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.

AMMI and GGE Biplot Analysis for Stability of Yield in Mid-early Pigeonpea [Cajanus cajan (L.) Millspaugh] Genotypes

Background: The genotype × environment interaction greatly influences the success of breeding and in multi-location trials complicates the identification of superior genotypes for a single location, due to magnitude of genotype by location interaction are often greater than genotype by year interaction. This necessitates genotype evaluation in multi environments trials in the advanced stages of selection. Methods: Nine elite pigeonpea genotypes of mid-early duration were evaluated in six diverse locations in randomized complete block design with three replications during kharif, 2019 to ascertain the stable genotypes, environments discrimination and genotype by environment crossovers using AMMI and GGE biplot stability models. Result: The results in the present investigation revealed that first two principal components explained 73.4% of variation interaction, while, 80.50% in GGE biplot. Both the models identified WRGE-126 (G6) as stable performer with high yield (1733 kg ha-1) and among the locations Tandur (E1) measured as the ideal environment. Whereas, the environments, Adilabad (E3) and Warangal (E4) were observed representative with better discriminating ability.

Bayesian Genomic Linear Regression

The Bayesian paradigm for parameter estimation is introduced and linked to the main problem of genomic-enabled prediction to predict the trait of interest of the non-phenotyped individuals from genotypic information, environment variables, or other information (covariates). In this situation, a convenient practice is to include the individuals to be predicted in the posterior distribution to be sampled. We explained how the Bayesian Ridge regression method is derived and exemplified with data from plant breeding genomic selection. Other Bayesian methods (Bayes A, Bayes B, Bayes C, and Bayesian Lasso) were also described and exemplified for genome-based prediction. The chapter presented several examples that were implemented in the Bayesian generalized linear regression (BGLR) library for continuous response variables. The predictor under all these Bayesian methods includes main effects (of environments and genotypes) as well as interaction terms related to genotype × environment interaction.

Bayesian and Classical Prediction Models for Categorical and Count Data

In this chapter, we explain, under a Bayesian framework, the fundamentals and practical issues for implementing genomic prediction models for categorical and count traits. First, we derive the Bayesian ordinal model and exemplify it with plant breeding data. These examples were implemented in the library BGLR. We also derive the ordinal logistic regression. The fundamentals and practical issues of penalized multinomial logistic regression and penalized Poisson regression are given including several examples illustrating the use of the glmnet library. All the examples include main effects of environments and genotypes as well as the genotype × environment interaction term.

Linear Mixed Models

The linear mixed model framework is explained in detail in this chapter. We explore three methods of parameter estimation (maximum likelihood, EM algorithm, and REML) and illustrate how genomic-enabled predictions are performed under this framework. We illustrate the use of linear mixed models by using the predictor several components such as environments, genotypes, and genotype × environment interaction. Also, the linear mixed model is illustrated under a multi-trait framework that is important in the prediction performance when the degree of correlation between traits is moderate or large. We illustrate the use of single-trait and multi-trait linear mixed models and provide the R codes for performing the analyses.

Partial Results Regarding the Genetic Determinism of Growth Process in a Native Horse Breed using Wither’s Height

Study of variability in domestic animal populations is the foundation of quantitative genetics. Based on statistical methods, the weights of the total phenotype variation that belong to its different fractions (causal components) are quantified: variation due to gene additive effect, variation due to allelic and non-allelic interactions, variation due to environment (general and special), variation due to genotype-environment interaction and possibly variation due to the association between genotype and environment. In this study, during 2017-2020, we used the method of analysis of variance with two sources of variation. The material was represented by 538 individuals from Hucul horse breed analyzed at 18, 30 and 42 months old). The heritability of character was 0.3402±0.1546 (18 months), 0.5549±0.2225 (30 months), 0.4506±0.1895 (42 months), suggest that this is a hereditary condition that follows a quantitative model of inheritance, where the influence of additive genetic factors is moderate to intense. We can conclude that, in this native breed and for this character, a significant share of the phenotypic value is due to the additive effect of genes.

GGE-BIPLOT OF MULTIVARIATE INDEX TO SELECT MAIZE PROGENIES FOR EFFICIENT ASSOCIATION WITH Azospirillum brasiliense1

ABSTRACT Maize is widely cultivated in Brazil, and nitrogen is a major nutrient for its yield. Azospirillum brasiliense bacteria help in plant nutrient supply; however, maize-Azospirillum symbiosis is not very efficient and requires selection of genotypes with a more efficient association. Multivariate indexes facilitate selection using a single value, and GGE-biplot analysis enables the visualization of the genotype-environment interaction from this value. The present study aimed to select progenies that effectively associate with the bacteria and study the efficiency of progeny selection using a multivariate index observed in the GGE-biplot method. The experiments were conducted in two cities in the state of Mato Grosso do Sul. In a simple 16 × 16 lattice, 256 genotypes were evaluated in the presence and absence of diazotrophic bacteria. PH, SL, SD, FI, HGM, SS, and GY were measured for the construction of a selection index. Genotypes exhibited significant genotype–environment interactions for all evaluated traits, allowing their use in the selection index. High-yield genotypes were not those with the highest selection index values. The traits GY, SD, HGM, SS, SL, and PH contributed the most to the construction of the index. The no-till system may have contributed to the weaker response of maize inoculated with Azospirillum brasiliense. Genotype 96 had the highest values of the characteristics used to calculate the GISI, along with the stability between environments.

Evaluation of stability in maize hybrids using univariate parametric methods

Genotype × environment interaction is one of the complex issues of breeding programs to produce high-yielding and compatible cultivars. Interaction of genotype × environment and make the more accurate selection, the performance and stability of hybrids need to be considered simultaneously. This study aimed to investigate stable genotypes with yield using 12 maize hybrids in different climatic conditions of Iran. The experimental design used was a randomized complete blocks design in three replications in two cropping years in Karaj, Birjand, Shiraz, and Arak stations. The simple analysis of variance performed on grain yield of genotypes indicated that all hybrids studied each year and station were significantly different in grain yield. Also, the combined analysis results showed a significant effect on the environment, the effects of genotype, and the interaction of genotype × environment and t in the studied hybrids different. Comparing Duncan's mean on the data obtained from the research, KSC705 genotypes with an average yield of 7.21 and KSC704 genotype with an average yield of 7.04 were identified as high yield cultivars. In order to identify stable cultivars, six stability parameters were used. KSC260 and KSC707 genotypes had stability Based on the environmental variance, also had stability based KSC705, KSC707 genotype on environmental the coefficient of variation, and KSC260 genotypes had stability based methods of genotype and environment interaction. As well as based on Eberhart and Russell regression coefficient had the stability to KSC400 and SC647 genotypes. Also, they were identified as the most stable genotypes based on the detection coefficient method, KSC707, and KSC703 genotypes.

Acrocomia spp.: neglected crop, ballyhooed multipurpose palm or fit for the bioeconomy? A review

Acrocomia spp., a genus of wild-growing palms in the neotropics, is rapidly gaining interest as a promising multipurpose crop. Diverse products can be derived from various components of the palm, the oils being of highest interest. Acrocomia shows similar oil yield and fatty acid composition to the African oil palm (Elaeis guineensis). It is, however, able to cope with a wider range of environmental conditions, including temporary water scarcity and lower temperatures, thus potentially a more sustainable alternative to its tropical counterpart. Acrocomia’s research history is recent compared to other traditional crops and thus knowledge gaps, uncertainty, and challenges need to be addressed. This review attempts to assess the acrocomia’s preparedness for cultivation by highlighting the state-of-the-art in research and identifying research gaps. Based on a systematic literature search following a value web approach, it (a) provides a comprehensive overview of research topics, (b) shows the development of publication activities over time and the drivers of this development, and (c) compiles main findings to assess the acrocomia’s preparedness for commercial cultivation. Our results confirm its multipurpose characteristic as a potential feedstock for manifold sectors. Research has continued to increase over the last decade, especially on A. aculeata and is driven by the interest in bioenergy. Increasing knowledge on botany has contributed to understanding the genetic diversity and genus-specific biology. This has enabled applied research on seed germination and propagation toward domestication and initial plantation activities, mostly in Brazil. Main research gaps are associated with genotype–environment interaction, planting material, crop management, and sustainable cropping systems. Overall, we conclude that acrocomia is at an early phase of development as an alternative and multipurpose crop and its up-scaling requires the integration of sustainability strategies tailored to location-based social-ecological conditions.