Addressing land use change and uncertainty in the life-cycle assessment of wheat-based bioethanol

Greenhouse gas intensity of palm oil produced in Colombia addressing alternative land use change and fertilization scenarios

Land use change implications for large-scale cultivation of algae feedstocks in the United States Gulf Coast

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).

The production of six regionally important cellulosic biomass feedstocks, including pine, eucalyptus, unmanaged hardwoods, forest residues, switchgrass, and sweet sorghum, was analyzed using consistent life cycle methodologies and system boundaries to identify feedstocks with the lowest cost and environmental impacts. Supply chain analysis models were created for each feedstock calculating costs and supply chain requirements for the production 453,592 dry tonnes of biomass per year. Cradle-to-gate environmental impacts from these supply systems were quantified for nine mid-point indicators using SimaPro 7.2 LCA software. Conversion of grassland to managed forest for bioenergy resulted in large reductions in GHG emissions, due to carbon sequestration associated with direct land use change. However, converting forests to energy cropland resulted in large increases in GHG emissions. Production of forest-based feedstocks for biofuels resulted in lower delivered cost, lower greenhouse gas (GHG) emissions and lower overall environmental impacts than the studied agricultural feedstocks. Forest residues had the lowest environmental impact and delivered cost per dry tonne. Using forest-based biomass feedstocks instead of agricultural feedstocks would result in lower cradle-to-gate environmental impacts and delivered biomass costs for biofuel production in the southern U.S. Introduction. Production of cellulosic biofuels and other bio-based products are expected to increase national energy independence, improve rural economies, and reduce greenhouse gases (GHG) compared to conventional transportation fuels (Demirbas 2008). To ensure greenhouse gas (GHG) emission reductions and a sustainable bioenergy industry, the Energy Independence and Security Act (EISA) established the life cycle greenhouse gas (GHG) thresholds (percent reduction) compared to the 2005 base line, with reductions of 20% for renewable fuels, 50% for advance fuels, 50% for biomass-based fuels and 60% for cellulosic biofuels (EPA 2012). The feedstock type used for biofuels conversion can play a central role in determining the overall GHG emissions as well as the financial and technological feasibility of a renewable biofuel. This study evaluated six potential biomass supply system scenarios for renewable energy production (liquid and/or solid fuels) in the southern U.S. Supply chain logistics, delivered cost and environmental burdens of these biomass feedstocks were qualified and quantified from cradle-to-gate. Feedstocks analyzed included loblolly pine, eucalyptus, unmanaged hardwood, forest residues, switchgrass and sweet sorghum. Previous studies have revealed feedstock production and delivery as the single largest contributor to the financial feasibility of bioenergy Proceedings of the International Symposium on Sustainable Systems and Technologies (ISSN 2329-9169) is published annually by the Sustainable Conoscente Network. Melissa Bilec and Jun-ki Choi, co-editors. ISSSTNetwork@gmail.com. Copyright © 2013 by Jesse S. Daystar, Carter W. Reeb, Ronalds Gonzalez, Richard A. Venditti. Licensed under

PurposeGreenhouse gas (GHG) emissions from land use and land-use change (LULUC) are major contributors to the climate change impact of agricultural products. The widely used method recommended by PAS 2050 when the previous land use is unknown has several limitations. The aim of this study was to develop a method to estimate GHG emissions from both direct land-use change (LUC) and land management changes (LMC), to be implemented in the French agricultural and food life cycle inventory database Agribalyse.MethodsThe proposed method uses 50 m × 50 m spatially explicit land conversion data at the departmental scale with a shared-responsibility approach and regionalised carbon (C) stocks, in line with recent advances in LULUC accounting. It also includes GHG emissions associated with changes in hedgerow area and CO2 removals by the soil and biomass. We calculated reference values for five agricultural land-use categories (field crops and temporary grassland, vegetables and flowers, permanent grassland, vineyards, and orchards) in 94 departments of metropolitan France and mean national results for 26 agricultural products. Total net GHG emissions from LULUC at the national scale were calculated for the aggregate land-use category cropland per previous land-use category: cropland, grassland, forest, settlement, hedgerow, and others.Results and discussionTotal net GHG emissions of LULUC from cropland in France in 2020 were equivalent to those estimated by the French National Emissions Inventory Agency, with a large contribution from grassland conversions, followed by forest and hedgerow conversions. Large CO2 removals by the soil were also estimated, associated mainly with LMC. GHG emissions per hectare varied widely among land-use categories and departments, ranging from − 2570 to 4969 kg CO2-eq∙ha−1∙year−1. For products assessed at the national scale, including LMC GHG emissions decreased total net GHG emissions per kilogramme without LULUC by 8–46%, while including LUC GHG emissions increased the latter by 4–68% (except for baled grass from permanent grassland in the north-western lowlands, which decreased from net GHG emissions to net GHG removals).Conclusions and recommendationsWhen data at the farm scale are not available, we recommend using the results of this method, which are in line with IPCC guidelines, instead of those of PAS 2050. For other studies that assess LULUC, we recommend (i) using spatially explicit land conversion data, (ii) using regionalised C stocks, (iii) including changes in hedgerow area, and (iv) including CO2 removals by the soil, especially those associated with LMC and establishment of permanent grassland.

This study conducted a life cycle assessment (LCA) investigating energy, land occupation, greenhouse gas (GHG) emissions, fresh water consumption and stress-weighted water use from production of export lamb in the major production regions of New South Wales, Victoria and South Australia. The study used data from regional datasets and case study farms, and applied new methods for assessing water use using detailed farm water balances and water stress weighting. Land occupation was assessed with reference to the proportion of arable and non-arable land and allocation of liveweight (LW) and greasy wool was handled using a protein mass method. Fossil fuel energy demand ranged from 2.5 to 7.0 MJ/kg LW, fresh water consumption from 58.1 to 238.9 L/kg LW, stress-weighted water use from 2.9 to 137.8 L H2O-e/kg LW and crop land occupation from 0.2 to 2.0 m2/kg LW. Fossil fuel energy demand was dominated by on-farm energy demand, and differed between regions and datasets in response to production intensity and the use of purchased inputs such as fertiliser. Regional fresh water consumption was dominated by irrigation water use and losses from farm water supply, with smaller contributions from livestock drinking water. GHG emissions ranged from 6.1 to 7.3 kg CO2-e/kg LW and additional removals or emissions from land use (due to cultivation and fertilisation) and direct land-use change (due to deforestation over previous 20 years) were found to be modest, contributing between –1.6 and 0.3 kg CO2-e/kg LW for different scenarios assessing soil carbon flux. Excluding land use and direct land-use change, enteric CH4 contributed 83–89% of emissions, suggesting that emissions intensity can be reduced by focussing on flock production efficiency. Resource use and emissions were similar for export lamb production in the major production states of Australia, and GHG emissions were similar to other major global lamb producers. The results show impacts from lamb production on competitive resources to be low, as lamb production systems predominantly utilised non-arable land unsuited to alternative food production systems that rely on crop production, and water from regions with low water stress.

In life cycle assessment studies, greenhouse gas (GHG) emissions from direct land-use change have been estimated to make a significant contribution to the global warming potential of agricultural products. However, these estimates have a high uncertainty due to the complexity of data requirements and difficulty in attribution of land-use change. This paper presents estimates of GHG emissions from direct land-use change from native woodland to grazing land for two beef production regions in eastern Australia, which were the subject of a multi-impact life cycle assessment study for premium beef production. Spatially- and temporally consistent datasets were derived for areas of forest cover and biomass carbon stocks using published remotely sensed tree-cover data and regionally applicable allometric equations consistent with Australia’s national GHG inventory report. Standard life cycle assessment methodology was used to estimate GHG emissions and removals from direct land-use change attributed to beef production. For the northern-central New South Wales region of Australia estimates ranged from a net emission of 0.03 t CO2-e ha–1 year–1 to net removal of 0.12 t CO2-e ha–1 year–1 using low and high scenarios, respectively, for sequestration in regrowing forests. For the same period (1990–2010), the study region in southern-central Queensland was estimated to have net emissions from land-use change in the range of 0.45–0.25 t CO2-e ha–1 year–1. The difference between regions reflects continuation of higher rates of deforestation in Queensland until strict regulation in 2006 whereas native vegetation protection laws were introduced earlier in New South Wales. On the basis of liveweight produced at the farm-gate, emissions from direct land-use change for 1990–2010 were comparable in magnitude to those from other on-farm sources, which were dominated by enteric methane. However, calculation of land-use change impacts for the Queensland region for a period starting 2006, gave a range from net emissions of 0.11 t CO2-e ha–1 year–1 to net removals of 0.07 t CO2-e ha–1 year–1. This study demonstrated a method for deriving spatially- and temporally consistent datasets to improve estimates for direct land-use change impacts in life cycle assessment. It identified areas of uncertainty, including rates of sequestration in woody regrowth and impacts of land-use change on soil carbon stocks in grazed woodlands, but also showed the potential for direct land-use change to represent a net sink for GHG.

The recent development of biomass production for energy purposes has spurred interest in the effects of the land-use changes (LUC) it triggers worldwide, and a surge in the number of scientific articles dealing with this topic. The processes leading from increased biomass demand to environmental impacts in relation to LUC may be analyzed as a three-step causal chain starting with the identification of reorganization of agricultural and forestry systems, the assessment of LUC occurring in response to these drivers, and the associated environmental impacts. Here we set out to review the impacts of land-use changes induced by non-food biomass production on greenhouse gases emissions. The selected body of 162 articles displays the following salient features: most articles deal with LUC triggered by biofuel production, the predominant direct LUCs are forest or grassland conversions into annual or perennial crops, and annual crops conversion into perennial crops; and while Europe and North America come first in terms of direct LUC location, a large number of articles also deal with direct LUCs occurring in South America and Asia. We show that peer-reviewed literature does not sign a blank check to non-food biomass. The number of articles evidencing a net reduction in GHG emissions following a diversion of food/feed crops towards non-food products is only 50% higher than the number of articles drawing opposite conclusions. As the LUC-related carbon intensity of biofuels strongly depends on where the feedstock is grown and which land-use it replaces, we investigated whether specific land-use change patterns can be tied to certain types of feedstocks. Contrary to our expectations, direct forest and grassland conversion is significantly less often considered for second generation feedstocks or wood.

AimClimate is often the sole focus of global change research in mountain ecosystems although concomitant changes in land use might represent an equally important threat. As mountain species typically depend on fine‐scale environmental characteristics, integrating land use change in predictive models is crucial to properly assess their vulnerability. Here, we present a modelling framework that aims at providing more comprehensive projections of both species’ distribution and abundance under realistic scenarios of land use and climate change, and at disentangling their relative effects.LocationSwitzerland.MethodsWe used the ring ouzel (Turdus torquatus), a red‐listed and declining mountain bird species, as a study model. Based on standardized monitoring data collected across the whole country, we fitted high‐resolution ensemble species distribution models to predict current occurrence probability, while spatially explicit density estimates were obtained from N‐mixture models. We then tested for the effects of realistic scenarios of land use (land abandonment versus farming intensification) and climate change on future species distribution and abundance.ResultsOccurrence probability was mostly explained by climatic conditions, so that climate change was predicted to have larger impacts on distribution and abundance than any scenarios of land use change. In the mid‐term (2030–2050), predicted effects of environmental change show a high spatial heterogeneity due to regional differences in climate and dominant land use, with farming intensification identified as an important threat locally. In the long term (2080–2100), climate models forecast a marked upward range shift (up to +560 m) and further population decline (up to −35%).Main conclusionsOur innovative approach highlights the spatio‐temporal heterogeneity in the relative effects of different environmental drivers on species distribution and abundance. The proposed framework thus provides a useful tool not only for better assessing species’ vulnerability in the face of global change, but also for identifying key areas for conservation interventions at a meaningful scale.

With urban areas responsible for a significant share of total anthropogenic emissions, greenhouse gas (GHG) emissions due to land-use change (LUC) induced by peri-urban (PU) development have the potential to be considerable. Despite this, there is little research into the transition from PU cropland to housing in terms of contribution to global warming. This paper presents a cross-sectoral integrative method for prospective climate change evaluation of PU LUC. Specifically, direct LUC (dLUC) GHG emissions from converting PU cropland to greenfield housing were examined. Additionally, GHG emissions due to displaced crop production inducing indirect LUC (iLUC) elsewhere were assessed. GHG impacts of dLUC and iLUC were each determined to be approximately 8 per cent of total GHG emissions due to a greenfield housing development displacing PU cropland. This magnitude of dLUC and iLUC emissions suggests that both have importance in future land-use decision making with respect to PU environments.

Modification of the Earth’s surface i.e. land use change, is the main human activity for survival and is the key player in the management of natural resources, including water. Little attention has, however, been given to understand the role the territorial vegetation changes may play in strategic management of water resources. In the basin of Aswa northern Uganda, the changes in land use due to complex demographic and social economic factors is among the numerous challenges faced in management of the limited water resources in the area. The aim of the current study was to explore the opportunities land use changes in the basin may offer to water resources management, looking mainly at the expansion in future agriculture and afforestation as the critical land use change issues. The study was structured into four broad objectives: The first objective was to generate the reference land use dataset (1986 & 2001). The available techniques (the supervised and the unsupervised image classification) were explored using Landsat multi-spectral images. Through careful evaluation, the supervised image classification with the best classification accuracy of 81.48% was used to generate 1986 and 2001 land use maps. The second objectives of the study was to generate experimental land use scenarios required for testing the effect of spatial land use policies on hydrologic processes in the basin. The Multi-criteria-GIS methodology was developed and six experimental land use scenarios were generated using simple but consistence set of bio-physical and socio-economic parameters. The third objective was to customise the hydrologic process model SWAT that was used to simulate the hydrologic impact of the land use change scenarios. The calibration of the hydrologic model SWAT used monthly historical streamflow records from 1970 to 1974 recorded at the basin outlet. The model was manually calibrated using the Nash-Sutcliffe coefficient as objective function. The efficiency of the model during calibration was 0.46. Validation of the model using an independence monthly streamflow records from 1975 to 1978 was done and the model efficiency was 0.66, much better than in calibration period. The forth and last objective of the study was to simulate the hydrologic processes in the reference years and the hydrologic processes impacted by the land use change scenarios and to evaluate how this impact affects water resources management strategies. An independent validation of the model to identify the validity of extending the optimal parameters set in simulation of 2001 and land use change hydrologic processes was carried out by comparing the simulated actual evapotranspiration fraction with estimated actual evapotranspiration fraction obtained using surface energy balance method and the thermal MODIS images. Validation indicated acceptable model performance in simulating 2001 hydrologic processes, with a spatial correlation coefficient of 0.45. The application of the model in simulations of the hydrologic processes in the reference years noted that 2001 had more water yield than 1986 by 9.2 mm. The analysis of the impact of land use change in the reference years indicated an increase of 2.52 mm of water yield in the year 2001. Simulation of the hydrologic impact of the experimental land use indicated that Land use types, which in this study were restricted to plantation forest and generic agriculture, land use extent and location of the land use with respect to precipitation rate and amount, greatly influence the hydrologic process of the basin and the net water yield. It was noted that the water yield of the basin can be significantly decreased by over 15%, if more than 37% of the plantation forests are introduced in the wet zone. In the dry sub-basins however, afforestation of up to 42% had insignificant effect on water yield, which could therefore be exploited so as to offset the afforestation pressure in the wet sub-basin while at the same time enhancing the basin water yield. The effect of agricultural land use change on water yield was however less sensitive to climatic zones. 53% increase in agricultural land cover responded with an increase in water yield by about 27%.

Converting land to biofuel feedstock production incurs changes in soil organic carbon (SOC) that can influence biofuel life‐cycle greenhouse gas (GHG) emissions. Estimates of these land use change (LUC) and life‐cycle GHG emissions affect biofuels' attractiveness and eligibility under a number of renewable fuel policies in the USA and abroad. Modeling was used to refine the spatial resolution and depth extent of domestic estimates of SOC change for land (cropland, cropland pasture, grassland, and forest) conversion scenarios to biofuel crops (corn, corn stover, switchgrass, Miscanthus, poplar, and willow) at the county level in the USA. Results show that in most regions, conversions from cropland and cropland pasture to biofuel crops led to neutral or small levels of SOC sequestration, while conversion of grassland and forest generally caused net SOC loss. SOC change results were incorporated into the Greenhouse Gases, Regulated Emissions, and Energy use in Transportation (GREET) model to assess their influence on life‐cycle GHG emissions of corn and cellulosic ethanol. Total LUC GHG emissions (g CO2eq MJ−1) were 2.1–9.3 for corn‐, −0.7 for corn stover‐, −3.4 to 12.9 for switchgrass‐, and −20.1 to −6.2 for Miscanthus ethanol; these varied with SOC modeling assumptions applied. Extending the soil depth from 30 to 100 cm affected spatially explicit SOC change and overall LUC GHG emissions; however, the influence on LUC GHG emission estimates was less significant in corn and corn stover than cellulosic feedstocks. Total life‐cycle GHG emissions (g CO2eq MJ−1, 100 cm) were estimated to be 59–66 for corn ethanol, 14 for stover ethanol, 18–26 for switchgrass ethanol, and −7 to −0.6 for Miscanthus ethanol. The LUC GHG emissions associated with poplar‐ and willow‐derived ethanol may be higher than that for switchgrass ethanol due to lower biomass yield.

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Oil palm (OP) plantations account for 1.7 % of global CO2 emissions. Numerous studies have focused primarily on greenhouse gas (GHG) emissions from peatlands, constituting 20% of total OP area in the two largest OP producing countries, Indonesia and Malaysia. Few studies have investigated the potential for reducing GHG emissions in OP plantations. Strategies to reduce emissions and sequester carbon must consider how different practices affect production and the environment. Understanding the spatial distribution of GHG intensity and how the environment affects GHG intensity is therefore key to sustainable oil palm production.GHG intensity was used as a metric to map the potential for sustainable OP plantations. GHG intensity represents the GHG emissions / removals (ton C ha-1) per unit of oil palm yields (ton ha-1). The approach for analysing the change in GHG emissions/ removals, referred to as the IPCC tier 1 method, is based on changes in soil organic carbon due to C and N emissions in drained peatlands and the associated change in aboveground biomass due to land use change. Changes in GHG intensity were investigated spatially for a case study in an industrial OP plantation located in Riau Province, Indonesia, from 2015 to 2019. Linear regression was used to analyse the relationships between GHG intensity and agri-environmental variables including NDVI, NPP, GPP, evapotranspiration, soil moisture in the root zone, soil moisture in deeper layer, C and N emissions from organic soils, and soil organic carbon (SOC).The results show that around 90% of the new oil palm plantations in 2019 were converted from timber plantation, swamp scrubland, and bare land in 2015. Consequently, biomass growth from land use change acted as a carbon sink in this period. However, drained organic soils contributed significantly to GHG emissions. The change in GHG intensity in OP plantation in this study varied spatially from emitting (0.19 to 4.10 Ton C eq Ton-1 yields) to removing the GHG (0.23 to 2.40 Ton C eq Ton-1 yields). Among the environmental variables, NDVI and soil moisture showed the strongest relationship with GHG emissions/ removals (R2 = 0.23,   p value = < 2.2e-16) and yields (R2 = 0.2   p value = < 2.2e-16) in OP plantations.These initial findings are advantageous for spatially identifying potential OP plantations that remove or emit GHG. Understanding the relationship between GHG emissions/removals and yields to environment variables provides insight into monitoring and enhancing OP sustainability, both from production and environmental perspectives. Future work will examine non-linear approaches to better model this relationship.   

Greenhouse gas (GHG) emissions resulting from the Land Use, Land-Use Change, and Forestry sector (LULUCF) are estimated and reported in National Communications to the United Nations Framework Convention on Climate Change (UNFCCC). By definition, the LULUCF sector is a “greenhouse gas (GHG) inventory sector that covers emissions and removals of greenhouse gases resulting from direct human-induced land use, land-use change and forestry activities”. In principle, the annual GHG national inventory should be transparent, consistent, comparable, complete, and accurate. Also, it should be able to systematically account for all changes in land use and forest cover over many years. In this context, it is essential to investigate the development of an automated approach for mapping local GHG emissions/removals from the LULUCF sector for integration at the national level. In view of that, the aim of this work was to develop a semi-automated model for estimating GHG emissions and removals form the LULUCF sector at the local level. The specific objectives were to 1) map changes in land use and forest cover between two consecutive years, and 2) assess GHG emissions and removals from the LULUCF sector. The methodology of work comprised the use of Geographic Object-Based Image Analysis (GEOBIA) for modelling changes in the LULUCF sector and, subsequently, estimating GHG emissions/removals between two consecutive years. The combined use of Very High Resolution (VHR) SPOT imagery (2.5 m colour) and field data was involved in identifying and mapping land-use changes between 2014 and 2015. Subsequently, GHG emissions and removals were estimated using customized features in GEOBIA and following the 2003 Intergovernmental Panel on Climate Change “Good Practice Guidance for Land Use, Land-Use Change and Forestry”, which adopts a land use category-based approach to estimate emissions/removals from all land categories and all relevant GHGs. An accuracy assessment of the initial classification was conducted with the use of reference data. The overall classification accuracy of the LULUCF mapping in 2014 was found to be 83%, while the Kappa Index of Agreement (KIA) was 0.74. The developed GEOBIA model estimated for the year 2015 net annual GHG removals of -1.613 Gg of CO2 eq. (i.e., an approximate increase of 12.7% in removals between 2014 and 2015). Future work will involve further development of the model to account for all possible changes in the LULUCF sector and test the transferability of the model to other sites.