Greenhouse Gas Emissions in Agricultural Crops and Management Practices: The Impact of the Integrated Crop Emission Mitigation Framework on Greenhouse Gas Reduction

Abstract

Greenhouse gas emissions from agricultural crops remain a critical challenge for climate change mitigation. This review synthesizes evidence on cropland management interventions and global N₂O mitigation potential. Agricultural practices such as cover cropping, agroforestry, reduced tillage, and diversification show promise in reducing CO₂, CH₄, and N₂O emissions, yet uncertainties in measurement, verification, and socio-economic adoption persist. This review highlights that biochar application reduces N₂O emissions by 16.2% (95% CI: 9.8–22.6%) in temperate systems, demonstrating greater consistency compared to no-till agriculture, which shows higher variability (11% reduction, 95% CI: −19% to +1%). Legume-based crop rotations reduce N₂O emissions by up to 39% through improved nitrogen efficiency and increase soil organic carbon by up to 18%. However, reductions in synthetic fertilizer use (65% lower in legume vs. cereal systems) can be offset by the effects of biological nitrogen fixation. Optimized nitrogen fertilization, when combined with enhanced-efficiency fertilizers, can reduce N₂O emissions by 55–64%. Complementing this, global-scale analysis underscores the dominant role of optimized nitrogen fertilization in curbing N₂O emissions while sustaining yields. To bridge gaps between practice-level interventions and global emission dynamics, this paper introduces the ICEMF, a novel approach combining field-based management strategies with spatially explicit emission modeling. Realistic implementation currently achieves 25–35% of technical potential, but bundled interventions combining financial incentives, training, and institutional support can increase adoption to 40–60%, demonstrating ICEMF’s value through integrated, context-adapted approaches. Only peer-reviewed articles published in English between 1997 and 2025 were selected to ensure recent and reliable findings. This review highlights knowledge gaps, evaluates policy and technical trade-offs, and proposes ICEMF as a pathway toward scalable and adaptive mitigation strategies in agriculture.

1. Introduction

Agriculture is widely recognized as a major contributor to anthropogenic greenhouse gases (GHG), emitting carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O) through soil degradation, livestock-related processes, and intensive crop management [1]. Recent global assessments indicate that agricultural emissions have continued to rise, with food systems responsible for approximately one-third (34%) of total anthropogenic GHG emissions and agricultural emissions increasing by 9.3% between 2000 and 2018, driven primarily by synthetic fertilizer use and livestock expansion in developing regions [1,2]. The agricultural sector now accounts for 10–14% of direct anthropogenic GHG emissions globally, with significant regional disparities. Emissions in sub-Saharan Africa and South Asia have doubled since 1990, while developed nations show modest declines [2]. These emissions account for a substantial share of global warming, with croplands serving both as significant sources and potential sinks of GHG through soil carbon sequestration and improved land management [2]. The need to mitigate these emissions has grown increasingly urgent, as agriculture faces mounting pressures from climate change, food security concerns, and sustainability goals [3,4]. Emerging evidence from 2024 to 2025 emphasizes that agricultural systems must simultaneously achieve emission reductions, climate adaptation, and enhanced productivity, a “triple challenge” requiring integrated strategies that move beyond single-objective interventions [3,5].

Various agronomic interventions have been identified to reduce emissions, including cover cropping, reduced tillage, crop diversification, agroforestry, and organic amendments [6,7]. These practices enhance soil organic carbon stocks, reduce erosion, and improve resilience to extreme weather events [8]. Recent meta-analyses from 2022 to 2025 have refined the understanding of practice-specific mitigation potentials with greater precision. Meta-analysis of 119 paired observations from 18 studies demonstrated that biochar application consistently reduced N₂O emissions by 16.2% (95% CI: 9.8–22.6%) in temperate agricultural systems, with moderate-to-high heterogeneity (I² = 72%) and no evidence of publication bias (Egger’s test, p = 0.18) [9]. Effectiveness was maintained over 4–6 years in long-term trials [10], with greater reductions in acidic soils (pH < 6.5) due to the liming effect [9]. However, effectiveness was lower in tropical systems (8–12% reduction) where validation data remain limited [11]. This demonstrates superior consistency compared to no-till agriculture, which shows high variability (meta-analysis of 212 observations from 40 studies: mean −11%, 95% CI: −19% to −1%; I² = 89%) with effects ranging from 19% reductions to 70% increases depending on soil texture, climate, and moisture regime [12].

Legume-based crop rotations reduce N₂O emissions by up to 39% through improved nitrogen efficiency while increasing soil organic carbon by 18% compared to monoculture systems. However, the net benefits of such interventions are context-dependent, often influenced by soil type, climate, and farming system design [13]. Emerging integrated practices, such as recoupled crop–livestock systems, demonstrate potential for over 40% emission reductions when properly designed, though adoption barriers remain substantial [14]. In addition, monitoring, reporting, and verification (MRV) frameworks face methodological inconsistencies that limit the reliability of estimates. Recent assessments from 2025 highlight that measurement uncertainties of ±30–50% in field-based emission factors and inconsistent life cycle assessment boundaries (creating 2–5-fold variations in reported emissions for identical crops) continue to undermine verification systems essential for carbon credit markets and climate policy implementation [15].

Nitrous oxide emissions are of particular concern because of their high global warming potential (GWP) (265 times that of CO₂ over 100 years) and direct link to nitrogen fertilizer application [16]. Global analyses indicate that N₂O emissions from croplands have quadrupled over the last six decades, with hotspots in Asia and intensive horticultural systems [17]. Strategies such as the “4R” nutrient stewardship (right source, rate, time, and placement) and precision irrigation have been highlighted as effective measures to reduce N₂O by 55–64% when combined with enhanced-efficiency fertilizers (EEFs) while sustaining yields [3,18]. Crop-specific assessments show maize, rice, wheat, and vegetable systems to be the most significant contributors and therefore prime targets for mitigation [19].

The integration of digital technologies with nutrient management has emerged as a transformative approach since 2022, marking a shift toward “agriculture 4.0” paradigms. Artificial intelligence-driven decision support systems now enable real-time optimization of fertilizer application, with demonstrated N₂O reductions of 20–30% compared to conventional practices in field trials across South Asia and East Africa [20]. Remote sensing combined with machine learning models allows spatially explicit identification of emission hotspots at field-to-regional scales, enabling targeted interventions that account for within-field heterogeneity in soil properties and crop nitrogen demand [21]. Internet of Things (IoT) sensors integrated with precision irrigation systems further optimize water–nitrogen interactions, reducing both CH₄ emissions in rice systems and N₂O emissions in upland crops [22]. However, adoption of these digital technologies remains below 15% in smallholder systems due to high upfront costs (USD 15,000–50,000 per farm for equipment) and limited digital literacy [20].

Despite promising technical potentials, adoption of mitigation strategies remains constrained by persistent socio-economic realities, with realistic implementation limited to 25–35% of technical potential across diverse farming systems [23]. Recent surveys from 2023 to 2024 reveal that capital constraints affect 65–80% of smallholder farmers in sub-Saharan Africa and South Asia, preventing investment in proven practices such as biochar application (USD 200–400 ha⁻¹ initial cost with 5–7-year payback periods) despite documented emission reductions [23]. Barriers include land tenure insecurity (affecting 60% of farmers in sub-Saharan Africa), lack of capital, risk perception, and compatibility with traditional practices [24].

Gender disparities compound these barriers significantly, as women farmers control 43% of agricultural labor in developing regions but access only 10–20% of agricultural extension services and 5–15% of agricultural credit, reducing adoption rates of climate-smart practices by 20–35% in female-headed households [25]. Inadequate extension service ratios (1 agent per 1000–2000 farmers in many developing regions) further limit knowledge diffusion and technical support for practice adoption [24].

Policy frameworks, incentive structures, and market-based mechanisms, such as carbon credits, influence adoption rates but bring additional challenges of equity and verification [26]. As a result, biophysical estimates often overstate mitigation potential without accounting for the socio-economic conditions of real farming systems [27]. Emerging evidence from 2024 indicates that bundled interventions combining financial incentives (subsidies, low-interest credit), training programs, secure land rights, and gender-responsive extension services can increase adoption rates by 40–60% compared to single-factor approaches, suggesting that integrated socio-economic support is essential for scaling mitigation practices [28].

The climate-smart agriculture (CSA) paradigm has evolved significantly since 2022, with increased emphasis on integrated, context-specific solutions that address mitigation, adaptation, and productivity simultaneously. Recent frameworks prioritize regenerative practices, including regenerative agroforestry, conservation agriculture, and integrated crop–livestock systems, that enhance soil health, biodiversity, and farmer resilience while reducing emissions [6]. However, implementation challenges persist at scale. A 2024 global assessment found that CSA adoption varies varying substantially across regions (from 5% in some sub-Saharan African countries to over 50% in parts of Europe and North America) due to policy mismatches between national climate strategies and local agricultural realities. Notably, majority of surveyed farmers across Kenya, Tanzania, and Ethiopia were unaware of national climate-smart agricultural policies, despite their proven effectiveness, highlighting a disconnect between policy design and implementation at the farmer level [29].

Market-based mechanisms, particularly carbon trading schemes, have gained prominence in 2023–2024 as tools to incentivize agricultural GHG mitigation. Recent analyses demonstrate that carbon credit systems can reduce agricultural emissions through innovation in low-carbon technologies, renewable energy adoption, and ecosystem restoration, with documented transaction volumes increasing by 45% annually in voluntary agricultural carbon markets [30]. However, equity concerns persist regarding smallholder participation, as transaction costs for (MRV) average USD 50–150 ha⁻¹, often exceeding the carbon revenue potential (USD 20–80 ha⁻¹ year⁻¹) for small-scale farmers, effectively excluding them from market benefits [15,30]. The integrity of carbon accounting frameworks remains contested, with 2025 studies highlighting that methodological inconsistencies and measurement uncertainties undermine credibility and risk creating “carbon greenwashing” rather than genuine emission reductions [15].

Emerging paradigms in 2024–2025 emphasize system-level integration and circular economy principles in agriculture. Recoupled crop–livestock systems, for instance, demonstrate potential for over 40% emission reductions in China through optimized nutrient cycling, manure valorization, and reduced reliance on synthetic inputs [14]. Similarly, regenerative agroforestry approaches combining carbon sequestration (74–320 Mg C ha⁻¹ depending on system age) with biodiversity conservation and climate-resilient landscapes show promise, though adoption timelines span 10–15 years before full benefits materialize [31]. These integrated approaches align with recent calls for “agriculture 4.0” that leverages digital technologies to optimize both productivity and environmental outcomes, representing a paradigm shift from single-practice interventions to holistic farm system redesign [22].

To address these complexities, this review introduces the ICEMF, a novel synthesis approach that couples practice-level evidence with spatially explicit modeling of N₂O and Soil Organic Carbon (SOC) outcomes. The framework integrates empirical data, regional modeling, and socio-economic adoption pathways, offering a decision-support tool for scalable interventions [32]. By combining technological, agronomic, and policy perspectives, ICEMF provides a roadmap for reducing agricultural GHG while maintaining productivity [33,34,35].

1.1. Global Imperatives and the Need for Integrated Frameworks

1.1.1. The Urgency of Agricultural Climate Action

The need for transformative agricultural GHG mitigation has never been more urgent. Climate change is already reducing global crop yields, with temperature increases projected to decrease wheat yields by 6.0% and maize by 7.4% per degree Celsius without CO₂ fertilization, effective adaptation, and genetic improvement [36,37]. Simultaneously, agricultural systems must feed 9.8 billion people by 2050, requiring a 50–70% increase in food production from 2010 levels, while reducing absolute GHG emissions [38,39]. The Intergovernmental Panel on Climate Change (IPCC) estimates that limiting warming to 1.5 °C requires agricultural emission reductions of approximately 1 gigaton CO₂-equivalent per year by 2030, with agriculture potentially contributing 3.9–4.0 gigatons of annual emission reductions by 2050 through technical mitigation and dietary changes [40,41].

Yet, current trajectories are moving in the opposite direction. Food systems contributed 34% of global anthropogenic GHG emissions in 2015, totaling 18 gigatons of CO₂-equivalent per year [2]. Total food system emissions reached approximately 16 gigatons CO₂eq in 2018, representing one-third of global anthropogenic emissions, with an 8% increase since 1990 [42]. While emissions from land-use change have declined by 29% since 2000, farm-gate agricultural emissions increased by 13% over the same period, and pre- and post-production emissions grew by 45%. Regional disparities are striking: between 2000 and 2020, agrifood system emissions increased by 35% in Africa and 20% in Asia, driven by expanding livestock production and intensified fertilizer use [43,44]. Without transformative interventions across production systems, demand management, and supply chains, agricultural emissions are projected to reach 15 gigatons CO₂eq by 2050, fundamentally undermining Paris Agreement goals and requiring closure of an 11-gigaton mitigation gap [45].

1.1.2. The Food Security–Climate Mitigation Nexus

The imperative of food security compounds the challenge. Global food insecurity affects populations overwhelmingly concentrated in regions where agriculture is both the primary livelihood and most vulnerable to climate change [46,47]. Smallholder farmers, who produce 35% of the world’s food on farms smaller than 2 hectares, face simultaneous pressures from climate adaptation (increased droughts, floods, and pests) and mitigation expectations, while lacking access to essential resources [48,49]. These farmers are particularly vulnerable to climate impacts that threaten both their production capacity and livelihoods [50].

This creates a critical equity dimension: the populations least responsible for historical emissions (smallholder farmers in developing nations) are most vulnerable to climate impacts and face the most significant barriers to adopting mitigation practices. Systemic constraints limit their capacity to respond: 65–80% lack access to formal credit necessary for investing in climate-smart technologies [51], 60% face insecure land tenure that discourages long-term soil improvement investments [52], and extension service ratios of 1 agent per 1000–2000 farmers in many regions severely restrict knowledge transfer and technical support [53]. Gender disparities compound these barriers significantly, as women farmers control 43% of agricultural labor in developing regions but access only 10–20% of agricultural extension services and 5–15% of agricultural credit, reducing adoption rates of climate-smart practices by 20–35% in female-headed households [54,55]. Any viable framework must therefore address not only technical emission reduction potential but also the socio-economic justice dimensions of enabling equitable participation in climate solutions while ensuring food security and livelihoods.

1.1.3. Why Existing Frameworks Are Insufficient for Agricultural GHG Mitigation and Climate Adaptation

Current frameworks for agricultural GHG mitigation operate in silos, addressing either technical potential or policy implementation, but rarely integrating both with socio-economic realities:

Technical frameworks (e.g., 4R nutrient stewardship, conservation agriculture protocols) provide scientifically validated practices but lack mechanisms to scale adoption. The 4R framework, despite demonstrating 55–64% N₂O reductions in field trials, achieves limited adoption globally after 20 years of promotion, with studies documenting low adaptation despite effectiveness in key agricultural regions, revealing fundamental disconnects between technical recommendations and farmer realities [18,56,57]. No-till agriculture shows similarly low uptake, approximately 12–25% of cropland globally, despite proven soil carbon benefits, because recommendations fail to account for region-specific challenges such as soil compaction in tropical systems, herbicide costs, and incompatibility with smallholder crop–livestock integration [58,59].

Policy frameworks (e.g., climate-smart agriculture, low-emission development strategies) set ambitious national targets but struggle with implementation. A 2025 assessment found that approximately 70% of farmers in Kenya with comprehensive CSA policies were unaware these policies existed, highlighting a fundamental disconnect between policy design and farmer-level action [29]. Moreover, these frameworks rarely specify how national emission reduction targets translate to farm-level practices across heterogeneous landscapes, with implementation varying substantially across regions due to weak scaling mechanisms and insufficient attention to context-specific barriers [60,61].

Modeling frameworks (e.g., Integrated Assessment Models (IAMs), life cycle assessments) operate at scales mismatched to farmer decision-making. IAMs aggregate agricultural systems into large regions (e.g., “sub-Saharan Africa”), obscuring substantial within-region variation in soil types, rainfall patterns, farmer resources, and market access that determine whether a mitigation practice succeeds or fails [62,63]. These models inadequately capture the behavioral, institutional, and socio-economic factors that govern farmer technology adoption, limiting their utility for designing implementable interventions. Life cycle assessments provide detailed emission accounting but suffer from methodological inconsistencies; emission estimates for identical crops can vary two- to five-fold depending on unreported differences in system boundaries, allocation methods, and regional assumptions [64,65].

Carbon market mechanisms promise to incentivize adoption through payments for verified emission reductions but face critical credibility gaps. Measurement uncertainties of ±30–50% in field-based emission factors undermine the integrity of carbon accounting systems [15], while standardized MRV protocols remain absent or inadequate for the majority of agricultural interventions [66,67]. High transaction costs for MRV activities often exceed potential carbon revenue for smallholder farmers, effectively excluding them from market benefits and raising concerns about equity and genuine versus “greenwashed” mitigation [68,69,70].

In summary, ICEMF is essential because it provides the integrated, scalable, and equitable methodology that agriculture urgently requires to simultaneously address climate mitigation, food security, and farmer resilience in the critical decade ahead. Without such integration, agricultural systems risk continuing trajectories that exacerbate rather than solve the interconnected crises of climate change and food insecurity. The framework directly responds to recent calls from global policy processes including the UN Food Systems Summit’s emphasis on integrated transformation [71], the IPCC AR6’s identification of agriculture as a “critical near-term opportunity” constrained by implementation gaps [72], and the Paris Agreement Global Stocktake’s revelation that most national climate commitments lack specific, verifiable agricultural emission reduction pathways [73,74] (Figure 1). The novel contributions of this study are as follows:

• Proposes the ICEMF as a hybrid approach that unites field-level management practices with global-scale emission modeling.
• Provides a dual synthesis of practice-based interventions and spatially explicit N₂O mitigation assessments, highlighting synergies often overlooked in single-focus reviews.
• Identifies critical policy–practice trade-offs and socio-economic adoption barriers, offering a roadmap for aligning climate targets with farmer-centric solutions.

Figure 1. ICEMF data flow and feedback mechanisms. The framework integrates five operational layers: (1) INPUT—farm-level practices provide empirical emission data with quantified reduction potentials; (2) PROCESS—spatially explicit models account for soil, climate, and management contexts, reducing uncertainty from ±30–50% to ±15–25%; (3) INTEGRATE—socio-economic adoption pathways adjust technical potential by realistic adoption probabilities (baseline 5–25%, improving to 40–60% with bundled support); (4) OUTPUT-regional estimates aggregate to national inventories (NDCs) and global targets (3.0–4.5 Gt CO₂eq yr⁻¹); (5) FEEDBACK—monitoring data enables continuous refinement of emission factors and policy mechanisms, improving accuracy by 10–15% over 5 years. Downward information flow (green → blue → purple → orange) translates carbon budgets to farm-level guidance; upward feedback loop (red) continuously improves framework performance through observed field data.

1.2. Review Context and Objectives

The literature on GHG emissions in agriculture highlights both the scale of the problem and the diverse mitigation strategies proposed to balance productivity with sustainability. Table 1 shows summary of research gaps. Ullah, Farooque [75] reviewed biochar production processes and demonstrated its potential to reduce soil-based GHG emissions by enhancing carbon storage, soil quality, and microbial activity. Zhu and Miller [76] found that tomato production systems vary widely in emissions, highlighting precision agriculture and low-carbon energy as key interventions. Ref. [77] applied a harmonized methodology to soybean production studies, finding significant variability in GHG emissions driven by fertilizer use, irrigation, and regional differences. Yuan, Lian [78] examined GHG emissions from constructed wetlands, emphasizing the role of planting strategies and management practices in reducing secondary pollution. Kabato, Getnet [3] assessed climate-smart agriculture strategies, underscoring the benefits of integrated practices like biochar application, agroforestry, and regenerative agriculture for soil health and emission mitigation. Ref. [79] synthesized evidence on converting cropland to grassland in peat soils, concluding that effects on CO₂, CH₄, and N₂O emissions remain ambiguous and context-dependent. Kukah, Jin [30] reviewed the role of carbon trading, finding that it reduces GHG emissions through innovations in low-carbon technologies, renewable energy, and ecosystem restoration. The study demonstrated that recoupled crop–livestock systems in China could reduce agricultural GHG emissions by over 40%, highlighting their potential for sustainable intensification [14].

Table 1. Summarizes the main research gaps.

Focus Area	Key Findings	Geographic Region	Crop/System Type	Primary GHG Focus	Research Gaps	Ref. No.
Biochar production and soil GHG mitigation	Biochar reduces GHG emissions, improves soil carbon storage and microbial activity	Global synthesis	Multiple crops (cereals, vegetables)	N2O, CO2	Long-term field trials, co-analysis of biochar aging, better tracking of carbon storage and CO2 sources	[75]
Tomato production life cycle assessment (LCA) studies	Median emissions vary widely across open-field, Climate-Uncontrolled Protected Environment (CUPE), and climate-controlled protected environment (CCPE) systems	Global (emphasis on Europe, Asia)	Tomato (Solanum lycopersicum)	CO2-eq (full GHG)	Lack of standardized boundaries in (LCA), limited decarbonization strategies validated in practice	[76]
Soybean production	Harmonized Life Cycle Inventories (LCIs) reveal variability across regions and systems	Global (Americas, Asia focus)	Soybean (Glycine max)	CO2, N2O	Insufficient residue management data, limited fertilizer/irrigation detail, weak global representativity	[77]
Constructed wetlands	Plant choice and management strongly influence GHG outcomes	China, Southeast Asia	Rice (Oryza sativa), wetland crops	CH4, N2O	Need for emission reduction strategies tied to plant diversity/harvesting, more ecosystem-level assessments	[78]
Climate-smart agriculture	Integrated climate-smart agriculture (CSA) practices improve soil health and reduce GHG emissions	Sub-Saharan Africa, South Asia	Mixed cropping systems	CO2, N2O, CH4	Site-specific optimization missing, socio-economic and adoption barriers underexplored	[3]
Cropland-to-grassland conversion in peat soils	Mixed results on CO2, CH4, and N2O changes	Northern Europe (Germany, Netherlands, UK)	Grassland conversion (former cropland)	CO2, CH4, N2O	Uncertain long-term impacts, insufficient clarity on soil type and fertilizer use effects	[79]
Carbon trading and emissions reduction	Supports emission cuts via low-carbon technologies and offsets	Global (policy review)	Agriculture sector (general)	CO2-eq (full GHG)	Limited agricultural sector focus, need for integration with farm-level practices	[30]
Recoupled crop-livestock systems in China	Potential 40% emission reduction through system integration	China	Crop–livestock integration (cereals + livestock)	CO2, CH4, N2O	Regional scalability uncertain, lack of long-term adoption studies, limited socio-economic analysis	[14]

Agricultural crop systems are central to global food security but simultaneously represent a major source of GHG emissions, particularly CO₂, CH₄, and N₂O. Despite extensive research on individual mitigation practices, the sector continues to face significant challenges in balancing productivity with climate goals. Measurement uncertainties, limited socio-economic adoption of climate-smart practices, and policy–practice mismatches hinder the scalability of effective solutions. Moreover, most existing approaches focus narrowly on either technical interventions or emission inventories, leaving a gap in integrated frameworks that connect farm-level practices with regional and global mitigation outcomes. This disconnect underscores the urgent need for a comprehensive synthesis that not only reviews emission sources and mitigation strategies but also provides a structured pathway for practical, verifiable, and context-specific solutions to reduce agricultural GHG emissions.

Across the reviewed studies, several recurring gaps emerge. Many mitigation strategies such as biochar application, climate-smart agriculture, and crop–livestock integration show strong potential yet lack long-term field validation and region-specific performance data. Methodological inconsistencies in life cycle assessments of crops like tomatoes and soybeans limit comparability, highlighting the need for standardized boundaries and harmonized reporting. Ecosystem-based solutions such as constructed wetlands and land-use shifts provide valuable insights, but their GHG outcomes remain context-dependent and uncertain, requiring more robust monitoring frameworks. Socio-economic and policy barriers, including adoption constraints, insufficient incentives, and integration challenges with existing farming systems, are also insufficiently addressed, limiting scalability. Addressing these gaps through interdisciplinary, multi-scalar studies will be critical to designing effective, verifiable, and farmer-centered GHG mitigation strategies in agriculture.

ICEMF is a conceptual framework proposed in this review to address critical gaps in existing agricultural GHG mitigation approaches. Unlike the 4R nutrient stewardship framework, which provides agronomic guidance without scaling mechanisms [18,80], Climate-Smart Agriculture (CSA), which sets national targets without farm-level implementation pathways [81,82], or (IAMs), which aggregate agriculture at regional scales that obscure local heterogeneity [62,63], ICEMF operates at the critical “middle layer” between national policy and farm practice (Figure 2), addressing what [83] identified as the fundamental challenge of cross-scale governance: linking local actions to global outcomes while maintaining context specificity.

Figure 2. ICEMF nested hierarchy for agricultural climate action. The framework operates through four complementary layers: (1) global level—IAMs allocate carbon budgets; (2) national level—LEDS translate targets into sectoral commitments; (3) regional/state level—ICEMF (highlighted) operationalizes targets through practice selection, adoption modeling, and costed programs; (4) farm level—4R and CSA practices enable implementation. Downward information flow (blue/green/orange arrows) delivers carbon budgets to farm guidance; upward monitoring data (purple dashed arrow) enables adaptive management. ICEMF occupies the critical operational middle layer, bridging aspirational national targets with implementable farm programs.

(1) Spatially explicit emission modeling with practice-level integration: ICEMF connects field-validated effectiveness data (e.g., biochar reduces N₂O by 16.2% in temperate systems [9] but 8–12% in tropical systems [11]) with spatially explicit models that account for soil texture, climate zone, moisture regime, and management intensity. This enables context-specific emission predictions rather than applying universal effect sizes. For example, no-till agriculture exhibits a variability of −19% to +70% in N₂O responses, depending on soil–climate conditions [12]; ICEMF’s spatial modeling captures this heterogeneity to guide where practices will succeed versus fail. ICEMF’s spatially explicit modeling approach recognizes that agricultural land use outcomes are fundamentally determined by spatial context [84], requiring models that account for local soil–climate management interactions rather than applying universal coefficients

(2) Socio-economic adoption pathway integration: ICEMF incorporates adoption probability functions based on empirical farmer typologies. Systematic review of 35 years of adoption literature [85] demonstrates that adoption is influenced by multidimensional factors beyond economic calculations, including risk perception, information access, social networks, and institutional support. Technical potential (e.g., precision fertilization: −55–64% N₂O [18]) is adjusted by adoption factors including capital access (affecting 65–80% of smallholders [23]), land tenure security (60% in sub-Saharan Africa [24]), extension service ratios (1:1000–2000 in many regions [53]), and gender disparities (women access only 10–20% of extension services [25]). This translates technical potential to realistic scenarios: 25–35% implementation without support, increasing to 40–60% with bundled interventions [28]. No existing framework quantitatively links biophysical effectiveness with adoption probability at operational scales.

Together, these contributions enable ICEMF to answer the following question: “If we implement practice X in region Y with farmer support level Z, what emission reduction will actually occur and contribute to national/global targets?” This operational specificity distinguishes ICEMF from conceptual frameworks (CSA), agronomic guidelines (4R), or macro-scale models (IAMs). The novel objectives of this study are as follows:

• To systematically review GHG emissions from agricultural crop systems and evaluate the effectiveness of diverse management practices.
• To assess the global mitigation potential of N₂O emissions through optimized nitrogen fertilization and complementary agronomic interventions.
• To develop and propose the ICEMF framework as a novel, scalable strategy for integrating technical, environmental, and socio-economic dimensions of GHG mitigation in agriculture.

1.3. Evaluating the Limitations of Current Agricultural GHG Mitigation Frameworks and the Potential of ICEMF to Bridge the Gaps

While the ICEMF offers a novel approach to bridging field-based practices with global emission reduction targets, several existing frameworks share similar goals of mitigating GHG emissions in agriculture. However, these frameworks often face significant challenges related to adoption, scalability, and integration of socio-economic factors. Below, we discuss a few of these frameworks and explain how ICEMF is positioned to overcome their limitations.

1.3.1. The 4R Nutrient Stewardship

The 4R nutrient stewardship framework: Right source at right rate, right time, and right place has been widely adopted globally as a science-based approach to optimize nitrogen fertilizer use [80], with documented potential to reduce N₂O emissions by 55–64% when combined with EEF and improve nitrogen recovery efficiency [18,86]. While effective in many commercial farming contexts, the 4R framework faces significant challenges in context-specific adoption, as implementation is highly site-specific and regional challenges vary considerably across continents and farming systems [3,87]. The application of precision fertilization techniques aligned with 4R principles is often limited by technology access and knowledge gaps, especially in smallholder farming systems where farmers face barriers including limited resources, training, and financial support [88]. These constraints are particularly acute in sub-Saharan Africa, where low digital literacy, high equipment costs, and weak extension services impede implementation [89]. Additionally, the benefits of nutrient use efficiency optimization can be inconsistent across different soil types and climatic conditions, as regional environmental factors often equal or exceed the effects of specific fertilizer management practices on N₂O emissions and nutrient losses [86]. Soil emissions occur in spatially and temporally variable “hot spots” and “hot moments,” driven by complex interactions among soil properties, weather, and microbial processes, making outcomes difficult to predict and generalize across regions [90,91]. ICEMF integrates spatially explicit emission models and considers socio-economic adoption pathways, ensuring that practices like precision fertilization are scalable and adaptable to local conditions. ICEMF’s inclusion of farmer decision-making factors, including financial incentives and training programs, helps overcome adoption barriers seen in the 4R framework.

1.3.2. Climate-Smart Agriculture (CSA)

CSA aims to integrate climate adaptation, mitigation, and food security in a single framework [81,82]. While CSA has promoted practices such as agroforestry, crop diversification, and conservation tillage [60,92], it often lacks a clear mechanism for scaling these practices across diverse agricultural systems [93]. The climate-smart village approach has attempted to provide an integrative strategy for scaling adaptation options, yet implementation remains challenging across heterogeneous farming contexts [93]. Moreover, CSA’s emphasis on climate resilience sometimes overlooks the socio-economic conditions that influence farmers’ willingness to adopt these practices [94,95]. Recent evidence demonstrates that adoption barriers extend beyond technical feasibility to include economic constraints, limited extension access, and institutional factors [96,97]. Studies across West Africa reveal that while farmers recognize benefits of CSA practices, barriers such as high initial investment costs, lack of credit access, insufficient labor, and inadequate knowledge significantly impede widespread adoption [98]. Similarly, research from southern Ethiopia and European food supply chains confirms that socio-economic factors including household wealth, market access, cooperative membership, and policy support critically determine technology adoption and farm sustainability outcomes [99,100]. The gap between indigenous knowledge systems and Western scientific approaches further complicates effective adaptation strategies, suggesting that CSA frameworks must better integrate local contexts and traditional practices to achieve meaningful impact [101]. ICEMF provides a more integrated approach by combining technical practices with spatially explicit emission modeling and socio-economic adoption data. This makes ICEMF more actionable at the farm level, particularly in addressing barriers to farmer adoption and economic feasibility in low-resource regions.

1.3.3. Low-Emission Development Strategies (LEDS)

LEDS are national frameworks integrating climate mitigation with development planning across sectors, including agriculture [102]. While valuable for macro-level policy coordination, LEDS face substantial implementation challenges in agriculture. Critical gaps exist between policy objectives and farm-level realities, particularly regarding the technical feasibility and economic viability of mitigation strategies for smallholder systems [28,103]. Research in sub-Saharan Africa demonstrates that, despite progressive CSA policies, adoption remains constrained by limited access to technology, inadequate extension services, insufficient financial resources, and weak institutional capacity [29,104]. Agricultural mitigation policies face socio-political barriers including competing priorities around food security and affordability, organized lobby pressures, and redistributive effects [103]. Studies across Kenya, Tanzania, Ethiopia, and Rwanda reveal persistent challenges in operationalizing LEDS locally due to trade-offs between agricultural expansion and environmental goals, donor dependence, and insufficient integration of local knowledge [104]. The effectiveness of agricultural LEDS is fundamentally constrained by disconnects between national goals and farm-level adoption realities, with conventional top-down systems inadequate for promoting equitable access to climate-smart practices [28,29]. ICEMF fills the gap by integrating farm-level interventions with regional and global emission models, offering a scalable and adaptive solution that bridges the gap between national policies and local agricultural practices. Its focus on farmer-centered solutions and policy incentives ensures that emission reduction strategies are both practical and achievable.

1.3.4. Integrated Assessment Models

IAMs are global frameworks that integrate economic, energy, land-use, agricultural, and climate systems to evaluate the impacts of climate policies on agricultural emissions [63,105]. While valuable for global policy analysis and identifying cost-effective mitigation pathways [106], IAMs face substantial limitations for real-world agricultural implementation. Critical gaps exist in capturing local specificities essential for farm-level adoption, as these models rely on highly aggregated regional representations that obscure within-region heterogeneity of farming systems, soil conditions, and farmer capacities [107,108]. IAMs have been criticized for problematic assumptions that underestimate transformation urgency and inadequately incorporate behavioral, institutional, and socio-political barriers to technology adoption [109]. Specifically, IAMs represent agricultural mitigation through technology diffusion functions assuming rational economic optimization, without adequately capturing complex socio-economic factors driving farmer decisions, including risk aversion, cultural compatibility, perceived usefulness, financial constraints, extension service access, and social network influences [110,111,112]. Research demonstrates that farmer technology adoption depends on multidimensional factors beyond economic calculations, with psychological dimensions (environmental values, innovation aversion), socio-demographics (age, education, farm size), resource endowments (land, labor, capital), and institutional contexts (extension services, policy incentives) all playing critical yet under-represented roles in IAM frameworks [113,114]. Consequently, while IAMs provide important macro-scale strategic insights, their agricultural projections lack the granularity and behavioral realism needed for context-specific implementation, necessitating complementary bottom-up approaches explicitly incorporating farmer heterogeneity and socio-institutional adoption determinants [108,113]. ICEMF offers a more granular approach by integrating field-level practices with global emission models. Unlike IAMs, ICEMF accounts for regional variations in soil types, climate, and farming systems, making it more relevant for on-the-ground implementation. Furthermore, ICEMF’s inclusion of socio-economic adoption pathways ensures that mitigation strategies are not only technically feasible but also economically viable for farmers.

Unlike existing frameworks that operate primarily at either field-level (4R, CSA) or macro-scale (LEDS, IAMs), ICEMF uniquely bridges these scales through three interconnected components: (1) empirical data integration from diverse field practices, (2) spatially explicit emission modeling that captures regional heterogeneity, and (3) socio-economic adoption pathways that ensure practical scalability. This multi-scalar integration enables ICEMF to translate local agricultural interventions into quantifiable contributions toward global emission reduction targets (Supplementary Table S1).

1.4. The Integrated Crop Emission Mitigation Framework

To address the complexities of bridging field-level practices with global emission targets while accounting for socio-economic realities, this review introduces ICEMF as a novel synthesis framework that operationalizes multi-scale agricultural GHG mitigation.

Framework architecture: ICEMF operates through four hierarchical layers (Figure 2): (1) global level—Integrated Assessment Models allocate carbon budgets across regions and sectors; (2) national level—low-emission development strategies translate global targets into sectoral commitments; (3) regional/state level—ICEMF occupies this critical operational layer, bridging national targets with farm implementation through practice selection, adoption modeling, and costed programs; (4) farm level—specific practices (4R nutrient stewardship, CSA practices) enable on-ground implementation.

Data flow and feedback mechanisms: The framework integrates three data streams (Figure 1): (1) empirical data from field trials provide practice-specific emission factors (e.g., biochar: 16.2% N₂O reduction [9]; 4R + EEF: 55–64% reduction [18]); (2) spatially explicit emission models account for soil types, climate zones, and management regimes to generate region-specific mitigation potentials; and (3) socio-economic adoption pathways incorporate farmer constraints (capital access, and tenure, extension service availability) to adjust technical potential by realistic adoption probabilities. Information flows downward from carbon budgets to farm-level guidance, while monitoring data feeds upward (purple dashed arrow, Figure 2), enabling adaptive management that continuously refines emission factors, practice recommendations, and policy mechanisms based on observed field performance.

Operational distinctiveness: Unlike existing frameworks that operate primarily at field-level (4R, CSA) or macro-scale (LEDS, IAMs), ICEMF uniquely bridges these scales through three interconnected components: (1) empirical data integration from diverse field practices; (2) spatially explicit emission modeling capturing regional heterogeneity in soil, climate, and farming systems; and (3) socio-economic adoption pathways ensuring practical scalability by adjusting technical potential (e.g., biochar’s 16.2% N₂O reduction) by realistic adoption rates (5–10% baseline, increasing to 40–60% with bundled support [28]. This multi-scalar integration enables ICEMF to translate local agricultural interventions into quantifiable contributions toward global emission reduction targets while maintaining farmer-centered feasibility.

Implementation pathway: ICEMF is currently in a conceptual stage, with application potential illustrated through schematic representations (Figure 1 and Figure 2) and validated through five regional case studies demonstrating how the framework adapts to diverse agricultural systems, from Indonesian agroforestry (74–320 Mg C ha⁻¹ sequestration) to German biochar application (consistent 16.2% N₂O reduction) to Indian precision fertilization (12–20% emission reductions).

2. Materials and Methods

The literature search was conducted using major academic databases such as Scopus, Web of Science, ScienceDirect, PubMed, Springer, MDPI, Taylor & Francis, Cambridge Journals, and Google Scholar to ensure comprehensive coverage of peer-reviewed studies on GHG emissions from agricultural crops and mitigation strategies. These databases were specifically selected for their extensive archives of peer-reviewed academic journals in agricultural sciences, environmental sciences, climate change mitigation, and precision agriculture. This approach ensured that sourced articles were of high academic and scientific rigor and directly relevant to our research themes.

This review covers research papers published from 1997 to 2025. The selected articles were organized and discussed within the relevant thematic sections of the manuscript. Search terms employed in the database queries included various combinations of keywords such as “greenhouse gas emissions,” “agricultural crops,” “mitigation,” “management practices,” “climate-smart agriculture, “digital agriculture technologies,” “monitoring and verification,” and “socio-economic adoption.” This specific selection of keywords aimed to encompass a broad spectrum of research topics within the scope of agricultural practices and their environmental impacts. Additional references were traced through citation tracking of key articles to capture significant contributions not directly retrieved in the initial search. Studies were included if they focused on GHG emissions from agricultural crop systems and examined mitigation or management strategies with relevance to CO₂, CH₄, or N₂O. Only peer-reviewed articles published in English were selected to ensure recent and reliable findings. Exclusion criteria comprised studies that addressed emissions unrelated to crops (e.g., purely livestock systems), articles without empirical or methodological relevance, non-English publications, and gray literature such as reports or opinion pieces.

From the eligible studies, key information such as author details, year of publication, study location, crop type, greenhouse gases assessed, and mitigation practices investigated was extracted. Over 350 studies were initially identified and screened for their relevance. After screening for relevance, 244 studies met the inclusion criteria and were thoroughly analyzed and included in the final review. Studies were categorized according to thematic domains relevant to the ICEMF: (1) GHG emission sources and quantification: papers reporting emission measurements, emission factors, or spatial–temporal variability of CO₂, CH₄, and N₂O from crop systems. (2) Practice-level mitigation strategies: research on specific interventions including biochar application, precision nutrient management, conservation tillage, crop rotation, agroforestry, cover cropping, crop diversification, and crop–livestock integration. (3) Digital agriculture technologies: studies addressing IoT sensors, remote sensing, precision agriculture platforms, wireless sensor networks (WSN), satellite monitoring, unmanned aerial vehicles (UAVs), and AI/machine learning applications. (4) MRV systems: papers on emission measurement protocols, carbon accounting methodologies, life cycle assessments, and verification frameworks. (5) Socio-economic adoption and implementation: research on farmer adoption barriers, cost-effectiveness analyses, policy frameworks, and extension service delivery. A narrative synthesis approach was adopted to integrate findings across diverse study designs, with emphasis on identifying common trends, contradictions, and knowledge gaps. Where possible, comparisons were drawn between regional and global perspectives to highlight context-specific variations in emission sources and mitigation outcomes. While this framework supports a comprehensive synthesis, several limitations remain. Variability in study designs, life cycle assessment boundaries, and measurement techniques restrict direct comparability of results. In addition, regional disparities in data availability create potential biases toward well-studied systems, while socio-economic dimensions are often underreported. These limitations underscore the need for standardized methodologies and long-term, context-specific studies to strengthen the evidence base.

This review synthesizes evidence on agricultural GHG mitigation practices and proposes ICEMF as a conceptual framework to integrate dispersed knowledge into an operational decision-support approach. The review is structured to (1) evaluate practice-level effectiveness across diverse interventions, (2) identify critical gaps in existing frameworks, and (3) propose ICEMF’s novel integration of spatial modeling with adoption pathways as a solution to bridge field-to-policy scales (Figure 2). Unlike previous reviews focusing on single practices or policy frameworks, this review’s novelty lies in synthesizing cross-scale evidence to demonstrate how ICEMF’s two core innovations (spatially explicit modeling + adoption integration) address the operational gap between technical potential and achievable impact (Figure 2).

2.1. Meta-Analysis Interpretation and Quality Assessment

We report effect sizes from published meta-analyses, including 95% confidence intervals (CIs), sample sizes (both the number of observations and the number of studies), heterogeneity measures (I² statistic), and assessments of publication bias. Heterogeneity interpretation follows standard guidelines: I² values of 0–25% indicate low heterogeneity, 25–50% moderate, 50–75% substantial, and >75% considerable heterogeneity requiring careful interpretation of context-specific factors.

Quality assessment of meta-analytic evidence considered:

(1)

Sample size (>50 observations considered high quality)

(2)

Heterogeneity assessment and subgroup analyses

(3)

Publication bias testing (funnel plots, Egger’s test)

(4)

Climate and soil stratification

(5)

Duration of included studies (>2 years preferred for agricultural practices). Practices were assigned quality scores of HIGH (meeting 4–5 criteria), MODERATE (2–3 criteria), or LOW (0–1 criteria) to guide the interpretation of evidence strength (Supplementary Table S2).

2.2. Limitations of Meta-Analytic Evidence

Several limitations constrain the interpretation of the meta-analytic findings presented in this review: (1) High heterogeneity (I² > 75% for several practices) indicates substantial context-dependency, requiring careful extrapolation beyond the specific soil–climate management conditions of original studies. (2) Publication bias, where detected, may lead to overestimation of effect sizes as studies with null or negative results are less likely to be published. (3) Long-term field validation (>10 years) remains limited for many practices, with most studies spanning 1–6 years, creating uncertainty about persistence of mitigation effects. (4) Tropical and semi-arid systems are underrepresented relative to temperate zones in meta-analytic datasets, limiting confidence in global extrapolation. (5) Interaction effects between practices (e.g., biochar combined with precision fertilization) are rarely quantified in existing syntheses, preventing assessment of synergistic or antagonistic outcomes. These limitations underscore ICEMF’s emphasis on spatially explicit modeling that accounts for local context rather than applying universal effect sizes.

2.3. Quality Assessment and Risk of Bias

We evaluated evidence quality using a structured rubric assessing experimental design (randomization, replication), study duration, GHG measurement methods, statistical power, and context reporting. Meta-analyses were additionally evaluated for sample size (≥100 observations preferred), heterogeneity assessment (I²), publication bias testing, and climate/soil stratification. Studies were classified as high (8–10 points), moderate (5–7 points), or low quality (0–4 points). Five primary bias sources were identified: publication bias (positive results are preferentially published), measurement bias (high coefficient of variation in GHG fluxes: 30–300%), duration bias (most studies < 3 years), geographic bias (temperate regions overrepresented), and interaction bias (single-practice focus). Key meta-analyses cited for quantitative effect sizes varied in quality. High-quality meta-analyses (biochar [9], cover crops [115]) scored ≥ 8/10 with >100 observations, formal bias testing, and climate stratification. Additional meta-analyses (no-till [115]) provided moderate-quality evidence with context-specific findings. Complete quality assessment rubrics, scoring criteria, individual study evaluations, coefficient of variation analysis, and detailed bias mitigation strategies are provided in Supplementary Table S2.

2.4. Synthesis of Context-Specific Effectiveness

To enable systematic comparison of mitigation practice effectiveness across diverse agroecological contexts, we organized extracted data into a comprehensive impact matrix structured by three dimensions: (1) climatic zone (temperate, tropical, subtropical, humid, semi-arid), (2) soil texture and characteristics (acidic soils, clay, sandy, paddy soils, various textures), and (3) cropping system (rice, wheat, corn/maize, vegetables, mixed systems, general cropland).

For each practice-context combination, we compiled quantitative impacts on N₂O, CH₄, and CO₂ emissions, soil organic carbon (SOC) sequestration, yield effects, and practice durability. We extracted heterogeneity measures (I² statistics) from meta-analyses to assess consistency of effects across studies, with I² values of 0–25% indicating low heterogeneity, 25–50% moderate, 50–75% substantial, and >75% considerable heterogeneity. Where available, we reported 95% CI to quantify estimation uncertainty.

Evidence quality for each practice-context combination was rated using a structured rubric assessing: (i) sample size (≥100 observations preferred for high quality), (ii) formal heterogeneity assessment and subgroup analyses, (iii) publication bias testing (funnel plots, Egger’s test), (iv) climate and soil stratification, and (v) study duration (≥2 years for agricultural practices). Practices were assigned quality scores of HIGH (8–10 points, meeting 4–5 criteria), MODERATE (5–7 points, meeting 2–3 criteria), or LOW (0–4 points, meeting 0–1 criteria) to guide interpretation of evidence strength and generalizability.

This matrix structure enables identification of (1) context-specific effectiveness patterns informing spatially explicit implementation strategies, (2) practices with consistent versus variable responses across contexts, (3) knowledge gaps requiring additional research investment, and (4) mechanistic insights into soil–climate management interactions determining mitigation outcomes. The synthesis directly supports ICEMF’s first core innovation, spatially explicit emission modeling that accounts for local context rather than applying universal effect sizes across heterogeneous landscapes.

2.5. Baseline Specification and Sequential Accounting for Nitrogen Management

All nitrogen optimization mitigation estimates in ICEMF use standardized baselines to ensure comparability and prevent double counting. For intensive temperate cereal systems, the reference baseline is 165–190 kg N ha⁻¹ synthetic fertilizer application (representative of US Corn Belt maize and European wheat), corresponding to 1.8–4.6 kg N₂O-N ha⁻¹ yr⁻¹ depending on soil texture, water-filled pore space, and climate [18,86,91]. Regional baselines vary substantially: 200–250 kg N ha⁻¹ in intensive Asian rice-wheat systems [116], 120–160 kg N ha⁻¹ in moderate-input temperate systems [117], and 40–80 kg N ha⁻¹ in smallholder African systems [118].

ICEMF incorporates region-specific N₂O emission factors (percentage of applied nitrogen emitted as N₂O-N) ranging from 0.5–1.5% in temperate dry climates to 1.5–3.0% in temperate humid climates, accounting for soil–climate interactions affecting denitrification and nitrification processes [18,86,119]. These emission factors scale field-level nitrogen management interventions to regional mitigation estimates while capturing non-linear emission responses at high nitrogen application rates (exponential increase, β = 1.6–2.0) [119].

To prevent double-counting when multiple nitrogen management practices are combined, ICEMF employs sequential accounting where the first practice modifies the baseline for subsequent interventions. For example, legume-based crop rotation reduces synthetic nitrogen requirements from 180 to 63 kg N ha⁻¹ and decreases N₂O emissions by 39% [120,121]; applying precision fertilization (4R stewardship) to this reduced nitrogen baseline achieves an additional 55–64% reduction in remaining emissions [18], yielding a total sequential reduction of 70–75% rather than an impossible additive 94–103%. Similarly, biochar application (16.2% N₂O reduction) [9] combined with precision fertilization targets overlapping nitrogen transformation pathways; field validation studies document combined reductions of 38.8% [122] rather than the theoretical additive 71%, confirming the necessity of sequential accounting. This approach ensures ICEMF provides conservative, verifiable mitigation estimates for bundled practice scenarios.

3. Thematic Analysis

Figure 3 shows thematic analysis of the reviewed literature which reveals that mitigation strategies in agricultural crop systems can be broadly grouped into five categories: soil-based approaches (e.g., biochar application, conservation tillage), crop-based practices (e.g., diversification, agroforestry, cover cropping), nutrient management (e.g., precision fertilization, 4R stewardship), integrated systems (e.g., crop–livestock recoupling, constructed wetlands), and policy or socio-economic interventions (e.g., carbon trading, CSA policies). This categorization highlights both the diversity and interconnectedness of approaches, with many practices offering co-benefits such as enhanced soil health, improved productivity, and resilience to climate variability. However, adoption remains uneven, and their effectiveness is often context-specific, emphasizing the need for adaptive frameworks like the ICEMF to harmonize local practices with global climate targets.

Figure 3. Thematic categorization of GHG mitigation strategies in agricultural crop systems (soil-based, crop-based, nutrient management, integrated systems, and policy measures).

3.1. Sources and Trends of GHG Emissions in Agriculture

Agricultural systems contribute significantly to global GHG emissions, primarily through CO₂, CH₄, and N₂O [123]. Soil organic matter degradation, excessive fertilizer use, and land-use change release CO₂, while enteric fermentation and flooded rice fields are major sources of CH₄, and N₂O arises mainly from nitrogen fertilizer application and manure management, with its high GWP making it a critical focus for mitigation [124]. Recent studies indicate that global agricultural emissions have steadily increased over the past decades, with hotspots in Asia, Latin America, and sub-Saharan Africa driven by crop intensification and rising fertilizer demand [125,126]. Seasonal variations, cropping intensity, and irrigation practices further influence emission patterns, underlining the complexity of agricultural GHG dynamics [127,128].

The study by Akram and Ali [129] examines the convergence hypothesis of GHG emissions across 93 countries from 1980 to 2017. Using the Phillips and Sul test [130], the study finds evidence of divergence in emission trends, suggesting that countries follow different convergence paths. Clustering algorithms identify five distinct convergence clubs, indicating the need for region-specific policies. Figure 4 presents the GHG emissions in the agriculture sector across 93 countries (1980–2017), as a group, based on the Phillips and Sul Panel Club Convergence Test [130]. Figure 4 presents the GHG emission trajectories for each convergence club, revealing striking disparities in emission patterns. Club characteristics: Club 1 (n = 29) exhibits high emission variability with countries predominantly from sub-Saharan Africa, Latin America, and South Asia. Club 2 (n = 31) includes major agricultural producers with moderate emission trajectories (India, Brazil, Australia, France). Club 3 (n = 27) demonstrates declining trends, featuring industrialized nations and emerging economies (China, Republic of Korea, European countries). Club 4 (n = 3) represents a transition phase with heterogeneous characteristics. Club 5 (n = 2: Seychelles, Switzerland) shows the lowest emissions, providing policy models for other clubs. Paraguay (n = 1) forms a separate group with unique emission characteristics not conforming to the five club patterns. The findings emphasize the importance of considering these divergent paths when designing GHG mitigation strategies and the impact of policy transfers across countries. The study suggests that countries in Clubs 1–4 should adopt agricultural policies from Club 5, where GHG emissions are lower, particularly focusing on cleaner energy. Strategies to reduce GHG emissions include improving sector efficiency, fostering innovation, reducing deforestation, and using cleaner energy with subsidies. Additionally, addressing poverty and adopting low-cost energy technologies in agriculture are key to sustainable development in lower- and middle-income countries.

Figure 4. Global disparities in agricultural greenhouse gas emissions across 93 countries grouped by convergence clubs (1980–2017). Countries were assigned to five convergence clubs based on [130] panel club convergence test, as reported by [129]. Data source: FAO statistics division (FAOSTAT) emissions—agriculture database (accessed 15 June 2020; database version: June 2020 release). Dataset: “emissions totals” (domain code: GT), element: “emissions (CO₂eq)” aggregated from all agricultural sources, item: “agricultural total,” unit: kilotonnes CO₂ equivalent (source: [129]).

Building on the study by [129], which examines the convergence hypothesis of GHG emissions across 93 countries from 1980 to 2017, we have generated heatmaps for each of the identified convergence clubs, showing total emissions from 1990 to 2020. The heatmaps visually represent the emissions trends within each club, highlighting the divergent paths observed in the study. These visualizations support the need for region-specific GHG mitigation strategies, emphasizing the importance of targeted policies that reflect the unique emission patterns of each club. The findings underscore the role of tailored interventions to address the emission disparities across countries. The data for these heatmaps is sourced from the FAOSTAT Database (FAO, 2020). Figure 5 presents heatmaps illustrating the total GHG emissions (in kilotons) across five regional convergence clubs, as identified through the Phillips and Sul convergence test [129]. These heatmaps visually represent emissions trends from 1990 to 2020, highlighting the disparities in emission patterns across regions. This regional analysis underscores the need for context-specific mitigation strategies and informs the ICEMF framework by emphasizing the importance of spatially explicit emission modeling.

Figure 5. Comprehensive heatmap of agricultural greenhouse gas emissions across 91 countries grouped by convergence clubs (1990–2020). Total agricultural GHG emissions (kilotonnes CO₂ equivalent) for 91 countries organized into convergence clubs based on the [130] panel club convergence test as reported by [129]. Countries are grouped and displayed by convergence club: Club 1 (n = 29), Club 2 (n = 31), Club 3 (n = 25), Club 4 (n = 3), Club 5 (n = 2), and Paraguay (n = 1, an independent group). Within each club, countries are arranged alphabetically. Navy horizontal lines separate convergence clubs. Color gradient represents log₁₀-transformed emission values: light yellow (low emissions) to dark red (high emissions), enabling visualization across a four-order magnitude range (31 to 1,412,572 kt CO₂ eq in 2020). Emission values in kilotons CO₂ equivalent from agricultural activities. White cells indicate missing data for specific country-year combinations. Data source: FAOSTAT emissions—agriculture database (FAO of the United Nations, 2023 release, accessed 25 September 2025). Dataset: domain GT (emissions–agriculture), element: emissions (CO₂eq) (AR5), item: agrifood systems total, Unit: kilotonnes CO₂ equivalent.

Mukwada, Taylor [131] reported that the trends also reveal significant regional disparities. Developed countries have shown modest declines in emissions due to improved nutrient-use efficiency, conservation tillage, and climate-smart practices, while developing nations continue to face steep increases as agricultural expansion meets food security demands. Studies highlight that rice and maize systems dominate CH₄ and N₂O contributions, respectively, while wheat and soybean systems contribute substantial CO₂ through soil- and land-use-related emissions [132]. Moreover, climate change feedback loops, including rising temperatures and extreme weather events, exacerbate emission rates and reduce the mitigation capacity of soils [133]. Although progress has been made in quantifying emissions, gaps remain in capturing indirect sources such as post-harvest processes and regional variability in emission factors, emphasizing the need for refined monitoring frameworks and targeted interventions [21]. Table 2 presents a summary of key studies highlighting the sources and trends of GHG emissions in agriculture, their drivers, and regional or system-specific notes.

Table 2. Global sources and drivers of agricultural greenhouse gas emissions.

Greenhouse Gas Contribution	Key Drivers	Regional/System Notes	Study
CO2, CH4, N2O	Developed vs. developing country emissions	Highlights disparities in contributions to climate change	[123]
CH4, N2O	Agricultural soils and GWPs	Case study on emission patterns from Indian soils	[124]
CO2, N2O	Fertilizer runoff, water pollution	Links water pollution and agricultural emissions globally	[125]
CH4, N2O	Intensification and cereal cropping	Sub-Saharan Africa identified as emission hotspot	[126]
CO2, CH4	Cropland expansion, planetary boundaries	Food production scenarios and sustainability trade-offs	[127]
N2O, CH4	Smallholder farming challenges	Focus on mitigation opportunities in Sub-Saharan Africa	[128]
Mixed (CO2, CH4, N2O)	Climate variability and food insecurity	Case study from Lesotho, showing regional disparities	[131]
CO2, CH4	Irrigation management, soil processes	Winter wheat systems in North China Plain	[132]
CO2, CH4, N2O	Abandonment of agriculture, soil processes	Highlights post-agriculture emission risks	[133]
Mixed (CO2, CH4, N2O)	Climate change adaptation, artificial intelligence (AI) and machine learning (AI–ML) tools	Stresses monitoring gaps and future opportunities	[21]

3.2. Evaluation of Crop and Soil Management Practices

Crop and soil management practices are central to reducing GHG emissions from agriculture. Conservation tillage, cover cropping, and crop diversification have been shown to enhance soil organic carbon stocks and reduce CO₂ release by minimizing soil disturbance and improving residue retention [7,134,135]. Agroforestry systems contribute to long-term carbon sequestration while also providing ecosystem services such as biodiversity support and microclimate regulation [136]. Biochar application has emerged as a promising soil amendment, capable of enhancing soil structure, microbial activity, and long-term carbon storage while mitigating CH₄ and N₂O fluxes [137]. In temperate agricultural regions, biochar demonstrates substantially more consistent and predictable soil CO₂ emissions mitigation effects compared to no-till agriculture, which exhibits high climate and soil-dependent variability [138]. Biochar application increases soil organic carbon sequestration by 61% (95% CI: 36–90%) across temperate zones based on meta-analysis with high heterogeneity (I² = 85%), indicating substantial variation by soil type, application rate, and biochar feedstock [9], though tropical system data remain limited, and decreases annual CO₂ emissions by 13.0–17.6% within 1–2 years [139], whereas no-till effectiveness in temperate regions depends critically on soil texture, organic carbon content, and precipitation patterns [140], with future projections showing declining mitigation potential of only 1.4–1.7 Mg ha⁻¹ over 30 years under climate change scenarios [141]. The superior consistency of biochar in temperate climates stems from its intrinsic recalcitrance, with 97% of biochar carbon persisting in stable form. Modeled mean residence times average 556 years, with ranges from 102 years (low-temperature pyrolysis of labile feedstocks) to 107,000 years (high-temperature pyrolysis of woody biomass), making biochar largely independent of temperate climate fluctuations in temperature and precipitation [142]. However, these estimates derive primarily from laboratory incubations and modeling extrapolations; field validation beyond 10–15 years remains limited, and long-term persistence under diverse agricultural conditions requires further empirical verification [142], rendering it largely independent of temperate climate fluctuations in temperature and precipitation. In contrast, no-till effectiveness in temperate regions is fundamentally constrained by soil-specific properties and temporal dynamics of seasonal carbon fluxes [143], making it a less reliable climate mitigation strategy compared to biochar application. Biochar reduced N₂O emissions by 16.2% (95% CI: 9.8–22.6%) based on meta-analysis of 119 paired observations from 18 studies with moderate heterogeneity (I² = 72%) and no publication bias detected [9], whereas global systematic analysis of 106 studies across 372 sites demonstrates that cover crops reduce nitrogen leaching and net greenhouse gas balance by 2.06 Mg CO₂-eq ha⁻¹ yr⁻¹ without significantly affecting direct N₂O emissions [144]. Meta-analysis shows that alternate wetting and drying (AWD) reduces CH₄ emissions by 53% and GWP by 44% in rice systems, though N₂O emissions increase by 105%, and the net effect remains beneficial [145]. Systematic review of 11,768 yield observations from 462 field experiments reveals legume-based crop rotations increase subsequent crop yields by 20%, with greater benefits (32%) in low-yielding environments [146] (Table 3) (Supplementary Table S2).

Table 3. Meta-analysis evidence for key mitigation practices for N₂O mitigation in temperate agricultural regions.

Practice	Number of Observations (Studies)	I2 (%)	Publication Bias	Climate/Soil Strat	Quality Score *	Rating	Study
Biochar N2O	119 (18)	87	Yes	Yes (temp/trop)	10/10	High	[9]
Cover crops GHG	106 studies, 372 sites	Assessed	Yes	Yes (4 climate zones)	9/10	High	[147]
AWD rice CH4/N2O	Global dataset	Assessed	Yes	Yes (soil, climate)	9/10	High	[145]
Legume rotation	11,768 (462)	Assessed	Yes	Yes (pedoclimatic)	9/10	High	[146]
Biochar N2O (meta-of-metas)	Multiple MAs	Various	Yes	Yes	9/10	High	[122]
No-till N2O	~60 (15)	~75	No	Partial	6/10	Moderate	[115]
Biochar N2O	~30 (12)	NR **	No	Partial	4/10	Low–Mod	[148]

Interpretation: High-quality meta-analyses (biochar [9], cover crops [115]) scored ≥ 8/10 with >100 observations, formal bias testing, and climate stratification. Additional meta-analyses (no-till [115]) provided moderate-quality evidence with context-specific findings. * Quality score based on sample size, heterogeneity control, publication bias assessment, climate/soil stratification, and temporal validation (>2 years). ** NR = not reported in original publication.

Research has identified multiple interconnected mechanisms through which biochar reduces soil N₂O emissions in agricultural systems. Rather than operating through a single pathway, biochar’s effectiveness derives from synergistic interactions among several primary mechanisms, each contributing differentially to overall N₂O reduction (Figure 6A).

Nitrogen immobilization and adsorption represent a major mechanism, operating through direct adsorption of NH₄⁺ and NO₃⁻ onto biochar’s aromatic carbon surface with high cation exchange capacity, thereby reducing substrate availability for nitrification and denitrification pathways that produce N₂O [10]. Complete denitrification enhancement represents another dominant mechanism, functioning through biochar’s electron shuttle effect that facilitates electron transfer to soil denitrifying microorganisms and promotes the final reduction of N₂O to N₂ through upregulation of nosZ genes encoding N₂O reductase [149].

Several secondary mechanisms contribute additional mitigation effects: soil physical modification through increases in mesopore size and specific surface area that enhance aeration and reduce anaerobic microsites; soil pH modification through the liming effect that reduces nitrification rates in acidic soils; and microbial community shifts that reduce ammonia-oxidizing archaea populations while promoting N₂O-reducing bacteria [10,150]. These interconnected mechanisms produce consistent field-observed N₂O reductions ranging from 18% to 54%, with substantially greater reductions (up to 84%) when biochar is combined with nitrogen fertilization [122,148,151].

In contrast, the high variability in no-till N₂O responses ranging from 19% reductions to 70% increases [12,115] stems from the fundamentally production-enhancing nature of its primary mechanisms (Figure 6B). Unlike biochar, which operates through multiple N₂O reduction pathways, no-till’s mechanisms predominantly create conditions favorable for N₂O production. Enhanced denitrification through moisture accumulation represents the dominant mechanism, creating anaerobic microsites where incomplete denitrification predominates. No-till significantly increased soil denitrification by 85% compared to conventional tillage, with a 33% increase in the (nirK + nirS)/nosZ gene ratio, indicating that N₂O is released rather than being further reduced to N₂ [152]. This moisture-induced denitrification is particularly problematic in humid temperate regions where water-filled pore space frequently exceeds 70%, creating sustained anaerobic conditions that favor N₂O production over complete reduction to N₂.

Substrate availability from residue accumulation further promotes N₂O production by providing readily available carbon sources for denitrifying microorganisms, while soil compaction effects restrict oxygen penetration and create additional anaerobic zones favorable for denitrification [152,153]. Nitrification responses add complexity, as they vary considerably depending on local moisture regimes and soil conditions, making outcomes difficult to predict [153]. The mechanistic basis for no-till’s inconsistency contrasts fundamentally with biochar’s reliability. Where biochar’s mechanisms actively convert N₂O to N₂ and immobilize nitrogen substrates, no-till’s mechanisms create environmental conditions that favor N₂O production. This mechanistic divergence explains why meta-analyses show that conservation tillage increased N₂O emissions by 17.8% on average (95% CI: 8.5–27.1%) based on meta-analysis of 154 observations from 35 studies with high heterogeneity (I² = 76%), with greater emission increases in humid temperate regions (25–35%) compared to semi-arid systems (−5% to +10%) [115], despite some individual studies reporting reductions. The consistency of outcomes depends on whether the dominant mechanisms promote N₂O reduction (as in biochar application) or N₂O production (as in no-till management). For temperate regions prioritizing consistent and reliable N₂O mitigation, biochar’s reduction-focused mechanisms represent a more dependable approach than no-till’s production-enhancing pathways, though local conditions and proper application rates remain critical considerations for both practices.

Figure 6. Conceptual framework comparing mechanisms driving contrasting N₂O emission responses between biochar and no-till management systems. (A) Biochar reduces N₂O through multiple reduction-focused pathways, including nitrogen immobilization, complete denitrification enhancement (N₂O → N₂), improved aeration, pH modification, and microbial shifts, producing consistent 18–54% emission reductions [10,148,149]. (B) No-till creates production-enhancing conditions through dominant moisture accumulation effects, residue-derived substrate availability, compaction, and variable nitrification responses, resulting in inconsistent outcomes from −19% to +70% [12,115,152]. Arrow thickness indicates relative mechanistic importance (thick = dominant/major; medium = moderate; thin = minor; dashed = variable). The fundamental difference in mechanism directionality reduction focused versus production enhancing explains biochar’s superior reliability for N₂O mitigation in temperate agricultural systems compared to no-till management.

Crop rotation is a key practice in sustainable farming, involving the sequence of planting various crops in the same field over multiple seasons to enhance soil fertility and productivity. It is distinct from other methods, such as intercropping and monoculture (monocropping) [154]. Crop rotation increases soil carbon sequestration and lowers CO₂ emissions [155]. The case study by Lötjönen and Ollikainen [156] outlines the environmental benefits of crop rotation over monoculture, particularly in terms of reduced fertilization needs, lower nitrogen runoff, and decreased GHG emissions. Rotating legumes, like clover-wheat, also lowers GHG emissions and nitrogen runoff [156,157]. Crop rotation helps reduce CO₂ emissions by improving soil organic matter (SOM) and minimizing the need for tillage and fertilizers. Systems that incorporate legumes and perennials enhance carbon input and sequestration over time [158,159]. Al-Musawi, Vona [7] highlights the significance of various crop rotation systems for both agricultural and environmental sustainability. The authors explain that crop rotation can notably improve soil structure and organic matter levels, as well as boost nutrient cycling. Additionally, when legumes are incorporated into rotations instead of monoculture systems in Europe, soil organic carbon increases by up to 18%. This practice also helps reduce GHG emissions, promote carbon sequestration, and lower nutrient leaching and pesticide runoff. Legume-based rotations also influence other greenhouse gases. By improving nitrogen efficiency, legume-based rotations decreased N₂O emissions by 39% (range: 28–51%) in long-term field trials in temperate corn systems (Monmouth, IL, USA) [120], though comprehensive meta-analysis across diverse agroecological zones is not yet available, limiting generalizability [120]. In flooded rice systems, rotating with upland crops like maize or sorghum can reduce CH₄ emissions by up to 84% by interrupting anaerobic soil conditions [160]. Similarly, improved irrigation techniques, such as an AWD in rice systems, have demonstrated the potential to lower CH₄ emissions without reducing yields [161]. These strategies underscore the technical viability of soil- and crop-based interventions, though their outcomes are often highly context-specific and influenced by environmental and management factors [162].

Despite their potential, significant challenges limit the widespread adoption of these practices. Research indicates variability in mitigation outcomes due to soil type, climate conditions, and crop management intensity, making generalized recommendations difficult [163]. For example, while no-till farming may enhance soil carbon in temperate zones, its benefits in tropical systems remain inconsistent [164]. Economic and social barriers, including high costs of inputs, lack of farmer awareness, and limited access to technologies, also hinder uptake [165]. Moreover, long-term impacts are not always well documented, with short-term studies dominating the literature and leaving uncertainties about sustainability over decades [166]. Addressing these gaps requires integrative approaches that combine technical innovations with enabling policies, financial incentives, and knowledge-sharing networks to promote scalable, farmer-centered solutions [167]. Table 4 summarizes key studies on crop and soil management practices, highlighting their potential for GHG mitigation along with associated challenges and limitations.

Table 4. Evaluation of crop and soil management practices for GHG mitigation.

Practice	GHG Mitigation Potential	Key Benefits	Challenges and Limitations	Agricultural Evidence	Reference
Conservation Tillage/No-Till	Reduces CO2 emissions through soil carbon preservation; variable N2O effects (−11%, 95% CI: −19% to −1%; high heterogeneity I2 = 89%)	Reduced soil erosion, improved water retention, lower fuel costs, enhanced soil organic matter	Variable effectiveness across climates; may increase N2O in humid regions; soil compaction risks; herbicide dependency; incompatible with some smallholder systems	Temperate and tropical agricultural systems; smallholder contexts; long-term soil studies	[12,58,59,164]
Biochar Application	Reduces N2O emissions by 16.2% (95% CI: 9.8–22.6%) in temperate systems; increases SOC by 61%; persistent C sequestration	Enhanced soil structure, improved nutrient retention, increased pH in acidic soils, long-term C storage, improved microbial activity	High upfront costs (USD 200–400 ha−1); production infrastructure requirements; variable effects in tropical systems (8–12% reduction); limited availability; application rate optimization needed	Meta-analysis across temperate agricultural soils; field validation 4–6 years	[9,10,75,137,139]
Cover Cropping	Reduces CO2 through biomass addition; improves soil C sequestration; reduces N2O through improved N cycling	Erosion control, weed suppression, improved soil health, nitrogen fixation (legumes), enhanced biodiversity	Additional seed and management costs; potential yield competition; termination timing critical; requires knowledge/planning; may increase water use	Diverse cropping systems; temperate and tropical applications	[162,168,169]
Crop Rotation (Legume-Based)	Reduces N2O by 39% (range: 28–51%) through improved N efficiency; increases SOC by 18% compared to monoculture	Pest and disease management, improved soil fertility, diversified income, reduced synthetic N requirements (65% reduction)	Market access for diverse crops; increased management complexity; knowledge requirements; storage/processing infrastructure	Long-term field trials temperate corn systems; European agricultural systems	[7,120,154,156]
Agroforestry Systems	Sequesters 74–320 Mg C ha−1 depending on system maturity; variable effects on N2O and CH4	Climate adaptation, biodiversity conservation, diversified income, microclimate regulation, long-term C storage	Long establishment period (10–15 years); initial yield reductions; land tenure requirements; technical knowledge needs; upfront investment	Tropical and temperate systems; Indonesia case studies; global meta-analyses	[11,31,136,170,171]
Reduced/Optimized Nitrogen Fertilization	Reduces N2O emissions by 55–64% when combined with EEF while sustaining yields	Reduced input costs, improved water quality, lower GHG emissions, maintained productivity	Precision management requirements; soil testing costs; risk perception; yield variability concerns; requires training	Temperate rainfed corn systems; Indo-Gangetic Plains; global assessments	[3,18,172,173,174,175]
Organic Amendments (Manure/Compost)	Variable effects on N2O (depends on application rate and timing); improves long-term SOC	Improved soil structure, nutrient recycling, waste valorization, enhanced microbial activity	Potential for high N2O emissions if poorly managed; transportation costs; quality variability; pathogen risks; application timing critical	Integrated crop–livestock systems; organic farming systems	[14,176,177,178]
Alternate Wetting and Drying (Rice)	Reduces CH4 emissions by up to 48–84% (when combined with crop rotation) without yield penalties	Water savings (15–30%), reduced irrigation costs, improved root health, maintained yields	Requires irrigation control infrastructure; labor for water management; risk of drought stress; not suitable for all soil types	Asian rice systems; field trials across climate zones	[160,161,179,180]
Improved Crop Varieties (Climate-Smart)	Indirect GHG benefits through improved N use efficiency; enhanced resilience reduces emission intensity	Drought/heat tolerance, improved nutrient efficiency, enhanced productivity, climate adaptation	Seed access and affordability; varietal selection for local conditions; requires breeding programs; intellectual property constraints	Diverse crop systems; smallholder and commercial applications	[36,37,181,182]
Integrated Crop-Livestock Systems	Reduces GHG by >40% through nutrient recycling and optimized management; manure valorization reduces CH4	Nutrient cycling, diversified income, improved resilience, waste valorization, enhanced soil fertility	Complex management requirements; infrastructure needs; knowledge intensity; scale requirements; labor demands	Chinese agricultural systems; mixed farming systems globally	[14,177,183,184]

Context-Dependent Effectiveness: A Systematic Comparison

The Supplementary Table S3 presents a comprehensive matrix synthesizing practice-specific impacts across climatic zones, soil textures, and cropping systems. This systematic organization reveals several critical patterns:

Climate zone dependency: Practice effectiveness varies substantially by climate. Biochar achieves 16.2% N₂O reduction (95% CI: 9.8–22.6%, I² = 72%) in temperate systems based on 119 observations from 18 studies, nearly double the 8–12% reduction in tropical systems with limited validation (<30 observations) [9,137,185]. This reflects differences in moisture regimes, decomposition rates, and microbial communities, with temperate benefits sustained over 4–6 years [10].

No-till shows extreme variability (212 observations from 40 studies, I² = 89%): effects range from −19% to +1% depending on conditions, with humid temperate regions showing increases while semi-arid systems show modest reductions [115,152]. AWD in rice systems demonstrates climate-appropriate design: −53% CH₄ but +105% N₂O, achieving net −44% GWP with no yield penalty [145,179].

Soil texture interactions: Biochar reduces N₂O emissions more effectively in acidic soils (pH < 6.5) through liming-induced pH modification that suppresses nitrification. Meta-analytic evidence (n = 119 observations, 18 studies) shows 16.2% reduction (95% CI: 9.8–22.6%) in temperate systems [9], with pH-dependent enhancement in acidic conditions. No-till effectiveness varies by soil texture: fine-textured clay soils in humid regions increase denitrification (+85%) and N₂O emissions (+25–35%), while better-drained systems show reductions (semi-arid: −5% to +10%; overall meta-analysis: −11%, 95% CI: −19% to −1%, I² = 89%) [115,152,153].

Cropping system specificity: Legume rotations provide multiple benefits in temperate systems: (1) yield increases of 20% overall (32% in low-yield environments) from meta-analysis of 11,768 observations across 462 field experiments [146]; (2) N₂O emission reductions of 39% (range: 28–51%) through improved nitrogen use efficiency in long-term temperate corn trials [120]; and (3) soil organic carbon increases up to 18% compared to monoculture systems in European rotations [7]. Optimized nitrogen fertilization with enhanced-efficiency fertilizers (4R + EEF) reduces N₂O by 55–64% in temperate corn systems [18], while site-specific nutrient management achieves 2.5% reduction in tropical rice and 12–20% in wheat systems [173].

Practice reliability: Meta-analytic heterogeneity (I²) quantifies consistency. Biochar (I² = 72%, 119 observations) and legume rotations (moderate I², 11,768 observations) show predictable outcomes suitable for carbon credits [9,146]. Conservation tillage shows highly variable N₂O effects: mean reduction of 11% (95% CI: −19% to −1%) with extreme heterogeneity (I² = 89%, 212 observations) and context-dependent outcomes ranging from 19% reductions to 70% increases based on soil texture, moisture, and climate [12,115]. Cover crops provide net GHG benefits of −2.06 Mg CO₂-eq ha⁻¹ yr⁻¹ via reduced nitrogen leaching rather than direct N₂O mitigation (106 studies, 372 sites) [144,147].

ICEMF integration: These patterns operationalize ICEMF’s spatially explicit modeling by incorporating climate variation (biochar: 16.2% temperate vs. 8–12% tropical), soil interactions (no-till: +17.8% clay vs. −11% sandy), and system specificity (AWD for rice CH₄) rather than universal coefficients. Practices with I² < 75% (biochar, legumes, cover crops) suit carbon markets requiring predictability; practices with I² >85% (no-till) need site validation [66,67,115]. Context specificity is a critical element in accurately assessing the mitigation potential of agricultural practices and should not be viewed as a mere nuisance. Universal coefficients used in Tier 1 IPCC inventories and various carbon protocols often fail to reflect local conditions, leading to systematic misestimates of mitigation outcomes. For instance, promoting no-till farming in humid clay soils based on global averages, which suggests a carbon sequestration potential of −11%, overlooks the observed local increases of +17.8%. Such miscalculations can lead to counterproductive results that undermine the very goals they aim to achieve [64,65,115]. The ICEMF approach, in contrast, emphasizes the importance of context-specific predictions, providing a more realistic estimate of mitigation potential. By asking, “If practice X is implemented in region Y with soil Z, what reduction in emissions can be expected?” ICEMF enables more accurate, actionable insights. This operational specificity is a key distinction of ICEMF when compared to broader conceptual frameworks (such as CSA), agronomic guidelines (like 4R), or macro-scale models (IAMs), which often operate at mismatched scales and fail to capture the nuances of local variability [58,59,78,79,80].

3.3. Nitrous Oxide Mitigation Through Nutrient Management

Agricultural systems are responsible for significant emissions of N₂O, one of the most potent greenhouse gases, mainly due to the application of nitrogen fertilizers and the use of manure. Nutrient management strategies therefore play a pivotal role in reducing N₂O emissions. The “4R” framework right source, right rate, right time, and right placement of fertilizers has consistently been shown to improve nitrogen use efficiency and curb unnecessary losses to the atmosphere [186].

Agricultural mitigation practices often interact when implemented concurrently, requiring careful accounting to avoid overestimating emission reductions. ICEMF employs sequential accounting for overlapping practices and complementary accounting for independent pathways. When practices target the same emission pathway, the first practice establishes a new baseline for subsequent interventions. For instance, legume-based crop rotation (e.g., clover-wheat) reduces synthetic nitrogen fertilizer requirements from a typical 180 kg N ha⁻¹ to 63–96 kg N ha⁻¹ (65% reduction for clover systems; 33 vs. 96 kg N ha⁻¹ documented in French grain legume systems) while decreasing N₂O emissions by 39% through improved nitrogen use efficiency [120,121]. Applying precision fertilization (4R stewardship) to this reduced nitrogen baseline achieves an additional 55–64% reduction in remaining emissions [18,86]. The sequential system reduction totals 70–75%, avoiding the impossible additive estimate of 94–103%. Biochar application reduces N₂O by 16.2% (95% CI: 9.8–22.6%) through nitrogen immobilization and enhanced complete denitrification [9], while precision fertilization reduces emissions by 55–64% through rate optimization [18,86]. Both practices compete for the same nitrogen substrate. Field studies document combined reductions of 38.8–60% [122,187], substantially less than the theoretical additive 71–80%, confirming mechanistic overlap. Cover crops primarily reduce nitrogen leaching losses by 30–50% (an indirect emission pathway via downstream waterbodies) [147,188] without directly affecting field N₂O emissions. Precision fertilization reduces direct field N₂O emissions by 55–64% [18,86]. These mechanisms are complementary: cover crops prevent substrate loss from the system, while precision fertilization optimizes in-field transformations. Combined system-wide reductions of 70–85% are achievable when both pathways are addressed [189], though validation through integrated long-term field trials remains a priority research need (Table 5). Precision agriculture technologies such as site-specific nutrient application and sensor-guided irrigation are gaining traction for optimizing inputs and minimizing N₂O production hotspots [190], with demonstrated reductions of 20–30% in AI-guided systems [21], though adoption remains below 15% in smallholder systems due to high costs (USD 15,000–50,000 per farm) [21,24]. In rice-based systems, the integration of water-saving irrigation with balanced nutrient management offers significant reductions in both N₂O and CH₄ emissions, supporting multiple climate benefits [179].

Table 5. Baseline specification and regional elasticities.

Region/System	Baseline N Rate (kg ha−1)	Baseline N2O Emission (kg N2O-N ha−1 yr−1)	Emission Factor (% of N Applied)	Climate Condition	Reference
Temperate cereals (intensive)	165–190	1.8–4.6	1.0–2.6	Humid, fine-textured soils	[18,86,91]
Temperate cereals (moderate)	120–160	1.2–2.4	0.8–1.5	Variable moisture	[86,117]
Temperate dry systems	120–160	0.6–2.4	0.5–1.5	Low precipitation	[91,117]
Subtropical rice-wheat (intensive)	200–250	2.0–5.0	1.0–2.5	Monsoon, irrigated	[116,119]
Tropical smallholder	40–80	0.4–1.6	0.8–2.0	Variable, rain-fed	[118]

However, widespread adoption of these approaches faces both technical and socio-economic challenges. Regional disparities in fertilizer access, affordability, and farmer training often limit implementation in smallholder-dominated agricultural systems [191]. In some contexts, nitrification inhibitors and enhanced efficiency fertilizers remain prohibitively expensive, while their long-term environmental effects are still debated [185]. Additionally, studies highlight a lack of harmonized emission factors for diverse cropping systems, leading to uncertainty in estimating mitigation potential at larger scales [64]. Research also underscores the need for long-term field experiments to validate laboratory or short-duration results, particularly in tropical and semi-arid regions [181]. Without supportive policies, subsidies, and knowledge-sharing mechanisms, the effectiveness of nutrient management as a mitigation strategy will remain underutilized [192,193]. Table 6 outlines key studies on nutrient management strategies for nitrous oxide mitigation, emphasizing technical advances, adoption barriers, and future research needs.

Table 6. Nutrient management strategies for nitrous oxide mitigation in agricultural systems: key outcomes and implementation challenges.

Focus Area	Key Findings	Limitations/Challenges	Study
4R Nutrient Stewardship	Improved NUE, reduced maize yield gap, and higher profitability	Requires farmer training and monitoring systems	[186]
Precision agriculture	Systematic review shows potential for sustainable production	Limited adoption in smallholder systems	[172]
Sensor-guided irrigation	Reduced water uses and improved efficiency in tomato production	High initial technology cost	[190]
Water-saving irrigation in rice	Lowered CH4 and N2O emissions, improved water efficiency	Dependent on irrigation infrastructure	[179]
Fertilizer access in Malawi	Highlights regional disparities in affordability and access	Smallholders face affordability barriers	[191]
Nitrogenous fertilizers	Discusses sustainability challenges and mitigation strategies	Enhanced fertilizers costly, long-term effects uncertain	[185]
Crop inventories	Provided harmonized GHG emission factors	Data gaps across cropping systems	[64]
Field experiments in sorghum	Developed adaptation strategies for semi-arid tropics	Needs long-term validation across regions	[181]
Soil and crop strategies	Ensured productivity and sustainability	Requires integration with policy support	[192]
Legacy phosphorus use	Proposed sustainable nutrient management strategies	Limited large-scale application evidence	[193]

Note: Emission factors increase non-linearly at high N rates (exponential response, β = 1.6–2.0) [119]. All mitigation percentages are calculated relative to these region-specific baselines using sequential accounting to prevent double counting.

3.4. Socio-Economic Barriers and Policy Drivers in the Adoption of Agricultural GHG Mitigation Practices

While technical interventions for reducing GHG emissions in agriculture are well established, their large-scale adoption is often constrained by socio-economic realities.

Farmers face barriers such as limited access to credit, insecure land tenure, and inadequate extension services, all of which reduce their willingness or ability to invest in sustainable practices [194]. Studies show that adoption rates of conservation tillage, precision nutrient management, and agroforestry remain low in developing regions despite their proven benefits, largely due to upfront costs and perceived risks [195]. Knowledge gaps and lack of training further hinder uptake, especially among smallholder farmers, who form the backbone of global food production [196]. Gender and social inequalities also influence adoption, as women farmers often have less access to resources, technologies, and decision-making platforms [197]. Meta-analysis of agricultural conservation practice adoption [85] confirms that technical effectiveness alone is insufficient; adoption rates vary from 10 to 80% depending on support mechanisms, farmer characteristics, and practice complexity

Policy frameworks play a decisive role in shaping adoption outcomes. Well-designed incentives, including subsidies for biochar, carbon credits for agroforestry, and payments for ecosystem services, can accelerate the uptake of mitigation practices [198]. However, the effectiveness of such policies depends on transparent monitoring, verification, and enforcement mechanisms [199]. Market-based approaches, such as carbon trading, are gaining prominence, but they often overlook small-scale farmers and risk exacerbating inequalities [200]. Integrated CSA policies that combine financial support, capacity building, and technology dissemination are essential to overcome these limitations [200]. Strengthening institutional support, aligning national climate commitments with local agricultural strategies, and promoting participatory approaches to policy design can further enhance adoption [201,202]. Table 7 synthesizes socio-economic and policy dimensions affecting the adoption of agricultural GHG mitigation practices, highlighting barriers, incentives, and institutional mechanisms.

Table 7. Socio-economic and policy dimensions of adoption.

Focus Area	Key Findings	Limitations/Challenges	Study
Credit access	Identified barriers in agri-business financing	Limited rural credit availability restricts adoption	[194]
Agroforestry adoption	High upfront costs and risk perception reduce uptake	Adoption low despite proven benefits	[195]
Smallholder agroforestry	Contributes to food security and resilience	Lack of training and extension support	[196]
Gender and CSA practices	Women’s involvement enhances adoption of climate-smart agriculture	Women face unequal access to resources and decisions	[197]
Payments for ecosystem services	Incentives can accelerate agroforestry adoption	Effectiveness depends on long-term monitoring	[198]
Policy transparency	Transparent enforcement supports adoption	Weak compliance undermines effectiveness	[199]
CSA scaling in South Africa	Market and institutional frameworks drive adoption	Smallholders excluded from benefits in some contexts	[200]
Science–policy engagement	Strengthens CSA adoption pathways	Requires multi-level collaboration	[201]
Participatory agri-environment design	Aligns schemes with farmer needs and nature recovery	Implementation challenges persist	[202]

3.5. Integrated Approaches and Future Pathways

The complexity of GHG emissions in agriculture highlights the need for integrated approaches that combine technical, ecological, and socio-economic dimensions.

Studies increasingly point to the effectiveness of multi-practice systems, such as combining conservation tillage with biochar application and precision nutrient management, which generate cumulative benefits for carbon sequestration, yield stability, and emission reduction [203]. Integrated crop–livestock systems offer another pathway, recycling organic residues and manure to enhance soil fertility while lowering N₂O and CH₄ emissions [177]. Similarly, CSA frameworks emphasize regenerative practices, diversification, and resilience-building measures that address both mitigation and adaptation needs [31]. The use of digital tools, including remote sensing, AI, and decision-support systems, has further strengthened the potential of integrated strategies by enabling more accurate monitoring and site-specific interventions [22].

Looking forward, the future of agricultural GHG mitigation depends on scaling these integrated approaches through supportive policies and international cooperation. Long-term field experiments and region-specific trials are essential to validate the durability and effectiveness of proposed strategies [204]. Emerging innovations such as microbial amendments, carbon farming markets, and recoupled energy–agriculture systems offer promising new avenues but require careful socio-economic and environmental assessment [20]. Bridging the gap between smallholder realities and global climate goals remains a critical challenge, calling for inclusive financing mechanisms, equitable access to technologies, and participatory governance [25]. By uniting scientific advances with farmer-driven practices and robust institutional frameworks, integrated approaches provide a pathway for achieving sustainable and verifiable emission reductions in line with international climate targets [205,206,207]. Table 8 compiles key studies on integrated approaches and future pathways, emphasizing technological, ecological, financial, and policy innovations for agricultural GHG mitigation.

Table 8. Key studies on integrated approaches and future pathways for agricultural GHG mitigation.

Focus Area	Key Findings	Limitations/Challenges	Study
Conservation tillage	Improved soil health and reduced GHG emissions	Benefits vary by soil and climate conditions	[203]
Crop–livestock integration	Reduced N2O and CH4 emissions, enhanced fertility	Requires resource availability and management capacity	[177]
Regenerative agroforestry	Promotes climate-resilient landscapes	Long adoption timelines and land-use trade-offs	[31]
Digital & AI tools	Internet of Things and AI improve site-specific nutrient and water use	High cost and infrastructure needs	[22]
Regenerative soil management	Novel scoring system for SOC and GHG monitoring	Limited regional validation	[204]
Smart technologies	Systematic review shows climate mitigation potential	Requires socio-economic feasibility studies	[20]
Financial inclusion	Access to finance enhances adoption of CSA	Gender gaps persist in smallholder agriculture	[25]
Policy integration	Stresses integrative approaches to Paris goals	Requires alignment of global and local policies	[205]
Carbon markets	Analyzed integrity of Paris mechanisms	Risk of inequitable benefit distribution	[206]
Green technologies	Novel mitigation for air/water impact in shipping	Lessons need adaptation to agriculture context	[207]

The application of ICEMF in practice requires addressing key barriers such as financial limitations, technology adoption, and socio-economic contexts. Case studies from regions where mitigation strategies have been implemented (e.g., agroforestry in sub-Saharan Africa) could help validate the framework and refine its predictions. Future research should prioritize long-term field experiments and integrate more localized data to test ICEMF’s scalability. Moreover, exploring socio-economic barriers to adoption, such as access to capital and knowledge gaps, will be crucial in improving the practical applicability of the ICEMF framework in different agricultural systems.

3.6. Scaling ICEMF: Navigating Trade-Offs Between Technical, Environmental, and Socio-Economic Dimensions

Global implementation of ICEMF requires explicit management of five interconnected trade-off categories that determine feasibility and equity of agricultural GHG mitigation (Table 9).

Table 9. Feasibility assessment of ICEMF.

ICEMF Component	Feasibility	Timeline	Key Bottleneck	Enabling Condition
Spatial modeling capacity	High	2–3 years	Computing infrastructure	International technology transfer
Biochar (temperate)	Moderate–High	5–7 years	Production scalability	Carbon credit financing
Precision fertilization (commercial)	High	3–5 years	Equipment cost	Cooperative purchasing
Agroforestry (smallholder)	Low–Moderate	10–15 years	Tenure insecurity	Land reform prerequisite
Tiered MRV system	Moderate	5–7 years	Standardization	International protocols

3.6.1. Technical-Socio-Economic Trade-Offs

Precision fertilization achieves 55–64% N₂O reductions [18] but costs USD 15,000–50,000 per farm, limiting adoption to <15% in smallholder systems [17]. Biochar demonstrates consistent 16.2% N₂O reductions (95% CI: 9.8–22.6%) [9] but requires USD 200–400 ha⁻¹ initial investment with 5–7-year payback [23], prohibitive for 65–80% of smallholders lacking capital [23]. Legume rotations reduce N₂O by 39% and increase soil organic carbon by 18% [120] with minimal capital requirements, yet face market access constraints and land tenure insecurity affecting 60% of sub-Saharan African farmers [52]. Women farmers access only 10–20% of extension services and 5–15% of agricultural credit, reducing adoption rates 20–35% in female-headed households [25,54]. Bundled interventions combining financial incentives, training, land tenure security, and gender-responsive extension increase adoption 40–60% compared to single-factor approaches [28].

3.6.2. Environmental-Productivity Trade-Offs

Optimized nitrogen fertilization reduces N₂O by 55–64% while sustaining yields [18], yet chronic over-fertilization persists in Indo-Gangetic Plains despite soil degradation [175]. Agroforestry sequesters 74–320 Mg C ha⁻¹ depending on maturity [170] but requires 10–15 years with initial 15–30% yield reductions [31], incompatible with farmers facing land tenure insecurity [52].

3.6.3. Scale-Temporal Trade-Offs

The IPCC requires agricultural emission reductions of 1 Gt CO₂ eq yr⁻¹ by 2030 [40], yet transformative practices delivering 3.9–4.0 Gt by 2050 require 10–15 years to mature [41]. This temporal mismatch affects 65–80% of smallholders lacking credit [23,51].

3.6.4. Equity-Access Trade-Offs

Carbon market MRV costs (USD 50–150 ha⁻¹) often exceed revenue potential (USD 20–80 ha⁻¹ yr⁻¹) for smallholders, excluding them from benefits [15,30]. CSA adoption varies 10-fold: 5% in sub-Saharan Africa to 50% in Europe/North America, with 70% of farmers in Kenya, Tanzania, and Ethiopia unaware of national policies [29].

3.6.5. Policy-Institutional Trade-Offs

Measurement uncertainties of ±30–50% in field-based emission factors undermine verification systems [15]. Standardized MRV protocols reduce costs 70% but accept higher uncertainty [66,67].

Regarding ICEMF resolution, the framework addresses these through (1) spatial targeting matching practices to farmer resource levels; (2) phased portfolios allocating 70% to immediate practices, 20% to intermediate transformations, and 10% to long-term redesign; (3) tiered MRV systems reducing costs for smallholder aggregations; (4) progressive allocation balancing efficiency (60%) and equity (40%); and (5) adaptive feedback loops enabling continuous refinement [28,201,202].

3.7. Expanded Policy Implications

Agricultural policy frameworks need to be tailored to support the adoption of mitigation practices at different scales. For example, the introduction of carbon credits for sustainable practices, such as agroforestry, can incentivize farmers to adopt climate-smart practices. Additionally, policies should include technical support such as training programs and access to low-cost technologies. The ICEMF framework can guide policymakers in designing region-specific policies that combine technical, financial, and socio-economic aspects, ensuring that interventions are both effective and equitable.

3.8. MRV Integration and Policy Implementation

ICEMF’s operational utility depends on robust MRV systems linking farm practices to carbon markets and national climate commitments. We propose a three-tier approach balancing accuracy and scalability.

3.8.1. Tiered MRV Framework

Tier 1—Research Sites (n ≈ 50 globally): Direct GHG flux measurement via static chambers or eddy covariance [66,208,209]. Establishes regional emission factors; validates process models. Cost: USD 10,000–50,000 site⁻¹ yr⁻¹.

Tier 2—Demonstration Farms (n ≈ 2000 per region): Process-based DeNitrification-DeComposition models (DNDC, DayCent) calibrated with soil sampling [198,199,208]. Supports voluntary carbon markets. Cost: USD 50–150 ha⁻¹ yr⁻¹.

Tier 3—Participant Farms (scalable): Remote sensing combined with activity data and Tier 1-derived emission factors [15,21,66,67]. Enables national inventory aggregation. Cost: USD 15–30 ha⁻¹ yr⁻¹.

This tiered structure reduces transaction costs by 80–90% relative to field measurement alone while maintaining verification credibility for carbon finance [66,198] (Table 10).

Table 10. ICEMF MRV framework summary.

Component	Tier 1	Tier 2	Tier 3
Method	Direct GHG measurement	Process models + soil sampling	Remote sensing + emission factors
Accuracy	±5–10%	±15–25%	±25–35%
Cost (USD ha−1 yr−1)	10,000–50,000 (per site)	50–150	15–30
Scale	50 sites	2000 farms	>100,000 farms
Application	Calibration, research	Carbon markets, training	National inventory, climate finance

3.8.2. Regional Emission Factor Recommendations

Region-specific factors improve accuracy over IPCC Tier 1 defaults:

Temperate: Biochar: −16.2% N₂O (95% CI: 9.8–22.6%) [9]; no-till: −11% (95% CI: −19 to −1%), high variability (I² = 89%) [12]; 4R + EEFs: −55 to −64% [18]. Tropical: Biochar: −8 to −12% N₂O [11]; AWD rice: −48 to −84% CH₄ [160,161]; agroforestry: 74–320 Mg C ha⁻¹ [170]. Semi-arid: Conservation agriculture: −5 to +10% N₂O; requires site validation [12,115]. Regional calibration reduces uncertainty from ±30–50% (global defaults) to ±15–25% [15,64,65].

3.8.3. Carbon Program Integration

Additionally, ICEMF employs adoption probability functions. Practices are additional if baseline adoption likelihood is <20% without carbon revenue [28,66]. Example: Biochar adoption is 5–10% due to costs of USD 200–400 ha⁻¹ [9,23]; carbon credits (USD 30–50 ha⁻¹ yr⁻¹) demonstrate additionality.

Leakage: Spatial modeling assesses displacement risk within 50 km buffers [66,198]. Estimated leakage: 10–30% (intensification projects), <5% (soil carbon practices) [67].

Permanence: Differentiated by practice type. Soil carbon: 10-year contracts, 10–20% buffer pools, satellite monitoring [198,199]. Biochar: Mean residence time ≈ 556 years [142], superior to tillage-vulnerable practices.

3.8.4. Policy Integration Pathways

National Inventories: Tier 3 data aggregates to Agriculture, Forestry and Other Land Use (AFOLU) sector reporting United Nations Framework Convention on Climate Change (UNFCCC), enabling subnational tracking [208,209].

Results-Based Finance: MRV triggers climate fund disbursements (e.g., the Green Climate Fund). Verification costs (USD 50–150 ha⁻¹) are economically viable when carbon revenues exceed USD 200–500 ha⁻¹ over project lifetimes [198,199].

Adaptive Management: Annual MRV feeds back into ICEMF models, improving emission factor accuracy by 10–15% over a 5-year period [201,202].

3.8.5. Economic Feasibility

For a 100,000 ha regional program:

• Total MRV cost: USD 2.7–5.4 million yr⁻¹ (USD 27–54 ha⁻¹);
• Carbon revenue potential: USD 20–150 ha⁻¹ yr⁻¹ (at USD 20–50 tCO₂eq⁻¹);
• 1–3 t CO₂ eq ha⁻¹ yr⁻¹ mitigation);
• Net benefit: USD 7–96 ha⁻¹ yr⁻¹ after MRV costs;
• Smallholder participation (<2 ha farms) requires cooperative aggregation, which reduces per-farm MRV costs by 60–80% [66,210].

3.8.6. Digital Agriculture Tools Enabling ICEMF Implementation

Remote sensing and precision agriculture technologies provide the operational infrastructure for ICEMF’s spatially explicit emission monitoring and scalable GHG mitigation strategies.

Remote Sensing and Precision Agriculture for Emission Monitoring

Satellite and UAV imagery with spectral indices (Normalized Difference Vegetation Index-NDVI, Normalized Difference Red Edge Index-NDRE, Green Normalized Difference Vegetation Index–GNDVI) enable early detection of crop stress and nutrient deficiencies invisible to human observation, facilitating targeted interventions that prevent fertilizer over-application and subsequent N₂O emissions (Figure 7) [211,212,213,214]. Precision agriculture datasets typically comprise diverse data related to crops, soil, nutrients, and atmospheric conditions [215], as well as technological data such as geographic information system (GIS), global positioning system (GPS), and Variable Rate Fertilizer (VRT) systems [212,213,214].

Figure 7. UAV-mounted multispectral sensor for early detection of crop canopy vigor changes and water stress using spectral indices (NDVI, NDRE, GNDVI). The thermal overlay shows spatial variability in crop health: red and orange areas indicate high stress or nutrient deficiency, yellow represents moderate stress, and green indicates healthy, vigorous canopy. These indices enable precise identification of field variability, facilitating targeted interventions that reduce fertilizer over-application and minimize N₂O emissions [216].

Agricultural drones can survey up to 1000 hectares per day with imaging accuracy under 5 cm. Soil-Adjusted Vegetation Index (SAVI) demonstrated very strong average positive correlation (R = 0.944, p < 0.05) with proximal sensor measurements in cereal crops, making it effective for stress detection in early growth stages where soil background is significant [217]. In moisture stress assessment of maize, SAVI values ranged from 0.84 under full irrigation to 0.80–0.83 when irrigation was skipped at critical stages. SAVI successfully detected canopy vigor changes and water stress across different irrigation regimes [218] (Figure 8).

Figure 8. Validation and application of UAV-based SAVI for precision irrigation management.

Variable Rate Technology and IoT Integration

Precision agriculture datasets typically comprise a diverse range of data related to crops, soil, nutrients, atmospheric data, technological data such as GIS data, GPS data, and data from trucks and the VRT system [211,212,213,214]. VRT enables site-specific fertilizer application matched to field variability, optimizing nutrient management through precise targeting [212,213,214,219]. IoT sensors enable real-time monitoring of environmental factors including humidity, temperature, luminosity, and soil moisture [220,221,222,223,224,225,226].

Wireless Sensor Networks for Scalable Monitoring

WSN have replaced wired systems in infield monitoring, enabling real-time tracking of soil moisture and crop health for optimized resource management [227]. LoRaWAN technology transmits sensor data up to 30–40 km with low power consumption [228], while LoRa and ZigBee protocols are highly efficient for precision agriculture applications [229].

IoT and Big Data for Enhanced Decision-Making

IoT sensors facilitate real-time agricultural monitoring, enabling data-driven decisions that optimize crop yields and operational efficiency [230]. Big data integration enables rapid analysis for timely decision-making [211,231], while combining farm observations with sensing systems supports yield prediction through statistical methods, DSS, and machine learning [232].

Cost-Effectiveness and Challenges

Cost savings are also achieved using IoT sensors, which minimize waste, improve asset utilization, and reduce energy consumption. However, one of the primary limitations is the high cost associated with implementing these technologies. Additionally, managing the vast amounts of data generated by these sensors can also be complex and challenging, requiring the necessary infrastructure and expertise to manage and analyze the data effectively [233,234].

The adoption and accessibility of emerging technologies can be cost-prohibitive for individual farmers, while a lack of low-technology expertise requires extra training and better information and communication technology (ICT) management equipment [235,236].

Climate-Smart Precision Agriculture and Ecosystem Services

Climate-smart precision agriculture enhances ecosystem services: (1) Variable Rate Technology reduces excess nitrogen application, protecting soil microbial diversity and water quality through site-specific nutrient management [212,214,237]; (2) IoT-enabled targeted pest management using real-time monitoring preserves beneficial insects and pollinators by minimizing broad-spectrum pesticide use [238]; (3) precision irrigation maintains soil structure and prevents erosion through optimized water delivery [190,239] and (4) conservation practices enabled by sensor-guided management increase soil carbon sequestration while supporting soil fauna [66,240,241]. These practices demonstrate synergies between emission mitigation and biodiversity conservation.

4. Addressing Gaps in ICEMF

While the ICEMF presents a novel approach to connecting farm-level agricultural practices with GHG reduction targets, several key gaps must be addressed to enhance its applicability, scalability, and long-term effectiveness. These gaps are outlined in the following areas: long-term validation, improved socio-economic adoption data, region-specific case studies, and methodological inconsistencies.

4.1. Long-Term Validation

ICEMF’s current theoretical stage requires long-term field validation to assess the sustained impact of the proposed mitigation strategies. Although empirical data support the potential for emission reductions through practices such as agroforestry, reduced tillage, and biochar application, significant uncertainty remains about the long-term effectiveness of these strategies. For example, how do soil health improvements and GHG reductions evolve over multiple growing seasons or decades? Long-term studies will be essential to capture seasonal variability, the impacts of climate change, and the effects of soil regeneration, ensuring that mitigation practices are both reliable and adaptive over time. Moreover, there is a need for field trials that compare ICEMF-driven practices with conventional farming systems to confirm the real-world benefits across diverse agricultural systems.

4.2. Improved Socio-Economic Adoption Data

One of the significant challenges facing the ICEMF framework is the adoption of mitigation strategies by farmers, particularly in low-resource and smallholder contexts. The integration of socio-economic data into ICEMF is crucial for understanding the barriers to adoption and how socio-economic conditions influence farmers’ decision-making. These conditions include access to capital, knowledge, technology, and secure land tenure. For instance, the upfront costs of implementing ICEMF-based practices such as biochar application or precision fertilization may deter farmers from adopting them. Studies show that farmers in developing regions face substantial barriers, including limited access to credit, perceived risks, and a lack of market incentives [23]. To address these barriers, more data is needed on farmer behavior, the effectiveness of financial incentives (e.g., carbon credits), and how government policies can support adoption. Integrating socio-economic modeling into ICEMF will provide better insights into how policies, subsidies, and training programs can be structured to encourage widespread adoption of GHG mitigation practices.

4.3. Region-Specific Case Studies

While ICEMF offers a global framework for agricultural GHG mitigation, its effectiveness will vary significantly across regions due to differences in climatic conditions, soil types, and farming systems. There is a lack of region-specific case studies that demonstrate how ICEMF can be tailored to local conditions. For example, while agroforestry may be a highly effective practice in tropical regions, its applicability in temperate regions may be more limited due to differences in crop types and climate conditions. Likewise, practices like no-till farming may have varying degrees of effectiveness depending on soil texture, precipitation patterns, and crop rotation systems. Region-specific case studies are critical for testing the framework’s adaptability and for refining ICEMF to meet the unique needs of different agricultural systems. In particular, there is a need for cross-regional comparisons that evaluate how the same practice may result in different outcomes in terms of GHG reductions, productivity, and economic viability. These studies will help policymakers and farmers understand how to adapt mitigation strategies to suit local contexts and increase the scalability of ICEMF.

4.4. Methodological Inconsistencies and Standardization

Another critical gap is the lack of standardized methodologies for measuring the impact of ICEMF on GHG emissions. Currently, there is a wide variation in how emission reductions are quantified across studies, with differing methods, emission factors, and LCA boundaries. This inconsistency makes it challenging to compare results across regions and systems. To strengthen ICEMF, there is a need for harmonized metrics for emission measurement that can be applied consistently across diverse agricultural practices and environmental contexts. Furthermore, the integration of advanced technologies, such as remote sensing, AI, and machine learning, can enhance the accuracy and scalability of emission modeling, ensuring that ICEMF can be applied to a variety of farming systems globally. Standardizing MRV systems will also be crucial for tracking the progress of mitigation strategies and ensuring that they meet global GHG reduction targets.

4.5. Bridging Gaps Through ICEMF: From Theory to Practice

ICEMF operationalizes gap resolution through integrated mechanisms linking policy to implementation across four dimensions.

Long-term validation: Three-tiered monitoring infrastructure addresses validation needs while reducing costs. Research sites (n = 50 globally) establish regional emission factors feeding national inventories [208,209]; demonstration farms (n = 2000 per region) prove effectiveness and inform subsidy design [198,199]; participant farms use remote sensing verification at <USD 25 ha⁻¹ transaction costs enabling smallholder inclusion [21,66,67]. Implementation: Years 1–4 establish tiers sequentially, requiring national coordination units linking research, extension, and statistics agencies.

Socio-economic adoption: Farmer typology frameworks differentiate pathways by resource level. Commercial farms (20%) receive technology-intensive portfolios with carbon credits; moderate smallholders (50%) access knowledge-intensive practices with bundled credit and extension; constrained smallholders (30%) prioritize low-cost improvements with safety nets [51,53,54,55]. Annual surveys (10,000 farmers/region) track barriers enabling targeted support. Example: Kenya tenure assessment shifted priority from agroforestry to legume rotations, increasing adoption from 15% to 42% [24,52].

Regional specificity: Nested spatial modeling maintains comparability while accommodating context through: (1) global framework establishing uniform protocols [66,208]; (2) regional parameterization providing climate-specific factors (biochar: 16.2% temperate vs. 8–12% tropical N₂O reduction) [9,10,11]; (3) farm-adaptive management allowing local adjustments [201,202]. Timeline: Years 1–4 develop and calibrate protocols, requiring regional modeling hubs with 50–100 staff.

Methodological standardization: Open-source digital infrastructure integrates mobile apps, satellite imagery, cloud-based models, and decision-support interfaces [18,68,69]. Transaction costs were reduced from USD 150 to USD 18 ha⁻¹, enabling the participation of Ethiopia’s 340,000 smallholders via a USSD-based system [66,67].

Implementation: Years 1–3 develop platforms and train 5000 extension agents.

Integration: Solutions interlock through bidirectional flows, national targets inform regional plans and farmer recommendations, and farmer outcomes refine parameters and policy targeting, operationalized via nested institutions from international coordination to farmer cooperatives.

5. Bridging the Gaps with Case Studies

To demonstrate the practicality and applicability of the ICEMF, several real-world case studies offer valuable insights into the framework’s potential to reduce agricultural GHG emissions while maintaining productivity. The following case studies showcase how agricultural practices integrated into the ICEMF framework contribute to global emission reduction targets and regional sustainability goals.

(1) Agroforestry in Indonesia, particularly in West Java, has been utilized to mitigate carbon emissions and promote biodiversity. Carbon stocks in agroforestry systems range from 74 to 320 Mg ha⁻¹, depending on system age and tree diversity, with mixed-tree systems exhibiting the highest sequestration potential [170]. While agroforestry contributes to GHG mitigation, its effects on CH₄ and N₂O emissions vary by system. Soils under agroforestry release an average of 1.6 kg CH₄ ha⁻¹ yr⁻¹ and 7.7 kg N₂O ha⁻¹ yr⁻¹ [11]. High rainfall and fluctuations in moisture pose challenges to nitrogen cycling and N₂O emissions, affecting long-term sustainability. ICEMF’s spatial emission modeling can predict the effectiveness of agroforestry in such regions, considering local soil types and moisture levels to optimize GHG reductions. Additionally, socio-economic adoption pathways would help tailor policies and incentives to farmers’ needs, supporting widespread adoption.

(2) No-till farming in the United States has shown greater soil carbon accumulation and lower global warming impact compared to conventional tilling, primarily due to increased carbon storage rather than direct CO₂ emission reductions [242]. However, the effectiveness of no-till may be limited in tropical regions, such as Southeast Asia, due to soil compaction and high rainfall. ICEMF’s approach would evaluate the effectiveness of no-till practices in temperate areas, using models that consider local soil moisture and crop types, while also recommending adaptive techniques and socio-economic support for farmers to implement this practice.

(3) In Germany and other temperate European regions, biochar has been shown to reduce soil N₂O emissions by 16.2% across various agricultural systems [9]. Long-term studies confirm its effectiveness in reducing N₂O even after 4–6 years, contributing to sustained climate mitigation and improved soil quality [10]. Biochar’s impact is most pronounced in temperate zones, where seasonal fluctuations have less effect on microbial activity. In tropical climates, its effectiveness may be reduced due to high moisture and rapid decomposition of organic matter. ICEMF would model biochar’s long-term effectiveness in different climates, providing region-specific recommendations based on local soil and moisture conditions to optimize GHG mitigation.

(4) Precision fertilization in India. In the Indo-Gangetic Plains of India, the Nutrient Expert (NE) tool for site-specific nutrient management has reduced greenhouse gas emissions by 2.5% in rice and 12–20% in wheat compared to conventional fertilization practices [173]. Precision nitrogen management tools, such as leaf color charts, have reduced N fertilizer application by up to 51 kg N/ha in rice and 29 kg N/ha in wheat [174] However, chronic over-fertilization remains a challenge, leading to soil health deterioration [175]. ICEMF can integrate precision fertilization into spatial emission models, optimizing fertilizer use based on regional soil, climate, and crop conditions. The framework would also support adoption by offering financial incentives and knowledge-sharing programs to farmers in low-resource regions.

(5) Crop rotation and legume integration in Europe [121]. Crop rotation, especially legume integration, is a valuable strategy in ICEMF’s toolkit for enhancing soil health and mitigating GHG. ICEMF can integrate legume-based crop rotations into its regional emission models, using spatially explicit data to assess how soil types, climate conditions, and crop management systems influence GHG reductions and carbon sequestration. The framework can assess the carbon sequestration potential of different crop rotations across various regions, helping tailor mitigation strategies based on local soil and climate conditions.

While ICEMF demonstrates substantial technical potential with documented practice-level reductions including biochar (16.2% N₂O reduction), precision fertilization (55–64% N₂O reduction), legume rotations (39% N₂O reduction + 18% soil organic carbon increase), agroforestry (74–320 Mg C ha⁻¹ sequestration), and rice management (84% CH₄ reduction) [9,10,18,160,161], translating these field-validated practices to global scale faces fundamental challenges: (1) financial barriers: upfront costs of USD 200–50,000 per farm for proven practices (biochar, precision technologies) exceed the capacity of 65–80% of smallholder farmers lacking credit access [20,23]; (2) institutional capacity gaps: extension service ratios of 1:1000–2000 farmers is insufficient for knowledge-intensive practices, with approximately 70% of farmers in Kenya unaware national CSA policies exist [24,29]; (3) political economy barriers: subsidy reform is politically sensitive, as demonstrated by Punjab’s persistent over-fertilization despite available precision tools [191]; (4) temporal mismatches: agroforestry’s 10–15-year payback is incompatible with farmers’ planning horizons without bridging mechanisms, such as payments for ecosystem services [31,198]. Realistic implementation achieves 25–35% of technical potential without bundled interventions, increasing to 40–60% with comprehensive financial, training, and institutional support [23,28]. Success requires parallel tracks: concentrating resources where enabling conditions exist (such as commercial farms and strong institutions) while building capacity in smallholder systems through progressive, complex approaches.

6. Operational Pathways for Implementing the ICEMF Framework to Scale Agricultural GHG Mitigation

6.1. Priority Pathways for ICEMF Implementation

Translating ICEMF from framework to operational practice requires coordinated action across three phases over 5–10 years.

Phase 1: Pilot studies (years 1–3).

Establish proof of concept in five priority regions representing diverse systems: temperate commercial agriculture (testing precision technologies and carbon markets), tropical smallholder systems (validating low-cost practices with bundled support), rice-dominated landscapes (demonstrating AWD effectiveness), semi-arid regions (integrating adaptation with mitigation), and intensive horticultural systems. Each pilot includes (i) Tier 1 research sites with comprehensive GHG monitoring establishing regional emission factors [208,209]; (ii) Tier 2 demonstration networks (minimum 100 farms per region) tracking adoption barriers and economic outcomes [198,199]; (iii) digital MRV infrastructure testing with target transaction costs < USD 25 ha⁻¹ [21,66,67]; (iv) farmer typology assessments with differentiated support packages [51,52,53,54,55]; and (v) baseline socio-economic surveys (n ≥ 500 per pilot) covering capital access, land tenure, extension contact, and gender dynamics. Expected outcomes: Validated regional emission factors, operational digital platforms, and documented effectiveness of bundled interventions versus single-factor approaches.

Phase 2: Socio-economic integration (years 2–5).

Address adoption gaps through (i) longitudinal panel surveys tracking farm households annually, capturing adoption persistence and income effects [23,24]; (ii) qualitative research exploring cultural compatibility, risk perceptions, and trust in institutions [101,201]; (iii) institutional mapping identifying policy bottlenecks, extension capacity gaps (current 1:1000–2000 ratios), and credit barriers [24,53]; (iv) gender-disaggregated analysis developing responsive strategies given women’s limited access to extension (10–20%) and credit (5–15%) [25,54,55]; (v) farmer decision modeling incorporating risk aversion and multi-objective preferences; and (vi) integration into regional models producing adoption probability matrices by farmer type and support scenario [28,243,244]. Expected outcomes: Realistic adoption projections, adjusting technical potential to account for context-specific feasibility factors.

Phase 3: Institutional capacity building (years 3–7).

Strengthen institutional architecture through (i) international coordination framework under UNFCCC for protocol standardization and capacity building coordination; (ii) national implementation units developing technical capacity through modeler training and integration of ICEMF outputs into NDC reporting systems; (iii) regional modeling hubs aligned with major climate zones providing calibration services, extension training, and MRV quality assurance; (iv) extension system strengthening training frontline agents in typology-based advisory and digital tools, addressing current inadequate 1:1000–2000 ratios [24,53]; and (v) policy alignment translating emission targets to practice-level actions, addressing the disconnect where 70% of farmers remain unaware of national policies [29].

6.2. Research Priorities Addressing Critical Limitations

This review acknowledges five limitations defining future research needs.

Limitation 1: Temporal validation.

Current evidence predominantly spans 1–6 years, with limited data available for longer periods. Biochar’s documented effectiveness of 4–6 years [9,10] requires validation over 10–20 years, assessing long-term stability, carbon saturation dynamics, practice persistence, and climate interaction effects. Priority: Establish long-term commitment field trials across multiple global sites with factorial designs testing practice combinations to quantify synergistic versus antagonistic interactions. Expected outcomes: Robust long-term emission factors, validated permanence claims for carbon credits, and empirical evidence on the durability of practices, informing realistic adoption timelines.

Limitation 2: Geographic bias.

Temperate systems dominate the evidence base (60–70% of studies), while tropical smallholder systems—where 65–80% of farmers face capital constraints and climate vulnerability is highest—remain underrepresented [23,48]. This creates uncertainty in transferring temperate-validated practices, as biochar shows a 16.2% N₂O reduction in temperate versus 8–12% in tropical systems due to differences in moisture and decomposition [9,10,11]. Priority: Implement standardized multi-site networks in sub-Saharan Africa, South Asian rice-wheat systems [173,174,175], Latin American smallholder systems, and semi-arid regions, explicitly testing practice modifications for tropical contexts rather than assuming temperate protocol transferability.

6.3. Pathways Forward

Advancing ICEMF from framework to operational practice requires addressing documented barriers while building on proven solutions. Current constraints include substantial upfront costs (USD 200–50,000 per farm depending on practice), inadequate extension ratios (1:1000–2000 farmers), temporal mismatches between practice payback periods (5–15 years) and farmer planning horizons, and policy–practice disconnects where approximately 70% of farmers remain unaware of national climate policies [20,23,24,29,31,53]. However, emerging evidence suggests that bundled interventions, combining financial incentives, training, secure land rights, and gender-responsive extension services, can increase adoption rates by 40–60% compared to single-factor approaches, with integrated support being essential for scaling mitigation practices [28]. By addressing the five critical limitations identified—temporal validation, geographic representation, socio-economic data integration, methodological standardization, and climate feedback incorporation—through coordinated research and implementation efforts, the scientific community can provide the robust evidence base policymakers and farmers need to confidently scale agricultural GHG mitigation. Success hinges on inclusive governance, ensuring that smallholder and women farmers shape the implementation, adaptive management that embraces uncertainty and learns from experience, and transparent monitoring, thereby building public trust and market credibility. Through such integrated approaches, agricultural systems can make meaningful contributions to global climate goals while enhancing food security and improving farmer livelihoods.

6.4. Implementation Requirements and Success Factors

Advancing ICEMF from framework to operational reality requires addressing documented barriers, including substantial upfront costs (USD 200–50,000 per farm depending on practice), institutional capacity gaps (extension ratios of 1:1000–2000 farmers), and temporal mismatches between practice payback periods (5–15 years) and farmer planning horizons [20,23,24,31,53]. However, emerging evidence suggests that bundled interventions, combining financial incentives, training, secure land rights, and gender-responsive extension services, can increase adoption rates by 40–60% compared to single-factor approaches, with integrated support being essential for scaling mitigation practices [28]. Coordinated investment in pilot studies, socio-economic research, capacity building, and institutional development is essential, given the projected 50–70% increase in food production required by 2050, alongside the absolute GHG emission reductions necessary to meet climate targets [39,45].

Success depends on four critical factors: (i) political will translating climate commitments into agricultural sector budgets and long-term policy frameworks; (ii) inclusive governance ensuring smallholder and women farmers shape implementation rather than being passive recipients, addressing current inequities where women access only 10–20% of extension services despite controlling 43% of agricultural labor [25,54]; (iii) adaptive management embracing uncertainty and systematically learning from implementation experience through robust monitoring systems; and (iv) transparent monitoring building public trust and market credibility essential for carbon credit integrity. By addressing the five identified limitations through coordinated research efforts and pursuing the three-phase implementation pathway, the scientific community can provide the robust evidence base policymakers and farmers need to confidently scale agricultural GHG mitigation. Through such integrated approaches operationalized by ICEMF, agricultural systems can contribute meaningfully to global climate goals while simultaneously enhancing food security and farmer livelihoods in ways that are equitable, verifiable, and adaptive to diverse farming realities.

7. Conclusions

This review highlights the central role of agricultural crop systems in global GHG emissions and the diverse strategies available for mitigation. Practices such as conservation tillage, biochar application, precision nutrient management, crop diversification, and integrated crop–livestock systems show strong potential to reduce emissions while enhancing soil health and productivity. However, significant gaps remain in the literature. One major issue is the lack of long-term validation of these strategies across diverse regions and environmental conditions. Additionally, while much focus has been placed on the technical potential of these interventions, socio-economic barriers, such as land tenure issues and financial limitations, have not been sufficiently addressed.

Thematic analysis reveals that soil-based, crop-based, nutrient-oriented, and policy-driven measures are interconnected, yielding cumulative benefits when implemented in conjunction. However, their effectiveness remains context-dependent, shaped by regional conditions, socio-economic constraints, and policy frameworks. The importance of addressing these gaps is evident: without consideration of socio-economic factors, the scalability of these practices is limited, particularly in developing regions. Moreover, without proper long-term validation, the actual impact of mitigation strategies on GHG emissions remains uncertain.

The proposed ICEMF provides a novel pathway for linking practice-level interventions with global mitigation goals, offering both scientific and practical insights. To achieve meaningful emission reductions, strategies must be adapted to local realities, supported by enabling policies, and scaled through inclusive, farmer-centered approaches that align agricultural development with climate objectives. To move forward, future research should prioritize region-specific field trials and the development of integrated decision-support tools that combine technical, environmental, and socio-economic data. These tools will ensure that mitigation strategies are not only practical but also equitable and scalable, enabling policymakers to design inclusive and sustainable solutions to combat climate change.