be adopted by farmers.

Bean is now considered as one of the most important products in the Middle East, Africa, China and parts of Europe and Australia in human and animal nutrition (Turpin et al., 2002). Bean is a suitable herb in rotation with cereals. Because it can break the cycle of many common diseases of grains and reduce the population of nematodes. The model is a tool that helps us interpret and understand the world in which we live (Boote et al., 2003). Precise prediction of the phenology of crops is one of the essential features of simulation models. From models, improved crop production management can be used to predict the probable dates of harvesting or predicting final yields, or more actively, to predict the timing of phenological events. The production and distribution of dry matter in crop simulation models is largely regulated by the timing of development stages. Understanding how the bean's phenological development response to day length and temperature, creates a model for predicting the time of occurrence of different stages of development of this crop. The aim of this study was to obtain a model for predicting phenological stages, maximum leaf area index, economic and biological yield, as well as harvest index of bean plant under Gonbad conditions.

For the main data, the values obtained from field experiments were carried out at the research farm of the Faculty of Agricultural Sciences and Natural Resources of Gonbad-e-Kavou’s University under favorable rainfed conditions and free of pests, weeds and bean diseases. The experiment was a factorial based on randomized complete block design with four replications in two years. Planting density was at four levels (8, 12, 16 and 20 plants/m2) and sowing date was in three levels (13 November, 1391, 13 December, 1391, 13 December 2012 and 7 December 2011, December 4, 2011 and December 17, 2011). The model used in this study was similar to that of Soltani et al, (1999) for chickpea. Meteorological and climatic data was from 1994 to 2014 of Gonbad. 24 different scenarios including different combinations of density and planting dates were defined for two years in the model. In order to compare simulated data with field experiment data, evaluation indices, explanation factor (R2), root mean square error, normalized root mean square error and line 1: 1 were used. The higher the R2 value from the linear regression of functions and the 1: 1 line closer to one, indicates a high correlation between simulated and observed values and is a sign of a better description of the model in simulating the desired attribute.

The coefficient of explanation between the predicted values and obtained from field experiments on day to flowering was 0.89. The root mean square error and its normalized value were 2.99 and 1.29 days, respectively. Also, comparison of the number of days to harvest obtained from field experiments with values obtained from the model showed that all of these values are in the range of 15% up and down the 1:1 line. The results showed that the model also well predicted day to pod setting. So that the explanation coefficient, root mean square error and normalized root mean square error, between the data obtained from field experiments and the predicted data of the model were equal to 0.81, 2.99, and 12.0, respectively. Regarding the day to the physiological maturity, the coefficient of explanation, root mean square error and normalized root mean square error were 0.9, 5.84, and 12.0, respectively, which all represent a good prediction of the model. The explanation coefficient (R2) was obtained by analyzing the linear regression of the functions between day to harvest and the predicted values equal to 0.91.

The results showed that the model could well predict the phenological stages of the faba bean, with the exception of the day to the emergence. This prediction was at the highest level in the stages leading to bean harvest, so that the highest coefficient of explanation with a value of 0.91 was found for the number of days to bean harvest. Our study showed that the above model cannot predict well the maximum leaf area index, yield, biological yield and harvest index in dune weather conditions and under our farm experiments.

Maize in response to salinity stress exhibits different responses during different growth stages and reproductive growth stage is the most sensitive stage of plant growth. The objective of the present study was to evaluate the ability of salinity simulator models to estimate maize (SC704) yield, during reproductive growth stage. Process-physical models including Maas and Hoffman, van Genuchten and Hoffman, Dirksen et al. and Homaee et al. models were used to access this objective. In order to create real growth conditions in saline soils, natural saline water of Nough lake in Rafsanjan, Kerman province, Iran, with electrical conductivity of 42.6 dS.m-1, was used and diluted with normal fresh water for preparation of salinity treatments studied in this experiment. Treatments used in this study were five salinity levels of 1, 2, 4, 6 and 8 dS/m with a non-saline water (as check treatment) that were studied in three replications. The results of modified efficiency coefficient (E'), modified agreement index (d') and coefficient of residual mass (CRM) statistics showed that the van Genuchten and Hoffman model with the highest accuracy (E'=0.87, d'=0.91) was the most accurate model for shoot height, while for shoot dry weight, the Nonlinear model of Homaee et al. (with the accuracy of E'=0.86 and d'=0.90) and for final grain yield, the Maas and Hoffman model (with the accuracy of E'=0.94 and d'=0.96), had the most accuracy and the best estimate. In total, the results of this study showed that salinity simulation models had good ability to estimate maize yield under salinity stress and better management of allocation of low quality water resources in different stages of plant growth. In other words, the four models of Maas and Hoffman, van Genuchten and Hoffman, Dirksen et al. and Homaee et al. can be effective tools in using saline water sources with varying degrees of salinity to achieve optimal maize production. Therefore, by precisely determining the optimal model for each growth stage and accepting the risk of yield loss due to salinity of irrigation water, low-quality water resources can also be considerably utilized in maize yield production.

Food security is a main subject in the world. Many international and governmental organizations to ensure the nutritional needs of human are being explored. According to the restrictions on agricultural lands, an increase in production per area unit is a way to improve food security. Despite high yielding varieties, these varieties do not reach their potential yield due to lack of access to favorable environmental conditions. Therefore, yield gap will be created between potential yield and actual yield. Before any progress in the agronomic operations of crop, it is necessary to identify the crop’s potential yield and to calculate the gap between potential and actual yield. Therefore, the aim of this study was to evaluate the historical trend in different yield gaps and to calculate different mean yield gaps in the studied region.

Potential and conventional yields were simulated using CropSyst model over 1981-2008 period and researchers’ potential yields over 1994-2008 period, maximum achievable yield over 1989-2008 period and actual yield over 1981-2008 period were collected from Agricultural Research Center, and Agriculture Organization. In this study, four types of yield gap were calculated as the difference between simulated potential yield and actual yield (yield gap 1), the difference between researchers’ potential yield and actual yield (yield gap 2), the difference between maximum achievable yield and actual yield (yield gap 3) and the difference between the simulated conventional yield and actual yield (yield gap 4).

The trend of changes in actual yield compared with both simulated potential and conventional yields over 1981-2008 period showed the gap between simulated potential and conventional yields with actual yield were decreasing. The decrease in the yield gaps was due to decrease in the trend of changes in simulated potential and conventional yields and increase in the trend of changes in actual yield over the studied period. According to the results, to increase the simulated potential yield, the varieties should be selected which have high radiation use efficiency. While for reducing the yield gap between simulated conventional yield and actual yield, the varieties with high radiation use efficiency should be cultivated under unlimited water and nitrogen condition. The trend of changes in yield gap between researchers’ potential yield and the actual yield was fixed yields over the period of 1994-2008 which was due to unchanged trend of both yields over this period. The trend of changes of the gap between actual yield and maximum achievable yield was increasing over 1989-2008. This was indicated the better management operations of prior farmers to achieve maximum yield, while other farmers did not work hardly to enhance the yield over the studied years. The simulated mean potential and conventional yield, the researchers’ potential yield, the maximum achievable yield and the actual yield were 6.35, 6.6, 6.0, 4.42 and 3.4 t ha-1, respectively, over 1994-2008 period. The differences between these yield levels with the actual yield were about 3.0, 1.0, 3.2 and 2.6 t ha-1.

It can be concluded that the difference between the management operations performed for achieving actual yield and the other yield levels can be created a yield gap and the more management differences, the higher yield gap.

Climate changes and phenomena such as drought are effective in the yield of agricultural products. Replacing crisis management with risk management is one of the solutions for these phenomena. With risk assessment before crisis, the amount of damages will be reduced to the minimum amount. In this research, the risk assessment of drought by Monte Carlo method will be used in order to reduce the damages caused by drought as a natural and uncontrollable phenomenon on saffron product. The monthly Standardized Precipitation Index (SPI) of drought and the monthly average temperature are calculated as independent variables in the yield distribution function. The relationship between independent variables (temperature and SPI) and dependent variable (saffron yield) is established using Artificial Neural Network (ANN). After that, 2000 random data from independent variables are generated using MATLAB and 2000 simulated yields generated by a trained artificial neural network. Then, the cumulative distribution of the simulated yields are determined and these yields are standardized in order to unification of the yield data of each city. The risk factor is calculated by choosing a reference station and using the cumulative distribution. The relative risks of the stations are considered after drawing the diagram of Yield-Risk standard factor. The results of the research show that most of the studied years are in normal range and the drought frequency in the four stations of Khorasan Jonoobi province is twice the stations in Khorasan Razavi. Furthermore, the artificial neural network with a correlation coefficient of 0.85 could predict the yield of the product very well. The similarity of the cumulative distribution diagram of the real yield with the cumulative distribution of the yields simulated by Monte Carlo indicates that the results are correct. At the end The results of this research show that Ghayen has the highest relative risk compared to the reference station (Torbat-e- Heydariyeh) and Nehbandan has the lowest one.