Evaluation of Indian ginseng [Withania somnifera (L.) Dunal] breeding lines and genotype-by-environment interaction across production environments in western India

In order to find location-specific and broadly adapted genotypes for total root alkaloid content and dry root yield along with additive main effects and multiplicative interactions (AMMI) and genotype (G) main effects plus genotype × environment (E) interaction in Indian ginseng (Withania somnifera (L.) Dunal), (GGE) biplot analyses were used in the current study. Trials were carried out in a randomized complete block design (RCBD) over three succeeding years viz., 2016-2017, 2017-2018 and 2018-2019 at three different locations (S. K. Nagar, Bhiloda and Jagudan). Analysis of variance (ANOVA) for AMMI for dry root yield revealed that the environment, genotype, and GE interaction, respectively, accounted for significant sums of squares of 35.31%, 24.89%, and 32.96%. For total root alkaloid content, a significance of 27.59% of total sum of squares was justified by environment, 17.72% by genotype and 43.13% by GEI. Nine experimental trials in total were taken into consideration as contexts for the GEI analysis in 16 genotypes, including one check. AMMI analysis showed that genotypes, SKA-11, SKA-27, SKA-23 and SKA-10 were superior for mean dry root yield and SKA-11, SKA-27 and SKA-21 had better performance for total root alkaloid content across environment. The GGE biplot analysis showed genotypes SKA-11, SKA-27, SKA-10 desirable for dry root yield and SKA-26, SKA-27, SKA-11 for total root alkaloid content. As a result of the GGE and AMMI biplot techniques, SKA-11 and SKA-27 were determined to be the most desired genotypes for both total root alkaloid content and dry root yield. Further, simultaneous stability index or SSI statistics identified SKA-6, SKA-10, SKA-27, SKA-11 and AWS-1 for higher dry root yield, whilst SKA-25, SKA-6, SKA-11, SKA-12 and AWS-1 for total alkaloid content from root. Based on trait variation, GGE biplot analysis identified two mega-environments for dry root yield and a total of four for total root alkaloid content. Additionally, two representative and discriminating environments-one for dry root production and the other for total root alkaloid content were found. Location-specific and breeding for broad adaptation could be advocated for improvement and release of varieties for Indian ginseng.

Crop model determined mega-environments for cassava yield trials on paddy fields following rice

The Cropping System Model (CSM)-MANIHOT-Cassava provides the opportunity to determine target environments for cassava (Manihot esculenta Crantz) yield trials by simulating growth and yield data for various environments. The aim of this research was to investigate whether cassava production on paddy fields in Northeast, Thailand could be grouped into mega-environments using the model. Simulations for four different cassava genotypes grown on paddy field following rice harvest was conducted for various soil types and the weather data from 1988 to 2017. The genotype main effect plus genotype by environment interaction (GGE biplot) technique was used to group the mega-environments. The analyses of yearly data showed inconsistent results across years for environment grouping and for the winning genotypes of the individual environment group. An analysis using GGE biplot with the average value of the simulated storage root dry weight (SDW) for 30 years indicated that all 41 environments were grouped into two different mega-environments. This study demonstrated the ability of the CSM-MANIHOT-Cassava to help identify the mega-environments for cassava yield trials on paddy field during off-season of rice that could help reduce both time and resources.

Genome-wide QTL mapping of yield and agronomic traits in two widely adapted winter wheat cultivars from multiple mega-environments

Quantitative trait loci (QTL) analysis could help to identify suitable molecular markers for marker-assisted breeding (MAB). A mapping population of 124 F_5:7recombinant inbred lines derived from the cross ‘TAM 112’/‘TAM 111’ was grown under 28 diverse environments and evaluated for grain yield, test weight, heading date, and plant height. The objective of this study was to detect QTL conferring grain yield and agronomic traits from multiple mega-environments. Through a linkage map with 5,948 single nucleotide polymorphisms (SNPs), 51 QTL were consistently identified in two or more environments or analyses. Ten QTL linked to two or more traits were also identified on chromosomes 1A, 1D, 4B, 4D, 6A, 7B, and 7D. Those QTL explained up to 13.3% of additive phenotypic variations with the additive logarithm of odds (LOD(A)) scores up to 11.2. The additive effect increased yield up to 8.16 and 6.57 g m⁻² and increased test weight by 2.14 and 3.47 kg m⁻³ with favorable alleles from TAM 111 and TAM 112, respectively. Seven major QTL for yield and six for TW with one in common were of our interest on MAB as they explained 5% or more phenotypic variations through additive effects. This study confirmed previously identified loci and identified new QTL and the favorable alleles for improving grain yield and agronomic traits.