Agricultural Greenhouse Gas Emissions Research Articles

We need to act now to ensure global food security, and reduce agricultural greenhouse gas emissions

ABSTRACT To feed a rapidly growing human population in an era of climate change and increasingly uncertain weather—such as drought and flood—humanity will need crops with increased yields, greater resiliency to extreme weather, and more resistance to disease. Appropriately enough in the Century of Biology, that means turning to genetic tools such as CRISPR.

Assessing agricultural greenhouse gas emission mitigation by scaling up farm size: An empirical analysis based on rural household survey data

Agriculture is a major contributor to greenhouse gas (GHG) emissions. Farm size affects agricultural production inputs and thus has impacts on agricultural GHG emissions. However, the effects and mechanisms behind this are still unclear. In this paper, we identified the effects and mechanisms of farm size on agricultural GHG emissions, based on survey data about over 20,000 rural households in China from 2009 to 2016. Firstly, we calculated the agricultural CO2, CH4, and N2O emissions using the life-cycle analysis (LCA). Secondly, the impacts of farm size on GHG emissions intensity were explored with a fixed effect model, based on the long-term rural household survey data. Finally, the mechanisms were tested by the mediation effect model. The results showed that a 1 % increase in farm size, on average, could reduce the GHG emissions intensity of rural households by 0.245 % from 2009 to 2016. The mechanism analysis showed that the larger farm size reduced GHG emissions intensity mainly by reducing the non-fixed input intensity and raising fixed input investment. By identifying the impacts and mechanisms of farm size on agricultural GHG emissions, this paper aims to provide insights for policymakers to achieve China's goal of reaching carbon neutrality by 2060.

Assessing Agricultural Impact on Greenhouse Gases in the European Union: A Climate-Smart Agriculture Perspective

With the increasing concern about climate change and its impacts on agriculture, understanding the dynamics of greenhouse gas (GHG) emissions in the European Union (EU) agricultural sector is essential for devising effective mitigation strategies. This study aims to assess the impact of agriculture on GHG within the EU and to examine how climate-smart agricultural practices can affect these emissions. The research investigates the complex relationship between agricultural activities and GHG emissions within the European Union during the period of 2017–2022 using structural equation modeling based on data from Eurostat and the European Commission. Furthermore, the study examines the influence of the digital economy on labor productivity in agriculture, recognizing the pivotal role of digital technologies in fostering climate-smart agricultural practices. The findings unveil significant positive influences encompassing the digital economy, agricultural productivity, agricultural output, and GHG emissions, underscoring the imperative of integrating climate-smart methodologies into agricultural frameworks. However, the influence of digital technologies is not significant as a result of opposing forces. Digital technologies exert positive indirect influences by increasing agricultural productivity and agricultural output, while they have negative influences by improving production processes through automation and precision agriculture. Digitalization and climate-smart agricultural practices have a significant potential to improve the efficiency and sustainability of the agricultural sector, contributing to food security and environmental protection by reducing GHG emissions. This study highlights the EU’s potential to achieve its environmental objectives through the reduction of GHG emissions and the enhancement of resilience within the agricultural sector, emphasizing the necessity of adopting climate-smart strategies.

Smart Agriculture and Greenhouse Gas Emission Mitigation: A 6G-IoT Perspective

Smart farming has emerged as a promising approach to address the agriculture industry’s significant contribution to greenhouse gas (GHG) emissions. However, the effectiveness of current smart farming practices in mitigating GHG emissions remains a matter of ongoing debate. This review paper provides an in-depth examination of the current state of GHG emissions in smart farming, highlighting the limitations of existing practices in reducing GHG emissions and introducing innovative strategies that leverage the advanced capabilities of 6G-enabled IoT (6G-IoT). By enabling precise resource management, facilitating emission source identification and mitigation, and enhancing advanced emission reduction techniques, 6G-IoT integration offers a transformative solution for managing GHG emissions in agriculture. However, while smart agriculture focuses on technological applications for immediate efficiency gains, it also serves as a crucial component of sustainable agriculture by providing the tools necessary for long-term environmental supervision and resource sustainability. As a result, this study also contributes to sustainable agriculture by providing insights and guiding future advancements in smart farming, particularly in the context of 6G-IoT, to develop more effective GHG mitigation strategies for smart farming applications, promoting a more sustainable agricultural future.

Effects of Long-Term Controlled-Release Urea on Soil Greenhouse Gas Emissions in an Open-Field Lettuce System.

Controlled-release urea (CRU) fertilizers are widely used in agricultural production to reduce conventional nitrogen (N) fertilization-induced agricultural greenhouse gas emissions (GHGs) and improve N use efficiency (NUE). However, the long-term effects of different CRU fertilizers on GHGs and crop yields in vegetable fields remain relatively unexplored. This study investigated the variations in GHG emissions at four growth stages of lettuce in the spring and autumn seasons based on a five-year field experiment in the North China Plain. Four treatments were setup: CK (without N application), U (conventional urea-N application), ON (20% reduction in urea-N application), CRU (20% reduction in polyurethane-coated urea without topdressing), and DCRU (20% reduction in polyurethane-coated urea containing dicyandiamide [DCD] without topdressing). The results show that N application treatments significantly increased the GHG emissions and the lettuce yield and net yield, and DCRU exhibited the lowest N2O and CO2 emissions, the highest lettuce yield and net yield, and the highest lettuce N content of the N application treatments. When compared to U, the N2O emission peak under CRU and DCRU treatments was notably decreased and delayed, and their average N2O emission fluxes were significantly reduced by 10.20-20.72% and 17.51-29.35%, respectively, leading to a significant reduction in mean cumulative N2O emissions during the 2017-2021 period. When compared to U, the CO2 fluxes of DCRU significantly decreased by 8.0-16.54% in the seedling period, and mean cumulative CO2 emission decreased by 9.28%. Moreover, compared to U, the global warming potential (GWP) and greenhouse gas intensity (GHGI) of the DCRU treatment was significantly alleviated by 9.02-17.13% and 16.68-20.36%, respectively. Compared to U, the N content of lettuce under DCRU was significantly increased by 6.48-17.25%, and the lettuce net yield was also significantly increased by 5.41-7.71%. These observations indicated that the simple and efficient N management strategy to strike a balance between enhancing lettuce yields and reduce GHG emissions in open-field lettuce fields could be obtained by applying controlled-release urea containing DCD without topdressing.

Balancing farm profit and greenhouse gas emissions along the dairy production chain through breeding indices

Breeding is a promising greenhouse gas (GHG) mitigation option for the dairy sector that offers potential permanent and cumulative effects. However, there is limited understanding of how genetic traits affect GHG emissions from the dairy production chain and how breeding indices could be used to find a balance between GHG emissions and farm profit. Using a typical Chinese dairy farm as a case study, we developed a novel method to address these gaps. The farm comprised of 1523 Holstein-Friesian dairy cows and 1429 young stock. The average milk yield at the farm was 11,533 kg per cow per year. Life cycle assessment was combined with an existing bio-economic model to determine the emission intensity values (IV) of six genetic traits: milk yield, protein yield, fat yield, calving interval, productive life, and incidence of clinical mastitis. The IVs and economic values of the traits were used to form different breeding indices, of which the economic and environmental consequences were assessed. Results showed that for the next generation, breeding animals with optimal indices could reduce carbon dioxide equivalents per ton of fat-and-protein-corrected milk by six to 10 kilogrammes, while increasing profitability by 822 to 1355 Chinese Yuan per cow unit. Different indices can balance farm profit and GHG emissions to different degrees. However, the indices with higher profit showed less potential in reducing GHG emissions. This study provides insights into how breeding strategies could contribute to GHG mitigation in the dairy sector.

Agricultural greenhouse gas emissions, fertilizer consumption, and technological innovation: A comprehensive quantile analysis

This paper investigates the asymmetric inter-linkages and causality between agricultural greenhouse gas emissions, fertilizer consumption, and technological innovation in four main agricultural countries i.e., China, the United States, Japan and Canada, by employing monthly data from 1990 to 2019. Accordingly, Quantile-on-Quantile (QQ) regression and two causality-in-quantile approaches are applied to conduct a comprehensive quantile analysis of the asymmetric relationship for all quantiles of the above distribution. Our results exhibit a positive association between agricultural greenhouse gas emissions, fertilizer consumption, and technological innovation in China, the United States and Canada. Besides, the strength of the positive association depends largely on the development level of agricultural greenhouse gas emissions, fertilizer consumption, and technological innovation. However, except for the positive impact between fertilizer consumption and agricultural greenhouse gas emissions, I find a negative nexus between agricultural greenhouse gas emissions and technological innovation in Japan. Compared with the other three sample countries, Japan has done the best in agricultural greenhouse gas emission mitigation. The results also demonstrate that agricultural greenhouse gas emissions, fertilizer consumption, and technological innovation follow a bidirectional quantile-causality relation in all sample countries. Overall, I find that fertilizer consumption does increase agricultural greenhouse gas emissions, and that technological innovation has not played a full role in mitigating greenhouse gas emissions in most countries. Finally, our findings have significant implications for formulating reasonable emission reduction measures in the agricultural sector.

Assessing uncertainties and discrepancies in agricultural greenhouse gas emissions estimation in China: A comprehensive review

Due to diverse methodologies and data sources, China's agricultural greenhouse gas (AGHG) emissions estimates demonstrate significant variability. A quantitative review was conducted on the prevalence and application of various accounting methods to clarify and reduce discrepancies in AGHG emissions estimation techniques and data application. Using a systematic PRISMA framework, 4257 global studies were analyzed, yielding 681 AGHG emission records from 57 papers and four databases explicitly focusing on China. The analysis reveals similarities between Chinese and global research trends, concentrating on environmental science, soil science, and ecology. Furthermore, the spatial scale and methodologies for China's AGHG emissions estimation are relatively centralized, with 53.21% of the research targeting site-specific scales and 74.12% employing inventory methods and statistical analysis. Regarding the variability in China's AGHG emission estimates between 1996 and 2020, estimates ranged from 100 to 800 Mt. C-eq, with the highest estimate being approximately 25 times the minimum within the same period. Notably, compared to the average AGHG emissions calculated for 1996–2020 (284.7 to 548.7 Mt. C-eq), estimates from the FAO and Our World in Data were higher by an average of 114.7% and 81.9%, respectively. These findings underscore the need to enhance remote sensing and survey data application and standardize AGHG emissions estimation methodologies and boundaries to ensure comparability across various accounting methods and datasets. This study conducts a quantitative analysis to identify and bridge the gaps in AGHG emission evaluation techniques, laying the groundwork for establishing a more comprehensive and resilient framework for AGHG emissions assessments.

Accounting for Greenhouse Gas Emissions in the Agricultural System of China Based on the Life Cycle Assessment Method

Agricultural systems contribute nearly one-third of global anthropogenic GHG emissions and are an important source of GHGs globally. The clarification of the GHG emission pattern from agriculture is of paramount importance in the establishment of an agricultural emission reduction mechanism and the realization of China’s dual-carbon target. Based on the life cycle assessment method (LCA), this paper comprehensively quantifies the greenhouse gas emissions from the agricultural system in China, encompassing rice, wheat, and corn cultivation as well as animal husbandry including cows, horses, donkeys, mules, camels, pigs, and sheep. The analysis covers the period 2000–2020 and examines the spatial distribution, temporal trends, and structural changes in the greenhouse gas emissions within China’s agriculture sector. The main results are as follows: (1) from 2000 to 2020, China witnessed a consistent upward trajectory in its total agricultural GHG emissions, exhibiting an average annual growth rate of 0.73%. Notably, methane (CH4) emissions emerged as the largest contributor, displaying an overall fluctuating pattern. Carbon dioxide (CO2) emissions demonstrated intermittent increases with a noteworthy annual growth rate of 3%, signifying the most rapid expansion within this context. Conversely, nitrous oxide (N2O) emissions experienced decline over the specified period. (2) GHG emissions from cultivation demonstrate an upward trajectory, primarily driven by the CH4 emissions originating from rice cultivation and CO2 resulting from straw incineration. Conversely, GHG emissions stemming from animal husbandry declined, with the primary source being CH4 emissions arising from animal enteric fermentation. Agricultural N2O emissions predominantly arise due to manure management and nitrogen fertilizer application. (3) Agricultural greenhouse gas emissions exhibit significant variations in spatial distribution, gradually concentrating towards the North China Plain, the middle and lower reaches of the Yangtze River, and the northeast. Specifically, agricultural CH4 emissions are progressively concentrated in China’s pivotal rice-growing regions, encompassing the middle and lower reaches of the Yangtze River Plain, as well as livestock breeding areas like Inner Mongolia. Agricultural CO2 emissions primarily concentrate in dryland crop production zones such as North China and Northeast China. Meanwhile, Agricultural N2O emissions predominantly occur in Inner Mongolia and the North China Plain. China’s agricultural greenhouse gas emissions in 2020 show a significant spatial clustering effect, with hotspots primarily concentrated in Shandong, Anhui, Henan, and other regions and cold spots focused in the western and southern areas. The emission patterns of agricultural GHGs are closely intertwined with farming practices, regional development levels, and national policy; hence, tailored measures for emission reduction should be formulated based on specific crop types, livestock categories, agricultural production activities, and regional development characteristics.

Structural decomposition analysis of agricultural Non-CO2 greenhouse gas emission intensity in China

Agriculture is China's primary emitter of non-CO2 greenhouse gas (GHGs) emissions, and it is crucial to understand the drivers and contributions of agricultural non-CO2 GHGs emission intensity (ANCGI) for formulating effective emission reduction policies. Based on a decomposition analysis approach, this study explains the factors that driving the change of ANCGI from the perspectives of environment, economy, and scale. And by calculating the contribution of these three factors in 31 Chinese provinces between 2000 and 2019 using the Logarithmic Mean Divisia Index (LMDI) technology. The findings show that the ANCGI in China has declined by about 3.6% per year on average over the past 20 years, and the main factor affecting its decline is the economic level, contributing more than one third. The ANCGI is negatively impacted by the environment, with a contribution value of −1.60%; and the scale effect contributes 1.21% to the decrease in the ANCGI. Therefore, improving the level of agriculture environment in each province and transitioning to a high-quality development model are important ways to reduce the ANCGI in China.

Analysis of the dynamics and structure of greenhouse gas emissions in Russian agriculture

The problem of assessing the carbon footprint in the economy has recently received much attention in many countries around the world, including Russia. Given the importance of the problem of climate change, the urgent task is to reduce greenhouse gas emissions to achieve international climate goals outlined by the Paris Climate Agreement and the Strategy for the socio-economic development of a country with low greenhouse gas emissions until 2050.Currently, the main global trend in the development of economic sectors is the implementation of ESG principles and a focus on the use of energy-saving technologies, therefore, special attention in all sectors of the economy is paid to the issues of accounting for greenhouse gas emissions and assessing their impact on climate change. Accounting, monitoring and justification of directions for reducing the carbon footprint is also an important task in the context of the formation of the Green Economy. Agriculture is in third place in terms of emissions after energy sector and industry sector. Regular monitoring of greenhouse gas emissions by source is necessary to effectively address the emissions problem and develop cost-effective public policy measures to ensure sustainable agricultural development and develop export potential.The purpose of the study is a comprehensive study of the volumes and sources of greenhouse gas emissions, the role of agriculture in the formation of the carbon footprint. The study examines in detail the volume and structure of greenhouse gas emissions in long-term dynamics for 1990–2022. The use of a number of statistical methods made it possible to establish a reduction in emissions in the agricultural sector, which is explained by a sharp reduction in resource potential and the increased use of resource-saving technologies in recent years.

Can we design food taxes to reduce agricultural greenhouse gas emissions in China? A perspective from animal protein intake of low-income populations

PurposeStudies on developed economies showed that imposing taxes on animal-based foods could effectively reduce agricultural greenhouse gas emissions (AGHGEs), while this taxation may not be appropriate in developing countries due to the complex nutritional status across income classes. Hence, this study aims to explore optimal tax rate levels considering both emission reduction and nutrient intake, and examine the heterogenous effects of taxation across various income classes in urban and rural China.Design/methodology/approachThe authors estimated the Quadratic Almost Ideal Demand System model to calculate the price elasticities for eight food groups, and performed three simulations to explore the relative optimal tax regions via the relationships between effective animal protein intake loss and AGHGE reduction by taxes.FindingsThe results showed that the optimal tax rate bands can be found, depending on the reference levels of animal protein intake. Designing taxes on beef, mutton and pork could be a preliminary option for reducing AGHGEs in China, but subsidy policy should be designed for low-income populations at the same time. Generally, urban residents have more potential to reduce AGHGEs than rural residents, and higher income classes reduce more AGHGEs than lower income classes.Originality/valueThis study fills the gap in the literature by developing the methods to design taxes on animal-based foods from the perspectives of both nutrient intake and emission reduction. This methodology can also be applied to analyze food taxes and GHGE issues in other developing countries.

Spatial-temporal characteristics and driving factors' contribution and evolution of agricultural non-CO2 greenhouse gas emissions in China: 1995-2021.

Comprehending the spatial-temporal characteristics, contributions, and evolution of driving factors in agricultural non-CO2 greenhouse gas (GHG) emissions at a macro level is pivotal in pursuing temperature control objectives and achieving China's strategic goals related to carbon peak and carbon neutrality. This study employs the Intergovernmental Panel on Climate Change (IPCC) carbon emissions coefficient method to comprehensively evaluate agricultural non-CO2 GHG emissions at the provincial level. Subsequently, the contributions and spatial-temporal evolution of six driving factors derived from the Kaya identity were quantitatively explored using the Logarithmic Mean Divisia Index (LMDI) and Geographical and Temporal Weighted Regression (GTWR) methods. The results revealed that the distribution of agricultural non-CO2 GHG emissions is shifting from the central provinces to the northwest regions. Moreover, the dominant driving factors of agricultural non-CO2 GHG emissions were primarily economic factor (EDL) with positive impact (cumulative promotion is 2939.61 million metric tons (Mt)), alongside agricultural production efficiency factor (EI) with negative impact (cumulative reduction is 2208.98 Mt). Influence of EDL diminished in the eastern coastal regions but significantly impacted underdeveloped regions such as the northwest and southwest. In the eastern coastal regions, EI gradually became the absolute dominant driver, demonstrating a rapid reduction effect. Additionally, a declining birth rate and rural-to-urban population migration have significantly amplified the driving effects of the population factor (RP) at a national scale. These findings, in conjunction with the disparities in geographic and socioeconomic development among provinces, can serve as a guiding framework for the development of a region-specific roadmap aimed at reducing agricultural non-CO2 GHG emissions.

Soil aggregate stability governs field greenhouse gas fluxes in agricultural soils

Agriculture is responsible for 30–50% of the yearly CO2, CH4, and N2O emissions. Soils have an important role in the production and consumption of these greenhouse gases (GHGs), with soil aggregates and the inhabiting microbes proposed to function as biogeochemical reactors, processing these gases. Here we studied, for the first time, the relationship between GHG fluxes and aggregate stability as determined via laser diffraction analysis (LDA) of agricultural soils, as well as the effect of sustainable agricultural management strategies thereon. Using the static chamber method, all soils were found to be sinks for CH4 and sources for CO2 and N2O. The application of organic amendments did not have a conclusive effect on soil GHG fluxes, but tilled soils emitted more CO2. LDA was a useful and improved method for assessing soil aggregate stability, as it allows for the determination of multiple classes of aggregates and their structural composition, thereby overcoming limitations of traditional wet sieving. Organic matter content was the main steering factor of aggregate stability. The presence of persistent stable aggregates and the disintegration coefficient of stable aggregates were improved in organic-amended and no-tilled soils. Predictive modelling showed that, especially in these soils, aggregate stability was a governing factor of GHG fluxes. Higher soil CH4 uptake rates were associated with higher aggregate stability, while CO2 and N2O emissions increased with higher aggregate stability. Altogether, it was shown that sustainable agricultural management strategies can be used to steer the soil's aggregate stability and, both consequently and outright, the soil GHG fluxes, thereby creating a potential to contribute to the mitigation of agricultural GHG emissions.

Use of anaerobic digestate to substitute inorganic fertilisers for more sustainable nitrogen cycling

Fertiliser applications significantly contribute to agricultural greenhouse gas emissions. While crop yields and quality depend on the type and amount of chemical fertiliser supplied, there are limitations to fertiliser use, as continuous application of inorganic fertilisers can negatively affect soil fertility and result in mineral leaching. Organic fertiliser produced by Anaerobic Digestion (AD) of sewage waste (AD digestate) has been proposed as a substitute for chemical fertilisers. To investigate the potential for the use of AD digestate in pasture systems, grassland field trials were conducted on three independent farm enterprises in Ireland. AD digestate and chemical fertilisers were applied separately or in various combinations and the effects on soil mineral content, grass yield and quality compared. The analyses revealed significant improvement of soil nitrification activity along with improved fodder yield and quality. In particular, digestate application increased crude protein content of fodder by up to 22.8 % compared to fodder from plots without digestate application, that had up to 14.8% crude protein. The increase could be attributed to the white clover percentage increase from 2% (no AD digestate) to up to 31% on plots where AD digestate was applied. Strikingly, grass yields were 1.5-2-fold higher than the five-year national average (of 59.7 ± 4 kg DM/ha/day) on the plots where AD digestate was applied, either alone or in combination with chemical fertilisers. The results of this study highlight the potential of AD digestate application as a more sustainable alternative to inorganic fertilisers in grass-based pasture systems.

Exploring the potential impact of household photovoltaic systems on low-carbon production behavior in rural areas: unveiling the pro-environmental spillover effect

Introduction: China, as the world’s largest emitter of carbon dioxide, faces significant challenges in agricultural greenhouse gas emissions. The Chinese government has been actively promoting household photovoltaic (PV) power generation, which has great potential for application in rural areas. This study aims to explore whether the promotion of household PV systems in rural areas has a positive impact on farmers’ low-carbon production behavior and to analyze the influencing factors and mechanisms. This research fills the research gap in the analysis of the promotion of household PV systems and farmers’ low-carbon production behavior, providing scientific evidence to support policymakers in promoting widespread use of household PV systems and facilitating the transition of farmers to low-carbon production methods.Methods: This study adopts a qualitative research method and analyzes interview data and semi-structured questionnaire survey data from 48 farmers. By collecting, organizing, comparing, and extracting information and employing the research process of grounded theory, the researchers summarize the model of household PV-driven low-carbon production behavior.Results: The study finds that the installation of household PV systems indeed promotes farmers to adopt more low-carbon production behaviors. Farmers who install household PV systems show a greater willingness to reduce the use of fertilizers and pesticides, conserve water resources, and improve land utilization, among others. They perceive the positive effects of household PV systems and their own capacity for environmental protection, enhancing confidence and motivation to engage in low-carbon production behaviors.Discussion: Existing research methods have mainly relied on theoretical deduction combined with quantitative empirical approaches when exploring farmers’ pro-environmental spillover behaviors. However, these methods often start from the perspectives of either egoism or altruism, resulting in biased tendencies toward negative spillover or positive spillover. Nevertheless, neither egoism nor altruism fully captures the decision-making process when deeply understanding farmers’ production, life, and decision-making processes. The installation of household PV systems can change factors such as farmers’ knowledge, skills, cognition, and resources, enhancing their green self-efficacy and helping them acquire more knowledge and skills in renewable energy. Therefore, this research adopts a qualitative research method to more accurately reflect farmers’ decision-making process and provides practical recommendations to promote farmers’ active transition to pro-environmental spillover behaviors.Conclusion: This study fills the research gap in the analysis of the promotion of household PV systems and farmers’ low-carbon production behavior, providing practical recommendations for policymakers to facilitate farmers’ positive behavioral changes. Qualitative research methods enable a more realistic understanding and promotion of farmers’ pro-environmental spillover behaviors by deeply understanding their contexts. The study offers targeted suggestions to policymakers to drive farmers’ transition to low-carbon production methods.

The Greenhouse Gas Budget of Terrestrial Ecosystems in East Asia Since 2000

AbstractEast Asia (China, Japan, Koreas, and Mongolia) has been the world's economic engine over at least the past two decades, exhibiting a rapid increase in fossil fuel emissions of greenhouse gases (GHGs) and has expressed the recent ambition to achieve climate neutrality by mid‐century. However, the GHG balance of its terrestrial ecosystems remains poorly constrained. Here, we present a synthesis of the three most important long‐lived greenhouse gases (CO2, CH4, and N2O) budgets over East Asia during the decades of 2000s and 2010s, following a dual constraint approach. We estimate that terrestrial ecosystems in East Asia is close to neutrality of GHGs, with a magnitude of between −46.3 ± 505.9 Tg CO2eq yr−1 (the top‐down approach) and −36.1 ± 207.1 Tg CO2eq yr−1 (the bottom‐up approach) during 2000–2019. This net GHG sink includes a large land CO2 sink (−1229.3 ± 430.9 Tg CO2 yr−1 based on the top‐down approach and −1353.8 ± 158.5 Tg CO2 yr−1 based on the bottom‐up approach) being offset by biogenic CH4 and N2O emissions, predominantly coming from the agricultural sectors. Emerging data sources and modeling capacities have helped achieve agreement between the top‐down and bottom‐up approaches, but sizable uncertainties remain in several flux terms. For example, the reported CO2 flux from land use and land cover change varies from a net source of more than 300 Tg CO2 yr−1 to a net sink of ∼−700 Tg CO2 yr−1. Although terrestrial ecosystems over East Asia is close to GHG neutral currently, curbing agricultural GHG emissions and additional afforestation and forest managements have the potential to transform the terrestrial ecosystems into a net GHG sink, which would help in realizing East Asian countries' ambitions to achieve climate neutrality.

Transition to cellular agriculture reduces agriculture land use and greenhouse gas emissions but increases demand for critical materials

Cellular agriculture, that is, the production of cultured meat and microbial proteins, has been developed to provide food security for a growing world population. The use of green energy technologies is recommended to ensure the sustainability of changing traditional agriculture to a cellular one. Here, we use a global dynamic model and life-cycle assessment to analyze scenarios of replacing traditional livestock products with cellular agriculture from 2020 to 2050. Our findings indicate that a transition to cellular agriculture by 2050 could reduce annual greenhouse gas emissions by 52%, compared to current agriculture emissions, reduce demand for phosphorus by 53%, and use 83% less land than traditional agriculture. A maximum 72% replacement of livestock products with cellular agriculture using renewable energy is possible based on the 2050 regional green energy capacities. A complete transition can be achieved but requires 33% of the global green energy capacities in 2050. Further, the accelerated demand for critical materials will not exceed their primary production capacities, except for tellurium. We conclude that a transition to cellular agriculture is possible with environmental benefits and provide a benchmark to study different alternatives to animal-based diets.

The Russia-Ukraine war decreases food affordability but could reduce global greenhouse gas emissions

The Russian invasion of Ukraine has the potential to exacerbate food insecurity around the world, as both countries are major exporters of grains and other agricultural products. In this model-based scenario study, medium-term effects of the war are quantified on agricultural production, trade flows, market prices, food security, land use, and greenhouse gas emissions. The scenarios assess the possible consequences of macro-economic and agricultural production impacts in Ukraine, trade sanctions against Russia, and conflict-related energy price developments for global trade, food security, and greenhouse gas emissions. From a food security perspective, we conclude that there is enough food on the global level, but higher food and energy prices cause problems for low-income populations, spending a large part of their income on staple foods. Agricultural production and area expansion in parts of the world other than Ukraine and Russia could pose a risk to biodiversity and lead to higher greenhouse gas emissions related to land. However, total greenhouse gas emissions might decrease as lower emissions from less use of fossil energy due to higher energy and fertilizer prices in the whole economy dominate additional emissions resulting from land use change.

Hayvan Sayısında Artış İklim Değişikliğini Etkiler mi? Türkiye Örneği

The Agricultural Sector is one of the sectors that cause the most significant greenhouse gas emissions due to using fertilizers, agricultural mechanization, paddy cultivation, and especially animal husbandry. Although the agricultural sector causes climate change, it is one of the sectors most affected by climate change. Reducing greenhouse gas emissions from agricultural production is essential for the sustainability of agriculture and the food security of people. This study analyzes long and short-term cointegration between agricultural greenhouse gas emissions and livestock activities in Türkiye using data covering 1990-2019. According to the analysis results, using the Autoregressive Distributed Lag (ARDL) bound testing method, a positive and statistically significant relationship was found between the number of cattle, bovine animals, poultry, and CO2 emissions. In the long run, a 1% increase in the number of bovine animals (BA), sheep and goats (SG), and poultry (P) in Türkiye will increase CO2 emissions by 0.87, 0.09, and 0.09%, respectively. In the short term, only a positive and significant relationship was found between the number of bovine animals and CO2 emissions. These results reveal that policymakers should evaluate people’s efforts to increase animal production to ensure food security and policies to reduce greenhouse gas emissions.