Sixteen polymorphic Simple sequence repeat (SSR) markers were used to determine the genetic diversity and varietal identification among 38 soybean (Glycine max (L.) Merr.) genotypes which are at present under seed multiplication chain in India. A total of 51 alleles with an average of 2.22 alleles per locus were detected. The polymorphic information content (PIC) among genotypes varied from 0.049 (Sat_243 and Satt337) to 0.526 (Satt431) with an average of 0.199. The pair wise genetic similarity between soybean varieties varied from 0.56 to 0.97 with an average of 0.761. These 16 SSR markers successfully distinguished 12 of the 38 soybean genotypes. These results suggest that used SSR markers are efficient for measuring genetic diversity and relatedness as well as identifying varieties of soybeans. Diverse genetic materials may be used for genetic improvements of soybean genotypes.

ICRISAT scientists, working with Indian programme counterparts, developed the world's first cytoplasmic-nuclear male sterility (CMS)-based commercial hybrid in a food legume, the pigeonpea [Cajanus cajan (L.) Millsp.]. The CMS, in combination with natural outcrossing of the crop, was used to develop viable hybrid breeding technology. Hybrid ICPH 2671 recorded 47% superiority for grain yield over the control variety 'Maruti' in multilocation on-station testing for 4 years. In the on-farm trials conducted in five Indian states, mean yield of this hybrid (1396 kg/ ha) was 46.5% greater than that of the popular cv. 'Maruti' (953 kg/ha). Hybrid ICPH 2671 also exhibited high levels of resistance to Fusarium wilt and sterility mosaic diseases. The outstanding performance of this hybrid has led to its release for cultivation in India by both a private seed company (as 'Pushkal') and a public sector university (as 'RV ICPH 2671'). Recent developments in hybrid breeding technology and high yield advantages realized in farmers' fields have given hope for a breakthrough in pigeonpea productivity.

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Key message Comparative assessment identified naïve interaction model, and naïve and informed interaction GS models suitable for achieving higher prediction accuracy in groundnut keeping in mind the high genotype × environment interaction for complex traits. Abstract Genomic selection (GS) can be an efficient and cost-effective breeding approach which captures both small- and large-effect genetic factors and therefore promises to achieve higher genetic gains for complex traits such as yield and oil content in groundnut. A training population was constituted with 340 elite lines followed by genotyping with 58 K ‘Axiom_Arachis’ SNP array and phenotyping for key agronomic traits at three locations in India. Four GS models were tested using three different random cross-validation schemes (CV0, CV1 and CV2). These models are: (1) model 1 (M1 = E + L) which includes the main effects of environment (E) and line (L); (2) model 2 (M2 = E + L + G) which includes the main effects of markers (G) in addition to E and L; (3) model 3 (M3 = E + L + G + GE), a naïve interaction model; and (4) model 4 (E + L + G + LE + GE), a naïve and informed interaction model. Prediction accuracy estimated for four models indicated clear advantage of the inclusion of marker information which was reflected in better prediction accuracy achieved with models M2, M3 and M4 as compared to M1 model. High prediction accuracies (> 0.600) were observed for days to 50% flowering, days to maturity, hundred seed weight, oleic acid, rust@90 days, rust@105 days and late leaf spot@90 days, while medium prediction accuracies (0.400–0.600) were obtained for pods/plant, shelling  %, and total yield/plant. Assessment of comparative prediction accuracy for different GS models to perform selection for untested genotypes, and unobserved and unevaluated environments provided greater insights on potential application of GS breeding in groundnut.

Foliar fungal diseases especially late leaf spot (LLS) and rust are the important production constraints across the peanut growing regions of the world. A set of 340 diverse peanut genotypes that includes accessions from gene bank of International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), elite breeding lines from the breeding program, and popular cultivars were screened for LLS and rust resistance and yield traits across three locations in India under natural and artificial disease epiphytotic conditions. The study revealed significant variation among the genotypes for LLS and rust resistance at different environments. Combined analysis of variance revealed significant environment (E) and genotype × environment (G×E) interactions for both the diseases indicating differential response of genotypes in different environments. The present study reported 31 genotypes as resistant to LLS and 66 to rust across the locations at 90 DAS with maturity duration 103 to 128 days. Twenty-eight genotypes showed resistance to both the diseases across the locations, of which 19 derived from A. cardenasii, five from A. hypogaea, and four from A. villosa. Site regression and Genotype by Genotype x Environment (GGE) biplot analysis identified eight genotypes as stable for LLS, 24 for rust and 14 for pod yield under disease pressure across the environments. Best performing environment specific genotypes were also identified. Nine genotypes resistant to LLS and rust showed 77% to 120% increase in pod yield over control under disease pressure with acceptable pod and kernel features that can be used as potential parents in LLS and rust resistance breeding. Pod yield increase as a consequence of resistance offered to foliar fungal diseases suggests the possibility of considering ‘foliar fungal disease resistance’ as a must-have trait in all the peanut cultivars that will be released for cultivation in rainfed ecologies in Asia and Africa. The phenotypic data of the present study will be used for designing genomic selection prediction models in peanut.