An Integrated Novel Triangulation Framework for Monitoring Ecosystem Health and Sustainability under Regenerative Landscapes

Pavan Kumar Yeggina¹Ajay Kumar Mishra^1*Kranthi Kumar Challa¹Piyush Kumar Maurya¹Manas Ranjan Sahoo¹Mayank Sharma²Jasmer Dhingra³Amit Kumar Srivastava¹Sheetal Sharma¹

• ¹IRRI South Asia Regional Centre, Varanasi, India
• ²MicroSave Consulting, Lucknow, India
• ³IDH Group Limited, Manchester, United Kingdom

Accelerated climate change and unsustainable agricultural practices have intensified greenhouse gas (GHG) emissions and degraded soil health, particularly in intensively cultivated landscapes. This study presents an integrated geospatial and biophysical triangulation framework to monitor ecosystem health and sustainability in the rice-based systems of Chhindwara district, Madhya Pradesh, India. The objectives were to evaluate GHG emissions, assess land use change, analyze soil health, and identify socio-economic drivers influencing agricultural sustainability under a regenerative agriculture (RA) paradigm. A combination of high-resolution satellite imagery, field-based soil sampling (n=430), socio-economic surveys, and carbon stock modeling using the InVEST tool was employed. Land Use and Land Cover (LULC) changes over a decade (2011– 2021 year) were mapped using Random Forest classification. GHG emissions were estimated using the Cool Farm Tool, while Water Use Efficiency (WUE) and biodiversity indices were evaluated across villages and seasons. In Sausar, there was a 4.6% decrease in agricultural area, a 6.6% rise in dense vegetation, and noticeable soil degradation. Mokhed had a higher soil organic content (1.07%) than Sausar (0.51%), and its carbon stocks were higher (13-35 Mg C/ha) than Sausar's (7-13 Mg C/ha). Furthermore, Mokhed's GHG emissions during the Kharif season were much greater at 4000 kg CO2 eq/ha than those during the Rabi season, which were just 1500 kg CO₂ eq/ha. WUE varied by season and crop, with Rabi vegetables achieving the maximum WUE at 10.4 kg/ha-mm and cotton demonstrating the lowest efficiency at 1.4 kg/ha-mm in Kharif , underscoring the potential of targeted interventions. The framework demonstrated that integrating geospatial, biophysical, and socio-economic data provides actionable insights for climate-resilient agriculture. The findings support landscape-level planning for soil restoration, GHG emission mitigation, and sustainable intensification in rainfed regions to escalate RA transitioning and benefits for the local communities.