Spatial big data analytics for sustainable intensification of pulse crops in South Asia

Chandrashekhar Biradar, Rajkumar Singh, Mukunda Behera, Ashutosh Sarker, Shiv Kumar Agrawal, Xiangming Xiao, Geli Zhang, Khaled El-Shamaa, Girish Pujar, Layal Atassi, Sat Kumar Tomar, Prasenjit Acharya, Rabi Sahoo, Nigamananda Swain, Jalal Omary, Praveen Prajapati, Rajib Nath, Parvesh Chandna, Murali Krishna Gumma, Abhineet Jain. (7/5/2018). Spatial big data analytics for sustainable intensification of pulse crops in South Asia.

Abstract

Sustainable intensification has become a foremost goal of agricultural research and development in the recent years. The mono-crop based production systems in South Asia retains nearly 30 percent of cultivated area left as fallows, while demand for the nutritional food grains continues to rise. Several initiatives are underway towards bridging the gaps and achieving sustainable food as well as nutritional security. One of the opportunities lies in the potential use of the crop-fallows for growing pulses. The lentil, grasspea and chickpea are most preferred food legume crops for fallow intensification. However often lack of the adequate and updated information on the crop follow areas, its dynamics, soil moisture, suitability range, etc. lead to poor intervention on the ground level. The recent advances in harnessing the power of big-data analytics, artificial intelligence and machine learning with access to near real-time remote sensing, in-situ observations including drones, soil moisture, water harvesting provided a unique capability for quantifying suitable matrix for intensification of pulses such as lentil, grasspea and chickpea in the rice-fallows. The dynamics of the rice fallows has been mapped using multi-source satellite data (MODIS, Landsat, Sentinel, Worldview, SMOS, AMSR-E and UAVs/drones) and in-situ observations. The hotspots (priority areas) were delineated with respect to multi-year maps (2000-2016) of fallow dynamics such a start and end dates, length of the fallows, biophysical parameters for specific pulse crops and varieties. The high-resolution farm typology was built along with soil moisture regimes and suitable area for rain water harvesting for providing supplemental irrigation during dry season. Resultant big data analytics envisage empowering decision makers, researchers, extension systems and farmers by providing a holistic perspective to fine tune decisions and actions from on-ground implementation to resource management, sustainable intensification and values chains.