Pizhūhishhā-yi zirā̒ī-i Īrān

Introduction Biotic and abiotic stresses are among the most significant factors reducing productivity in arid and semi-arid regions, where water is the primary limiting factor for plant growth. Sorghum possesses several advantageous physiological characteristics including drought and salinity tolerance, higher water-use efficiency compared to other forage crops, relatively high yield, desirable forage quality, and suitability for storage as both dry fodder and silage which make it particularly valuable in these environments. Strategies to utilize saline water effectively include selecting salt-tolerant cultivars, applying saline water during growth stages of lower sensitivity, and blending saline and non-saline water to reduce overall salinity. Although sorghum exhibits relative tolerance to soil and irrigation-water salinity, leaf-area expansion, carbon assimilation, stem elongation, and dry-matter accumulation are significantly constrained under high salinity. Materials and Methods This experiment was conducted over two growing seasons (2019–2020 and 2020–2021) at the Zahak Agricultural Research Station (Sistan, Iran), using a split-plot arrangement in a randomized complete block design with three replications. Main-plot treatments were three irrigation-water salinity levels: non-saline (2–3 dS m-1, control), moderate (4–6 dS m-1), and severe (6–8 dS m-1). Sub-plot treatments comprised six promising forage sorghum genotypes (Pegah, Mansour, Speedfeed, KFS15, KFS16, and KFS17). Based on soil test recommendations, ammonium phosphate (250 kg ha-1) and urea (100 kg ha-1) were applied at planting; an additional 100 kg ha-1 urea was top-dressed when plants reached 35–40 cm in height. Sowing occurred on 15 March each year. Each sub-plot consisted of six 5-meter-long rows with 50 cm spacing between rows and 6 cm spacing between plants. At the panicle emergence stage, after removing the two border rows and 0.5 m from both ends of each plot, samples were harvested from an area of approximately 8 m². Recorded traits included days to flowering, plant height, tiller number, stem diameter, leaf number, leaf area, fresh and dry forage yields.Results and Discussion The combined analysis of variance revealed that plant height, stem diameter, tiller number, leaf number, leaf area, protein content, fresh forage yield, and dry forage yield were significantly affected (p ≤ 0.01) by salinity, genotype, and their interaction. Under non-saline conditions (2–3 dS m-1), ‘Mansour’ produced the highest fresh forage yield (146.94 t ha-1), leaf area (261 cm2), and protein content (15.03%). The Speedfeed cultivar under normal irrigation ranked second with a protein content of 14.75%, following Mansour. The lowest protein content was observed in lines KFS17 and KFS16, with means of 11.70% and 11.85%, respectively, under high-salinity irrigation. Protein content declined in all genotypes with increasing salinity; ‘Mansour’ exhibited the greatest reduction (23.7%). Under severe salinity (6–8 dS m-1), ‘Pegah’ maintained superior plant height, stem diameter, tiller and leaf numbers, and achieved higher fresh forage yield (72.01 t ha-1) and dry forage yield (21.30 t ha-1) yields than the other entries. Fresh forage yields decreased significantly across all genotypes as salinity rose (p ≤ 0.01); the lowest fresh yield (38.96 t ha-1) was recorded in KFS15 under severe salinity. The highest dry forage yields under non-saline irrigation were obtained by ‘Mansour’ (38.71 t ha-1) and KFS17 (38.40 t ha-1), whereas the lowest dry forage yield (22.33 t ha-1) occurred in KFS16 at 6–8 dS m-1.Conclusion The study demonstrated significant genotypic differences in the response of forage sorghum to irrigation water salinity. Although all measured traits declined with increasing salinity, 'Mansour' performed best under non-saline conditions, whereas 'Pegah' exhibited greater tolerance under moderate and high salinity by maintaining higher biomass production and crude protein content. These findings suggest that selecting genotypes based on their salinity tolerance can optimize forage yield and quality in salt-affected environments. Given the increasing salinity of irrigation water in the Sistan region, the cultivation of 'Pegah' represents a practical and resilient strategy for sustaining forage production and supporting local livestock systems.

Introduction The escalating degradation of water and soil resources, coupled with global warming, population growth, and climate change, necessitates a reevaluation of food production systems. Corn (Zea mays L.), as the third most strategic crop worldwide, faces increasing demand due to rising food needs. Optimizing seed yield under varying environmental conditions is critical, with arbuscular mycorrhizal fungi (AMF) and plant growth-promoting rhizobacteria (PGPR) playing pivotal roles by enhancing nutrient uptake (e.g., phosphorus and nitrogen) and improving plant resilience to stresses and pathogens. However, the complex eco-physiological interactions influencing seed yield remain poorly understood, underscoring the need for advanced modeling tools. Traditional regression models often fail to capture non-linear and interactive relationships among factors such as photosynthesis rates, nutrient levels, and plant anatomical traits. Consequently, machine learning models like Deep Neural Networks (DNN) and Transformers have emerged as powerful alternatives for precision agriculture. This study aims to identify key features affecting corn seed yield using stepwise regression and compare eight machine learning algorithms, Enhanced DNN, Transformer, XGBoost, SVM, ANN, ANFIS, LightGBM, and SVR, to determine the most accurate model for predicting yield and unveiling hidden eco-physiological relationships.Materials and Methods The experiments were conducted over two consecutive years at the research farm of Ferdowsi University of Mashhad, Iran (latitude 36°15'N, longitude 59°28'E, altitude 985 m), located in the Kashafroud River basin. The region features a semi-arid climate with an average annual rainfall of 252 mm and a mean temperature of 15°C, with loamy soil of moderate organic carbon content. The dataset comprised 96 samples and 73 features, collected over two growing seasons, including plant traits (e.g., leaf chlorophyll content via SPAD index, nitrogen concentration) and soil characteristics. Of these, 32 were primary features (e.g., photosynthesis rate, leaf area index, canopy temperature), and 41 were engineered interaction features (e.g., Pmax, Mean_Leaf Area Index, Canopy Temp, 1_Root Colonization), designed based on domain knowledge of agro-ecophysiology, soil ecology, AMF, and PGPR. Data were randomly split (random_state = 42) into 70% training (67 samples), 15% validation (14 samples), and 15% test (15 samples) sets, with standardization applied using StandardScaler. Feature selection employed stepwise backward regression, reducing 73 features to 13 key variables (e.g., Canopy Temp_3, % P plant) based on adjusted R², multicollinearity, and variance inflation factor (VIF). Among the 15 machine learning algorithms evaluated, eight were configured with model-specific architectures, including an Adaptive Neuro-Fuzzy Inference System (ANFIS) implemented using TensorFlow-based layers, a Transformer model with two attention heads, an Artificial Neural Network (ANN) with three hidden layers, and a Support Vector Regression (SVR) model with a radial basis function (RBF) kernel. Model performance was evaluated using the coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and Willmott's index of agreement (d). In addition, the Shapiro–Wilk test was used to assess the normality of model residuals.Results and Discussion Stepwise regression yielded a model with an adjusted R² of 58.53% and a predictive R² of 51.06%, identifying 13 features (e.g., Canopy Temp_3, coefficient = 0.2451, p = 0.002). Machine learning results identified ANFIS (R² = 0.555), the Transformer model (R² = 0.545), and ANN (R² = 0.518) as the top-performing models, whereas SVM (R² = 0.325) exhibited the lowest performance. The Nemenyi diagram ranked ANN first (mean rank = 1.50, Willmott's d = 0.848), followed by the Transformer model, with a critical distance of 1.11 indicating significant differences (α = 0.05) between the highest- and lowest-ranked models. Taylor diagrams highlighted the superior performance of the Transformer model (RMSE = 2.834, R² = 0.545), with ANFIS and ANN performing similarly. SHAP plots revealed that interactions such as Leaf Area Index–Dry Matter Yield were among the most influential predictors, while SVR shared nine features with the regression model. Correlation analysis grouped the features into physiological (e.g., SPAD_Mean) and yield-related (e.g., Dry Matter Yield) categories, with strong positive correlations (e.g., SPAD_2 and cob diameter, r = 0.82) and strong negative correlations (e.g., Canopy Temp_Mean and Root Colonization, r = −0.82). Skewed distributions in KDE plots underscored the need for non-linear models. Force plots (e.g., LightGBM sample) showed features like Leaf Area Index_Dry Matter Yield (+280.047) driving predictions, while loss curves for Enhanced DNN indicated effective convergence.ConclusionThe study suggests that neural network-based models excel in capturing complex eco-physiological interactions, with canopy temperature and root-nitrogen interactions as key predictors, offering insights for sustainable corn production despite data size limitations.

IntroductionNanotechnology is actually one of the new technologies that has recently entered the agricultural field. Nanoparticles are atomic or molecular assemblies with minimum dimensions between 1-100 nanometers. One of the first effects of reducing the size of particles to below 100 nanometers and converting them into nanoparticles is an increase in surface area to volume, which causes more atoms to be placed on the surface compared to the volume, and subsequently changes the physical and chemical properties of the particles. In the meantime Nanosilver is known as a stress factor in plants due to its unique properties such as small size and high specific surface area, which can cause plant damage by inducing oxidative stress. Increasing of resistance to abiotic stresses in some plants is done through external application of various organic compounds. In this study, the role of sodium hydrosulfide (NaHS) and sodium nitroprusside (SNP) in reducing the negative effects of nanosilver on the physiology, growth and yield of soybean cultivar Williams was investigated.Materials and MethodsThe experiment was conducted at the research farm of the Faculty of Agriculture, Shahrood University of Technology. The evaluated traits included shoot dry matter, forage yield, chlorophyll a and b contents, relative water content, plasma membrane stability, flavonoid content, anthocyanin content, and leaf soluble sugar concentration. The experiment was carried out as a factorial experiment in a randomized complete block design with three replications with three levels of nanosilver (0, 1.5 and 3 g L-1), three levels of NaHS (0, 0.5 and 1 mM) and two levels of SNP (0 and 120 μM) in 1402. After testing, the obtained data was analyzed by SAS statistical software (version 9.1). The comparison of means was done based on minimum mean difference test (LSD) at 5 % probability level. The shape of the mold was studied using excel software.Results and DiscussionThe results showed that nanosilver reduced yield, relative leaf water content, and pigment concentration, while the combined use of NaHS and SNP reduced stress-induced damage by increasing the levels of protectants such as flavonoid, anthocyanin, and soluble sugars. In the treatment containing 3 g L-1 of silver nanoparticles, 0.5 mM sodium hydrosulfide, and no sodium nitroprusside, the chlorophyll a content of 0.78 mg g-1 fresh leaf weight showed the lowest value among all treatments. The highest soluble sugar content was recorded in plants treated with 1.5 g L-1 silver nanoparticles. The amount of flavonoid in plants sprayed with 1.5 g L-1 nanosilver was significantly increased as the amount of this trait in the application of 0.5 mM and non - consuming sodium hydrosulfide increased 0.0771 and 0.0769 g.g-1 fresh weight of leaves and increased 48 percent. he highest anthocyanin content was observed in the control treatment (0 g L⁻¹ nanosilver) and in the 1.5 g L⁻¹ nanosilver treatment in the absence of sodium hydrosulfide. All other treatment combinations significantly reduced anthocyanin content compared with the control. Nanosilver application reduced plasma membrane stability. The plasma membrane stability of the control plants was 71.55%, which decreased to 68.9% following application of 1.5 g L⁻¹ nanosilver; however, this reduction was not statistically significant. Increasing the nanosilver concentration to 3 g L⁻¹ resulted in a significant decline in plasma membrane stability, with a mean value of 61.6%. The shoot dry matter content in the control plants was 109.6 g m-2. in the effect of spraying of plants with 1.5 g L-1 nanosilver and non - consumption of sodium nitroprusside and sodium hydrosulfide to 99.5 g.m-2 and by infecting plants with 3 g L-1 nanosilver with a significant drop of 51.7 g m-2. The highest grain yield (420.3 g m-2) was obtained in the treatment without nanosilver and with the use of 1 mM NaHS, which showed a 36% increase compared to the control (270 g m-2).ConclusionOverall, the application of NaHS and SNP under nanosilver stress conditions left effective protective effects and can be suggested as an efficient approach for managing stresses caused by heavy metals in plants.

Introduction Forage sorghum (Sorghum bicolor (L.) Moench), a drought-tolerant C4 crop, is increasingly vital for sustainable livestock feed production in arid and semi-arid regions like Iran, where climate change and water scarcity threaten agricultural productivity. Despite its resilience, the limited availability of high-quality seeds hampers its widespread adoption. Plant growth regulators (PGRs), such as Medax Top (containing mepiquat chloride and prohexadione calcium), offer a promising approach to enhance grain yield and seed quality by modulating plant growth and assimilate allocation. This study aimed to evaluate the effects of Medax Top foliar application on grain yield, seed quality, and physiological traits of two open-pollinated forage sorghum cultivars under water-limited conditions, providing insights into optimizing seed production for sustainable agriculture.Materials and Methods A two-year field experiment (2023-2024) was conducted at the Seed and Plant Improvement Institute, Karaj, Iran, using a factorial split-plot of treatment arrangement in complete randomized block design with three replications. Main plots comprised factorial combination of Medax Top doses (0, 0.5, 1, and 2 L ha-1) and application timings (3-4 and 6-8 leaf stages), while subplots included two sorghum cultivars (Mansour and Behesht). The trial was established in a semi-arid climate (265 mm annual rainfall, 14°C mean temperature) with drip irrigation. Measured traits included leaf SPAD-value, leaf area index (LAI), grain yield, seed germination percentage, grain starch, total carbohydrates, soluble sugars, and forage yield. Data were analyzed using SAS 9.1, with means compared via LSD’s test (P ≤ 0.05) after confirming variance homogeneity across years. Results and Discussion Medax Top application significantly influenced physiological and agronomic traits. As the application rate of Medax Top increased, leaf SPAD -value, grain yield, grain starch content, carbohydrates, soluble sugars and seed germination percentage significantly increased. However, these positive changes were accompanied by a reduction in LAI and forage yield. Foliar application at the 3-4 leaf stage, compared to the 6-8 leaf stage, significantly increased starch content and grain yield while decreasing forage yield. Application at the 3-4 leaf stage yielded higher grain production (2976 kg ha-1) than the 6-8 leaf stage (2723 kg ha-1), likely due to reduced competition between vegetative and reproductive sinks in early development duration. The 2 L ha-1 of Medax Top maximized grain yield and germination percentage, alongside enhancing SPAD-value and carbohydrate content, reflecting improved photosynthetic efficiency and assimilate storage. However, forage yield decreased by 34% at this dose, indicating a shift in resource allocation from vegetative to reproductive growth. The Mansour cultivar demonstrated significant superiority over the Behesht in terms of starch content, carbohydrates, soluble sugars, and forage yield, showcasing superior genetic potential. The maximum grain soluble sugar content (6.88%) was observed in the Mansour cultivar with a ModaxTop application rate of 2 L ha-1 in the first year, while the highest grain yield (4542 kg ha-1) was achieved in the same cultivar with a dose of 1 L ha-1 in the second year. Lower doses (0.5-1 L ha-1) better supported forage production, balancing vegetative biomass retention.Conclusion This study demonstrated that foliar application of Medax Top effectively improved grain yield and seed quality in open-pollinated forage sorghum cultivars. A rate of 2 L ha⁻¹ applied at the 3–4 leaf stage was identified as the optimum treatment for seed production, particularly in the 'Mansour' cultivar. In forage production systems, however, lower application rates are recommended to minimize reductions in biomass. Overall, a foliar application of Medax Top at 2 L ha⁻¹ is recommended to maximize seed yield and quality, whereas an application rate of 1 L ha⁻¹ is more suitable for combined seed and forage production. These findings highlight the potential of plant growth regulators to optimize sorghum production according to specific production objectives, seed, forage, or both, in water-limited environments, thereby enhancing agricultural resilience and food security.Acknowledgement The authors express gratitude to the Seed and Plant Improvement Institute, the Agricultural Research, Education, and Extension Organization (AREEO) for providing laboratory facilities and technical support in this research [Project number 03-03-0305-024-010312]. Also, thanks to the support of the Vice Chancellor for Research and Technology of Tarbiat Modares University.

IntroductionIn recent years, application of conservation tillage methods has been widely considered in the world, and the use of conventional tillage methods has become outdated in some parts of the world. Conservation tillage systems are usually implemented in arid and semi-arid regions. In semi-arid regions, the key to increasing crop production is to maximize surface water infiltration. In addition, legumes are the main source of protein in developing countries, with about twice the protein content of cereals, and are a source of Inexpensive, good-quality protein and a suitable complement to cereal protein. These plants are very important in low-input farming systems and have a special place in the rotation of some agricultural systems in the world, especially in dry regions, and play a significant role in food production in these countries. Therefore, a field experiment was conducted to evaluate the effects of different tillage systems on nutrient concentrations, root traits, and forage yield of forage legumes at the research farm of the Lorestan Agricultural and Natural Resources Research and Education Center, located 8 km southwest of Khorramabad, Iran, during the 2022–2023 and 2023–2024 growing seasons.Materials and MethodsThis experiment was conducted as a split plot based on a randomized complete block design with three replications. Tillage methods (conventional, low-tillage, and no-tillage) were considered as the main factor, and forage legumes (Broujerd landrace faba bean, Lamei cultivar vetch, Grass pea, Barakat and Feyz cultivar faba bean) were considered as secondary factors. The water required by the plants was supplied by normal rainfall during the growing season, and no irrigation was performed. Disking, minimum tillage included a chisel plow or a combined toothed roller cultivator, and no-tillage involved no tillage operations. Results and DiscussionThe results of the analysis of variance showed that the main effect of tillage system and forage legumes on all measured traits and the interaction effect of tillage × forage legumes on forage yield, root length, root volume, and the concentration of copper, zinc, and phosphorus elements in aerial parts were significant. Based on the results of the tillage system × forage legume planting showed that the highest forage dry weight (2820.9 kg ha-1) and copper concentration (50.17 ppm) were observed in the conventional tillage system and Lamei variety vetch. The highest forage fresh yield (10671.54 and 10581.19 kg ha-1), root length (33.89 and 33.39 cm) and phosphorus concentration (0.85 and 0.84%) were observed in the conventional and low-tillage systems and Lamei variety vetch. Also low-tillage system and Lamei variety vetch had the highest zinc concentration (50.69 ppm) and root volume (11.74 cm3). The conventional tillage system increased root diameter by 32%, fresh forage weight by 26%, dry weight by 21%, potassium by 27%, iron by 6%, and manganese by 8% and soil respiration by 22% compared to the no-till system. Also, the results of comparing the average effect of planting different forage legumes on measured traits showed that the highest yield, root traits, and nutrient elements were observed in the Lamei variety vetch. ConclusionOverall, the results highlight the importance of selecting appropriate tillage systems and forage legume species to optimize forage yield and quality. Among the evaluated species, vetch exhibited superior performance under the environmental conditions of this study. Furthermore, conventional and minimum tillage significantly influenced forage yield, root characteristics, and nutrient concentrations by altering the soil's physical, chemical, and biological properties in the Khorramabad region.AcknowledgementHereby, we would like to express our gratitude to the respected professors and laboratory staff of the Khorramabad Research Center for their guidance and assistance during the project.

Introduction Lentil is the third most important legume in the cold-season legume family. Legumes, as the second most important food source after cereals, account for about 613000 hectares of the annual cultivated area of crops in Iran. In terms of cultivated area, lentil is in third place among legumes after chickpeas and beans, and the provinces of Hamedan, Lorestan, Kerman, Fars and Bushehr are the main centers of production of this plant. Crop growth simulation models can play an important role in allowing farmers and planners to make decisions about the feasibility of systems (crops and technologies). These useful tools greatly facilitate the optimization of crop production and management strategies. Crop growth models also provide a useful tool in researches to simulate potential growth and yield. The aim of this study was to calibrate and validate the SSM-iCrop2 model for simulation of growth and yield of lentil in order to investigate the effect of sowing date and different irrigation under Kermanshah and Azna regions using the sub models associated with phenology, dry matter production and distribution and the changes in leaf area.Materials and Methods This study was conducted at Agricultural and Natural Resources, Razi University and in a leading farmer's farm in the Azna region. The SSM-iCrop2 model was used. This model has been tested and proved for a wide range of crops. This model requires limited and easily available input information. In order to collect the necessary information for model calibration and evaluation, experiments were conducted using a split factorial design with three replications in 2022 and 2023. In this way, one experiment was conducted to extract the parameters required for model calibration under the Kermanshah region in 2022, and then two other experiments were conducted to extract the information required to model validation under the Kermanshah and Azna regions in 2023. Treatments included supplementary irrigation (no irrigation, one irrigation at flowering stage, and two irrigations at flowering and seed filling stages) as the main factor, sowing date (February 17, March 21, March 20 for Kermanshah, March 6, 16, and 24 for Azna), and cultivar (Bile Savar, Kimia, and Gachsaran) as secondary factors. Measured data of phenological development stages and grain yield were used to evaluate the model. Model evaluation indices included linear regression fitting between observed and simulated data and their comparison with the slope of the 1:1 line, root mean square error (RMSE), normalized root mean square error (nRMSE), mean error of approximation (MBE), and Wilmot's agreement index (d).Results and Discussion The model calibration results indicated appropriate accuracy of the plant parameter values used in the model structure, which led to very accurate prediction of growth and yield characteristics. The evaluation results showed acceptable accuracy of SSM-iCrop2 in simulating the process of changes in developmental stages and grain yield of cultivars in the studied treatments. The RMSE value of the developmental stage from sowing to physiological maturity in Kermanshah for the cultivars Bileh Savar, Kimia and Gachsaran was 2.5, 2.5 and 3.4 days, respectively, and in Azna it was 12.9, 12.5 and 11.8 days, respectively. The RMSE value of grain yield in Kermanshah ranged from 1.4 to 2.15 and from 4 to 7 g m-2 in Azna. Several studies have been conducted on the evaluation of the SSM-iCrop2 model in simulating grain yield in different crops. Among them is the research on soybean by Nehbandani et al. (2015), who reported satisfactory results regarding the model evaluation. They stated that this model can be used to determine the most suitable sowing and harvesting dates, grain yield, and other phenological stages of soybean.Conclusion The results indicated that the SSM-iCrop2 model accurately estimated the phenological development and grain yield of lentil cultivars. Despite variations in sowing dates and irrigation regimes, the model successfully simulated lentil growth and yield using a relatively small number of input parameters. Model evaluation demonstrated that SSM-iCrop2 can accurately simulate the developmental stages and grain yield of lentil in the Kermanshah and Azna regions. Based on these findings, the model is suitable for predicting lentil growth and yield and can be applied to studies assessing lentil performance under diverse climatic conditions and management practices.

Introduction Soil salinity is a major abiotic stress limiting agricultural productivity worldwide, threatening food security particularly in arid and semi-arid regions where irrigation practices and poor drainage exacerbate salt accumulation in the root zone. It is estimated that over 800 million hectares of land are affected by salinity, a figure that continues to rise due to climate change and unsustainable farming practices. High salt concentrations in the soil disrupt plant water uptake, induce ionic toxicity, and cause oxidative damage, ultimately reducing crop yields. Breeding crops for increased tolerance to abiotic stresses is very difficult due to the complexity of inheritance of traits related to tolerance to these stresses, which often involve multiple genes with minor effects and strong interactions with environmental conditions. Consequently, conventional breeding programs have made limited progress in developing salt-tolerant crop varieties. In recent years, non-breeding or non-transgenic approaches such as the use of symbiotic microorganisms in plants have been considered, which has led to increased tolerance to abiotic stresses and achieved promising results. These beneficial microbes can alleviate stress effects through various mechanisms, including phytohormone production, nutrient solubilization, and induction of host defense responses.Plants have distinct microbial communities in their different organs, including roots, stems, leaves, and seeds, collectively referred to as the plant microbiome. There is a lot of evidence about the role of the microbiome in growth, development, response to biotic and abiotic stresses, and increased adaptation to the environment. In particular, endophytic microorganisms, those that reside within plant tissues, have gained attention as natural partners that can enhance host resilience. Halophytes, plants naturally adapted to high-salinity environments, harbor unique microbial communities that have co-evolved with their hosts under extreme conditions. These halophyte-associated microbes have shown remarkable ability to confer stress tolerance to conventional crops, but the mechanisms remain poorly understood, limiting their practical application. This study investigated the potential of Penicillium chrysogenum, an endophytic fungus isolated from seeds of the halophyte Bassia scoparia, to improve salinity tolerance in maize (Zea mays L.), a globally important cereal crop sensitive to salt stress. Our research aimed to (1) characterize the salt tolerance of P. chrysogenum under in vitro conditions, (2) evaluate its effects on maize growth under salt stress, and (3) analyze physiological responses in inoculated plants, including ion content and photosynthetic performance. Materials and Methods The fungal strain was isolated from surface-sterilized B. scoparia seeds collected from saline regions in Iran. Surface sterilization was performed using ethanol and sodium hypochlorite to ensure that only endophytic microorganisms were recovered. Molecular identification was performed using ITS sequencing of the ribosomal DNA region, and the sequence was compared against public databases to confirm taxonomic affiliation. The salinity tolerance of the fungus was evaluated on potato dextrose agar (PDA) medium containing NaCl concentrations ranging from 0 to 4 M, with radial growth measured daily. Maize seeds (cv. Hido) were surface-sterilized and inoculated with a fungal spore suspension (10⁶ spores/mL) or sterile water for controls. Seeds were grown under controlled conditions with three salinity levels (0, 30, and 60 mM NaCl) in a growth chamber. Subsequently, greenhouse tests were conducted under two treatments of 150 mM NaCl and normal irrigation to evaluate performance under more realistic stress levels. Drought tests were also conducted on plants in the greenhouse by withholding irrigation for defined periods. Growth parameters including germination rate, root length, shoot length, and fresh and dry biomass were recorded. Ion content (Na⁺, K⁺) was determined using flame photometry, and photosynthetic parameters such as chlorophyll content and net photosynthetic rate were measured using a portable photosynthesis system. Experiments included laboratory, growth chamber, and greenhouse trials with five replicates per treatment arranged in a completely randomized design. Data were analyzed using analysis of variance (ANOVA) followed by Tukey's honestly significant difference test (p < 0.05) for mean comparisons.Results and Discussion In this study, an endophytic fungus, identified by ITS sequencing as Penicillium chrysogenum, was isolated from the seed microbiome of the halophyte B. scoparia. This fungus exhibited optimal growth at 1 M NaCl, with substantial mycelial development even at 2 M, indicating strong halotolerance superior to many soil saprophytes. Although it did not significantly affect seed germination percentage or rate, fungal inoculation improved early seedling growth traits and biomass under both saline and normal conditions, suggesting that the primary benefits occur after emergence. Greenhouse experiments confirmed its positive effects on shoot and root development under salt stress, while no beneficial effects were observed under drought conditions, indicating a stress-specific mechanism rather than a general growth promotion effect.Interestingly, photosynthetic performance and Na⁺/K⁺ ratios remained unchanged in inoculated plants compared to non-inoculated controls, suggesting that the fungus may enhance salt tolerance through mechanisms such as salt detoxification via osmotic adjustment, enhancement of antioxidant enzyme activity, or production of fungal metabolites that protect cellular structures, rather than altering ion uptake or transport. Given the fungus’s native origin from a halophyte host and its potential role in stress adaptation, P. chrysogenum represents a promising candidate for developing biological solutions to improve crop resilience in saline environments. Further field trials and molecular studies are recommended to optimize its application, including dose-response experiments and transcriptomic analysis of host plant responses.Conclusion This study demonstrated that Penicillium chrysogenum, an endophytic fungus isolated from the halophyte Bassia scoparia, can promote maize growth under salt stress. Despite no significant effect on germination rate, fungal inoculation improved seedling establishment and biomass production in both normal and saline conditions. The fungus showed strong halotolerance and may enhance salt stress resilience through mechanisms unrelated to ion uptake or photosynthetic changes, potentially involving detoxification pathways or antioxidant activity. Given its native origin and efficacy under saline conditions, P. chrysogenum holds potential as a bio-inoculant for improving crop performance in salt-affected soils. Further field trials and molecular studies are recommended to better understand and harness its functional mechanisms for agricultural application.

Introduction Environmental stresses are important factors in reducing agricultural production worldwide. Plants are continuously exposed to various stresses under natural and agricultural conditions, and water scarcity is the most important limiting factor for crop yield in most parts of the world. Drought stress can cause morphological, physiological, and biochemical changes in crop plants. Cytokinin application and foliar application of micronutrients can have beneficial effects on plant photosynthesis and wheat yield under moisture stress conditions.Materials and Methods This study was conducted as a split-plot factorial experiment based on a randomized complete block design with three replications in two regions, Karaj and Hamadan, during the 2019–2020 growing seasons. Irrigation regimes, including three levels—(i) irrigation at 40% available soil moisture depletion throughout the growing season (control), (ii) normal irrigation from planting to the pollination stage followed by irrigation at 60% available soil moisture depletion, and (iii) normal irrigation from planting to the pollination stage followed by irrigation cutoff until maturity—were assigned to the main plots. The factorial sub-factors included low-consumption nano-elements at five levels (control, zinc, iron, selenium, and a combined application of the three elements) and cytokinin application timing at four levels (control, flowering stage, milking stage, and flowering + milking stages). Measured traits included grain yield, biological yield, and chlorophyll fluorescence parameters.Results and Discussion The results indicate that the effects of irrigation, cytokinin and Nano-micronutrient treatments on the studied traits were significant, but the interaction effects of these treatments were not significant. Drought stress has an inhibitory effect on various photosynthetic activities, especially photosystem II activity, in wheat. In the present study, it was determined that chlorophyll fluorescence parameters along with chlorophyll content have a special role in investigating the effects of drought stress on plant photosynthetic systems. Therefore, by applying drought stress, the rate of transpiration, gas exchange, maximum photochemical efficiency of photosystem II, maximum fluorescence (Fm) and photosynthesis rate decreased. However, the use of cytokinin hormone and the combined application of micronutrients significantly increased the rate of photosynthesis, stomatal conductance, transpiration intensity, maximum photochemical efficiency of photosystem II, maximum fluorescence (Fm), minimum fluorescence (F0), relative water content of leaf and chlorophyll content. Normal irrigation treatment increased the stable fluorescence (26.68%) and the variable fluorescence index (24.78%) compared to the normal irrigation treatment until pollination and then completely stopped irrigation. Zinc + iron + selenium elements improved the stable fluorescence (26.16%) and variable fluorescence (21.48%) compared to the control treatment. Foliar application of cytokinin at pollination + grain milking increased the stable fluorescence (15.34%) and variable fluorescence (20.74%) compared to the control. Drought stress had an inhibitory effect on various activities of the photosynthetic apparatus, especially the activity of photosystem II, in wheat, but the application of micronutrients and the hormone cytokinin reduced the inhibitory effect. The improvement of photosynthesis by cytokinin and micronutrients under stress may be due to the effect of these substances in maintaining leaf chlorophyll. It seems that cytokinin and micronutrients have inhibitory effects on the functioning of the photosynthetic apparatus. According to the results obtained, it can be concluded that the decrease in the quantum yield of photosystem II is mainly due to the occurrence of disorder in the chloroplast and the decrease in chlorophyll also confirms this issue, because chlorophyll fluorescence is directly related to the activity of chlorophyll in the reaction of photosystems and can be used as a criterion for measuring the efficiency of the photosystem.Conclusion The combined application of nano micronutrients (zinc + iron + selenium) led to an increase in wheat yield compared to the application of each of them individually, so the combined foliar application of these three elements is recommended to increase wheat yield. Cytokinin foliar application at the pollination + grain milk stage increased wheat yield compared to application at other stages, although various sources consider the application of nano micronutrients and cytokinin foliar application important for water stress tolerance, but the results of the present study showed that in conditions of low irrigation and stress, these strategies are not recommended to reduce the negative effects of deficit irrigation.

IntroductionThis study aimed to utilize unconventional water resources to cultivating the strategic oilseed crop Camelina sativa, also investigating the effects of foliar application of L-amino acids and salicylic acid (SA) on morpho-physiological characteristics of Camelina cultivars. Soil and water salinity are the most significant problems in Khuzestan Province, leading ‎to reduced agricultural productivity. High salinity in Khuzestan soils significantly reduces ‎the growth and productivity of many crops. However, employing foliar spraying of amino acids ‎and salicylic acid can serve as a magnificent method to reduce the impact of salt stress in ‎saline soils. The current investigation intends to explore the effect of foliar spraying of ‎amino acids and salicylic acid on the Camelina sativa L. under sugarcane (Saccharum officinarum) drainage water ‎irrigation. Camelina‏ ‏as a low input oilseed crop has a few positive‏ ‏traits that make it possible ‎to cultivate in Iran. The main advantages of Camelina like drought and salinity tolerance and ‎high seed oil content make it valuable especially in less‏ ‏productive lands. Due to the large volumes of drainage water produced in the sugarcane industry, recycling this water and using it to irrigate salt-tolerant crops—other than sugarcane—can serve as a valuable source of supplemental irrigation. With proper management, drainage water recycling can also offer additional benefits, such as conserving conventional freshwater resources needed for expanding crop cultivation in the region.Materials and MethodsThe experiment was conducted during the 2022–2023 and 2023–2024 growing seasons using a split-split plot test based on a randomized complete block design in four replications at the sugarcane industry of Hakim Farabi, located in southern Khuzestan Province. Water sources as main factor included river water (control), alternate irrigation (alternating river water and sugarcane drainage water), sugarcane drainage water irrigation. Sub-factor was foliar applications at flowering stage included control (no application), L-amino acids at 1.5 and 3 L ha-1, salicylic acid at 1 and 2 mM, and cultivars (Soheil and Sepehr) as sub-sub-factor. The amino acids used included alanine, arginine, aspartic acid, cysteine, glutamic acid, glutamine, glycine, histidine, isoleucine, leucine, lysine, methionine, phenylalanine, proline, serine, threonine, tryptophan, tyrosine, and valine from stock solutions of 1 and 1.5 L ha-1. Results and DiscussionResults indicated that under river water irrigation (Karun River), the Sepehr cultivar treated with 3 L ha⁻¹ L-amino acids exhibited a 53% higher seed yield compared to the untreated Soheil control. The lowest yield was observed under sugarcane drainage water irrigation. However, the Soheil cultivar showed a 35% yield advantage under sugarcane drainage water when treated with 3 L ha-1L-amino acids compared to the untreated Sepehr control. The highest seed yield over the two years was obtained from river water irrigation combined with a foliar application of 3 L ha-1 of amino acids in the Sepehr cultivar (3609 kg ha-1). In contrast, the lowest seed yield was recorded for the Sepehr cultivar under irrigation with sugarcane drain water. The best seed yield in the Soheil cultivar was also achieved with a foliar application of 3 L ha-1 of amino acids (1691 kg ha-1). The Sepehr cultivar exhibited higher grain and biological yields under river water irrigation. Foliar applications of amino acids and salicylic acid significantly improved seed yield, biological yield, harvest index, 1000-seed weight, number of siliques per plant, number of seeds per silique, silique length, and plant height under both drainage water and non-drainage water irrigation conditions. However, under drainage water irrigation, Sepehr yielded less than the Sohail cultivar. Overall, foliar application of L-amino acids and salicylic acid appeared to function as effective growth regulators, alleviating stress by enhancing nutrient uptake and ultimately improving seed yield.ConclusionThis study concluded that foliar applications of amino acids and salicylic acid enhanced the growth and development of Camelina by improving nutrient absorption, mitigating stress effects, and increasing seed yield. The integration of L-amino acids and SA alleviated stress under unconventional water irrigation, demonstrating their potential for sustainable Camelina production. River water with amino acid supplementation yielded optimal results, while sugarcane drainage water required amino acid amendments to enhance productivity.

IntroductionBlack cumin (Nigella sativa L.) is a medicinal crop in the Ranunculaceae family, a dicotyledonous, herbaceous and annual plant. Nigella sativa is one of the eight species of this genus that can be cultivated naturally in different parts of Iran. Grains of nigella contains about 40% oil and 1.4% essential oil. The main fatty acids in nigella grain are oleic acid, linoleic acid and palmitic acid. In dry and semi-dry regions, such as west of Iran, water deficit is the most important factor that reduce the growth and production of crops. Plant's growth depends on the adequate amount of nutrients in the rhizosphere. Among nutrients, nitrogen has a particular importance. Because nitrogen plays a role in the formation of amino acids, proteins, nucleic acids and other cell compounds. Using the correct methods of mineral nutrition in plants reduces the effects of drought stress. Silicon is one of these elements. Although silicon is not an essential element for plant growth, it plays an important role in reducing the harmful effects of drought stress. The aim of this experiment was to investigate the effects of silicon foliar application and nitrogen fertilizer in modulating the negative impacts of drought stress on growth, grain yield and phonologic stages of nigella. Materials and MethodsThis experiment was carried out in the research farm of Razi University during two crop years, 2020-21 and 2021-22. The research was done in the form of two separate experiments, one under non-drought conditions and the other under drought stress conditions. Each experiment was laid out as a split plot in the form of a randomized complete blocks design (RCBD) in three replications. The main factor was the amount of nitrogen fertilizer in three levels (0, half, and equivalent to the recommended amount, respectively contain 0, 125 and 250 kg ha-1 urea) and the sub-factor including silicon foliar spraying in four concentrations (0, 3, 6 and 9 mM). The measured traits were included grain yield, HI, biomass, capsules per plant, grains per capsule, 1000 grain weigh, grains per plant, days to flowering, days to physiologic ripening, grain filling period and grain filling rate. To analysis of variance of the data, the combined analysis model was used with SAS, 9.4 software. The Bartlett test was used to confirm the homogeneity of variances. In combined analysis, year was considered as a random effect and nitrogen and silicon as fixed effects. The means comparison was performed by LSD test at the probability level of 5%. Results and DiscussionIn normal moisture conditions and without silicon spraying, the application of 250 kg ha-1 urea was more suitable for the investigated traits. So that the highest values ​​of grain yield (1453 kg ha-1), number of capsules per plant (33.6 capsules), number of grains per capsule (73.8 grains), number of grains per plant (2514 grains), days to physiological maturity (106.6 days) and grain filling rate (125.7 mg day-1) were obtained at 250 kg ha-1 urea. In the absence of nitrogen, silicon was not effective. But with nitrogen application, 6 and 9 mM silicon foliar spraying were effective. In general, under without drought stress conditions, the interaction effect of 250 kg ha-1 urea × 6 mM silicon was found as the superior treatment. Under drought stress conditions and without silicon spraying, 125 kg ha-1 urea was more suitable than 250 kg ha-1 urea and also the no-nitrogen treatment. But in silicon spraying treatments, the use of 250 kg ha-1 nitrogen achieved better results. At 125 kg ha-1 nitrogen, foliar spraying of silicon 6 mM was more suitable. But at 250 kg ha-1 nitrogen, foliar spraying of 9 mM silicon was better. According to the obtained results, under drought stress conditions, the consumption of 250 kg ha-1 urea × silicon foliar application of 9 mM was determined as the best treatment.ConclusionIn general, the results of this experiment showed that silicon foliar spraying was able to significantly reduce the negative effects of water deficiency in nigella sativa. Therefore, in order to increase the yield of nigella, along with the application of nitrogen fertilizer as much as the recommended amount, it is recommended to foliar spraying of 6 mM silicon under normal moisture conditions and 9 mM under drought stress conditions.

IntroductionThis study undertook a detailed comparison of two supervised machine-learning algorithms—Random Forest (RF) and eXtreme Gradient Boosting (XGBoost)—to predict irrigated wheat (Triticum aestivum L.) yield across 20 counties in Razavi Khorasan Province. Both models were trained on 70 % of the dataset (years 1383–1402) and tested on the remaining 30 %. Hyperparameter tuning was performed via a grid search coupled with five-fold cross-validation.Materials and MethodsThe two algorithms were initially trained and optimized using 70% of the data (approximately 14 years per county) and subsequently tested on the remaining 30% (approximately 6 years). Hyperparameter tuning was performed through a grid search combined with five-fold cross-validation. Hyperparameter Tuning was performed using Gridsearch over key parameters (e.g., number of trees n_estimators, maximum depth max_depth, learning rate for XGBoost) with 5-fold cross-validation. Afterwards, both models were evaluated and validated using RMSE (Root Mean Squared Error), R² (Coefficient of Determination), MAE (Mean Absolute Error), Willmott’s d (Index of Agreement). Two machine learning algorithms, Random Forest and XGBoost, were developed to predict wheat performance at the district level. The performance values were initially predicted numerically using regression and then divided into three distinct classes using statistical percentiles: Low: 0th to 33rd percentile; Medium: 33rd to 66th percentile; High: 66th to 100th percentile. This classification was based on both the actual and predicted values for each district. The results of this classification are presented in the form of a confusion matrix for each model. Using three indices—Aridity Index, Seasonal Intensity of Temperature, and Growing Degree Days—districts in the province were clustered into three climatic zones. Then, wheat performance in these zones was analyzed based on two models. In this study, the machine learning algorithms Random Forest and XGBoost were implemented in Python 3.3 using the Scikit-learn library. DataFrame preparation and the clustering of the province’s counties into climatic zones were carried out using R 4.3.2 and ArcGIS 10.8.2.Results and DiscussionAlghoritms Performance: On average, RF reduced prediction error by ~19 % (395 vs. 492 kg ha-1 RMSE) and achieved a slightly higher agreement with observed yields (d=0.467 vs. 0.419). Random forest showed Best RF Performance for Khalilabad (RMSE = 141.19 kg ha-1, MAE = 125.68 kg ha-1, d = 0.37) and Bardeskan (RMSE = 187.69 kg ha-1, d = 0.80); Worst RF Performance was obtained for Quchan (RMSE = 667.65 kg ha-1, d = 0.36) and Torbat-e Heydarieh (RMSE = 581.33 kg ha-1, d = 0.47). Best XGBoost Performance was obtained for Torbat-e Jam (RMSE = 269.36 kg ha-1, d = 0.77), Neyshabur (RMSE = 377.91, d = 0.78). Worst XGBoost Performance resulted for Quchan (RMSE = 943.99 kg ha-1, d = 0.25) and Torbat-e Heydarieh (RMSE = 786.20 kg ha-1, d = 0.39). In 6 out of 20 counties (Khaf, Mahvelat, Kalat-e Nader, Kashmar, Chenaran, Fariman) both RF and XGBoost performed nearly identical errors (ΔRMSE < 15 kg ha-1), indicating similar predictive power under those local conditions.Feature Importance: Daily Minimum Temperature (Tmin): Ranked #1 in RF’s importance list; ranked #3 in XGBoost. Seasonal Tmin (TminGS): Consistently #3 in both models. Other Key Predictors: Precipitation over the growing season (Prec, PGS), Growing Degree Days (GDDGS), and Evapotranspiration (ETGS) all contributed substantially, though with 4th–8th ranks markedly lower in RF than in XGB.Classifiction of Yield into Three Performance Classes: Using percentile thresholds—Low (0–33rd), Medium (34–66th), High (67–100th)—the models were also evaluated as classifiers. Low-Performing Counties Needing Intervention Seven counties (35 % of the sample)- Quchan, Torbat-e Heydarieh, Sarakhs, Kalat-e Nader, Gonabad, Neyshabur, Taybad- fell in the Low performance class across both models. These areas should be prioritized for targeted agronomic management and resource allocation.Cluster-Based Insights: Counties were grouped into three agro-climatic clusters: Very Dry & Hot (4 counties): RF outperformed XGB in all (e.g., Bardeskan, Khalilabad, Mahvelat, Sarakhs). Semi-humid Cooler (6 counties): Mixed results-RF won in 4 (Chenaran, Kalat-e Nader, Mashhad, Nishapur); XGB was slightly better in 2 (Fariman, Quchan). Warm Semi-arid (10 counties): RF superior in 7; equivalence in Taybad & Torbat-e Heydarieh; XGB edged ahead only in no counties here.ConclusionOverall, the Random Forest model showed better results for predicting wheat yield in Razavi Khorasan province, especially in most counties. Although the XGBoost model has higher potential for modeling complex patterns, Random Forest performed more accurately in conditions of greater data dispersion. In conclusion, the results of this study emphasize that by utilizing climatic and agricultural data, machine learning algorithms can be optimized not only to achieve high accuracy but also to provide a clear interpretation of the contribution of each variable in wheat yield using the SHAP tool.

IntroductionSunflower (Helianthus annuus L.) is one of the most significant oilseed crops, valued for its high content of unsaturated fatty acids, which contributes to the production of high-quality oil. Although this plant exhibits relative adaptability to Iran's climatic conditions, it demonstrates moderate sensitivity to drought stress. Drought stress poses a major challenge to crop production by directly affecting plant growth and yield. While sunflowers can tolerate mild drought conditions, severe drought significantly disrupts their physiological responses. Reduced regulation of transpiration and limited leaf expansion are key factors contributing to the plant's vulnerability to severe drought. This stress condition results in excessive production of reactive oxygen species (ROS), intensifying oxidative stress. Consequently, oxidative stress leads to chloroplast degradation, decreased photosynthetic capacity, leaf shrinkage and thickening, reduced leaf area, and morphological alterations such as impaired stomatal movement. Brassinolides, which belong to the class of plant steroid hormones, play a crucial role in regulating plant growth and development, including cell division and elongation, organogenesis, delayed leaf senescence, and enhanced resistance to environmental stress. By modulating plant responses to drought, brassinolides can mitigate the adverse effects of oxidative stress and improve overall plant performance.Materials and MethodsTo investigate the effect of brassinolide under water deficit conditions on antioxidant enzyme activity and certain agronomic traits of the sunflower cultivar Oscar, an experiment was conducted in a split-plot design based on a randomized complete block design (RCBD) with three replications. The main factor consisted of irrigation regimes at three levels (full irrigation at 100% field capacity, moderate drought at 75% field capacity, and severe drought at 50% field capacity based on Class A pan evaporation). The sub-factor included brassinolide foliar application at three concentrations (0 (distilled water), 0.1 and 0.5 mg L-1). In this study, traits such as relative water content (RWC) of leaves, antioxidant enzyme activities (catalase, peroxidase, and superoxide dismutase), leaf area index (LAI), filled and unfilled capitulum weight, seed yield, and oil yield were evaluated.Results and DiscussionThe results indicated that drought stress and brassinolide foliar application significantly affected the morphophysiological indices and agronomic performance of sunflower. Severe stress led to a reduction in relative leaf water content (by 18.79%) and an increase in antioxidant enzyme activity. At this stress level, leaf area index, grain yield, and oil yield decreased by 57.48%, 61.56%, and 70.01%, respectively, compared to full irrigation. This reduction highlights disruptions in water uptake and retention as well as diminished grain-filling capacity under drought conditions. On the other hand, the simple effect of foliar application of 0.5 mg/L brassinolide increased catalase and peroxidase activities by 18.58% and 36.40%, respectively. This increase highlights the role of brassinolide in alleviating oxidative stress and enhancing the plant’s physiological condition. Furthermore, the highest leaf area index was recorded under full irrigation combined with a 0.5 mg/L brassinolide foliar application. Under severe stress conditions, grain yield and oil yield at the highest level of brassinolide application increased by 83.57% and 66.66%, respectively, compared to the treatment without foliar application.ConclusionThe findings of this study demonstrate that brassinolide foliar application, particularly under drought stress conditions, can be an effective strategy for enhancing both the quantitative and qualitative performance of sunflower. Brassinolide enhances antioxidant enzyme activities, mitigates the negative effects of oxidative stress, and improves plant physiological traits, thereby preserving photosynthetic potential and promoting vegetative and reproductive growth. Based on the results, foliar application of brassinolide at 0.5 mg L-1 yielded the most significant improvements in leaf area index, seed yield, and oil yield, underscoring its effectiveness in optimizing plant growth even under limited water availability. Therefore, the application of brassinolide under optimal irrigation regimes, particularly during moderate to severe drought stress, is recommended as a viable approach to enhancing sunflower productivity and sustainability.

IntroductionPotato (Solanum tuberosum L.) is a globally significant crop whose productivity is substantially influenced by seed tuber quality and nutrient management. The Sante variety, prized for its disease resistance and processing qualities, shows particular sensitivity to these factors. Recent studies highlight that seed tuber size is a critical determinant of early growth vigor, influencing both carbohydrate reserves and meristematic potential. Larger tubers (>20 mm in diameter) generally exhibit 30–40% higher initial growth rates due to their greater sprouting capacity and faster establishment of photosynthetic activity. Concurrently, phosphate biofertilizers containing Pseudomonas and Bacillus strains have shown remarkable efficacy in potato systems, improving phosphorus availability by 50-70% through organic acid secretion and phosphatase activity. The interaction between tuber size and biofertilization remains underexplored, particularly regarding source-sink relationships during tuber initiation. Preliminary evidence suggests synergistic effects, where larger tubers' inherent advantages are amplified by biofertilizer-induced nutrient mobilization. This study investigates these dynamics in Sante potatoes, hypothesizing that optimal tuber size combined with dual-phase biofertilizer application will maximize yield components through: (1) enhanced canopy development, (2) improved phosphorus use efficiency, and (3) superior photoassimilate partitioning. The findings will advance precision management strategies for seed potato production systems.Materials and Methods A field experiment using the Sante potato cultivar was conducted in a factorial design in a randomized complete block design with three replications in the 1401 crop year. The microtubers were produced in the previous year through tissue culture and potting in greenhouse cultivation. The treatments included tuber size at three levels up to 20, 20-25 and 25-30 mm and application of Barvar 2 phosphate biofertilizer at three levels without phosphate biofertilizer (control), one application of phosphate biofertilizer (at the time of planting by dipping the tubers with biofertilizer) and two applications of phosphate biofertilizer at the time of planting and 4-6 leaves. Land preparation operations included semi-deep ploughing in spring and two stages of vertical disking. The seeds were sown in rows and ridges with row spacing of 75 cm and plant spacing of 20 cm. Each plot consisted of 6 rows, each 5 m in length. Irrigation was carried out regularly and according to the plant's water requirements during the growth period. Weed control was carried out manually. During the growth period, sampling of experimental plots was carried out every 10 days by randomly picking 5 plants from the assigned rows, and leaf area, leaf dry matter and total dry matter were measured to evaluate growth indices. At the time of harvest, the number of stems, number of tubers, tuber dry matter, and total tuber yield were measured for each plot, and the harvest index was calculated. SAS version 9.4 software was used to analyse the data, and Microsoft Excel software was used to create graphs.Result and Discussion The results indicated that larger microtubers (25–30 mm) significantly (p < 0.01) enhanced growth and yield parameters. Total biomass (6,201 kg ha⁻¹), number of tubers per plant (4.8), and tuber yield (28,337 kg ha⁻¹) increased by 41%, 23%, and 63%, respectively, compared to smaller microtubers (15–20 mm). Two applications of phosphate biofertilizer also increased leaf area index (27.58%), tuber dry matter (33.19%), and harvest index (5.69%) compared to the control. The interaction of these two factors showed that the combination of 25-30 mm microtubers with two applications of biofertilizer produced the highest tuber yield (31524 kg ha-1) and harvest index (89.03%).Conclusion This study investigated the effect of potato tuber size and phosphate biofertilizer application on the growth and yield of the new potato variety Sante. The results showed that tuber size and phosphate biofertilizer application had a significant effect on potato growth and yield indices. Larger tubers (25 to 30 mm) provided more energy for germination and early growth due to greater nutrient storage, which led to faster leaf formation and increased leaf area index (LAI). Also, applying phosphate biofertilizer twice had a greater effect on leaf area, stem number, tuber number, tuber yield, tuber dry matter, and total biomass compared to applying phosphate biofertilizer once and without fertilizer. The interaction between tuber size and phosphate biofertilizer was also significant for all measured traits. The highest yield and plant growth were observed in the combined treatment of 25-30 mm microtubers and two applications of phosphate biofertilizer. These results indicate that the use of larger microtubers, along with optimal use of phosphate biofertilizer can be used as an effective strategy to improve potato growth and yield under field conditions. Finally, this study emphasizes that choosing the appropriate size of microtubers and properly managing the use of phosphate biofertilizer can help increase productivity and sustainability in potato production. These findings can be used as a guide for farmers and researchers to optimize cultivation conditions and increase potato yield.

IntroductionThe nation's substantial maize (Zea mays L.) requirements are currently met through imports. Given the scarcity of arable land and water resources, enhancing yield per unit area is the only viable solution to augment domestic production. Significant progress can be made toward this goal by optimizing production management to narrow the yield gap. Utilizing a reliable simulation model can play a pivotal role in reducing the yield gap and achieving optimal yields through evaluating various management practices. Effective management strategies are essential to optimize maize production and ensure food security. While field experiments can provide valuable insights, they can be resource-intensive, time-consuming, and even impractical due to complex interactions between environmental and management factors. Crop simulation models offer a powerful alternative, enabling the exploration of various scenarios and the identification of optimal management practices. These models simulate plant growth and development in response to climate variables, soil conditions, management inputs, and genetic traits, providing valuable information for decision-making. Accurate parameterization is crucial for reliable crop model predictions. This study aims to parameterize and evaluate the SSM-iCrop model for predicting grain maize yield and nitrogen dynamics in Iran.Materials and Methods Simple Simulation Models (SSM), were initially developed for soybean yield prediction in 1986. The model has since been refined to simulate various crops, including maize. SSM-iCrop simulates daily plant growth and development processes, such as phenology, leaf area development, dry matter production, yield formation, and water and nitrogen dynamics. The model has been successfully used in various studies for different plants. Moreover, comparisons of this model with other crop models have shown its effectiveness in simulating yield. The SSM-iCrop model requires input data on weather parameters (minimum and maximum temperature, precipitation, and solar radiation), soil properties, cultivar-specific parameters, and management practices (planting date, plant density, irrigation, and nitrogen fertilization). The SSM-iCrop model was parameterized and calibrated in this study using data from various studies conducted in Iran between 2001 and 2022. The calibration process involved adjusting plant parameters within a reasonable range, as determined by scientific literature, to minimize the difference between simulated and observed data. The parameters that yielded the best fit were selected as the final estimates. Lastly, the model was evaluated using different studies.Results and Discussion The SSM-iCrop model accurately simulated key maize growth stages, including emergence, tasseling, silking, and physiological maturity (RMSE = 13.22, CV = 17.4, r = 0.97). The model also accurately predicted leaf area index (RMSE = 0.4, CV = 6.2, r = 0.98), biological yield (RMSE = 431.3, CV = 23.9, r = 0.66), and grain yield (RMSE = 161.9, CV = 16.9, r = 0.81). Compared to previous studies, such as Zeinali et al. (2016), the current study demonstrated superior performance in grain yield prediction. While Zeinali et al. (2016) did not explicitly simulate nitrogen dynamics, the current study considered nitrogen processes. Manschadi et al. (2021) reported high accuracy in simulating grain maize yield in Austrian conditions. The difference in accuracy between the two studies may be attributed to the quality of the observed data, as accurate parameterization is crucial for model performance. Although this study incorporated nitrogen fertilization treatments and examined the impact of nitrogen-related parameters on yield, dry matter production, and leaf area, specific nitrogen-related traits were not evaluated. This is because existing research has primarily focused on the overall effects of nitrogen fertilization on yield and yield components, often neglecting the measurement of nitrogen content in critical plant tissues such as leaves and grains.Conclusion The performance of the SSM-iCrop model for simulating key maize growth stages, leaf area index, and biological and grain yield was suitable. This model is a valuable tool for simulating maize yield and optimizing management practices in Iran. By simulating the impact of different environmental factors and management strategies, the model can help farmers and policymakers make informed decisions to improve maize production and ensure food security.

IntroductionCowpea (Vigna unguiculata L.) is a valuable food legume from the Fabaceae family. This crop originated from West Africa (Nigeria) and was then transferred to the Middle East and Europe. Cowpea is suitable for cultivation in tropical regions. In addition to its multipurpose uses (green pods, dry seeds, animal feed), it also has high agronomic value. Despite breeding research worldwide to increase cowpea production, its yield is low in most growing regions. Therefore, to increase yield, agronomic management practices should be studied alongside breeding efforts. One of the key factors in farm management and improving the productivity of legumes, including cowpea, is the application of micronutrients, particularly iron.Materials and MethodsIn the present study, the responses of 12 cowpea lines (C1 to C12) along with the Mashhad cultivar to the application of iron nanofertilizer were investigated in four regions of Iran (Karaj, Dezful, Khomein and Shiraz). The experiments were conducted in a strip plot based on a randomized complete block design with three replications in 2023. Genotypes were placed in horizontal plots and treatments of using or not using iron nanofertilizer were placed in vertical plots. Some morphological traits including yield and yield components of genotypes were evaluated in different environments.Results and DiscussionThe results showed that the effects of location, nanofertilizer and genotype on all studied traits were significant. The interaction effect of location and nanofertilizer was significant on all traits except biomass, harvest index and 100-grain weight, but the interaction effect of location and genotype was significant on all traits except the number of grains per pod. The interaction effect of genotype and nanofertilizer was significant on all traits except plant height, biomass and grain yield. All traits, except the number of seeds per pod, were significantly affected by the three-way effect of location, genotype and nanofertilizer. Lines C4 and C5 were the earliest and the most late-maturing genotypes, respectively. The highest biomass yield of 3810 kg ha-1 was related to the Mashhad cultivar, and the biomass yield in all lines was lower than that of the Mashhad cultivar. Plant height and number of branches per plant varied from 55.9-72.7 and 5.4-7.4, respectively. Only four lines, along with the Mashhad cultivar, had a grain harvest index higher than the average. The highest grain harvest index was related to C1 line (38.77%). All 13 genotypes studied responded positively to the use of iron nanofertilizer and had better growth compared to the conditions of no nanofertilizer use. The highest average percentage increase was related to the number of seeds per plant (27.25%). The use of iron nanofertilizer accelerated the maturity of the genotypes. In the absence of iron nanofertilizer, the number of pods per plant and the number of seeds per pod, and consequently the number of seeds per plant, were significantly lower than when iron nanofertilizer was applied. In line C11, although the values ​​of these traits were lower than the average of the genotypes in the absence of iron nanofertilizer, it showed the greatest response to iron nanofertilizer application. Yield components (number of pods per plant, number of seeds per pod and 100-seed weight) showed a significant increase (17.4%, 8.8% and 3.2% respectively) under the influence of iron nanofertilizer application. Grain yield also increased by 10.8%. In accordance with our experimental results, it has been reported in various studies that the use of nanofertilizers significantly increases the yield of crop plants compared to treatments without the use of nanofertilizers. The main reason for this is the improvement of the growth of plant components and metabolic processes such as photosynthesis, which causes greater accumulation of photosynthetic products and their transport to the economic organs of the plant.ConclusionBased on the environmental index, the evaluated locations differed from each other. Accordingly, Dezful region had the highest value of environmental index. Line C1, along with lines C4 and C11, were the best genotypes. Although the use of iron nanofertilizers improved the growth and development of the studied genotypes, since there are limited reports on the biological consequences of nanofertilizer use and their absorption by plants, and numerous questions remain about the fate and behavior of nanomaterials in plants, the use of these types of fertilizers should be done with more caution.

IntroductionThe importance of sesame (Sesamum indicum L.) as a key crop in numerous regions can be attributed to its adaptability to dry climates, the nutritional value of its oil, and its health advantages. The necessity of improving the sesame plant is important by its comparatively low yield. The use of improved cultivars by plant breeding and breeding methods has resulted in a higher yield and higher quality of crops, which in the case of sesame plants include a greater increase in the seed yield and improvement in its quality. In a breeding program, increasing genetic diversity enhances the ability to select superior genotypes, and a more efficient selection process contributes to greater breeding success. The use of local sesame cultivars is particularly valuable because of their potential to generate improved genotypes. Understanding the relationships between yield and other related traits allows these traits to be used more effectively as selection indicators for genetic improvement. The nature of the relationship between yield and its components determines what appropriate traits should be used in plant breeding.Materials and MethodsTo discover the correlations among seed yield and some important agricultural traits in order to determine the direct and indirect effects of each trait on seed yield and finally to choose the ideal genotypes in terms of various characteristics, a total of 36 local sesame cultivars were evaluated in 3 regions (Karaj, Moghan and Jiroft) by using a randomized complete block design for two years (2017-2018). A total of 14 quantitative traits were studied in this study, including measurements like the number of days from germination to the beginning of flowering, the number of days from germination to the beginning of maturity, the height of the first capsule from the plant crown, the height of the plant, the number of branches, the number of capsules in one plant, the number of seeds in one Capsule, seed weight of one capsule, the weight of 1000 seeds, capsule length, capsule width, capsule diameter, biological performance and seed yield.Results and DiscussionThe calculation of simple correlation coefficients showed that the height of the first pod from the plant crown, seed weight of a capsule, biological yield, number of seeds in a capsule and plant height have the highest correlation coefficients with seed yield. The height of the first capsule from the plant crown, the number of seeds in the capsule and the height of the plant were demonstrated by path analysis had the most positive direct effect and the number of days until the start of maturity had the most negative direct effect on seed yield and it is suggested that they be used as selection indicators for the improvement of seed yield. Five components were identified through the results of the principal components analysis which explained 77.92% of the variations in the data. Out of all the genotypes analyzed in terms of yield, genotypes 78-730, 78-229, and 78-570, displayed the greatest seed yield on the biplot generated by the first and second components. All evaluated traits led to the identification of four separate groups through cluster analysis. Overall, the results indicated that the cultivars in the first group were late-flowering types characterized by tall plants and high yield. The second group consisted of late-flowering cultivars with few capsules and low yield. The third group included early-flowering, early-maturing cultivars with long capsules, while the fourth group comprised cultivars with small capsules but a high number of seeds.ConclusionIt was shown by cluster analysis that there was no connection between the classification of genotypes and their geographical placement and predominantly, the genotypes were classified by their physical distinctions and morphological differences. It can be concluded from the results that principal components analysis and cluster analysis exhibit similarities in their ability to segregate cultivars and genotypes. Their analysis outcomes give us a better understanding of the genetic structure and helps identifying specific genetic populations that have the potential to improved breeding programs.

IntroductionChickpea (Cicer arietinum L.) is one of the most significant crops in the legume family, ranking third globally after beans and peas. This plant plays a crucial role in biological stabilization and serves as an important source of nutrition for humans, as well as animal feed and forage. The majority of chickpea production in Iran occurs through spring sowing under rain-fed conditions, where terminal drought stress particularly during seed filling significantly impacts growth and productivity, leading to yield reductions of up to 70%. To enhance plant growth under dryland conditions, using compounds that improve drought tolerance and metabolic activities in plants represents a practical strategy. Among the identified compounds, humic acid (HA) and salicylic acid (SA) have shown promise. Humic acid has a significant effect on root growth and the emergence of lateral roots and increases the levels of auxins, cytokinins, and gibberellins in plants Salicylic acid stimulates flowering and enhances defensive responses to water deficiency conditions. External application of SA reduces cellular lipid peroxidation and hydrogen peroxide accumulation, thereby mitigating membrane damage in leaves of water-stressed plants. Thus, this study aims to investigate the effects of varying levels of humic acid and salicylic acid on the yield and quality of chickpea seeds.Materials and MethodsThis study was conducted as a factorial experiment based on a randomized complete block design with four replications. The experimental factors included foliar spraying of humic acid at three levels: 0, 3, and 6 L ha-1 as the first factor, and salicylic acid at four levels: 0, 50, 100, and 150 mg L-1 as the second factor. The plants were sprayed with these treatments at two stages: during vegetative growth and just before flowering. The following variables were measured in this experiment: number of pods per plant, seeds per square meter, weight of 100 seeds, grain yield, biological yield, harvest index, and the content of protein, potassium, phosphorus, iron, zinc, and manganese in the seeds. The experimental data were analyzed using SAS 9.1 statistical software, and the comparison of the means was conducted based on the Least Significant Difference (LSD) test at the 1% and 5% probability levels.Results and DiscussionResults revealed significant effects of salicylic acid on pod number per plant, seed number per square meter, 100 seed weight and seed and biological yield, while humic acid did not have a significant effect on chickpea yield and yield components. The highest number of pods per plant (20.95) was achieved with the application of 150 mg L-1 of salicylic acid. Foliar spraying with this concentration led to increases of 35.27% in the number of seeds per square meter and 26.80% in the weight of 100 seeds, compared to the untreated control. Additionally, the application of 150 mg L-1 salicylic acid resulted in the highest seed yield (1488.85 kg ha-1) and biological yield (3451.93 kg ha-1). Results indicated significant effects of both humic and salicylic acid on the protein content and nutrient levels (potassium, phosphorus, iron, zinc, and manganese) of chickpea seeds. As the concentration of salicylic acid and humic acid increased, so did the levels of protein and potassium in the seeds. Conversely, foliar applications of humic acid and salicylic acid on chickpea plants not only enhanced the nutrient content and protein levels in chickpea seeds but also improved the overall quality of the seeds.ConclusionThis study highlights the effectiveness of foliar application of humic acid at 6 L ha⁻¹ and salicylic acid at 150 mg L⁻¹ in improving growth, yield, and nutritional quality of chickpeas under dryland conditions. Salicylic acid particularly enhanced reproductive success and biomass allocation, whereas humic acid improved nutrient availability and metabolic activity. These findings suggest that simultaneous applications of HA and SA could serve as effective strategies for boosting chickpea productivity and quality under challenging dryland farming conditions.

IntroductionIntercropping is considered as one of the components of sustainable agriculture, where two or more species are grown in the same location to take advantage of the beneficial effects between the species. The competition among species for resource use can be facilitative, conflicting, or neutral. In many intercropping systems, plants from the legume and cereal families are cultivated with the aim of creating a complementary relationship between species and to enhance resource use efficiency. One practical way to increase organic matter in agricultural lands is through conservation tillage and returning plant residues to the soil. Corn (Zea mays L.) is the third most important cereal in the world, after wheat and rice. Pinto beans (Phaseolus vulgaris L.) are among the most consumed legumes, playing a crucial role in providing the protein that humans need. The purpose of this research was to evaluate the performance, competitive indicators, and economic benefits of intercropping of corn and beans influenced by tillage systems, crop residues, and planting patterns in the conditions of Shahrekord.Materials and MethodsThe experiment was performed using split-split plot based on a randomized complete block design with three replications in Agricultural Research Field of Shahrekord during 2016–2018. Tillage with two levels (minimum, and no-tillage) and three levels of crop residues (30, 60, and 90% of straw yield of wheat) and five intercropping patterns including corn and bean sole cropping, corn and bean ratio with 2:2, 3:1 and 1:3 were considered as main, sub and sub-sub plots, respectively. After measuring the yield of corn and beans, in order to evaluate the efficiency and competition in intercropping, the indices of land equivalent ratio, relative crowding coefficient, aggressivity and competition ratio were calculated. Also, in order to measure the economic usefulness of intercropping, system productivity index, Intercropping Advantage, and Monetary Advantage Index were used.Results and DiscussionBased on the results obtained, the interaction of tillage × crop residues × plant patterns had a significant effect on the yield of corn and beans (P ≤ 0.05). Based on the average comparison results, the highest corn yield was obtained in sole corn cultivation under no-till conditions and using 60% of residues in the second year of the experiment (1.9317 kg ha-1). The yield of bean seeds, the highest yield was related to sole bean cultivation in low tillage conditions and the use of 60% plant residues (2933.91 kg ha-1). The interaction of tillage × crop residues × plant patterns on total land equivalent ratio was significant (P≤0.05). The plant pattern of 2 corn: 2 beans had the highest amount of LER (1.71) compared to other intercropping patterns. The maximum value of total relative crowding coefficient and competition index for corn was obtained in the plant pattern of 2 corn: 2 beans. Also, the positive values of the aggressivity index for the corn showed the competitive advantage of this plant compared to beans. The corn canopy has a larger volume and height compared to the bean canopy, so corn is a stronger competitor in absorbing light and other resources than beans. The corn canopy has a larger volume and height compared to the bean canopy, so corn is considered a stronger competitor in absorbing light and other resources than beans. The system productivity index was positive across all intercropping ratios, indicating the overall effectiveness of intercropping. The highest intercropping advantage (5859.42) and monetary advantage index (5988.62) were observed in the 2 corn: 2 beans planting pattern. The increased monetary advantage index in this ratio can be attributed to the higher land equivalent ratio and total relative crowding coefficient achieved in this treatment.ConclusionAccording to the results obtained from the evaluation of the land equivalent ratio and the relative crowding coefficient, intercropping advantage and monetary advantage index, it can be stated that the 2 corn: 2 bean planting pattern was superior in terms of competition and economic usefulness compared to other intercropping patterns. Based on the findings of this research, the positive values of the aggressivity index and the increase in the competition index indicate a competitive advantage of corn over beans in intercropping. Therefore, it can be said that the 2 corn: 2 beans, in addition to creating diversity and sustainability through maximizing the biological potential of the species, is significantly effective in enhancing financial advantage and the efficiency of using agricultural lands.

IntroductionThe growing human population, global warming, depletion of water and soil resources, and climate change make it imperative to reconsider food production methods in agricultural systems. Improving resource use efficiency and enhancing productivity are key strategies to address these challenges. Reducing the gap between the actual yield currently achieved on farms and the yield that could be achieved using the best environmentally compatible cultivars and the best water, soil and plant management practices is a key solution to overcome the challenge of feeding the world's growing population. The first step in addressing the yield gap is to determine how much and how it is distributed.Materials and MethodsTo estimate the potato (Solanum tuberosum L.) yield gap in Khorasan Razavi province and to determine the contribution of water and nitrogen to it, two separate field experiments were conducted based on randomized complete block design (RCBD) with water and nitrogen limitation conditions for potato cultivars in the city of Quchan during the growing season of 2018-2019. The first experiment was conducted with three irrigation levels of 100, 75, and 50% water requirement, and used two potato cultivars. The second experiment was conducted with four levels of nitrogen, including 0, 50, 100, and 150 kg of pure nitrogen, and also included two potato cultivars.Results and DiscussionThe results showed that increasing nitrogen fertilizer application improved many of the evaluated characteristics of two potato varieties. However, with the increase in nitrogen use, nitrogen use efficiency (productivity) decreased. The potential yield of potatoes in different regions of Khorasan Razavi province was estimated using the DSSAT model. The data obtained from one of the field experiments, including dry matter, leaf area, phenology (developmental stages), and yield, were used to calibrate the model. After determining the potential yield and estimating the yield gap, the contribution of water and nitrogen to the yield gap was identified. Based on the protocol provided by the Global Yield Gap Atlas, the province was clustered into three regions. Region 1 (R1) includes the cities of Quchan and Fariman; Region 2 (R2) comprises Golmakan, Neyshabour, Torbat-e Hydarie, Mashhad, and Dargaz, which lie between regions 1 and 3; and Region 3 (R3) includes the cities of Torbat-e Jam, Gonabad, Khaf, Kashmar, Sabzevar, Sarakhs, and Bardaskan. The model was calibrated and validated with the data obtained from the field experiment. Long-term weather data and average actual yield were collected for each station, and the potential yield in each station was simulated using the model. Then, the difference between the potential yield and the actual yield was calculated, and the yield gap was determined for each area. Afterward, using the model, the potential yield was recalculated under water and nitrogen limitation conditions, and the contribution of water and nitrogen to the yield gap was assessed.ConclusionIn R1, the yield gap varied between 40.5 and 57.7 ton ha-1. The average yield gap during 10 years was estimated at 48.8 and 31.7 ton ha-1 for R1 and R2, respectively. According to the DSSAT model's results, R3 had a lower potential yield than the other two regions. The average contribution of water and nitrogen limitations to the potato yield gap in R1 and R2 was calculated. Accordingly, in R1, the impact of water and nitrogen limitations was 12.1 and 18 ton ha-1, and in R2, it was 10.9 and 8.3 ton ha-1, respectively. Although narrowing the yield gap depends on the climatic conditions of each region, selecting a compatible crop variety, optimizing planting date, and adopting appropriate plant density are among the most effective crop management strategies to reduce the yield gap, regardless of climatic differences.

IntroductionEnsuring food security has significant importance to countries with arid and semiarid climates and inadequate water for irrigation, like Iran which is quite vulnerable to climate change consequences. Strategic decision-making is crucial for effective production of agricultural crops especially cereals. Wheat is a strategic crop for achieving food security in Iran which is cultivated both rain-fed and irrigated. Crop models are mathematical expressions of the plant growth and development under different environmental and management conditions. These models’ performance and accuracy rely on high-quality and long-term input data especially observed or generated climatic databases. The aim of this study is to simulate the yield of irrigated Pishtaz cultivar wheat in seven cities of Razavi Khorasan province using DSSAT crop model and AgMERRA reanalysis data.Materials and MethodsIn this study, required daily weather data of seven stations located across Khrosan Razavi province namely Mashhad, Neishabour, Gonabad, Torbet-Haidaryeh, Torbet-Jam, Sabzevar, and Kashmer for the period of 1980-2010 were collected and used. The observed daily data included rainfall, maximum and minimum temperatures, wind speed, and sunshine hours. Corresponding period AgMERRA reanalysis data were also retrieved from the database to be used as an alternative input. AgMERRA is a global gridded daily weather dataset that was originally generated using NASA’s MERRA model (the National Aeronautics and Space Administration, Modern-Era Retrospective Analysis for Research and Applications). The AgMERRA global gridded climate dataset (0.25×0.25) has a horizontal resolution of approximately 25 km. It provides daily, high-resolution, and continuous meteorological datasets for the period 1980-2010. It is proven to be useful for agricultural and meteorological studies. Annual irrigated wheat yield data, soil information (including texture, depth, nitrogen content, and moisture), and management data i.e. variety, planting date, planting depth, and row spacing were obtained from agricultural stations across the province. The Crop Simulation Model (CERES-wheat module) of the DSSAT version 4.6 was used to simulate irrigated wheat yield. DSSAT (Decision Support System for Agrotechnology Transfer) is a package of several dynamic simulation models for over 42 crops that has been tested and applied for more than 30 years in more than174 countries with acceptable results. The reported genetic coefficients for selected wheat variety from previous studies in the region were used. The statistical indices, including the coefficient of determination (R2), root mean square error (RMSE) and normalized root mean square error (NRMSE) were used for comparisons and evaluation of the model performance for both runs using observed and reanalysis weather data.Results and DiscussionThe comparison between observed and reanalysis AgMERRA climate data in all seven study stations revealed a good agreement with correlation coefficient ranging from 0.67 to 0.92 and highest correlation was observed in air temperature time series. Besides, the error indies range for AgMERRA dataset determined as MAE from 3.87 to 4.11 and RMSE from 4.93 to 7.76. The model was run for simulation of Pishtaz variety yield, which has already been calibrated and evaluated in Khorasan Razavi province, using observed and AgMERRA climatic data. According to NRMSE, RMSE, and R2 statistical indices application of observed climatic data for simulation of the selected wheat yield is more accurate with R2 between 0.63-0.72 in study stations compared to AgMERRA data application with R2 ranging from 0.50 to 0.67.ConclusionAccording to statistical metrics, the use of observed data comparing to AgMERRA reanalysis provided better estimations of irrigated wheat in all study stations. Although the AgMERRA may also be used as a suitable alternate data with acceptable accuracy. Therefore, the climate datasets can be recommended as an input of crop models in regions with limited or non-reliable climate data. Further studies using another climate datasets and other crops is required for more scrutiny.AcknowledgmentAuthors would like to acknowledge the Seed and Plant Improvement Institute, Iran Ministry of Agriculture, and also Iran Meteorological Organization for their assistance and providing required data.