Soil organic carbon stability and greenhouse gas mitigation through AI-enabled precision land leveling and nutrient management: A systematic review

Abstract

Soil organic carbon (SOC) is the largest terrestrial carbon pool and a key indicator of soil health, fertility, and climate mitigation potential. However, intensive agricultural practices have accelerated SOC depletion and contributed substantially to greenhouse gas (GHG) emissions, particularly nitrous oxide (N₂O) and carbon dioxide (CO₂) (Lal, 2015) [27]. Precision agriculture, particularly precision land leveling (PLL) and site-specific nutrient management (SSNM), has emerged as an effective strategy to enhance resource use efficiency and reduce environmental footprints. Precision land leveling reduces surface microtopography and improves water distribution, which enhances root penetration and reduces anaerobic soil zones that produce CH₄ emissions in flooded systems (Singh et al., 2018) [54]. Site-specific nutrient management optimizes fertilizer application by tailoring rates and timing to spatial heterogeneity in soil nutrient status, significantly reducing excess nitrogen that drives N₂O emissions (Dobermann & Cassman, 2004; Robertson & Vitousek, 2009) [8, 46]. Artificial intelligence (AI) including machine learning (ML), remote sensing (RS), and data-driven decision support systems have enabled dynamic, real-time analysis of soil conditions, crop status, and carbon fluxes. AI models have been used to predict SOC dynamics with high accuracy (Ranjan et al., 2021) [45], identify N₂O hot spots (Sun et al., 2022) [49], and optimize nutrient prescriptions (Zhang et al., 2023) [50]. A meta-analysis of 65 field studies revealed that laser leveling combined with optimized nutrient application reduced N₂O emissions by up to 35% and increased SOC stocks by 12-18% across cereal cropping systems. In rice-wheat systems, AI-enabled SSNM reduced fertilizer nitrogen use by 20-30%, while maintaining or increasing grain yield by 5-10%, concurrently decreasing N₂O emissions by 25% (FAO, 2022; Naresh et al., 2021) [11, 37]. This systematic review synthesizes evidence on the effects of AI-integrated PLL and SSNM on SOC stability and GHG mitigation, examining changes in SOC fractions (e.g., particulate and mineral-associated organic carbon) and field-level emissions of CO₂, N₂O, and CH₄. We also identify key research gaps, including the need for long-term SOC monitoring, microbial carbon dynamics (Cotrufo et al., 2013) [7], and AI model transferability across agro-ecologies. The review concludes that AI-enabled precision interventions can significantly enhance SOC sequestration, reduce GHG emissions, and support climate-smart agriculture, but emphasizes the need for integrated soil-crop models and multi-scale validation studies.