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Peanut (Arachis hypogaea L.) is one of the important oilseed crops of the Indian subcontinent and fungal diseases like early leaf spot (ELS) and late leaf spot (LLS) caused by Cercospora arachidicola and Phaeoisariopsis personata, respectively, are major peanut cultivation constraints. Defensins are basically antimicrobial peptides that have been implicated in plant defense against various microbial attacks. Transgenic peanut plants, developed through Agrobacterium mediated transformation of de-embryonated cotyledons and overexpressing a synthetic defensin fusion gene from fenugreek (Tfgd2) and radish (RsAFP2) linked by a linker peptide, were found to have enhanced resistance to the ELS and LLS infection over the wild type (cv. GG 20). Both transformed and untransformed lines were characterized for leaf spot diseases using a detached leaf assay. PCR and RT-PCR analyses confirmed stable integration and expression of these genes in peanut transgenics. This investigation provides further evidence that a fusion product of two plant defensins can be successfully implemented as a means of imparting resistance to multiple fungal pathogens through genetic engineering in peanut. [ABSTRACT FROM AUTHOR]

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. 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. [ABSTRACT FROM AUTHOR]

Key message: Groundnut has entered now in post-genome era enriched with optimum genomic and genetic resources to facilitate faster trait dissection, gene discovery and accelerated genetic improvement for developing climate-smart varieties. Cultivated groundnut or peanut (Arachis hypogaea), an allopolyploid oilseed crop with a large and complex genome, is one of the most nutritious food. This crop is grown in more than 100 countries, and the low productivity has remained the biggest challenge in the semiarid tropics. Recently, the groundnut research community has witnessed fast progress and achieved several key milestones in genomics research including genome sequence assemblies of wild diploid progenitors, wild tetraploid and both the subspecies of cultivated tetraploids, resequencing of diverse germplasm lines, genome-wide transcriptome atlas and cost-effective high and low-density genotyping assays. These genomic resources have enabled high-resolution trait mapping by using germplasm diversity panels and multi-parent genetic populations leading to precise gene discovery and diagnostic marker development. Furthermore, development and deployment of diagnostic markers have facilitated screening early generation populations as well as marker-assisted backcrossing breeding leading to development and commercialization of some molecular breeding products in groundnut. Several new genomics applications/technologies such as genomic selection, speed breeding, mid-density genotyping assay and genome editing are in pipeline. The integration of these new technologies hold great promise for developing climate-smart, high yielding and more nutritious groundnut varieties in the post-genome era. [ABSTRACT FROM AUTHOR]

With 3 figures and 7 tables Abstract With an objective of identification of highly informative set of SSR markers in cultivated groundnut ( Arachis hypogaea L.), a total of 4485 markers were used for screening using a set of 20 parental genotypes of 15 mapping populations. Although 3582 (79.9%) markers provided scorable amplification, only 1351 (37.3%) markers showed polymorphism. Polymorphism information content (PIC) value ranged from 0.10 (GM742) to 0.89 (S009) with an average of 0.31. Similarly, number of alleles ranged from 2 to 14 with an average of 3.2 alleles. In general, the SSR markers based on dinucleotide repeats displayed higher PIC value and number of alleles. Based on these polymorphism features, 199 markers with >0.50 PIC values have been identified. Polymorphism features of these markers along with the primer sequences, for the first time, for a total of 946 SSR markers have been provided. It is anticipated that the identified set of highly informative markers, instead of starting from the random set of SSR markers, should be very useful to initiate molecular genetics and breeding studies in cultivated groundnut. [ABSTRACT FROM AUTHOR]

• Genotype by Environment by Management (GxExM) interactions hampers crop improvement. • Crop simulation revealed large/variable yield gaps (∼70 %). • Filling 80 % of yield gap would increase production by 8 M tons. • Modelling output and biophysical traits were clustered (HPU). • Grouping by HPU reduced GxExM interactions and will help refine breeding targets. The high degree of Genotype by Environment by Management (GxExM) interactions is a serious challenge for production and crop improvement efforts. This challenge is especially true for a crop like groundnut that is often grown as a rainfed crop in diverse environments and management, leading to considerable production fluctuations among regions and seasons. Developing a means to characterize the drivers of variable yield and to identify region specific breeding objectives were the main motivations for this research, using groundnut production in India, as a case study for rainfed crops. Historically, five groundnut production areas have been considered by Indian crop improvement programs. Our objectives were to assess the relevance of this zonation system and possibly to re-define production areas with a higher degree of similarities into homogeneous production units (HPUs). Towards this, we used yield gap analysis and the geo-biophysical characters of the production region to understand and deal with GxExM interactions. Weather and soil data, crop parameters, and management information data were collected and groundnut production was simulated at the district scale over 30 consecutive years. Consequently, the geographic distribution of the potential yields and the yield gaps were first estimated to understand the main production limitations in a given region. Large and variable yield gaps (with a mean of ∼70 %) were observed and results revealed a readily exploitable production gap (∼ 8 M tons), which might be bridged by following recommended agronomic practices. Water deficit limited the yield potential by an average of 40 %, although with large variability among districts. However, large and variable yield gaps remained. To resolve the unexplained variation, principal component and cluster analysis of agronomic model output together with geo-biophysical indicators for each district were carried out. This resulted in seven HPUs, having well-defined production-limiting constraints. Grouping by HPU greatly reduced variance in actual and simulated yields, as compared to grouping across all groundnut production zones in India. The HPU based approach delimited precise geographic regions within which HPU-specific GxM products could be designed by crop improvement programs to boost productivity. [ABSTRACT FROM AUTHOR]