Data strategy for environmental assessment of agricultural regions via LCA: case study of a French catchment

Purpose

Various approaches have been carried out to extrapolate environmental assessments of farms to the regional level, some of them oversimplified and thus leading to high uncertainty. Key challenges include selection of a representative sample, construction of a farm/land use typology, the extrapolation strategy and dealing with data limitations. This work proposes a method for addressing these issues by means of statistically supported approaches.

Methods

We applied a novel approach combining a sampling strategy, estimation of farm-level environmental impacts via life cycle assessment (LCA), a farm typology based on principal component analysis, a statistical method for extending the farm sample given data constraints and finally linear extrapolation based on regional production and land use, taking into account the regional import–export balance. The approach was applied to a French case study, the Lieue de Grève catchment in the dairy-intensive Brittany region. A decision flowchart was developed to generalise the approach for similar applications dealing with farm and LCA data constraints. Additionally, innovative farm practices were modelled and their impacts propagated to the regional level.

Results and discussion

The typology developed identified “dairy”, “beef”, “dairy + beef” and “swine” farms as the dominant farm types in the region. While swine farms had the highest mean impacts per hectare, dairy and dairy + beef farms had impacts two to five times as high as those of beef and swine farms, when extrapolated to the entire catchment. Multiple linear regressions based on an extended farm and LCA dataset were used to predict environmental impacts of dairy farms lacking LCA results, thus increasing their sample size before extrapolation. The inclusion of farm and LCA data from a neighbouring region did not contribute to the accuracy of predicted impacts, as determined by comparing them to those of the farm closest to the dairy cluster’s centre, but rather produced significantly larger coefficients of variation. Results of tests of including two extra-regional farm and LCA datasets helped determine decision rules for the decision flowchart. Modelling of innovative agricultural practices yielded regional impacts consistent with previous estimates.

Conclusions

This approach provides a generalisable approach for farm typologies, data handling and regional extrapolation of farm-level LCAs, applicable to estimate environmental impacts of any agricultural area if requirements of a representative farm sample are met. We demonstrate the utility of the method for estimating effects of innovative agricultural practices on a region’s impacts by modelling practices on virtual farms and extrapolating their results.

     

Metabolomic profiling of maternal hair suggests rapid development of intrahepatic cholestasis of pregnancy

Introduction

Intrahepatic cholestasis of pregnancy (ICP) is a common maternal liver disease; development can result in devastating consequences, including sudden fetal death and stillbirth. Currently, recognition of ICP only occurs following onset of clinical symptoms.

Objective

Investigate the maternal hair metabolome for predictive biomarkers of ICP.

Methods

The maternal hair metabolome (gestational age of sampling between 17 and 41 weeks) of 38 Chinese women with ICP and 46 pregnant controls was analysed using gas chromatography–mass spectrometry.

Results

Of 105 metabolites detected in hair, none were significantly associated with ICP.

Conclusion

Hair samples represent accumulative environmental exposure over time. Samples collected at the onset of ICP did not reveal any metabolic shifts, suggesting rapid development of the disease.

     

Crop metabolomics: from diagnostics to assisted breeding

Background

Until recently, plant metabolomics have provided a deep understanding on the metabolic regulation in individual plants as experimental units. The application of these techniques to agricultural systems subjected to more complex interactions is a step towards the implementation of translational metabolomics in crop breeding.

Aim of Review

We present here a review paper discussing advances in the knowledge reached in the last years derived from the application of metabolomic techniques that evolved from biomarker discovery to improve crop yield and quality.

Key Scientific Concepts of Review

Translational metabolomics applied to crop breeding programs.

     

Optimization of fecal sample preparation for untargeted LC-HRMS based metabolomics

Introduction

Collecting feces is easy. It offers direct outcome to endogenous and microbial metabolites.

Objectives

In a context of lack of consensus about fecal sample preparation, especially in animal species, we developed a robust protocol allowing untargeted LC-HRMS fingerprinting.

Methods

The conditions of extraction (quantity, preparation, solvents, dilutions) were investigated in bovine feces.

Results

A rapid and simple protocol involving feces extraction with methanol (1/3, M/V) followed by centrifugation and a step filtration (10 kDa) was developed.

Conclusion

The workflow generated repeatable and informative fingerprints for robust metabolome characterization.

     

A generalized computational structure for regional life-cycle assessment

Purpose

Regional life-cycle assessment (LCA) is gaining an increasing attention among LCA scholars and practitioners. Here, we present a generalized computational structure for regional LCA, discuss in-depth the major challenges facing the field, and point to a direction in which we believe regional LCA should be headed.

Methods

Using an example, we first demonstrate that when there is regional heterogeneity (be it due to environmental conditions or technologies), average data would be inadequate for estimating the life-cycle impacts of a product produced in a specific region or even that of an average product produced in many regions. And when there is such regional heterogeneity, an understanding of how regions are connected through commodity flows is important to the accuracy of regional LCA estimates. Then, we present a generalized computational structure for regional LCA that takes into account interregional commodity flows, can evaluate various cases of regional differentiation, and can account for multiple impact categories simultaneously. In so doing, we show what kinds of data are required for this generalized framework of regional LCA.

Results and discussion

We discuss the major challenges facing regional LCA in terms of data requirements and computational complexity, and their implications for the choice of an optimal regional scale (i.e., the number of regions delineated within the geographic boundary studied).

Conclusions

We strongly recommend scholars from LCI and LCIA to work together and choose a spatial scale that not only adequately captures environmental characteristics but also allows inventory data to be reasonably compiled or estimated.

     

Social life cycle assessment of average Irish dairy farm

Purpose

The majority of sustainability studies of dairy farms focused on environmental performance and profitability; however, social aspect has been hardly assessed. This study aims to investigate the social impacts of dairy farm via a case study using a social life cycle assessment framework.

Methods

The assessment was carried out applying the social LCA Guideline by UNEP-SETAC. Nineteen suitable social indicators were selected from four stakeholder categories of the guideline. Characterization and normalization were further developed based on data availability. National farm survey data was used as foreground data for farm activities, supplemented with background data from public database and life cycle working environment (LCWE) data by Gabi database. All indicators were divided into three groups: functional unit-related quantitative indicators, non-functional unit-related quantitative indicators and semi-quantitative indicators.

Results and discussion

Irish dairy farming has positive social impacts on value chain actors and society, predominantly positive impacts for local community and generally positive values for workers. The main negative impacts are health and safety issue, equal opportunity for workers, and safe and healthy living conditions for the local community. Possible actions to improve the social performance include introducing more efficient and robotic milk production systems; applying better handling methods and using real time decision support to operational management for emissions reduction.

Conclusions

This study is the first attempt of social LCA in Ireland. It demonstrated a possible method to carry out SLCA for Irish dairy sector. The results identified the positive and negative social hotspot of dairy farm with recommendation for future improvement.

     

TarMet: a reactive GUI tool for efficient and confident quantification of MS based targeted metabolic and stable isotope tracer analysis

Introduction

Untargeted and targeted analyses are two classes of metabolic study. Both strategies have been advanced by high resolution mass spectrometers coupled with chromatography, which have the advantages of high mass sensitivity and accuracy. State-of-art methods for mass spectrometric data sets do not always quantify metabolites of interest in a targeted assay efficiently and accurately.

Objectives

TarMet can quantify targeted metabolites as well as their isotopologues through a reactive and user-friendly graphical user interface.

Methods

TarMet accepts vendor-neutral data files (NetCDF, mzXML and mzML) as inputs. Then it extracts ion chromatograms, detects peak position and bounds and confirms the metabolites via the isotope patterns. It can integrate peak areas for all isotopologues automatically.

Results

TarMet detects more isotopologues and quantify them better than state-of-art methods, and it can process isotope tracer assay well.

Conclusion

TarMet is a better tool for targeted metabolic and stable isotope tracer analyses.

     

The effect of local climate and soil drainage on the environmental impact of grass-based milk production

Purpose

System modelling and life cycle assessment (LCA) were used to assess the climate change, acidification and eutrophication impacts of milk production using spring calving pasture-based system. The objective of the study was to evaluate the effect of climate and soil resources on the environmental impact per unit milk produced at the farm gate from low-cost, grass-based rotational-grazing dairy production.

Methods

A dairy system model, Dairy_sim, designed to identify optimum grass-based spring calving production systems considering the interaction between climate and soil resources was tested using the Irish National Dairy Blueprint and then used to assess regional differences of system management with well, moderately, mixed moderately-poorly and poorly drained soil resources available. Life cycle assessment was used to quantify environmental impacts of climate and soil drainage status. The Dairy_sim output was used as activity data for the LCA model.

Results and discussion

Differences were found in the management tactics influenced by climate and drainage resource. The impact of poor drainage reduced stocking rate, increased housing time and had greater need for later cut silage and more reliance on silage. Climate change, acidification and eutrophication impacts were greater for optimum management on poorly drained soil. The climate change ranged from 1.06 kg CO₂ eq./kg (well drained) to 1.18 kg CO₂ eq./kg (poorly drained) of energy corrected milk (ECM). The acidification and eutrophication ranged from 3.87 to 6.85 g SO₂ eq./kg ECM and 2.69 to 3.64 g PO₄ eq./kg ECM, respectively. Around 50% of poorly drained soil resource can be easily accommodated in dairy systems with little increase in environmental impact, where poor drained portion is utilised for silage.

Conclusions

LCA combined with a system optimization model revealed how dairy farm management practises constrained by poor land resource increased the environmental impact per unit product.

     

Omgaan met somberheid op oudere leeftijd: een kwalitatieve studie

Background

To gain new insights for support for older people with low mood, we explored the perceptions of ‘screenpositive’ older people on underlying causes and possible solutions.

Design and method

We conducted two in-depth interviews with 38 participants (≥77 years) who screened positive for depressive symptoms in general practice. To investigate the influence of the presence of complex health problems, we included 19 persons with and 19 without complex problems. Complex problems were defined as a combination of functional, somatic, psychological or social problems.

Results

All participants used several cognitive, social or practical coping strategies. Four patterns emerged: mastery, acceptance, ambivalence, and need for support. Some participants, especially those with complex problems, were ambivalent about possible interventions.

Conclusion

Most older participants perceived their coping strategies as sufficient. General practitioners can support self-management by exploring the (effectiveness of) personal coping strategies, providing information, elaborating on perceptions of risks and discussing alternative options with older persons.

     

A comparative life cycle assessment of the sugarcane value chain in the province of Tucumán (Argentina) considering different technology levels

Purpose

The purpose of this work is to quantify the environmental impact of the sugarcane industry in Tucumán (Argentina) through the life cycle analysis (LCA). The distinctive feature is the consideration of different technology levels (TLs) in the agricultural stage: high (HTL), medium (MTL), and low (LTL).

Methods

The scope of the study covers the agricultural and industrial stages through a “from cradle to gate” approach (from sugarcane cultivation until production of finished products: sugar and alcohol). The system is divided into Agriculture, Sugar Factory, and Distillery. Data used for the inventory are mainly provided by local experts, sugarcane growers, and processing companies. The characteristics of each TL are taken from a regional classification. For the impact assessment, the CML 2001 model (nine impact categories) is used.

Results and discussion

Regardless of the TL, in most of the impact categories, an important contribution attributable to the use of synthetic agrochemicals is evident. As for the comparison among TLs, the ethanol produced with HTL has less impact values than the ones produced with MTL and LTL in seven categories. These results can be mainly explained by the better cultural yields obtained with HTL, and to the fact that sugarcane is not burnt before harvesting in HTL as it is in MTL and LTL.

Conclusions

This study explores the implications of using different TLs for the agricultural tasks on the sugarcane supply chain in Tucumán, which is characterized by a vertically nonintegrated productive scheme. If practices associated to HTL are implemented, a reduction of the environmental impact is observed in most categories. It is necessary to compare these results with economic and social implications to ensure sustainability of the sugarcane value chain.

     

A lost opportunity for science: journals promote data sharing in metabolomics but do not enforce it

Introduction

Data sharing is being increasingly required by journals and has been heralded as a solution to the ‘replication crisis’.

Objectives

(i) Review data sharing policies of journals publishing the most metabolomics papers associated with open data and (ii) compare these journals’ policies to those that publish the most metabolomics papers.

Methods

A PubMed search was used to identify metabolomics papers. Metabolomics data repositories were manually searched for linked publications.

Results

Journals that support data sharing are not necessarily those with the most papers associated to open metabolomics data.

Conclusion

Further efforts are required to improve data sharing in metabolomics.

     

Pseudo current density maps of electrophysiological heart,nerve or brain function and their physical basis

Background

In recent years the visualization of biomagnetic measurement data by so-called pseudo current density maps or Hosaka-Cohen (HC) transformations became popular.

Methods

The physical basis of these intuitive maps is clarified by means of analytically solvable problems.

Results

Examples in magnetocardiography, magnetoencephalography and magnetoneurography demonstrate the usefulness of this method.

Conclusion

Hardware realizations of the HC-transformation and some similar transformations are discussed which could advantageously support cross-platform comparability of biomagnetic measurements.

     

Interaction between contrasting rice genotypes and soil physical conditions induced by hydraulic stresses typical of alternate wetting and drying irrigation of soil

Background and aims

Drought events, agricultural practices and plant communities influence microbial and soil abiotic parameters which can feedback to fodder production. This study aimed to determine which soil legacies influence plant biomass production and nutritional quality, and its resistance and recovery to extreme weather events.

Methods

In a greenhouse experiment, soil legacy effects on Lolium perenne were examined, first under optimal conditions, and subsequently during and after drought. We used subalpine grassland soils previously cultivated for two years with grass communities of distinct functional composition, and subjected to combinations of climatic stress and simulated management.

Results

The soil legacy of climatic stress increased biomass production of Lolium perenne and its resistance and recovery to a new drought. This beneficial effect resulted from higher nutrient availability in soils previously exposed to climatic stresses due to lower competitive abilities and resistance of microbial communities to a new drought. This negative effect on microbial communities was strongest in soils from previously cut and fertilized grasslands or dominated by conservative grasses.

Conclusion

In subalpine grasslands more frequent climatic stresses could benefit fodder production in the short term, but threaten ecosystem functioning and the maintenance of traditional agricultural practices in the long term.

     

Untargeted metabolomics suffers from incomplete raw data processing

Introduction

Untargeted metabolomics is a powerful tool for biological discoveries. To analyze the complex raw data, significant advances in computational approaches have been made, yet it is not clear how exhaustive and reliable the data analysis results are.

Objectives

Assessment of the quality of raw data processing in untargeted metabolomics.

Methods

Five published untargeted metabolomics studies, were reanalyzed.

Results

Omissions of at least 50 relevant compounds from the original results as well as examples of representative mistakes were reported for each study.

Conclusion

Incomplete raw data processing shows unexplored potential of current and legacy data.

     

Media optimization for extracellular amylase production by <Emphasis Type="Italic">Pseudomonas balearica</Emphasis> vitps19 using response surface methodology

Background

In this study, we optimized the process for enhancing amylase production from Pseudomonas balearica VITPS19 isolated from agricultural lands in Kolathur, India.

Methods

Process optimization for enhancing amylase production from the isolate was carried out by Response Surface Methodology (RSM) with optimized chemical and physical sources using Design expert v.7.0. A central composite design was used to evaluate the interaction between parameters. Interaction between four factors–maltose (C-source), malt extract (Nsource), pH, and CaCl₂ was studied.

Results

The factors pH and CaCl₂ concentration were found to affect amylase production. Validation of the experiment showed a nearly twofold increase in alpha amylase production.

Conclusion

Amylase production was thus optimized and increased yield was achieved.

     

Life cycle inventory modeling of phosphorus substitution,losses and crop uptake after land application of organic waste products

Purpose

Life cycle assessments (LCAs) that attempt to provide advice on treatment options for phosphorus (P) containing organic waste products encounter problems related to the quantification of mineral P fertilizer substitution, P loss and crop P uptake after land application. The purpose of this study was to develop a relatively easy to use life cycle inventory model, known as PLCI, that could be used to estimate these values.

Methods

A life cycle inventory model for P was developed, which estimates the effect of an application of organic waste followed by ordinary fertilizer management in the modeling period. This was compared with a simulation without the initial waste application. The difference in mineral P fertilizer application (substitution), P loss and crop P uptake was then calculated and expressed as a proportion of the amount of waste applied. As an example, the effect of an initial application of mineral fertilizer, sewage sludge and ash on two farm types was simulated. These results were applied in an LCA case study of different sewage sludge treatment options.

Results and discussion

Farm type influenced the P fertilizer substitution, loss and crop uptake factors. The application on an arable farm showed a substitution of 28 to 31%, relatively low P loss and a large spread in crop P uptake for the different P sources, compared with the pig farm. Application on a pig farm showed no mineral P substitution. For substitution, mineral fertilizer outperformed waste product fertilizer with a short modeling period, due to higher immediate P availability, which was not the case with a long period. The LCA case study showed that the P substitution factor had an influence on the environmental impact categories climate change and depletion of reserve-based abiotic resources while the P loss factor influenced freshwater eutrophication. Application of the P loss and substitution factors generated from the PLCI model resulted in higher environmental burdens and lower savings than using conventional factors.

Conclusions

The soil P status mainly affected P substitution and loss, with the fertilizer type only having a small influence when soils had a low P status. The PLCI model can facilitate more coherent and rigorous estimates of P substitution and loss to be used in LCA studies involving application of waste products on agricultural land. This is important since P substitution and loss can have an important influence on impact categories, such as freshwater eutrophication and resource depletion.

     

Quantitative analysis of tetrahydrofolate metabolites from <Emphasis Type="Italic">clostridium autoethanogenum</Emphasis>

Introduction

Quantification of tetrahydrofolates (THFs), important metabolites in the Wood–Ljungdahl pathway (WLP) of acetogens, is challenging given their sensitivity to oxygen.

Objective

To develop a simple anaerobic protocol to enable reliable THFs quantification from bioreactors.

Methods

Anaerobic cultures were mixed with anaerobic acetonitrile for extraction. Targeted LC–MS/MS was used for quantification.

Results

Tetrahydrofolates can only be quantified if sampled anaerobically. THF levels showed a strong correlation to acetyl-CoA, the end product of the WLP.

Conclusion

Our method is useful for relative quantification of THFs across different growth conditions. Absolute quantification of THFs requires the use of labelled standards.

     

KniMet: a pipeline for the processing of chromatography–mass spectrometry metabolomics data

Introduction

Data processing is one of the biggest problems in metabolomics, given the high number of samples analyzed and the need of multiple software packages for each step of the processing workflow.

Objectives

Merge in the same platform the steps required for metabolomics data processing.

Methods

KniMet is a workflow for the processing of mass spectrometry-metabolomics data based on the KNIME Analytics platform.

Results

The approach includes key steps to follow in metabolomics data processing: feature filtering, missing value imputation, normalization, batch correction and annotation.

Conclusion

KniMet provides the user with a local, modular and customizable workflow for the processing of both GC–MS and LC–MS open profiling data.

     

A methodology for elucidating regulatory mechanisms leading to changes in lipid profiles

Introduction

It is difficult to elucidate the metabolic and regulatory factors causing lipidome perturbations.

Objectives

This work simplifies this process.

Methods

A method has been developed to query an online holistic lipid metabolic network (of 7923 metabolites) to extract the pathways that connect the input list of lipids.

Results

The output enables pathway visualisation and the querying of other databases to identify potential regulators. When used to a study a plasma lipidome dataset of polycystic ovary syndrome, 14 enzymes were identified, of which 3 are linked to ELAVL1—an mRNA stabiliser.

Conclusion

This method provides a simplified approach to identifying potential regulators causing lipid-profile perturbations.

     

Effect of experimental gingivitis induction and erythritol on the salivary metabolome and functional biochemistry of systemically healthy young adults

Introduction

Understanding the changes occurring in the oral ecosystem during development of gingivitis could help improve prevention and treatment strategies for oral health. Erythritol is a non-caloric polyol proposed to have beneficial effects on oral health.

Objectives

To examine the effect of experimental gingivitis and the effect of erythritol on the salivary metabolome and salivary functional biochemistry.

Methods

In a two-week experimental gingivitis challenge intervention study, non-targeted, mass spectrometry-based metabolomic profiling was performed on saliva samples from 61 healthy adults, collected at five time-points. The effect of erythritol was studied in a randomized, controlled trial setting. Fourteen salivary biochemistry variables were measured with antibody- or enzymatic activity-based assays.

Results

Bacterial amino acid catabolites (cadaverine, N-acetylcadaverine, and α-hydroxyisovalerate) and end-products of bacterial alkali-producing pathways (N-α-acetylornithine and γ-aminobutyrate) increased significantly during the experimental gingivitis. Significant changes were found in a set of 13 salivary metabolite ratios composed of host cell membrane lipids involved in cell signaling, host responses to bacteria, and defense against free radicals. An increase in mevalonate was also observed. There were no significant effects of erythritol. No significant changes were found in functional salivary biochemistry.

Conclusions

The findings underline a dynamic interaction between the host and the oral microbial biofilm during an experimental induction of gingivitis.