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A plant breeding program involves hundreds of experiments, each having number of entries, genealogy information, linked experimental design, lists of treatments, observed traits, and data analysis. The traditional method of arranging breeding program information and data recording and maintenance is not centralized and is always scattered in different file systems which is inconvenient for retrieving breeding information resulting in poor data management and the loss of crucial data. Data administration requires a significant amount of manpower and resources to maintain nurseries, trials, germplasm lines, and pedigree records. Further, data transcription in scattered spreadsheets and files leads to nomenclature and typing mistakes, which affects data analysis and selection decisions in breeding programs. The accurate data recording and management tools could improve the efficiency of breeding programs. Recent interventions in data management using computer-based breeding databases and informatics applications and tools have made the breeder's life easier. Because of its digital nature, the data obtained is improved even further, allowing for the acquisition of images, voice recording and other specific data kinds. Public breeding programs are far behind the industry in the use of data management tools and softwares. In this article, we have compiled the information on available data recording tools and breeding data management softwares with major emphasis on potato breeding data management.

Indian dill seed (Anethum sowa Roxb. ex Fleming) is an important member of family Apiaceae. It is a significant seed spice having many medicinal properties. But, this aromatic herb is still orphan from breeding and crop improvement perspective as no serious attention has been given by breeders. To swift the breeding program, molecular markers play the main role. But due to the paucity of markers, breeding is slower in dill seed. Therefore, an attempt was made to develop the genomic SSR markers in dill seed through next-generation sequencing. A total of 2119.51 Mbp of raw data were generated on the Miseq NGS platform during survey sequencing. In 2,25,956 contigs created by Spades assembler, a total 48,951 repeat motifs were identified. A set of 20,294 primer pairs (dimer to hexamer repeats) were produced. Among detected repeat motifs, 48.89% was mononucleotides. Of 12 primers, 10 (83%) primers could be successfully amplified in dill seed and produced 11 amplicons. During cross-genera amplification of markers, 9 out of 10 primers could be successfully amplified in related genera. Developed markers can also be used to initiate the molecular breeding program, association mapping and to assess the evolutionary relationship among seed spices. Keywords: Anethum, Seed spices, SSR, Transferability

Sorghum is widely grown as a failsafe crop in semi-arid regions particularly in post rainy season. Though the effect of drought on crop performance is studied widely there are few studies illustrating the association of fodder quality and agronomic traits under drought. To study the interactions we evaluated a set of 24 cultivars under drought for three years in post rainy season. The effect of drought was evident in delayed flowering (by 2 days) and reduce plant height (by 0.98 cm) compared to control. The fodder digestibility traits were reduced (in vitro organic matter digestibility by 2.25 times) under drought. All the plant growth and yield parameters recorded higher heritability compared to fodder quality parameters (

Summary: Heterogeneous and multidisciplinary data generated by research on sustainable global agriculture and agrifood systems requires quality data labeling or annotation in order to be interoperable. As recommended by the FAIR principles, data, labels, and metadata must use controlled vocabularies and ontologies that are popular in the knowledge domain and commonly used by the community. Despite the existence of robust ontologies in the Life Sciences, there is currently no comprehensive full set of ontologies recommended for data annotation across agricultural research disciplines. In this paper, we discuss the added value of the Ontologies Community of Practice (CoP) of the CGIAR Platform for Big Data in Agriculture for harnessing relevant expertise in ontology development and identifying innovative solutions that support quality data annotation. The Ontologies CoP stimulates knowledge sharing among stakeholders, such as researchers, data managers, domain experts, experts in ontology design, and platform development teams. The Bigger Picture: Digital technology use in agriculture and agrifood systems research accelerates the production of multidisciplinary data, which spans genetics, environment, agroecology, biology, and socio-economics. Quality labeling of data secures its online findability, reusability, interoperability, and reliable interpretation, through controlled vocabularies organized into meaningful and computer-readable knowledge domains called ontologies. There is currently no full set of recommended ontologies for agricultural research, so data scientists, data managers, and database developers struggle to find validated terminology. The Ontologies Community of Practice of the CGIAR Platform for Big Data in Agriculture harnesses international expertise in knowledge representation and ontology development to produce missing ontologies, identifies best practices, and guides data labeling by teams managing multidisciplinary information platforms to release the FAIR data underpinning the evidence of research impact.

With high global demand, seeds and gum of cluster bean are an essential raw material for various industries. Worldwide the low yield and productivity of guar (Cyamopsis tetragonoloba) create a large gap between demand and supply of guar. Therefore, to upsurge guar production, there is a need to improve the guar at genetic level. The genetic improvement of cluster bean is slower due to insufficient genomic resources, co-dominant marker system especially simple sequence repeats (SSRs) and inadequate information on the variability of germplasm. For development of microsatellite markers in cluster bean, DNA survey sequencing was carried out using the Miseq NGS system. DNA sequencing generated 73,934 sequences which harboured microsatellite repeat sequences. Successfully, a total of 15,399 marker-pairs were designed. The amplicon size ranged between 101–385 base-pairs. To validate the primers and to analyse the diversity in released varieties and germplasm of cluster bean, successfully amplifiable 21 primers pairs were used. With a mean of 2.05 alleles per primers, 21 bands were detected. The polymorphism information content (PIC) for polymorphic markers fluctuated between 0.04 - 0.67 (mean = 0.3). With the average dissimilarity coefficient of 0.11, four clusters were noticed with NJ analysis. The grouping pattern of genotypes indicated a low genetic variability in the guar gene pool. The results of this study suggested that instead of re-circulation of parental lines, hybrid breeding using diverse parents followed by selection should be performed for genetic improvement of cluster bean. Keywords: Cluster bean, Diversity, DNA marker, NGS, SSR

Complete atrioventricular (AV) block in association with Takotsubo syndrome (TS) has been well recognized, but the cause and effect relationship has not been elucidated. We describe a 78-year-old female who presented with complete AV block but one week later developed new-onset, diffuse T-wave inversions, QT prolongation, and acceleration of junctional escape rate. Left ventriculogram revealed features typical of TS. One year after permanent pacemaker implantation, complete AV block persisted despite the reversal of wall motion defects implying that conduction abnormality was the trigger of TS rather than its consequence.