Geo-Big Data in Digital Augmentation and Accelerating Sustainable Agroecosystems

Abstract

Digital technologies are penetrating into the agriculture sector rapidly but lack dynamics, granularity and demand-driven advisory. At the same time, agri-food systems transformation marching towards economically viable and ecologically sustainable options for healthy people and a healthy planet. Such transformation requires a systematic quantification and characterization of farming system dynamics and farm typology at much higher spatial and temporal granularity with real-time analytics and advisory. The rise of digital augmentation to quantify land use pattern, farming systems and site-specific recommendation on a real-time basis has become possible by recent advances in big-data analytics, cloud computing, along with smartphone-enabled decisions making at the farm level to address the gaps at multiple domain levels in demand-driven agroecological interventions. In the chapter, we discussed ongoing digital augmentation efforts for accelerating agroecological transition through sustainable intensification of rice fallows in India. Systematic characterization of these rice fallows can be sustainably intensified to grow short-duration legume crops, vegetables and oilseeds between two crops. Near-real-time mapping of active rice-growing areas, their dynamics in terms of length of fallows, corresponding soil moisture to map suitable areas for specific crops and varieties for cropping system diversification and sustainable intensification to increase land/water productivity, nutrition, crop yield and overall system-level resilience. The capabilities and limitations of mapping rice-based farming systems using Sentinel series of optical and microwave satellite imagery during the kharif season in monsoon climate have been discussed. The chapter shows a new methodological approach for multi-scale mapping at the macro to micro levels to assist informed decision making.