University of Guilan Cereal Research 2252-0163 16 1 2026 03 21 Simulation of irrigation and nitrogen management effects on rice yield and green/blue water productivity using ORYZA2000 Simulation of irrigation and nitrogen management effects on rice yield and green/blue water productivity using ORYZA2000 1 18 9546 10.22124/cr.2026.31892.1882 FA Parvin Zolfaghary Ph. D. Graduate, Department of Water Science and Engineering, Faculty of Water and Soil, Gorgan University of Agricultural Science and Natural Resources, Gorgan, Iran Abutaleb Hezarjaribi Associate Professor, Department of Water Science and Engineering, Faculty of Water and Soil, Gorgan University of Agricultural Science and Natural Resources, Gorgan, Iran Mojtaba Rezaei Research Assistant Professor, Rice Research Institute of Iran, Agricultural Research, Education and Extension Organization (AREEO), Rasht, Iran Ebrahim Amiri Professor, Department of Water Engineering, La. C., Islamic Azad University, Lahijan, Iran Journal Article 2025 10 05 Introduction: The projected 60% increase in global food demand by 2050, along with growing limitations on water and nitrogen resources, highlights the urgent need to optimize input use in strategic crops such as rice. As a major irrigated cereal with high water requirements, rice production faces rising challenges related to water scarcity, low nitrogen use efficiency, and environmental concerns stemming from excessive fertilizer application. Effective irrigation and fertilization management—especially through distinguishing between green water (soil moisture from rainfall) and blue water (surface and groundwater withdrawals)—is essential for improving resource use efficiency. Process-based models like ORYZA2000 provide powerful tools for analyzing these management strategies. As a validated model widely used for rice systems, ORYZA2000 supports the evaluation of scenarios aimed at increasing productivity while reducing dependence on limited water resources under changing climatic conditions. Materials and Methods: This study was conducted over two consecutive growing seasons at the experimental farm of the National Rice Research Institute in Rasht, Iran. A split-plot arrangement within a randomized complete block design with three replications was used, with 16 treatment combinations. Irrigation treatments included continuous flooding and irrigation 1, 3, and 5 days after surface water disappearance, while nitrogen levels consisted of 0, 60, 90, and 120 kg ha⁻¹. The hybrid cultivar ‘Bahar,’ known for its high yield potential and adaptability to Gilan’s climate, was grown. Field measurements of grain yield, biomass, leaf area index, and nitrogen uptake were used to calibrate and validate the ORYZA2000 model. Outputs from water balance simulations were then used to estimate green and blue water productivity indices and major water balance components. Results and Discussion: The highest grain yield (7.2 t ha⁻¹) occurred under irrigation 3 days after water disappearance combined with 120 kg N per ha. The ORYZA2000 model showed strong performance in simulating grain yield (NRMSE = 7–12%), biomass (NRMSE = 6–12%), total nitrogen (NRMSE = 8–15%), grain nitrogen (NRMSE = 9–10%), and leaf area index (NRMSE = 14–29%). For all variables except leaf area index, the model efficiency (EF) exceeded 0.65, and coefficients of determination (R² > 0.70) indicated a good match between simulated and observed data. Across two years, the highest simulated water productivity indices were also obtained from the I3N4 treatment (irrigation three days after water disappearance + 120 kg N), including irrigation water productivity (WPI = 1.32), irrigation plus rainfall productivity (WPI+R = 0.84), transpiration productivity (WPT = 2.0), evapotranspiration productivity (WPET = 1.12), and evapotranspiration plus percolation productivity (WPETQ = 0.89). Separation of indices into green and blue components showed that this treatment provided the highest values for most blue and green water productivity indices. However, green water productivity (WPg) and green water transpiration productivity (WPTg) were highest under I4N4 (irrigation 5 days after water disappearance + 120 kg N) and I1N4 (continuous flooding + 120 kg N), respectively. Conclusion: Combined irrigation and nitrogen management plays a crucial role in enhancing rice yield and water productivity. The ORYZA2000 model demonstrated strong accuracy in simulating management scenarios and water balance components, making it a valuable decision-support tool for water and fertilizer management in rice systems. Incorporating the distinction between green and blue water further supports strategies to reduce irrigation demand and increase overall productivity under water-limited conditions. The model identified the most efficient irrigation-nitrogen combination for maximizing both blue and green water productivity. This is particularly important where irrigation water is dominant; for example, alternate wetting and drying with a three-day interval plus 120 kg N ha⁻¹ was the most effective treatment. These findings can guide resource optimization strategies at both farm and policy levels under varying levels of dependence on water sources. Introduction: The projected 60% increase in global food demand by 2050, along with growing limitations on water and nitrogen resources, highlights the urgent need to optimize input use in strategic crops such as rice. As a major irrigated cereal with high water requirements, rice production faces rising challenges related to water scarcity, low nitrogen use efficiency, and environmental concerns stemming from excessive fertilizer application. Effective irrigation and fertilization management—especially through distinguishing between green water (soil moisture from rainfall) and blue water (surface and groundwater withdrawals)—is essential for improving resource use efficiency. Process-based models like ORYZA2000 provide powerful tools for analyzing these management strategies. As a validated model widely used for rice systems, ORYZA2000 supports the evaluation of scenarios aimed at increasing productivity while reducing dependence on limited water resources under changing climatic conditions. Materials and Methods: This study was conducted over two consecutive growing seasons at the experimental farm of the National Rice Research Institute in Rasht, Iran. A split-plot arrangement within a randomized complete block design with three replications was used, with 16 treatment combinations. Irrigation treatments included continuous flooding and irrigation 1, 3, and 5 days after surface water disappearance, while nitrogen levels consisted of 0, 60, 90, and 120 kg ha⁻¹. The hybrid cultivar ‘Bahar,’ known for its high yield potential and adaptability to Gilan’s climate, was grown. Field measurements of grain yield, biomass, leaf area index, and nitrogen uptake were used to calibrate and validate the ORYZA2000 model. Outputs from water balance simulations were then used to estimate green and blue water productivity indices and major water balance components. Results and Discussion: The highest grain yield (7.2 t ha⁻¹) occurred under irrigation 3 days after water disappearance combined with 120 kg N per ha. The ORYZA2000 model showed strong performance in simulating grain yield (NRMSE = 7–12%), biomass (NRMSE = 6–12%), total nitrogen (NRMSE = 8–15%), grain nitrogen (NRMSE = 9–10%), and leaf area index (NRMSE = 14–29%). For all variables except leaf area index, the model efficiency (EF) exceeded 0.65, and coefficients of determination (R² > 0.70) indicated a good match between simulated and observed data. Across two years, the highest simulated water productivity indices were also obtained from the I3N4 treatment (irrigation three days after water disappearance + 120 kg N), including irrigation water productivity (WPI = 1.32), irrigation plus rainfall productivity (WPI+R = 0.84), transpiration productivity (WPT = 2.0), evapotranspiration productivity (WPET = 1.12), and evapotranspiration plus percolation productivity (WPETQ = 0.89). Separation of indices into green and blue components showed that this treatment provided the highest values for most blue and green water productivity indices. However, green water productivity (WPg) and green water transpiration productivity (WPTg) were highest under I4N4 (irrigation 5 days after water disappearance + 120 kg N) and I1N4 (continuous flooding + 120 kg N), respectively. Conclusion: Combined irrigation and nitrogen management plays a crucial role in enhancing rice yield and water productivity. The ORYZA2000 model demonstrated strong accuracy in simulating management scenarios and water balance components, making it a valuable decision-support tool for water and fertilizer management in rice systems. Incorporating the distinction between green and blue water further supports strategies to reduce irrigation demand and increase overall productivity under water-limited conditions. The model identified the most efficient irrigation-nitrogen combination for maximizing both blue and green water productivity. This is particularly important where irrigation water is dominant; for example, alternate wetting and drying with a three-day interval plus 120 kg N ha⁻¹ was the most effective treatment. These findings can guide resource optimization strategies at both farm and policy levels under varying levels of dependence on water sources.

University of Guilan Cereal Research 2252-0163 16 1 2026 03 21 Carbon Footprint Analysis of Rice Cultivation in Gilan Province through Life Cycle Assessment and Traditional Knowledge Data Carbon Footprint Analysis of Rice Cultivation in Gilan Province through Life Cycle Assessment and Traditional Knowledge Data 19 34 9527 10.22124/cr.2026.32678.1888 FA Maedeh Omidi Nowbijar Ph.D. Graduate, Department of Rangeland Management, Faculty of Rangeland and Watershed Management, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran Hossein Barani Associate Professor, Department of Rangeland Management, Faculty of Rangeland and Watershed Management, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran Mohamad Rahim Forouzeh Associate Professor, Department of Rangeland Management, Faculty of Rangeland and Watershed Management, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran. Ahmad Abedi Sarvestani Associate Professor, Department of Agricultural Extension and Education, Faculty of Agricultural Management, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran Journal Article 2025 11 29 Introduction: Rice (Oryza sativa) is a strategic agricultural crop that plays a pivotal role in food security and livelihoods in Iran and worldwide. In this context, a comprehensive assessment of greenhouse gas emissions from rice cultivation systems is essential for achieving sustainable production and Carbon footprint serves as an indicator for estimating both direct and indirect greenhouse gas emissions.Aligned with global efforts to mitigate climate change, the role of traditional knowledge systems in carbon management has increasingly garnered attention. The objective of this study was to investigate the carbon footprint of rice cultivation and the associated Traditional knowledge related to its influencing factors in Gilan Province during the 2022 cropping year. Materials and Methods: A mixed-methods design was applied, integrating quantitative and qualitative approaches. The CF was calculated for three farm sizes (<0.5 ha, 0.5–1 ha, >1 ha) using life cycle assessment based on Intergovernmental Panel on Climate Change guidelines. Traditional knowledge was explored through an ethnographic method and analyzed with coding in MAXQDA 2020. Data normality was assessed using the Shapiro-Wilk test, and due to the non-normal distribution of some data, non-parametric Kruskal-Wallis tests was conducted using SPSS software. For pairwise comparisons, Dunn’s post-hoc test with Bonferroni correction was applied at a 5% significance level. Results and Discussion: The average CF of rice production was 1.23 kg CO₂eq/kg.year. Although no statistically significant differences were observed among farm sizes, larger farms tended to have slightly lower CF values. The mean global warming potential of the system was 3031 kg CO₂eq/ha.year. Irrigation practices and methane emissions during the growing season accounted for approximately 64% of total emissions, while nitrogen-based fertilizers and associated N₂O emissions contributed about 10%. Additional sources included fuel, labor, and seed inputs. Farmers recognized Traditional knowledge as effective in reducing CF, particularly through organic fertilizer management (50%), mechanization (40%), and seed practices (10%). Conclusion: Mitigating the CF of rice production in Gilan requires an integrated strategy that merges formal scientific approaches with Traditional knowledge. Priority measures include transforming irrigation practices, optimizing fertilizer application, promoting local rice cultivars, and combining farmers’ experiential knowledge with energy-efficient mechanization. Introduction: Rice (Oryza sativa) is a strategic agricultural crop that plays a pivotal role in food security and livelihoods in Iran and worldwide. In this context, a comprehensive assessment of greenhouse gas emissions from rice cultivation systems is essential for achieving sustainable production and Carbon footprint serves as an indicator for estimating both direct and indirect greenhouse gas emissions.Aligned with global efforts to mitigate climate change, the role of traditional knowledge systems in carbon management has increasingly garnered attention. The objective of this study was to investigate the carbon footprint of rice cultivation and the associated Traditional knowledge related to its influencing factors in Gilan Province during the 2022 cropping year. Materials and Methods: A mixed-methods design was applied, integrating quantitative and qualitative approaches. The CF was calculated for three farm sizes (<0.5 ha, 0.5–1 ha, >1 ha) using life cycle assessment based on Intergovernmental Panel on Climate Change guidelines. Traditional knowledge was explored through an ethnographic method and analyzed with coding in MAXQDA 2020. Data normality was assessed using the Shapiro-Wilk test, and due to the non-normal distribution of some data, non-parametric Kruskal-Wallis tests was conducted using SPSS software. For pairwise comparisons, Dunn’s post-hoc test with Bonferroni correction was applied at a 5% significance level. Results and Discussion: The average CF of rice production was 1.23 kg CO₂eq/kg.year. Although no statistically significant differences were observed among farm sizes, larger farms tended to have slightly lower CF values. The mean global warming potential of the system was 3031 kg CO₂eq/ha.year. Irrigation practices and methane emissions during the growing season accounted for approximately 64% of total emissions, while nitrogen-based fertilizers and associated N₂O emissions contributed about 10%. Additional sources included fuel, labor, and seed inputs. Farmers recognized Traditional knowledge as effective in reducing CF, particularly through organic fertilizer management (50%), mechanization (40%), and seed practices (10%). Conclusion: Mitigating the CF of rice production in Gilan requires an integrated strategy that merges formal scientific approaches with Traditional knowledge. Priority measures include transforming irrigation practices, optimizing fertilizer application, promoting local rice cultivars, and combining farmers’ experiential knowledge with energy-efficient mechanization.

University of Guilan Cereal Research 2252-0163 16 1 2026 03 21 The effect of nitrogen levels on appearance traits and cooking and nutritional quality of rice grain under direct seeding in wet bed The effect of nitrogen levels on appearance traits and cooking and nutritional quality of rice grain under direct seeding in wet bed 35 48 9743 10.22124/cr.2026.32887.1891 FA Fatemeh Habibi Rice Research Institute of Iran (RRII), Agricultural Research, Education and Extension Farzin Pouramir Rice Research Institute of Iran (RRII), Agricultural Research, Education and Extension Mina Ebrahimi Rice Research Institute of Iran (RRII), Agricultural Research, Education and Extension Journal Article 2026 01 03 Introduction: Nitrogen (N) management is a critical factor influencing rice yield and grain quality, particularly under wet-bed direct seeding, where crop establishment and nutrient dynamics differ from conventional transplanting systems. Cooking and eating quality in rice, including amylose content, gelatinization temperature, and protein content, depends not only on the genetic characteristics of the variety but also on environmental factors, particularly the nitrogen application rate. Materials and Methods: This study evaluated the effects of five N application rates (0, 60, 90, 120, and 150 kg N ha⁻¹ as urea) on paddy yield, milling quality, grain physical traits, cooking and physicochemical properties, and nutritional composition of three rice cultivars, namely Hashemi, Anam, and Shiroudi. The experiment was conducted at the Rice Research Institute of Iran (Rasht) using a split-plot arrangement in a randomized complete block design with three replications. After harvest, paddy samples were dried, dehulled, milled, and head rice was separated to determine milling and quality-related traits. Data analysis was conducted using SAS software, and treatment means were separated by the Least Significant Difference (LSD) test at the 5% probability level. Results and Discussion: Paddy yield responded significantly to N rate and cultivar. Maximum yields of Hashemi (4.20 t.ha⁻¹) were obtained at 90 kg N ha⁻¹, whereas Anam and Shiroudi achieved their highest yields at 120 kg N ha⁻¹ (4.79 and 6.56 t.ha⁻¹, respectively), with no significant yield increase at higher N rates. Increasing N generally improved milling recovery and head rice yield, while increasing raw grain length and slenderness. In contrast, cooked grain length and elongation ratio declined with increasing N. Grain protein content increased markedly with N application, while amylose content decreased from approximately 24.8% at 0 N to 21.3% at 150 kg N ha⁻¹, accompanied by an increase in gelatinization temperature. Grain Mn concentration increased with higher N supply, whereas Fe and Zn responses varied depending on cultivar and N rate. Pasting properties were also significantly affected: peak and final viscosity decreased by 12–25% at high N rates, while setback viscosity and pasting temperature increased, indicating less desirable cooking quality under excessive N application. Conclusion: Overall, the results demonstrate that rice cultivars exhibit genotype-specific and non-linear responses to N under wet-bed direct seeding. Optimized N rates of approximately 90 kg N ha⁻¹ for Hashemi and 120 kg N ha⁻¹ for Anam and Shiroudi provided the best compromise between high paddy yield, milling performance, and acceptable cooking and nutritional quality, whereas excessive N application offered limited yield benefits but adversely affected grain quality. Introduction: Nitrogen (N) management is a critical factor influencing rice yield and grain quality, particularly under wet-bed direct seeding, where crop establishment and nutrient dynamics differ from conventional transplanting systems. Cooking and eating quality in rice, including amylose content, gelatinization temperature, and protein content, depends not only on the genetic characteristics of the variety but also on environmental factors, particularly the nitrogen application rate. Materials and Methods: This study evaluated the effects of five N application rates (0, 60, 90, 120, and 150 kg N ha⁻¹ as urea) on paddy yield, milling quality, grain physical traits, cooking and physicochemical properties, and nutritional composition of three rice cultivars, namely Hashemi, Anam, and Shiroudi. The experiment was conducted at the Rice Research Institute of Iran (Rasht) using a split-plot arrangement in a randomized complete block design with three replications. After harvest, paddy samples were dried, dehulled, milled, and head rice was separated to determine milling and quality-related traits. Data analysis was conducted using SAS software, and treatment means were separated by the Least Significant Difference (LSD) test at the 5% probability level. Results and Discussion: Paddy yield responded significantly to N rate and cultivar. Maximum yields of Hashemi (4.20 t.ha⁻¹) were obtained at 90 kg N ha⁻¹, whereas Anam and Shiroudi achieved their highest yields at 120 kg N ha⁻¹ (4.79 and 6.56 t.ha⁻¹, respectively), with no significant yield increase at higher N rates. Increasing N generally improved milling recovery and head rice yield, while increasing raw grain length and slenderness. In contrast, cooked grain length and elongation ratio declined with increasing N. Grain protein content increased markedly with N application, while amylose content decreased from approximately 24.8% at 0 N to 21.3% at 150 kg N ha⁻¹, accompanied by an increase in gelatinization temperature. Grain Mn concentration increased with higher N supply, whereas Fe and Zn responses varied depending on cultivar and N rate. Pasting properties were also significantly affected: peak and final viscosity decreased by 12–25% at high N rates, while setback viscosity and pasting temperature increased, indicating less desirable cooking quality under excessive N application. Conclusion: Overall, the results demonstrate that rice cultivars exhibit genotype-specific and non-linear responses to N under wet-bed direct seeding. Optimized N rates of approximately 90 kg N ha⁻¹ for Hashemi and 120 kg N ha⁻¹ for Anam and Shiroudi provided the best compromise between high paddy yield, milling performance, and acceptable cooking and nutritional quality, whereas excessive N application offered limited yield benefits but adversely affected grain quality.

University of Guilan Cereal Research 2252-0163 16 1 2026 03 21 Grain and forage yield response of two maize (Zea mays L.) cultivars to planting date and nitrogen management under different environmental conditions Grain and forage yield response of two maize (Zea mays L.) cultivars to planting date and nitrogen management under different environmental conditions 49 66 9687 10.22124/cr.2026.32918.1892 FA Mohsen Zavareh Department of Plant Production Engineering and Genetics, Faculty of Agricultural Sciences, University of Guilan 0000-0002-3673-8256 Roghayeh Jamaeili Department of Plant Production Engineering and Genetics, Faculty of Agricultural Sciences, University of Guilan Mohammad Rahmani Seed and Plant Certification and Registration Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj Journal Article 2025 12 14 Introduction: Maize (Zea mays L.) is one of the most important field crops worldwide and plays a critical role in food security due to its high genetic diversity, broad adaptability, substantial dry matter production, and favorable nutritional value. Because maize yield and productivity are strongly influenced by environmental conditions, genetic characteristics, and agronomic management, the adoption of region-specific management strategies is essential for achieving sustainable production. Accordingly, the present study aimed to optimize the production of two maize cultivars under contrasting environmental conditions in Guilan Province through the management of planting date and nitrogen fertilizer application. Materials and Methods: The experiment was conducted as a factorial split-plot design within a randomized complete block design (RCBD) with four replications at two locations, Rasht and Bandar Anzali, Guilan Province. Nitrogen fertilizer levels (0, 180, and 360 kg ha⁻¹, applied as urea) were assigned to main plots, while the factorial combination of two maize cultivars (KSC704 and Kousha) and three planting dates (May 26, June 16, and July 6) was allocated to subplots. Grain yield, biomass yield, and yield components were measured. Data were subjected to analysis of variance (ANOVA) and stepwise regression analysis using SAS software. Results and Discussion: Combined analysis of variance revealed that the four-way interaction of planting date × nitrogen fertilizer × cultivar × location had significant effects on leaf area, ear dry weight, and biomass yield. In addition, the interaction of planting date × nitrogen fertilizer × cultivar significantly influenced the number of kernels per row and grain yield. The number of kernels per ear was significantly affected by the interactions of planting date × nitrogen fertilizer × cultivar and location × nitrogen fertilizer × cultivar. For 100-kernel weight, significant interactions were observed for location × planting date × cultivar and nitrogen fertilizer × planting date × cultivar, whereas the main effect of cultivar was significant for the number of rows per ear. Mean comparisons indicated that cultivar KSC704 consistently outperformed Kousha in terms of both grain and biomass production across all experimental conditions. The highest grain yield of KSC704 (10,579.55 kg ha⁻¹) was obtained at the May 26 planting date with the application of 360 kg ha⁻¹ nitrogen fertilizer, which was approximately 31% higher than the maximum grain yield achieved by cultivar Kousha. The highest biomass yield of KSC704 was jointly recorded at the May 26 and June 16 planting dates under 360 kg ha⁻¹ nitrogen application in Bandar Anzali. Stepwise regression analysis showed that the number of kernels per ear, 100-kernel weight, and biomass yield together explained 97% of the variation in grain yield. Conclusion: Given the significant superiority of cultivar KSC704 over Kousha, planting this cultivar on May 26 combined with the application of 360 kg ha⁻¹ nitrogen fertilizer is recommended to achieve maximum grain yield. However, when forage production is the primary objective, planting cultivar KSC704 in Bandar Anzali between May 26 and June 16 with 360 kg ha⁻¹ nitrogen fertilizer represents the optimal management strategy. Introduction: Maize (Zea mays L.) is one of the most important field crops worldwide and plays a critical role in food security due to its high genetic diversity, broad adaptability, substantial dry matter production, and favorable nutritional value. Because maize yield and productivity are strongly influenced by environmental conditions, genetic characteristics, and agronomic management, the adoption of region-specific management strategies is essential for achieving sustainable production. Accordingly, the present study aimed to optimize the production of two maize cultivars under contrasting environmental conditions in Guilan Province through the management of planting date and nitrogen fertilizer application. Materials and Methods: The experiment was conducted as a factorial split-plot design within a randomized complete block design (RCBD) with four replications at two locations, Rasht and Bandar Anzali, Guilan Province. Nitrogen fertilizer levels (0, 180, and 360 kg ha⁻¹, applied as urea) were assigned to main plots, while the factorial combination of two maize cultivars (KSC704 and Kousha) and three planting dates (May 26, June 16, and July 6) was allocated to subplots. Grain yield, biomass yield, and yield components were measured. Data were subjected to analysis of variance (ANOVA) and stepwise regression analysis using SAS software. Results and Discussion: Combined analysis of variance revealed that the four-way interaction of planting date × nitrogen fertilizer × cultivar × location had significant effects on leaf area, ear dry weight, and biomass yield. In addition, the interaction of planting date × nitrogen fertilizer × cultivar significantly influenced the number of kernels per row and grain yield. The number of kernels per ear was significantly affected by the interactions of planting date × nitrogen fertilizer × cultivar and location × nitrogen fertilizer × cultivar. For 100-kernel weight, significant interactions were observed for location × planting date × cultivar and nitrogen fertilizer × planting date × cultivar, whereas the main effect of cultivar was significant for the number of rows per ear. Mean comparisons indicated that cultivar KSC704 consistently outperformed Kousha in terms of both grain and biomass production across all experimental conditions. The highest grain yield of KSC704 (10,579.55 kg ha⁻¹) was obtained at the May 26 planting date with the application of 360 kg ha⁻¹ nitrogen fertilizer, which was approximately 31% higher than the maximum grain yield achieved by cultivar Kousha. The highest biomass yield of KSC704 was jointly recorded at the May 26 and June 16 planting dates under 360 kg ha⁻¹ nitrogen application in Bandar Anzali. Stepwise regression analysis showed that the number of kernels per ear, 100-kernel weight, and biomass yield together explained 97% of the variation in grain yield. Conclusion: Given the significant superiority of cultivar KSC704 over Kousha, planting this cultivar on May 26 combined with the application of 360 kg ha⁻¹ nitrogen fertilizer is recommended to achieve maximum grain yield. However, when forage production is the primary objective, planting cultivar KSC704 in Bandar Anzali between May 26 and June 16 with 360 kg ha⁻¹ nitrogen fertilizer represents the optimal management strategy.

University of Guilan Cereal Research 2252-0163 16 1 2026 03 21 Evaluation of grain and biological yield stability of maize lines using MGIDI index under environmental stresses conditions Evaluation of grain and biological yield stability of maize lines using MGIDI index under environmental stresses conditions 67 82 9691 10.22124/cr.2026.33190.1895 FA Mona Bordbar Ph.D. Student, Department of Plant Production and Genetics, Faculty of Agriculture, Urmia University, Urmia, Iran Reza Darvishzadeh Professor, Department of Plant Production and Genetics, Faculty of Agriculture, Urmia University, Urmia, Iran Hadi Alipour Associate Professor, Department of Plant Production and Genetics, Faculty of Agriculture, Urmia University, Urmia, Iran Journal Article 2025 12 16 In this study, 86 maize genotypes sourced from multiple research centers (Razi University of Kermanshah, Agricultural Research Center of Mashhad, and Institute of Plant Breeding and Seedling Preparation of Karaj) were evaluated under four environmental conditions: non-salinity (SN), salinity stress (SS), optimal phosphorus (PN), and phosphorus deficiency stress (PS) to assess the stability of grain yield (GY) and biological yield (BY). The experiment was conducted in an open field at the College of Agriculture, Urmia University, using a completely randomized design with three replications during the 2017-2018 cropping season (1396 in the Persian calendar). The Multi-Trait Genotype Ideotype Distance Index (MGIDI) was employed as a multivariate tool for selecting stable genotypes by integrating traits such as grain yield and biological yield alongside parametric and non-parametric stability indices. Composite analysis of variance revealed that the grain yield and biological yield of the studied maize lines were significantly influenced (at P ≤ 0.01) by environment, genotype, and genotype × environment interaction. The MGIDI index for grain yield and biological yield facilitated the selection of lines that maximized selection differential and genetic gain. Heritability estimates across environments for these two traits were high, approaching 90%. Based on the results of this index, lines No. 9, 22, and 27 were desirable for grain yield, while lines No. 1, 9, 11, 13, and 27 excelled in biological yield and stability for both traits. Line No. 9 (P14L1Kahriz) and Line No. 27 (P16L12Kahriz) were present in selections for both traits and are recommended as the superior lines. Overall, the findings of the present study demonstrated that the MGIDI index can successfully select desirable maize genotypes across the four environments for the two key traits of grain yield and biological yield. Therefore, combining the MGIDI index with stability statistics provides a simple, comprehensive, and reliable approach for selecting high-yielding and stable maize lines, which can serve as a model for future studies. In this study, 86 maize genotypes sourced from multiple research centers (Razi University of Kermanshah, Agricultural Research Center of Mashhad, and Institute of Plant Breeding and Seedling Preparation of Karaj) were evaluated under four environmental conditions: non-salinity (SN), salinity stress (SS), optimal phosphorus (PN), and phosphorus deficiency stress (PS) to assess the stability of grain yield (GY) and biological yield (BY). The experiment was conducted in an open field at the College of Agriculture, Urmia University, using a completely randomized design with three replications during the 2017-2018 cropping season (1396 in the Persian calendar). The Multi-Trait Genotype Ideotype Distance Index (MGIDI) was employed as a multivariate tool for selecting stable genotypes by integrating traits such as grain yield and biological yield alongside parametric and non-parametric stability indices. Composite analysis of variance revealed that the grain yield and biological yield of the studied maize lines were significantly influenced (at P ≤ 0.01) by environment, genotype, and genotype × environment interaction. The MGIDI index for grain yield and biological yield facilitated the selection of lines that maximized selection differential and genetic gain. Heritability estimates across environments for these two traits were high, approaching 90%. Based on the results of this index, lines No. 9, 22, and 27 were desirable for grain yield, while lines No. 1, 9, 11, 13, and 27 excelled in biological yield and stability for both traits. Line No. 9 (P14L1Kahriz) and Line No. 27 (P16L12Kahriz) were present in selections for both traits and are recommended as the superior lines. Overall, the findings of the present study demonstrated that the MGIDI index can successfully select desirable maize genotypes across the four environments for the two key traits of grain yield and biological yield. Therefore, combining the MGIDI index with stability statistics provides a simple, comprehensive, and reliable approach for selecting high-yielding and stable maize lines, which can serve as a model for future studies.

University of Guilan Cereal Research 2252-0163 16 1 2026 03 21 Optimization of maize hybrids selection in preliminary environmental trails: comparison of classical and spatial approaches Optimization of maize hybrids selection in preliminary environmental trails: comparison of classical and spatial approaches 83 100 9686 10.22124/cr.2026.33205.1896 FA Mohammadreza Shiri Research Associate Professor, Seed and Plant Improvement Institute, Agricultural Research Education and Extension Organization (AREEO), Karj, Iran Hamid Najafinezhad Research Associate Professor, Kerman Agricultural and Natural Resources Research and Education Center, Agricultural Research Education and Extension Organization (AREEO), Kerman, Iran Sharareh Fareghi Research Assistant Professor, Kermanshah Agricultural and Natural Resources Research and Education Center, Agricultural Research Education and Extension Organization (AREEO), Kermanshah, Iran Journal Article 2026 01 16 Introduction: In plant breeding, identification of high-yield genotypes and suitable stability for introducing new cultivars or selection of high potential genotypes in preliminary breeding trials is very important. However, experimental design may not be sufficient to consider field heterogeneity in the plot area. The aim of this study was to evaluate the potential of spatial models to correct spatial trends and improve prediction of genotype values and increase accuracy of selection of genotypes in comparison with classic statistical design. Materials and Methods: In this study, data analysis were conducted based on a two - step procedure. In the first step, the data of each experiment were modified separately using spatial correction models (SpATS, AR1×AR1 and SpATS + AR1×AR1 combined model) to remove the spatial heterogeneity and systematic environmental effects. Then, in the second step, the corrected data entered the Multi-Environment Trials based on factor Analytic (FA) to accurately model the genotype × environment interaction and to identify superior genotypes based on yield and stability. For this purpose, an experiment was conducted with 105 hybrid maize hybrids in α -lattice design with two replications and five incomplete blocks per replication in three stations of Karaj, Kerman and Kermanshah. Results and Discussion: The results showed that the SpATS model effectively eliminate large - scale spatial variations and decreases the error of prediction (RMSE) in comparison with raw data and other models. Comparison of SpATS spatial model with the alpha lattice design showed that the SpATS model significantly improved the accuracy of the yield and ranking of genotypes. After correction of data with SpATS, Multi-Environment Trials were performed based on factor Analytic (FA) to model the interaction of genotype × environment and genotypes with high yield and suitable stability were identified. Kermanshah station with high genetic variance, had more genotype discriminate and was the most suitable station for identification of superior genotypes. The FA2 biplot indicated that genotypes located near the origin, such as H80, H102, H33, and H22, exhibited broad adaptability and high stability. In contrast, genotypes positioned farther from the origin, including H60, H24, H86, H93, and H91, showed stronger genotype × environment interactions and greater dependence on environmental conditions. Genotypes such as H24, H35, H26, H28, and H34 demonstrated the most favorable combination of FA1 and FA2 scores. In addition, the stability index WAASB and the combined performance–stability index WASSBY were employed for genotype selection. Genotypes H24, H30, H26, and H49, with high WASSBY values, exhibited a desirable balance between yield performance and environmental stability. Based on performance and stability indices, despite the superiority of five hybrids (H24, H26, H30, H35, and H49), approximately 20% of the top-performing genotypes were advanced to the next stage of the breeding program in order to maintain genetic diversity and enhance breeding opportunities. Conclusion: Overall, the results of this study suggest that the application of spatial models, particularly SpATS, while accounting for field heterogeneity in maize trials, in combination with FA analysis, improves data analysis and the prediction of genotypic values. This integrated approach provides an efficient and reliable framework for increasing the accuracy of selecting superior genotypes in multi-environment trials. Introduction: In plant breeding, identification of high-yield genotypes and suitable stability for introducing new cultivars or selection of high potential genotypes in preliminary breeding trials is very important. However, experimental design may not be sufficient to consider field heterogeneity in the plot area. The aim of this study was to evaluate the potential of spatial models to correct spatial trends and improve prediction of genotype values and increase accuracy of selection of genotypes in comparison with classic statistical design. Materials and Methods: In this study, data analysis were conducted based on a two - step procedure. In the first step, the data of each experiment were modified separately using spatial correction models (SpATS, AR1×AR1 and SpATS + AR1×AR1 combined model) to remove the spatial heterogeneity and systematic environmental effects. Then, in the second step, the corrected data entered the Multi-Environment Trials based on factor Analytic (FA) to accurately model the genotype × environment interaction and to identify superior genotypes based on yield and stability. For this purpose, an experiment was conducted with 105 hybrid maize hybrids in α -lattice design with two replications and five incomplete blocks per replication in three stations of Karaj, Kerman and Kermanshah. Results and Discussion: The results showed that the SpATS model effectively eliminate large - scale spatial variations and decreases the error of prediction (RMSE) in comparison with raw data and other models. Comparison of SpATS spatial model with the alpha lattice design showed that the SpATS model significantly improved the accuracy of the yield and ranking of genotypes. After correction of data with SpATS, Multi-Environment Trials were performed based on factor Analytic (FA) to model the interaction of genotype × environment and genotypes with high yield and suitable stability were identified. Kermanshah station with high genetic variance, had more genotype discriminate and was the most suitable station for identification of superior genotypes. The FA2 biplot indicated that genotypes located near the origin, such as H80, H102, H33, and H22, exhibited broad adaptability and high stability. In contrast, genotypes positioned farther from the origin, including H60, H24, H86, H93, and H91, showed stronger genotype × environment interactions and greater dependence on environmental conditions. Genotypes such as H24, H35, H26, H28, and H34 demonstrated the most favorable combination of FA1 and FA2 scores. In addition, the stability index WAASB and the combined performance–stability index WASSBY were employed for genotype selection. Genotypes H24, H30, H26, and H49, with high WASSBY values, exhibited a desirable balance between yield performance and environmental stability. Based on performance and stability indices, despite the superiority of five hybrids (H24, H26, H30, H35, and H49), approximately 20% of the top-performing genotypes were advanced to the next stage of the breeding program in order to maintain genetic diversity and enhance breeding opportunities. Conclusion: Overall, the results of this study suggest that the application of spatial models, particularly SpATS, while accounting for field heterogeneity in maize trials, in combination with FA analysis, improves data analysis and the prediction of genotypic values. This integrated approach provides an efficient and reliable framework for increasing the accuracy of selecting superior genotypes in multi-environment trials.