Estimation of nitrous oxide and methane emission from livestock of urban agriculture in Beijing

Objective: To prepare a greenhouse gas inventory within the state of Alagoas for 2019, using data and factors from livestock and agriculture. Theoretical framework: Preparing an inventory is an important tool for understanding climate change that is directly related to greenhouse gas (GHG) emissions, mostly resulting from anthropogenic activities. In Brazil, agriculture stands out as one of the main sources of emissions, driven by the production of grains and animals for domestic and foreign consumption. Due to the difficulty of direct measurement, emissions are often assessed through inventories. The GHG emissions inventory is one of the basic tools that provides relevant data to direct certain public policies, or civil society actions, to reduce GHG emissions into the atmosphere, given that the state of Alagoas has few scientific productions on GHG sources and emissions. Methodology: Emissions were calculated by multiplying the emission coefficient following the methodology proposed by the IPCC, GHG Protocol, using the multiplication of the emission coefficient also indicated by the IPCC, MCTIC, EMBRAPA by the total number of heads or by the number of hectares of each crop in 2019 to calculate emissions. The results were then transformed into figures to facilitate understanding and analysis. Results and Discussions: N2O emissions from temporary and permanent crops, and N2O and CH4 emissions from flooded rice crops; as well as CH4 and N2O emissions from the livestock sector, totaling 10.53 MtCO2e of emissions in 2019. Comparing with other results, differences were noted in the total emissions due to the adoption of different methodologies and factors, as well as in what to measure, so this article presents advances and limitations, contributing to a greater depth of understanding of emissions. Research Implications: This research contributes to the scientific literature, public policy actions and environmental planning, as well as understanding and analyzing the impacts on the agricultural sector, and methodological and scientific advances, and the promotion of new research and data updates that can guide new research and updates. Originality/value: This study provides a regional focus on Alagoas, with consistent applications of international methodologies (IPCC, GHG Protocol) in local contexts. It serves as a basis for local public policies, environmental and climate impacts, expanding knowledge about the state's emissions, and encouraging innovation in local methods, as well as awareness and social mobilization.

Blue revolution for food security under carbon neutrality: A case from the water-saving and drought-resistance rice

Methane emissions along biomethane and biogas supply chains are underestimated

The global animal food chain has a large contribution to the global anthropogenic greenhouse gas (GHG) emissions, but its share and sources vary highly across the world. However, the assessment of GHG emissions from livestock production is subject to various uncertainties, which have not yet been well quantified at large spatial scale. We assessed the uncertainties in the relations between animal production (milk, meat, egg) and the CO2, CH4, and N2O emissions in Africa, Latin America and the European Union, using the MITERRA-Global model. The uncertainties in model inputs were derived from time series of statistical data, literature review or expert knowledge. These model inputs and parameters were further divided into nine groups based on type of data and affected greenhouse gas. The final model output uncertainty and the uncertainty contribution of each group of model inputs to the uncertainty were quantified using a Monte Carlo approach, taking into account their spatial and cross-correlation. GHG emissions and their uncertainties were determined per livestock sector, per product and per emission source category. Results show large variation in the GHG emissions and their uncertainties for different continents, livestock sectors products or source categories. The uncertainty of total GHG emissions from livestock sectors is higher in Africa and Latin America than in the European Union. The uncertainty of CH4 emission is lower than that for N2O and CO2. Livestock parameters, CH4 emission factors and N emission factors contribute most to the uncertainty in the total model output. The reliability of GHG emissions from livestock sectors is relatively high (low uncertainty) at continental level, but could be lower at country level.

Human demand for livestock products has increased rapidly during the past few decades largely due to dietary transition and population growth, with significant impact on climate and the environment. The contribution of ruminant livestock to greenhouse gas (GHG) emissions has been investigated extensively at various scales from regional to global, but the long-term trend, regional variation and drivers of methane (CH4 ) emission remain unclear. In this study, we use Intergovernmental Panelon Climate Change (IPCC) Tier II guidelines to quantify the evolution of CH4 emissions from ruminant livestock during 1890-2014. We estimate that total CH4 emissions in 2014 was 97.1 million tonnes (MT) CH4 or 2.72 Gigatonnes (Gt) CO2 -eq (1 MT = 1012 g, 1 Gt = 1015 g) from ruminant livestock, which accounted for 47%-54% of all non-CO2 GHG emissions from the agricultural sector. Our estimate shows that CH4 emissions from the ruminant livestock had increased by 332% (73.6 MT CH4 or 2.06 Gt CO2 -eq) since the 1890s. Our results further indicate that livestock sector in drylands had 36% higher emission intensity (CH4 emissions/km2 ) compared to that in nondrylands in 2014, due to the combined effect of higher rate of increase in livestock population and low feed quality. We also find that the contribution of developing regions (Africa, Asia and Latin America) to the total CH4 emissions had increased from 51.7% in the 1890s to 72.5% in the 2010s. These changes were driven by increases in livestock numbers (LU units) by up to 121% in developing regions, but decreases in livestock numbers and emission intensity (emission/km2 ) by up to 47% and 32%, respectively, in developed regions. Our results indicate that future increases in livestock production would likely contribute to higher CH4 emissions, unless effective strategies to mitigate GHG emissions in livestock system are implemented.

Greenhouse gas (GHG) emissions were a major causal factor of global warming that further impacts climate change. This study aimed to inventory the sources of greenhouse gas emissions from the livestock sector in Bangka Belitung. The GHG emissions in the livestock sector was calculated using the Tier-2 method based on guidance from IPCC 2006. Secondary data were collected from multiple sources, including livestock population, enteric CH4 emission factors, and the production and management of local livestock manure. The results of the calculation of GHG emissions in Bangka Belitung from 2018-2022 showed a significant increase from 25.54 to 33.32 Gg CO2 eq, with an accumulation of 139.43 Gg CO2 eq over five years. Beef cattle became the largest contributor to GHG emissions, with enteric fermentation CH4 emissions of 104.34 Gg CO2 eq, accounting for 91.90% of the total CH4 emissions from enteric fermentation sources and 74.84% of the total GHG emissions in Bangka Belitung. The largest contributor to GHG emissions was 78.62% or 109.62 Gg CO2 eq from enteric fermentation sources of ruminants, while N2O emissions from manure management reached 29.10 Gg CO2 eq, and the smallest CH4 emissions were 0.70 Gg CO2 eq, sourced from livestock manure

ABSTRACTPatches of dung and urine are major contributors to the feedlot gas emissions. This study investigated the impacts of dung deposition frequency (partly reflecting animal stocking density of a feedlot), dairy feedlot floor conditions (old floor indicated with the presence of consolidated manure pad [CMP] vs. new floor with the absence of consolidated manure pad [CMPn]), and application of dicyandiamide (DCD) and hydroquinone (HQ) on nitrous oxide (N2O) and methane (CH4) emissions from patches in the laboratory, and the integrative impacts were expressed in terms of global warming potential (CO2-equivalent). Dung deposition frequency, feedlot floor condition, and application of inhibitors showed inverse impacts on N2O and CH4 emissions from patches. Greenhouse gas (GHG) emissions from the dung, urine, and dung+urine patches on the CMP feedlot surface were approximately 7.48, 87.35, and 7.10 times those on the CMPn feedlot surface (P < 0.05). Meanwhile, GHG emissions from CMP and CMPn feedlot surfaces under high deposition frequency condition were approximately 10 and 1.7 times those under low-frequency condition. Moreover, application of HQ slightly reduced the GHG emission from urine patches, by 14.9% (P > 0.05), while applying DCD or DCD+HQ significantly reduced the GHG, by 60.3% and 65.0%, respectively (P < 0.05). Overall, it is necessary to include feedlot management such as animal stocking density and feedlot floor condition to the process of determining emission factors for feedlots. In the future, field measurements to quantitatively evaluate the relative contribution of nitrification and denitrification to the N2O emissions of feedlot surfaces are highly required for effective N2O control.Implications: This study shows that feedlot CH4 and N2O emissions inversely respond to the dicyandiamide (DCD) application. Applying DCD significantly reduces GHG emissions of feedlot urine patches. Feedlot floor condition and stocking density strongly impact feedlot GHG emissions. Including feedlot floor condition and stocking density in the feedlot EF determining process is necessary.

This chapter outlines the role of livestock in the production of greenhouse gases (GHGs) that contributes to climate change. Livestock contribute both di‐ rectly and indirectly to climate change through the emissions of GHGs such as carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O). As animal production systems are vulnerable to climate change and are large contribu‐ tors to potential global warming, it is vital to understand in detail enteric CH4 emission and manure management in different livestock species. Methane emissions from livestock are estimated to be approximately 2.2 billion tonnes of CO2 equivalents, accounting for about 80% of agricultural CH4 and 35% of the total anthropogenic CH4 emissions. Furthermore, the global livestock sec‐ tor contributes about 75% of the agricultural N2O emissions. Other sources of GHG emission from livestock and related activities are fossil fuels used for as‐ sociated farm activities, N2O emissions from fertilizer use, CH4 release from the breakdown of fertilizers and from animal manure, and land-use changes for feed production. There are several techniques available to quantify CH4 emission, and simulation models offer a scope to predict accurately the GHG emission from a livestock enterprise as a whole. Quantifying GHG emission from livestock may pave the way for understanding the role of livestock to climate change and this will help in designing appropriate mitigation strat‐ egies to reduce livestock-related GHGs.

Identifying a sustainable rice-based cropping system via on-farm evaluation of grain yield, carbon sequestration capacity and carbon footprints in Central China

Integrative effects of soil tillage and straw management on crop yields and greenhouse gas emissions in a rice–wheat cropping system

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High latitude ecosystems are prone to phenological mismatches due to climate change- driven advances in the growing season and changing arrival times of migratory herbivores. These changes have the potential to alter biogeochemical cycling and contribute to feedbacks on climate change by altering greenhouse gas (GHG) emissions of carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) through large regions of the Arctic. Yet the effects of phenological mismatches on gas fluxes are currently unexplored. We used a three-year field experiment that altered the start of the growing season and timing of grazing to investigate how phenological mismatch affects GHG exchange. We found early grazing increased mean GHG emission to the atmosphere despite lower CH4 emissions due to grazing-induced changes in vegetation structure that increased uptake of CO2. In contrast, late grazing reduced GHG emissions because greater plant productivity led to an increase in CO2 uptake that overcame the increase in CH4 emission. Timing of grazing was an important control on both CO2 and CH4 emissions, and net GHG exchange was the result of opposing fluxes of CO2 and CH4. N2O played a negligible role in GHG flux. Advancing the growing season had a smaller effect on GHG emissions than changes to timing of grazing in this study. Our results suggest that a phenological mismatch that delays timing of grazing relative to the growing season, a change which is already developing along in western coastal Alaska, will reduce GHG emissions to the atmosphere through increased CO2 uptake despite greater CH4 emissions.

Globally, agriculture is directly responsible for 14% of annual greenhouse gas(GHG) emissions and induces an additional 17% through land use change, mostlyin developing countries (Vermeulen et al 2012). Agricultural intensification andexpansion in these regions is expected to catalyze the most significant relativeincreases in agricultural GHG emissions over the next decade (Smith et al 2008,Tilman et al 2011). Farms in the developing countries of sub-Saharan Africa andAsia are predominately managed by smallholders, with 80% of land holdingssmaller than ten hectares (FAO 2012). One can therefore posit that smallholderfarming significantly impacts the GHG balance of these regions today and willcontinue to do so in the near future.However, our understanding of the effect smallholder farming has on theEarth’s climate system is remarkably limited. Data quantifying existing andreduced GHG emissions and removals of smallholder production systems areavailable for only a handful of crops, livestock, and agroecosystems (Herrero et al2008, Verchot et al 2008, Palm et al 2010). For example, fewer than fifteenstudies of nitrous oxide emissions from soils have taken place in sub-SaharanAfrica, leaving the rate of emissions virtually undocumented. Due to a scarcity ofdata on GHG sources and sinks, most developing countries currently quantifyagricultural emissions and reductions using IPCC Tier 1 emissions factors.However, current Tier 1 emissions factors are either calibrated to data primarilyderived from developed countries, where agricultural production conditions aredissimilar to that in which the majority of smallholders operate, or from data thatare sparse or of mixed quality in developing countries (IPCC 2006). For the mostpart, there are insufficient emissions data characterizing smallholder agricultureto evaluate the level of accuracy or inaccuracy of current emissions estimates.Consequentially, there is no reliable information on the agricultural GHG budgetsfor developing economies. This dearth of information constrains the capacity totransition to low-carbon agricultural development, opportunities for smallholdersto capitalize on carbon markets, and the negotiating position of developingcountries in global climate policy discourse.Concerns over the poor state of information, in terms of data availability andrepresentation, have fueled appeals for new approaches to quantifying GHGemissions and removals from smallholder agriculture, for both existing conditionsand mitigation interventions (Berry and Ryan 2013, Olander et al 2013).Considering the dependence of quantification approaches on data and the currentdata deficit for smallholder systems, it is clear that in situ measurements must bea core part of initial and future strategies to improve GHG inventories and

Mitigation of greenhouse gas emissions and reduced irrigation water use in rice production through water-saving irrigation scheduling, reduced tillage and fertiliser application strategies

The objective of this work was the application of 2006 Intergovernmental Panel on Climate Change (IPCC) Guidelines for the estimation of methane and nitrous oxide emissions from the waste sector in Argentina as a preliminary exercise for greenhouse gas (GHG) inventory development and to compare with previous inventories based on 1996 IPCC Guidelines. Emissions projections to 2030 were evaluated under two scenarios—business as usual (BAU), and mitigation—and the calculations were done by using the ad hoc developed IPCC software. According to local activity data, in the business-as-usual scenario, methane emissions from solid waste disposal will increase by 73% by 2030 with respect to the emissions of year 2000. In the mitigation scenario, based on the recorded trend of methane captured in landfills, a decrease of 50% from the BAU scenario should be achieved by 2030. In the BAU scenario, GHG emissions from domestic wastewater will increase 63% from 2000 to 2030. Methane emissions from industrial wastewater, calculated from activity data of dairy, swine, slaughterhouse, citric, sugar, and wine sectors, will increase by 58% from 2000 to 2030 while methane emissions from domestic will increase 74% in the same period. Results show that GHG emissions calculated from 2006 IPCC Guidelines resulted in lower levels than those reported in previous national inventories for solid waste disposal and domestic wastewater categories, while levels were 18% higher for industrial wastewater. Implications: The implementation of the 2006 IPCC Guidelines for National Greenhouse Inventories is now considering by the UNFCCC for non-Annex I countries in order to enhance the compilation of inventories based on comparable good practice methods. This work constitutes the first GHG emissions estimation from the waste sector of Argentina applying the 2006 IPCC Guidelines and the ad doc developed software. It will contribute to identifying the main differences between the models applied in the estimation of methane emissions on the key categories of waste emission sources and to comparing results with previous inventories based on 1996 IPCC Guidelines.