The Geospatial Understanding of Climate-Smart Agriculture and REDD+ Implementation: Indian Perspective

Firoz Ahmad 1 , Asim Farooq 2 , Laxmi Goparaju 1 , and Javed Rizvi 3
  • 1 , 231001, Mirzapur, India
  • 2 School of Civil and Resource Engineering, University of Science and Technology, Beijing, China
  • 3 , New Delhi, India


Geospatial technology has an enormous capacity to analyze large and diversified datasets for evaluating the hidden spatial relationship which provides a better comprehension of the subject and helps significantly in policymaking and planning future strategies.

This study has examined the relationship among diversified remote sensing and GIS datasets such as GHG emission from cropland, rice cultivation area, agro-ecological region, Land use/Land cover (LULC) categories, long-term NDVI (1982−2006) based negative changes, agriculture vulnerability, drought-prone area and future (2021, 2050) climate change anomalies (RCP-6) of India for better understanding and knowledge of the GHG emission scenario, vegetation health, LULC, agriculture vulnerability, and future climate change impact. The LULC analysis revealed that 49.6% (1 628 959 km²) of the geographical area was found to be under category ‘cropland’. The 32.5% of the total cropland areas are used for rice cultivation whereas around 76% of this rice cultivation area is producing high GHG emission (>1000 Mg CO2 e/yr.). LULC categories ‘Cropland’ and ‘Plantation’ show the long-term (1982−2006) negative change equivalent to 19.7 and 70.2% respectively. Similarly, around 56% of LULC categories representing the forest show the long-term negative change whereas the maximum change (139 867 km²) was found in the category of ‘Deciduous Broadleaf Forest’. The 30.6% of the LULC category of ‘cropland’ falls in very high agriculture vulnerable areas whereas 31.7% of the same category falls in the drought-prone area. The significant increase in temperature and abrupt rainfall patterns were observed during Kharif and Rabi seasons in the future. Such variation of climate parameter in the future not only adversely affect the agriculture crop production but also the natural vegetation of India.

The outcomes of the present study would support the policymakers of India to implement the climate-smart agriculture (CSA) and REDD+ on an urgent priority based on a proper evaluation of the socio-economic condition of the poor people. It will certainly help in the reduction of GHG emission, forest amelioration, will bring the resilience in livelihood and mitigate the poverty among the rural communities for the betterment of people.

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