afshin soltani

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

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

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

Conclusion :

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

The demand for food is influenced by population size, type of diet and the amount of loss/waste of Agricultural products. In the present study, the amount of demand affected simultaneously by the influencing factors for the present time period, 2030 and 2050 were examined. The study examined 48 different scenarios derived from combining three levels of loss/waste reduction (0, 25, and 50%), four different food baskets, and four different fertility rates (including the current constant (1.8), low (1.5), medium (2.5) and high (3.3) fertility rates) for each of the 2030- and 2050-time frames, and calculated the demand values for plant products and blue water footprint required for each scenario. The amount of blue water footprint required to produce the products in demand was calculated according to the current farm management conditions of agricultural production. In the calculation of blue water footprint, the effect of climate change for the time points of 2030 and 2050 was also taken into cosideration. The results showed that the current demand for plant products in the country is 128.74 million tons per year. Assuming a population increase at the current fertility rate (1.8), the demand will be 149.25 million tons in 2030 and 166.06 million tons in 2050. The amount of irrigation water required to meet the demands in is under the current conditions, 152.47 billion m3 in 2030 and 176.34 billion m3 in 2050. Currently, the amount of water allocated to the agricultural sector is 86 billion m3, which is more than the environmental capacity. To achieve a sustainable environment, the amount of water allocated to the agricultural sector must be reduced to 39 billion m3. If the environmental protection is included, the country's self-sufficiency rate in producing plant products from a water perspective will be 25.6% in 2030 and 22.1% in 2050. With a 25% reduction in loss/waste, the amount of water needed to produce the products in demand will be 140.28 billion m3 in 2030 and 162.18 billion m3 in 2050. However, the use of a plant-based diet can significantly reduce the amount of demand for agricultural products and subsequently the demand for water.

Documenting the production process in agriculture includes gathering all the information and activities that show the production process of a product from the stage of seed bed preparation to harvesting. In this view, the farm is considered as a production unit, and all processes from the time of preparation of this production unit for cultivation until the product leaves the farm after harvesting are included in the scope of documentation. Considering that the management of the production process in agricultural systems has a significant effect on the quantity of yield, production efficiency, the efficiency of input consumption and finally the environmental effects of production, monitoring and improving the processes leading to the production of agricultural products in order to reduce the challenges associated with agricultural management in Agricultural systems are inevitable. From a scientific point of view, it is possible to improve such management processes by using various techniques, among which continuous improvement and re-engineering of processes are among the most important ones. Golestan province is considered one of the most important areas for the production of faba bean (Vicia faba L.) in the country due to the climate and soil conditions, therefore, without a comprehensive understanding of the agricultural operations, it is difficult to take steps to meet the needs of farmers and eliminate production obstacles. In this study, it is tried that all the researchers and farmers become familiar with the production process of faba bean in Golestan province in an integrated and documented manner and this research is a basis for effective planning to meet the needs of farmers, reduce the consumption of inputs and reduce the environmental effects by changing the method it will be farm management.

This research was carried out in two crop years 2019-2020 and 2020-2021 in Golestan province and in Gorgan, Aliabad Katul and Aqqola cities. To document the production and analyze the performance gap, 445 farms that had sufficient diversity in different aspects, including farm management, were selected, and all information related to the farm and production management was collected using questionnaires, observations and measurements. In each region, every year, different fields were examined in terms of agricultural management factors, and observations related to the field, planting, planting and harvesting were recorded. In order to analyze the data, absolute and cumulative frequency distribution was used. In these investigations, the scope of changes and the method of performing each management operation performed in the bean farms and also the proportion of farmers who used different methods of each of these management operations were determined.

The results showed that soybeans are cultivated more than other crops (41%) before faba bean. Faba bean is planted in these areas between 14 October and 25 November. In these areas, 97% of farmers use reversible plows (one to two times) to prepare the bean planting bed. The amount of seed used is in the range of 60 to 100 kg.ha-1, and in 56% of the fields it is planted with a linear machine, in 43% with a row machine, and in 2% by hand spraying and then mixing the seeds with the soil using a disc. According to the distribution of rainfall in the region, 92% of faba bean cultivation is carried out under rainfed condition. Farmers use 0 to 115 kg.ha-1 of pure nitrogen fertilizers, 0 to 96 kg ha-1 of pure phosphorus and 0 to 72 kg.ha-1 of pure potassium fertilizers to meet their fertilizer needs. Investigations showed that pesticides were used in 338 out of 445 farms, herbicides were used in 356 farms, and fungicides were used in 293 farms, and the number of times pesticides, herbicides, and fungicides were used varied between 1 and 3 times. Faba bean is harvested in these areas from late April to early June, depending on weather conditions, planting date and variety. The yield of faba bean varied from 780 to 4187 kg.ha-1.

The results of this research can clarify the details of the faba bean production process for students and researchers to carry out future projects and also provide information on the common consumption of inputs to those interested and be useful in the implementation of growth and yield simulation plans under common conditions by means of average crop plants.

After wheat, rice is the main food of Iranian people and has a special role in nutrition, income and job creation in the agricultural sector. Despite the undeniable importance of this crop, it is considered one of the water-intensive plants in the agricultural sector, and due to the water crisis in the country, the continuation of the cultivation of this crop in many regions of the country is facing many problems. The purpose of this research was to evaluate the potential yield (yield in the absence of water, nutrients, pests and diseases) and to estimate the blue and green water footprint of rice in the agro-ecological zones (AEZ) of the entire country. Water footprint calculations were done with the SSM-iCrop2 model for the time period of 2006-2015. The results show that parts of the northern regions of the country, such as Gilan, Mazandaran and Golestan provinces, which are located in the vicinity of the Caspian Sea, have less blue water footprint and more green water footprint, and are suitable areas for rice cultivation. Other provinces of the country, such as Khuzestan, Bushehr, Fars and South Khorasan, which are mainly located in the warm regions of the country, have more blue water footprints and less green water footprints, therefore, these provinces are not suitable for rice cultivation at all.

Today, the DeNitrification DeComposition (DNDC) is model used to anticipate soil organic carbon (SOC) turnover and crop growth under various field management practices. Gorgan County is an important region for soybean production in Iran.  We aimed to 1) validate the DNDC model for modeling SOC dynamic in croplands under soybean cropping, 2) simulate the total topsoil (0–30 cm) SOC stocks of soybean cropping systems, 3) quantify the spatial distributions of carbon sequestration potential of soybean-grown croplands by Geographic Information System (GIS) techniques. In this research, soil samples were taken from 150 fields at depth of 0–30 cm before soybean cultivation and after crop harvesting. In this research, we used the site simulation type of DNDC model for simulation and denitrification/decomposition procedure. Inputs in the DNDC model included information on survey region, climate, soil, crop properties, and farmland management practices. The soil and crop properties categorized into farming management practices such as fertilization, tillage, grazing or plant cutting, and irrigation. The climatic data were obtained from one meteorological station located within the study area. To continue, crop parameters were provided based on field survey and laboratory work. Also, the soil properties (including texture, bulk density, pH, SOC, soil total N, field capacity, wilting point, hydro-conductivity point, porosity and clay fraction) were obtained from sampling sites distributed in soybean croplands of Gorgan county. Results indicated that the DNDC model can simulate the SOC values for soybean fields. Based on the results, there was correlation between the simulated and measured data for SOC. The average concentration and storage of carbon sequestration were as 3.97 and 1.42 Mg ha-1 for observed situation and in predicted situations obtained as 2.60, and 1.42 Mg ha-1, respectively. The highest content of SOC was related to the east, southeast, and central parts toward the south of the county, which was affected by several factors such as soil bulk density, regional climatic condition, using conservation cropping systems, improved irrigation systems, and fertilization management type. The study provided new information on how improvements in the process-based DNDC model in Iran. Therefore, it can be utilized to determine SOC change and dynamism and carbon sequestration potential on the regional scale.

The aim of present study was to investigate the effects of NPK chemical fertilizer management on parameters related to lodging, yield components, grain yield and NPK nutrient uptake in local rice cultivars. The experiment was conducted in farms located in Mazandaran province, Sari, during 2017 and 2018. The experiment was performed as split plot in a randomized complete block design with four replications. The different doses of fertilizer treatments at five levels including F1: N250P150K150, F2: N200P100K100, F3: N150P75K75, F4: N100P50K50, and F5: Control (N0P0K0) as main plot and local rice cultivars at two levels of Sang Tarom and Tarom Hashemi were considered as sub-plots. The results showed that the highest third and fourth internodes lodging index were observed in N250P150K150 and N200P100K100 treatments. Application of N200P100K100 treatment increased the number of fertile tillers per hill (13.25) and finally produced highest grain yield (4806 kg/ha-1), although there was no significant difference with N250P150K150 and N150P75K75 treatments. When the doses of NPK fertilizers increased, the concentration of NPK in grain and straw and as well as grain protein content also enhanced. There was no statistically significant difference between the two cultivars in terms of grain yield and concentration of N and K in plant, but the concentration of P in grain for Tarom Hashemi cultivar was about 8.9% higher than Sang Tarom. According to the findings of this study, the application of N150P75K75 treatment is a better option to improving the grain yield of rice cultivars along with a decrease in chemical fertilizers and subsequent reduced fertilizers costs and environmental damages.

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.

Common bean (Phaseolus vulgaris L.) due to its high nutritional value has an effective role in ensuring food security of the community. In Iran, protein supply is mainly dependent on plant products, and any action to improve the yield and production of protein-rich crops, which are headed by legumes, is of particular importance. Increasing yields by optimizing production management and eliminating yield gaps is the most appropriate way to increase crop production and improve food security. Therefore, accurate estimation of potential yield and yield gap and crop production is essential for sustainable food supply. The present study aims to estimate the yield gap and production and water productivity of bean in its main climate zones in Iran based on the Global Yield Gaps Atlas (GYGA) project at the Gorgan University of Agricultural Sciences and Natural Resources was done in 2016. In order to estimate the beans yield gap and water productivity in Iran according to GYGA protocol, first the data related to farmers' yield (Ya) and bean harvested areas and production in the country in a period of 15 years from 2001 to 2015 from the Ministry of Agriculture of Iran was prepared. Then the distribution map of beans in the country was prepared. By combining the distribution map of the bean harvested areas and the climatic zoning map of the country, the main climatic zones (DCZs) of bean production were identified. Then, reference weather stations (RWSs) were selected according to the level of each climatic zone. In order to estimate the potential yield (Yp) and water productivity (Wp) based on weather data and major soil type and agronomic management meteorological in each of the selected areas, the SSM-iCrop2 simulation model was used, which was locally calibrated and evaluated. Finally, the bean yield gap (Yg) was calculated from the difference between the potential and actual yield of each RWSs was upscaled to DCZs and country-level. The results of comparing the average of beans actual yield reported by the Ministry of Agriculture of Iran with the actual yield calculated according to the GYGA protocol for the country with RMSE, CV and r values of 84 kg ha-1, 4% and 0.96 respectively, which indicated using This protocol can estimate the average yield of bean in the country with high accuracy. The average beans actual yield in Iran during the years 2001 to 2015 varied between 1.6 and 2.3 ton ha-1. Also, the average actual yield in the main climatic zones of production was 1.9 and between 1.1 (climatic zone 4202 in Germi) to 2.3 ton ha-1 (in climatic zone 3003 in Avaj). Bean potential yield was estimated from 3.4 (in climate zone 4202 in Germi) to 5.4 ton ha-1 (in climate zone 4103 in Hamedan and Bijar) with an average of 4.5 ton ha-1. Based on results, in the main climatic zones of bean production in Iran, there is a yield gap of 1.8 to 3.5 (average 2.6) ton ha-1, equivalent to 46 to 67% (average 57%). The average water productivity potential for bean in Iran was estimated to be 0.76 kg m-3. According to the results, if the factors causing the yield gap are eliminated by optimizing the management of bean production and cultivation and bringing the yield of bean fields to attainable yield (80% of potential yield), is increasing grain yield from the current value of 1.9 to 3.6 ton ha-1 with the same harvested areas, bean production in Iran will increase from the current 222,705 to 415,822 ton, which is equivalent to 46% increase in production and is considered an important step in improving food security.

There has been a long-standing dispute between organizations associated with water resources (such as the Ministry of Energy and Agricultural Jihad) over the amount of withdrawal water for agriculture at the national and provincial levels. The purpose of this study was to estimate the withdrawal water for irrigated agriculture in Fars Province based on water balance modelling under the condition of “farmers". For this purpose, the SAWA system was used. The system estimates were obtained based on 10-year meteorological data (2011–2021) and 5-year crop area statistics (2017–2021). The largest crop area belonged to wheat (32%), fruits (24%), and barley (7%). Regarding net applied irrigation water volume, fruits (39%), wheat (14%), and rice (7%) had the greatest shares. The highest net applied irrigation water belonged to date, rice and pomegranate, respectively, with 23651, 14489, and 10160 m3.ha-1. Water balance analysis showed that the highest irrigation occurs in June (1024 million m3) and the lowest in December (70.8 million m3). The average volume of withdrawal water for agriculture was estimated at 6565 million m3 per year with a range of 6228–7076 million m3 per year, which was less than the estimate of the Ministry of Energy (i.e., 7991 million m3 per year). Part of this difference is due to the use of different statistical years.

Cotton is a critical crop in Golestan Province, yet there is a notable discrepancy between actual and attainable yields, largely due to suboptimal agricultural management practices. In each region, specific factors contribute more significantly to the yield gap, influenced by local resources and farmer experience. Identifying these factors and their contributions can guide targeted improvements in agricultural management. This study aims to identify the yield-limiting factors for cotton and estimate the yield gap in Kordkuy County. 

This study assessed 57 cotton farms in Kordkuy County during 2020. Data on agricultural management practices—including seedbed preparation, cultivar choice, seed source, planting timing, weed control, pesticide application, and seed consumption—were collected through field visits and farmer interviews. Actual yields were recorded. The Comparative Performance Analysis (CPA) method was employed to determine the contribution of each factor to the yield gap. Data were analyzed using SAS software, with quantitative variables examined through linear regression and qualitative variables assessed via a completely randomized design. Mean comparisons were performed using the Least Significant Difference (LSD) test at the 5% significance level. 

The study revealed a substantial yield gap of 56.7% in Kordkuy cotton farms. Key factors affecting yield were identified as pest issues (49%), seed disinfection (31%), pre-planting irrigation (10.2%), weed presence (6.7%), and planting method (2.9%). Addressing these issues—by controlling pests, performing seed disinfection and pre-planting irrigation, mechanizing planting, and managing weeds—could potentially increase yields from 2,192 kg/ha to 5,061 kg/ha. Without optimal management, the yield gap remains at 2,869 kg/ha.

The findings highlight that optimizing management practices related to plant density, intra-row spacing, planting dates, mechanized planting, seed disinfection, pre-planting irrigation, pest control, and weed management can significantly boost cotton yields. These insights are valuable for the Agricultural Jihad Organization and extension workers aiming to enhance cotton production.

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.

Several management factors play a decisive role in the extent of yield. Higher yields may be achieved by identifying and improving the factors that cause yield gap. The present study was conducted to identify the yield gap and the contribution of factors affecting it in irrigated wheat in Jelin city.

This experiment was conducted as a survey in 50 wheat fields in Jelin city in the growing year of 2021. These farms were diverse in terms of area, crop management and yield. Information such as farm area, experience (record) of farmers, previous crop, planting date, planting method, cultivars used, seedbed preparation operation, seeding rate, herbicide, fungicide, irrigation method, water source, base fertilizer, top dressing and the time of harvest were collected and completed through field surveys during the growing season and questions from farmers. At the end of the growing season, the actual yield harvested by the farmers was recorded. Analysis of quantitative traits was done using simple linear regression method and analysis of qualitative traits was carried out in the form of an unbalanced completely randomized design. Mean comparison was done using the LSD method at five percent probability level. Finally, the relationship between actual yield and more than 50 management variables was investigated using stepwise regression and comparative performance analysis (CPA) and SAS software.

The results showed that there is a significant relationship between wheat grain yield and variables such as plant and spike density per square meter, planting date, amount of herbicide and fungicide, amount of potassium sulfate application, amount of fertilizer and the number of (times) of fertilizer splits. Also, qualitative traits such as fungicide (application/ non-application) and triple superphosphate fertilizer (application/ non-application) had a significant effect on grain yield. There was a gap of 3160 kg/ha between the actual yield (3528 kg/ha) and the attainable yield (6688 kg/ha). management variables such as seedbed moisture, Tirgan variety, planting date, amount of fertilizer, amount of micronutrients, NPK application, fungicide consumption (application/non-application), Altocombiنشریه پژوهشهای تولید گیاهی، دوره ،31شماره 1403 ،2 172 (cyproconazole + carbendazim) (application/ non-application) contributed to yield and its gap. Fertilizer top dressing with 17.7% and seedbed moisture with 2.7% had the highest and lowest share in yield gap, respectively. The use of Tirgan variety (14.8 percent), planting date (13.3 percent), amount of micronutrients (16.5 percent), NPK (11.7 percent), fungicide application (9.6 percent) and Altocombi fungicide (7 (13 percent) also contributed to yield gap.

Considering that among the management factors, some variables have a greater effect on yield gap, wheat yield in Jelin city may be improved by optimizing the mentioned items. Therefore, it is necessary to prioritize the improved management of these variables in the farms of Jelin city

The present study was conducted in order to estimate the yield gap of faba bean in the main production areas of this plant in Golestan province, including Gorgan, Ali Abadkatol and Aq Qola cities, and to identify the factors that cause it and to determine the contribution of each of them.

Based on this, the information related to the production management of 445 faba bean farms in Gorgan, Aliabadkatol and Aqqola regions in 1398-1399 and 1399-1400 was collected and the yield gap was estimated using the comparative performance analysis (CPA) method.

 The average, maximum, and gap yield of faba bean seeds were 2742, 4000, and 1258 kg per hectare, respectively. Additionally, based on the results obtained, the most important reasons for the yield gap and the contribution of each of them in creating the yield gap were: planting date 21%, pests 15 percent, the amount of pure nitrogen (N) 14 percent, the number of disc times 14 percent, the amount of seed used 12 percent, diseases 12 percent, waterlogging 5 percent, weeds 3 percent, non-use of working row 2 percent and improper planting depth 1 percent.

In general, by optimizing the management of faba bean production and eliminating the mentioned factors of yield gap, it is possible to increase the yield of faba bean in the regions of Gorgan, Ali Abadkatol and Aq Qala of Golestan province by 46 percent (equal to 1258 kg per hectare) compared to the current amount increased.

Preparation and proper implementation of water scarcity adaptation programs at the provincial level requires diverse, reliable and integrated information related to the province's water resources. To obtain this information in an integrated and dynamic manner, a system called ‘System for regional Agricultural Water balance and water Accounting’ (SAWA) was prepared for Fars province. First, the irrigated agricultural lands of the province were divided into 17 homogenous Agro-Ecological-Zones (AEZ). Then, a crop model (SSM-iCrop2) was calibrated and set up to simulate the growth, yield and field water balance of 35 agricultural plants under potential and farmers’ conditions in the 17 AEZ. The simulations were done using weather data of 2011-2021. The outputs of the system are produced on a daily basis and for the end of the growing season. The system is also able to produce monthly outputs of water balance that are essential information for water scarcity adaptation programs such as cropping pattern. Some of the outputs of the system are crop planting date and the date of bud burst in trees, the time of occurrence of important phenological stages, total biomass, leaf area index and field water balance components such as runoff, evaporation, transpiration, deep drainage, weeds’ transpiration and applied irrigation water. The outputs of this system are available for each plant or all plants in each of the zones, counties and the whole province. The testing of the system showed that the simulated yields and applied irrigation water are in satisfactory agreement with the measured ones.

In order to investigate irrigation management and the effects of different amounts of nitrogen fertilizer on hybrid corn SC704 experimental in the form of crushed plots in the form of complete randomized block design in three replications during two crop years 2015 and 2016 in Golestan province, in Katul was done. The main factor of irrigation cycle by day in four levels including 7, 14, 21 and 28 days and the secondary factor of nitrogen fertilizer in four levels included 60, 120, 180 and 240 kg.ha-1. The results of analysis of variance showed a significant effect between irrigation cycle and nitrogen on Weigh of 1000 grains, grain yield, seed nitrogen and biomass yield. The results showed that 28 days of irrigation reduced grain yield, seed nitrogen, seed phosphate and seed potassium compared to 7 to 14 days of irrigation. The increase in nitrogen fertilizer to 180 and 240 kg.ha-1 increased the yield of the grain by affecting the biological yield and yield components seed nitrogen, seed phosphate and seed potassium. However, nitrogen consumption in higher amounts under stress conditions (21 to 28 day Interval Irrigation) not only did not have a positive effect on yield, but also reduced grain yield by 180 to 240 kg.ha-1. The highest grain yield was obtained in the combination of 7 and 14 day irrigation treatment and nitrogen level of 240 to 180 kg.ha-1 and 12209, 11416, 10147 kg.ha-1. The results of the average comparison between the two years showed that the highest number of grains per corn was 583, the Number of grains per row as 40.3 and the Weigh of 1000 grains was 138.9 grams and seed nitrogen 3.991 µg.g-1 DW in the second year.

Increasing water and soil salinity and the damage caused by it on plant products make it necessary to investigate the effects of salinity on crop plants. Studying the physiological traits changes under stress conditions is a suitable method for identifying the factors that affect crop tolerance to salinity stress and selecting tolerant cultivars.

A factorial experiment was conducted based on a randomized complete block design with three replications in the research greenhouse of the faculty of Plant Production, Gorgan University of Agricultural Sciences and Natural. Experiment factors included the applying four salinity levels with Hoagland solution (no application of salinity as control, application of 30, 60, and 80 mM NaCl) and different soybean cultivars (Hill, LBK, BP, Hog, Deer, Gorgan 3, Sahr, Hobbit, Williams, JK, and LWK). In this experiment, soybean seeds were inoculated with Rhizobium japonicum bacteria and planted in pots containing sand. The irrigation was done by distilled water from the planting until the appearance of the main leaf, and after that, Hoagland's solution (without nitrogen) continued. Seedlings were harvested after 60 days and Na+ and K+ percentage, Na+/K+ ratio, proline, nitrogen percentage, nitrogen yield, and total dry weight were measured. Analysis of variance and comparison of the means (LSD test) was done by the SAS software (version 9).

The results show that with increasing the salinity, sodium percentage, Na+/K+ ratio, and proline amount increased and total dry weight, potassium percentage, and nitrogen yield decreased. Deer cultivar with the highest percentage of nitrogen at the levels of 0, 30, 60, and mM NaCl and the lowest percentage of nitrogen reduction (28%) have the lowest total dry weight reduction (54%) from the control treatment to the 80 mM treatment. Hag cultivar have the lowest increase in sodium percentage and the lowest percentage decrease in potassium absorption and the lowest Na+/K+ ratio compared to other varieties. LWK, LBK, and Sahar cultivars had the lowest tolerance to salinity with the highest amount of sodium accumulation, the highest Na+/K+ ratio, and low production of proline, and dry matter. The results of correlation coefficients showed that there was the most positive and significant correlation between total dry weight and potassium percentage (0.904**) and nitrogen percentage (0.902**).

According to the obtained results, it seems that the tolerant cultivars deal with salinity stress with mechanisms such as more potassium absorption and osmotic potential regulation. Among the investigated cultivars and according to the measured indicators, the Deer cultivar was recognized as a tolerant cultivar and can be used for cultivation in saline lands and the Hag cultivar can be used in breeding programs.

The evaluation of rangelands needs some studies about the physical effects and climatic factors on the changes of rangelands during different years. Evaluating changes in crown cover, plant composition, production rate, status, capacity and trend of rangelands during different years is very important. beneficiaries are often faced with unpredictable climate and natural condition changings, which rangelands of this group of experimental farmers is necessary for sustainable management and ecological health. The goals of this research are to investigate the understanding of climate change from the point of view of farmers in Rineh, Polour, Kangarchal and Poshtkoh regions in Mazandaran province. And its effect is on the beneficiary’s efforts and it will provide suggestions to reduce the effect of climate change and increase production of rangelands. In this regard, some questions were formulated and asked to the local users through a questionnaire, and SPSS software was used to determine users' opinions and compare their views in four sites. Also, four index including plants, herbal production, rangeland’s ecosystem (situation, soil and wild life), livestock and grazing systems, herd management and livelihood system were evaluated in four sites. The results of the average comparison showed that a large number of operators, about 41.7% believe that climate change which has occurred in these sites are happened in recent years. Also, the reduction of snowfall, the production of rangelands, the number of livestock shows that 37.7% of the respondents agree with the increases of immigration. For this purpose, in order to reduce the negative effects of climate changes are essential for a comprehensive education, proper planning and providing financial services and implementing plans for the proper use of water resources to deal with climate changes.

  Intensification of agriculture along with increasing use of chemical pesticides can have significant environmental effects on other non-target organisms as well as various environments such as air, soil and water. Increasing concerns about the negative effects of pesticide resulted some indicators that measure the environmental risk of each pesticide. One of these indicators is the Environmental Impact Quotient (EIQ), which has been used repeatedly to monitor environmental pesticides in a wide range of agricultural and horticultural fields. The purpose of this study is to investigate the environmental effects of pesticides used in wheat fields in Bandar-e-Turkeman county in 2019-2020 year.

To implement the model, information about the name, amount of consumption and times of application of pesticides used by farmers were collected in the form of questionnaires and face-to-face interviews with 59 farmers. The Environmental Impact Quotient (EIQ) of these pesticides was extracted from version 2021 of the Integrated Pest Management (IPM) site. The environmental effect of used pesticides in each wheat field was obtained from the sum of EIQ of pesticides in the amount of consumption and dose of pesticides per hectare. The calculations of this model were done in Excel version 2013.

The result showed that the final EIQ score for all toxins had the highest impact on the ecological section. Imidacloprid (92.88), Carbendazim (86.00), Acetamiprid (71.95), Cyproconazole (67.00) and Deltamethrin (65.15) had the highest toxicity for the ecological section, respectively. Based on the results, Carbendazim was introduced as the most dangerous and the insecticides Acetamiprid and Imidacloprid were the least dangerous toxin for the farm workers. Calculation of environmental impact of pesticides per hectare showed that there was a high numerical variation among surveyed fields. So that the lowest of index related to fields No. 25, 55 and 42 they had about 6.31, 7.01 and 7.02 environmental impact, respectively. In these fields, no pesticides were used during the wheat growing period and only at the time of planting, carboxin fungicide was used for seeds at the rate of 0.45 and 0.50 kg/ha. The fields No. 40, 10, 30 and 35 recorded with EIQ-FUR equal to 41.49, 33.20, 31.20, 30.85 and 30.67, respectively. In these fields, we were observed the highest environmental impact per hectare. In continue, the distribution map of this index in studied fields was produced. The results showed that there is a different variety in terms of pesticide application in fields, therefore target points can be determine to farmers for reduce of pesticide application and field with high environmental impact.

Among the three components of the EIQ index, including risk to farm workers, consumers, and ecology, the numerical values of these three components can vary depending on location. The current values of this index do not take into account differences in the technology of application or use of personal protective equipment. It can be concluded that the EIQ model requires changes in the direction of localization with the study areas, including the number of farm workers, specifying the type of tools and equipment used and separating the type of developed or developing countries. In general, in these fields, the management of pest, the identification and management of weeds diversity can be effective in controlling the amount of pesticide consumption and reduce environmental risks.

We would like to thank Gorgan University of Agricultural Sciences and Natural Resources, wheat farmers and agricultural Jihad management of Bandar-e-Turkeman county for their cooperation in conducting this research.

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

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

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

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

This research was conducted to study of diversity and weed population structure of wheat agroecosystems of Bandar-e-Torkeman county in 2018-2019 year. Diversity indices such as Shannon-Wiener and Simpson for fields and frequency, relative frequency, relative uniformity and relative density indices for all weed species were calculated and measured in two time stages, before and after chemical treatment with herbicides. Obtained data from diversity indices were moved in the GIS media and weed species maps of wheat fields were prepared. In this study, we were identified 18 species from 9 plant families. The checking of plant species showed that 66.67% and 33.33% were belonged to annual and perennial, also, about 27.77% and 72.23% were narrow-leaved and broad-leaved, respectively. Gramineae species include Avena ludoviciana, Lolium temulentum, Phalaris minor, Alopecurus myosuroides and Polygon monspeliensis, Fabaceae species include Medicago scutellata, Polygonom avicular, Vicia villosa and Oxalis corniculata and Compositea species such as Helianthus annuus and Cirsium arvense, had about 27.77, 22.22 and 11.11 percent of the available plant species, that were recognized as the most important plant families in wheat fields of county, respectively. In this study, weed distribution maps in wheat fields of the county before and after chemical control were prepared in three frequency groups of 50-100, 30-50 and less than 30% and showed an almost uniform distribution of weeds in surveyed region.

 Occurrence of drought and reduction of rainfall in the future will limit the cultivation of irrigated crops. Thus, it is probable that a part of the present irrigated lands and orchards of Iran may be unavailable for the cultivation of irrigated crops, but it is possible to cultivate rainfed crops in these lands. However, the available potential for cultivation of rainfed crops with respect to the soil type, climate and other factors is not known. Limited water resources, on the one hand, and the growing population along with increasing the need to produce food, on the other, make it necessary to have a comprehensive, practical and accurate program. Therefore, research on this issue is essential. In this study, production potential of rainfed wheat (Triticum aestivum), barley  (Hordeum vulgare L.), chickpea (Cicer arietinum L.), lentil (Lens culinaris Medik) and canola (Brassica napus)  in irrigated lands (fields and orchards) was modeled.

  Weather stations (position and distribution), long-term weather data (15 to 30 years), HC27 soil map, crop management data plant parameters were used to determine the yield in this study using SSM-iCrop2 model. In each zone, the yield was determined and compared with the actual data. In other words, the model output were compared with the actual current rainfed yields of each province and then it was determined that whether the model precision was sufficient for this study. Other calculations (determining the average yield of provinces) and generation of maps were done using ArcGIS V.10.2. The yield obtained by farmers in these lands was considered as 50 and 70 percent of yield potential. Also, the yields were categorized into four classes of excellent, good, medium and non-suitable. This classification is based on economic- agronomic profit of crop harvest.

 The results of this study showed that the conditions of rainfed production in each province of the country is suitable/appropriate for some crops and unsuitable/inappropriate for some other. In case the yield of farmers reached 70 percent of yield potential by optimum management, all provinces will be classified into the upper average group (3, 18 and 10 provinces in excellent, good and medium groups) for wheat. For barley 30 (3, 10 and 17 provinces in excellent, good and medium groups), for chickpea 30 (3, 6 and 21 provinces in excellent, good and medium groups), for lentil 31 (13 and 18 and 10 provinces in good and medium groups) and for canola 30 (4, 5 and 21 provinces in excellent, good and medium groups) provinces will be placed in the upper average group. Based on 70 percent of yield potential of canola, barley and chickpea, only on province is placed in non-suitable group. On the other hand, in case the yield of farmers reaches 50 percent of yield potential due to improper management, for wheat 30 (2, 2 and 26 provinces in excellent, good and medium groups), for barley 28 (4 and 24 provinces in good and medium groups), for chickpea 18 (4 and 14 provinces in good and medium groups), for lentil 28 (3 and 10 provinces in good and medium groups) and for canola 25 (5 and 20 provinces in good and medium groups) will be classified into the upper average group. Based on 50 percent of yield potential of rainfed wheat, barley, canola, chickpea and lentil, 1, 3, 6, 13 and 3 provinces were placed in non-suitable group, respectively.

 According to the results of this study, a major part of the provinces will be placed in medium and non-suitable groups in case of improper management, and agricultural productions will not satisfy the needs of the country. Therefore, it is necessary to pay a special attention to agronomic management of rainfed crops, as the agricultural production of the country will not be acceptable unless 70 percent of yield potential is achieved.

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.

decres gap between actual yield and potencial yield (gap yield) is important way to incrace yield in unit. In order enhance yield, gap yield in any area determinted and factor participant be identified in gap yield in preduct crop plant. This investigation to order determination gap yield and limited factor to yield and contribution of each of them to soybean in Aliabad Katol area by using CPA method and boundary line analysis.

To carry out this study, Information on production management of 120 soybean farms in Aliabadabad district of Golestan province was collected in 2016. Management factors included field characteristics (area, previous crop, herbicides, previous crop, field position), seedbed preparation (type, number and time of tillage), sowing (amount of seed consumed, cultivar type, seed source, sowing date, Plant density), fertilizers and micronutrients (type of fertilizer, amount and timing of fertilizer application), pesticides (type and amount of herbicides and pesticides), use of biological control (Trichogram and Braun wasps), harvest (grain yield and harvest date). The yield was determined using CPA method and boundary line analysis.

The results showed that soybean yield potential in this region was 3921 kg / ha and soybean yield loss in Aliabad region was estimated at 30%. Delay in planting in this region was 8.4% of yield and suitable plant density of 15.5%, planting and seed consumption were 6.3% and 8.5% of seed yield, respectively. It was also found that not using herbicide before planting would decrease grain yield by 17.45%. According to the results, irrigation with 28.4% had the highest share in determining soybean production potential in the study area. The highest grain yield response to urea fertilizer application and irrigation was two-fold; increasing nitrogen up to 65 kg urea and 3 times irrigation increased grain yield and consuming greater than 65 kg ha-1 and more than 3 irrigation times. It did not change grain yield per hectare. The relationship between delay in planting and plant density with seed yield was dentate. 10-day delay in planting leads to increased yield, but yields maximum grain yield in fields that have 22 days past optimal sowing date.nsity with seed yield was dentate. 10-day delay in planting leads to increased yield, but yields maximum grain yield in fields that have 22 days past optimal sowing date.

In general, soybean yield in this area is related to resource limitation and crop management is second in importance, which can increase soybean production by 30% with proper irrigation and nitrogen use.

 There are only a limited number of maps available regarding the distribution of agricultural and horticultural lands in the country. The existence of these maps can play an important role in various fields such as agricultural planning and development, climate change assessment, yield gap, and food security analysis, livestock production systems management, ecosystem service, fertilizer use management, agricultural optimal cropping pattern determination and other studies related to agriculture, forestry, and rangeland. For example, one of the relevant applications of these maps is their use in combination with climate zoning maps and soil maps to select the target meteorological stations in plant production simulation studies with different practical purposes. The SPAM project (Spatial Production Allocation Model) in IFPRI (International Food Policy Institute) is the main source of these kinds of maps. The accuracy of SPAM maps is acceptable at providence or higher scales because they are prepared based on provincial data. However, these maps may not be precise on a smaller scale than the province. The SPAM data bank does not cover all main crops and none of the horticultural plants. Therefore, in this study, the land use maps of 33 main agricultural and horticultural plants were produced at the country scale using the simplified SPAM method.

To generate these maps, a spatial overlay of land distribution (total rainfed land raster, total irrigated land raster, and total garden products raster for target crop) with polygon boundaries of cities was produced in ArcGIS. The value of the pixels in each county must be calculated in the created map. To do this, first, the number of pixels with land in each county was counted on this map. Then the area under cultivation of the target plant in each county was divided by the number of pixels counted in the same county. The number obtained in each county was considered as the pixel value of the land in that county for the target plant. The assumptions about the production of distribution maps for each plant in rainfed and irrigated Conditions are 1- To generate the distribution maps of each rainfed or irrigated annual plant; it is assumed that the distribution of the land of the given crop is the same as the distribution of all rainfed or irrigated lands within each county. It should be noted that this assumption can be incorrect if only the land distribution of the crop is considered in a particular crop year. Due to crop rotation, the distribution of a crop in the lands of a county changes every year. If the land distribution of a particular crop in a county is considered for a period of 3 years or more, the probability of target crop planting within each part of the county lands will be high. Because in the present study, information from the period 2014 to 2016 has been used, it is assumed that during this period, the distribution of lands the target rainfed and irrigated is similar to the distribution of total rainfed and irrigated lands in the given county. 2- In the case of horticultural crops that are perennial, it is assumed that the gardens of the target crop are distributed uniformly in the horticultural lands of that county. Therefore, the distribution of a particular horticultural crop in a county was similar to that of the total distribution of horticultural lands in that county.

The findings of this study suggest that SPAM2005 maps for various crops in Iran were less accurate than SPAM2010 maps at the province level. While the area of crops in SPAM maps was similar to statistical data at the province level, the spatial distribution of crops required modification. This issue became apparent when the grassland distribution map was compared with the crops SPAM maps, indicating that some crop land in SPAM maps was incorrectly located where grasslands should be. The approach used in this study resolved this problem and generated new spatial distribution maps for crops with higher accuracy.

This study utilized county-level data from over 400 counties to produce more accurate maps than the original SPAM maps. The advantages of the maps produced in this study compared to SPAM maps are (1) The use of up-to-date crop area information to generate the maps; (2) The use of crop area data at the county scale to increase map accuracy; (3) The application of country-specific land distribution maps to generate the maps; and (4) The production of distribution maps for all major horticultural and agricultural crops in the country, creating a comprehensive database in this field.

Crop simulation models are very useful tools for the evaluation of plant growth and development processes. Crop-simulating models may be used to estimate yield and evaluate climatic, plant, and management parameters on yield. Also, it may be used to predict crop water requirements under different conditions. Crop models should be evaluated and parameterized to simulate crop growth and development. Parameterization is used for precise simulation of crop growth and development and can estimate the best and most appropriate values for model parameters obtained via observed data or calibration. The objectives of this study were to describe the SSM-iCrop2 model, determine plant parameters, and evaluate alfalfa (Medicago sativa L.) in its major production regions using the SSM-iCrop2 model in Iran.

SSM-iCrop2 crop simulation model is a simplified form of SSM crop models which is suitable for the simulation of growth, development, and yield of different crops under different environmental conditions and large-scale estimation of crop production, especially in the evaluation of nutrient availability and climatic effects. This model includes sub-models of phenology, leaf expansion and senescence, dry matter production and distribution, and soil water balance. Daily weather data, agronomic management, soil properties, and plant parameters are required for simulation in this model. The present study investigates the performance of the SSM-iCrop2 model regarding the prediction of single cuts and overall cuts, phonologic stages, and water requirement of alfalfa under changing climatic conditions in Iran. To simulate the growth, development and yield of alfalfa using SSM-iCrop2 model in Iran, the major irrigated alfalfa production provinces, including East Azarbaijan, Hamedan, West Azarbaijan, Sistan and Baluchestan, Khorasan Razavi, Esfahan, Kordestan, Ghazvin, Ardabil, Markazi, Fars, Zanjan, Chaharmahal and Bakhtiyari and Tehran were identified based on the data available in Ministry of Agriculture statistics. Then, field experiment data required for model parameterization and estimation were collected from these provinces.

According to the results of the SSM-iCrop2 model parameterization, two cultivars with different leaf area indices (high-yielding and low-yielding) were identified in major alfalfa production provinces. The model was evaluated using independent experimental data that had not been used for parameterization. The evaluation results for alfalfa yield showed that the observed single-cut forage yield ranged from 112 to 640 g.m-2 with an average of 330 g.m-2; the observed total annual forage yield ranged from 646 to 4042 g.m-2 with an average of 1717 g.m-2; and the water requirement of alfalfa obtained from the NETWAT software was between 5140 to 12690 m3 ha-1 with an average of 8746 m3 ha-1. The predicted single-cut forage yield, predicted total annual forage yield, and alfalfa water requirement ranged from 189 to 457 g.m-2 with an average of 351 g.m-2, 693 to 3296 g.m-2 with an average of 1654 g.m-2, and 4093 to 16874 m3 ha-1 with an average of 10940 m3.ha-1, respectively. Overall, in the evaluation of observed versus simulated alfalfa forage yield, 31 points were obtained for single-cut yield with a correlation coefficient (r) of 0.79, root mean square error (RMSE) of 88.3 g.m-2, and coefficient of variation (CV) of 26.78%; and 21 points were obtained for annual yield with an r of 0.90, RMSE of 344.4 g.m-2, and CV of 20.05%. The evaluation results also showed that the observed versus simulated alfalfa water requirement had an r of 0.43, RMSE of 3503 m3 ha-1, and CV of 40%.

The results obtained from parameterization and evaluation of the SSM-iCrop2 model show that the mentioned model presents a logical prediction and accurate estimation of model parameters for yield and water requirement of alfalfa crops in Iran. Thus, this model may be used for the prediction of alfalfa yield under different climates and management conditions.