The motivation is grounded in stark numbers. Global meat production has grown more than fourfold since 1961, rising from 71 million tonnes to 337 million tonnes in 2020, and cattle production has doubled over the same period. Livestock are indispensable to human nutrition, but they are also a major climate burden. Ruminants produce between 250 and 500 litres of methane per animal per day through enteric fermentation, the microbial digestion process in the rumen. Of the estimated 86 teragrams of methane released annually by domesticated livestock, dairy cattle alone account for approximately 18.9 teragrams. Lactating cows, which eat more than dry cows or heifers, emit roughly twice as much methane as their non-lactating counterparts. Projections suggest that methane emissions from dairy farming could rise by 30 percent by 2050 if current practices continue.

In arid regions such as Qatar, the problem is compounded by heat. Cattle are sensitive to the temperature-humidity index, or THI, a combined measure of air temperature and relative humidity that indicates heat stress. When the THI exceeds the animals’ thermoneutral zone, cows respond with sweating, altered respiration, and elevated skin temperature, and milk production suffers. Conventional open sheds or naturally ventilated barns with water spraying and fogging struggle to maintain acceptable THI under Qatar’s extreme ambient temperatures and intense solar irradiance, and these open systems allow methane to escape uncontrolled into the atmosphere. The new design closes that loop, both thermally and chemically.

The proposed barn houses 100 mature lactating cows weighing 500 kilograms or more in a tie-stall configuration, following established reference designs for manure collection. The architectural model, built in Autodesk Revit, incorporates insulated walls and roof elements that cut the overall heat-transfer coefficients dramatically — from 2.242 to 0.139 W/m²/K for the walls and from 3.440 to 0.105 W/m²/K for the roof. Insulation proved to be far more than a comfort measure: sensitivity analysis showed it reduces monthly cooling loads by at least 15 percent, a substantial saving given that cooling is the single largest energy consumer in the design. The building envelope is modelled against Doha’s weather data using ASHRAE Fundamentals methods, accounting for conduction through the envelope, solar heat gain through windows, metabolic heat from the animals themselves, and ventilation loads.

At the heart of the climate-control strategy is a vapor-compression HVAC system consisting of an air-handling unit and a chiller, sized with Carrier’s Hourly Analysis Program and ducted according to the equal-friction method with a friction loss of 1 pascal per metre. The system maintains a barn setpoint of 18°C — comfortably within the thermal comfort zone for dairy cows — and regulates humidity between 50 and 60 percent through integrated humidifier and dehumidifier components. Air is distributed through 24 supply diffusers and 12 exhaust diffusers, each 450 millimetres square, mounted in a 5-metre-high ceiling. The target air velocity at cow level is between 1 and 2 metres per second, fast enough to remove heat, moisture, and harmful gases without causing drafts that stress the animals. Crucially, the ventilation system is closed and mechanical, which means the exhaust air — and the methane it carries — can be routed somewhere useful rather than vented to the sky.

To verify that the air actually moves the way the designers intended, the team ran computational fluid dynamics simulations in ANSYS Fluent 2022 using the standard k–ε turbulence model, solving the continuity, momentum, energy, and species-transport equations for the airflow around the animals. The CFD results predict temperatures of approximately 20°C around the animals and air velocities consistently within the 1–2 m/s target band, with generally uniform circulation across the animal zone. The species-transport formulation also allowed the researchers to estimate methane concentration in the barn air, which depends on cow weight, ventilation rate, and air density. For cows above 500 kilograms, an emission factor of 3.5 to 4.5 applies; at the design conditions of 18°C and 46 litres per second of ventilation per cow, the modelled methane concentration sits near the lower end of a 0–3 percent parametric range used to characterise the downstream power cycle.

That downstream component is a Brayton cycle, the same thermodynamic arrangement used in gas-turbine power plants, consisting of a compressor, combustion chamber, and turbine. In a conventional Brayton cycle, ambient air enters the compressor, is compressed from 101 to 1000 kilopascals, and is heated by burning fuel. Here, the innovation is twofold. First, the compressor intake is not ambient air but the methane-containing exhaust stream drawn from the barn, which carries more chemical energy than air alone. At 1500 K and 1000 kPa, methane has a specific enthalpy of 4943 kJ/kg compared with 1637 kJ/kg for air, so even dilute methane enriches the working fluid. Second, the combustion fuel is not natural gas but cow manure, which has a heating value of 11,729 kJ/kg. Combustion gases leave the chamber at approximately 1200 K and expand through the turbine to generate electricity. Mass and energy balances for each component were solved using the first law of thermodynamics, with a fuel-to-air ratio of 1:10.

The performance numbers are nuanced and honest. Across the analysed methane concentrations of 0 to 3 percent, power output and cycle efficiency rise only slightly with methane enrichment: at 1 percent methane, the model predicts 17.68 kW of power at a cycle efficiency of 21.34 percent, while at 3 percent these figures reach 17.77 kW and 21.6 percent. The researchers are explicit that the electrical output is governed primarily by the manure fuel; the dilute methane in the recovered ventilation air contributes only marginally to power. Its principal role is greenhouse-gas mitigation through thermal oxidation — controlled combustion in the high-temperature chamber converts methane to carbon dioxide and water. Because carbon dioxide has a far lower global warming potential than methane (25 versus a much higher value for methane over 100 years), this conversion yields a substantial net climate benefit.

The emissions accounting quantifies that benefit precisely. Using a 100-year global warming potential of 25 for methane and the stoichiometric combustion reaction CH₄ + 2O₂ → CO₂ + 2H₂O, the researchers calculate that one gram of methane produces 2.75 grams of carbon dioxide. For the 100-cow barn, the system is modelled to capture and process approximately 18 tonnes of methane annually, corresponding to a 400.5-tonne CO₂-equivalent reduction in methane-attributable emissions — an 89 percent reduction in the greenhouse-gas burden directly attributable to methane at the barn boundary. The authors caution that this figure excludes indirect emissions, such as grid electricity used for cooling, which would be addressed in a full life-cycle assessment.

The researchers are equally candid about the study’s boundaries. This is a conceptual design and feasibility study, not an experimentally validated or economically optimised system. The CFD and thermodynamic results are numerical predictions that would benefit from experimental validation or comparison with field data. Methane capture efficiency, leakage, maintenance requirements, safety controls, techno-economic assessment, and full life-cycle analysis were all outside the present scope. Performance is also sensitive to operating conditions: methane concentration in the exhaust air rises with cattle weight and falls as ventilation rate increases, creating a design tension between air quality, cooling demand, and methane enrichment that future work must resolve. The authors recommend testing the concept across different geographies, cattle types, and ventilation strategies before advancing it toward practical implementation.

Even with those caveats, the significance of the design lies in its integration. Previous efforts have attacked the problem piecemeal — dietary manipulation and breeding to reduce enteric methane, anaerobic digestion to convert manure to biogas, or barn designs focused solely on animal welfare. Earlier polygeneration studies by some of the same authors demonstrated that methane and manure from dairy farms could yield 17 MW of electricity and 1350 cubic metres of freshwater per day, or drive systems with overall energy efficiencies of up to 81.6 percent. The new work is the first, according to the team’s comparison of the literature, to fold barn-level THI design, methane mitigation, and power generation into a single architectural and thermodynamic scheme — so that the building that houses the cows is also the machine that cools them, scrubs their methane, and powers the farm.

The research, published in the journal Air Quality, Atmosphere & Health by a team at Fujian Agriculture and Forestry University led by Jiadong Zhang, Tao Xu, Shengquan Wang, Shaoxiong Wu and Lingxin Bao, examines agricultural carbon emission efficiency—often abbreviated ACEE—across all 31 Chinese provinces from 2010 to 2022. ACEE is a measure that captures how effectively a region converts agricultural inputs into grain output relative to the carbon it emits in the process. A high ACEE score means a province is producing more food per unit of agricultural carbon, integrating the twin objectives of grain production growth and multi-source emission reductions into a single quantitative framework.

The concept of climate-smart agriculture, or CSA, was developed by the Food and Agriculture Organization of the United Nations as a paradigm that pursues three goals simultaneously: sustainably increasing agricultural productivity, adapting and building resilience to climate change, and reducing or removing greenhouse gas emissions wherever possible. In practice, CSA encompasses technologies such as water-saving irrigation, straw-return—the practice of working crop residues back into the soil rather than burning them—and no-tillage planting, which minimizes soil disturbance and the carbon losses associated with it. While these practices have been widely adopted in parts of the developing world and are increasingly embedded in agricultural policy in Europe and North America, their implementation in China has been uneven, largely because local levels of agricultural sustainability vary so dramatically across the country’s vast and ecologically diverse territory.

To map that unevenness, the researchers first constructed a comprehensive indicator system for ACEE, drawing on emission accounting methods consistent with the Intergovernmental Panel on Climate Change guidelines for national greenhouse gas inventories. Agricultural emissions in China arise from multiple sources, including nitrogen fertilizer application, rice paddies, livestock, soil management and the energy consumed by farm machinery. Rather than treating these as a monolithic total, the team’s framework integrates both the desired output—grain production—and the undesired outputs of various emission streams, reflecting the reality that a province cannot simply cut emissions by producing less food.

The efficiency calculations were performed using a technique known as super-efficiency slacks-based measurement, or super-efficiency SBM, an advanced form of data envelopment analysis. Conventional efficiency analysis struggles to rank decision-making units that all sit on the “efficient frontier”—the boundary representing the best achievable performance. The super-efficiency variant solves this by allowing efficient units to exceed a score of one, effectively ranking them against a frontier from which they have been temporarily removed. This matters in a national comparison, because without it, many provinces would simply tie at maximum efficiency and the analysis could not distinguish, say, a moderately efficient grain belt from an exceptional one.

To track how the distribution of ACEE has evolved over the twelve-year study window, the team then applied kernel density estimation, a non-parametric statistical method that reconstructs the underlying probability distribution of efficiency scores from observed data without imposing assumptions about its shape. This allowed the researchers to detect subtle shifts in the ” geography” of Chinese agricultural carbon performance that simple provincial averages would obscure. Their findings are striking: the national average ACEE remained broadly stable over the period, but the spatial distribution exhibited an asymmetric pattern the authors describe as “high-value contraction” and “low-value stability.” In other words, provinces at the top of the efficiency distribution appear to have become more tightly clustered—converging on a shared high-efficiency profile—while lower-performing provinces held their positions without marked improvement. Within China’s three major regions, internal disparities in ACEE remained evident, with varying degrees of polarization, suggesting that the gap between leaders and laggards has not closed and, in some places, may have widened.

Having quantified where efficiency is high and low, the study’s central contribution lies in explaining why. Guided by an analytical framework built around climate-smart agriculture, the researchers examined explanatory factors across three dimensions: CSA technology, policy support and the social environment. For this they turned to the Geodetector model, a spatial analysis tool designed to measure how much of the spatial variation in a variable can be explained by a stratifying factor. Geodetector works by comparing the within-stratum variance of the outcome variable to its total variance; the resulting q-statistic ranges from zero to one and expresses the explanatory power of each factor. Unlike conventional regression, Geodetector makes no assumption about linearity and is robust to multicollinearity, which makes it well suited to disentangling the effects of interrelated social, technological and environmental variables.

The Geodetector results pointed clearly to technology. The adoption levels of three CSA technologies—water-saving irrigation, straw-return and no-tillage planting—showed relatively strong explanatory power for the spatial disparities in ACEE. Provinces where these practices had penetrated more deeply tended to be provinces where agricultural carbon efficiency was higher, even after accounting for other conditions. But the single most important finding of the spatial analysis may be about interaction rather than individual factors: the explanatory power of factor combinations significantly exceeded their independent contributions. This is a classic signature of synergistic causation, in which technologies or conditions that are only moderately powerful on their own become highly consequential when deployed together. A water-saving irrigation system paired with supportive policy instruments and a favorable social environment, for instance, delivers efficiency gains that no single component could achieve alone.

To translate that insight into actionable strategy, the researchers integrated dynamic qualitative comparative analysis—QCA—into their framework. QCA is a set-theoretic method rooted in the work of Charles Ragin that treats cases, in this study provinces, as configurations of conditions rather than as independent data points. Rather than asking whether factor X has an average effect on outcome Y across all cases, QCA asks which combinations of conditions are sufficient, or necessary, to produce the outcome. The dynamic extension of the method allows these configurations to be examined across time, capturing how the recipe for high efficiency may change as regions develop. Configurational methods are increasingly favored in sustainability research precisely because they embrace what scholars call causal complexity: multiple, different routes to the same outcome, with conditions substituting for one another in some configurations and complementing one another in others.

The QCA analysis identified four differentiated configuration pathways that enhance ACEE under the CSA framework. The team labeled these pathways as those driven by “policy and environment,” by “technology and policy,” by “technology, policy and environment” jointly, and by “technology” alone. Each represents a distinct recipe that a province can follow. A policy-and-environment pathway suggests that in some regions, strong governmental support combined with favorable social and natural conditions can deliver high efficiency even without leading-edge technology adoption. A technology-driven pathway indicates that in other regions, the diffusion of CSA practices itself is sufficient to propel efficiency gains. The combined pathways, meanwhile, confirm the Geodetector’s finding that the most reliable route to high performance is the deliberate stacking of technological, institutional and social conditions.

The policy implications are significant, both for China and for the wider world. China is simultaneously the world’s largest agricultural producer and a major agricultural emitter, and its stated “dual carbon” goals—peaking carbon emissions before 2030 and achieving carbon neutrality before 2060—cannot be met without transforming the farm sector. The study suggests that a one-size-fits-all national CSA mandate would be a mistake. Provinces should instead be matched to the pathway that fits their existing endowments: regions with strong fiscal and institutional capacity might lead with policy and environmental measures, while agronomically advanced regions could accelerate technology-led transitions. The finding that factor interactions outperform individual factors also cautions against fragmented, siloed interventions—subsidizing a single technology in isolation is unlikely to replicate the gains seen where technology is embedded in supportive governance and social context.

The research also carries a note of urgency. The “high-value contraction, low-value stability” pattern implies that the provinces best positioned to improve may be plateauing at high efficiency while the laggards remain stuck, a dynamic that could entrench regional inequality in agricultural sustainability. Because ACEE integrates food production with emission performance, stagnation among low-efficiency provinces threatens both climate objectives and food security, the very trade-off CSA is designed to resolve.

The work was supported by the Natural Science Foundation of Fujian Province and the Special Fund for Science and Technology Innovation of Fujian Agriculture and Forestry University. The corresponding author is Lingxin Bao of the College of Computer and Information Sciences at Fujian Agriculture and Forestry University. While the methodology is grounded in Chinese data, the framework—linking an integrated efficiency indicator, spatial diagnostics, and configurational pathway analysis—offers a transferable template for any nation wrestling with how to feed a growing population on a warming, carbon-constrained planet. As climate pressures intensify, the study’s core message is clear: the future of low-carbon agriculture will be won not by single silver-bullet technologies, but by smartly assembled combinations of technology, policy and social conditions tailored to each region’s circumstances.

Composting, the biodegradation of organic matter by microbes under controlled aerobic conditions, serves as a natural method to recycle manure, food remains, and sewage sludge. This process releases essential nutrients back to soils while producing humus-like material that enhances soil structure and fertility. Nevertheless, during composting, significant quantities of nitrogen escape into the atmosphere primarily as ammonia (NH3) and nitrous oxide (N2O), a potent greenhouse gas. Simultaneously, carbon is lost through emissions of methane (CH4) and carbon dioxide (CO2). These gaseous losses not only diminish the nutrient value of compost but also contribute directly to global warming, posing a serious dilemma for policymakers and agronomists striving to balance environmental goals.

In this expansive analysis, researchers compiled and synthesized data from 848 composting experiments conducted worldwide, spanning manure, food waste, and sewage sludge feedstocks. By applying sophisticated machine learning algorithms, they quantitatively identified 19 key management parameters that collectively influence emissions of NH3, N2O, CH4, and CO2. This systemic approach transcends traditional trial-and-error methods, illuminating precise operational factors critical to optimizing compost emissions. The enhanced understanding thereby paves the way for designing evidence-based composting protocols that can minimize greenhouse gas release while maximizing nutrient retention.

The study’s findings emphasize the scale of global greenhouse gas emissions attributable to composting operations. On an annual basis, the composting of organic waste releases approximately 747 kilotonnes of nitrogen as ammonia (NH3-N), 81 kilotonnes of nitrogen as nitrous oxide (N2O-N), and 592 kilotonnes of carbon as methane (CH4-C). When converted into carbon dioxide equivalents (CO2e), the total emission burden reaches an estimated 61 million tonnes (Mt) per year. These figures highlight the urgency of developing mitigation strategies that can significantly curtail composting’s carbon footprint while sustaining its agronomic functionality.

Central to the optimization framework is the manipulation of composting management parameters such as aeration regimes, substrate carbon-to-nitrogen (C/N) ratios, moisture content, temperature control, and the inclusion of specific additives. Aeration, for instance, modulates oxygen availability, directly affecting microbial respiration pathways and the balance between nitrification and denitrification processes that produce nitrous oxide. Similarly, adjusting the C/N ratio ensures an optimal nutrient environment that suppresses excessive nitrogen volatilization. Through fine-tuning these variables, operators can substantially reduce emissions while still facilitating effective organic matter decomposition.

Under a scenario envisioned by the researchers—where composting management is optimized using insights unearthed through machine learning—the composting chain could be transformed from a net greenhouse gas emitter releasing 40.1 Mt CO2e annually to a net carbon sink absorbing 15.1 Mt CO2e. This remarkable reversal would not only conserve nutrients vital for crop growth but also contribute meaningfully to climate change mitigation by sequestering more carbon than is emitted. Achieving such a transition embodies a paradigm shift, elevating composting from a waste management tool to a proactive climate solution.

The geographic distribution of these optimized outcomes reveals important regional contributions. Among global players, China, Brazil, and the United States emerge as the top three countries with the highest carbon sink potential within the composting sector. Collectively, these nations could realize approximately 65% of total emission reductions achievable under best-practice composting strategies. This underscores the considerable influence of national waste handling practices and policies on global greenhouse gas trajectories and highlights priority areas for investment and capacity building.

The research leverages the power of big data analytics and machine learning not only to characterize emission profiles but also to predict the environmental impacts of hypothetical management adjustments before field implementation. This predictive capability accelerates innovation, enabling practitioners to tailor composting processes for site-specific conditions and waste types, thereby enhancing scalability and adaptability. Furthermore, it assists regulators and stakeholders in developing science-based guidelines aligned with emission reduction targets.

Despite the significant advancements, challenges remain in translating these findings into widespread practice. Composting sites exhibit heterogeneity in feedstock composition, technological infrastructure, and operational expertise, all of which may impact the feasibility of optimized protocols. Moreover, the economic costs and labor requirements associated with precise parameter control need careful consideration to ensure adoption by farmers, municipalities, and commercial operators, especially in resource-limited contexts.

Nonetheless, the demonstration that composting’s environmental footprint can be drastically reduced without compromising nutrient recycling galvanizes efforts to mainstream optimized organic waste management. This could complement parallel strategies such as anaerobic digestion, biochar application, and sustainable fertilizer use to forge integrated food system solutions that decrease emissions at multiple points along the supply chain—from production to consumption to waste recovery.

Beyond carbon emission mitigation, enhancing compost quality through improved processing techniques supports soil health restoration—combatting erosion, enhancing water retention, and rebuilding microbial biodiversity. These ecosystem benefits contribute to long-term agricultural resilience in the face of climate change and population growth, positioning composting as a multifunctional technology with both environmental and social dividends.

In summary, the innovative cross-disciplinary research presented in this landmark study provides a roadmap to unlock the full potential of composting as a climate-smart practice. By embracing machine learning-driven optimization of management parameters, composting operations globally can transition toward becoming significant carbon sinks, substantially lowering greenhouse gas emissions while promoting sustainable nutrient cycling. This work serves as an inspiring proof of concept for the integration of artificial intelligence into environmental stewardship frameworks.

As nations struggle to meet ambitious greenhouse gas reduction commitments under international agreements, the importance of scalable and affordable mitigation technologies becomes paramount. Composting—long lauded for its circular economy value—now stands poised to evolve into a pivotal climate solution through data-driven refinement of its processes. Future policies that incentivize adoption of machine learning-optimized compost practices have the potential to deliver transformative impacts at the intersection of agriculture, waste management, and climate action.

Ultimately, this research illuminates the untapped potential that lies in re-envisioning traditional organic waste treatment methods through the lens of cutting-edge technology. The combined power of data science, microbial ecology, and engineering innovation provides new levers to address persistent environmental challenges. Harnessing these synergies will be essential to advancing towards a more sustainable, resilient, and low-carbon food system globally.

Subject of Research:
Article Title:
Article References: Zhang, L., Yang, J., Liu, J. et al. Machine learning-optimized composting strategies can enhance nutrient recycling and transform food system waste into a net carbon sink. Nat Food (2026). https://doi.org/10.1038/s43016-026-01361-w
Image Credits: AI Generated
DOI: https://doi.org/10.1038/s43016-026-01361-w
Keywords: composting, machine learning, greenhouse gases, nutrient recycling, carbon sink, ammonia emissions, nitrous oxide, methane, carbon dioxide, organic waste management, sustainable agriculture, climate change mitigation, circular economy, waste-to-resource

The research team, comprising Adam, A.K., Sadhu, T., and Mondal, B.P. among others, meticulously analyzed a variety of low-carbon agricultural techniques, ranging from no-till farming to the implementation of cover crops. Each of these practices has been shown to significantly reduce greenhouse gas emissions while simultaneously improving soil health. The results, set to be published in the 2025 issue of Discover Sustainability, indicate a promising future for the agriculture sector amidst a climate crisis, potentially setting a standard for other nations to follow.

Farmers who have adopted these low-carbon techniques report not only a decrease in their carbon footprint but also an increase in crop resilience. For instance, practices such as crop rotation and agroforestry have demonstrated a remarkable ability to improve biodiversity, which is crucial for sustainable agriculture. These methods help in maintaining soil fertility, thus reducing the need for chemical fertilizers that often lead to environmental degradation. Such innovations reflect what could be a revolutionary shift in agricultural practice in the developing world.

Moreover, the researchers emphasize the socio-economic benefits of low-carbon agriculture. By adopting these environmentally friendly practices, farmers often see a reduction in costs related to inputs such as fertilizers and energy. This economic advantage enables farmers to invest in other areas of their agricultural operations, enhancing their overall productivity and potentially increasing their income. As such, the transition to sustainable practices not only aligns with environmental goals but also supports the livelihoods of farmers, forming a symbiotic relationship between ecological health and economic viability.

Furthermore, the study highlights the significance of policy support in facilitating the adoption of low-carbon practices. According to the authors, government initiatives that incentivize sustainable farming can play a crucial role in encouraging farmers to shift away from conventional methods. Such support could come in the form of subsidies for sustainable inputs, education programs, and financial assistance for transitioning to more sustainable practices. The alignment of policy with sustainable agriculture could create a robust framework for long-term change.

As the consequences of climate change become increasingly severe, the importance of adopting low-carbon practices cannot be overstated. The team notes that these agricultural innovations are not merely beneficial but necessary for adapting to the challenges of an unpredictable climate. Issues such as erratic weather patterns, prolonged droughts, and poor soil fertility can all undermine food security, especially in a country as populous as India.

Despite the urgent need for change, the research also acknowledges barriers to adopting these low-carbon practices. Social and economic factors, such as access to information, financing, and markets, can impede the transition. Thus, fostering a community of practice amongst farmers—where knowledge sharing and collaboration are prioritized—becomes essential. This collective approach can empower farmers, making them stakeholders in their own food security and resilience.

The implications of this research extend beyond India, serving as a blueprint for sustainable agriculture worldwide. As nations grapple with the dual challenges of food security and climate change, this study presents a viable pathway towards sustainable practices that could be tailored to various contexts. The lessons drawn from India’s experience can resonate with agricultural communities globally, especially in developing countries facing similar environmental concerns.

In light of these findings, the role of education becomes paramount. Training programs aiming to disseminate knowledge of low-carbon practices can equip farmers with the tools needed to innovate their methods. The research team argues that educational initiatives should not only focus on traditional farming techniques but also promote a holistic understanding of ecosystem services and sustainable practices’ benefits. Emphasizing environmental stewardship can foster a new generation of farmers who view themselves as integral parts of their ecosystem.

Ultimately, as a society, we must rethink our relationship with agriculture. The study calls for a transformation in how we perceive farming—from a mere means of food production to a vital contributor to ecological health and social welfare. By embracing low-carbon agricultural practices, we can pave the way for a future where food security is assured, and environmental sustainability is a reality.

In conclusion, the adoption of low-carbon agricultural practices offers a promising solution to the pressing challenges of climate change and food security in India and beyond. This important research underscores the interconnectedness of ecological resilience and economic sustainability, presenting a compelling narrative that urges immediate action. As farmers and policymakers begin to recognize the benefits of such practices, the tools for a more sustainable agricultural framework are within reach, promising a resilient future for generations to come.

Subject of Research: Low-carbon agricultural practices in India

Article Title: Low-carbon agricultural practices enhance climate resilience and food security in India

Article References:

Adam, A.K., Sadhu, T., Mondal, B.P. et al. Low-carbon agricultural practices enhance climate resilience and food security in India.
Discov Sustain (2025). https://doi.org/10.1007/s43621-025-01675-y

Image Credits: AI Generated

DOI: 10.1007/s43621-025-01675-y

Keywords: Low-carbon agriculture, climate resilience, food security, sustainable practices, India.