Integrated system approach becomes key pillar for sustainable intensification of agri-food systems while ensuring ecological functions under changing climate, diet and demography. The digitization of the agroecosystems become most essential entry point for any sustainable developmental entities whether it is plant genetics for breeding better varieties, crop diversification and intensification, efficient use of farm inputs, agronomic practices, stable economic return, to ecosystem services management. The functional production systems become more important than single commodity production systems. Recent advances in Earth Observation System (EOS), Open-Access (AO), Artificial Intelligence (AI), Machine Learning (ML), Information, and Communication Technologies (ICTs), Cloud Computing Platforms (CCP) along with smartphone-enabled Citizen Science (CS) increasingly make Big-Data analytics much smarter, interoperable and much useful than ever before, and create valuable baseline information for decision making. This has opened tremendous opportunities to address the knowledge gaps at multiple levels (e.g., data, yield, ecology, economy, resilience) for demand-driven ecological interventions across the scale (e.g., space, time and package). Ongoing efforts in big-data driven digital augmentation aim at quantifying functional production dynamics and drivers to target sitespecific sustainable developmental interventions and scaling the ecological intensification such as intensification of pulses in cerealbased systems (rice fallows), adoption of conservation agriculture, bridging the yield gaps, geo-localization of the research and impact reporting. Here we present some of the ongoing efforts in EOS Big Data for digital augmentation for accelerating agroecological intensification in different agroecologies and regions in the drylands.

This paper proposes a new interconnection network topology, called the Star varietalcube SVC(n,m), for large scale multicomputer systems. We take advantage of the hierarchical structure of the Star graph network and the Varietal hypercube to obtain an efficient method for constructing the new topology. The Star graph of dimension n and a Varietal hypercube of dimension m are used as building blocks. The resulting network has most of the desirable properties of the Star and Varietal hypercube including recursive structure, partionability, strong connectivity. The diameter of the Star varietal hypercube is about two third of the diameter of the Star-cube. The average distance of the proposed topology is also smaller than that of the Star-cube.

This paper attempts to derive the performance properties of the Leafycube (LC) interconnection network. The Leafycube is already observed to have quite superior topological properties in comparison to the other contemporary networks. The various performance parameters of the LC network are studied and compared with the existing HC and its variants. The routing and broadcasting algorithms are proposed and the time complexities are also compared. The paper attempts to evaluate the cost effectiveness, reliability and fault tolerance aspects of LC interconnection network in order to justify the novelty in the design of the proposed structure. The leafy structure helps to retain the original hypercube while improving the node packing density in the interconnection network.

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