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Changes in biotic parameters, primarily in those associated with land use, are a fundamental issue for assessing the greenhouse gas (GHG) balance of bioenergy production. From 2002 to 2017, the transition from manual to mechanized sugarcane harvesting in Brazil resulted in 10–20 Mg ha−1 y−1 of biomass, increasing incomes by 150% via bioelectricity. This study is a life cycle assessment (LCA) of using straw along with bagasse for bioethanol production. The assessment focused on the global warming potential (GWP), characterizing the biotic and fossil carbon flows for a functional unit of 1 MJ of 96% hydrated bioethanol production as modeled in GaBi. The model is based on the primary data for a specific cradle-to-gate Petrobras agro-industrial unit. The biotic flow includes the CO2 uptake and the agricultural and industrial emissions. The parametric equations depict the influence of leaving straw in the field on the sugarcane yield and the soil carbon emissions. The remaining CML impact categories are then assessed. Two straw utilization scenarios were studied as follows: the reference system (RS), in which straw is left in the field, and the straw-to-boiler system (SS), in which part or all of the straw is exported for energy conversion. Two sensitivity analyses were performed by considering the effect of the straw on the amount of sugar in the sugarcane juice (SA) and considering the use of a second-generation ethanol unit (2G) on the basis of the hydrolysis of both bagasse and straw. In the RS, 55% of the carbon is captured in the sugarcane (bagasse + juice), and 45% is captured in the straw. The agricultural GHG output is distributed between vinasse fertigation (9.2%) and the straw left on the soil (7.6%). The industrial GHG emissions include the burning of bagasse (46%), fermentation emissions (11%) and the pre-emissions embedded in EtOH 96% (26%). In the SS 100% scenario, the GWP increases by 161%, with the biotic GWP shifting from soil to boiler. In addition, the fossil fuel GWP decreases by 39% due to the use of only 86% of the initial planting area. These results highlight the fact that removing straw to use as extra biomass for electricity is associated with a consequent higher sugarcane yield with an overall higher environmental performance and efficiency when using the captured carbon to produce the same functional unit or the combined ethanol and electricity technological products. In the SA scenario, the GWP is directly correlated with the imposed ±10% variation in the sugar that is transformed into ethanol. In the 2G scenario, the GWP results show that second-generation bioenergy must optimize the amount of straw. The other categories show similar qualitative variations among the scenarios, which are related to fossil and mineral reserve processing or to additional boiler emissions. (Resume d'auteur)

International audience; Evaluating holistically environmental impacts of land planning policies implies to take into account several aspects, intimately related both to territorial features and to production-consumption patterns, which have a specific local character and a potential impact at different scales. To address these chal- lenges, life cycle thinking and assessment methods are crucial. Indeed, beyond the traditional application of Life Cycle Assessment as a product-oriented methodology, a new LCA-based approach called "terri- torial LCA" has gradually emerged to assess geographically or administratively defined systems. This paper aims to analyze how this new LCA-based approach differs from conventional LCA, highlighting main differences and added values. Territorial LCAs can be divided into two main approaches, i.e., i) type A, which focuses on the assessment of a specific activity or supply chain anchored in a given territory, and ii) type B, which attempts to assess all production and consumption activities located in a territory, including all environmental pressures embodied in trade flows with other territories. These two ap- proaches are described and compared according to the four LCA phases to highlight differences and similarities with conventional LCA. This comparison is based on a detailed case study analysis for each territorial LCA type and it shows that most of the differences are in the goal and scope definition, especially for the territorial LCA of type B where the functional unit definition is no more the starting point of the assessment. Concerning territorial LCA of type A, there are no main divergences with conventional LCA as territorial contextualization already exists in some LCA applications, even if not systematically applied. Improvements in the application may entail a comprehensive contextualization of the four LCA phases, developing the synergies with the use of Geographic Information System (GIS) tools. Other specific challenges affecting both type A and B are related to i) territorial unique intrinsic multi- functionality determined by all human activities located within its boundaries, ii) specific territorial characteristics (i.e., spatial variability and organization), and iii) multiscale issues and the consideration of interactions between territories.

In the past 10 years, 90% of cassava starch factories in Thailand have switched from fuel oil to renewable biogas, to cover part of their energy needs. The environmental benefits of switching to biogas have not been assessed quantitatively. To alleviate this, this study assessed 100-year greenhouse gas (GHG) emissions, or carbon footprint (CF), of cassava starch production for four factories in Thailand. Key results demonstrate that biogas reduces the carbon footprint of the Thai cassava starch industry as a whole by 0.9–1.0 million tons CO2eq/year, and highlight methodological precautions to collect LCI data and allocate GHG emissions between co-products with high moisture contents. The system boundaries included farm stage (production of cassava roots), transportation to factory and processing into native starch. The functional unit (FU) was one ton of native cassava starch at 13% water content. Biogas produced from the factory wastewater (95–200 m3/FU) was the main source of thermal energy for starch drying, and for on-site electricity production when excess biogas was available. The total CF of cassava starch was in the range 609–966 kg CO2eq/FU. Agricultural production contributed 60% of the carbon footprint, mainly from the use of nitrogen fertilizers. GHG emissions of root production varied widely due to (1) the diversity of farming practices even within a small radius (50 km), and (2) different agricultural yields in different regions. The contribution of the factory stage to the carbon footprint depended on the use of electricity, biogas and other fuels, ranging from 217 to 342 kg CO2eq/FU. Allocation rules such as wet weight or dry weight basis allocations affected the results markedly.

This study investigates the balance between economic and environmental performances of cotton cropping systems in Southern Punjab, Pakistan. Eco-efficiency analysis was performed using Data Envelopment Analysis to integrate economic and environmental performances, which were assessed through life cycle assessment. All 169 cotton cropping systems were individually analyzed. Special attention was paid to farm size as a possible factor of performances variation. The results show that pesticides and fertilizer use, field emissions, field operations and irrigation are the main sources of environmental impacts. It reveals that production of 1 kg of seed cotton delivered at farm gate generates a global warming potential of 3–3.4 kg CO 2e and requires 5–6 L of water. Eco-efficiency estimates of small, medium and large sized farms computed on per hectare basis are 0.86, 0.74 and 0.78, respectively, and 0.51, 0.52 and 0.50 respectively when computed on the basis of kilogram of seed cotton. No significant differences of eco-efficiencies per functional unit were observed across farm size categories. Small farms' higher profits counterbalance their significantly higher levels of eutrophication, and balance its overall eco-efficiency with other farm categories. A trade-off analysis tried to identify the farms that would epitomize sustainable cotton production; it shows that it is almost impossible to combine high economic return with low environmental impacts under current context. However some recommendations have been formulated with regards to pesticides and fertilizers use, which may be significantly reduced with no effect on yield, and potentially reduce environmental impacts.

Trout farming is the main fish production system in France. This article describes a system to classify trout farms based on environmental impacts calculated by life cycle assessment and technical and economic indicators. Since the number of surveyed farms was too small for a robust assessment, we combined principal component analysis (PCA) with a non-parametric bootstrap technique. French trout farms were surveyed to collect technical and economic indicators. The representativeness of the survey was verified by comparing it to a national inventory. Life cycle assessment was used to estimate environmental impacts of farms and the contribution of each production stage to impacts. PCA was used to evaluate both technical-economic and environmental indicators of the trout farms, which were separated into three groups based on the size of fish produced (pan-size, large and mixed-size, and very large). Non-parametric bootstrap was used to compare the groups and to test the significance of PCA results. Results validated the fish-farm classification system based on the size of fish produced and indicated that farm operations and fish feeding contributed the most to environmental impacts. The PCA method distinguished three groups via their technical indicators, with non-significant differences among the groups in environmental impacts. However, environmental indicators showed strong links with technical and economic indicators. In conclusion, bootstrapped PCA offers the ability to assess groups of trout production system when the sample size is too small and provides more conservative results by considering uncertainty. Future studies should focus on providing reliable data to reduce uncertainty.[br/]

LCAs (life cycle assessments) are often based on average data to produce a generic evaluation of a good or service. However, ignoring variability and induced uncertainty of LCA results reduces their significance, especially when dealing with agricultural processes that present high natural fluctuations. The objective of the study was to explore the robustness of LCA results when accounting for variable emissions data, illustrated by the case of slurry application techniques. Four application techniques were compared: band spreading, broadcast spreading, harrowing after surface application and direct injection. On the basis of the normalisation results, acidification, eutrophication and global warming potentials were selected. To estimate field nitrogen emissions, an original approach was developed based on relative nitrogen loss factors for each technique from a literature review. The calculated field emissions from different soil and climate conditions were considered equally probable and were propagated into a range of LCA result using the Monte Carlo method. Injection and harrowing both showed reduced acidification and eutrophication potentials compared to band spreading and broadcast spreading but had larger global warming potentials, which could be particularly important with injection. Harrowing consequently appeared as the best compromise. Despite the large range of LCA results, robust conclusions could be drawn. To achieve a more refined comparison between the techniques, the use of process-based models in contrasted situations is suggested.

International audience; The environmental evaluation of fruits and vegetables in life cycle assessment (LCA) requires a proper estimation of pesticide emissions and associated (eco-)toxicity impacts. In response, we developed an approach to consistently combine state-of-the-art emission inventory and impact assessment models for assessing human toxicity and freshwater ecotoxicity impacts from pesticide applications, and tested our approach in an LCA case study on pesticides applied to an open-field tomato produced in Martinique (French West Indies). Our results show that impact scores vary over several orders of magnitude, mainly as function of differences in pesticide properties and application time in relation to crop growth stage. Overall, impacts related to pesticide field emissions leading to exposure to pesticide residues in crop harvest are a main contributor to LCA performance results for tomato produced in Martinique, with fertilizer and packaging manufacturing as other dominating aspects. While the proposed approach is applicable to refine currently LCA methods for assessing pesticides, large uncertainties remain. These are mostly related to the parametrization of impact factors for tropical species. Based on our findings, we recommend using initial emission distribution fractions in combination with steady-state characteriza- tion factors for environmental emissions and with time-dependent characterization factors for pesticide residues in crop harvest in LCA, while further improving the use of secondary emission fractions to allow for better consideration of local field, soil and climate characteristics.