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The phenolic compound contents and antioxidant activities of the leaf extracts of nine olive genotypes were determined, and the obtained data were analysed using chemometric techniques. In the crude extracts, 12 compounds belonging to the secoiridoids, phenylethanoids, and flavonoids were identified. Oleuropein was the primary component for all genotypes, exhibiting a content of 21.0 to 98.0 mg/g extract. Hydroxytyrosol, verbascoside, luteolin 7-O-glucoside, and luteolin 4′-O-glucoside were also present in noticeable quantities. Genotypes differed to the greatest extent in the content of verbascoside (0.45–21.07 mg/g extract). The content of hydroxytyrosol ranged from 1.33 to 4.03 mg/g extract, and the aforementioned luteolin glucosides were present at 1.58–8.67 mg/g extract. The total phenolic content (TPC), DPPH• and ABTS•+ scavenging activities, ferric reducing antioxidant power (FRAP), and ability to inhibit the oxidation of β-carotene-linoleic acid emulsion also varied significantly among genotypes. A hierarchical cluster analysis enabled the division of genotypes into three clusters with similarity above 60% in each group. GGE biplot analysis showed olive genotypes variability with respect to phenolic compound contents and antioxidant activities. Significant correlations among TPC, FRAP, the values of both radical scavenging assays, and the content of oleuropein were found. The contents of 7-O-glucoside and 4′-O-glucoside correlated with TPC, TEAC, FRAP, and the results of the emulsion oxidation assay.

Genotype × environment (GE) interaction can difficult soybean breeding programs to achieve the aim of more productive cultivars. Environment stratification is a way to circumvent this problem. However, multiyear data studies are difficult to work with, because they are, usually, unbalanced. GGE Biplot is an efficient method to find mega-environments, however, it allows for, at most, 30% of the unbalanced data. Thinking about how we could resolve this problem we came up with the idea of test a method that englobes GGE Biplot graphs, environment coincidence matrices and networks of environment. Wherefore, this work aimed to gather GGE Biplot graphs of a network of trials unbalance multiyear soybean via matrices of coincidence and networks of environment to optimize environmental stratification. Data from an experimental network of 43 trials was used, these experiments were implanted in 23 municipalities during the crop seasons of 2011/2012, 2012/2013, 2013/2014 and 2015/2016 in Brazil. The trials were implanted under an experimental block design with randomized treatments and approximately 30 genotypes were evaluated, of which most of these genotypes were not repeated between the trials evaluated. The GE interaction were statistically significant for all 43 trials. The step by step of our analyses was: GGE Biplots graphs were obtained; the environment coincidence matrices were calculated; the values of matrices were used to obtain the networks of environmental similarity. The study demonstrated that by the method was possible to identify, using unbalanced multiyear data, four mega-environments. The region under study can be represented by the municipalities of Palotina, Maracaju, Bela Vista do Paraíso and Rolândia. Therefore, integrating GGE Biplot graphs and networks of environmental similarity is an efficient method to optimize a soybean program by environment stratification.

Identifying adopted Green Super Rice (GSR) under different agro-ecological locations in Pakistan is crucial to sustaining the high productivity of rice. For this purpose, the multi-location trials of GSR were conducted to evaluate the magnitude of genetic variability, heritability, and stability in eight different locations in Pakistan. The experimental trial was laid out in a randomized complete block (RCB) design with three replications at each location. The combined analysis of variance (ANOVA) manifested significant variations for tested genotypes (g), locations (L), years (Y), genotype × year (GY), and genotype × location (GL) interactions revealing the influence of environmental factors (L and Y) on yield traits. High broad-sense heritability estimates were observed for all the studied traits representing low environmental influence over the expression of traits. Noticeably, GSR 48 showed maximum stability than all other lines in the univariate model across the two years for grain yield and related traits data. Multivariate stability analysis characterized GSR 305 and GSR 252 as the highest yielding with optimum stability across the eight tested locations. Overall, Narowal, Muzaffargarh, and Swat were the most stable locations for GSR cultivation in Pakistan. In conclusion, this study revealed that G×E interactions were an important source of rice yield variation, and its AMMI and biplots analysis are efficient tools for visualizing the response of genotypes to different locations.

Low soil nitrogen (low N) threatens maize production in sub-Sahara Africa (SSA). We examined the mode of gene action conditioning grain yield of intermediate maturing inbreds and evaluated lines in hybrid combinations for high yield, stability and tolerance to low N. Thirty-two sets of inbreds were crossed to three elite testers (87036, 1368 and 9071) to generate 96 F1 hybrids. The testcrosses plus four hybrid checks were evaluated under low (30 kg/ha) and high (90 kg/ha) N environments at three locations for 2 years in Ghana. Significant general combining ability (GCA) and specific combining ability (SCA) effects were detected for grain yield and most measured traits across test environments, indicating that both additive and non-additive gene action governed the inheritance of the traits. GCA effects were greater than SCA effects, indicating that most traits were controlled predominantly by additive gene action and that inbreds with positive significant GCA effects for grain yield and other traits would contribute favourable alleles to progenies across environments. Hybrid CZL 0001 × 9071 possessed high GY, increased EPP, desirable EHT and PLHT and was the highest yielding under each of two research conditions. Significant genetic correlations were observed between GY and PLHT, EPP, EHT, CA and PA implying that improvement of these traits would lead to significant gains in grain yield under low-N conditions. Hybrids CLWN 247 × 9071, ZM523B-29-2-1-1-B*6 × 9071, TZD II 68 × 1368 and P43SCRq Fs100-1-1-8 × 9071 were high-yielding, stable and low-N tolerant and should be tested on-farm and commercialized.

The study aimed to select high-yielding, farmers-preferred quality sweet potato varieties that are suitable to grow in various environmental conditions in Bangladesh. In this context, four popular sweet potato varieties (viz., ‘BARI Mistialu-8′, ‘BARI Mistialu-12′, ‘BARI Mistialu-14′, and ‘BARI Mistialu-15′) were used in the study. These varieties were released by Bangladesh Agricultural Research Institute (BARI). In the first season (2018–2019), these varieties were evaluated at nine locations, and in the second season (2019–2020), the same varieties were tested further, but only in three locations. The trial was set up in a randomized complete block design and repeated three times. After two years of observation, it was found that the fresh root yield was varied significantly due to the environment (E), genotypes (G), and their (G × E) interaction (p ≤ 0.01) by using genotype and genotype x environment (GGE) biplot analysis. The E and G × E interaction effects were found to the greater than the genotypes effect solely. In the first year, three varieties, namely ‘BARI Mistialu-8′, ‘BARI Mistialu-12′ and ‘BARI Mistialu-14′, were identified as balanced and comparatively higher in yield in nine locations. These three varieties also showed a similar trend with respect to root yield in tested three locations in the second year. Among the four varieties, ‘BARI Mistialu-12′ was found to be the highest root yielder, followed by ‘BARI Mistialu-8′ and ‘BARI Mistialu-14′. Across the locations, these varieties showed 57.89%, 61.50% and 44.30% higher yield than the local check cultivar. Therefore, these three varieties may be recommended as the best varieties of sweet potato throughout the country.

Climate change poses significant challenges to agriculture in Türkiye, where diverse climatic conditions demand resilient forage crops to meet rising roughage demands. This study evaluates the performance of Hungarian vetch (Vicia pannonica Crantz), a cold- and drought-tolerant legume, to enhance sustainable forage production. Eight genotypes (Line-5, Line-16, Line-23, Line-28, Tarm White, Aegean White, Budak, and Oguz) were tested in Bilecik and Bingol during the 2015–2016 and 2016–2017 growing seasons using a randomized complete block design with three replications. Key traits such as pods per plant, thousand-seed weight, biological yield, seed yield, straw yield, and harvest index were analyzed using Genotype plus Genotype-by-Environment (GGE) biplot analysis based on Principal Component Analysis (PCA). The distribution of these traits was also examined using box plots. Results showed significant variations: pods per plant ranged from 17.5 to 21.7, thousand-seed weight from 26.8 to 42.6 g, biological yield from 5710 to 8780 kg ha−1, seed yield from 826 to 1132 kg ha−1, straw yield from 4997 to 7643 kg ha−1, and harvest index (HI) from 13.9% to 21.0%. Aegean White excelled in seed yield, while Line-16 showed the highest harvest index. GGE biplot analysis highlighted harvest index as the primary variance contributor, emphasizing genotype-environment interactions for selecting adaptable cultivars for sustainable agriculture.

The winged bean is an underutilized legume that is adapted to the tropics. It has good prospects as a significant multi-purpose food crop including human nutrition, cattle feed, and environmental protection. However, little research attention has been given to the crop to address the identified constraints, especially low yield in Nigeria. To improve its yield potential, GGE biplot analysis was used to identify high-yielding and stable winged bean genotypes, previously collected from the continent of Asia, and Nigeria for yield improvement. Twenty winged bean genotypes were being evaluated under the rainfed condition at three locations (Ibadan, Ile-Ife, and Kishi) for two years, comprising six environments. The obtained results showed that the seed yield (SY) ranged from 805.61 kg.ha (Ibadan) to 1,096.35 kg.ha (Kishi), with SY of 988.42 kg.ha across the locations. The winged bean reached its first flowering, 50% flowering, 50% podding, and 70% physiological maturity in 74, 80, 93, and 137 days after being planted, respectively across the locations. The GGE biplot analysis showed that the principal component (PC) axes captured 71.5% of the total variation, where PC1 and PC2 were responsible for 36.6% and 34.9%, respectively. Genotype, environment, and their interaction had a significant effect on SY. Environments IB20 and IF20 were adjudged the most ideal environments to discriminate between the genotypes. Genotype Tpt-12 was identified as high-yielding and stable. Tpt-12 would be recommended for commercial farming in southwestern Nigeria. The selected high-yielding winged bean genotypes are hereby recommended as promising parental lines for the grain yield improvement in the winged bean improvement programs.

Pea weevil ( Bruchus pisorum ) is a damaging insect pest affecting pea production worldwide. The aim of this study was to identify sources of resistance to pea weevil in a Pisum spp. germplasm in multi-environment field tests. Seed infestation and larval development were assessed in each environment and subjected to a heritability-adjusted genotype and genotype x environment biplot analysis. Results showed that seed infestation and larval development are independent traits. Accessions P669 ( P. sativum ssp. elatius ) and P656 ( P. fulvum ) showed a stable reduction in seed infestation across environments. Meanwhile, accessions P314 ( P. sativum ssp. elatius ) and P1 ( P . abyssinicum ) showed a stable reduction in larval development. The most promising accession was P665 ( P. sativum ssp. syriacum) which showed resistance at both pod and seed levels. This study demonstrates the importance of environmental conditions for pea weevil infestation. Moreover, by submitting data to a CCA, the influence of climatic parameters over seed infestation and larval development has been elucidated.

Fusarium head blight (FHB) is one of the major biotic constraints to wheat due to its direct detrimental effects on yield quality and quantity. To manage the disease, the deployment of resistant genotypes is ideal in terms of effectiveness, eco-friendliness, and sustainability of production. The study was conducted to determine the responses of different wheat genotypes to FHB, and to identify suitable and stable wheat genotype(s) regarding the FHB resistance and yield performance. A field study was carried out using eleven bread wheat genotypes in seven locations in southern Ethiopia during the 2019 main cropping season. A randomized complete block design with three-time replicates was applied in this study. The results showed that the lowest mean FHB severity (11.33%) and highest mean yield (4.54 t/ha) were recorded at Bonke. Conversely, the highest mean FHB severity (83.38%) and the lowest mean yield (0.94 t/ha) were observed at North Ari. It was also showed that maximum mean FHB severity (49.25%) and minimum mean yield (2.95 t/ha) were recorded on the genotype Hidase under crosswise assessment. Across locations, a minimum mean FHB severity (17.54, 18.83, and 21.31%) and maximum mean yield (3.92, 3.96, and 3.93 t/ha) were noted from the Shorima, Bondena, and Wane genotypes, respectively. GGE biplot analysis and various comparison tests for FHB severity revealed a higher percentage of variation concerning FHB resistance reactions due to the environment (47% as an interactive element), followed by genotype by environment interaction (21%). AMMI analysis revealed genotype, environment, and genotype by environment interaction had a total variation of 7.10, 58.20, and 17.90% for yield performance, respectively. The inconsistency between genotype responses to FHB and yield performance demonstrated that the environmental component was responsible for significant variability in FHB reaction, yield performance, and the dominance of cross-over interaction. However, the greatest level of resistance to FHB was comparatively found in the genotypes Shorima, Bondena, Wane, and Huluka across locations. Considering both FHB resistance response and yield stability, in most environments, Shorima, Bondena, Wane, and Huluka genotypes were suggested for consideration of cultivation where they are well-performed under the pressure of FHB. North Ari and Hulbareg were acknowledged as more discriminating environments than the others for test genotypes against FHB. Bonke and Chencha were considered ideal environments for selecting superior genotypes with good yield performance.