IPCC Tier Research Articles

Prompt active restoration of peatlands substantially reduces climate impact

Restoration of peatlands after peat extraction could be a benefit to the climate system. However a multi-year ecosystem-scale assessment of net carbon (C) sequestration is needed. We investigate the climate impact of active peatland restoration (rewetting and revegetating) using a chronosequence of C gas exchange measurements across post-extraction Canadian peatlands. An atmospheric perturbation model computed the instantaneous change in radiative forcing of CO2 and CH4 emissions/uptake over 500 years. We found that using emission factors specific to an active restoration technique resulted in a radiative forcing reduction of 89% within 20 years compared to IPCC Tier 1 emission factors based on a wide range of rewetting activities. Immediate active restoration achieved a neutral climate impact (excluding C losses in the removed peat) about 155 years earlier than did a 20 year delay in restoration. A management plan that includes prompt active restoration is key to utilizing peatland restoration as a climate change mitigation strategy.

Detailed global modelling of soil organic carbon in cropland, grassland and forest soils.

Assessments of the global carbon (C) cycle typically rely on simplified models which consider large areas as homogeneous in terms of the response of soils to land use or consider very broad land classes. For example, “cropland” is typically modelled as an aggregation of distinct practices and individual crops over large regions. Here, we use the process-based Rothamsted soil Carbon Model (RothC model), which has a history of being successfully applied at a global scale, to calculate attainable SOC stocks and C mineralization rates for each of c. 17,000 regions (combination of soil type and texture, climate type, initial land use and country) in the World, under near-past climate conditions. We considered 28 individual crops and, for each, multiple production practices, plus 16 forest types and 1 grassland class (total of 80 classes). We find that conversion to cropland can result in SOC increases, particularly when the soil remains covered with crop residues (an average gain of 12 t C/ha) or using irrigation (4 t C/ha), which are mutually reinforcing effects. Attainable SOC stocks vary significantly depending on the land use class, particularly for cropland. Common aggregations in global modelling of a single agricultural class would be inaccurate representations of these results. Attainable SOC stocks obtained here were compared to long-term experiment data and are well aligned with the literature. Our results provide a regional and detailed understanding of C sequestration that will also enable better greenhouse gas reporting at national level as alternatives to IPCC tier 2 defaults.

No-tillage did not increase organic carbon storage but stimulated N2O emissions in an intensively cultivated sandy loam soil: A negative climate effect

Although numerous studies have been conducted on the effects of no-tillage on carbon (C) sequestration in agricultural systems, there is still no consensus on the balance between the potential of C sequestration and nitrous oxide (N2O) or nitric oxide (NO) emissions. A no-tillage field experiment in the North China Plain was established in 2006 and the influence of no-tillage on N2O and NO emissions was monitored under an annual wheat-maize cropping system. The study included four treatments: no-tillage (NT) and conventional tillage (CT) soils amended with N fertilizer at a rate of 225 kg N ha–1 for wheat and 195 kg N ha–1 for maize (NTN and CTN) and without N fertilizer (NT0 and CT0). Three years of no-tillage significantly (p < 0.05) increased soil organic C (SOC) content by 12.2% in the 0–5 cm soil layer, possibly due to the surface aggregation of organic C derived from crop roots and exudates, but did not alter SOC pool in the 0–30 cm profile. Annual N2O emissions in the NT0 and CT0 treatments were 0.53 and 0.57 kg N2O-N ha–1, respectively, and were significantly (p < 0.05) increased to 0.96 kg N2O-N ha–1 in CTN and to 1.23 kg N2O-N ha–1 in NTN. Remarkable differences in N2O emissions between CTN and NTN were observed during the maize growing season. In contrast, NO emissions were not affected by the tillage regimes regardless of N fertilization. The mean ratios of NO/N2O fluxes in N-unfertilized plots were 0.26–0.29 and 1.79–2.11 for the maize and wheat season, respectively, indicating that both NO and N2O were primarily derived from denitrification during the maize growing season and from nitrification under wheat cultivation. Under N-fertilized plots, the ratios increased to 1.44–2.02 and 5.00–6.03 for the maize and wheat season, respectively, with significantly (p < 0.05) lower values in NTN plots than in CTN plots. The N2O emission factors for N applied in the wheat-maize rotation system were 0.16% and 0.09% for NTN and CTN, respectively, which was far lower than the IPCC Tier 1 default value (1.0%), primarily due to the absence of irrigation after fertilization in maize season and low temperature in wheat season. The results suggest that the 3-year no-tillage regime with residue removal did not substantially increase C storage in the 0–30 cm profile, but stimulated N2O emissions primarily by increasing denitrification.

Impacts of intensifying or expanding cereal cropping in sub-Saharan Africa on greenhouse gas emissions and food security.

Cropping is responsible for substantial emissions of greenhouse gasses (GHGs) worldwide through the use of fertilizers and through expansion of agricultural land and associated carbon losses. Especially in sub-Saharan Africa (SSA), GHG emissions from these processes might increase steeply in coming decades, due to tripling demand for food until 2050 to match the steep population growth. This study assesses the impact of achieving cereal self-sufficiency by the year 2050 for 10 SSA countries on GHG emissions related to different scenarios of increasing cereal production, ranging from intensifying production to agricultural area expansion. We also assessed different nutrient management variants in the intensification. Our analysis revealed that irrespective of intensification or extensification, GHG emissions of the 10 countries jointly are at least 50% higher in 2050 than in 2015. Intensification will come, depending on the nutrient use efficiency achieved, with large increases in nutrient inputs and associated GHG emissions. However, matching food demand through conversion of forest and grasslands to cereal area likely results in much higher GHG emissions. Moreover, many countries lack enough suitable land for cereal expansion to match food demand. In addition, we analysed the uncertainty in our GHG estimates and found that it is caused primarily by uncertainty in the IPCC Tier 1 coefficient for direct N2 O emissions, and by the agronomic nitrogen use efficiency (N-AE). In conclusion, intensification scenarios are clearly superior to expansion scenarios in terms of climate change mitigation, but only if current N-AE is increased to levels commonly achieved in, for example, the United States, and which have been demonstrated to be feasible in some locations in SSA. As such, intensifying cereal production with good agronomy and nutrient management is essential to moderate inevitable increases in GHG emissions. Sustainably increasing crop production in SSA is therefore a daunting challenge in the coming decades.

CALCULATING ENERGY-INDUCED CARBON FOOTPRINT: BATMAN UNIVERSITY CASE

This study determined the energy-induced carbon footprint arising from energy consumption in Batman University. It is important for universities to do acts toward control the greenhouse gas emission based on energy consumption while performing activities. Therefore, there is need to know the components of carbon footprint. Electric energy, natural gas energy, and various oils as the energy consumption components in these institutions were scrutinized. Petrol, diesel fuel, and fuel oil were utilized as types of oil. It is seen that the component which considerably specifies the carbon footprint is 3.161.238 ton CO 2 value and electric energy. Natural gas and fuel oil use contribute to carbon footprint at high ratios in winter months especially arising from heating. 361 ton CO 2 based on natural gas use and 1.256.445 ton CO 2 based on fuel oil use are other components which specify the carbon footprint. As a result, the primary carbon footprint for Batman University was determined by IPCC Methodology and Tier 1 approach; it is 3.162.855,445 ton CO 2 for 2017.

Evaluation of the performance of existing mathematical models predicting enteric methane emissions from ruminants: Animal categories and dietary mitigation strategies

The objective of this study was to evaluate the performance of existing models predicting enteric methane (CH4) emissions, using a large database (3183 individual data from 103 in vivo studies on dairy and beef cattle, sheep and goats fed diets from different countries). The impacts of dietary strategies to reduce CH4 emissions, and of diet quality (described by organic matter digestibility (dOM) and neutral-detergent fiber digestibility (dNDF)) on model performance were assessed by animal category. The models were first assessed based on the root mean square prediction error (RMSPE) to standard deviation of observed values ratio (RSR) to account for differences in data between models and then on the RMSPE. For dairy cattle, the CH4 (g/d) predicting model based on feeding level (dry matter intake (DMI)/body weight (BW)), energy digestibility (dGE) and ether extract (EE) had the smallest RSR (0.66) for all diets, as well as for the high-EE diets (RSR = 0.73). For mitigation strategies based on lowering NDF or improving dOM, the same model (RSR = 0.48 to 0.60) and the model using DMI and neutral- and acid-detergent fiber intakes (RSR = 0.53) had the smallest RSR, respectively. For diets with high starch (STA), the model based on nitrogen, ADF and STA intake presented the smallest RSR (0.84). For beef cattle, all evaluated models performed moderately compared with the models of dairy cattle. The smallest RSR (0.83) was obtained using variables of energy intake, BW, forage content and dietary fat, and also for the high-EE and the low-NDF diets (RSR = 0.84 to 0.86). The IPCC Tier 2 models performed better when dietary STA, dOM or dNDF were high. For sheep and goats, the smallest RSR was observed from a model for sheep based on dGE intake (RSR = 0.61). Both IPCC models had low predictive ability when dietary EE, NDF, dOM and dNDF varied (RSR = 0.57 to 1.31 in dairy, and 0.65 to 1.24 in beef cattle). The performance of models depends mostly on explanatory variables and not on the type of data (individual vs. treatment means) used in their development or evaluation. Some empirical models give satisfactory prediction error compared with the error associated with measurement methods. For better prediction, models should include feed intake, digestibility and additional information on dietary concentrations of EE and structural and nonstructural carbohydrates to account for different dietary mitigating strategies.

Soil-atmosphere exchange of nitrous oxide in two Tanzanian croplands: Effects of nitrogen and stover management

Cropland intensification is needed to meet the demand for food in sub-Saharan Africa (SSA). This process requires a dramatic increase in resource inputs, including fertilizer-nitrogen (N) and organic residues (e.g., stover), which alter the soil-atmosphere exchange of nitrous oxide (N2O). The dearth of N2O emission data for SSA croplands, however, limits our ability to define regional and global N2O flux and mitigation opportunities. In two soils planted with maize in Tanzania (Iringa, sandy Alfisols; Mbeya, clayey Andisols), we conducted year-round measurements for 2 consecutive years to quantify N2O emissions in response to increasing N rates and in combination with maize stover incorporation. Rainfall and the resulting soil moisture, rather than soil temperature, were important environmental drivers of N2O emissions in these fields. Applied N stimulated N2O fluxes across soil types but with different magnitudes—lower in Iringa because of the dominance of nitrification in N2O production and higher in Mbeya likely from promoted denitrification when the water-filled pore space was >47%. N2O emission increased exponentially or linearly with N rate, depending on the year. The direct N2O emission factors were well below the 1% of the IPCC Tier 1 method, ranging from 0.13% to 0.26% in Iringa and from 0.24% to 0.42% in Mbeya, for a N rate of 50–150 kg N ha−1 during the study. Compared with N application alone, stover plus N did not significantly alter maize yield, but did raise N2O emissions significantly (P ≤ 0.06). Consequently, stover incorporation markedly increased the emission factor (up to 0.46% in Iringa and 1.29% in Mbeya) as well as yield-scaled N2O emissions. Our results suggest that linear and exponential emission responses can occur in SSA croplands and challenge the promotion of combining stover with fertilizer-N as resource input management in this region.

Carbon stocks for different land cover types in Mainland Tanzania

BackgroundDeveloping countries participating in the mitigation mechanism of reducing emissions from deforestation and forest degradation (REDD+), are required to establish a forest reference emission level (FREL), if they wish to seek financial support to reduce carbon emissions from deforestation and forest degradation. However, establishment of FREL relies heavily on the accurate estimates of carbon stock as one of the input variable for computation of the emission factors (EFs). The product of an EF and activity data, such as the area of deforestation, results in the total emissions needed for establishment of FREL. This study presents the carbon stock estimates for different land cover classes based on an analysis of Tanzania’s national forest inventory data generated through the National Forest Resources Monitoring and Assessment (NAFORMA).ResultsCarbon stocks were estimated in three carbon pools, namely aboveground, belowground, and deadwood for each of the three land cover classes (i.e. Forest, non-forest, and wetland). The weighted average carbon stock was 33.35 t C ha−1 for forest land, 4.28 t ha−1 for wetland and 5.81 t ha−1 for non-forest land. The uncertainty values were 0.9% for forest land, 11.3% for wetland and 1.8% for non-forest land. Average carbon stocks for land cover sub-classes, which make up the above mentioned major land cover classes, are also presented in our study.ConclusionsThe values presented in this paper correspond to IPCC tier 3 and can be used for carbon estimation at the national scale for the respective major primary vegetation type for various purposes including REDD+. However, if local based estimates values are needed, the use of auxiliary data to enhance the precision of the area of interest is recommended.

Introducing a new tool for greenhouse gas calculation tailored for cropland: rationale, operational framework and potential application

The new GHG calculator named SECTOR (Source-selective and Emission-adjusted GHG CalculaTOR for Cropland) is based on the IPCC Tier 2 approach for rice as well as other crops. The new features of SECTOR facilitate high flexibility in terms of entering newly obtained emission factors, easy data transfer from crop statistics for entering activity data and detailed specifications of GHG scenarios. A new procedure of entering frequency-based data on current water management practices was also developed. Moreover, the tool allows deviating from the 2006 IPCC Guidelines by considering field records with high background levels of N2O emissions in the overall assessment of GHG emissions.This article assesses different applications of the tool, namely as add-ons to field measurements, for GHG calculation at national/sectorial scale and within measurement, reporting and verification of development projects. SECTOR is downloadable in the form of templates that can be used to develop custom versions with varying levels of disaggregated data entries at different scales. A case study for rice production in one Vietnamese province demonstrates the potential to display GHG results in combination with GIS. SECTOR can easily be adjusted to incorporate new emission factors and calculation procedures expected in forthcoming revisions of the IPCC Guidelines.HighlightsA new GHG calculation tool was developed based on the IPCC Tier 2 approach for rice as well as other cropsFeatures of the tool facilitate high flexibility in terms of emission factors, easy data transfer from crop statistics for entering activity data and detailed specification of GHG scenariosThis article assesses different user groups and possible applications of the toolA case study for rice production in one Vietnamese province demonstrates the potential to display GHG results in combination with GIS

Contextualized re-calculation of enteric methane emission factors for small ruminants in sub-humid Western Africa is far lower than previous estimates.

Given the projected growth of methane emission by ruminants in developing countries, there is a clear need for reliable estimates of their contribution to greenhouse gas emissions. Existing studies have rarely considered sheep and goats. The objective of this study was to predict enteric fermentation methane emission factors (EFs) for Djallonké sheep and West African Dwarf goats, following the 2006 IPCC Tier 2 methodology. Estimated enteric methane emission factors, expressed per head of animal per year, were 2.3kg CH4 and 2.0kg CH4 for sheep and goats species, respectively. Compared with the generic Tier 1 emission factor of 5kg CH4 head proposed by the IPCC for small ruminants in the sub-Saharan Africa region, our suggested values are 56% and 60% lower for sheep and goat, respectively. These lower values took account of the particular flock structure of both sheep and goats. These estimates also accounted for differences in live weight according to age and corresponding estimated feed intake. This work is a step forward in the revision of small ruminant emission factors and can further support assessment of mitigation strategies in Senegalese livestock farming systems.

Revisiting IPCC Tier 1 coefficients for soil organic and biomass carbon storage in agroforestry systems

Agroforestry systems comprise trees and crops, or trees and pastures within the same field. Globally, they cover approximately 1 billion hectares of land and contribute to the livelihoods of over 900 million people. Agroforestry systems have the capacity to sequester large quantities of carbon (C) in both soil and biomass. However, these systems have not yet been fully considered in the approach to C accounting developed by the Intergovernmental Panel on Climate Change, largely due to the high diversity of agroforestry systems and scarcity of relevant data. Our literature review identified a total of 72 scientific, peer-reviewed articles associated with biomass C storage (50) and with soil organic carbon (SOC) (122), containing a total of 542 observations (324 and 218, respectively). Based on a synthesis of the reported observations, we are presenting a set of Tier 1 coefficients for biomass C storage for each of the eight main agroforestry systems identified, including alley cropping, fallows, hedgerows, multistrata, parklands, shaded perennial-crop, silvoarable and silvopastoral systems, disaggregated by climate and region. Using the same agroforestry classification, we are presenting a set of stock change factors (FLU) and SOC accumulation/loss rates for three main land use changes (LUCs): cropland to agroforestry; forest to agroforestry; and grassland to agroforestry. Globally, the mean SOC stock change factors (± confidence intervals) were estimated to be 1.25 ± 0.04, 0.89 ± 0.07, and 1.19 ± 0.10, for the three main LUCs, respectively. However, these average coefficients hide huge disparities across and within different climates, regions, and types of agroforestry systems, highlighting the necessity to adopt the more disaggregated coefficients provided herein. We encourage national governments to synthesize data from local field experiments to generate country-specific factors for more robust estimation of biomass and SOC storage.

Carbon and blue water footprints of California sheep production.

While the environmental impacts of livestock production, such as greenhouse gas emissions and water usage, have been studied for a variety of US livestock production systems, the environmental impact of US sheep production is still unknown. A cradle-to-farm gate life cycle assessment (LCA) was conducted according to international standards (ISO 14040/44), analyzing the impacts of CS representing five different meat sheep production systems in California, and focusing on carbon footprint (carbon dioxide equivalents, CO2e) and irrigated water usage (metric ton, MT). This study is the first to look specifically at the carbon footprint of the California sheep industry and consider both wool and meat production across the diverse sheep production systems within California. This study also explicitly examined the carbon footprint of hair sheep as compared with wooled sheep production. Data were derived from producer interviews and literature values, and California-specific emission factors were used wherever possible. Flock outputs studied included market lamb meat, breeding stock, 2-d-old lambs, cull adult meat, and wool. Four different methane prediction models were examined, including the current IPCC tier 1 and 2 equations, and an additional sensitivity analysis was conducted to examine the effect of a fixed vs. flexible coefficient of gain (kg) in mature ewes on carbon footprint per ewe. Mass, economic, and protein mass allocation were used to examine the impact of allocation method on carbon footprint and water usage, while sensitivity analyses were used to examine the impact of ewe replacement rate (% of ewe flock per year) and lamb crop (lambs born per ewe bred) on carbon footprint per kilogram market lamb. The carbon footprint of market lamb production ranged from 13.9 to 30.6 kg CO2e/kg market lamb production on a mass basis, 10.4 to 18.1 kg CO2e/kg market lamb on an economic basis, and 6.6 to 10.1 kg CO2e/kg market lamb on a protein mass basis. Enteric methane (CH4) production was the largest single source of emissions for all CS, averaging 72% of total emissions. Emissions from feed production averaged 22% in total, primarily from manure emissions credited to feed. Whole-ranch water usage ranged from 2.1 to 44.8 MT/kg market lamb, almost entirely from feed production. Overall results were in agreement with those from meat-focused sheep systems in the United Kingdom as well as beef raised under similar conditions in California.

Global ammonia emissions from synthetic nitrogen fertilizer applications in agricultural systems: Empirical and process-based estimates and uncertainty.

Excessive ammonia (NH3 ) emitted from nitrogen (N) fertilizer applications in global croplands plays an important role in atmospheric aerosol production, resulting in visibility reduction and regional haze. However, large uncertainty exists in the estimates of NH3 emissions from global and regional croplands, which utilize different data and methods. In this study, we have coupled a process-based Dynamic Land Ecosystem Model (DLEM) with the bidirectional NH3 exchange module in the Community Multiscale Air-Quality (CMAQ) model (DLEM-Bi-NH3 ) to quantify NH3 emissions at the global and regional scale, and crop-specific NH3 emissions globally at a spatial resolution of 0.5°×0.5° during 1961-2010. Results indicate that global NH3 emissions from N fertilizer use have increased from 1.9±0.03 to 16.7±0.5TgN/year between 1961 and 2010. The annual increase of NH3 emissions shows large spatial variations across the global land surface. Southern Asia, including China and India, has accounted for more than 50% of total global NH3 emissions since the 1980s, followed by North America and Europe. Rice cultivation has been the largest contributor to total global NH3 emissions since the 1990s, followed by corn and wheat. In addition, results show that empirical methods without considering environmental factors (constant emission factor in the IPCC Tier 1 guideline) could underestimate NH3 emissions in context of climate change, with the highest difference (i.e., 6.9TgN/year) occurring in 2010. This study provides a robust estimate on global and regional NH3 emissions over the past 50years, which offers a reference for assessing air quality consequences of future nitrogen enrichment as well as nitrogen use efficiency improvement.

A new Tier 3 method to calculate methane emission inventory for ruminants

Livestock is the main source of methane (CH4) emissions. It is important to accurately determine emissions from ruminants that meet standardized international guidelines for national greenhouse gas inventories. A new method to improve the accuracy of CH4 emissions that complies with IPCC rules for a Tier 3 method is described and evaluated. This method, developed by INRA (French Institute for Agricultural Research), was applied to the French inventory of CH4 emissions by ruminants and compared with the IPCC Tier 2 method. For enteric CH4, depending on the animal category, the INRA CH4 emission estimates lay between 88% and 114% of IPCC's. The INRA/IPCC ratio for enteric emission was close to unity and did not differ between methods (P = 0.43) for adult cows (i.e. most cattle). In France, feedlot manure is stored in aerobic conditions, and so the INRA/IPCC fit for manure emission was poorer (P < 0.05). The INRA/IPCC fit for enteric CH4 was very close between methods to that for total CH4 (P = 0.39), enteric CH4 representing 93% of total emissions. The main improvement is the use of a robust equation (from numerous data and diets), based on digestible organic matter intake (DOMI) corrected for the digestive interactions, to predict CH4 consistently from enteric and manure sources. It was developed for the French livestock inventory but is customizable for other countries. This new improved CH4 estimation method, based on equations from a large literature database, complies with IPCC rules for a Tier 3 method.

Nitrous oxide emissions after incorporation of winter oilseed rape (Brassica napus L.) residues under two different tillage treatments

AbstractThe aim of this study was to investigate the effect of crop residues from winter oilseed rape on N2O emissions from a loamy soil and to determine the effect of different tillage practices on N2O fluxes. We therefore conducted a field experiment in which crop residues of winter oilseed rape (Brassica napus L., OSR) were replaced with 15N labelled OSR residues. Nitrous oxide (N2O) emissions and 15N abundance in the N2O were determined for a period of 11 months after harvest of OSR and in the succeeding crop winter wheat (Triticum aestivum L.) cultivated on a Haplic Luvisol in South Germany. Measurements were carried out with the closed chamber method in a treatment with conventional tillage (CT) and in a treatment with reduced soil tillage (RT). In both tillage treatments we also determined N2O fluxes in control plots where we completely removed the crop residues. High N2O fluxes occurred in a short period just after OSR residue replacement in fall and after N‐fertilization to winter wheat in the following spring. Although N2O emissions differed for distinct treatments and sub‐periods, cumulative N2O emissions over the whole investigation period (299 days) ranged between 1.7 kg and 2.4 kg N2O‐N ha−1 with no significant treatment effects. More than half of the cumulative emissions occurred during the first eight weeks after OSR replacement, highlighting the importance of this post‐harvest period for annual N2O budgets of OSR. The contribution of residue N to the N2O emission was low and explained by the high C/N‐ratio fostering immobilization of mineral N. In total only 0.03% of the N2O‐N emitted in the conventional tillage treatment and 0.06% in the reduced tillage treatment stemmed directly from the crop residues. The 15N recovery in the treatments with crop residues was 62.8% (CT) and 75.1% (RT) with more than 97% of the recovered 15N in the top soil. Despite our measurements did not cover an entire year, the low contribution of the OSR residues to the direct N2O emissions shows, that the current IPCC tier 1 approach, which assumes an EF of 1%, strongly overestimated direct emissions from OSR crop residues. Furthermore, we could not observe any relationship between tillage and crop residues on N2O emission, only during the winter period were N2O emissions from reduced tillage significantly higher compared to conventional tillage. Annual N2O emission from RT and CT did not differ.

Evaluation of four modelling approaches to estimate nitrous oxide emissions in China's cropland

Process-based models are useful tools to integrate the effects of detailed agricultural practices, soil characteristics, mass balance, and climate change on soil N2O emissions from soil - plant ecosystems, whereas static, seasonal or annual models often exist to estimate cumulative N2O emissions under data-limited conditions. A study was carried out to compare the capability of four models to estimate seasonal cumulative N2O fluxes from 419 field measurements representing 65 studies across China's croplands. The models were 1) the DAYCENT model, 2) the DNDC model, 3) the linear regression model (YLRM) of Yue et al. (2018), and 4) IPCC Tier 1 emission factors. The DAYCENT and DNDC models estimated crop yields with R2 values of 0.60 and 0.66 respectively, but both models showed significant underestimation for all measurements. The estimated seasonal N2O emissions with R2 of 0.31, 0.30, 0.21 and 0.17 for DAYCENT, DNDC, YLRM, and IPCC, respectively. Based on RMSE, modelling efficiency and bias analysis, YLRM performed well on N2O emission prediction under no fertilization though bias still existed, while IPCC performed well for cotton and rapeseed and DNDC for soybean. The DAYCENT model accurately predicted the emissions with no bias across other crop and fertilization types whereas the DNDC model underestimated seasonal N2O emissions by 0.42 kg N2O-N ha−1 for all observed values. Model evaluation indicated that the DAYCENT and DNDC models simulated temporal patterns of daily N2O emissions effectively, but both models had difficulty in simulating the timing of the N2O fluxes following some events such as fertilization and water regime. According to this evaluation, algorithms for crop production and N2O emission should be improved to increase the accuracy in the prediction of unfertilized fields both for DAYCENT and DNDC. The effects of crop types and management modes such as fertilizations should also be further refined for YLRM.

Soil carbon inventory to quantify the impact of land use change to mitigate greenhouse gas emissions and ecosystem services

Currently the land use and land use change (LULUC) emits 1.3 ± 0.5 Pg carbon (C) year−1, equivalent to 8% of the global annual emissions. The objectives of this study were to quantify (1) the impact of LULUC on greenhouse gas (GHG) emissions in a subtropical region and (2) the role of conservation agriculture to mitigate GHG emissions promoting ecosystem services. We developed a detailed IPCC Tier 2 GHG inventory for the Campos Gerais region of southern Brazil that has large cropland area under long-term conservation agriculture with high crop yields. The inventory accounted for historical and current emissions from fossil fuel combustion, LULUC and other minor sources. We used Century model to simulate the adoption of conservation best management practices, to all croplands in the region from 2017 to 2117. Our results showed historical (1930–2017) GHG emissions of 412 Tg C, in which LULUC contributes 91% (376 ± 130 Tg C), the uncertainties ranged between 13 and 36%. Between 1930 and 1985 LULUC was a major source of GHG emission, however from 1985 to 2015 fossil fuel combustion became the primary source of GHG emission. Forestry sequestered 52 ± 24 Tg C in 0.6 Mha in a period of 47 years (1.8 Tg C Mha−1 year−1) and no-till sequestered 30.4 ± 24 Tg C in 2 Mha in a period of 32 years (0.5 Tg C Mha−1 year−1) being the principal GHG mitigating activities in the study area. The model predictions showed that best management practices have the potential to mitigate 13 years of regional emissions (330 Tg C in 100 years) or 105 years of agriculture, forestry and livestock emissions (40 Tg C in 100 years) making the agriculture sector a net carbon (C) sink and promoting ecosystem services.

Nitrous Oxide and Ammonia Emissions from Cattle Excreta on Shortgrass Steppe.

Grazing cattle redistribute nitrogen (N) consumed in forage through urine and feces patches. The high concentration of N in these patches often exceeds the uptake demands of the local plant community, thereby providing ideal conditions for losses of reactive N. However, knowledge on nitrous oxide (NO) and ammonia (NH) emissions from excretal patches on shortgrass steppe grassland is limited. We studied the effect of cattle urine (1002 kg N ha) and feces (1021 kg N ha) patches on NO and NH emissions in two sites with contrasting vegetation: (i) cool-season (C3) 'Bozoisky-Select' Russian wildrye [ (Fisch.) Nevski], pasture (C3Past) and (ii) C4-dominated native shortgrass steppe rangeland (C4SS). Nitrous oxide and NH were measured using semi-static and semi-open chambers, respectively. Cumulative NO emissions were 217 and 173% greater and cumulative volatile NH emissions were 339 and 157% greater on C3Past compared with C4SS from the urine and feces treatments, respectively. Nitrous oxide emission factors were 0.20 and 0.05% for urine and 0.07 and 0.03% for feces on C3Past and C4SS, respectively. Our findings suggest that using the IPCC Tier 1 default emission factor (2%, 95% CI = 0.7-6%) to estimate NO emissions from cattle excretal patches on shortgrass steppe grassland would result in a significant overestimation for these dryland systems. Ammonia emission factors were 35 and 10% for urine and 7 and 5% for feces on C3Past and C4SS, respectively. With the exception of the urine treatment on C3Past, observed NH emissions were consistent with the IPCC Tier 1 default assumption that 20% (95% CI = 5-50%) of excretal N is volatilized as NH+NO.

A new approach for improving emission factors for enteric methane emissions of cattle in smallholder systems of East Africa – Results for Nyando, Western Kenya

In Africa, the agricultural sector is the largest sector of the domestic economy, and livestock, are a crucial component of agriculture, accounting for ~45% of the Kenyan agricultural GDP and >70% of African agricultural greenhouse gas (GHG) emissions. Accurate estimates of GHG emissions from livestock are required for inventory purposes and to assess the efficacy of mitigation measures, but most estimates rely on TIER I (default) IPCC protocols with major uncertainties coming from the IPCC methodology itself. Tier II estimates represent a significant improvement over the default methodology, however in less developed economies the required information is lacking or of uncertain reliability. In this study we developed an alternative methodology based on animal energy requirements derived from field measurements of live weight, live weight change, milk production and locomotion to estimate intake. Using on-farm data, we analysed feed samples to produce estimates of digestibility by season and region, then and used these data to estimate daily methane production by season, area and class of animal to produce new emission factors (EF) for annual enteric CH4 production. Mean Dry Matter Digestibility of the feed basket was in the range of 58–64%, depending on region and season (around 10% greater than TIER I estimates). EFs were substantially lower for adolescent and adult male (30.1, 35.9 versus 49kg CH4) and for adolescent and adult female (23.0, 28.3 versus 41kg), but not calves (15.7 versus 16kg) than those given for “other” African cattle in IPCC (Tier I) estimates. It is stressed that this study is the first of its kind for Sub-Sharan Africa relying on animal measurements, but should not automatically be extrapolated outside of its geographic range. It does however, point out the need for further measurements, and highlights the value of using a robust methodology which does not rely on the (often invalid) assumption of ad libitum intake in systems where intake is known or likely to be restricted.

How do farm models compare when estimating greenhouse gas emissions from dairy cattle production?

The European Union Effort Sharing Regulation (ESR) will require a 30% reduction in greenhouse gas (GHG) emissions by 2030 compared with 2005 from the sectors not included in the European Emissions Trading Scheme, including agriculture. This will require the estimation of current and future emissions from agriculture, including dairy cattle production systems. Using a farm-scale model as part of a Tier 3 method for farm to national scales provides a more holistic and informative approach than IPCC (2006) Tier 2 but requires independent quality control. Comparing the results of using models to simulate a range of scenarios that explore an appropriate range of biophysical and management situations can support this process by providing a framework for placing model results in context. To assess the variation between models and the process of understanding differences, estimates of GHG emissions from four farm-scale models (DairyWise, FarmAC, HolosNor and SFARMMOD) were calculated for eight dairy farming scenarios within a factorial design consisting of two climates (cool/dry and warm/wet)×two soil types (sandy and clayey)×two feeding systems (grass only and grass/maize). The milk yield per cow, follower:cow ratio, manure management system, nitrogen (N) fertilisation and land area were standardised for all scenarios in order to associate the differences in the results with the model structure and function. Potential yield and application of available N in fertiliser and manure were specified separately for grass and maize. Significant differences between models were found in GHG emissions at the farm-scale and for most contributory sources, although there was no difference in the ranking of source magnitudes. The farm-scale GHG emissions, averaged over the four models, was 10.6 t carbon dioxide equivalents (CO2e)/ha per year, with a range of 1.9 t CO2e/ha per year. Even though key production characteristics were specified in the scenarios, there were still significant differences between models in the annual milk production per ha and the amounts of N fertiliser and concentrate feed imported. This was because the models differed in their description of biophysical responses and feedback mechanisms, and in the extent to which management functions were internalised. We conclude that comparing the results of different farm-scale models when applied to a range of scenarios would build confidence in their use in achieving ESR targets, justifying further investment in the development of a wider range of scenarios and software tools.