Bridging conservation agriculture and cloud computing: Quantitative approaches to soil organic carbon dynamics and water productivity in rice-wheat systems: A systematic review

Abstract:

Rice-wheat systems (RWS) of the Indo-Gangetic Plains sustain more than 400 million people but are increasingly constrained by groundwater depletion (0.3-1.0 m yr-¹ decline in intensively irrigated zones), declining soil organic carbon (SOC <0.5% in many alluvial soils), rising energy costs, and climatic variability marked by terminal heat stress and erratic rainfall. Conservation Agriculture (CA) built upon zero or reduced tillage, crop residue retention (≥30% surface cover), and crop diversification, has demonstrated measurable gains in soil carbon sequestration and crop water productivity. Parallel advances in cloud computing, remote sensing, and IoT-enabled soil monitoring now allow quantitative, near real-time assessment of these benefits at field to landscape scales.
Conservation Agriculture (CA) can enhance total SOC stocks by 0.3-0.8 Mg C ha-¹ yr-¹, with particulate organic carbon increasing by 15-35% and microbial biomass carbon by 20-40% within 3-7 years of adoption. Improvements in macro-aggregate stability (10-25%) and reduced bulk density (3-8%) contribute to better carbon stabilization and root proliferation. Residue retention of 5-7 t ha-¹ in rice-wheat rotations improves labile carbon pools and increases available nitrogen by 8-15%, enhancing nutrient cycling efficiency.
Water-related gains under CA are equally significant. Zero-till wheat following residue-retained rice commonly reduces irrigation requirement by 15-30% and improves infiltration rates by 20-40%. Water productivity improvements of 0.2-0.5 kg grain m-³ have been recorded compared to conventional puddled-transplanted systems. Reduced evaporation losses (10-25%) and moderated soil temperature (1.5-3.0°C reduction) further stabilize moisture regimes during critical growth stages.
Cloud-based decision support platforms integrate satellite-derived evapotranspiration (ET), normalized difference vegetation index (NDVI), soil moisture sensors, and crop growth simulation models to generate high-resolution water balance maps and SOC prediction layers. These systems enhance irrigation scheduling efficiency by 20-35% and improve SOC stock estimation accuracy by approximately 15-25% over traditional sampling-only approaches. Machine learning algorithms applied to multi-year datasets enable spatial mapping of SOC fractions and water productivity variability across heterogeneous farm landscapes.
The convergence of CA practices with cloud-enabled analytics provides a scalable pathway for climate-resilient intensification of rice-wheat systems. By linking carbon sequestration metrics with water-use optimization, this integrated framework strengthens sustainability, enhances resource-use efficiency, and supports evidence-based policy and farm-level decision-making under changing climatic conditions.