Genetic Variability and G×E Interactions in a Diverse Set of Groundnut Accessions

Background: Knowledge of the genetic diversity for various agronomic traits and their interaction with the environment and subsequent classification of genotypes will be beneficial for identification of divergent and stable sources of agronomic traits. Methods: A set of 96 groundnut germplasm accessions belonging to four botanical groups were evaluated for three years (2017 to 2019) for pod yield and component traits using AMMI analysis and subsequently accessions were classified based Euclidean cluster analysis. Result: Among different botanical groups, Virginia genotypes matured late and possessed high SPAD chlorophyll meter readings (SCMR) and pod yield compared to Spanish types. The component traits of pod maturity like days to flowering (first and 50%) showed low heritability and high genotype × environment interaction (GEI) and significant negatively affected sound mature kernel (SMK) and shelling per centage (SP). The cumulative contribution of environment and GEI component to the total variance was the highest in the expression of SP (67%) followed by days to maturity (54%) and days to 50% flowering (52%). Euclidean distance-based cluster analysis grouped the 96 accessions into five major clusters. Cluster I had accessions with higher pod yield, whereas cluster V contained accessions with low SLA, high SCMR and moderate pod yield. High yielding as well as stable accessions identified based on AMMI stability value (ASV) are NRCG 17332, 10076, 17268, 17197, 17108, 10106, 10089 and 17165. Trait specific as well as stable accessions identified in the present study can be useful donors for groundnut breeding programme.

Stability Analysis of Rice Hybrids for Grain Yield in Telangana through AMMI and GGE Bi-plot Model

An investigation was carried out on fifteen rice genotypes to identify stable rice hybrids across six different agroclimatic zones in Telangana state using AMMI and GGE bi-plot analyses during July to November, 2020. Analysis of variance clearly showed that environments contributed highest (65.47%) in total sum of squares followed by genotypes×environments (21.19%) indicating very greater role played by environments and their interactions in realizing final grain yield. AMMI analysis revealed that rice hybrids viz., RNRH 39 (G6), 27P31 (G14) and RNRH 15 (G1) were recorded higher mean grain yield with positive IPCA1 scores. The hybrids, JGLH 275 (G11) and JGLH 365 (G15) were plotted near to zero IPCA1 axis indicating that these hybrids are relatively more stable across locations. GGE bi-plot genotype view depicts that the hybrids, JGLH 365 (G15) and US 314 (G8) were inside the first concentric circle and found to be more stable across environments. GGE bi-plot environment view showed that Rudrur (E4) location was the most ideal environment. However, Warangal (E6) and Jagtial (E1) locations were poor and most discriminating. Depending on dispersion of environments in different directions, six locations were partitioned into three mega zones as first zone comprised of four locations viz., Kunaram (E2), Kampasagar (E3), Rudrur (E4) and Rajendranagar (E5) whereas highly dispersed Jagtial (E1) and Warangal (E6) were identified as two separate mega environments. The bi-plot view identified that 27P31 (G14), JGL 24423 (G2) and RNRH 39 (G6) were the best performing genotypes in first zone comprising four locations.

SEEDLING EMERGENCE STABILITY OF BAMBARA GROUNDNUT [VIGNASUBTERRANEA L. (VERDC.)] UNDER SAVANNA AND HUMID RAIN FOREST AREAS CONDITIONS DURING TWO CROPPING SEASONS IN COTEDIVOIRE

The yield of Bambara groundnut [Vignasubterranea L. (Verdc.)] is extremely low and unpredictable. Erratic yields have been attributed, at least in part, to variable or poor field establishment due to seedling emergence. The aim of this study wasto evaluate under field conditions, the effects of genotype and environment on seedling emergence in Bambara groundnut. Field experiment was conducted with 15 landraces in Northern and Southern regions of Cote dIvoire. A randomized complete block design with five replications was used in each environment. Estimated variables included number of day for emergence (DFE), mean emergence time (MET), emergence index (EI), time to 50% emergence (T50) and seedling emergence percentage (EP). Analysis of variance revealed significant (P < 0.05) differences between genotypes, locations and the interaction genotype x location for all traits studied. Location x year x genotype interactions were only significant for DFE, EI and EP.The highest DFE and T50 and MET were observed with the genotype Ci7. The least DFE, MET and T50 were obtained with genotypes Ci1, Ci2, Ci10, Ci11, Ci12, Ci13 and Ci22. Highest seedling emergence percentage was recorded with genotype Ci15 (93.29%) in Korhogo and Ci4 in Korhogo (90.85%) and Abidjan (89.59 %). Lowest EP was observed with the genotype Ci3 (42.33%).Correlations indicated that genotype with higher emergence percentage, emerged faster (low DFE, MET, T50) and exhibited higher emergence index (EI). AMMI analysis showed that Ci1, Ci4, Ci7, Ci11, Ci14 and Ci15 were the ideal genotypes because they expressed high and stable EP.

Grain yield stability analysis using AMMI and GGE biplot models in different breeding zones of Bangladesh

During the 2018-2019 Boro season (dry season), 70 rice genotypes were examined with alpha lattice experimental design with the goal of measuring grain yield stability analysis. Results indicated that AMMI analysis explained 100% of the G×E variance, while captured 81.74% variance. Based on the GGE and AMMI analysis, the most stable and high yielding genotype was identified G41 followed by G22, G26, G58, G24 and G61. The AMMI 1 biplot analysis revealed that the first primary component of interaction (IPC1) factor was responsible for 64.2 % variation due to G × E interaction. On other hand, the second primary component (PC2) factor accounted for 35.8% variation of the G × E interaction. These two-primary component (PC1 and PC2), all together accounted for 100% variation of the G × E interaction. The contribution of G68 was highest to the interaction followed by G70, G58, G42, G61, G45, G38, G14, G33, G60, G53, and G9. Best environment analysis indicated that the ranking was Rajshahi < Gazipur < Cumilla. GGE biplot analysis accounted for 81.74% variation comprising two principal components PC1 and PC2 with 45.62% and 36.12% variations respectively. Rajshahi was more stable than Gazipur. Based on environment analysis genotypes, G22, G26, G58, and G44 can be recommended as best stable genotypes that breeding zone. However, the genotype G61 was identified adapted to Cumilla breeding zone.

Yield Stability and Genotype Environment Interaction of Water Deficit Stress Tolerant Mung Bean (Vigna radiata L. Wilczak) Genotypes of Bangladesh

Water deficit stress is a critical abiotic constraint to mung bean production that affects plant growth and development and finally reduces crop yield. Therefore, a field experiment was conducted at five diverse environments using four water stress-tolerant genotypes, namely BARI Mung-8, BMX-08010-2, BMX-010015, and BMX-08009-7, along with two popular cultivated varieties (check) of BARI Mung-6 and BARI Mung-7 to evaluate more stable tolerant genotypes across the country. Stability analysis was performed based on the grain yield. The combined analysis of variance showed significant variations among genotypes, environments, and their interactions. The AMMI analysis of variance indicated that genotype accounted for 91% of the total sum of squares for grain yield, followed by genotype × environment interaction (5%), and environment (4%). Partitioning of interaction indicated that the first three interaction principal components (IPCA1–IPCA3) were highly significant (p ≤ 0.01). Using these significant IPCAs, AMMI stability parameters and non-parameter indices BMX-010015 was found stable across the environment based on yield traits and grain yield. The BMX-08010-2 genotype also showed significant regression coefficient (bi) more than unity, and non-significant deviation from regression (S2di) values, indicating suitable for a favorable environment considering grain yield. So, based on the stability analysis (Eberhart and Russell), additive main effects, and multiplicative interactions (AMMI) analysis, the BMX-010015 and BMX-08010-2 could be suitable for having tolerance to water deficit stress.

ADAPTABILITY AND STABILITY FOR IRON AND ZINC IN COWPEA BY AMMI ANALYSIS

ABSTRACT Iron and zinc deficiency is one of the main problems affecting vulnerable populations in the Colombian Caribbean, thereby generating malnutrition from the consumption of foods with low content of essential minerals. The objective of this study was to evaluate the genotype-environment interaction for iron and zinc accumulation in grains in 10 cowpea bean genotypes by additive main effects and multiplicative interaction (AMMI) model and to select the most stable ones to stimulate their planting or as parents in the genetic improvement program. Nine promising lines and a commercial control were evaluated using the randomized complete block design with 10 treatments and four replications in 10 environments of the northern Colombia in the second semester of 2017 and first of 2018. The adaptability and stability analysis was done using AMMI model. The results showed highly significant differences at the level of environments, genotypes, and genotype-environment interaction for iron and zinc, demostrating a differential adaptability of genotypes in the test environments. Genotypes 2 and 3 expressed greater adaptability and stability for iron contents in the seed; while genotype 1, recorded it for zinc contents. These three genotypes outperformed the commercial control and, therefore, can be recommended for planting or be used as parents in the genetic improvement program.

Wheat genotypes evaluated for GxE interactions in Central Zone of the country by AMMI analysis

Highly significant effects of environments, GxE interaction and genotypes were observed for cropping years 2017-18 and 2018-19. Further analysis of interactions sum of squares bifurcated into seven significant multiplicative interactions principal components to assess the performances of genotypes as per AMMI based measures. For the first year of study wheat genotypes (G5, G6, G7) had top ranked by EV2, D2, ASV, ASV1 and ASTAB2 measures. MASV & MASV1 pointed towards G7, G8, G6 wheat genotypes. Association among these measures displayed graphically in a biplot analysis. Largest cluster comprised of D2, D3, D5, D7, ASV, ASV1, ASTAB2, EV2, EV3, EV5, ASTAB3, ASTAB5, ASTAB7 measures. Wheat genotypes (G1, G11, G3) pointed by EV2, D2, ASV, ASV1 and ASTAB2 values for the second year. MASV settled for G11, G7, G13 whereas MASV1 pointed towards G11, G7, G2. Biplot analysis based on first two PC’s observed largest group had clubbed measures D2, ASV, ASTAB2, EV5, MASV, MASV1, EV3, D3, D5, D7, EV7, ASTAB3 ASTAB5, ASTAB7. AMMI based measures would be useful to identify and recommend genotypes with high, stable and predictable yield across environments.

AMMI Appended with BLUP to Assess the Superiority of Hullness Barley Genotypes Evaluated under Coordinated System of the Country

Highly significant effects of environments (E), G×E interaction and genotypes (G) had expressed by AMMI analysis for hulless barley genotypes under coordinated barley improvement program. Environment effects explained 69.9% and 59.7% whereas Interaction effects accounted for 17% and 20.9% during cropping seasons of 2018-19 and 2019-20, respectively. Stability measure WAASB based on all significant interaction principal components ranked suitability of DWRB204, K1149 genotypes. Superiority index while weighting 0.65 and 0.35 for mean yield & stability ranked DWRB204, Karan 16 as of stable performance with high yield barley genotypes. Ranks as per composite measures MASV1 and MASV found NDB943, KB1750 as desirable genotypes. Lower values ASTAB measure achieved by Karan 16, NDB943. Biplot graphical analysis as per 40.4% of variation of the measures exhibited MASV1 clubbed with ASTAB, EV, SIPC, Za, W6, WAASB and MASV measures. Measure IPCA1 clubbed with SI corresponding yield based. W2, W3, W4 measures observed in different group. For the second-year lower value of WAASB measure had observed for PL891, KB1843, NDB943. Ranking of genotypes as per Superiority index found Karan16, UPB1086 as of stable performance with high yield. MASV1 and MASV identified Karan16, DWRB216 genotypes of choice for these locations. Barley genotypes Karan16, DWRB216 were selected as per values of ASTAB measure accounted AMMI analysis with BLUP of genotypes yield values. About 78.1% of variation of the measures under biplot analysis observed MASV1 grouped with ASTAB, EV, SIPC, and MASV. While Za joined together with W2, W3, W4, W5, W6, WAASB to form separate group.