adel pedram

Genotype-environment interaction is one of the most important limiting factors in breeding programs. A comprehensive study of genotype-environment interaction requires powerful statistical methods. Different methods for evaluating the interaction effect of genotype-environment have been proposed by different researchers. Therefore, in the present study, the role of this phenomenon on the sugar yield of sugar beet hybrids and the identification of stable hybrids were studied based on the AMMI, GGE bi-plot, and MTSI stability index methods. For this study, a total of 20 sugar beet genotypes were utilized, comprising 15 recently developed hybrids and five control cultivars (Sina, Dena, Novodora, Modex and Loriquet). Phenotypic assessments of experimental genotypes were conducted in 2021 crop year at four agricultural research stations located in Karaj, Mashhad, Shiraz and Miandoab. These selected sites differed in terms of altitude, latitude and longitude, atmospheric temperature and precipitation, and physical and chemical characteristics of soil. The experiments at each research station were carried out using a randomized complete block design with four replications. Each genotype was planted in a separate plot, consisting of three cultivation rows with a length of eight m and a distance of 50 cm interrow. Throughout the growing season, weed control, irrigation, fertilizer application, and other field management activities were performed based on the recommendations of experts. Additionally, regular monitoring and prevention of pests and diseases specific to sugar beet were conducted at each research station. Stability analysis methods of AMMI, GGE bi-plot, and MTSI stability index were used to analyze the genotype-environment interaction. The results of a combined analysis of variance confirmed the significant effects of environment and genotype on all traits at a one percent probability level. The interaction between them was significant at one and five percent probability levels for all traits except the sugar content and white sugar content. Analysis of the multiplicative effect of the AMMI model showed that the first two components are significant at the one and five percent probability levels, respectively, and together explain 92.20 percent of the interaction variations. The bi-plot of mean yield and the first principal component of the interaction confirmed the superiority of genotype no. 20 due to its high sugar yield and stability. The results obtained from the GGE bi-plot method showed that the first and second components together explain 84.16% of the variations in total sugar yield. According to the GGE bi-plot, Karaj, Shiraz, and Miandoab had a relatively similar reaction in terms of genotype yield rank and in these environments, genotype no. 20 was stable. The reaction of the Mashhad environment was different from the other three environments and in that genotype no. 9 had suitable stability. Based on the results of the MTSI index, four genotypes of 18, 2, 20, and 17 were identified as stable genotypes. In general, the selected genotypes based on the MTSI caused a favorable selection differential in all traits. Among the traits, except root yield, other traits had good selection differential and selection gain. On the other hand, genotype no. 15 had the highest value of MTSI stability index and was unfavorable genotype in terms of studied traits. In general, among breeding hybrids, in the first the hybrid obtained from crossing of (7112 × SB36) × S1– 960132 (genotype no. 2) and then the hybrid obtained from crossing of (7112 × SB36) × S1- 970063 (genotype no. 9) can be used as promising hybrids in final evaluation programs until the introduction of new hybrids. The studied sites were not very closely correlated to be suggested that a site be abandoned to reduce costs for future research; In contrast, most of the tested environments had a high differentiation capability and could make a good distinction among genotypes in terms of sugar yield in genotype-environment interaction studies of sugar beet cultivars.

In order to identification of effective traits on root yield and also determination of cause-and-effect relationships between in 20 different sugar beet cultivars, an experiment was carried out in West Azarbaijan Agricultural and Natural Resources Research and Education Center, Miandoab Research Station based on RCB design with four replications in 2017 crop season. The assayed traits included root yield, sugar yield, white sugar yield, sugar content, white sugar content, extraction coefficient of sugar, root α-amino nitrogen, sodium and potassium, alkalinity coefficient and molasses sugar. The results of analysis of variance showed that difference between studied cultivars for amino nitrogen (α-N) all traits was significant at the 0.05 probability level and for other traits was significant at the 0.01 probability level. Phenotypic and genotypic coefficients of variation for most traits were high, indicating relatively high variability in studied cultivars for evaluated traits. Correlation coefficients between traits showed that there is the most negative and significant correlation between root yield trait with sugar content (-0.62), white sugar content (-0.53), alkalinity coefficient (-0.52), potassium (-0.49), sodium (-0.45) and also molasses sugar content (-049) traits, and the most positive and significant correlation with sugar yield (0.95), white sugar yield (0.90) and extraction coefficient of sugar (0.58) traits, respectively. In multiple regression analysis by stepwise method, sugar content and white sugar content, amino nitrogen and alkalinity coefficient traits were entered into model, respectively, that explained 82 percent of root yield variations. Based on correlation, stepwise regression and path analysis results, among the studied traits, amino nitrogen (0.35) and white sugar content
(-0.31) traits with most direct effect were effective on root yield and will be useful in identifying genotypes.

In the present study, the resistance of 30 common commercial sugar beet cultivars against root rot caused by Rhizoctonia solani and Fusarium oxysporum was evaluated in West Azerbaijan province during 2017 and 2018. Cultivars were inoculated in pots under controlled greenhouse conditions. The experiment was conducted in completely randomized design with 15 replications and 5 controls for each treatment. Ranking based on root rot intensity caused by inoculation of R. solani was done eight weeks after inoculation with a scale of 1-9. Harvest Index was also calculated for each treatment. Ranking based on leaves color yellowing and root rot caused by F. oxyporum were recorded weekly from the second week after inoculation with 0-5 index. The mean of area under disease progress curve (AUDPC) was calculated for the disease development comparison among cultivars. Also, in order to compare the cultivars damping off under normal condition, a field experiment was conducted in a randomized complete block design. Missing or dying plants were counted three time after thinning and weeding. Dying plants were identified and transferred to the laboratory for diagnosis of the disease. The results of AUDPC calculation were obtained under field conditions and resistant cultivars were identified. The white sugar yield of cultivars per unit area was calculated. The results obtained from the cultivars resistance in the field and greenhouse with the genetic characteristics of the cultivars introduced by the sugar beet seeds companies were compared and resistant cultivars with high yield were introduced. The BTS233, Flores, Delta, and Murialle showed relative resistance to both pathogens and showed significant yield per hectare and are recommended in fields with contamination.

Testing plant varieties under different regions is necessary to assess their stability. In this study, 17 sugar beet genotypes were evaluated in a randomized complete block design with six replications in five regions including Karaj, Hamedan, Miandoab, Mashhad, and Shiraz. After checking the homogeneity of mean squares in five experiments by Bartlett’s test, analysis of the combined was performed for sugar yield. In this analysis, location, genotype and genotype × location interaction effects were significant. Based on regression coefficient and deviation from regression parameters, genotypes G2, G6, G9 and G13 were the most stable genotypes. Genotypes G2, G3, G5 and G6 had the highest stability based on Shukla’s stability variance and Wrick’s ecovalence. Based on superiority measure, genotypes G6 and G9 and based on coefficient of variation genotypes G2 and G5 were identified as the most stable genotypes. Using coefficient of determination, genotypes G3, G5 and G6 were selected and by Tai’s regression method, genotypes G2, G6 and G12 genotypes were selected as the most stable genotypes having sugar yield higher than average. Based on the above mentioned methods, genotype G2 was recognized as the most suitable genotype for sugar yield stability.

In order to study the effect of seed priming on and the growth of sugar beet seedling under stress condition, an experiment was conducted in the laboratory and greenhouse of the Department of Agronomy, Faculty of Agriculture, Urmia University in 2017. Experimental units were arranged in factorial based on completely randomized design with three replications. The first factor was four salinity levels (0 as control), 5, 10, and 15dS/m of sodium chloride and the second factor was priming treatments including magnetic water, salicylic acid, calcium chloride, and sodium chloride for 8 h and non-priming (control). The effect of salinity levels on all studied traits was significant (P < 0.01) except for the seedling germination. The effect of seed priming on all the studied traits was significant (P < 0.01). Furthermore the interaction of the two treatments on germination index and seedling length was significant (P < 0.01) as well as for shoot length (P < 0.05). Results showed that 15 dS/m salinity level reduced final germination percentage, radicle length, radicle dry weight, shoot dry weight, seedling weight, seedling vigor index, speed of emergence, uniformity of emergence, percent of seed emergence, and speed of emergence-coefficient by 8.89, 33.33, 51.35, 46.41, 44.44,25.23, 12.39, 13.05, and 49.05% and increased mean time to germination by 56.46% compared with control, while priming with NaCl significantly increased final germination percentage, radicle length, radicle dry weight, shoot dry weight, seedling weight, and seedling vigor index by 14.55, 9.95, 84.21, 27.77, 22.72, 58.94, 26.23, 17.32, 10.07 and 9.09% and reduced mean time to germination by 13.14%. The highest germination index, stem length, and seedling length and also the lowest mean time to germination were obtained at zero salinity level and priming with sodium chloride. In addition, priming with sodium chloride at other salinity levels was able to moderate the effect of salinity stress on these indices and was recognized as the best priming treatment.

Using resistant cultivars is the best way to reduce damages caused by sugar beet root rot and rhizomania. In order to develop resistant cultivar to these diseases, sugar beet genotypes were evaluated under artificial inoculation to rhizoctonia and one of the pollinators was selected as the most resistant population to rhizoctonia (SB19) in 2003. Self-fertile lines and hybrids were derived from non-inoculated roots of this population in 2004 and 2005, respectively. Selected hybrids were compared in terms of yield in different regions for two years and also pollinator lines were evaluated in terms of resistance under micro plot conditions in Hamedan. Results showed that sugar yield of a hybrid that its pollinator was selected as Ekbatan cultivar’s pollinator was 10.2 t.ha-1 and decease index of its pollinator was 2.16. Average sugar yield of Ekbatan in infested and non- infested regions with rhizoctonia was 9.1 and 7.0 t.ha-1, respectively. According to the results of yield and resistance to rhizoctonia evaluation, 10 superior lines were selected. In order to obtain resistant hybrid to rhizoctonia as well as tolerant torhizomania, 18 hybrids were developed by crossing those 10 superior lines with some male sterile lines that were resistant to rhizomania. Yield comparison of the hybrids was performed in three different regions that were naturally infested with rhizoctonia and rhizomania during 2010 and 2011. Two-years averages of sugar yield for (7112*SB36)*S1-24 hybrid that was released as Ekbatan in those regions were 8.7, 8.9 and 9.3 ton.ha-1, respectively. In general, results indicated that yield and resistance of Ekbatan cultivar was similar to the foreign resistant cultivar. The time needed to reach 90% seed germination by Ekbatan was 80 hours that is faster than that for Flores and Jolgeh cultivars. This indicates the higher germination rate of this cultivar.

To investigate the effect of different seed primings on quantitative and qualitative characteristics of two sugar beet cultivars a factorial experiment based on randomized complete block design (RCBD) with three replications, was carried out in both laboratory and field at the Agricultural and Natural Resources Research Center of West Azarbaijan province in 2015-16. The factors were two cultivars (Ekbatan and 7233) and five kinds of seed primings (seed hardening, priming with nano fertilizer, priming with pigeon manure extract, hydropriming and control). The results of combined analysis of variance showed that there were significant differences between genotypes for root yield and white sugar content. It was also, revealed that primings for all traits, except percent of sugar extraction, alkalinity and white sugar content, were significant. In this study, cultivar 7233, as compared with Ekbatan, produced higher root yield and sugar content. Furthermore, hydropriming produced higher germination percentage (96.50%), speed of germination (9.56 seeds/day), root yield (80.33 t.ha-1), sugars content (23.11%), sugar yield (18.38 t.ha-1) than other seed primings. Based on stepwise regression analysis, four traits, like sugars content, root yield, sugar extraction percentage and molasses sugar, justified 98% of white sugar yield variations. Thus, these four traits were identified as the most effective ones for white sugar yield. It can be, therefore, concluded that hydropriming would be a proper seed priming to improve the quantitative and qualitative characteristics of sugar beet.