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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. Abstract 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.

The use of near infrared reflectance spectroscopy (NIRS) to evaluate the nutritional quality of peanut kernels has potential applications in plant breeding as a rapid, non-destructive tool for seed/plant selection, and in quality control. We investigated the feasibility of applying NIRS to the estimation of essential mineral composition in peanut kernels using two sample sets: A, comprising 56 diverse genotypes (N = 163); and B, comprising nine genotypes grown in five distinct environments (N = 156). Essential mineral composition was analyzed by inductively coupled plasma-optical emission spectroscopy (ICP-OES) and -mass spectrometry (ICP-MS). Calibration models were developed by partial least squares (PLS) regression, and explored a variety of data pre-treatments. Models allowing approximate estimation of K (RPDCV 2.25, r CV 2 0.800, RPDP 2.22) and Mg (RPDCV 2.24, r CV 2 0.786, RPDP 1.74), and to a lesser extent Ca (RPDCV 1.85, r CV 2 0.649, RPDP 1.52) and P (RPDCV 1.77, r CV 2 0.634, RPDP 1.65), were developed for Set B, but poorer calibrations were obtained for Set A. This level of accuracy does not allow accurate prediction, but permits approximate quantification that may be useful in plant improvement programs for screening breeding populations. The results are remarkable because NIRS is rarely applied to analytes present at such low concentrations, especially inorganic constituents that are not inherently NIR-absorbent. Further analysis of more diverse peanut samples is warranted to confirm batch-to-batch accuracy and to improve the robustness of calibrations.