Linear and non-linear impact of key agricultural components on greenhouse gas emissions

Global climate change triggered by greenhouse gases (GHGs) puts incomparable threats to the environment and food security. Agriculture is one of the key drivers of environmental deterioration, which is linked to GHG emissions and labeled ultrasensitive to climate change. However, there is a scarcity of research exploring the nexus between agriculture and GHG emissions in Bangladesh. Thus, the present study empirically investigates the dynamic impacts of agricultural land expansion, agricultural value added, crop production, livestock production, fisheries production, energy use in agriculture, fertilizer consumption, and forest land on GHG emissions in Bangladesh. Time series data from 1990 to 2018 were utilized by employing the Dynamic Ordinary Least Squares (DOLS) approach. The empirical findings reveal that a 1% increase in agricultural land, crop production index, livestock production index, fisheries production, energy use in agriculture, and fertilizer consumption will increase GHG emissions by 0.25%, 0.29%, 0.40%, 0.18%, 0.46%, and 0.28% in the long run. Conversely, a 1% increase in agricultural value added and the forest land may lead to GHG emissions reduction by 0.32% and 1.44% in the long run. The estimated results are robust to alternative estimators such as fully modified least squares (FMOLS) and canonical cointegrating regression (CCR). This research contributes to the existing literature by shedding light on the GHG emissions from the agriculture sector of Bangladesh. This article put forward policy recommendations on sustainable and climate-smart agriculture that would enhance productivity and resilience while reducing emissions from the agriculture sector.

A model for fossil energy use in Danish agriculture used to compare organic and conventional farming

A model to estimate total direct energy use in agriculture was developed for Turkey by using the data obtained from various study reports, survey results, and administrative data. The model was applied to provide the quantification of direct energy use in Turkey agriculture for the period of 2008. Direct energy use in agriculture was estimated by direct energy use in crop production, in bovine, sheep and goats production, and in poultry production. According to the model results, total direct energy use in agriculture in Turkey for 2008 is estimated as 4862 kTEP. Diesel energy use in crop production has the highest share in total direct energy use in agriculture with 84%. This is followed by electricity use for agricultural irrigation (8%), energy use in poultry production (5%), energy use in bovine and sheep and goats production (3%), and energy used for heating in greenhouse production (0.1%). Comparing total direct energy use in agriculture data of European Union (0.15 TEP ha -1 yr -1 ) with the data obtained in this study (0.12 TEP ha -1 yr -1 ), the value of Turkey was lower.

Climate change caused by different anthropogenic activities is a subject of attention globally. There is a concern on how to maintain a clean environment and at the same time achieve optimal use of land. To this end, this study examines the causal effects of land use including agricultural, forestry, and other land categories on greenhouse gas (GHG) emissions. The data for China is collected over the period 1990 to 2012 for the empirical examination. By employing vector error correction model (VECM), it is found that there is significant long-run causality among variables. However, in the short run expectedly, only land under agriculture has strong causality with the GHG emissions. The results in case of variance decomposition analysis highlight that land under agriculture and other use significantly causes the GHG emissions in the long run. Further, impulse responses of variables are also measured with the Cholesky one standard deviation. The results are robust and support the argument that different land uses cause GHG emissions in China. The study provides insights for policy makers to improve the activities occurring on agricultural and other land uses. Assessment of overall potential, including bio energy, needs to include analysis of trade-offs and feedbacks with land-use competition. Many positive linkages with sustainable development and with adaptation exist but are case and site specific as they depend on scale, scope, and pace of implementation.

Nexus between climate change, agricultural output, fertilizer use, agriculture soil emissions: Novel implications in the context of environmental management

Trajectory, driving forces, and mitigation potential of energy-related greenhouse gas (GHG) emissions in China's primary aluminum industry

In this paper the author analyses agricultural land use changes in the urban shadow of the Sydney metropolitan region and clarifies their sustainability on a micro-scale using the example of Castlereagh area, Penrith City. Castlereagh area is situated around the western suburbs of Sydney city centre, and is characterised by competition between agricultural and urban land use. In this area, rural and agricultural land use has generally developed since the colonial period. Although definite changes from agricultural to urban land use are not apparent with the advancement of urbanization, some kinds of agricultural use have changed to others in terms of function and quality since the 1990s. This sustainability of agricultural land use changes based on land, climate, and historical conditions as a suitable region for agriculture, and accessibility to the urban market for agricultural products, and land use policy of city planning and land use zoning.In Castlereagh area dairy farming and sheep grazing have traditionally developed with advantageous land and climate conditions for grass production. In particular, suburban dairy farming was important for town milk production. Although there were a few trends of conversion from dairy farming to sheep grazing, because of a decreasing agricultural labour force, the framework of traditional pastoral farming still remained until the 1980s. Since the 1990s most aspects of pastoral farming have changed into horse raising, horticulture, and hobby farming with the enlargement of urban land use for residential and factory sites. Such farming has been most apparent among all kinds of land use in the 1990s.Under an environment of urban shadow, both horse raising and horticulture have developed due to their suitability for expanding urban land use and farmland subdivisions. Agricultural land use changes are supported by economic factors such as capital intensity and high profitability. On the other hand, hobby farming is less intensive and rather unprofitable, and is developed for the mental satisfaction of aged and the urban residents, rather than the advancement of urbanisation and land subdivision. Therefore, agricultural land use changes into hobby farming are supported by non-economic factors such as productive aging and mental satisfaction. On the whole, a series of agricultural land use changes are identified for their sustainability, and are supported by economic and non-economic factors. In particular, hobby farming plays an important role in holding back urban sprawl and maintaining agricultural land use.

Fossil-fuel based energy use in agriculture leads to CO2 and non-CO2 emissions. We focus on emissions generated within the farm gate for crop and livestock production and from fisheries, providing information relative to the period 1970–2019 for both energy use and the associated greenhouse gas (GHG) emissions. Country-level information is generated from UNSD and IEA data on energy in agriculture, forestry and fishing, relative to use of: gas/diesel oil, motor gasoline, liquefied petroleum gas (LPG), natural gas, fuel oil and coal. Electricity used within the farm gate is also quantified, while recognizing that the associated emissions are generated elsewhere. We find that in 2019, annual emissions from energy use in agriculture were about 523 million tonnes (Mt CO2eq yr−1), and up to 1,029 Mt CO2eq yr−1 when including electricity. They increased 7 % since 1990. The largest emission increases from on-farm fuel combustion were from LPG (32 %), whereas significant decreases were observed for coal (−55 %), natural gas (−50 %), motor gasoline (−42 %) and fuel oil (−37 %). Conversely, use of electricity and the associated indirect emissions increased three-fold over the 1990–2019 period, thus becoming the largest emission source from energy use in agriculture since 2005. Overall the global trends were a result of counterbalancing effects: marked decreases in developed countries in 2019 compared to 1990 (−273 Mt CO2eq yr−1) were masked by slightly larger increases in developing and emerging economies (+339 Mt CO2 eq yr−1). The information used in this work is available as open data at: https://zenodo.org/record/5153241 (Tubiello and Pan, 2021). The relevant FAOSTAT (FAO, 2021) emissions database is maintained and updated annually by FAO.

Fossil-fuel based energy use in agriculture leads to CO2 and non-CO2 emissions. We focus on emissions generated within the farm gate for crop and livestock production and from fisheries, providing information relative to the period 1970–2019 for both energy use and the associated greenhouse gas (GHG) emissions. Country-level information is generated from UNSD and IEA data on energy in agriculture, forestry and fishing, relative to use of: gas/diesel oil, motor gasoline, liquefied petroleum gas (LPG), natural gas, fuel oil and coal. Electricity used within the farm gate is also quantified, while recognizing that the associated emissions are generated elsewhere. We find that in 2019, annual emissions from energy use in agriculture were about 523 million tonnes (Mt CO2eq yr−1), and up to 1,029 Mt CO2eq yr−1 when including electricity. They increased 7 % since 1990. The largest emission increases from on-farm fuel combustion were from LPG (32 %), whereas significant decreases were observed for coal (−55 %), natural gas (−50 %), motor gasoline (−42 %) and fuel oil (−37 %). Conversely, use of electricity and the associated indirect emissions increased three-fold over the 1990–2019 period, thus becoming the largest emission source from energy use in agriculture since 2005. Overall the global trends were a result of counterbalancing effects: marked decreases in developed countries in 2019 compared to 1990 (−273 Mt CO2eq yr−1) were masked by slightly larger increases in developing and emerging economies (+339 Mt CO2 eq yr−1). The information used in this work is available as open data at: https://zenodo.org/record/5153241 (Tubiello and Pan, 2021). The relevant FAOSTAT (FAO, 2021) emissions database is maintained and updated annually by FAO.

The majority of peatlands in Germany were drained for agriculture and other land use and therefore make a significant contribution to greenhouse gas (GHG) emissions from land use, land use change and forestry (LULUCF). In Germany, they correspond to around 7.5% of the total emissions and 44% of emissions from agriculture and agriculturally used land (UBA 2022). According to the Climate Protection Act, the LULUCF sector should have a sinking capacity of 40 million tonnes of CO2 equivalent by 2045 (German Federal Council 2021). If the water level is raised accordingly, peatlands have enormous GHG reduction potential. A comprehensive data basis is needed for monitoring and evaluating climate protection measures on peatlands. In this context, the project “Copernicus lights green”, which focusses on satellite applications in grassland monitoring, developed satellite-based indicators with Copernicus data that are suitable for characterizing the hydrological condition of peatland areas under agricultural use. In addition to the intensity of use, reflected by mowing events, overflowing or surface water caused by waterlogging was considered as an indirect proxy for the water level of the organic soils. This contribution presents the method and results of an approach that estimates the duration and extent of waterlogged areas based on monthly composites of satellite data time series from Sentinel-1 and -2. The work builds on a random forest classifier using the Framework for Operational Radiometric Correction for Environmental monitoring (FORCE) (Frantz, 2020) that detects waterlogged areas in agricultural land on organic soils. Due to the heterogeneity of agricultural land use in Germany and its varying open ground frequency as well as the lack of availability of cloud-free images in the winter months, an approach considering a combination of two models according to the vegetation period was developed. It optimizes the selection of training data and input features in order to generate reliable information on a monthly basis. The chosen study area in Lower Saxony in Germany showed good prediction results in 2018 and 2019, whereas the resulting model predictions achieved an F1 score between 85-91% with a variability of 2-5%. This provides a methodological base for comprehensive monitoring.

Abstract. The Paris Agreement commits 197 countries to achieve climate stabilisation at a global average surface temperature less than 2 ∘C above pre-industrial times using nationally determined contributions (NDCs) to demonstrate progress. Numerous industrialised economies have targets to achieve territorial climate neutrality by 2050, primarily in the form of “net zero” greenhouse gas (GHG) emissions. However, particular uncertainty remains over the role of countries' agriculture, forestry, and other land use (AFOLU) sectors for reasons including the potential trade-offs between GHG mitigation and food security, a non-zero emission target for methane as a short-lived GHG, and the requirement for AFOLU to act as a net sink to offset residual emissions from other sectors. These issues are represented at a coarse level in integrated assessment models (IAMs) that indicate the role of AFOLU in global pathways towards climate stabilisation. However, there is an urgent need to determine appropriate AFOLU management strategies at a national level within NDCs. Here, we present a new model designed to evaluate detailed AFOLU scenarios at national scale using the example of Ireland, where approximately 40 % of national GHG emissions originate from AFOLU. GOBLIN (General Overview for a Backcasting approach of Livestock INtensification) is designed to run randomised scenarios of agricultural activities and land use combinations within biophysical constraints (e.g. available land area, livestock productivities, fertiliser-driven grass yields, and forest growth rates). Using AFOLU emission factors from national GHG inventory reporting, GOBLIN calculates annual GHG emissions out to the selected target year for each scenario (2050 in this case). The long-term dynamics of forestry are represented up to 2120 so that scenarios can also be evaluated against the Paris Agreement commitment to achieve a balance between emissions and removals over the second half of the 21st century. Filtering randomised scenarios according to compliance with specific biophysical definitions (GHG time series) of climate neutrality will provide scientific boundaries for appropriate long-term actions within NDCs. We outline the rationale and methodology behind the development of GOBLIN, with an emphasis on biophysical linkages across food production, GHG emissions, and carbon sinks at a national level. We then demonstrate how GOBLIN can be applied to evaluate different scenarios in relation to a few possible simple definitions of “climate neutrality”, discussing opportunities and limitations.

Reply to L Aleksandrowicz et al.

The three main farm products from Canadian agriculture, i.e., proteins, vegetable oils, and carbohydrates, account for 98% of the land in annual crops in Canada. The intensities and efficiencies of these field crops in relation to their Greenhouse Gas (GHG) emissions were assessed for their value as land use change indicators. To facilitate spatial comparisons, this assessment was carried out at the Ecodistrict (ED) scale. The Unified Livestock Industry and Crop Emissions Estimation System (ULICEES) model was modified to operate at the ED scale, and used to quantify the GHG emission intensity of protein. GHG emissions were also calculated for plant products not used for livestock feed. The livestock GHG emissions and GHG-protein intensities estimated using ED scale inputs to ULICEES were reasonably close to GHG-protein intensities generated by the version of ULICEES driven by provincial scale census data. Carbohydrates were split into two groups, i.e., whether or not they supported livestock. Annual farm product data at 5-year intervals were used to generate GHG emissions from all farm operations. The range of GHG emissions from all farm operations in Western Canada was from 42 to 54 Mt CO2e between in 1991 and 2011, while GHG emissions from livestock ranged from 22 to 34 Mt CO2e over the same period. The Eastern Canadian GHG emissions from all farm operations declined gradually from 24 to 22 Mt CO2e over the period, with most of the eastern GHG emissions being from livestock. Ruminant livestock accounted for most of the livestock GHG emissions, particularly in the west. Provincial scale GHG emission efficiencies of the four farm product groups were assessed on a per-unit of GHG emissions basis for 2006. The most GHG-efficient province for protein was Ontario, whereas the most GHG-efficient province for all three plant products was Saskatchewan. The coastal provinces were the least GHG-efficient sources of all four farm product groups.

Due to its nature, agricultural land use depends on local site characteristics such as production potential, costs and external effects. To assess the relevance of the modifying areal unit problem (MAUP), we investigated as to how a change in the data resolution regarding both soil and land use data influences the results obtained for different land use indicators. For the assessment we use the example of the greenhouse gas (GHG) emissions from agriculturally used organic soils (mainly fens and bogs). Although less than 5 % of the German agricultural area in use is located on organic soils, the drainage of these areas to enable their agricultural utilization causes roughly 37 % of the GHG emissions of the German agricultural sector. The abandonment of the cultivation and rewetting of organic soils would be an effective policy to reduce national GHG emissions. To assess the abatement costs, it is essential to know which commodities, and at what quantities, are actually produced on this land. Furthermore, in order to limit windfall profits, information on the differences of the profitability among farms are needed. However, high-resolution data regarding land use and soil characteristics are often not available, and their generation is costly or the access is strictly limited because of legal constraints. Therefore, in this paper, we analyse how indicators for land use on organic soils respond to changes in the spatial aggregation of the data. In Germany, organic soils are predominantly used for forage cropping. Marked differences between the various regions of Germany are apparent with respect to the dynamics and the intensity of land use. Data resolution mainly impairs the derived extent of agriculturally used peatland and the observed intensity gradient, while its impact on the average value for the investigated set of land-use indicators is generally minor.

Agriculture and the related primary industry is an increasingly energy demanding sector. Energy is needed to different extent in all the stages of the agri-food chain. In many cases, energy cost may represent a significant proportion of the total agricultural production cost, including the cost of manufacturing and transportation of various chemicals and fertilisers. A modified and standardized energy analysis and benchmarking process is described in this paper. It is shown that energy use in agriculture varies considerably, depending on the cropping enterprise and the farming systems. Opportunities to reduce energy use and costs and greenhouse gas emissions in agriculture are discussed.