Energy balances and greenhouse gas emissions of palm oil biodiesel in Indonesia

The objectives of this study are to identify the energy balance of Indonesian palm oil biodiesel production, including the stages of land use change, transport and milling and biodiesel processing, and to estimate the amount of greenhouse gas emissions from different production systems, including large and small holder plantations either dependent or independent, located in Kalimantan and in Sumatra. Results show that the accompanied implications of palm oil biodiesel produced in Kalimantan and Sumatra are different: energy input in Sumatra is higher than in Kalimantan, except for transport processes; the input/output ratios are positive in both regions and all production systems. The findings demonstrate that there are considerable differences between the farming systems and the locations in net energy yields (43.6 to 49.2 GJ t-1 biodiesel yr-1) as well as greenhouse gas emissions (1969.6 to 5626.4 kg CO2eq t-1 biodiesel yr-1). The output to input ratios are positive in all cases. The largest greenhouse gas emissions result from land use change effects, followed by the transesterification, fertilizer production, agricultural production processes, milling and transportation. Ecosystem carbon payback times range from 11 to 42 years.

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).

Dairy farming is a major source of greenhouse gas (GHG) emissions in agriculture. There are numerous scientific studies analysing GHG flows and testing GHG reduction methods in dairy farming, yet very few scientific papers cover all the relevant GHG flows. GHG flows that are difficult to quantify, such as C sequestration in soils, the effects of land-use change (LUC) or the energy input used to produce capital equipment, are not always considered.This paper describes the development and application of a model for energy and GHG accounting in dairy farming. This new model enables all relevant nutrient, energy and GHG flows to be modelled at farm level. This then forms the basis for system analysis and derivation of GHG mitigation strategies. The model was used on 18 organic and 18 con-ventional farms in Germany. Calculated CO2-eq emissions per kg of Energy Corrected Milk (ECM) were 995 g on average for organic farms (org) and 1,048 g on average for conventional farms (con). The largest contribution (55 % (org) and 43 % (con)) to total GHG emissions came from enteric methane emissions (549 g CO2-eq (kg ECM)-1 (org) and 449 g CO2-eq (kg ECM)-1 (con)). On the organic dairy farms, there was an increase in soil humus and therefore carbon storage and sequestration in soils, whereas the GHG emissions for the conventional farms included CO2 emissions from LUC due to soybean usage. The significantly higher energy input in the conventional systems resulted from the production of energy-intensive concentrates, mineral fertilisers and pesticides, and transportation (imported feed).This study shows that there are many factors that influence GHG emissions in dairy farming, and that these factors often interact with each other. An increase in productivity is one of several optimisation strategies; however, it must not be at the expense of productive lifetime or require an extremely high amount of concentrates. GHG reduction in dairy farming requires farm-specific optimisation approaches due to the heterogeneity of production systems.

Climate-smart livestock production at landscape level in Kenya

Energy and greenhouse gas balances of cassava-based ethanol

Energy balance and greenhouse gas emissions of biodiesel production from oil derived from wastewater and wastewater sludge

Anaerobic treatment of palm oil mill effluents: potential contribution to net energy yield and reduction of greenhouse gas emissions from biodiesel production

基于生命周期评价的上海市水稻生产碳足迹研究

Southland has witnessed a pronounced change in its agricultural landscape in recent years.Greater profitability of dairy relative to sheep farming has led to a large number of dairy conversions over the last 20 years, with the scope for further substantive conversions into the future.The economic and social benefits have been extensively reported, but less is understood about the environmental impacts associated with this land use change.To investigate the potential effect of land use change from sheep and beef to dairy on economic and environmental outcomes in the Southland region of New Zealand, farm-scale enterprise simulation models were linked with spatially explicit land resource information.By overlaying individual farm parcels with land resource information, land area and topography data for each farm were attained.Estimated pasture production (PP) for each land use capability (LUC) Class provided indicative data for the modelling exercise on the productive use of the land across the region.The approach provided a method for the expansion of farm scale modelling to a regional scale.A representative DairyNZ Production System 3 was used to investigate the influence of increasing dairy cow numbers and associated inputs at the farm level.A representative sheep and beef farm was also modelled.To account for a dairy support area, used to carry dry cows during the winter, a second step involved the modelling of a larger System 3 dairy farm that included a milking platform area and an adjacent support area.This farm system was considered for regional up-scaling to allow for a more comprehensive capture of nutrient losses and financial outcomes.Estimates of annual nitrogen (N) leaching values from dairy farms ranged from 21 to 44 kg N/ha, and were higher for farms with greater pasture production potential, due to the greater amount of N cycling and increased number of urine patches from the higher number of livestock numbers carried.Annual N leaching from the sheep and beef farms ranged from 8 to 17 kg N/ha.Annual greenhouse gas (GHG) emissions were also higher from farms with greater productive potential, ranging from 7.1 to 15.4 t CO 2 -e/ha for dairy and from 2.1 to 6.9 t CO 2 -e/ha for sheep and beef farms.In contrast to leaching, GHG emissions were higher from poorly-drained soils compared with well-drained soils; annual nitrous oxide (N 2 O) emissions accounted for 22% and 35% of total GHG emissions from dairy farms on well-and poorly-drained soils, respectively, and up to 40% from sheep and beef farms on poorly-drained soils.The new dairy farms resulting from conversion would largely fall in an N leaching range of 25 to 31 kg N/ha and have GHG emissions of 7.0 to 10.5 t CO 2 -e/ha.Depending on future regional regulations that may be implemented, a large number of potential dairy farms might leach more N than the allowable limit, and mitigation techniques will need to be implemented.A shift in land use from the current 15% of land area under dairying to a potential 46% led to a large increase in regional profit (76%).The environmental impact from this land use change, however, became substantial, with regional nitrate leaching increasing by 34% and GHG emissions by 24%.Conversion of more farms into dairying increased farm profit, N leaching and GHG emissions in the region compared with the current situation.It must be noted, however, that the up-scaling of potential dairy conversion was based on land resources defined by the productive potential of the landscapes found in Southland and that the actual level of conversion could differ substantially if additional or different farming scenarios were tested.

Disaggregated greenhouse gas emission inventories from agriculture via a coupled economic-ecosystem model

Crop diversification is essential in lowland rice cropping systems to achieve sustainability, improve soil health, and as a climate-resilient practice to reduce greenhouse gas (GHG) emissions. A life cycle assessment (LCA) was conducted for the farms in the west-coast region of India to assess the environmental impact of the rice–rice and rice–cowpea cropping systems. The life cycle impact assessment (LCIA) was evaluated in a “cradle-to-gate” perspective. A higher energy consumption was found in the rice–rice system (32,673 vs. 18,197 MJ/ha), while the net energy output was higher in the rice–cowpea system (211,071 vs. 157,409 MJ/ha). Energy consumption was 44% lower in the rice–cowpea system, which was coupled with a higher energy efficiency (11.6 vs. 4.8), attributed to the lower energy consumption and the higher energy output. Further, the results indicated an energy saving potentialin the rice–cowpea system due to the higher use of renewable resources such as farmyard manure. Field emissions, fertilizer production, and fuel consumption were the major contributors to the greenhouse gas (GHG) emissions in both cropping systems. The total GHG emissions were 81% higher in the rice–rice system (13,894 ± 1329 kg CO2 eq./ha) than in the rice–cowpea system (7679 ± 719 kg CO2 eq./ha). The higher GHG emissions in the rice–rice system were largely due to the higher use of fertilizers, diesel fuel, and machinery. Hence, diversifying the winter rice with a cowpea crop and its large-scale adoption on the west coast of India would provide multiple benefits in decreasing the environmental impact and improving the energy efficiency to achieve sustainability and climate resilience in rice-based cropping systems.

This study presents a life-cycleanalysis of greenhouse gas (GHG)emissions of biodiesel (fatty acid methyl ester) and renewable diesel(RD, or hydroprocessed easters and fatty acids) production from oilseedcrops, distillers corn oil, used cooking oil, and tallow. Updateddata for biofuel production and waste fat rendering were collectedthrough industry surveys. Life-cycle GHG emissions reductions forproducing biodiesel and RD from soybean, canola, and carinata oilsrange from 40% to 69% after considering land-use change estimations,compared with petroleum diesel. Converting tallow, used cooking oil,and distillers corn oil to biodiesel and RD could achieve higher GHGreductions of 79% to 86% lower than petroleum diesel. The biodieselroute has lower GHG emissions for oilseed-based pathways than theRD route because transesterification is less energy-intensive thanhydro-processing. In contrast, processing feedstocks with high freefatty acid such as tallow via the biodiesel route results in slightlyhigher GHG emissions than the RD route, mainly due to higher energyuse for pretreatment. Besides land-use change and allocation methods,key factors driving biodiesel and RD life-cycle GHG emissions includefertilizer use and nitrous oxide emissions for crop farming, energyuse for grease rendering, and energy and chemicals input for biofuelconversion.

Addressing concerns about future food supply and climate change requires management practices that maximize productivity per unit of arable land while reducing negative environmental impact. On-farm data were evaluated to assess energy balance and greenhouse gas (GHG) emissions of irrigated maize in Nebraska that received large nitrogen (N) fertilizer (183 kg of N · ha(-1)) and irrigation water inputs (272 mm or 2,720 m(3) ha(-1)). Although energy inputs (30 GJ · ha(-1)) were larger than those reported for US maize systems in previous studies, irrigated maize in central Nebraska achieved higher grain and net energy yields (13.2 Mg · ha(-1) and 159 GJ · ha(-1), respectively) and lower GHG-emission intensity (231 kg of CO(2)e · Mg(-1) of grain). Greater input-use efficiencies, especially for N fertilizer, were responsible for better performance of these irrigated systems, compared with much lower-yielding, mostly rainfed maize systems in previous studies. Large variation in energy inputs and GHG emissions across irrigated fields in the present study resulted from differences in applied irrigation water amount and imbalances between applied N inputs and crop N demand, indicating potential to further improve environmental performance through better management of these inputs. Observed variation in N-use efficiency, at any level of applied N inputs, suggests that an N-balance approach may be more appropriate for estimating soil N(2)O emissions than the Intergovernmental Panel on Climate Change approach based on a fixed proportion of applied N. Negative correlation between GHG-emission intensity and net energy yield supports the proposition that achieving high yields, large positive energy balance, and low GHG emissions in intensive cropping systems are not conflicting goals.

A systematic review and meta-analysis were used to assess the current state of knowledge and quantify the effects of land use change (LUC) to second generation (2G), non-food bioenergy crops on soil organic carbon (SOC) and greenhouse gas (GHG) emissions of relevance to temperate zone agriculture. Following analysis from 138 original studies, transitions from arable to short rotation coppice (SRC, poplar or willow) or perennial grasses (mostly Miscanthus or switchgrass) resulted in increased SOC (+5.0 ± 7.8% and +25.7 ± 6.7% respectively). Transitions from grassland to SRC were broadly neutral (+3.7 ± 14.6%), whilst grassland to perennial grass transitions and forest to SRC both showed a decrease in SOC (−10.9 ± 4.3% and −11.4 ± 23.4% respectively). There were insufficient paired data to conduct a strict meta-analysis for GHG emissions but summary figures of general trends in GHGs from 188 original studies revealed increased and decreased soil CO2 emissions following transition from forests and arable to perennial grasses. We demonstrate that significant knowledge gaps exist surrounding the effects of land use change to bioenergy on greenhouse gas balance, particularly for CH4. There is also large uncertainty in quantifying transitions from grasslands and transitions to short rotation forestry. A striking finding of this review is the lack of empirical studies that are available to validate modelled data. Given that models are extensively use in the development of bioenergy LCA and sustainability criteria, this is an area where further long-term data sets are required.

Effects of land-use change on the carbon balance of 1st generation biofuels: An analysis for the European Union combining spatial modeling and LCA