Evaluation of ear yield stability of organic sweet corn hybrids at different elevations in the humid tropical climate of Indonesia

Super sweet is classified as a special type of corn, due to the presence of genes which promote sugar accumulation in grains, significantly reducing starch content in the endosperm. This study aimed to evaluate the agronomic performance of experimental super sweet corn hybrids, carrying the gene shrunken-2, in order to identify and select promising genotypes for Southern Brazil. The experiments were carried out in two sowing seasons in 2017/18, in the experimental field of UNICENTRO, Guarapuava-PR, Brazil. Thirty-two experimental hybrids resulting from an 8x4 partial diallel among super sweet corn inbred lines from four distinct populations were evaluated along with two commercial hybrids (BRS Vivi and Tropical Plus) used as checks. Traits of agronomic and commercial interest were evaluated: male flowering (MF), husk covering index (HCI), yield of husked ears (YHE), grain yield (GY), commercial prolificacy (CPR), percentage of commercial ears (PCE), color (COL) and soluble solids (SS) of the grains. We verified significant differences among the experimental hybrids, except for COL. There was no significant effect of sowing season regarding to SS. Genotypes x seasons interaction was significant for YHE, GY, CPR, PCE and COL. There are promising experimental hybrids showing performances superior to commercial super sweet corn hybrids used in Brazil. The experimental hybrids D2-61 x D5-41 and D3-10 x D5-43 were superior to the other experimental genotypes. The experimental hybrid D2-61 x D5-41 shows potential to meet the demands of the current super sweet corn market.

Sweet corn is used as food in the milky stage of endosperm, when its kernel is tender, succulent and sweet. It is consumed in form of fresh ears, or it is industrially processed. Breeding of sweet corn has several equally important aims that are directed by the market demands and different modes of consumption. The ear yield, in sweet corn is the most important but not the only main goal of breeding. In the two year study (2013, 2014) we observed the effect of the genotype, year and their interactions on the yields of 8 sweet corn hybrids. Two of the hybrids were commercial and six were experimental hybrids. The field experiment was arranged according to the RCBD with four replications. Hybrids were harvested 23 days after pollination, i.e. silking. Average yield in 2013 was significantly higher (12.19 t ha-1) than in 2014 (11.49 t ha- 1). In 2013 it ranged from 10.21 t ha-1 for the experimental hybrid ZP 489/1su, up to 13.52 t ha-1 for the commercial hybrid ZP 355su. In 2014 the lowest yielding hybrid was ZP 485/1su (10.14 t ha-1) while the highest yielding was ZP 486/1su (13.41 t ha-1). On average those two were also the highest (13.19 t ha-1) and the lowest yielding (10.66 t ha-1) hybrids. Statistical analysis showed that the effect of genotype and the year, as well as their interactions had significant impact on the yield performances of sweet corn hybrids.

Linseed (Linum usitatissimum L.) is one of the most prominent industrial oilseed crops cultivated for both seed and fiber. Lack of stable genotypes across the linseed production area is one of the problems. Thirteen linseed genotypes were planted in randomized complete block design with three replications at six linseed major growing agro-ecologies of North, central and Southeastern Ethiopia (Werabe, D\Markos, Welkite, Holeta, Kulumsa and Adiet) in 2021/2022 cropping season. With the objectives of determining the effects of GEI, on oil yield of linseed and identifying better performing and well adapted linseed genotypes than the control variety, and to prepare for registration and release of selected high oil yielding genotypes in the different linseed agro-environment conditions of Ethiopia. The oil yield subjected to the combined analysis of variance showed a highly significant (p<0.01) effect of genotype, location, and genotype x location interactions (GLI). Similarly the combined AMMI ANOVA for oil yield revealed that there were highly significant differences among genotypes, locations and genotype by location interactions and accounted 22.11%, 31.40% and 46.49% of the total variations respectively. The highest percentages of environmental variations are an indication that environment is the major factor that influences the yield performance of linseed oil yield in Ethiopia. In addition, the first two IPCAs were significant and accounted for 80.77% of the total interactions sum squares. Six stability measures viz Eberhart and Russell analysis (bi and S2di), Additive Main Effect and Multiplicative Interaction (AMMI) model, AMMI Stability Value (ASV),Yield Stability Index (YSI),Genotype Main Effect and Genotype by Environment Interaction Effect (GGE) bi plot analysis Model were used to evaluate the stable genotypes across the testing locations. Genotypes 10097(G2), 10103 (G3) and 239716 (G7) were more stable by Eberhart and Russell analysis and AMMI Stability Value. Genotypes 10103 (G3) and 208360 (G8) were more stable by Yield Stability Index. Genotypes 208360 (G8) and 10103 (G3) were selected as better genotypes that appeared in the five and four locations by AMMI analysis, respectively. According to one-year data, the six locations are grouped into one mega environment for linseed production with one winning genotype and genotype 208360 (G8) was an ideal genotype, while location A diet was an ideal environment by GGE analysis. Genotypes 208360 (G8), 234005 (G4)and 10103 (G3) are the three of the best performing genotypes than the other genotypes and control variety (Berene) in oil yield across locations. Therefore, those three highest oil yielder genotypes have a potential to be registered in Ethiopia. However, this trail need to be repeated for one more season, and or three of the best performing genotypes will be verified along with the check on farmers' fields for release.

Linseed (Linum usitatissimum L.) is one of the most prominent industrial oilseed crops cultivated for both seed and fiber. Lack of stable genotypes across the linseed production area is one of the problems. Thirteen linseed genotypes were planted in randomized complete block design with three replications at six linseed major growing agro-ecologies of North, central and Southeastern Ethiopia (Werabe, D\Markos, Welkite, Holeta, Kulumsa and Adiet) in 2021/2022 cropping season. With the objectives of determining the effects of GEI, on oil yield of linseed and identifying better performing and well adapted linseed genotypes than the control variety, and to prepare for registration and release of selected high oil yielding genotypes in the different linseed agro-environment conditions of Ethiopia. The oil yield subjected to the combined analysis of variance showed a highly significant (p<0.01) effect of genotype, location, and genotype x location interactions (GLI). Similarly the combined AMMI ANOVA for oil yield revealed that there were highly significant differences among genotypes, locations and genotype by location interactions and accounted 22.11%, 31.40% and 46.49% of the total variations respectively. The highest percentages of environmental variations are an indication that environment is the major factor that influences the yield performance of linseed oil yield in Ethiopia. In addition, the first two IPCAs were significant and accounted for 80.77% of the total interactions sum squares. Six stability measures viz Eberhart and Russell analysis (bi and S2di), Additive Main Effect and Multiplicative Interaction (AMMI) model, AMMI Stability Value (ASV),Yield Stability Index (YSI),Genotype Main Effect and Genotype by Environment Interaction Effect (GGE) bi plot analysis Model were used to evaluate the stable genotypes across the testing locations. Genotypes 10097(G2), 10103 (G3) and 239716 (G7) were more stable by Eberhart and Russell analysis and AMMI Stability Value. Genotypes 10103 (G3) and 208360 (G8) were more stable by Yield Stability Index. Genotypes 208360 (G8) and 10103 (G3) were selected as better genotypes that appeared in the five and four locations by AMMI analysis, respectively. According to one-year data, the six locations are grouped into one mega environment for linseed production with one winning genotype and genotype 208360 (G8) was an ideal genotype, while location A diet was an ideal environment by GGE analysis. Genotypes 208360 (G8), 234005 (G4)and 10103 (G3) are the three of the best performing genotypes than the other genotypes and control variety (Berene) in oil yield across locations. Therefore, those three highest oil yielder genotypes have a potential to be registered in Ethiopia. However, this trail need to be repeated for one more season, and or three of the best performing genotypes will be verified along with the check on farmers' fields for release.

Smallholder wheat farmers of Ethiopia frequently use landraces as seed sources that are low yielders and susceptible to diseases due to shortage of seeds of adapted improved bread wheat varieties. Developing novel improved varieties with wider adaptability and stability is necessary to maximize the productivity of bread wheat. Hence, a multi-location field trial was conducted across four locations in south Ethiopia during the 2022/23 main cropping season with the objective of estimating the magnitude of genotype by environment interaction (GEI) effect, and determine the stable genotype among the 10 Ethiopian bread wheat advanced selections using a randomized complete block design (RCBD) with three replications. The data recorded from all plots on 13 agronomic traits and the three wheat rust diseases were computed using appropriate statistical software. The results showed that individual and combined analysis of variance (ANOVA) exhibited the presence of highly significant variability (P<0.01) among the locations, genotypes, and GEI effects for most of the traits including grain yield. The additive main effects and multiplicative interaction (AMMI) ANOVA for main effects; location, genotype and GEI revealed significant variation among the selections with 82.0%, 8.7% and 9.3% share of sum square variation, respectively. The genotype plus genotype by environment interaction (GGE) bi-plot analysis explained 92.44% of the total variation observed. AMMI and GGE-biplot analyses indicated G11, G9, G10, and G8 as high yielders and well-adaptive in the favourable locations. AMMI stability value (ASV) and Yield stability index (YSI) showed G5 and G8 as highly stable and adaptive selections across locations. Overall, the study identified that G8 as the most stable and adaptive selection, while G11 was the top yielder cultivar across locations. Therefor it was suggested that seeds of G8 can be grown across all the locations, whereas G11, G9, and G10 can be grown in the favourable environments and similar agro-ecologies in the east African region.

The identification of stable genotypes with high yield in diverse multiple‐stress environments is important to increase maize (Zea mays L.) grain yield under tropical environments. Our objective was to assess the yield performance and stability of experimental hybrids and broad‐based populations of tropical maize across diverse environments in Southeastern Brazil. We evaluated two sets of maize genotypes for grain yield: 190 experimental hybrids along with 6 commercial hybrids and 45 population hybrids along with their 10 parental populations across 8 environments in Southeastern Brazil. Multiple statistical methods were used and compared in the analyses. Combined analysis of variance indicated that genotypic main effect (G), environmental main effect (E), and genotype by environment interaction were highly significant (p &lt; 0.0001) for grain yield. The E accounted for 42% of the total variation for both hybrids and populations, and they were more similar within the growing season than between seasons, mainly for populations. Low nitrogen (N) stress was a key factor in hybrid evaluation and recommendation, particularly under drought stress conditions. Among the environment classification methods, genotype main effect plus genotype × environment interaction (GGE) biplot provided more accurate information about environments grouping and selection of the genotypes than the Eberhart and Russell method. Harmonic mean of the relative performance of the predicted genetic values (HMRPGV) based on mixed models ranked the hybrids and populations according to mean grain yield and stability, penalizing hybrids, and populations with lower stability. Therefore, we recommend the GGE biplot and HMRPGV for genotype evaluation based on multi‐environment trials data. These methods identified 92V2144 and 92V2033 as the most promising hybrids for favorable and 92V2141, 92V2153, and 92V2137 as the most promising for unfavorable environments. 92VX033 and 92VX043 were identified as broadly adapted and stable populations across multiple environments in Southeastern Brazil.

This study analyzed the genotype x environment interaction (GE) for the juice productivity (JuProd) of 12 yellow passion fruit varieties (Passiflora edulis Sims. f. flavicarpa Deg.) using additive main effects and multiplicative interaction (AMMI) model and auxiliary parameters. The experiments were conducted in eight environments of Bahia State, Brazil, using a randomized block design with three replications. Analysis of variance showed significant effects (P ≤ 0.01) for environments, genotypes, and GE interaction. The first two interaction principal component axes (IPCAs) explained 81.00% of the sum of squares of the GE interaction. The AMMI1 and AMMI2 models showed that varieties 09 and 11 were the most stable. Other parameters, namely, the AMMI stability value (ASV), yield stability (YSI), sustainability, and stability index (StI), indicated that other varieties were more stable. These varying results were certainly a consequence of methodological differences. In contrast, the ranking of varieties for each of the stability parameters showed significant positive correlations (P ≤ 0.05) between IPCA1 x (ASV, YSI), JuProd x (StI, YSI), YSI x ASV, and StI x YSI. Cluster analysis based on the genotypic profile of the effects of the GE interaction identified three groups that correlated with the distribution of varieties in the AMMI1 biplot. However, the classification of stable genotypes was limited because the association with the productivity was not included in the analysis. Variety 08 showed the most stable and productive behavior, ranking above average in half of the environments, and it should be recommended for use.

Yield stability in sweet corn and its dependence on G x E interaction were investigated in a series of two way experiments. Five Romanian sweet corn hybrids were tested in three years (2008-2010) in three locations of Central Transylvania in different soils and climatic conditions. The experiments were organized in a split plot design in which, on a general level of organic fertilization (40 t/ha manure), four levels of mineral N fertilization were applied (kg/ha, active matter): N0, typical for organic technologies; N50, corresponding to the low-input (sustainable) system; N100 and N150 customary with conventional system of agriculture. Based on ear yield data registered for hybrids in locations x years x cropping system, a phenotypic index (Pi) was computed for each sweet corn hybrid illustrating the stability of their cob yields, with and without husks. The share of genotypic and G x E effects in the total value of Pi have been estimated. In different agricultural systems the tested hybrids were classified differently based on their Pi values. It is concluded that, at least for the time being, the initiation of an organic breeding program for sweet corn, in Romania, is not economically justified since all semiearly and semilate tested hybrids yielded satisfactorily under organic agricultural practices. Moreover, among the recently released sweet corn hybrids one can find certain genotypes highly suitable to organic (i.e. ‘Deliciul verii’, ‘Estival’) or low input (‘Dulcin’, ‘Estival’) agricultural practices. These three hybrids recorded the highes ear yields (with and without husks) over years, locations and agricultural systems.

The most important task of increasing food production prompts us to pay increasing attention to the cultiva-tion of such highly productive vegetable crops as sweet corn. In conditions of small-scale production, sweet corn is one of the most profitable crops. At the summer market for fresh produce, demand is always high. Over the past decade, the demand among Russian agricultural producers for sweet corn hybrids has in-creased significantly. The State register currently contains about 99 hybrids and populations of sweet corn, including 5 hybrids of ARRSIC breeding – Karamelka (FAO 120), Marmeladka and Serenada (FAO 160), Lakomka, Uslada (FAO 250). This article presents the results of the breeder’s work of the FSBSI ARRSI of corn on new mid-early hybrids of sweet corn creation in the conditions of the Predgorniy district of the Stav-ropol region. The productivity of new sweet corn hybrids for 2022-2023 had appraised. Over two years of re-search in boghara conditions, the ears yield without wrappers at technical ripeness amounted to 16.0-22.8 t/ha. A sweet corn hybrid was identified on the work results base, which meets the standard requirements (ear yield at technical ripeness, plant and of ear attachment height, ear size, grain size, grain color, cob color, as well as resistance to smut damage against a natural background), required for sweet corn of first generation hybrids for their further use in the food industry (canning, freezing). As a result of work on creating new sweet corn hybrids, new lines with high combining ability have been identified.

&lt;p class="Abstrak"&gt;Visualization of GGE biplot analyses was able to explain the genotype by environment interaction. This research was aimed to determine the yield stability of promising experimental maize hybrids in eight locations based GGE biplot method. Ten promising experimental maize hybrids and two commercial hybrid varieties as check, namely: HBSTK01, HBSTK03, HBSTK05, HBSTK06, HBSTK07, HBSTK08, HBSTK09, HBSTK10, HBSTK11, HBSTK13 and Bima 16 and Pertiwi 3 were evaluated in eight locations, ie. Bangka (Bangka Belitung), Probolinggo (East Java), Minahasa Utara (North Sulawesi), Donggala (Central Sulawesi), Soppeng, South Sulawesi, Gowa (South Sulawesi, Konawe (Southeast Sulawesi)and Lombok Barat (West Nusa Tenggara) from May to October 2013. The treatments were arranged in a randomized complete block design (RCBD) with 3 replications. Variable measured was grain yield. Analysis of variance was performed for data from each study site, to determine the performance of each genotype at each location. Yield stability analysis was performed by GGE biplot method using PB tools software. Results showed that genotype H9 (HBSTK11) had the highest biological stability with grain yield of 10.37 t/ha, higer than the overall mean yield. The best hybrid with the highest yield and good stability was hybrid H6 (HBSTK08) of 11.08 t/ha. This experimental hybrid is considered potential to be released as new hybrid variety. North Minahasa is considered the most suitable location for testing, whereas Konawe and West Lombok are least suitable, compared with the other locations.&lt;/p&gt;

Article Details: Received: 2020-11-30 | Accepted: 2020-12-09 | Available online: 2021-06-30 https://doi.org/10.15414/afz.2021.24.02.117-123 Multi-environment trials were conducted in two locations (Algiers and Setif) during two crop seasons in order to assess the responses of 17 genotype of barley (Hordeum vulgare L.) by evaluation of genotype-by-environment interactions (GEI) on grain yield and determine the stable genotypes. Results showed significant (p <0.001) effects of environment and genotypes and their interaction on grain yield. The genotypes had different behavior conducting to yield variation in the tested locations. So, selection could consider a specific adaptation of the genotypes and their yield stability. The Additive main effects and multiplicative interaction analysis is a useful tool allowing to explore important information on the obtained results; it revealed that 'Plaisant/ charan01' is the most stable genotype followed by 'Barberousse' and 'Barberousse/Chorokhod', while 'Begonia' and 'Plaisant' were unstable with specific adaptation to Setif location during 2018/19. the cultivar 'Express' presented a high productivity. Keywords: AMMI analysis, barley, genotype by environment interaction, grain yield, stability References Abdipur, M. & Vaezi, B. (2014). Analysis of the genotype-by-environment interaction of winter barley tested in the rain-fed regions of Iran by AMMi adjustment. Bulgarian Journal of Agricultural Science, 20(2), 421–427. https://www.agrojournal.org/20/02-27.html Chalak, L. et al. (2015). Performance of 50 Lebanese barley landraces (Hordeum vulgare L. subsp. vulgare) in two locations under rainfed conditions. Annals of Agricultural Sciences, 60(2), 325–334. http://dx.doi.org/10.1016/j.aoas.2015.11.005 Alfian, F. H. & Halimatus, S. (2016). On The Development of Statistical Modeling in Plant Breeding: An Approach of Row-Column Interaction Models (RCIM) For Generalized AMMI Models with Deviance Analysis. Agriculture and Agricultural Science Procedia, 9(1), 134–145. https://doi.org/10.1016/j.aaspro.2016.02.108 Bouzerzour, H. & Dekhili, M. (1995). Heritabilities, gains from selection and genetic correlations for grain yield of barley grown in two contrasting environments. Field Crops Research, 41(3), 173–178. http://dx.doi.org/10.1016/0378-4290(95)00005-B De Mendiburu, F. (2017). Agricolae: Statistical procedures for agricultural research. R package version, 1.2-8. Retrieved November 14, 2020 from https://tarwi.lamolina.edu.pe/~fmendiburu/ Dogan, Y. et al. (2016). Identifying of relationship between traits and grain yield in spring barley by GGE biplot analysis. Agriculture and Forestry, 62(4), 239–252. http://dx.doi.org/10.17707/AgricultForest.62.4.25 Farshadfar, E. et al. (2011). AMMI stability value and simultaneous estimation of yield and yield stability in bread wheat (Triticum aestivum L.). Australian Journal of Crop Science, 5(13), 1837–1844. http://www.cropj.com/farshadfar_5_13_2011_1837_1844.pdf Farshadfar, E. et al. (2012). GGE biplot analysis of genotype × environment interaction in wheat-barley disomic addition lines. Australian Journal of Crop Science, 6(6), 1074–1079. http://www.cropj.com/farshadfar_6_6_2012_1074_1079.pdf Gauch, H.G. (1988). Model selection and validation for yield trials with interaction. Biometrics, 44(3), 705–715. http://dx.doi.org/10.2307/2531585 Gauch, H.G. et al. (2008). Statistical analysis of yield trials by AMMI and GGE: Further considerations. Crop Science, 48(3), 866–889. https://doi.org/10.2135/cropsci2007.09.0513 Halimatus, S. & Alfian, F. H. (2016). AMMI Model for Yield Estimation in Multi-Environment Trials: A Comparison to BLUP. Agriculture and Agricultural Science Procedia, 9(1), 163–169. https://doi.org/10.1016/j.aaspro.2016.02.113 Vishnu, K. et al. (2016). AMMI, GGE biplots and regression analysis to comprehend the G × E interaction in multi-environment barley trials. Indian Journal of Genetics and Plant Breeding, 76(2), 202–204. https://dx.doi.org/10.5958/0975-6906.2016.00033.X Mirosavljevic, M. et al. (2014). Analysis of new experimental barley genotype performance for grain yield using AMMI biplot. Selekcija I semenarstvo, 20(1), 27–36. In Bosnian. http://dx.doi.org/10.5937/SelSem1401027M Peyman, S. et al. (2017). Evaluation of Genotype × Environment Interaction in Rice Based on AMMI Model in Iran. Rice Science, 24(3), 173–180. https://doi.org/10.1016/j.rsci.2017.02.001 Purchase, J.L. et al. (2000). Genotype × environment interaction of winter wheat (Triticum aestivum L.) in South Africa: II. Stability analysis of yield performance. South African Journal of Plant and Soil, 17(3), 101–107. http://dx.doi.org/10.1080/02571862.2000.10634878 Rodrigues, P.C. et al. (2016). A robust AMMI model for the analysis of genotype-by-environment data. Bioinformatics, 32(1), 58–66. http://dx.doi.org/10.1093/bioinformatics/btv533 Romagosa, I. & Fox, P.N. (1993). Genotype X environment interaction and adaption. In Hayward, M.D. et al. (eds.) Plant breeding principles and prospects. Plant Breeding Series. Dordrecht: Springer (pp. 373–390). https://doi.org/10.1007/978-94-011-1524-7_23 Temesgen, B. et al. (2015). Genotype X Environment Interaction and Yield Stability of Bread Wheat (Triticum aestivum L.) Genotype in Ethiopia using the Ammi Analysis. Journal of Biology, Agriculture and Healthcare, 5(11), 129–139. https:// www.iiste.org/Journals/index.php/JBAH/article/view/23245 Yan, W. et al. (2007). GGE biplot vs. AMMI analysis of genotype by environment data. 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&lt;p style="text-align: justify;"&gt;The objective of this research was to evaluate yield potential of 12 sweet corn promising hybrids from Plant Breeding Program (Bogor Agricultural University) and Indonesian Cereals Research Institute colection. This research was conducted at experimental field Indonesian Cereals Research Institute, in Maros, South Sulawesi, from June to August 2011. The genotypes used were : IM-12, IM-13, IM-14, IM-15, IM-16, IM-23, IM-24, IM-25, IM-34, IM-35, IM-45, IM-55, and three comercial varieties i.e. Super Sweet Corn, Sweet Boy, and Talenta. The design of this research was Randomized Complete Block Design with four replications. Data was analyzed with F-test then continued with Dunnett test (α=5%). Selection index was used for choosing the best genotype. Interaction between two factors, i.e. genotype and year, was analyzed with combined variance analysis using primary data from this research and secondary data from last year research (done from April to June 2010). Broad sense heritability was estimated from this two-factors analysis. The result from this research was the sweet corn productivity was not affected by genotype, but affected by genotype and year interaction. On the other hand, total soluble solid was affected by genotype, but not affected by interaction between genotype and year. Among characters evaluated, total soluble solid had highest broad sense heritability. Based on selection index, IM-16 was a promising hybrid and can be developed to be new commercial variety.&lt;/p&gt;&lt;p&gt;Keywords: yield trial, sweet corn hybrid, selection index, broad sense heritability&lt;/p&gt;

The sweet corn varieties included in the State Register of Plant Varieties Suitable for Cultivation in Ukraine were studied and a comparative assessment of hybrids was made. A general characteristic of sweet corn hybrids included in the State Register and recommended for cultivation was given. According to the State Register of Plant Varieties Suitable for Cultivation in Ukraine, as of 2024, there are over 100 sweet corn varieties and hybrids that are recommended for cultivation in different soil and climatic zones of Ukraine, as well as in closed ground. However, most of them are suitable for cultivation simultaneously in all zones. According to the international classification, sweet corn varieties and hybrids are divided into groups according to the sugar content in the grain, which is determined by their genetic type. Table 1 shows the classification of the main groups of sweet corn depending on the level of sweetness. According to the State Register of Plant Varieties Suitable for Distribution in Ukraine and in particular recommended for cultivation in the Forest-Steppe of Ukraine, hybrids of domestic and foreign selection are listed. Almost all hybrids of sweet corn are suitable for cultivation in all soil and climatic conditions, as evidenced by their adaptability and adaptability to certain conditions, namely soil and air humidity, temperature indicators, in particular temperature changes during the day, soil conditions. The direction of use of the studied hybrids of sweet corn is universal, which is why they can be consumed fresh, for preparing various dishes, canning and for processing. It is important to have in the State Register of hybrids of sweet corn of different maturity groups. It is thanks to the use of hybrids with different lengths of the growing season that conveyor cultivation can be ensured. The conveyor is most important for large commodity producers, as it prevents overripening of the beginnings. Ultra-early hybrids are harvested first, later as other maturity groups ripen. The use of hybrids of different maturity groups makes it possible to extend the period of fresh produce reaching the consumer. A comparative characteristic of sweet corn hybrids that were studied during 2020–2024 is presented. In total, 22 hybrids were analyzed, most of which are early and mid-early hybrids (the growing season varies from 66 to 84 days). The shortest ripening period was recorded for the Bonduelka (GSS-3071) and Spokusa F1 hybrids – 66–68 days and 70–72 days, respectively, which indicates their suitability for early sales of products. The Megaton F1 hybrid has the longest growing season – 84 days, which allows for high yields in conditions of a sufficient growing season. All of the above hybrids belong to the SH2 genetic type, which is characterized by an increased sugar content. In such sweet corn hybrids, the sugar content in the grain is 20-30%, which provides quite high taste qualities. Such increased sweetness of the grain is an important indicator for the consumer and the processing industry. As for the color of the seeds, most sweet corn hybrids have yellow grain color (for example, in the hybrids Accent F1, Jamala F1, Daenerys F1, etc.), which is traditional for sweet corn. However, recently, two-color sweet corn grain has been gaining popularity, which provides a rather attractive appearance and increases consumer interest. Some hybrids have yellow-white (for example, Cumberland F1, Overdale F1) or white seeds (Medunka F1), which can be an advantage in specialized market niches.

Super sweet corn hybrids shall present production and quality traits in order to meet farmer’s, industry and consumer’s expectations. The aim of this study was to select experimental super sweet corn hybrids based on the Z index (I Z ). We evaluated 64 experimental hybrids from crosses between inbred lines of different groups along with two check hybrids. The experiments were carried out in Guarapuava-PR in two sowing seasons in 2016. The evaluated traits were male flowering, ear length, ear diameter, soluble solids content, husked ear yield, unhusked ear yield, and grain yield. The traits which most contributed to the Z index were related to ear and grain yields and soluble solids content. The first sowing season was the most favorable for the expression of yield related traits. The experimental hybrid D3-30 x D5-46 showed high performance in both sowing seasons and the hybrid D2-17 x D5-46 stood out in the second sowing season, being both promising, showing high productivity and quality of ear, considering the Z index.

BackgroundThe performance of oat genotypes differs across environments due to variations in biotic and abiotic factors. Thus, evaluation of oat genotypes across diverse environments is very important to identify superior and stable genotypes for yield improvement.MethodsThe study aimed to assess the interaction (genotype‐by‐environment interaction; GEI) effect and determine the stability of grain yield in oat (Avena sativa L.) genotypes in Ethiopia using parametric and nonparametric stability statistics. Twenty‐four oat genotypes were evaluated in nine environments using a randomized complete block design replicated three times.ResultsThe pooled analysis of the variance of grain yield showed significant variations among genotypes, environments, and their interaction effects. Significant GEI revealed the rank order change of genotypes across environments. The environment main effect captured 44.62% of the total grain yield variance, while genotype and GEI effects explained 28.84% and 26.54% of the total grain yield variance, respectively. The grain yield stability was assessed based on 12 parametric and two nonparametric stability statistics. The results indicated that genotypes with superior grain yield‐ showed stable performance on the basis of the stability parameters of the genotypic superiority index (Pi), the Perkins and Jinks adjusted linear regression coefficient (Bi), and the yield stability index (YSI), indicating that selection using these stability parameters would be efficient for grain yield enhancement in oat genotypes. Spearman's rank correlation coefficients also showed that the stability parameters of Pi, Bi, and YSI had a significant positive association with grain yield. However, grain yield had an inverse correlation with the stability parameters of standard deviation, deviation from regression , the Hernandez desirability index (Dji), Wricke ecovalence (Wi), the Shukla stability variance (σi2), the AMMI stability value (ASV), and environmental variance , indicating that oat genotype selection using these stability parameters would not be efficient for yield enhancement because these stability parameters favor low‐yielding genotypes more, compared to high‐yielding ones.ConclusionsTherefore, G5, G8, G11, G12, G14, G16, G17, G19, and G22 genotypes were adaptable in all nine environments based on stability parameters of Pi, Bi, and YSI, and selection of these superior genotypes would improve grain yield in oat genotypes. However, the validity of this result should be confirmed by repeating the experiment in the same environments over two or more years.