افشین سلطانی

 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.

     Over-withdrawal of water in the agricultural sector of Iran is one of the most important factors that threatens the sustainability of agricultural production and natural resources. Therefore, appropriate approaches should be used to eliminate this factor. In this study, the water saving capacity in the irrigated agricultural sector of Fars province was examined using some options that decrease irrigation demand in fields and orchards. These options are: (1) optimization of cropping pattern with the aim of minimizing water use, (2) using shade net in gardens and vegetable farms, (3) using straw mulch in gardens and farms though implementation of conservation tillage, (4) changing the current flooded rice cultivation system to aerobic system, and (5) transferring vegetable production from the farm to the greenhouse. The effects of the options were assessed individually and in combination.
 The System for regional Agricultural Water balance and Accounting (SAWA) set up for irrigated agriculture lands in Fars province was used. The core of this system consists of a plant simulation model (SSM-iCrop2). For each of the water saving option of this study, a separate SAWA system was prepared and the amount of irrigation water used in agriculture in the province was estimated and compared. The system provides crop yield and irrigation water under farmer’s and potential conditions as influenced by climate, soil, management and crop. The system estimates were based on 10-year meteorological data (2011-2021), 5-year average crop area statistics (2017-2021), and yield, price, and cost of production data for plants in 2021.
 The results showed that the applied irrigation water in agriculture in the province for farmers' conditions (not for potential or optimal conditions) under the current cropping pattern is 4619 million cubic meters per year (net irrigation water without considering irrigation efficiency and uneven water distribution). Optimization of the cropping pattern reduced this figure by 35% (a reduction equivalent to 1616 million cubic meters per year). Optimizing the cropping pattern was done with the aim of minimizing the amount of irrigation water applied under farmers' conditions in the entire province provided that the farmers profits in the optimized pattern would not change and the area under cultivation of perennial plants and legumes, which play an important role in agricultural sustainability, would not decrease.The implementation of 2 t/ha straw mulch vias conservation tillage for all crops except for rice and alfalafa in the current and optimal cropping patterns reduced the net applied water by 8 and 10%, respectively (equivalent to 384 and 292 million cubic meters per year), the use of shade net (decreases solar radiation by 50%) during the summer months in the current and optimal cropping patterns reduced the applied water by 29 and 24%, respectively (equivalent to 1331 and 713 million cubic meters per year), and the use of aerobic rice cultivation system instead of flooding system in the current and optimal cropping patterns reduced the applied water by 4 and 1%, respectively (equivalent to 185 and 37 million cubic meters per year). Transferring vegetable production from the field to the greenhouse in both the current and optimal cultivation patterns was able to reduce the applied irrigation water by 4 percent (equivalent to 167 and 137 million cubic meters per year, respectively). However, combination of water saving options in the current and optimal cropping patterns could save irrigation water by 66 and 48% (equivalent to 3048 and 1444 million cubic meters per year), respectively.
 The findings of this study indicate that the evaluated options provide safe and effective solutions for addressing water scarcity in the irrigated agriculture of Fars Province. Therefore, it is recommended that executive officials, policymakers, and agricultural sector stakeholders work toward eliminating barriers and promoting the widespread adoption of these valuable options through evaluation and additional studies. By doing so, sustainable agricultural practices can be achieved, contributing to a healthier environment and preventing further ecological degradation.

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.

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.

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.

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.

Accurate knowledge of water balance components is necessary to optimize water consumption in agriculture. On the other hand, measuring water balance components is expensive and difficult. Therefore, the use of models that can simulate water balance values is important for water management in agriculture and water used by plants. Crop simulation models have been turned into essential tools for studying plant production systems. In the SSM-iCrop2 models, it is presumed that diseases and weeds are optimally managed and will not affect growth and yield. Additionally, except in cases where the model accounts for specific nutrients such as nitrogen, it is generally assumed that nutrient deficiencies are eliminated through fertilization. Therefore, parameterized and evaluated models are designed to fit these conditions. These factors are present in the field and affect crop growth and yield as well as water use. However, in several cases it is required to estimate yield and water balance components and irrigation water volume under grower conditions. Naturally, models parameterized using experiments are unable to simulate these conditions. Therefore, a model must be prepared so that it can simulate the real conditions of farmers. In this study, the SSM-iCrop2 model has been calibrated for the real conditions of farmers, and the purpose of this study is to use the SSM-iCrop2 model in simulating water performance and water balance for farmers.

In this study, the SSM-iCrop2 model was calibrated for farmers conditions using variables such as yield and harvest index, which are available for farmers’fields or are cheap to measure. The effect of factors such as pests and diseases, weeds and unsuitable nutrients, density and sowing date entered the model along with the calibration of three parameters of radiation use efficiency, maximum leaf area and maximum harvest index for farmers’ fields. Calibration was done by comparing the performance of farmers against the performance simulated by the model and by changing the parameters of radiation use efficiency (IRUE), maximum leaf area (LAIMX) and maximum harvest index (HIMAX). This calibration was done at Hashem Abad station in Gorgan for irrigated rice (paddy) and wheat. The simulated actual yield was calibrated with the actual yield. Due to the acceptable simulation of actual yields after calibration, it was presumed that other estimates made by the model are also reliable.

Measurement of water balance and other estimates of the model from growth and yield formation in the grower fields is expensive, but a calibrated model can estimate them at a low cost. In this study, it was shown that with the model calibrated for farmers' conditions, not other easily measured information (such as the irrigation water volume) can be obtained, with the assumption that the model accurately captures this information as well as performance. To evaluate the simulated real performance model, it was compared with the actual performance of farmers (Agricultural Jihad Report) after calibration. In addition to phenology, the SSM model simulates traits related to growth and yield, evapotranspiration values, irrigation water volume, runoff, available soil water during planting and harvesting, cumulative drainage, etc. The output of the model shows the amount of irrigation water is needed for a certain amount of performance in a given place (with specified rainfall and transpiration). The irrigation water volume calculated by the model was compared with the results of field tests from previous studies conducted by researchers at agricultural research centers. It was found that the model's output and the observed values were in good agreement. The root mean square error for rice and wheat was 216.6 and 157.6 kg per hectare, respectively, and the coefficient of variation and correlation coefficient were 4 and 85% for rice and 3 and 94% for wheat, respectively. Then, the irrigation water volume estimated by the model was evaluated and validated with the measured irrigation water volume in different crops (in Golestan province for different years). Based on the results of the evaluation, the coefficient of variation and the correlation coefficient for the simulated irrigation water volume were 8.9 and 98%, respectively, compared with the observed value. This calibration was done for rice (paddy) and irrigated wheat in the fields of Gorgan town, and the simulation and running were done using the meteorological statistics recorded in Hashem Abad weather station, Gorgan. Noting the fact that the actual yield has been simulated with good accuracy after the calibration, it was assumed that the other estimates of the model are also reliable. Thus, the calibrated model estimates them with low cost and appropriate accuracy and can complement field experiments.

This study discovered that the SSM_iCrop2 model, when calibrated for the conditions of farmers' fields, can accurately simulate both growth and yield traits as well as water balance characteristics. Notably, the model provides reliable estimates of irrigation water volume in farming scenarios, a crucial factor for agricultural planning and drought adaptation.

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.

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.

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

  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.

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.

For optimal production and maintaining its stability, environmental and weather conditions must be determined from the perspective of capabilities and limitations. For this purpose, it requires reliable regional data such as planting date, ripening time, plant density, soil, and meteorological information, which are generally not available for most regions. Obtaining this information is very time-consuming and expensive in many areas and is often simply not possible. Therefore, zoning can facilitate access to this information on a large scale. In other words, if the regions that are similar in terms of climate, soil, and management conditions are identified, the time and cost needed to collect information on a wide scale will be minimal.  

The present study was conducted for the agro-ecological zoning of the country. In this research, the existing climatic zones of the country were analyzed based on GYGA, and the existing soil zones of the country were analyzed based on the HC27 method.

The combination of climatic zones and soil, 198 polygons or agro-ecological zones were obtained for all agricultural lands of the country. The zones in which more than 1% of the country's agricultural lands are located include 28 zones, and in total, about 80% of the agricultural lands are located in these zones. The highest frequency percentage is related to agro-ecological zone 4103-5 with a frequency of 85.11%. Also, the frequency of agro-ecological codes (climate code 5003 with soil code 5) 5003-5 (28.7%) and (climate code 4003 with soil code 5) 5-4003 (93.4%) were placed next. Zoning can facilitate the selection of points for plant studies and other planning.

Each of these areas has a different climate and soil code, which indicates the specific production conditions of that area. From these agro-ecological zones, to improve studies and make agricultural management decisions, it is possible to prepare and complete the climate and soil information bank in each zone for use in simulation models of plant production, to facilitate the collection of information (such as management information, cultivar information plant) and the implementation of plant production simulation model to be used in studies related to the food security of the country. The current research was conducted to determine the main agro-ecological areas of agricultural production in the country so that simulation studies and other studies can be carried out in the main production location in each province. Therefore, it is necessary to know where the main production centers of each province were, what kind of climate and soil it has, and which meteorological station is the indicator of that region. In this research, the climate zones of irrigated, rainfed, garden, and pasture lands of the country were determined by using the Giga climate map. Based on this, more than 50% of water lands are located in climates 5003, 4003, 5002, 8003, and 6003, respectively. Also, the rainy lands are located in 4103, 4003, and 3103 climates respectively. Also, by using the HC27 soil map, the soil areas in the irrigated, rainfed, garden, and pasture lands of the country were determined. Therefore, more than 50% of water lands in soil codes 5 and 17; Rainy lands in Kodkhak 5 and 12; Garden lands were located in soil codes 5 and 12 and pastures were located in soil codes 5 and 17. By combining climatic zones and soil zones, agricultural-ecological zoning of the country was done, and finally, 198 zones were obtained. The zones in which more than 1% of the country's agricultural lands are located include 28 zones, in total, about 80% of the agricultural lands are located in these zones (Figure 7). The highest frequency percentage (11.85%) was related to the area with agroe-cological code 4103-5, which covered 1789965.8 hectares of agricultural land. Also, after that, the agro-ecological code 5003-5 has the highest frequency (7.28 percent), which covers 1100599.25 hectares of agricultural land in the country. In this research, after the agro-ecological zoning of the country's agricultural lands, several 198 zones were obtained, and after calculating the area covered by each zone, finally, 28 agro-ecological zones have an abundance percentage of more than 1%, and together they are about 80% (11813518.66 hectares). They cover the country's agricultural lands. These climate zones obtained can be used for food security studies and calculating and determining the production capacity of each region. On the other hand, considering that in agricultural studies, extensive and comprehensive information about climate and soil is needed for each region and access to this information is usually expensive and time-consuming, the use of agro-ecological zones resulting from this research can be necessary.

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.