The low grain iron and zinc densities are well documented problems in food crops, affecting crop nutritional quality especially in cereals. Sorghum is a major source of energy and micronutrients for majority of population in Africa and central India. Understanding genetic variation, genotype × environment interaction and association between these traits is critical for development of improved cultivars with high iron and zinc. A total of 336 sorghum RILs (Recombinant Inbred Lines) were evaluated for grain iron and zinc concentration along with other agronomic traits for 2 years at three locations. The results showed that large variability exists in RIL population for both micronutrients (Iron = 10.8 to 76.4 mg kg−1 and Zinc = 10.2 to 58.7 mg kg−1, across environments) and agronomic traits. Genotype × environment interaction for both micronutrients (iron and zinc) was highly significant. GGE biplots comparison for grain iron and zinc showed greater variation across environments. The results also showed that G × E was substantial for grain iron and zinc, hence wider testing needed for taking care of G × E interaction to breed micronutrient rich sorghum lines. Iron and zinc concentration showed high significant positive correlation (across environment = 0.79; p < 0.01) indicating possibility of simultaneous effective selection for both the traits. The RIL population showed good variability and high heritabilities (>0.60, in individual environments) for Fe and Zn and other traits studied indicating its suitability to map QTL for iron and zinc.

Sorghum, a cereal of economic importance ensures food and fodder security for millions of rural families in the semi-arid tropics. The objective of the present study was to identify and validate quantitative trait loci (QTL) for grain yield and other agronomic traits using replicated phenotypic data sets from three post-rainy dry sorghum crop seasons involving a mapping population with 245 F9 recombinant inbred lines derived from a cross of M35-1 × B35. A genetic linkage map was constructed with 237 markers consisting of 174 genomic, 60 genic and 3 morphological markers. The QTL analysis for 11 traits following composite interval mapping identified 91 QTL with 5-12 QTL for each trait. QTL detected in the population individually explained phenotypic variation between 2.5 and 30.3 % for a given trait and six major genomic regions with QTL effect on multiple traits were identified. Stable QTL across seasons were identified. Of the 60 genic markers mapped, 21 were found at QTL peak or tightly linked with QTL. A gene-based marker XnhsbSFCILP67 (Sb03g028240) on SBI-03, encoding indole-3-acetic acid-amido synthetase GH3.5, was found to be involved in QTL for seven traits. The QTL-linked markers identified for 11 agronomic traits may assist in fine mapping, map-based gene isolation and also for improving post-rainy sorghum through marker-assisted breeding.

Soybean [Glycine max (L.) Merr.] is the leading Indian oilseed crop grown under rainfed conditions. Meticulous understanding of genotype × environment interaction patterns is essential to develop superior and widely adaptable soybean varieties. In the current study, 32 soybean genotypes were evaluated at eight locations for two consecutive years. Additive main effect and multiplicative interaction ANOVA revealed that only 41.6% of variance was explained by the first two interaction principal component axes (IPCAs), leaving 58.4% to the remaining 13 IPCs. The weighted average of absolute scores (WAASB) stability index, a best linear unbiased prediction–based mixed model that takes in to account all the IPCAs, has been used in stability analysis. SL1171 (WAASB score, 4.09) was found to be highly stable among the genotypes under study, with grain yield (2,050.87 kg ha−1) lower than the grand mean (2,082.50 kg ha−1). A superiority index that allows weighting between mean performance and stability (WAASBY) was used to select stable and high yielding genotypes. MACS 1620 (WAASBY score, 74.47) was found to be high yielding (2,476.05 kg ha−1) and widely adaptable. A simultaneous selection index (i.e., multi‐trait stability index [MTSI]) has been used for selecting early‐maturing and high‐yielding genotypes. DSb 33 was found to have the lowest MTSI (0.001) and can be used as a parent for breeding for early maturity and higher yield. The 100‐seed weight was found to be positively correlated with grain yield and can be used in direct selection for grain yield. Through genotypic cluster analysis, NRC 146 was found to be more divergent, with the highest mean 100 seed weight (16.39 g), and therefore can be used as a parent for breeding solely for grain yield.

SUMMARYSorghum [Sorghum bicolor (L.) Moench] grown in India is of two adaptive types: rainy and post-rainy. The post-rainy sorghum is predominantly consumed by humans. While releasing new cultivars through multi-location testing, major emphasis is given to the superiority of new cultivars over existing cultivars, with very little emphasis on the genotype × environment interaction (GEI). To understand the complexity of GEI in post-rainy sorghum testing location trials, the multi-location evaluation data of two post-rainy seasons (2009/10 and 2010/11) under the All India Coordinated Sorghum Improvement Project were analysed. In both years, location explained the highest proportion of total sum of squares followed by the GEI effect and main effect of genotype. Additive main effects and multiplicative interaction (AMMI), stability values (ASV) and genotype + genotype × environment interaction (GGE) instability values recorded high correlation resulting in identification of the best performing cultivars. However, the rank correlations were lower, though still significant. A mixture of crossover and non-crossover GEI was a common occurrence in both years. ‘Which-won-where’ analysis suggested the existence of four possible mega-environments (ME) among post-rainy testing locations, with a few non-informative locations within ME. Mega-environments are characterized by soil type, rainfall pattern and moisture conservation practices. The present study indicated the possibility of reducing the number of test locations by eliminating non-representative highly correlated locations and suggested the need to breed for location-specific genotypes rather than genotypes with wider adaptability.

Sorghum is a major food crop in the semi-arid tropics of Africa and Asia. Enhancing the grain iron (Fe) and zinc (Zn) concentration in sorghum using genetic approaches would help alleviate micronutrient malnutrition in millions of poor people consuming sorghum as a staple food. To localize genomic regions associated with grain Fe and Zn, a sorghum F 6 recombinant inbred line (RIL) population (342 lines derived from cross 296B � PVK 801) was phenotyped in six environments, and genotyped with simple sequence repeat (SSR), DArT (Diversity Array Technology) and DArTSeq (Diversity Array Technology) markers. Highly significant genotype � environment interactions were observed for both micronutrients. Grain Fe showed greater variation than Zn. A sorghum genetic map was constructed with 2088 markers (1148 DArTs, 927 DArTSeqs and 13 SSRs) covering 1355.52 cM with an average marker interval of 0.6 cM. Eleven QTLs (individual) and 3 QTLs (across) environments for Fe and Zn were identified. We identified putative candidate genes from the QTL interval of qfe7.1, qzn7.1, and qzn7.2 (across environments) located on SBI-07 involved in Fe and Zn metabolism. These were CYP71B34, and ZFP 8 (ZINC FINGER PROTEIN 8). After validation, the linked markers identified in this study can help in developing high grain Fe and Zn sorghum cultivars in sorghum improvement programs globally.