Pyrosequencing reveals how pulses influence rhizobacterial communities with feedback on wheat growth in the semiarid Prairie

Background and Aims

Evidence shows that plants modify their microbial environment leading to the “crop rotation effect”, but little is known about the changes in rhizobacterial community structure and functionality associated with beneficial rotation effects.

Methods

Polymerase chain reaction (PCR) and 454 GS FLX amplicon pyrosequencing were used to describe the composition of the rhizobacterial community evolving under the influence of pea, a growth promoting rotation crop, and the influence of three genotypes of chickpea, a plant known as an inferior rotation crop. The growth promoting properties of these rhizobacterial communities were tested on wheat in greenhouse assays.

Results

The rhizobacterial communities selected by pea and the chickpea CDC Luna in 2008, a wet year, promoted durum wheat growth, but those selected by CDC Vanguard or CDC Frontier had no growth-promoting effect. In 2009, a dry year, the influence of plants was mitigated, indicated that moisture availability is a major driver of soil bacterial community dynamics.

Conclusion

The effect of pulse crops on soil biological quality varies with the crop species and genotypes, and certain chickpea genotypes may induce positive rotation effects on wheat. The strength of a rotation effect on soil biological quality is modulated by the abundance of precipitation.     

Exploring variability in methods and data sensitivity in carbon footprints of feed ingredients

Purpose

Production of feed is an important contributor to life cycle greenhouse gas emissions, or carbon footprints (CFPs), of livestock products. Consequences of methodological choices and data sensitivity on CFPs of feed ingredients were explored to improve comparison and interpretation of CFP studies. Methods and data for emissions from cultivation and processing, land use (LU), and land use change (LUC) were analyzed.

Method

For six ingredients (maize, wheat, palm kernel expeller, rapeseed meal, soybean meal, and beet pulp), CFPs resulting from a single change in methods and data were compared with a reference CFP, i.e., based on IPCC Tier 1 methods, and data from literature.

Results and discussion

Results show that using more detailed methods to compute N₂O emissions from cultivation hardly affected reference CFPs, except for methods to determine $ \mathrm{NO}_3^{-} $ leaching (contributing to indirect N₂O emissions) in which the influence is about ?7 to +12 %. Overall, CFPs appeared most sensitive to changes in crop yield and applied synthetic fertilizer N. The inclusion of LULUC emissions can change CFPs considerably, i.e., up to 877 %. The level of LUC emissions per feed ingredient highly depends on the method chosen, as well as on assumptions on area of LUC, C stock levels (mainly aboveground C and soil C), and amortization period.

Conclusions

We concluded that variability in methods and data can significantly affect CFPs of feed ingredients and hence CFPs of livestock products. Transparency in methods and data is therefore required. For harmonization, focus should be on methods to calculate $ \mathrm{NO}_3^{-} $ leaching and emissions from LULUC. It is important to consider LUC in CFP studies of food, feed, and bioenergy products.     

Development of a soil compaction indicator in life cycle assessment

Purpose

Integrating soil quality impacts in life cycle assessment (LCA) requires a global approach to assess impacts on soil quality that can be adapted to individual soil and climate contexts. We have developed a framework for quantifying indicators of impact on soil quality, valid for all soil and climate conditions, and considering both on-site and off-site agricultural soils. Herein, we present one of the framework’s impact indicators, which has not yet been quantified in detail in LCA studies: soil compaction.

Material and methods

The method includes guidelines and tools for estimating midpoint compaction impacts in topsoil and subsoil as a loss of soil pore volume (in cubic metre per functional unit). The life cycle inventory (LCI) and life cycle impact assessment are based on simulation modelling, using models simple enough for use by non-experts, general enough to be parameterised with available data at a global scale and already validated. Data must be as site specific and accurate as possible, but if measured data are missing, the method has a standardised framework of rules and recommendations for estimating or finding them. The main model used, COMPSOIL, predicts compaction due to agricultural traffic. Results are illustrated using a case study involving several crops in different soil and climate conditions: a representative pig feed produced in Brittany, France.

Results and discussion

Predicted compaction impacts result from the combination of site-specific soil, climate and management characteristics. The data necessary to the LCI are readily available from free soil and climate databases and research online. Results are consistent with compaction observed in the field. Within a soil type, predictions are most sensitive to initial bulk density and soil water content.

Conclusions

The method lays the foundation for possible improvement by refining estimates of initial soil conditions or adding models that are simple and robust enough to increase the method’s capacity and accuracy. The soil compaction indicator can be used in LCAs of bio-based materials and of waste management stages that consider composting. The framework includes other operational indicators (i.e. water erosion, soil organic matter change) to assess impact on soil quality. They complement other impact categories, providing increased ability to identify “impact swapping”.     

Coupling GIS and LCA for biodiversity assessments of land use

Purpose  

Geospatial details about land use are necessary to assess its potential impacts on biodiversity. Geographic information systems (GIS) are adept at modeling land use in a spatially explicit manner, while life cycle assessment (LCA) does not conventionally utilize geospatial information. This study presents a proof-of-concept approach for coupling GIS and LCA for biodiversity assessments of land use and applies it to a case study of ethanol production from agricultural crops in California.     

Potential impacts on ecosystem services of land use transitions to second‐generation bioenergy crops in GB

We present the first assessment of the impact of land use change (LUC) to second‐generation (2G) bioenergy crops on ecosystem services (ES) resolved spatially for Great Britain (GB). A systematic approach was used to assess available evidence on the impacts of LUC from arable, semi‐improved grassland or woodland/forest, to 2G bioenergy crops, for which a quantitative ‘threat matrix’ was developed. The threat matrix was used to estimate potential impacts of transitions to either Miscanthus, short‐rotation coppice (SRC, willow and poplar) or short‐rotation forestry (SRF). The ES effects were found to be largely dependent on previous land uses rather than the choice of 2G crop when assessing the technical potential of available biomass with a transition from arable crops resulting in the most positive effect on ES. Combining these data with constraint masks and available land for SRC and Miscanthus (SRF omitted from this stage due to lack of data), south‐west and north‐west England were identified as areas where Miscanthus and SRC could be grown, respectively, with favourable combinations of economic viability, carbon sequestration, high yield and positive ES benefits. This study also suggests that not all prospective planting of Miscanthus and SRC can be allocated to agricultural land class (ALC) ALC 3 and ALC 4 and suitable areas of ALC 5 are only minimally available. Beneficial impacts were found on 146 583 and 71 890 ha when planting Miscanthus or SRC, respectively, under baseline planting conditions rising to 293 247 and 91 318 ha, respectively, under 2020 planting scenarios. The results provide an insight into the interplay between land availability, original land uses, bioenergy crop type and yield in determining overall positive or negative impacts of bioenergy cropping on ecosystems services and go some way towards developing a framework for quantifying wider ES impacts of this important LUC.     

Life cycle assessment of the Peruvian industrial anchoveta fleet: boundary setting in life cycle inventory analyses of complex and plural means of production

Purpose

This work has two major objectives: (1) to perform an attributional life cycle assessment (LCA) of a complex mean of production, the main Peruvian fishery targeting anchoveta (anchovy) and (2) to assess common assumptions regarding the exclusion of items from the life cycle inventory (LCI).

Methods

Data were compiled for 136 vessels of the 661 units in the fleet. The functional unit was 1 t of fresh fish delivered by a steel vessel. Our approach consisted of four steps: (1) a stratified sampling scheme based on a typology of the fleet, (2) a large and very detailed inventory on small representative samples with very limited exclusion based on conventional LCI approaches, (3) an impact assessment on this detailed LCI, followed by a boundary-refining process consisting of retention of items that contributed to the first 95 % of total impacts and (4) increasing the initial sample with a limited number of items, according to the results of (3). The life cycle impact assessment (LCIA) method mostly used was ReCiPe v1.07 associated to the ecoinvent database.

Results and discussion

Some items that are usually ignored in an LCI’s means of production have a significant impact. The use phase is the most important in terms of impacts (66 %), and within that phase, fuel consumption is the leading inventory item contributing to impacts (99 %). Provision of metals (with special attention to electric wiring which is often overlooked) during construction and maintenance, and of nylon for fishing nets, follows. The anchoveta fishery is shown to display the lowest fuel use intensity worldwide.

Conclusions

Boundary setting is crucial to avoid underestimation of environmental impacts of complex means of production. The construction, maintenance and EOL stages of the life cycle of fishing vessels have here a substantial environmental impact. Recommendations can be made to decrease the environmental impact of the fleet.     

PestLCI 2.0: a second generation model for estimating emissions of pesticides from arable land in LCA

Purpose

The spatial dependency of pesticide emissions to air, surface water and groundwater is illustrated and quantified using PestLCI 2.0, an updated and expanded version of PestLCI 1.0.

Methods

PestLCI is a model capable of estimating pesticide emissions to air, surface water and groundwater for use in life cycle inventory (LCI) modelling of field applications. After calculating the primary distribution of pesticides between crop and soil, specific modules calculate the pesticide??s fate, thus determining the pesticide emission pattern for the application. PestLCI 2.0 was developed to overcome the limitations of the first model version, replacement of fate calculation equations and introducing new modules for macropore flow and effects of tillage. The accompanying pesticide database was expanded, the meteorological and soil databases were extended to include a range of European climatic zones and soil profiles. Environmental emissions calculated by PestLCI 2.0 were compared to results from the risk assessment models SWASH (surface water emissions), FOCUSPEARL (groundwater via matrix leaching) and MACRO (groundwater including macropore flow, only one scenario available) to partially validate the updated model. A case study was carried out to demonstrate the spatial variation of pesticide emission patterns due to dependency on meteorological and soil conditions.

Results

Compared to PestLCI 1.0, PestLCI 2.0 calculated lower emissions to surface water and higher emissions to groundwater. Both changes were expected due to new pesticide fate calculation approaches and the inclusion of macropore flow. Differences between the SWASH and FOCUSPEARL and PestLCI 2.0 emission estimates were generally lower than 2 orders of magnitude, with PestLCI generally calculating lower emissions. This is attributed to the LCA approach to quantify average cases, contrasting with the worst-case risk assessment approach inherent to risk assessment. Compared to MACRO, the PestLCI 2.0 estimates for emissions to groundwater were higher, suggesting that PestLCI 2.0 estimates of fractions leached to groundwater may be slightly conservative as a consequence of the chosen macropore modelling approach. The case study showed that the distribution of pesticide emissions between environmental compartments strongly depends on local climate and soil characteristics.

Conclusions

PestLCI 2.0 is partly validated in this paper. Judging from the validation data and case study, PestLCI 2.0 is a pesticide emission model in acceptable accordance with both state-of-the-art pesticide risk assessment models. The case study underlines that the common pesticide emission estimation practice in LCI may lead to misestimating the toxicity impacts of pesticide use in LCA.     

A framework for modelling the transport and deposition of eroded particles towards water systems in a life cycle inventory

Purpose

Topsoil erosion due to land use has been characterised as one of the most damaging problems from the perspective of soil-resource depletion, changes in soil fertility and net soil productivity and damage to aquatic ecosystems. On-site environmental damage to topsoil by water erosion has begun to be considered in Life Cycle Assessment (LCA) within the context of ecosystem services. However, a framework for modelling soil erosion by water, addressing off-site deposition in surface water systems, to support life cycle inventory (LCI) modelling is still lacking. The objectives of this paper are to conduct an overview of existing methods addressing topsoil erosion issues in LCA and to develop a framework to support LCI modelling of topsoil erosion, transport and deposition in surface water systems, to establish a procedure for assessing the environmental damage from topsoil erosion on water ecosystems.

Methods

The main features of existing methods addressing topsoil erosion issues in LCA are analysed, particularly with respect to LCI and Life Cycle Impact Assessment methodologies. An overview of nine topsoil erosion models is performed to estimate topsoil erosion by water, soil particle transport through the landscape and its in-stream deposition. The type of erosion evaluated by each of the models, as well as their applicable spatial scale, level of input data requirements and operational complexity issues are considered. The WATEM-SEDEM model is proposed as the most adequate to perform LCI erosion analysis.

Results and discussion

The definition of land use type, the area of assessment, spatial location and system boundaries are the main elements discussed. Depending on the defined system boundaries and the inherent routing network of the detached soil particles to the water systems, the solving of the multifunctionality of the system assumes particular relevance. Simplifications related to the spatial variability of the input data parameters are recommended. Finally, a sensitivity analysis is recommended to evaluate the effects of the transport capacity coefficient in the LCI results.

Conclusions

The published LCA methods focus only on the changes of soil properties due to topsoil erosion by water. This study provides a simplified framework to perform an LCI of topsoil erosion by considering off-site deposition of eroded particles in surface water systems. The widespread use of the proposed framework would require the development of LCI erosion databases. The issues of topsoil erosion impact on aquatic biodiversity, including the development of characterisation factors, are now the subject of on-going research.     

Application of robust optimization in matrix-based LCI for decision making under uncertainty

Purpose

When product systems are optimized to minimize environmental impacts, uncertainty in the process data may impact optimal decisions. The purpose of this article is to propose a mathematical method for life cycle assessment (LCA) optimization that protects decisions against uncertainty at the life cycle inventory (LCI) stage.

Methods

A robust optimization approach is proposed for decision making under uncertainty in the LCI stage. The proposed approach incorporates data uncertainty into an optimization problem in which the matrix-based LCI model appears as a constraint. The level of protection against data uncertainty in the technology and intervention matrices can be controlled to reflect varying degrees of conservatism.

Results and discussion

A simple numerical example on an electricity generation product system is used to illustrate the main features of this methodology. A comparison is made between a robust optimization approach, and decision making using a Monte Carlo analysis. Challenges to implement the robust optimization approach on common uncertainty distributions found in LCA and on large product systems are discussed. Supporting source code is available for download at https://github.com/renwang/Robust_Optimization_LCI_Uncertainty.

Conclusions

A robust optimization approach for matrix-based LCI is proposed. The approach incorporates data uncertainties into an optimization framework for LCI and provides a mechanism to control the level of protection against uncertainty. The tool computes optimal decisions that protects against worst-case realizations of data uncertainty. The robust optimal solution is conservative and is able to avoid the negative consequences of uncertainty in decision making.     

Estimating belowground nitrogen inputs of pea and canola and their contribution to soil inorganic N pools using 15N labeling

Background and aims

Crop species grown in a diversified crop rotation can influence soil N dynamics to varying degrees due to differences in the quantity and quality of the residues returned to the soil. The aim of this study was to quantify the contribution of N rhizodeposition by canola (Brassica napus L.) and pea (Pisum sativum L.) to the crop residue N balance and soil inorganic N pool.

Methods

Canola and pea were grown in a soil-sand mixture and were subject to cotton-wick ¹⁵N labeling in a greenhouse experiment. Nitrogen-15 recovered in the soil and roots were used to estimate N rhizodeposition.

Results

Belowground N, including root N and N rhizodeposits, comprised 70 % and 61 % of total crop residue N for canola and pea, respectively. Canola released the greatest amount of total root-derived N to the soil, which was related to greater root biomass production by canola. However, root-derived N in the soil inorganic N pool was greater under pea (13 %) than canola (4 %).

Conclusions

Our results show a significant belowground N contribution to total crop residue from pea and canola. Further investigation is required to determine whether input of the more labile N rhizodeposits of pea improves soil N supply to succeeding crops or increases the potential for N loss from the soil system relative to canola.     

Conservation agriculture, increased organic carbon in the top-soil macro-aggregates and reduced soil CO2 emissions

Background and aims

Conservation agriculture, the combination of minimal soil movement (zero or reduced tillage), crop residue retention and crop rotation, might have the potential to increase soil organic C content and reduce emissions of CO₂.

Methods

Three management factors were analyzed: (1) tillage (zero tillage (ZT) or conventional tillage (CT)), (2) crop rotation (wheat monoculture (W), maize monoculture (M) and maize-wheat rotation (R)), and (3) residue management (with (+r), or without (?r) crop residues). Samples were taken from the 0–5 and 5–10?cm soil layers and separated in micro-aggregates (< 0.25?mm), small macro-aggregates (0.25 to 1?mm) and large macro-aggregates (1 to 8?mm). The carbon content of each aggregate fraction was determined.

Results

Zero tillage combined with crop rotation and crop residues retention resulted in a higher proportion of macro-aggregates. In the 0–5?cm layer, plots with a crop rotation and monoculture of maize and wheat in ZT+r had the greatest proportion of large stable macro-aggregates (40%) and highest mean weighted diameter (MWD) (1.7?mm). The plots with CT had the largest proportion of micro-aggregates (27%). In the 5–10?cm layer, plots with residue retention in both CT and ZT (maize 1?mm and wheat 1.5?mm) or with monoculture of wheat in plots under ZT without residues (1.4?mm) had the greatest MWD. The 0–10?cm soil layer had a greater proportion of small macroaggregates compared to large macro-aggregates and micro-aggregates. In the 0–10?cm layer of soil with residues retention and maize or wheat, the greatest C content was found in the small and large macro-aggregates. The small macro-aggregates contributed most C to the organic C of the sample. For soil cultivated with maize, the CT treatments had significantly higher CO₂ emissions than the ZT treatments. For soil cultivated with wheat, CTR-r had significantly higher CO₂ emissions than all other treatments.

Conclusion

Reduction in soil disturbance combined with residue retention increased the C retained in the small and large macro-aggregates of the top soil due to greater aggregate stability and reduced the emissions of CO₂ compared with conventional tillage without residues retention and maize monoculture (a cultivation system normally used in the central highlands of Mexico).     

Initial soil C and land‐use history determine soil C sequestration under perennial bioenergy crops

In the UK and other temperate regions, short rotation coppice (SRC) and Miscanthus x giganteus (Miscanthus) are two of the leading ‘second‐generation’ bioenergy crops. Grown specifically as a low‐carbon (C) fossil fuel replacement, calculations of the climate mitigation provided by these bioenergy crops rely on accurate data. There are concerns that uncertainty about impacts on soil C stocks of transitions from current agricultural land use to these bioenergy crops could lead to either an under‐ or overestimate of their climate mitigation potential. Here, for locations across mainland Great Britain (GB), a paired‐site approach and a combination of 30‐cm‐ and 1‐m‐deep soil sampling were used to quantify impacts of bioenergy land‐use transitions on soil C stocks in 41 commercial land‐use transitions; 12 arable to SRC, 9 grasslands to SRC, 11 arable to Miscanthus and 9 grasslands to Miscanthus. Mean soil C stocks were lower under both bioenergy crops than under the grassland controls but only significant at 0–30 cm. Mean soil C stocks at 0–30 cm were 33.55 ± 7.52 Mg C ha^?1 and 26.83 ± 8.08 Mg C ha^?1 lower under SRC (P = 0.004) and Miscanthus plantations (P = 0.001), respectively. Differences between bioenergy crops and arable controls were not significant in either the 30‐cm or 1‐m soil cores and smaller than for transitions from grassland. No correlation was detected between change in soil C stock and bioenergy crop age (time since establishment) or soil texture. Change in soil C stock was, however, negatively correlated with the soil C stock in the original land use. We suggest, therefore, that selection of sites for bioenergy crop establishment with lower soil C stocks, most often under arable land use, is the most likely to result in increased soil C stocks.     

Soil carbon stock impacts following reversion of Miscanthus × giganteus and short rotation coppice willow commercial plantations into arable cropping

There are posited links between the establishment of perennial bioenergy, such as short rotation coppice (SRC) willow and Miscanthus × giganteus, on low carbon soils and enhanced soil C sequestration. Sequestration provides additional climate mitigation, however, few studies have explored impacts on soil C stocks of bioenergy crop removal; thus, the permanence of any sequestered C is unclear. This uncertainty has led some authors to question the handling of soil C stocks with carbon accounting, for example, through life cycle assessments. Here, we provide additional data for this debate, reporting on the soil C impacts of the reversion (removal and return) to arable cropping of commercial SRC willow and Miscanthus across four sites in the UK, two for each bioenergy crop, with eight reversions nested within these sites. Using a paired‐site approach, soil C stocks (0–1 m) were compared between 3 and 7 years after bioenergy crop removal. Impacts on soil C stocks varied, ranging from an increase of 70.16 ± 10.81 Mg C/ha 7 years after reversion of SRC willow to a decrease of 33.38 ± 5.33 Mg C/ha 3 years after reversion of Miscanthus compared to paired arable land. The implications for carbon accounting will depend on the method used to allocate this stock change between current and past land use. However, with published life cycle assessment values for the lifetime C reduction provided by these crops ranging from 29.50 to 138.55 Mg C/ha, the magnitude of these changes in stock are significant. We discuss the potential underlying mechanisms driving variability in soil C stock change, including the age of bioenergy crop at removal, removal methods, and differences in the recalcitrant of the crop residues, and highlight the need to design management methods to limit negative outcomes.     

Successive straw biochar application as a strategy to sequester carbon and improve fertility: A pot experiment with two rice/wheat rotations in paddy soil

Aims

A pot study spanning four consecutive crop seasons was conducted to compare the effects of successive rice straw biochar/rice straw amendments on C sequestration and soil fertility in rice/wheat rotated paddy soil.

Methods

We adopted 4.5 t ha^?1, 9.0 t ha^?1 biochar and 3.75 t ha^?1 straw for each crop season with an identical dose of NPK fertilizers.

Results

We found no major losses of biochar-C over the 2-year experimental period. Obvious reductions in CH₄ emission were observed from rice seasons under the biochar application, despite the fact that the biochar brought more C into the soil than the straw. N₂O emissions with biochar were similar to the controls without additives over the 2-year experimental period. Biochar application had positive effects on crop growth, along with positive effects on nutrient (N, P, K, Ca and Mg) uptake by crop plants and the availability of soil P, K, Ca and Mg. High levels of biochar application over the course of the crop rotation suppressed NH₃ volatilization in the rice season, but stimulated it in the wheat season.

Conclusions

Converting straw to biochar followed by successive application to soil is viable for soil C sequestration, CH₄ mitigation, improvements of soil and crop productivity. Biochar soil amendment influences NH₃ volatilization differently in the flooded rice and upland wheat seasons, respectively.     

Using a crop model to account for the effects of local factors on the LCA of sugar beet ethanol in Picardy region, France

Purpose

The results of published Life Cycle Assessments (LCAs) of biofuels are characterized by a large variability, arising from the diversity of both biofuel chains and the methodologies used to estimate inventory data. Here, we suggest that the best option to maximize the accuracy of biofuel LCA is to produce local results taking into account the local soil, climatic and agricultural management factors.

Methods

We focused on a case study involving the production of first-generation ethanol from sugar beet in the Picardy region in Northern France. To account for local factors, we first defined three climatic patterns according to rainfall from a 20-year series of weather data. We subsequently defined two crop rotations with sugar beet as a break crop, corresponding to current practice and an optimized management scenario, respectively. The six combinations of climate types and rotations were run with the process-based model CERES-EGC to estimate crop yields and environmental emissions. We completed the data inventory and compiled the impact assessments using Simapro v.7.1 and Ecoinvent database v2.0.

Results

Overall, sugar beet ethanol had lower impacts than gasoline for the abiotic depletion, global warming, ozone layer depletion and photochemical oxidation categories. In particular, it emitted between 28 % and 42 % less greenhouse gases than gasoline. Conversely, sugar beet ethanol had higher impacts than gasoline for acidification and eutrophication due to losses of reactive nitrogen in the arable field. Thus, LCA results were highly sensitive to changes in local conditions and management factors. As a result, an average impact figures for a given biofuel chain at regional or national scales may only be indicative within a large uncertainty band.

Conclusions

Although the crop model made it possible to take local factors into account in the life-cycle inventory, best management practices that achieved high yields while reducing environmental impacts could not be identified. Further modelling developments are necessary to better account for the effects of management practices, in particular regarding the benefits of fertiliser incorporation into the topsoil in terms of nitrogen losses abatement. Supplementary data and modelling developments also are needed to better estimate the emissions of pesticides and heavy metals in the field.     

Quantifying system uncertainty of life cycle assessment based on Monte Carlo simulation

Background, aim, and scope

Many studies evaluate the results of applying different life cycle impact assessment (LCIA) methods to the same life cycle inventory (LCI) data and demonstrate that the assessment results would be different with different LICA methods used. Although the importance of uncertainty is recognized, most studies focus on individual stages of LCA, such as LCI and normalization and weighting stages of LCIA. However, an important question has not been answered in previous studies: Which part of the LCA processes will lead to the primary uncertainty? The understanding of the uncertainty contributions of each of the LCA components will facilitate the improvement of the credibility of LCA.

Methodology

A methodology is proposed to systematically analyze the uncertainties involved in the entire procedure of LCA. The Monte Carlo simulation is used to analyze the uncertainties associated with LCI, LCIA, and the normalization and weighting processes. Five LCIA methods are considered in this study, i.e., Eco-indicator 99, EDIP, EPS, IMPACT 2002+, and LIME. The uncertainty of the environmental performance for individual impact categories (e.g., global warming, ecotoxicity, acidification, eutrophication, photochemical smog, human health) is also calculated and compared. The LCA of municipal solid waste management strategies in Taiwan is used as a case study to illustrate the proposed methodology.

Results

The primary uncertainty source in the case study is the LCI stage under a given LCIA method. In comparison with various LCIA methods, EDIP has the highest uncertainty and Eco-indicator 99 the lowest uncertainty. Setting aside the uncertainty caused by LCI, the weighting step has higher uncertainty than the normalization step when Eco-indicator 99 is used. Comparing the uncertainty of various impact categories, the lowest is global warming, followed by eutrophication. Ecotoxicity, human health, and photochemical smog have higher uncertainty.

Discussion

In this case study of municipal waste management, it is confirmed that different LCIA methods would generate different assessment results. In other words, selection of LCIA methods is an important source of uncertainty. In this study, the impacts of human health, ecotoxicity, and photochemical smog can vary a lot when the uncertainties of LCI and LCIA procedures are considered. For the purpose of reducing the errors of impact estimation because of geographic differences, it is important to determine whether and which modifications of assessment of impact categories based on local conditions are necessary.

Conclusions

This study develops a methodology of systematically evaluating the uncertainties involved in the entire LCA procedure to identify the contributions of different assessment stages to the overall uncertainty. Which modifications of the assessment of impact categories are needed can be determined based on the comparison of uncertainty of impact categories.

Recommendations and perspectives

Such an assessment of the system uncertainty of LCA will facilitate the improvement of LCA. If the main source of uncertainty is the LCI stage, the researchers should focus on the data quality of the LCI data. If the primary source of uncertainty is the LCIA stage, direct application of LCIA to non-LCIA software developing nations should be avoided.     

Spatiotemporal land use modelling to assess land availability for energy crops – illustrated for Mozambique

A method and tool have been developed to assess future developments in land availability for bioenergy crops in a spatially explicit way, while taking into account both the developments in other land use functions, such as land for food, livestock and material production, and the uncertainties in the key determinant factors of land use change (LUC). This spatiotemporal LUC model is demonstrated with a case study on the developments in the land availability for bioenergy crops in Mozambique in the timeframe 2005–2030. The developments in the main drivers for agricultural land use, demand for food, animal products and materials were assessed, based on the projected developments in population, diet, GDP and self‐sufficiency ratio. Two scenarios were developed: a business‐as‐usual (BAU) scenario and a progressive scenario. Land allocation was based on land use class‐specific sets of suitability factors. The LUC dynamics were mapped on a 1 km² grid level for each individual year up to 2030. In the BAU scenario, 7.7 Mha and in the progressive scenario 16.4 Mha could become available for bioenergy crop production in 2030. Based on the Monte Carlo analysis, a 95% confidence interval of the amount of land available and the spatially explicit probability of available land was found. The bottom‐up approach, the number of dynamic land uses, the diverse portfolio of LUC drivers and suitability factors, and the possibility to model uncertainty mean that this model is a step forward in modelling land availability for bioenergy potentials.     

Nitrogen use efficiency of organically fertilized white cabbage and residual effects on subsequent beetroot

Background and aims

The low N availability in organic cropping systems requires an efficient use of the limited N sources. The study aimed to analyze the N efficiency of organically fertilized white cabbage on a crop and crop rotation basis.

Methods

Effects of soil-incorporated lupine seedlings and seed meal on the N use efficiency (NUE) and individual NUE components of cabbage were investigated in field experiments. Cabbage was followed by beetroot to quantify residual fertilizer effects.

Results

Generally, NUE decreased with increasing N availability. Nitrogen uptake efficiency, however, was low at low N supply and increased curvilinearly to an asymptotic maximum. Variation in harvest index between and within experimental years was explained by differences in thermal growing time and initial cabbage growth, respectively. The increase in beetroot N supply by fertilizer treatments averaged 18 % of applied lupine seed N corresponding to 63 % of the incremental N in cabbage residues.

Conclusions

Dry matter partitioning alters during cabbage yield formation in favor of the harvest residue fraction if abiotic stress like water shortage occurs directly after crop establishment, being associated with reduced NUE. The residual effect depends largely on the re-utilization of incremental fertilizer N in cabbage residues and thus on the short-term net N mineralization of organic fertilizers.     

How to take time into account in the inventory step: a selective introduction based on sensitivity analysis

Purpose

Life cycle assessment is usually an assessment tool, which only considers steady-state processes, as the temporal and spatial dimensions are lost during the life cycle inventory (LCI). This approach therefore reduces the environmental relevance of certain results, as it has been underlined in the case of climate change studies. Given that the development of dynamic impact methods is based on dynamic inventory data, it seems essential to develop a general methodology to achieve a temporal LCI.

Methods

This study presents a method for selecting the steps, within the whole process network, for which dynamics need to be considered while others can be approximated by steady-state representation. The selection procedure is based on the sensitivity of the impacts on the variation of environmental and economic flows. Once these flows have been identified, their respective timescales are compared to the inherent timescales of the impact categories affected by the flows. The timescales of the impacts are divided into three categories (days, months, years) based on a literature review of the ReCiPe method. The introduction of a temporal dynamic depends on the relationship between the timescale of the environmental and economic flows on the one hand and that of the concerned impact on the other hand.

Results and discussion

This approach is illustrated by the life cycle assessment of palm methyl ester and ethanol from sugarcane. In both cases, the introduction of a temporal dynamic is limited to a small proportion of the total number of flows: 0.1 % in the sugarcane ethanol production and 0.01 % in the palm methyl ester production. Future developments of time integration in the LCI and in the life cycle impact assessment (LCIA) are also discussed in order to deal with the need of characterization functions and the recurrent problem of waiting times.

Conclusions

This work provides a method to select specific flows where the introduction of temporal dynamics is most relevant. It is based on sensitivity analyses and on the relationship between the timescales of the flows and the timescale of the involved impact. The time-distributed LCI generated by using this approach could then be coupled with a dynamic LCIA proposed in the literature.     

Assessing freshwater use impacts in LCA: Part I—inventory modelling and characterisation factors for the main impact pathways

Background, aim and scope

Freshwater is a basic resource for humans; however, its link to human health is seldom related to lack of physical access to sufficient freshwater, but rather to poor distribution and access to safe water supplies. On the other hand, freshwater availability for aquatic ecosystems is often reduced due to competition with human uses, potentially leading to impacts on ecosystem quality. This paper summarises how this specific resource use can be dealt with in life cycle analysis (LCA).

Main features

The main quantifiable impact pathways linking freshwater use to the available supply are identified, leading to definition of the flows requiring quantification in the life cycle inventory (LCI).

Results

The LCI needs to distinguish between and quantify evaporative and non-evaporative uses of ‘blue’ and ‘green’ water, along with land use changes leading to changes in the availability of freshwater. Suitable indicators are suggested for the two main impact pathways [namely freshwater ecosystem impact (FEI) and freshwater depletion (FD)], and operational characterisation factors are provided for a range of countries and situations. For FEI, indicators relating current freshwater use to the available freshwater resources (with and without specific consideration of water ecosystem requirements) are suggested. For FD, the parameters required for evaluation of the commonly used abiotic depletion potentials are explored.

Discussion

An important value judgement when dealing with water use impacts is the omission or consideration of non-evaporative uses of water as impacting ecosystems. We suggest considering only evaporative uses as a default procedure, although more precautionary approaches (e.g. an ‘Egalitarian’ approach) may also include non-evaporative uses. Variation in seasonal river flows is not captured in the approach suggested for FEI, even though abstractions during droughts may have dramatic consequences for ecosystems; this has been considered beyond the scope of LCA.

Conclusions

The approach suggested here improves the representation of impacts associated with freshwater use in LCA. The information required by the approach is generally available to LCA practitioners

Recommendations and perspectives

The widespread use of the approach suggested here will require some development (and consensus) by LCI database developers. Linking the suggested midpoint indicators for FEI to a damage approach will require further analysis of the relationship between FEI indicators and ecosystem health.