Pilot studies in the following areas: (click on titles for links to details)
1. Enhancing science culture in Agricultural Research Institutions: This study addresses recent concerns about science culture in public research institutions of National Agricultural Research System (NARS) in India. Specifically, the emphasis is on fostering three major attributes of science culture – research integrity, scientific creativity, and scientific integrity. A brief historical overview of science culture in general, and its evolution and adaptation in institutional contexts, and in agricultural science is discussed first. Next, the essential elements of the institutional framework that supported the thriving science culture of the green revolution years, and its subsequent adaptations in present NARS are analyzed. Third, the emerging factors which drive the need to enhance the agricultural science culture in NARS, how they enlarge the scope and complexity of agricultural research in multiple dimensions and towards smart agriculture, how this necessitates involvement of new paradigms, disciplines, collaborations, skill sets and mindsets, and how these change the business model of doing science, are explored. Finally, the policy, institutional, and behavioural pathways to enhance the science culture in NARS to stimulate innovation, and advance national capabilities to address current and emerging complex challenges of agriculture are identified. (http://naasindia.org/Policy%20Papers/policy%2093.pdf)
2. Big data analytics for climate smart agriculture: The integration of recent developments in big data analytics and climate change science with agriculture can greatly accelerate agricultural research and innovation for climate smart agriculture (CSA). CSA refers to an integrated set of technologies and practices that simultaneously improve farm productivity and incomes, increase adaptive capacity to climate change effects, and reduce green house gas emissions from farming. It is a multistage, multiobjective, data-driven, and knowledge based approach to agriculture, with the farm as the most fundamental unit for both strategic and tactical decisions. Three levels at which big data can enhance farmer field level insights and actionable knowledge for the practice of CSA are explored: (i) developing a predictive capability to factor climate change effects to scales relevant to farming practice, (ii) speeding up plant breeding for higher productivity and climate resilience, and (iii) delivery of customized and prescriptive real-time farm knowledge for higher productivity, climate change adaptation and mitigation.( http://insajournal.in/insaojs/index.php/proceedings/article/view/404)
3. Advancing drought monitoring, prediction, and management capabilities: A concept note was prepared for funding and support of an international workshop on ‘Advancing Drought Monitoring, Prediction and Management Capabilities’, by the Indo-UK Water Centre to: (i) synthesize the state-of-art of drought science and management, (ii) identify gaps between research knowledge and operational requirements, and (iii) lead to a road map for advancing operational capabilities for drought monitoring, prediction and management. Selected delegates from India(11) and UK (19) participated in the workshop organized at Lancaster UK during September18-20, 2018. (https://iukwc.org/sites/default/files/images/Lancaster%20Workshop%20Report.pdf).
4. Framework for application of machine learning approaches to high resolution drought monitoring and prediction (using diverse open source data and tools): Drought assessment requires diverse weather, soil, climate, crop and satellite data. All these data are available in public domain at diverse spatial, temporal and spectral (for satellite data) resolutions. Pilot studies have been initiated (with NAARM and ISRO) to develop a user friendly platform to project the various data at diverse resolutions to heterogeneous small farm scale resolutions for: (i) high resolution (30m) and high frequency (weekly) vegetation condition monitoring for drought at village level (https://iukwc.org/system/files/images/NH_Rao.pdf; https://drive.google.com/file/d/1VftbvwZTsiZIiQHWlxk-Yt2EXDTjG5Bt/view?usp=sharing), and (ii) explore applicability of machine learning methods to develop suitable indices for investigating drought duration, severity, intensity, onset and cessation at 1 km resolutions over a a large region (https://drive.google.com/file/d/1K0nVZjJwNiuaixm83trZwYKYiyNWC7PC/view?usp=sharing)
5.Monitoring surface water body dynamics with high resolution geospatial data and machine learning: Monitoring the dynamics of surface water bodies in arid and semi arid regions can be of great significance in water resources planning, management, and impact assessment of interventions. Recent ease of access and processing of historical and near real time high spatial and temporal resolution global remote sensing data via the Google Earth Engine (GEE) platform enables on the fly monitoring of changes in surface area of water bodies on the platform itself. In this study, the temporal changes in surface spread area of water bodies in Telangana state were monitored using vegetative indices(NDVI, NDWI and mNDWI), and machine learning algorithms (random forest) for the period 2013-18. The results showed a significant increase in both surface water area and groundwater levels in Telangana state, especially after 2015. This may be related to the activities of the Mission Bhagirath for statewide surface water body rejuvenation in 2015.
6. Open source GIS integrated modelling platform for Groundwater Security: Groundwater is central to national water, food and environmental security. Despite this, national effort in increasing quantitative understanding of aquifer systems’ behaviour through application of regional groundwater flow and transport models for surface-groundwater management has been limited in India, nor have groundwater models been integrated effectively into the core curriculum of Geohydrology or Groundwater Hydrology in Geology or Engineering education. A pilot study was initiated (in association with NGRI, NAARM and ISRO) to develop an integrated open source GIS (QGIS) and groundwater model (MODFLOW) enabled framework for groundwater management using open source FREEWAT plugin for QGIS. (FREEWAT was developed under the European Framework H2020 FREEWAT project).