The oil palm fruit forms (dura, pisifera and tenera) governed by the shell thickness gene (Sh) plays a major role in identification of fruit type and also influences palm oil yield. Identification of desired fruit type is a major asset to the breeders and oil palm workers for applications in breeding, seed certification and to reduce time, space and money spent on identification of fruit form. In the present study, we developed Sh gene specific primer pairs and bulk segregant analysis was done using 300 genomic and 8 genic SSR markers. We identified one cleaved amplified polymorphic site (CAPS) marker for differentiation of oil palm fruit type which produced two alleles (280 and 250bp) in dura genotypes, three alleles in tenera genotypes (550, 280, and 250bp) and one allele in pisifera genotypes (550bp). The shell allele sequencing results showed that two SNPs were present, of which SNP2 contributed for variation of fruit forms. The nucleotide ‘A’ was present in only dura genotypes, where as ‘T’ was present only in pisifera genotypes, which in turn led to the change of amino acid lysine to aspargine. The identified CAPS marker was validated on 300 dura, 25 pisifera and 80 tenera genotypes, 80 dura/ pisifera cross progenies and 60 lines of tenera/ tenera cross progeny. Association mapping of marker data with phenotypic data of eight oil yield related traits resulted in identification of seven significant QTLs by GLM approach, four by MLM approach at a significant threshold (P) level of 0.001. Significant QTLs were identified for fruit to bunch and oil to bunch traits, which explained R2 of 12.9% and 11.5% respectively. The CAPS marker used in the present study facilitate selection and timely distribution of desirable high yielding tenera sprouts to the farmers instead of waiting for 4–5 years. This saves a lot of land, time and money which will be a major breakthrough to the oil palm community.

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The traditional meat and poultry farms use a fixed quantity of supply, which creates an imbalance between demand and supply. Due to this imbalance, a huge amount is spent on balancing the requirements. There is an inequality among demand and supply since typical meat and poultry farms use a fixed amount of supply. A lot of money is spent trying to balance the requirements because of this mismatch. In addition, when connecting and building the meat and poultry farm system, the procedure ignores the impact on the environment. The owner’s primary goals are to retain massive profits and raise reliability. The classical method neglects the effect on the environment while linking and designing the meat and poultry farm system. The main aim of the owner is to increase the quality and maintain the maximum profit. This paper deals with the meat and poultry farms in two folds. In the first step, the IoT based system is implemented for the traceability and demand-supply monitoring. The second steps include optimization of the supply network to reduce the carbon emission from the transportation. Both steps take data analytics as an input to process the final result for the farm to run and optimize. Effective inventory optimization algorithms have been shown to be able to evaluate a significant portion of previous sales data and anticipate inventory future demand by taking seasonality and lead times into account. Revenue, productivity, and customer satisfaction are just a few of the business variables that these strategies may affect. Finally, the comparison is done with the traditional farm and supply chain on the points of demand-supply balance, cost, carbon emission, and wastage. It is found that the farms using data analytics to optimize the overall system perform better and with 37% more efficient than the traditional systems.

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The availability of large expressed sequence tag (EST) and whole genome databases of oil palm enabled the development of a data base of microsatellite markers. For this purpose, an EST database consisting of 40,979 EST sequences spanning 27 Mb and a chromosome-wise whole genome databases were downloaded. A total of 3,950 primer pairs were identified and developed from EST sequences. The tri and tetra nucleotide repeat motifs were most prevalent (each 24.75%) followed by di-nucleotide repeat motifs. Whole genome-wide analysis found a total of 245,654 SSR repeats across the 16 chromosomes of oil palm, of which 38,717 were compound microsatellite repeats. A web application, OpSatdb, the first microsatellite database of oil palm, was developed using the PHP and MySQL database (https://ssr.icar.gov.in/index.php). It is a simple and systematic web-based search engine for searching SSRs based on repeat motif type, repeat type, and primer details. High synteny was observed between oil palm and rice genomes. The mapping of ESTs having SSRs by Blast2GO resulted in the identification of 19.2% sequences with gene ontology (GO) annotations. Randomly, a set of ten genic SSRs and five genomic SSRs were used for validation and genetic diversity on 100 genotypes belonging to the world oil palm genetic resources. The grouping pattern was observed to be broadly in accordance with the geographical origin of the genotypes. The identified genic and genome-wide SSRs can be effectively useful for various genomic applications of oil palm, such as genetic diversity, linkage map construction, mapping of QTLs, marker-assisted selection, and comparative population studies.