Abstract:Assessing the effect of land management on soil quality is nowadays a key environmental concern, as the soil system is linked to major ecosystem services. There is a strong methodological shortage to integrate the impact of anthropogenic pressure on the soil system within large scale environmental frameworks, such as the Life Cycle Assessment. The LANCA® method was proposed to meet this need, integrating five impact categories of soil functions and directly applicable within the Life Cycle Assessment framework… Show more
“…Although several indexing strategies have been implemented for assessing and monitoring soil quality in different ecosystems around the world [42,[54][55][56][57][58][59][60], none have been applied to a restoration process in Amazonian soil conditions. For example, the GISQ can discriminate well between mature forest and covers dominated by grasses (pastures and silvopastoral systems) [49] and between covers with different intensity of use [48], but it has not yet proven if it can also discriminate the soil quality among different successional stages.…”
Successional processes in abandoned pastures in the Amazon region have been well-documented for the floristic component; however, soil succession has been poorly studied. This study assessed the physical, chemical and biological responses of soils in the Amazon region during the natural succession process in two main landscapes of the Colombian Amazon. Soil data on soil physico–chemical (bulk density, macroaggregates, pH and minerals) and biological (soil macrofauna) composition were evaluated along chronosequence with four successional stages: (i) degraded pastures, (ii) young (10–20-year-old), (iii) middle-age (25–40-year-old) and (iv) mature forests, in two different landscapes (hill and mountain). Individual soil variables and a synthetic indicator of soil quality (GISQ) were evaluated as tools for natural succession monitoring. The results corroborated the negative impact that cattle ranching has on Amazon soils. After 10 years of natural succession, the physico–chemical and biological soil components were widely restored. Less soil compaction and organic carbon occurred in older successional stages. Soil macrofauna richness and density increased along the chronosequence, with an evident association between the macrofauna composition and the macroaggregates in the soil. None of the individual soil properties or the GISQ indicator discriminated among natural succession stages; therefore, new soil quality indicators should be developed to monitor soil quality restoration in natural successions.
“…Although several indexing strategies have been implemented for assessing and monitoring soil quality in different ecosystems around the world [42,[54][55][56][57][58][59][60], none have been applied to a restoration process in Amazonian soil conditions. For example, the GISQ can discriminate well between mature forest and covers dominated by grasses (pastures and silvopastoral systems) [49] and between covers with different intensity of use [48], but it has not yet proven if it can also discriminate the soil quality among different successional stages.…”
Successional processes in abandoned pastures in the Amazon region have been well-documented for the floristic component; however, soil succession has been poorly studied. This study assessed the physical, chemical and biological responses of soils in the Amazon region during the natural succession process in two main landscapes of the Colombian Amazon. Soil data on soil physico–chemical (bulk density, macroaggregates, pH and minerals) and biological (soil macrofauna) composition were evaluated along chronosequence with four successional stages: (i) degraded pastures, (ii) young (10–20-year-old), (iii) middle-age (25–40-year-old) and (iv) mature forests, in two different landscapes (hill and mountain). Individual soil variables and a synthetic indicator of soil quality (GISQ) were evaluated as tools for natural succession monitoring. The results corroborated the negative impact that cattle ranching has on Amazon soils. After 10 years of natural succession, the physico–chemical and biological soil components were widely restored. Less soil compaction and organic carbon occurred in older successional stages. Soil macrofauna richness and density increased along the chronosequence, with an evident association between the macrofauna composition and the macroaggregates in the soil. None of the individual soil properties or the GISQ indicator discriminated among natural succession stages; therefore, new soil quality indicators should be developed to monitor soil quality restoration in natural successions.
“…It should be also noted that country-specific default values can in reality differ from actual site-specific values as was shown by Terranova et al (2021) [62]. There can be also differences in LANCA impacts between different geographical locations [63]. Therefore, for example, the wood biomethane case could have led to different results if the wood were from other ecoregions.…”
Targets to reduce global warming impacts of the transportation sector may lead to increased land use and negative land quality changes. The aim of this paper is to implement the Land Use Indicator Calculation in Life Cycle Assessment (LANCA®) model to assess land quality impacts and land use efficiencies (concerning occupation and transformation) of different example renewable transport energy systems for passenger cars. In addition, the land use impacts are normalized according to the Soil Quality Index building on LANCA® and included in the environmental footprint. The assessment is based on information from GaBi life cycle assessment software databases and on literature. Functional unit of the model is to provide annual drive of 18,600 km for a passenger car in the EU. The analysis includes examples of biomass, electricity, electricity to fuels and fossil-based energy systems. Our findings confirm previous research that biomass-based transport energy systems have risks to lead to significantly higher land occupation and transformation impacts than do fossil oil or electricity-based ones. According to the LANCA® model, methane from Finnish wood and German corn has the highest impacts on filtration and the physicochemical filtration reduction potential. Sugarcane ethanol and palm oil diesel systems, on the other hand, lead to the highest erosion potential. Electricity-based transportation energy systems appear to be superior to biomass-based ones from the perspectives of land occupation, land transformation, and soil quality impacts for the selected examples. Land quality impacts should be taken into account when developing and expanding renewable transportation energy systems. The paper shows that the LANCA® method is applicable for the assessment of transport systems in order to provide extended information on environmental sustainability, which should be included more often in future analysis. However, it can be challenging to interpret underlaying assumptions, especially when aggregated information is used from databases.
“…The strict dependence between erosion and slope could also be 4 observed looking at the European soil erosion map developed by Panagos (Panagos et al 2015a, b). This can be a problem when slope data are not available as is the case in some countries (Thoumazeau et al 2019) . The second more important factor is the C-factor whose influence is discussed hereafter (Sect.…”
Purpose
In this work, we study a land use impact model with the aim of obtaining spatially differentiated as opposed to default average characterisation factors. In particular, we study the application of LANCA®, a multi-indicator model with available country average characterisation factors expressing the alteration of the soil quality level of the current land use of one kind with respect to a reference situation.
Method
To this purpose, we use the LANCA® method documentation at a higher spatial resolution and apply all the required elemental steps. From a user perspective, we score the transparency of the method down to the basic methodological references and single out the source of errors that the user may incur when: (i) collecting the input data, (ii) selecting the appropriate soil/land classes and (iii) applying the individual calculation steps. For a greater insight, we couple the source of errors with a sensitivity analysis.
Results
In the comparison between a site-specific test area and the related country default values, we obtained relevant discrepancies regarding the erosion resistance and the physicochemical filtration of the soil. For example, we find that the erosion resistance potential is −1.06 * 10−3 kg m2 a−1 locally while the country default value is 13.1. We explain differences through the sensitivity analysis and having analysed in depth the underpinned soil erosion equation and the critical steps for its calibration. Together with systematic errors, we find that the method generally implies 9 scarcely guided steps out of 42, and one-third of the basic methodologies are not fully explained or accessible. These factors make the results related to Biotic Production, Mechanical Filtration, Physicochemical Filtration and Groundwater Regeneration user dependent and — in this sense — difficult to replicate.
Conclusions
From the analysis, we distil 7 main directions for improvement addressed to LANCA® and soil models especially in sight of a broader application of a regionalised life cycle impact assessment.
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