Background and objectives: The aim of this research was to select promising durum wheat lines in terms of stability and high grain yield by combining BLUP and AMMI methods and using REML/BLUP based on different indices.Materials and methods: In this study, 20 durum wheat genotypes were compared in five research stations of Karaj, Kermanshah, Khorramabad, Dezful and Darab based on randomized complete blocks design in 3 replications and in two growing seasons (2019-2021). Results:The results showed that the genotype × environment interaction was significant for grain yield. Comparison of the predicted averages of grain yield by BLUP method and HMGV, PRGV and HMRPGV statistics showed that genotypes G10, G8, G18, G9 and G14 with the highest predicted average were superior compared to genotypes others. According to the third type biplot, G1, G8, G10, G18 and G19 genotypes with high yield and low WAASB value were among the superior genotypes. Based on the polygonal biplot, G19, G1 and G10 genotypes with the lowest line slope were more stable. Based on the heat map of WAASB/Y, genotypes G8, G19, G18, G4, G10 and G1 were introduced as superior genotypes. The comparison between stability statistics and BLUP-based indices showed that there is a high agreement between their results.Conclusion: In general, according to the results of BLUP based on methods and stability statistics, genotypes G8, G10, G18 and G19 were introduced as stable genotypes and can be candidates for new cultivar introduction.

Abstract The present study examines the quantitative effects of uncertainty regarding government expenditure policy (current and construction expenditures) on Iran's economic activity. Uncertainty is a situation where future events or their probability of occurrence are not predicted. In the real world, the economy is full of uncertainty of economic factors, which leads to risk and danger in the decision-making space of economic factors and affects their behavior. In this article, we intend to use the structural vector autoregression model (SVAR), which are known as impulse models,Uncertainty effects created by the government's current expenditures and construction expenditures and other effective indicators such as,Measure the impact of oil prices and financial crises on economic activities such as production and employment. The data of the study was collected from the Central Bank website and the model was estimated using Eviuse software for the years 1365-1399. The findings showed that an impulse from the oil price area causes a 5% increase in production and a 1% increase in employment, also an impulse from the financial crisis area causes a 3% decrease in production and a 12% decrease in employment, respectively. The impulse from the area of uncertainty of current and construction expenses, causes a decrease in order,12 percent is produced and 7 and 5 percent is employed.

In this study, various statistical methodologies, including Additive Main effects and Multiplicative Interaction (AMMI) and Best Linear Unbiased Prediction (BLUP), were employed to identify high-yielding rainfed barley genotypes that are suitable for the cold and rainy regions of Iran. The experimental design comprised 25 barley cultivars and lines, along with three check cultivars, arranged in a randomized complete block design with four replications over three crop years (2017-2020). The AMMI analysis revealed that certain genotypes, specifically G15 and G21, demonstrated stability and adaptability across diverse environments, consistently yielding higher than other genotypes. Following the estimation of best linear unbiased predictions and conducting a stability analysis via the AMMI method, it was found that the highest yields were recorded in genotypes G6, G7, G15, G21, and G22, whereas the lowest yields were associated with genotypes G12, G25, G26, G27, and G28. According to the BLUP indices, genotypes G6, G15, G21, G20, G22, G17, G7, G9, and G19 were identified as superior in terms of grain stability and yield relative to the other genotypes. In the stability assessment utilizing a third-type biplot (yield versus WAASB (Weighted Average of Absolute Scores of the Best) index), it was noted that genotypes G2, G9, G10, G14, G16, G17, G19, G20, and G22 exhibited both high yield and stability. Furthermore, genotypes G4, G62, G7, G9, G10, G15, G16, G17, G19, G20, G21, and G22, which demonstrated the highest WAASBY (Weighted Average of Absolute Scores of the Best Yield) values, were classified as stable and high-yielding. Ultimately, when the first principal components in the AMMI analysis or GGE Biplot account for a lower percentage of genotype-environment interaction, it is advisable to employ methodologies that incorporate all significant principal components to effectively identify superior genotypes.

In this study 25 wheat genotypes including 22 advanced lines along with two durum (Saji and Zardak) and one bread (Sardari) wheat cultivars were grown in 19 diverisified environments in Kermanshsah, Ilam, Maragheh and Shirvan under both rainfed and supplemtary irrigation conditions during four cropping seasons (2006-2010) in Iran. The large magnitude of the GE interaction in combined-ANOVA of grain yield relative to genotypic effect, sugessted the possible existence of sub-environmental groups for the genotypes. The GGE biplot analysis showed that the cold environments were seprated from the warm environments and these two groups gave dissimilar results in ranking of genotypes. The moderate eviormnents were correlated with the both warm and cold environment groups. The genotypes G8, G17, G23 (Saji), G11, G21 and G19 could be recommended as genotypes with high yield and stability performance. The genotypes G21, G16, G8, G23 (Saji) and G17 were highly adapted to warm environments, while the genotypes G25 (Sardari), G24 (Zardak) and G6 were highly adapted to cold environments. The genotypes G20, G22 and G19 can be considered as genotypes with general adaptability.

The present study employs a parameter estimation method with time-varying coefficients and the Kalman Filter approach to conduct a state-space analysis of the convergence between the financial market and economic development in Iran from 1996 to 2023. The results obtained from the estimation of the state-space model (Kalman Filter) indicate that financial development, governance quality, rentier income, trade volume, and employment contribute to economic development by 6%, 0.2%, 7%, 10%, and 3%, respectively. Additionally, for every one percent increase in inflation rate and exchange rate, economic development declines by 2% and 7%, respectively. Moreover, the estimated parameters of the state-space model using the Kalman Filter reveal that the elasticity of economic development relative to financial development over the period under study is less than one. Specifically, between 1996 and 2005, the relevant sensitivity coefficient was 0.16. From 2010 to 2023, the elasticity of economic development with respect to financial development follows a declining trend. It is important to note that the implementation of financial market development processes does not necessarily result in growth or positive effects and requires structural alignment of the domestic economy, economic regulation, coordinated policies, and macroeconomic stability. Furthermore, an examination of the sensitivity of economic development to rentier income shows that the average sensitivity throughout the study period is less than one, approximately around 3%. In other words, with an increase in oil prices in the Iranian economy, capital is directed not into the productive and value-added sectors, but rather into imports. This diversion is a response to the country's stagflation conditions, intended to counter inflation. As a result, the productive sector faces serious harm, with many production units exiting the economic cycle. Capital that would otherwise be utilized in the productive economy remains stagnant and is inevitably redirected toward the black market and speculative activities.

Background and Objectives: The analyses of genotype × environment interaction in multi-environment yield trials are very important for evaluation, selection and recommendation of crop varieties. The purpose of this study was to identify high yielding and stable barley lines for introduction in different regions of warm climates of the Iran. Materials and Methods: 17 barley promising lines (with two checks Auxin and WB-94-3) will be evaluated in two years (2017 –,2019) using RCBD with three replications in five stations of warm zone including Ahvaz, Darab, Zabol, Gonbad and Moghan. Results: The results of variance analysis showed that the effects of location, genotype and genotype × location, genotype × year and genotype × year × location interactions were significant at the 0. 01 probability level. The polygon-view of GGE biplot led to the identification of four superior lines (G18, G10, G19, and G17), six poor lines and three mega-environments. Among the test locations, Gonbad had a high discriminating ability to show differences between the genotypes and Moghan showed the least of discriminating,Also, Gonbad showed the highest amount of broad sense heritability of grain yield (0. 780) and Moghan showed the lowest amount of broad sense heritability of grain yield (0. 018). Conclusion: Comparison of the studied lines with the ideal genotype showed that lines G18, G10, G19 and G17 were the closest lines to the ideal genotype with high yield and relative yield stability. Finally, these four lines were selected as the top lines for additional studies.

Purpose: Twenty genotypes of okra including hybrids and open pollinated varieties both from local and exotic sources were evaluated in the field to study their yield performance and disease response. Research method: Experiment was conducted following randomized complete block design with three replications. Findings: Analysis of variance indicates significant variation among the okra cultivars for all the characters’ studied except days to first germination. The highest number of fruits per plant (19.98) and maximum yield of green fruits per plant (279.59 g) and maximum yield (1.33 kg/m2) was obtained in the genotype G4 (F1-hybrid) followed by G7 and G18. The maximum edible fruit length was observed in G7 followed by G19 and G15. The maximum number of seeds perfruit was recorded in G4.The highest 100-seed weight was found in G4 followed by G17. On the contrary, the lowest seeds per fruit, 100-seed weight, and green fruit yield per plant were obtained from G6. The highest incidence of yellow vein mosaic virus (YVMV) was observed in the genotype G6 and the lowest in the genotype G19 followed by G8, G2, G4, G17 andG12. The cluster analysis grouped 20 genotypes into five cluster and maximum number of genotypes included in cluster 5. Strong positive correlation was observed between yield per plant and days to 50% germination, germination percentage, number of seeds per fruits and 100-seed weight (g). Limitations: No significant limitation to the report. Originality/Value: The results help to identify the suitable okra genotypes and generate breeding materials.

Salinity stress is one of the most important abiotic stresses in the arid and semi-arid areas of the world. To identify superior genotypes of barley with high grain performance and stability under salinity stress conditions, 18 promising genotypes along with two local checks (Mehr cultivar and MBS-97-6 line) were investigated in three saline regions (Yazd, Isfahan, and Birjand) in the 2020-21 and 2021-22 cropping seasons. Based on the results of means comparison, genotypes G19, G17, G1 (Check 1), G20 (Check 2), G4 and G8 genotypes showed the highest grain yield compared to the other genotypes. The results of the GGE biplot analysis indicated that the first two principal components in total and in averaged data of two years, accounted for 49.29% and 21.6%, 60.42% and 25.8% of the total variation of grain yield, respectively. The vertex genotypes were identified as G1, G2, G10, G11, G17, G18, G19 and G20. Isfahan had representativeness ability and was identified as the ideal location for the selection of superior genotypes. On the basis of results, genotypes Rojo/3/LB.Iran/Una8271//Gloria”S”/Com”S”/4/Kavir (G4), Roho / Mazorka // Trompilo /3/ Lignee527/Nk1272 // Jlb70-63 (G20) and Manal / Alanda-01 // 1-BC-0152 /4/ Rojo /3/ LB.Iran / Una8271 // Gloria”S” / Com”S”(G8) were recognized as the closest genotypes to the ideal genotype. Genotypes G4 and G20 were recognized as the best genotypes for cultivation in Yazd and G8 was recognized as the best genotype for cultivation in Isfahan and Birjand.