A review of greenhouse gas emissions from the agriculture sector in Africa

Agriculture sector holds an essential role in Latvia’s economy and play significant role in keeping rural areas as a habitable environment (approximately 32 % of the population lives in rural areas). The agricultural sector is responsible for 28.5 % (2018) of total non-European Union Emissions Trading System (non – EU ETS) greenhouse gas (GHG) emissions in Latvia. The largest part of emissions is related to agricultural soils (59.3 %) and enteric fermentation 32.6 % (mainly dairy and beef cattle). The GHG emissions trend of recent years shows a gradual and steady increase in GHG emissions for example between 2005 and 2018 +12.5 % and during the period 2013–2018 emissions increased by 2.12 %. According to Latvia’s National Energy and Climate Plan 2021–2030 (NECP), total GHG emissions in the agricultural sector are expected to increase in the period from 2020 to 2030, mainly in the enteric fermentation and agricultural soil categories. To achieve determined targets for Latvia’s non-EU ETS sector in 2030 and be on track to reach climate neutrality in 2050, the agricultural sector has to contribute to GHG emission mitigation. For the agricultural sector, improved food security and climate smart activities will be necessary to achieve GHG emission reduction. Existing policies and measures (WEM) as well as those which are included in the NECP as additional measures (WAM) were used to assess more suitable measures to move on climate smart agriculture (CSA), that could help to decrease GHG emissions at the farm and state level as well as is expected to contribute towards achieving the commitments in the plan. To achieve the aim of the study, a combination of the Delphi method together with multi-criteria analysis (MCA) is utilized to find a set of top GHG mitigation measures in the future. Results show that, in the future, the measure support the development of innovative technologies and solutions to promote resource efficiency in agriculture is essential to move on climate smart agriculture.

Climate-smart livestock production at landscape level in Kenya

Africa emits the lowest amounts of greenhouse gases (GHGs) into the global GHG budget. However, the continent remains the most vulnerable continent to the effects of climate change. The agricultural sector in Africa is among the most vulnerable sectors to climate change. Also, as a dominant agricultural sector, African agriculture is increasingly contributing to climate change through GHG emissions. Research has so far focused on the effects of GHG emissions on the agricultural and other sectors with very little emphasis on monitoring and quantifying the spatial distribution of GHG emissions from agricultural land in Africa. This study develops a new index: African Agricultural Land Greenhouse Gas Index (AALGGI) that uses scores and specific scale ranges for carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) to map the spatial variations in regional GHG emissions across Africa. The data for the three main GHGs (CO2, CH4, and N20) were downloaded from FAOSTAT. The data were analyzed through the newly developed African Agricultural Land Greenhouse Gas Index (AALGGI). This is an empirical index with scores ranging from 0 to 10, with higher scores indicating higher levels of emissions. The results show that Southern and North African regions have the lowest amounts of agricultural land GHG emissions, with AALGGIs of 3.5 and 4.5, respectively. East Africa records the highest levels of GHG emissions, with an AALGGI of 8 followed by West Africa with an AALGGI of 7.5. With the continental mean or baseline AALGGI being 5.8, East and Middle Africa are above the mean AALGGI. These results underscore the fact that though Africa, in general, is not a heavy emitter of GHGs, African agricultural lands are increasingly emitting more GHGs into the global GHG budget. The low AALGGIs in the more developed parts of Africa such as Southern and North Africa are explained by their domination in other GHG emitting sectors such as industrialization and energy. The high rates of emissions in East Africa and Middle Africa are mainly linked to intensive traditional farming practices/processes and deforestation. These findings underscore the need to further leverage climate change mitigation actions and policy in Africa and most importantly the co-benefits of mitigation and adaptations in the most vulnerable regions.

Better information on greenhouse gas (GHG) emissions and mitigation potential in the agricultural sector is necessary to manage these emissions and identify responses that are consistent with the food security and economic development priorities of countries. Critical activity data (what crops or livestock are managed in what way) are poor or lacking for many agricultural systems, especially in developing countries. In addition, the currently available methods for quantifying emissions and mitigation are often too expensive or complex or not sufficiently user friendly for widespread use.The purpose of this focus issue is to capture the state of the art in quantifying greenhouse gases from agricultural systems, with the goal of better understanding our current capabilities and near-term potential for improvement, with particular attention to quantification issues relevant to smallholders in developing countries. This work is timely in light of international discussions and negotiations around how agriculture should be included in efforts to reduce and adapt to climate change impacts, and considering that significant climate financing to developing countries in post-2012 agreements may be linked to their increased ability to identify and report GHG emissions (Murphy et al 2010, CCAFS 2011, FAO 2011).

Greenhouse gas (GHG) emissions, particularly carbon dioxide (CO₂) and methane (CH₄) from human activities, are the primary drivers of global warming. Additionally, methane contributes to ozone formation and therefore contributes to air pollution, posing risk to human health. Agriculture is a significant contributor to the global GHG emissions, with methane primarily emitted through enteric fermentation in livestock and manure management practices, while carbon dioxide largely arises from the use of machinery in various land management operations. Hence, to better understand and represent the intra - annual variability of GHG emissions within the agricultural sector, it is crucial to obtain spatial and temporal information about all contributing activities.Within the ARTEMIS project we are further developing and refining a dynamic emission model to capture the spatio-temporal variability of anthropogenic GHG and air pollutant emissions in Germany and its surroundings. Inside the emission model the spatial allocator estimates the total yearly emissions with the gridded GHG emission inventory of TNO - CAMS for Europe and UBA - GRETA for Germany.To account for temporal variability, different agricultural emission activities are parameterized individually. The temporal emission distribution for machinery use during land management operations gets estimated by deriving the emission timings from phenology observation data from the German Weather Service as well as using remote sensed phenology data from the COPERNICUS project. Additionally we incorporate an agricultural timer (Ge et al. 2020, 2022) developed to estimate the start of the growing season, which allows us to derive key dates such as sowing and manure application. The temporal variability of methane emissions from enteric fermentation are parameterized using literature-based emission factors linked to livestock feed intake and animal population data from national statistical agencies.These emission datasets were integrated into a LOTOS-EUROS model simulation to demonstrate their added value. The comparison using the new dynamic emission model indicated an improved representation of intra-annual GHG concentration variability. Furthermore also the depiction of the diurnal concentration cycle showed a better alignment with measured concentrations. Additionally, evaluation against ICOS tall tower measurements revealed improvements in correlation (up to 0.06) and reductions in root mean squared error (up to 15%) between modeled and observed concentrations at nearly all stations. These findings highlight the importance of disentangling the agricultural GHG emissions into seperate subsectors, enabling a more accurate depiction of temporal variability in anthropogenic emissions. We conclude that further improving the spatio-temporal emission information should be extended on other sectors such as the industry and energy, the road traffic or the landfills as well.

Mitigating Curtailment and Carbon Emissions through Load Migration between Data Centers

The decrease in the level of greenhouse gas (GHG) emissions from industry and agriculture is one of the biggest challenges that European Union (EU) countries have to face. Their economic development should occur under the conditions of limiting the pressure on the environment. The agricultural and industrial sectors play a key role in ensuring food security, technological progress, job security, social well-being, economic competitiveness, and sustainable development. The main purpose of this article was to identify and compare the level, trends, and variability in greenhouse gas emissions from industry and agriculture in EU countries in 2010–2019, to create classes of countries with similar gas emissions, and to analyze the average values of their economic conditions. The original contribution to the article was to investigate whether there is a relationship between the level of greenhouse gas emissions and the economic development of countries and other economic indicators characterizing the sectors of industry and agriculture. Empirical data were obtained from the Eurostat and Ilostat databases. Basic descriptive statistics, classification methods, multiple regression, and correlation methods were used in the study. The industrial and agricultural sectors in EU countries emit similar amounts of greenhouse gases into the environment. In the years 2010–2019, the percentage share of emissions from these sectors in total gas emissions was growing dynamically, but no evidence was found indicating that those countries that emitted the most greenhouse gases significantly reduced their emissions in the decade under review. Moreover, EU countries are still significantly and invariably differentiated in this respect. Greenhouse gas emissions from industry and agriculture are influenced by the economic characteristics of these sectors, such as the level of GDP per capita, the scale of investment by enterprises, the expenditure on research and development, as well as employment in these sectors. The findings of this study show that total greenhouse gas emissions from all sources increase with countries’ economic growth, while a higher level of support of EU countries for research and development, and a greater share of employment in both industry and agriculture, translate into higher greenhouse gas emissions from these sectors. These conclusions may be useful for decision makers in developed and developing countries, as well as those in the industrial and agricultural sectors, in controlling and verifying the possible causes of greenhouse gas emissions in terms of the need to reduce their negative role on the environment and human health.

Agricultural intensification scenarios, household food availability and greenhouse gas emissions in Rwanda: Ex-ante impacts and trade-offs

基于生命周期评价的上海市水稻生产碳足迹研究

Quantifying greenhouse gas (GHG) emissions is essential for mitigating global warming, and has become the task of individual countries assigned to the Paris agreement in the form of National Greenhouse Gas Inventory Reports (NIR). The NIR informs on GHG emissions and removals over national territory encompassing the 200-mile Exclusive Economic Zone (EEZ). However, apart from only a few countries, who have begun to report on coastal ecosystems, mostly mangroves, salt marshes, and seagrass meadows, the NIR does not cover or report on GHG sources and sinks of the 200-mile exclusive economic zone which, for Namibia and South Africa includes the Benguela Upwelling System (BUS). Based on our results, we estimated a CO2 uptake by the biological carbon pump of 18.5 ± 3.3 Tg C year−1 and 6.0 ± 5.0 Tg C year−1 for the Namibian and South African parts of the BUS, respectively. Even though it is assumed that the biological carbon pump already responds to global change and fisheries, uncertainties associated with estimates of the CO2 uptake by the biological carbon pump are still large and hamper a thorough quantification of human impacts on the biological carbon pump. Despite these uncertainties, it is suggested to include parameters such as preformed nutrient supply, carbon export rates, Redfield ratios, and CO2 concentrations measured at specific key sites into the NIR to stay focussed on the biological carbon pump and to support research addressing open questions, as well as to improve methods and observing concepts.

The world population is expected to grow to about 10 billion in 2050. To supply the future human population with food while sustaining a liveable planet, food should be produced sustainably. One of the most urgent environmental issues is climate change, induced by greenhouse gas (GHG) emissions. The dairy sector is a large contributor to GHG emissions. Important GHGs related to milk production are carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O), mainly emitted during feed production, enteric fermentation, and manure management. Diseases in dairy cows can reduce milk production, reproduction performance and longevity, and increase the amount of discarded milk. The objectives of this thesis were to estimate the impact of diseases (subclinical ketosis, clinical mastitis, and foot lesions) on GHG emissions, and to understand the relation between impact of diseases on GHG emissions and economic performance. First, a dynamic stochastic simulation model was developed to simulate the dynamics of the diseases and the associated production losses (reduced milk production, discarded milk, a prolonged calving interval, and removal (culling or dying on the farm)) per cow during one lactation. This model was combined with a life cycle assessment to quantify the impact of diseases on GHG emissions per ton fat-and-protein-corrected milk (kg CO2equivalents/t FPCM) from cradle to farm gate. Processes included were feed production, enteric fermentation, and manure management. The emissions of GHGs of cows with a disease increased on average by 21 (2.3%) kg CO2e/t FPCM per case of subclinical ketosis, by 58 (6.2%) kg CO2e/t FPCM per case of clinical mastitis, by 4 (0.4%) kg CO2e/ t FPCM per case of digital dermatitis, by 39 (4.3%) kg CO2e/ t FPCM per case of white line disease, and by 33 (3.6%) kg CO2e/ t FPCM per case of sole ulcer. An economic analyses was performed to estimate the costs of subclinical ketosis and related diseases. The total costs of subclinical ketosis were €130 per case per year. Comparing the impact of production contributors from a GHG emissions and economic perspective showed that a reduction in milk production had the highest impact on the economic performance, whereas removal and discarded milk had the highest impact on increase in GHG emissions. Prevalence, pathogen type, farm management (e.g. culling, feed, and manure), and prices (e.g. milk and feed) will affect the impact of production contributors on GHG emissions and economic performance. Therefore, specific farm analyses are needed to estimate the impact of diseases for a specific dairy farm. Diseases in dairy cows increase GHG emissions by approximately 0.4 Mton per year, which equals 15% of the Dutch governmental goal of GHG emission reductions in agriculture in 2030. Reducing diseases can decrease GHG emissions, can increase the income of the farmer, and can improve animal welfare. Therefore, reducing diseases can contribute to sustainable development of the dairy sector.

Greenhouse gas (GHG) emissions from agriculture contribute to climate change. The consequences of unsustainable agricultural activity are polluted water, soil, air, and food. The agricultural sector has become one of the major contributors to global GHG emissions and is the world’s second largest emitter after the energy sector, which includes emissions from power generation and transport. Latvian and Lithuanian agriculture generates about one fifth of GHG emissions, while Estonia generates only about one tenth of the country’s GHG emissions. This paper investigates the GHG trends in agriculture from 1995 to 2019 and the driving forces of changes in GHG emissions from the agricultural sectors in the Baltic States (Lithuania, Latvia, and Estonia), which are helpful for formulating effective carbon reduction policies and strategies. The impact factors have on GHG emissions was analysed by using the Logarithmic Mean Divisia Index (LMDI) method based on Kaya identity. The aim of this study is to assess the dynamics of GHG emissions in agriculture and to identify the factors that have had the greatest impact on emissions. The analysis of the research data showed that in all three Baltic States GHG emissions from agriculture from 1995 to 2001–2002 decreased but later exceeded the level of 1995 (except for Lithuania). The analysis of the research data also revealed that the pollution caused by animal husbandry activities decreased. GHG intensity declined by 2–3% annually, but the structure of agriculture remained relatively stable. The decomposition of GHG emissions in agriculture showed very large temporary changes in the analysed factors and the agriculture of the Baltic States. GHG emissions are mainly increased by pollution due to the growing economy of the sector, and their decrease is mainly influenced by two factors—the decrease in the number of people employed in the agriculture sector and the decreasing intensity of GHGs in agriculture. The dependence of the result on the factors used for the decomposition analysis was investigated by the method of multivariate regression analysis. Regression analysis showed that the highest coefficient of determination (R2 = 0.93) was obtained for Estonian data and the lowest (R2 = 0.54) for Lithuanian data. In the case of Estonia, all factors were statistically significant; in the case of Latvia and Lithuania, one of the factors was statistically insignificant. The identified GHG emission factors allowed us to submit our insights for the reduction of emissions in the agriculture of the Baltic States.

Major US electric utility climate pledges have the potential to collectively reduce power sector emissions by one-third

Energy-related activities are a major contributor of greenhouse gas (GHG) emissions. A growing body of knowledge clearly depicts the links between human activities and climate change. Over the last century the burning of fossil fuels such as coal and oil and other human activities has released carbon dioxide (CO2) emissions and other heat-trapping GHG emissions into the atmosphere and thus increased the concentration of atmospheric CO2 emissions. The main human activities that emit CO2 emissions are (1) the combustion of fossil fuels to generate electricity, accounting for about 37% of total U.S. CO2 emissions and 31% of total U.S. GHG emissions in 2013, (2) the combustion of fossil fuels such as gasoline and diesel to transport people and goods, accounting for about 31% of total U.S. CO2 emissions and 26% of total U.S. GHG emissions in 2013, and (3) industrial processes such as the production and consumption of minerals and chemicals, accounting for about 15% of total U.S. CO2 emissions and 12% of total ...

The most widely produced agricultural commodities in Indonesia are oil palm and paddy crops, which contribute the most to national income in the agricultural sector. On the other hand, in 2020, the agricultural sector contributed 9% to national greenhouse gas (GHG) emissions. This study aims to determine the effect of agricultural crop production, namely oil palm and paddy on GHG emissions in Indonesia’s agricultural sector from 2000-2020. Data for the study were obtained from the Directorate General of Climate Change, the Ministry of Environment and Forestry and BPS-Statistics Indonesia. The GHG effect of agricultural crop production were analyzed using multiple linear regression. The findings showed that the production of oil palm and paddy crops significantly affects the agricultural sector’s GHG emissions. Oil palm and paddy production have a significant level of 5% with a regression coefficients of 0.4958 and 0.0003, respectively. From the findings, it can be implied that an increase in oil palm and paddy production will raise Indonesia’s agricultural sector GHG emissions. The government can draw up regulations related to (1) protecting forest areas and peatlands close to community residential areas so that land conversion does not occur in oil palm plantations and (2) a sustainable agricultural system to increase paddy productivity, hoping that the agricultural sector’s GHG emissions will decrease.