Combined ANOVA Research Articles

GGE biplot analysis for cane yield and sugar yield in advanced clones of sugarcane (Saccharum sp. complex).

The present investigation was taken up to study the G × E interaction and stability in 14 sugarcane clones during 2020-2021 and 2021-2022 at four different locations namely Pantnagar, Kashipur, Dhanauri (Haridwar), and Dhakrani (Dehradun) for cane yield (CY) and sugar yield (SY) at the 10-month and 12-month stages. The research aimed to identify stable, high-yielding sugarcane clones adaptable to diverse environmental conditions, enhancing productivity and profitability for farmers in Uttarakhand, India. The combined ANOVA revealed significant differences among the clones (22.20% to 29.54% variation), environments (35% to 39.62% variation), and their interactions (19.91% to 24.16% variation) for CY and SY at both stages. To analyze the stability of genotypes and G × E interactions, the GGE biplot method was employed. The first two PCs explained 77.94% for CY, 74.39% for SY at the 10-month stage, and 81.01% for SY at 12-month stage of the total variation of the GGE model. The GGE biplots revealed that for CY, the mega-environment exhibited CoPant 16222 and CoPant 16223 as the winning genotypes. For SY at the 10-month stage, CoPant 17221 and CoPant 16222 were the best clones in two different mega-environments, while at the 12-month stage, the mega-environment showed CoPant 16222 and CoPant 16223 as the winning genotypes. Dehradun (2020) and Kashipur (2020) were identified as the best test environments for selecting widely and specifically adapted genotypes, respectively, for CY and SY at the 10-month as well as 12-month stages. In a nutshell, GGE biplot analysis identified the best-performing sugarcane clones and best test environments in Uttarakhand, India. Clone CoPant 16222 showed high mean performance and stability for cane and sugar yield, making it suitable for recommendation to farmers.

Yield performance and anthocyanin content of several purple-fleshed sweet potato clones grown in two locations in East Nusa Tenggara, Indonesia

Abstract. Arsa IGBA, Mau YS, Ndiwa ASS, Gandut YRY, Ishaq LF, Mahayasa INW. 2024. Yield performance and anthocyanin content of several purple-fleshed sweet potato clones grown in two locations in East Nusa Tenggara, Indonesia. Biodiversitas 25: 2276-2289. Several promising purple-fleshed sweet potato hybrid clones were identified to have high yield potential and, thus, can be evaluated for other superior traits and later be further processed for varietal registration and release. This study aimed to determine the tuber yield performance and anthocyanin content of several purple-fleshed sweet potato clones across two locations and to identify the best clones based on mean performance and stability. The study was conducted in the farmer's field in two different altitudes in West Timor, i.e., Mata Air Village, Kupang District (86 masl) and Netpala Village, Timor Tengah Selatan (TTS) District (1,090 masl) from April to September 2023. Six purple-fleshed sweet potato clones/varieties were assigned as treatment. The experiment was laid out in a complete block design in each location, and each treatment consisted of three replicates. Observed data included fresh storage root yield, number of storage roots per plant, storage root length, storage root tuber diameter, and tuber anthocyanin content. Observed data from the two trial locations were subjected to a combined ANOVA, cluster analysis, Principal Component Analysis (PCA), and GGE biplot analysis. The results showed that the genotype-by-location interaction effect was significant in all studied traits. Meanwhile, location or genotype significantly affected all traits except for the location, which did not significantly affect the anthocyanin content. The storage root yield ranged from ~19.95 t ha-1 to ~49.33 t ha-1, while the anthocyanin content ranged from ~28,64 mg/100 g to ~157,65 mg/100 g. The ranks of the tested genotypes based on the mean performance and stability are Undana UJ 7>Undana UJ 1>JPV-1>Antin 3>Undana UJ 6>Undana UJ 3 for storage root yield and JPV-1>Antin 3>Undana UJ 7>Undana UJ 6>Undana UJ 3>Undana UJ 1 for anthocyanin content.

Estimation of genetic diversity utilizing gene-targeted SCoT markers and morpho-chemotypic analyses in Senna alexandrina Mill. (Senna)

Senna, an industrial crop that generates foreign exchange, is planted predominantly in southern India. Senna leaves and pods grown in India are valued in international trade and the global market because they contain a high concentration of the chemical component sennosides. The current investigation was conducted in the CSIR-CIMAP experimental farm in Hyderabad, India. The experiment was designed in an RCBD and replicated three times using twenty-five senna accessions in the years 2020–21 and 2021–22. The combined ANOVA found that all fourteen of the traits studied were highly significant. The D2 analysis and λ1-λ2 values classified the 25 accessions into five clusters with 91.05 % diversity. The molecular diversity was also examined using SCoT markers. As SCoT markers target expressed portions of the genome, the variation among accessions reflects some of the morphological changes among them. For pharmaceutical applications, various lines Gen-1 and 2 (high in Sennosides A and B content) and Gen-4 can be hybridized with Gen-5 and 25 to produce generations high in pharmaceutically relevant Sennosides A and B. Considering genotypes Gen 1 and 2 rank first and second in terms of sennoside A and B contents in leaves (%) and pods, these two genotypes can be employed for large-scale cultivation and to transfer favorable traits in future breeding programs.

Genotype × environment interaction and selection parameters for high yielding wheat genotypes under irrigated and heat stress environment

AbstractIntroductionWheat is a significant contributor to the food and nutritional security of the world. Due to climatic constraints and heat stress condition, the potentiality of wheat to eradicate existing hunger and malnutrition has been severely limited all around the world.Materials and MethodsTo evaluate the performance and stability of elite wheat genotype under irrigated and heat stress environment, a field experiment was conducted during the wheat growing season of 2020, 2021 and 2022 under irrigated and heat stress environment that altogether created six distinct wheat growing environments.ResultsThe combined ANOVA revealed that all quantitative traits studied were significantly influenced by heat stress environments (p < 0.01). Which Won Where (WWW) model revealed, Bhairahawa lines (BL) 4407, Nepal lines (NL) 1384 and NL 1346 performed best under irrigated environments of 2020, 2021 and 2022 while BL 4407, NL 1384 and NL 1381 performed best under heat stress environment of 2020, 2021 and 2022. WWW model showed, NL 1369, NL 1386 and NL 1376 as the most stable genotypes across irrigated and heat stress environment. The phenotypic correlation, path analysis, network diagram, cluster analysis and cluster based principal component analysis analysis revealed traits, days to booting (DTB), plant height (Ph), spike length (SL), ten spike weight (TSW) and thousand kernel weight (TKW) are most closely associated with grain yield of wheat.ConclusionHigh yielding genotypes should be selected based on earliness in DTB, longer Ph, SL and higher TSW and TKW under both environments. Breeding for taller genotypes should specifically be focused to obtain high yielding genotypes under heat stress environments.

Influence of Environment by Spacing and Phosphorus Fertilizer Interaction on the Productivity of Faba Bean (Vicia faba L.) in the Major Nitisol Production Areas in Ethiopia

Though Faba bean is an important pulse crop grown in the highlands of Ethiopia, its productivity is far below its potential due to several constraints, such as optimum plant density and fertilizer rates meant for major faba bean growing areas. Hence, a field experiment was conducted to determine the appropriate seeding and fertilizer rates for faba bean production at Holeta, Jima, Kulumsa, and Sinana areas during the 2019 to 2021 main cropping seasons. Factorial combinations of phosphorus fertilizer (23, 46, and 69 kg P2O5/ha), intra-row spacing (7, 10, and 13 cm), and inter-row spacing (30, 40, and 50 cm) were laid out in a randomized complete block design with three replications. Due to the non-homogeneity of variances across locations, a separate combined ANOVA was done for Kulumsa and Jimma that were found homogeneous. Similarly, a separate combined ANOVA over years was done for Holeta. On the other hand, Sinana's two-year data lacked homogeneity across years and with either of the other testing sites. Hence, results of each year presented. Based on the ANOVA results, the lowest fertilizer rates of 23 kg P2O5/ha together with 10 cm intra-row spacing and 40 cm inter-row spacing, were found to be optimum for the study areas around Kulumsa and Jimma while, the highest fertilizer rate of 69 kg P2O5/ha together with 10 cm intra-row spacing and 40 cm inter-row spacing, were found to be economically optimal for faba bean production in West Shewa acidic nitisols. On the other hand, to get reliable results, the experiment should be repeated at least once in one growing season at Sinan testing sites.

Comprehensive Assessment of the Genotype-Environment Interaction and Yield Stability of Boro Rice Genotypes under Four Environments in Bangladesh Using AMMI Analysis.

Yield stability, alongside high yield potential and broad adaptation to various agroclimatic environments, is a key objective for rice breeders aiming to ensure food security. This study aimed to explore the most suitable and stable Boro rice genotypes for Bangladesh. Ten Boro rice genotypes underwent testing in four environments during the 2022 Boro season to investigate genotype-environment interaction (GEI) and yield stability performance. The experiment utilized three replications of a completely randomized block design. Yield stability performance was assessed through combined analysis and the additive main effects and multiplicative interaction (AMMI) model. The combined ANOVA revealed that the environment explained 10.23%, while GEI accounted for 9.17%, and the genotypes captured 80.60% of the variance, significantly impacting grain yield. Significance was observed in the environment, genotype main effects, and GEI. Analysis indicated that BRRI dhan 68 yielded the highest (6,754 kg·ha-1) and BRRI dhan 88 the lowest (5,620 kg·ha-1) among the investigated genotypes. In addition, genotypes BRRI dhan 84, BRRI dhan 81, and BRRI dhan 67 exhibited the highest grain yields. The Rangpur environment demonstrated considerable stability across the four environments with a high mean value of grain yield (7,206 kg·ha-1). Therefore, the AMMI model emerges as a valuable tool for identifying the most suitable and stable Boro rice genotypes with high-yielding potential across various regions in Bangladesh, as well as under diverse conditions.

Studies on Components of Genetic Variability in Indian Mustard (Brassica juncea (L.) Czern and Coss.) Genotypes across Five Locations in Northern Karnataka

Indian mustard is one of the prominent edible oil yielding crops cultivated in India, with oil content ranging from 37 to 48%. An experiment was conducted to evaluate the variability in eight promising genotypes of Indian mustard along with two checks, Dodda Sasive and NRCHB-101, laid in RCBD with three replications at five different locations across Karnataka. The observations were recorded for 12 biometric traits of mustard, including seed yield and oil content and analyzed using a combined ANOVA. Biometric tools like heritability and genetic advance are crucial components of genetic variability that serve to assess variation in the available germplasm and assist in the selection of a desirable genotype. The traits, number of secondary branches and racemes per plant have registered a high genotypic coefficient of variance as well as a high genetic advance as a percent of the mean indicating the possibility of rapid improvement. A minimal influence of the environment was noticed in the expression of 8 traits out of 12 that exhibited high heritability in a broad sense. Among all the traits, only seed yield exhibited a high genetic advance coupled with moderate heritability, which signifies that seed yield is governed by additive gene effects. The genotypes that exhibited superior performance with respect to yield across all the locations were TM-210, TM-136 and KMR (E) 16-11.

Comparative study of univariate and multivariate selection strategies based on an integrated approach applied to oilseed rape breeding

AbstractChoosing the right method to discover superior genotypes is always one of the major challenges that oilseed rape (Brassica napus L.) researchers deal with. This work aimed to compare the univariate and multivariate selection indices utilizing variance‐based, additive main effects and multiplicative interaction based, best linear unbiased prediction based, and genotype × environment/trait based strategies to determine the best‐performing (regarding stability, yield, and agronomic traits) oilseed rape genotypes. Utilized indices were categorized into five general methods, including univariate performance selection index (UPSI: mean performance), univariate stability selection indices (USSIs: environmental variance, Shukla's stability variance, AMMI stability value, modified AMMI stability value, modified AMMI stability index, and weighted average of absolute scores), multivariate performance selection indices (MPSIs: genotype by trait [GT], genotype by yield × trait [GYT], and ideotype‐design index [FAI‐BLUP]), univariate performance and stability selection indices (UPSSIs: coefficient of variability, harmonic mean of genotypic value [HMGV], relative performance of genotypic value [RPGV], WAASBY [WAASB and mean performance {Y}], and genotype + genotype × environment interaction [GGE]), and multivariate performance and stability selection index (MPSSI: MTSI). Data from 18 oilseed rape genotypes and two checks were obtained from trials conducted at eight locations in Iran during two consecutive crop years from 2018 to 2020. The grain yield (GY) and agronomic traits were evaluated. The significant genotype × environment interaction was found for GY and agronomic traits in the combined analysis of variance. In terms of GY, the selection indices GGE, HMGV, and RPGV (6.51), GYT (6.43), GT (5.35), MTSI (4.95), FAI‐BLUP (4.64), and WAASBY (3.97) achieved better‐than‐average genetic gains (GSs). Using an optimized model of USSIs (O‐USSIs), GS increased from −0.15 to 3.26 on average, which shows the efficiency of O‐USSIs in high‐yielding and stable genotypes selection. Based on Spearman's ranking correlation, a positive relationship was found among MPSIs, MPSSI, UPSSIs, and UPSIs, but not for USSIs. Based on an integrated model of selection indices, genotypes G2, G10, G11, G14, and G20 were detected as superior genotypes with high‐yielding, high‐stability, and desirable agronomic traits. It could be concluded that combining different approaches of genotype selection based on an integrated model can provide better answers to oilseed rape breeders.

Genotype x Environment Interaction and Oil yield Stability of Linseed (Linum usitatissimum L.) Genotypes in North, Central and Southeast Ethiopia

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.

Climate Smart Bread Wheat (Triticum aestivum L.) Variety Development for Optimum Moisture Areas of Ethiopia

A study was undertaken during the 2017–18 to 2018–19 cropping seasons at at eleven locations and/or seventeen environments in optimum moisture areas of Ethiopian to identify stable genotypes with high grain yield and release as a variety for optimum-moisture environments. Alpha-lattice design with three replications was used. The combined ANOVA revealed very highly significant differences (p≤0.001) among genotypes, environments, and GEI for yield and its components. The environment sum of squares contributed more than the genotype and GEI sum of squares for the total variance of all traits. When we consider the overall mean for grain yield, genotype ETBW8751 (5.12 t ha-1) the highest value followed by ETBW9554 (5.10 t ha-1) whereas the lowest grain yield was obtained from the genotype ETBW8804 (3.67 t ha-1). GGE and AMMI analysis explained almost similar amounts of variation; however, AMMI still show a slightly greater proportion than GGE during our study. According to AMMI and GGE analysis genotype 21 (ETBW9553) was more stable as well as high yielding followed by 22 (ETBW9554) and 2 (ETBW8751. Conversely, 15 (ETBW9547) was unstable, but high yielding. Hidasse had low yields but was unstable. ETBW9554 was validated on farmers’ fields and recommended for registration as a commercial variety and finally released in 2020 with its designated local name “Boru” for commercial production for mid to highland agro-ecologies in Ethiopia.

Growth Performance of Photoperiod-Sensitive Rice (Oryza sativa L.) Varieties in Different Soil Types under Rainfed Condition in Cambodia

In Cambodia, rice is predominantly produced in areas with rainfed lowland conditions where photoperiod-sensitive varieties are cultivated. A number of varieties have been released for rainfed lowland areas, and the rice grain yield has reportedly increased by approximately 12% over the past ten years. Moreover, great fluctuations in yield performance have been observed across different soil types of the rainfed ecosystems of Cambodia. Therefore, the present study aimed to analyze the grain yield and stability among ten popular varieties that were released for rainfed lowland ecosystems across the four different soil types in Cambodia in two years. The grain yield varied 566 g m−2 as the highest in clay soil and about 220 g m−2 as the lowest in sandy soil. A combined ANOVA revealed significant differences for the main effect of genotype, environment, and genotype-by-environment interaction (GEI) for all yield-related traits and grain yield per square meter. The principal component test results showed that the heterogeneity of grain yield was mainly attributable to the effect of environment, followed by the effect of genotype. In fertile conditions, a higher percentage of filled grains was supported by higher leaf N until the late stage with a wider flag leaf. In conditions of moderate fertility, larger numbers of panicles were supported by a higher percentage of productive culms with higher leaf N until the late stage. In conditions of poor fertility, a higher percentage of filled grains was supported by higher leaf N until the late stage, which is considered to be important for higher grain yield. The variety Phka Rumduol showed these preferable traits and produced higher yields in fertile to poor natural soil fertility conditions with moderate variation. This variety is considered to be more desirable and ideal due to its stability and higher grain yield. The other varieties, namely, Phka Mealdei, Phka Rumdeng, and CAR4, were identified as above-average yielders. Therefore, those varieties potentially may be recommended for cultivation in rainfed lowland rice ecosystems in Cambodia due to their high yields. CAR4 showed moderate variation at the same level as Phka Rumduol. From the point of stability, Phka Rumduol and CAR4 can be expected to excel.

Morphological diversity of ancho chile pepper landraces from Mexico

AbstractAncho (width) chile peppers have economic, social, culinary and cultural importance in Mexico and worldwide. This chile type considers divergent subtypes that altogether have not been analysed and therefore their morphological diversity has not been systematically described. The objectives were to describe the morphological diversity of ancho pepper landraces from Mexico, to identify groups of similarity and to define the traits with the higher contribution to the total variation. Eighty-six landraces of ancho chile peppers (red, ‘mulatos’, ‘miahuatecos’, ‘cristalinos’ and ‘huacle’), collected in six states of Mexico, and two commercial controls were evaluated in two localities, in a simple randomized complete block experimental design. We recorded 76 morphological traits. Statistical analysis included a combined ANOVA, Pearson's correlation coefficient, discriminant analysis, principal components and clusters. The morphological diversity in ancho chile peppers was mainly made up of fruit width, fruit wall thickness, stem diameter, corolla length, seed weight per fruit, plant height, stem length and pubescence. We defined four groups, which made it possible to differentiate ancho chile peppers of Puebla and the huacle chile pepper of Oaxaca from populations collected in the north and ‘Bajío’ (midland) parts of Mexico. Ancho chile peppers of Mexico showed wide morphological differences according to the type of chile pepper and seed collection regions. The traits that contributed the greatest morphological diversity were fruit width, fruit wall thickness, stem diameter, corolla length, seed weight per fruit, plant height, stem length and pubescence.

Sustainability on Different Canola (Brassica napus L.) Cultivars by GGE Biplot Graphical Technique in Multi-Environment

Knowledge about the extent of genotype in environment interaction is helpful for farmers and plant breeders. This is because it helps them choose the proper strategies for agricultural management and breeding new cultivars. The main contribution of this paper is to investigate genotype on environmental interaction using the GGE biplot method (Genotype and the Genotype-by-Environment) in ten canola cultivars. The experimental design was a randomized complete block design (RCBD) with three replications to assess the stability of grain yield of ten canola cultivars in five regions of Iran, including Birjand, Karaj, Kashmar, Sanandaj, and Shiraz, within two agricultural years of 2016 and 2017. The results of combined ANOVA illustrated that the effects of the environment, genotype × environment, and genotype were highly significant at 1%. Variance Analysis showed that three environmental impacts, genotype, and interaction of genotype in the environment effect, produced 68.44%, 18.63%, and 12.9% of the total variance. The GGE biplot graphs were constructed using PCA. The first principle component (PC1) explained 65.3%, and the second (PC2) explained 18.8% of the total variation. The research examined polygon diagrams to identify two top genotypes and four mega-environments. Also, the appropriate genotypes for each environment were diagnosed. Using the GGE biplot, it was possible to make visual comparisons and identify superior genotypes in canola. Accordingly,. The results obtained from graphical analysis indicated that Licord, Hyola 401 and Okapi genotypes showed the highest yield and were selected as the most stable genotypes. Also, Karaj region was chosen as a experimental region where the screening of genotypes was very suitable. Based on the ranking of the genotypes in the most suitable region (Karaj), Okapi genotype was selected as the desired genotype. In examining the heatmap drawn between the genotypes and the investigated environments, a lot of similarity between the genotypes of Sarigal, Hyola 401 and Okapi was observed in the investigated environments. The GGE biplot graphs enabled the detection of stable and superior environments and the grouping of cultivars and environments based on grain yield. The results of this research can be used both for extension and for future breeding programs. Our results provide helpful information about the canola genotypes and environments for future breeding programs.

Performance of some early-maturing groundnut (Arachis hypogaea L.) genotypes and selection of high-yielding genotypes in the potato-fallow system.

India imports the most edible oils because domestic demand exceeds production. Horizontally expanding groundnut production in non-traditional areas especially in the potato-paddy rice-fallow system is possible for increasing production and it requires trait-specific cultivars. Only 1% of oilseeds are grown in non-traditional regions. Nine interspecific groundnut derivatives were tested in potato-fallow system at Deesa, Gujarat, and Mohanpura, West Bengal, and non-potato fallow areas in Junagadh during Kharif 2020 to examine their performance and adaptability. Genotype-by-environment (G×E) interaction significantly affected pod yield and its components in the combined ANOVA. "Mean vs. stability" showed that the interspecific derivative NRCGCS 446 and variety TAG 24 were the most stable and valuable genotypes. GG 7 yielded more pods in Junagadh, whereas NRCGCS 254 yielded more in Mohanpur. Low heritability estimates and strong G×E interaction for flowering days showed complicated inheritance and environmental effects. The shelling percentage was significantly correlated with days to 50% blooming, days to maturity, SCMR, HPW, and KLWR, demonstrating negative connections between maturity, component characteristics, and seed size realisation.

Flag Leaf of Bread Wheat (Triticum aestivum L.) Genotypes and Relation with Yield Component under Rainfed Conditions

An experiment was carried out to assess flag leaf fresh and dry weight of advanced bread wheat genotypes and their relation with yield and yield components. A total of 25 bread wheat genotypes were tested during the 2017-2018 cycles in four locations in the Trakia region, Turkey. The experiment was conducted in a randomized complete blocks design (RCBD) with four replications. Grain yield (GY), spike number per square meter (SNM), kernel number per spike (KNS), spikelet number per spike (SNS), spike weight (SW), plant height (PH), peduncle length (PL), spike length (SL), flag leaf fresh weight (FLFW), flash leaf dry weight (FLDW) were investigated. The combined ANOVA revealed significant differences among genotypes (G), environments (E) and their interaction (G×E) for grain yield (p<0.01). The highest grain yield was performed by G21 (6807 kg ha-1) and G20 (6776 kg ha-1). Both genotypes also had higher yield potential across four environments. Therefore, they were considered stable genotypes. Flag leaf fresh and dry weight in wheat genotypes positively affected spike length, spikelet number per spike, kernel number per spike and spike weight. As the plant density per unit area increased, there was a decrease in flag leaf fresh and dry weight. Increasing in the spike number per square meter negatively affected and reduced flag leaf fresh and dry weight in wheat genotypes. Results showed that spike weight, flag leaf fresh and dry weight of the wheat genotypes could be used in the selection of wheat breeding study for yield components. The longest spike, the highest number of grains per spike and the number of spikelets were determined in G17, together with the yield above the average. In addition, G17 had the highest spike weight and flag leaf fresh and dry weight. For this reason, G17 has been determined that can be used in breeding studies due to its agronomic characteristics. The results of the research showed that flag leaf dry and fresh weight could be used for yield components in wheat breeding selection under rainfed conditions.

Response of Cotton Genotypes to Water-deficit Stress using Drought Tolerance Indices and Principal Component Analysis

Drought impacts on cotton cultivation and production are expected to worsen as a result of global warming and water-deficit stress. Drought tolerance indices and PCA analysis were used to evaluate drought stress responses in eleven cotton genotypes and fifteen indices' ability to identify drought-tolerant genotypes under normal and drought circumstances. Seed cotton yield (Kentar feddan-1) was significantly affected by genotypes, years, and their interaction (p ≤ 0.05 or 0.01) under normal and water-deficit stress conditions, according to a combined ANOVA. Except for error variance, all genetic parameters studied for seed cotton yield were higher in normal irrigation conditions than in water-deficit stress conditions. According to PCA analysis, The STI, MP, GMP, HM, ATI, SSPI, and TOL are suitable indicators and were similar in their ability to screen, rank and detect tolerant genotypes, due to positive correlations among each other and also the highest association with seed cotton yield in both irrigation conditions. The genotypes G4, G9, and G10 (Group A) seemed to be the most drought-tolerant and cotton productive based on mean performance, GxY heatmap analysis, drought tolerance indices, and PCA analysis. The results of our study's drought tolerance indices and PCA could be useful and appropriate for studying drought tolerance mechanisms and cotton yield improvement in Egypt.

GENOTYPE BY ENVIRONMENT INTERACTION AND STABILITY ANALYSIS OF ETHIOPIAN COMMERCIAL DURUM WHEAT (Triticum turgidum L.) CULTIVARS IN SOUTHERN ETHIOPIA

&lt;p class="abstrakinggris"&gt;The use of improved varieties with wider adaptability and stability is necessary to maximize the productivity of durum wheat. However, due to genotype by environment interaction (GEI) effect, superior genotypes in one environment could be inferior in other environments. Hence, a multi-environmental trial (MET) was commenced to determine the magnitude of GEI effect and identify stable durum wheat genotypes across environments. The MET was conducted using nine durum wheat cultivars in randomized completely block design with three replications across four contrasting locations in 2020 crop seasons. The result of combined analysis of variance showed the presence of significant differences among the main effects; environments, genotypes, and GEI effects for grain yield. The additive main effects and multiplicative interaction (AMMI) combined ANOVA for main effects of environment, genotype, and GEI revealed highly significant differences among cultivars with 66.2%, 26.4%, and 7.3% share of sum square variation, respectively, of the total variation. AMMI and genotype plus genotype by environment (GGE) Bi-plot analysis identified the cultivars Fetan, Denbi, and Mangudo as high yielders and adaptive to the favourable locations. AMMI stability value and yield stability index identified Mangudo as the most stable and adaptive cultivar across locations. The AMMI Bi-plot analysis showed that the first two interaction principal component analysis (IPCAs) captured 90.45% of the total interaction sum of squares (ISS), where IPCA1 took 55.61% and IPCA2 accounted for 34.84% of GEI effects. This study identified Mangudo as the most stable cultivar with acceptable yield while Fetan was the top yielder genotype across locations.&lt;/p&gt;

Assessment of environment effect on yield component in barley (Hordeum vulgare L.) genotypes under rainfed conditions

Purpose. This research was carried out to assess barley genotypes yield and quality parameters under various environmental conditions.&#x0D; Methods. The experiments were set up with 25 barley genotypes in a completely randomized blocks design with four replications at four locations in the 2018–2019 cycles in the Trakia region, Turkey. Data on grain yield, plant height, days of heading, 1000-kernel weight, test weight, protein ratio and grain uniformity were investigated.&#x0D; Results. The combined ANOVA revealed significant differences (p &lt; 0.01) among genotypes and environments for all parameters investigated. In the study genotype G4 (8514 kg ha−1) had a higher yield followed by G9 (8369 kg ha−1). The highest thousand kernel weight was 52.0 g in G14 and the test weight was 75.1 kg in G5. There was a significant difference among genotypes for protein ratio and genotype G22 had a higher protein ratio, followed by G23 and G24. The grain uniformity in barley is an essential parameter and G14 had a higher ratio of grain uniformity. Correlation analyses showed that a negative correlation was determined between grain yield with days of heading (r = −0.506**), plant height (r = −0.583**), and protein ratio (r = −0.542**). 1000-kernel weight and test weight were significantly positively correlated (r = 0.708**). Grain uniformity had a positive correlation with 1000-kernel weight (r = 0.898**) and test weight (r = 0.539**). Protein ratio was positively associated with plant height (r = 0.692**). According to stability analysis genotypes G9, G3, G15, G2, and G17 were adaptable to less fertile environmental conditions. It was determined that G10 and G16 were well adaptable to all environmental conditions and also were ideal in terms of higher-yielding ability and stability.&#x0D; Conclusions. While genotype G9 has high yield potential, G10 and G16 have high adaptability to different environmental conditions. The environmental effect was found to be very important according to the parameters examined. Early and short genotypes have higher yield potential. Environment E4 was the ideal environment because located close to the first concentric circle in the environment-focused biplot. Therefore, it should be regarded as the most suitable to select widely adapted barley genotypes.

Adaptability Study of Yield and Yield Related Trait Performance of Improved Bread Wheat (Triticum aestivum L.) Varieties in North Shewa Zone Oromia, Ethiopia

Bread Wheat (Triticum aestivum L.) is an important cereal crop, which receives the most attention of specialists in plant breeding and production as worldwide. The Knowledge of the interaction between genotypes and environment with yield and yield components is a key aspect of effective selection in crop improvement. Therefore, the objective of this study was: to identify Adaptable bread wheat variety/ies with high level of grain yield and yield stability across locations. The study used 16 bread wheat released varieties, against local check at Fitche Agricultural Research Center (FiARC) in 2020-2022 cropping season. Ten important agronomic traits data were evaluated. Analysis of variance noticed significant difference, among varieties in both separated and combined analysis of variance. The combined ANOVA and the additive main effects and multiplicative interactions (AMMI) analysis for grain yield across environments exhibited significantly affected by environments, which explained 76.6% of the total variation. The genotype and genotype environmental integration were significant and accounted for 8 and 13.1%, respectively. Principal component (PCA) 1 and 2 accounted for 6.5 and 3.5% of the GEI, respectively, with a total of 10% variation. Generally, Sanete and Dandaa varieties were identified for yielding ability and stability, and recommended for the study area and similar agro-ecologies.

Interpreting Variety–Location–Fertilizer Interactions to Enhance Foxtail Millet Productivity in Northern China

Foxtail millet (Setaria italica (L.) P. Beauv.) is an important traditional cereal crop in dryland ecological agriculture in China and is widely grown in India, the United States, and Nigeria. It is of significance to understand the variety–location–fertilizer (V–L–F) interaction for highly efficient production. Therefore, a two-year field experiment was conducted with six varieties in five locations, and data were analyzed by combined ANOVA analysis, redundancy analysis (RDA), and additive main multiplicative interaction (AMMI). The results showed that the mean sum of squares was significantly different among years, locations, varieties, fertilizations, and their interactions, except for Y–V and V–F interactions. The contributions of various factors to yield variation varied, location was the largest contributor (38.7%), followed by year (33.6%), and variety and fertilizer contributed 7.1% and 3.2%, respectively. JI25 was widely adapted, and its yield was stable and higher than that of others over diverse environments in two years. The RDA results showed that two principal components explained more than 66.1% of the yield variance, while more than 63.0% of the variances were clustered in the first factor. Excessive single rainfall or total rainfall and air temperature (especially minimum temperature) were significantly associated with the millet yield. The results offered an important reference for variety layout, natural resource potential mining, and formulation of efficient green cultural practices.