مدل ssm-icrop2

 Legumes are the second food source after cereals, which provide almost a quarter of protein for developing countries. Lentil is recognized as one of important legumes due to its favourable characteristics. The lintile production, like to other crops has been influenced by three factors: management, genetics, and environment. Improving crop production can be achieved by optimizing these factors. Thus, paying attention to the above-mentioned factors can reduce the severe climatic effects on crop production. Among the above-mentioned factors, management strategies e.g., optimal sowing date and using the optimal cultivar are considered to improve crop production. Initial soil moisture, which can affect crop germination, establishment and ultimately growth and yield, is another important strategy. Accordingly, the current research was conducted in order to simulate the effects of cultivar, sowing date, and initial soil moisture on lentil grian yield in different locations of Lorestan province.

 The study locations were Aligudarz, Nurabad, KhorramAbad, and Kuhdasht. Simple Simulation Models-iCrop2 (SSM-iCrop2) was used to simulate the lentil growth and development. The data required to run model included climatic, soil, management, and crop data. Daily long-term climatic data including maximum and minimum temperature, rainfall, and radiation were collected from Iran Meteorological Organization. The soil data included soil depth, soil water content at wilting point, soil water content at field capacity, and saturation water content, which were obtained from different data collection in the Ministry of Agriculture and Agricultural, the Natural Resources Research and Education Centers, and soil laboratories at each location and Food and Agriculture Organization and Global yield Gap Atlas.The management data such as palnt density, tillage, rows distance, and sowing depth were obtained by local experts from the Ministry of Agriculture and Agricultural and the Natural Resources Research and Education Centers at each location. The crop data e.g., the specific genetic coefficients of each cultivar were obtained form Amiri and Deihimfard (2018). The study treatments consisted of four sowing dates (21 January, 12 February, 4 March, and 30 April), two cultivars (early-maturity and late-maturity) and four initial soil moisture (32, 36, 42, and 58 mm). The model was run for 41 years (1980-2020). Initial soil water contents and sowing dates were obtained from a preliminary simulation experiment.

Averaged across all treatments, the highest grain yield was obtained at KhorramAbad (388 kg ha-1) due to higher rainfall during the growing season. In addition, the grain yield difference among the sowing dates ranged from kg ha-1 on 30 April to 364 kg ha-1 on 21 January. The reason for the higher grain yield on 21 January sowing date was the higher cumulative rainfall season (149.3 mm vs. 99.2 mm) and the lower mean temperature (22.9 °C vs. 23.9 °C) during the growing season compared to other sowing dates. Across locations, sowing dates, and cultivars, the highest grain yield (263 kg ha-1) was simulated under initial soil moisture of 58 mm. Also, on average across sowing dates, initial soil moisture contents, and locations, the early-maturity cultivar simulated more grain yield than late-maturity cultivar (405 kg ha-1 vs. 63 kg ha-1) due to the avoidance of drought stress at the end of the growing season. Considering different interactions, on average across locations, the highest grain yield was simulated by 704 kg ha-1 in the combination of early-maturity cultivar, early sowing date (21 January), and initial soil moisture of 58 mm.

 In general, the results indicated that the lentil grain yield varied among the various study treatments (sowing date, cultivar, and initial soil moisture) as well as different locations of Lorestan province. Lentil growers can increase the lentil production in Lorestan province and study locations by using the interaction of early planting date (21 January) ´ early-maturity cultivar (Kimia) ´ higher initial soil moisture (58 mm). It should be noted that the current research was conducted under water-limited condition and it is suggested that researchers focus on other limitimg and reducing factors in the future studies.

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.

Global climate change is among the most important agricultural and food security challenges. This study tries to investigate the effect of climate change on potential yield and water productivity of forage maize (Zea mays L.) in Iran. Two scenarios of RCP4.5 and RCP8.5 are used to predict the future climate (2050s) and climate data of 2001-2015 have been used as the base period. Potential yield is estimated using SSM-iCrop2 model according to the GYGA protocol and the climate changes for both scenarios are applied in the model. The results show that the climate change will not have a considerable effect on forage maize yield compared to the current conditions (85.6 ton ha-1) and will only lead to an increase of 0.9% and 1.6% in on both scenarios, respectively. This may be attributed to maize being a C4 plant and thus non-effectiveness of CO2 increase on its growth. Also, the temperature will remain in optimum range for maize in most of the main regions for forage maize cultivation areas in Iran. Water productivity in both scenarios will increase by 0.4% and 1.6%, compared to current conditions (10.4 kg m-3), respectively, which may be due to increased CO2 concentration and more closure of stomata. Also, improved water productivity in forage maize may be attributed to increase yield potential due to the fact that no considerable changes are observed in terms of the required water, evapotranspiration and irrigation times.

Sugar beet is an industrial and strategic crop regarding sugar production in Iran, however, accurate information on its yield potential (Yp) and yield gap (Yg) is limited. The aim of this study was to estimate the yield potential of sugar beet using GYGA protocol according to the current conditions for the first time in Iran. Based on this protocol, agro-climatic zones (CZ), reference weather stations (RWs) and buffers within each meteorological station in each climatic zone as well as the type of soil texture in the buffer range were determined and yield potential, yield gap and production were evaluated using SSM-iCrop2 model. Finally, 28 weather stations were selected for sugar beet in 13 climatic zones which cover 92.7% of total sugar beet cultivation areas. The modeling results estimated the yield potential and yield gap to be 11.39 and 6.23 M t, respectively. Actual yield and the yield potential were 46.663 and 102.986 tha-1, respectively resulting in yield gap of 56.323 t ha-1 for sugar beet production in Iran with a relative yield (RY) of 45%. This indicates that current management practices produce only less than half of the performance potential.

Yield gap analysis is a quantitative estimate of possible increase of the capacity to provide food for a specified area. It is an important component for designing strategies to supply food on a scale of regional, national, and global level. In this regard a study has been conducted to determine the extent and function of chickpea and lentil crop vacancy distribution at Gorgan University of Agricultural Sciences and Natural Resources during 2016-2018. Using SSM-iCrop2 model, the study simulates potential yield in chickpea and lentil producing regions in Iran. For this purpose, it employs the protocol of Atlas Gap Project, called GYGA protocol, to identify climatic zones and identify important meteorological stations, located in chickpea and lentil production areas in the country. After identifying the important stations, the performance potential for the station range is simulated and then the regional results are generalized to the whole country, based on the GYGA protocol. For dryland chickpeas in the country, the values of actual and potential yield as well as yield gap have been 0.43, 1.04, and 0.61 tons per hectare, respectively. In case of rainfed lentils in the country, the values of actual yield and potential along with yield gap have been 0.43, 1.10, and 0.67 tons per hectare, respectively. The present study can be used for better management in low-yield and high-yield areas of the country for these two products.