Genotype plus genotype by-environment interaction biplot and genetic diversity analyses on multi-environment trials data of yield and technological traits of cotton cultivars

ABSTRACT: Understanding the genetic diversity and overcoming genotype-by-environment interaction issues is an essential step in breeding programs that aims to improve the performance of desirable traits. This study estimated genetic diversity and applied genotype + genotype-by-environment (GGE) biplot analyses in cotton genotypes. Twelve genotypes were evaluated for fiber yield, fiber length, fiber strength, and micronaire. Estimation of variance components and genetic parameters was made through restricted maximum likelihood and the prediction of genotypic values was made through best linear unbiased prediction. The modified Tocher and principal component analysis (PCA) methods, were used to quantify genetic diversity among genotypes. GGE biplot was performed to find the best genotypes regarding adaptability and stability. The Tocher technique and PCA allowed for the formation of clusters of similar genotypes based on a multivariate framework. The GGE biplot indicated that the genotypes IMACV 690 and IMA08 WS were highly adaptable and stable for the main traits in cotton. The cross between the genotype IMACV 690 and IMA08 WS is the most recommended to increase the performance of the main traits in cotton crops.

Genotype by environment interaction analysis of wheat (Triticum aestivum L.) grain yield under rain-fed conditions in Zambia

Understanding genotype by environment interaction (GEI) is important for crop improvement because it aids in the recommendation of cultivars and the identification of appropriate production environments. The objective of this study was to determine the magnitude of GEI for the grain yield of wheat grown under rain-fed conditions in Zambia by using the additive main effects and multiplicative interaction (AMMI) model. The study was conducted in 2015/16 at Mutanda Research Station, Mt. Makulu Research Station and Golden Valley Agricultural Research Trust (GART) in Chibombo. During2016/17, the experiment was performed at Mpongwe, Mt. Makulu Research Station and GART Chibombo, Zambia. Fifty-five rain-fed wheat genotypes were evaluated for grain yield in a 5 × 11 alpha lattice design with two replications. Results revealed the presence of significant variation in yield across genotypes, environments, and GEI indicating the differential performance of genotypes across environments. The variance due to the effect of environments was higher than the variances due to genotypes and GEI. The variances ascribed to environments, genotypes, and GEI accounted for 45.79%, 12.96%, and 22.56% of the total variation, respectively. These results indicated that in rain-fed wheat genotypes under study, grain yield was more controlled by the environment than by genetics. AMMI biplot analysis demonstrated that E2 was the main contributor to the GEI given that it was located farthest from the origin. Furthermore, E2 was unstable yet recorded the highest yield. Genotype G47 contributed highly to the GEI sum of squares considering that it was also located far from the origin. Genotypes G12 and G18 were relatively stable because they were situated close to the origin. Their position indicated that they had minimal interaction with the environment. Genotype 47 was the highest-yielding genotype but was unstable, whereas G34 was the lowest-yielding genotype and was unstable.

Mega-Environment Analysis To Assess Adaptability, Stability, And Genomic Predictions In Grain Sorghum Hybrids

Multi-environment trials (MET) are fundamental for assessing genotype-by-environment interaction (GxE) effects, adaptability and stability of genotypes and provide valuable information about target regions. As such, a MET involving grain sorghum hybrid combinations derived from elite inbred lines adapted to diverse sorghum production regions was developed to assess agronomic performance, stability, and genomic-enabled prediction accuracies within mega-environments (ME). Ten females and ten males from the Texas A&M and Kansas State sorghum breeding programs were crossed following a factorial mating scheme to generate 100 hybrids. Grain yield, plant height, and days to anthesis were assessed in a MET consisting of ten environments across Texas and Kansas over two years. Genotype plus Genotype-by-block-of-environment biplot (GGB) assessed ME, while the "mean-vs-stability" view of the biplot and the Bayesian Finlay-Wilkinson regression evaluated hybrid adaptability and stability. A genomic prediction model including the GxE effect was applied within ME to assess prediction accuracy. Results suggest that grain sorghum hybrid combinations involving lines adapted to different target regions can produce superior hybrids. GGB confirmed distinct regions of sorghum adaption in the U.S. Further, genomic predictions within ME reported inconsistent results, suggesting that additional effects rather than the correlations between environments are influencing genomic prediction of grain sorghum hybrids.

Genetic Variation and Aflatoxin Accumulation Resistance among 36 Maize Genotypes Evaluated in Ghana

Aflatoxins are carcinogenic secondary metabolites produced predominantly by the fungi Aspergillus flavus and parasiticus. The toxin contaminate maize grains and threatens human food safety. Survey in Ghana revealed aflatoxin contamination of maize in excess of 941 ppb which is way beyond WHO and USA approved limits of 15 ppb and 20 ppb respectively. Host plant resistance is considered as the best strategy for reducing aflatoxins. This study was designed to (1) identify and select suitable maize lines that combine aflatoxin accumulation resistance and good agronomic traits under tropical conditions and (2) assess the genetic diversity among the exotic and locally adapted maize genotypes using significant morphological traits. Thirty-six maize genotypes, 19 from Mississippi State University, USA and 17 locally adapted genotypes in Ghana were evaluated for aflatoxin accumulation resistance and good agronomic characteristics across six contrasting environments using a 6x6 lattice design with three replicates. Five plants each per genotype were inoculated with a local strain of Aspergillus flavus inoculum at a concentration of 9 x 107/3.4 ml, two weeks after 50% mid silking. Total aflatoxin in the kernels were determined at harvest using HPLC method. Statistical analysis for agronomic traits and aflatoxin levels were performed using PROC GLM procedure implemented in SAS. The result indicated that genotype by environment interaction was significant (p < 0.05) for aflatoxin accumulation resistance and many other agronomic traits. Five genotypes (MP715, NC298, MP705, MP719, CML287 and TZEEI- 24) consistently displayed stable resistance across the environments and may serve as suitable candidates for developing aflatoxin resistant hybrids. Cluster analysis showed two distinct groups (locally adapted and exotic genotypes), an indication of re-cycled alleles per region. Broad sense heritability estimates for grain yield and aflatoxin accumulation resistance were moderately high, which could permit transfer of traits during hybrid development.

Genotype by Environment Interaction and Grain Yield Stability of Drought Tolerant Cowpea landraces in Ethiopia

Cowpea is one of the most important indigenous food and forage legumes in Africa. It serves as a primary source of protein for poor farmers in drought-prone areas of Ethiopia. The crop is used as a source of food, and insurance crop during the dry season. Cowpea is adaptable to a wide range of climatic conditions. Despite this, the productivity of the crop is generally low due to lack of stable and drought tolerant varieties. In this study, 25 cowpea genotypes were evaluated in five environments using a triple lattice design during the 2017 and 2018 main cropping seasons. The objectives of this study were to estimate the magnitude of genotype by environment interaction (GEI) and grain yield stability of selected drought tolerant cowpea genotypes across different environments. The additive main effect and multiplicative interaction (AMMI) model indicated the contribution of environment, genotype and GEI as 63.98 6%, 2.66% and 16.30% of the total variation for grain yield, respectively. The magnitudes of the GEI sum of squares were 6.12 times that of the genotypes for grain yield. The IPCA1, IPCA2 and IPCA3 were all significant and explained 45.47%, 28.05% and 16.59% of the GEI variation, respectively. The results from AMMI, cultivar superior measure (Pi), genotype plus genotype-by-environment (GGE) biplot yield stability index (YSI), and AMMI stability value (ASV) analyses identified NLLP-CPC-07-145-21, NLLP-CPC-103-B and NLLP_CPC-07-54 as stable and high yielding genotypes across environments. Thus, these genotypes should be recommended for release for production for drought prone areas. NLLP-CPC-07-143, Kanketi and CP-EXTERETIS were the least stable. The AMMI1 biplot showed that Jinka was a high potential and favorable environment while Babile was an unfavorable environment for cowpea production.

Assessment of genomic prediction reliability and optimization of experimental designs in multi-environment trials

Key message New forms of the coefficient of determination can help to forecast the accuracy of genomic prediction and optimize experimental designs in multi-environment trials with genotype-by-environment interactions. Abstract In multi-environment trials, the relative performance of genotypes may vary depending on the environmental conditions, and this phenomenon is commonly referred to as genotype-by-environment interaction (G$$\times$$ × E). With genomic prediction, G$$\times$$ × E can be accounted for by modeling the genetic covariance between trials, even when the overall experimental design is highly unbalanced between trials, thanks to the genomic relationship between genotypes. In this study, we propose new forms of the coefficient of determination (CD, i.e., the expected model-based square correlation between a genetic value and its corresponding prediction) that can be used to forecast the genomic prediction reliability of genotypes, both for their trial-specific performance and their mean performance. As the expected prediction reliability based on these new CD criteria is generally a good approximation of the observed reliability, we demonstrate that they can be used to optimize multi-environment trials in the presence of G$$\times$$ × E. In addition, this reliability may be highly variable between genotypes, especially in unbalanced designs with complex pedigree relationships between genotypes. Therefore, it can be useful for breeders to assess it before selecting genotypes based on their predicted genetic values. Using a wheat population evaluated both for simulated and phenology traits, and two maize populations evaluated for grain yield, we illustrate this approach and confirm the value of our new CD criteria.