ICAR-Central Plantation Crops Research Institute, India, has designed and developed a virgin coconut oil (VCO) cooker for the extraction of oil by the hot process. However, a number of VCO production processes being followed in India and elsewhere cause variations in the physicochemical properties, which in turn potentially affect the nutritional and medicinal properties of VCO. The physical and biochemical properties of VCO from the hot process (VCO-Hot), fermentation (VCO-Fer), expelled from dried gratings (VCO-EDG), centrifugation (VCO-Cen), and conventionally prepared copra coconut oil (CCO) were investigated in light of the design concept of the VCO cooker. The nutritionally important total phenolic content (mg GAE/100 g) and antioxidant capacity of all the VCOs were found to be in the range of 0.446 ± 0.041 (VCO-Cen) to 2.867 ± 0.152 (VCO-Hot) and 3.87 mM Trolox equivalent (TE) (VCO-Cen) to 11.31 mM TE (VCO-Hot), respectively. Multivariate analysis revealed that quality attributes viz., total phenol, total flavonoid, and cupric ion reducing antioxidant capacity of VCO-Hot defined by principal component 1. Hierarchical clustering showed that the VCO-Hot belonged to the group with high total phenolic and flavonoids content and strong antioxidant capacity. Comparative biochemical properties along with multivariate analysis differentiated the various VCO samples. Practical ApplicationsProduction of virgin coconut oil (VCO) by the hot process has been standardized by ICAR-CPCRI and the technology has been successfully adopted by several entrepreneurs. VCO has a tremendous export potential and hence has a greater demand in the international market. The quantum of VCO export from India has been 818 MT to various destinations such as the United States, Japan, Australia, United Kingdom, and Middle East (https://www.coconutboard.in). The export earnings of VCO have reached over Rs. 260 million in 2015-2016. The consumers are not aware of the different VCO production methods and the resultant properties of VCO (Manikantan

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Climate change and climate variability are projected to alter the geographic suitability of lands for crop cultivation. Early awareness of the future climate of the current cultivation areas for a perennial tree crop like coconut is needed for its adaptation and sustainable cultivation in vulnerable areas. We analyzed coconut’s vulnerability to climate change in India, based on climate projections for the 2050s and the 2070s under two Representative Concentration Pathways (RCPs): 4.5 and 8.5. Based on the current cultivation regions and climate change predictions from seven ensembles of Global Circulation Models, we predict changes in relative climatic suitability for coconut cultivation using the MaxEnt model. Bioclimatic variables Bio 4 (temperature seasonality, 34.4%) and Bio 7 (temperature annual range, 28.7%) together contribute 63.1%, which along with Bio 15 (precipitation seasonality, 8.6%) determined 71.7% of the climate suitability for coconuts in India. The model projected that some current coconut cultivation producing areas will become unsuitable (plains of South interior Karnataka and Tamil Nadu) requiring crop change, while other areas will require adaptations in genotypic or agronomic management (east coast and the south interior plains), and yet in others, the climatic suitability for growing coconut will increase (west coast). The findings suggest the need for adaptation strategies so as to ensure sustainable cultivation of coconut at least in presently cultivated areas.

Microsatellites are ubiquitously distributed, polymorphic repeat sequence valuable for association, selection, population structure and identification. They can be mined by genomic library, probe hybridization and sequencing of selected clones. Such approach has many limitations like biased hybridization and selection of larger repeats. In silico mining of polymorphic markers using data of various genotypes can be rapid and economical. Available tools lack in some or other aspects like: targeted user defined primer generation, polymorphism discovery using multiple sequence, size and number limits of input sequence, no option for primer generation and e-PCR evaluation, transferability, lack of complete automation and user-friendliness. They also lack the provision to evaluate published primers in e-PCR mode to generate additional allelic data using re-sequenced data of various genotypes for judicious utilization of previously generated data. We developed the tool (PolyMorphPredict) using Perl, R, Java and launched at Apache which is available at http://webtom.cabgrid.res.in/polypred/. It mines microsatellite loci and computes primers from genome/transcriptome data of any species. It can perform e-PCR using published primers for polymorphism discovery and across species transferability of microsatellite loci. Present tool has been evaluated using five species of different genome size having 21 genotypes. Though server is equipped with genomic data of three species for test run with gel simulation, but can be used for any species. Further, polymorphism predictability has been validated using in silico and in vitro PCR of four rice genotypes. This tool can accelerate the in silico microsatellite polymorphism discovery in re-sequencing projects of any species of plant and animal for their diversity estimation along with variety/breed identification, population structure, MAS, QTL and gene discovery, traceability, parentage testing, fungal diagnostics and genome finishing.