Historical information sheds new light on the intensification of flooding in the Central Mediterranean

Hydrological disasters, such as floods, can have dire consequences for human societies. Historical information plays a key role in detecting whether particular types of hydrological disasters have increased in frequency and/or magnitude and, if so, they are more likely attributable to natural or human-induced climatic and other environmental changes. The identification of regions with similar flood conditions is essential for the analysis of regional flooding regimes. To this end, we here present the longest existing flood reconstruction for the Eastern Liguria Area (ELA) in northwestern Italy, covering 1582 to 2022 CE, which offers a case study representative of the central Mediterranean region. An Annual Flood Intensification Index was developed to transform the historical data into a continuous annual hydrological time-series contained by a homogeneous data structure for the study-area. We found two change-points (trend breaks) in the reconstructed time-series, in 1787 and 1967, with only occasional heavy floods comparable to present-day disasters occurring before the first change-point, and an increasing intensification of floods after the second change-point up to the present day. The recent intensification of flooding in the ELA, associated with changes in land use and land cover, also appears to coincide with phases in which hydrological hazards have become more changeable and extreme in disaster-affected areas. This is evidenced by river basin responses to human-induced disturbances.

Uncertainties in the adaptation of alpine pastures to climate change based on remote sensing products and modelling

Over the last century, the management of pastoral systems has undergone major changes to meet the livelihood needs of alpine communities. Faced with the changes induced by recent global warming, the ecological status of many pastoral systems has seriously deteriorated in the western alpine region. We assessed changes in pasture dynamics by integrating information from remote-sensing products and two process-based models, i.e. the grassland-specific, biogeochemical growth model PaSim and the generic crop-growth model DayCent. Meteorological observations and satellite-derived Normalised Difference Vegetation Index (NDVI) trajectories of three pasture macro-types (high, medium and low productivity classes) in two study areas - Parc National des Écrins (PNE) in France and Parco Nazionale Gran Paradiso (PNGP) in Italy - were used as a basis for the model calibration work. The performance of the models was satisfactory in reproducing pasture production dynamics (R² = 0.52 to 0.83). Projected changes in alpine pastures due to climate-change impacts and adaptation strategies indicate that: i) the length of the growing season is expected to increase between 15 and 40 days, resulting in changes in the timing and amount of biomass production, ii) summer water stress could limit pasture productivity; iii) earlier onset of grazing could enhance pasture productivity; iv) higher livestock densities could increase the rate of biomass regrowth, but major uncertainties in modelling processes need to be considered; and v) the carbon sequestration potential of pastures could decrease under limited water availability and warming.

How Modelers Model: the Overlooked Social and Human Dimensions in Model Intercomparison Studies

There is a growing realization that the complexity of model ensemble studies depends not only on the models used but also on the experience and approach used by modelers to calibrate and validate results, which remain a source of uncertainty. Here, we applied a multi-criteria decision-making method to investigate the rationale applied by modelers in a model ensemble study where 12 process-based different biogeochemical model types were compared across five successive calibration stages. The modelers shared a common level of agreement about the importance of the variables used to initialize their models for calibration. However, we found inconsistency among modelers when judging the importance of input variables across different calibration stages. The level of subjective weighting attributed by modelers to calibration data decreased sequentially as the extent and number of variables provided increased. In this context, the perceived importance attributed to variables such as the fertilization rate, irrigation regime, soil texture, pH, and initial levels of soil organic carbon and nitrogen stocks was statistically different when classified according to model types. The importance attributed to input variables such as experimental duration, gross primary production, and net ecosystem exchange varied significantly according to the length of the modeler's experience. We argue that the gradual access to input data across the five calibration stages negatively influenced the consistency of the interpretations made by the modelers, with cognitive bias in "trial-and-error" calibration routines. Our study highlights that overlooking human and social attributes is critical in the outcomes of modeling and model intercomparison studies. While complexity of the processes captured in the model algorithms and parameterization is important, we contend that (1) the modeler's assumptions on the extent to which parameters should be altered and (2) modeler perceptions of the importance of model parameters are just as critical in obtaining a quality model calibration as numerical or analytical details.

Environmental drivers of dynamic soil erosion change in a Mediterranean fluvial landscape

Background

Rainfall and other climatic agents are the main triggers of soil erosion in the Mediterranean region, where they have the potential to increase discharge and sediment transport and cause long-term changes in the river system. For the Magra River Basin (MRB), located in the upper Tyrrhenian coast of Italy, we estimated changes in net erosion as a function of the geographical characteristics of the basin, the seasonal distribution of precipitation, and the vegetation cover.

Methods and findings

Based on rainfall erosivity and surface flow and transport sub-models, we developed a simplified model to assess basin-wide sediment yields on a monthly basis by upscaling the point rainfall input. Our calibration dataset of monthly data (Mg km^-2 month^-1, available for the years 1961 and 1963–1969) revealed that our model satisfactorily reproduces the net soil erosion in the study area (R² = 0.81). For the period 1950–2020, the reconstruction of an annually aggregated time-series of monthly net erosion data (297 Mg km^-2 yr^-1 on average) indicated a moderate decline in sediment yield after 1999. This is part of a long-term downward trend, which highlights the role played by land-use changes and reforestation of the mountainous areas of the basin.

Conclusion

This study shows the environmental history and dynamics of the basin, and thus the varying sensitivity of hydrological processes and their perturbations. Relying on a few climatic variables as reported from a single representative basin location, it provides an interpretation of empirically determined factors that shape active erosional landscapes. In particular, we showed that the most recent extreme storms associated with sediment yield have been characterised by lower cumulative rainfall, indicating a greater propensity for the basin to produce sediment more discontinuously over time.

Ensemble modelling, uncertainty and robust predictions of organic carbon in long-term bare-fallow soils

Simulation models represent soil organic carbon (SOC) dynamics in global carbon (C) cycle scenarios to support climate-change studies. It is imperative to increase confidence in long-term predictions of SOC dynamics by reducing the uncertainty in model estimates. We evaluated SOC simulated from an ensemble of 26 process-based C models by comparing simulations to experimental data from seven long-term bare-fallow (vegetation-free) plots at six sites: Denmark (two sites), France, Russia, Sweden and the United Kingdom. The decay of SOC in these plots has been monitored for decades since the last inputs of plant material, providing the opportunity to test decomposition without the continuous input of new organic material. The models were run independently over multi-year simulation periods (from 28 to 80 years) in a blind test with no calibration (Bln) and with the following three calibration scenarios, each providing different levels of information and/or allowing different levels of model fitting: (a) calibrating decomposition parameters separately at each experimental site (Spe); (b) using a generic, knowledge-based, parameterization applicable in the Central European region (Gen); and (c) using a combination of both (a) and (b) strategies (Mix). We addressed uncertainties from different modelling approaches with or without spin-up initialization of SOC. Changes in the multi-model median (MMM) of SOC were used as descriptors of the ensemble performance. On average across sites, Gen proved adequate in describing changes in SOC, with MMM equal to average SOC (and standard deviation) of 39.2 (±15.5) Mg C/ha compared to the observed mean of 36.0 (±19.7) Mg C/ha (last observed year), indicating sufficiently reliable SOC estimates. Moving to Mix (37.5 ± 16.7 Mg C/ha) and Spe (36.8 ± 19.8 Mg C/ha) provided only marginal gains in accuracy, but modellers would need to apply more knowledge and a greater calibration effort than in Gen, thereby limiting the wider applicability of models.

Analytical Method Performance Evaluation (AMPE)A Software Tool for Analytical Method Validation

A Windows-based software tool [Analytical Method Performance Evaluation (AMPE)] was developed to support the validation of analytical methods. The software implements standard statistical approaches commonly adopted in validation studies to estimate analytical method performance (limits of detection and quantitation, accuracy, specificity, working range, and linearity of responses) according to ISO 5725. In addition, AMPE proposes the application of innovative and unique approaches for the assessment of analytical method performance. Specifically, AMPE proposes the use of difference-based indexes to quantify the agreement between measurements and reference values, the use of pattern indexes to quantify methods bias with respect to specific external variables, and the application of fuzzy logic to aggregate into synthetic indicators the information collected independently via the different performance statistics traditionally estimated in validation studies. Aggregated measures are particularly useful for methods comparison, when more than one method is available for a specific analysis and itmay be of interest to identify the best performing one taking into account, simultaneously, the information available from different performance statistics. Illustrative examples of the type of outputs expected from AMPE-based validation sessions are given. The extensive data handling capabilities and the wide range of statistics supplied in the software package makes AMPE suitable for specific needs that may arise in different validation studies. The installation package, complete with a fully documented help file, is distributed free of charge to interested users along with input files exemplary of the type of entry data required to run validation data analyses.

Quantifying Uncertainty Due to Stochastic Weather Generators in Climate Change Impact Studies

Climate change studies involve complex processes translating coarse climate change projections in locally meaningful terms. We analysed the behaviour of weather generators while downscaling precipitation and air temperature data. With multiple climate indices and alternative weather generators, we directly quantified the uncertainty associated with using weather generators when site specific downscaling is performed. We extracted the influence of weather generators on climate variability at local scale and the uncertainty that could affect impact assessment. For that, we first designed the downscaling experiments with three weather generators (CLIMAK, LARS-WG, WeaGETS) to interpret future projections. Then we assessed the impacts of estimated changes of precipitation and air temperature for a sample of 15 sites worldwide using a rice yield model and an extended set of climate metrics. We demonstrated that the choice of a weather generator in the downscaling process may have a higher impact on crop yield estimates than the climate scenario adopted. Should they be confirmed, these results would indicate that widely accepted outcomes of climate change studies using this downscaling technique need reconsideration.

Use of identifiability analysis in designing phenotyping experiments for modelling forage production and quality

Agricultural systems models are complex and tend to be over-parameterized with respect to observational datasets. Practical identifiability analysis based on local sensitivity analysis has proved effective in investigating identifiable parameter sets in environmental models, but has not been applied to agricultural systems models. Here, we demonstrate that identifiability analysis improves experimental design to ensure independent parameter estimation for yield and quality outputs of a complex grassland model. The Pasture Simulation model (PaSim) was used to demonstrate the effectiveness of practical identifiability analysis in designing experiments and measurement protocols within phenotyping experiments with perennial ryegrass. Virtual experiments were designed combining three factors: frequency of measurements, duration of the experiment. and location of trials. Our results demonstrate that (i) PaSim provides sufficient detail in terms of simulating biomass yield and quality of perennial ryegrass for use in breeding, (ii) typical breeding trials are insufficient to parameterize all influential parameters, (iii) the frequency of measurements is more important than the number of growing seasons to improve the identifiability of PaSim parameters, and (iv) identifiability analysis provides a sound approach for optimizing the design of multi-location trials. Practical identifiability analysis can play an important role in ensuring proper exploitation of phenotypic data and cost-effective multi-location experimental designs. Considering the growing importance of simulation models, this study supports the design of experiments and measurement protocols in the phenotyping networks that have recently been organized.

The use of biogeochemical models to evaluate mitigation of greenhouse gas emissions from managed grasslands

Simulation models quantify the impacts on carbon (C) and nitrogen (N) cycling in grassland systems caused by changes in management practices. To support agricultural policies, it is however important to contrast the responses of alternative models, which can differ greatly in their treatment of key processes and in their response to management. We applied eight biogeochemical models at five grassland sites (in France, New Zealand, Switzerland, United Kingdom and United States) to compare the sensitivity of modelled C and N fluxes to changes in the density of grazing animals (from 100% to 50% of the original livestock densities), also in combination with decreasing N fertilization levels (reduced to zero from the initial levels). Simulated multi-model median values indicated that input reduction would lead to an increase in the C sink strength (negative net ecosystem C exchange) in intensive grazing systems: -64 ± 74 g C m^-2 yr^-1 (animal density reduction) and -81 ± 74 g C m^-2 yr^-1 (N and animal density reduction), against the baseline of -30.5 ± 69.5 g C m^-2 yr^-1 (LSU [livestock units] ≥ 0.76 ha^-1 yr^-1). Simulations also indicated a strong effect of N fertilizer reduction on N fluxes, e.g. N₂O-N emissions decreased from 0.34 ± 0.22 (baseline) to 0.1 ± 0.05 g N m^-2 yr^-1 (no N fertilization). Simulated decline in grazing intensity had only limited impact on the N balance. The simulated pattern of enteric methane emissions was dominated by high model-to-model variability. The reduction in simulated offtake (animal intake + cut biomass) led to a doubling in net primary production per animal (increased by 11.6 ± 8.1 t C LSU^-1 yr^-1 across sites). The highest N₂O-N intensities (N₂O-N/offtake) were simulated at mown and extensively grazed arid sites. We show the possibility of using grassland models to determine sound mitigation practices while quantifying the uncertainties associated with the simulated outputs.

Impacts of climate change adaptation options on soil functions: A review of European case-studies

Soils are vital for supporting food security and other ecosystem services. Climate change can affect soil functions both directly and indirectly. Direct effects include temperature, precipitation, and moisture regime changes. Indirect effects include those that are induced by adaptations such as irrigation, crop rotation changes, and tillage practices. Although extensive knowledge is available on the direct effects, an understanding of the indirect effects of agricultural adaptation options is less complete. A review of 20 agricultural adaptation case-studies across Europe was conducted to assess implications to soil threats and soil functions and the link to the Sustainable Development Goals (SDGs). The major findings are as follows: (a) adaptation options reflect local conditions; (b) reduced soil erosion threats and increased soil organic carbon are expected, although compaction may increase in some areas; (c) most adaptation options are anticipated to improve the soil functions of food and biomass production, soil organic carbon storage, and storing, filtering, transforming, and recycling capacities, whereas possible implications for soil biodiversity are largely unknown; and (d) the linkage between soil functions and the SDGs implies improvements to SDG 2 (achieving food security and promoting sustainable agriculture) and SDG 13 (taking action on climate change), whereas the relationship to SDG 15 (using terrestrial ecosystems sustainably) is largely unknown. The conclusion is drawn that agricultural adaptation options, even when focused on increasing yields, have the potential to outweigh the negative direct effects of climate change on soil degradation in many European regions.

Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N2 O emissions

Simulation models are extensively used to predict agricultural productivity and greenhouse gas emissions. However, the uncertainties of (reduced) model ensemble simulations have not been assessed systematically for variables affecting food security and climate change mitigation, within multi-species agricultural contexts. We report an international model comparison and benchmarking exercise, showing the potential of multi-model ensembles to predict productivity and nitrous oxide (N₂ O) emissions for wheat, maize, rice and temperate grasslands. Using a multi-stage modelling protocol, from blind simulations (stage 1) to partial (stages 2-4) and full calibration (stage 5), 24 process-based biogeochemical models were assessed individually or as an ensemble against long-term experimental data from four temperate grassland and five arable crop rotation sites spanning four continents. Comparisons were performed by reference to the experimental uncertainties of observed yields and N₂ O emissions. Results showed that across sites and crop/grassland types, 23%-40% of the uncalibrated individual models were within two standard deviations (SD) of observed yields, while 42 (rice) to 96% (grasslands) of the models were within 1 SD of observed N₂ O emissions. At stage 1, ensembles formed by the three lowest prediction model errors predicted both yields and N₂ O emissions within experimental uncertainties for 44% and 33% of the crop and grassland growth cycles, respectively. Partial model calibration (stages 2-4) markedly reduced prediction errors of the full model ensemble E-median for crop grain yields (from 36% at stage 1 down to 4% on average) and grassland productivity (from 44% to 27%) and to a lesser and more variable extent for N₂ O emissions. Yield-scaled N₂ O emissions (N₂ O emissions divided by crop yields) were ranked accurately by three-model ensembles across crop species and field sites. The potential of using process-based model ensembles to predict jointly productivity and N₂ O emissions at field scale is discussed.

Universal newborn hearing screening using A-TEOAE and A-ABR: The experience of a large public hospital

BACKGROUND

Universal newborn hearing screening (UNHS) aims to identify hearing loss in the early postnatal period; prompt detection of bilateral or unilateral hearing loss is mandatory for timely intervention.

METHODS

This retrospective study reports the results of the first two years of a UNHS program on 4,719 newborns in a large public Italian hospital. Screening was divided into two levels: automated transient otoacoustic emissions were used for first level; automated auditory brainstem response for second level. Second level included children with a "refer" response at first level and babies with a family history for hearing loss or other risk factors. Hearing loss diagnosis was made using clinical auditory brainstem response.

RESULTS

During first level, 254 (5.4% ) newborns were "refer". At retest, 130 (51.1% ) babies were PASS and 48 (18.8% ) were "refer". 76 babies dropped out (29.9% ). 146 babies (3.1% ) were referred to the second level: 48 for a "refer" response at first level and 98 for a PASS response but potential hearing loss due to risk factors. 24 babies dropped out (16.4% ). Out of 122 newborns tested in the second level, 105 (86.1% ) had a PASS response and 17 (13.9% ) were "refer". Our screening protocol identified 7 (0.14% ) babies with profound hearing loss; 5 had unilateral and 2 had bilateral hearing loss. 2 babies dropped out at diagnostic level (11.8% ).

CONCLUSIONS

A correct and early diagnosis of hearing loss is mandatory to prevent permanent consequences; the spread of hearing screening programs is the optimal solution to reach this goal.

Review and analysis of strengths and weaknesses of agro-ecosystem models for simulating C and N fluxes

Biogeochemical simulation models are important tools for describing and quantifying the contribution of agricultural systems to C sequestration and GHG source/sink status. The abundance of simulation tools developed over recent decades, however, creates a difficulty because predictions from different models show large variability. Discrepancies between the conclusions of different modelling studies are often ascribed to differences in the physical and biogeochemical processes incorporated in equations of C and N cycles and their interactions. Here we review the literature to determine the state-of-the-art in modelling agricultural (crop and grassland) systems. In order to carry out this study, we selected the range of biogeochemical models used by the CN-MIP consortium of FACCE-JPI (http://www.faccejpi.com): APSIM, CERES-EGC, DayCent, DNDC, DSSAT, EPIC, PaSim, RothC and STICS. In our analysis, these models were assessed for the quality and comprehensiveness of underlying processes related to pedo-climatic conditions and management practices, but also with respect to time and space of application, and for their accuracy in multiple contexts. Overall, it emerged that there is a possible impact of ill-defined pedo-climatic conditions in the unsatisfactory performance of the models (46.2%), followed by limitations in the algorithms simulating the effects of management practices (33.1%). The multiplicity of scales in both time and space is a fundamental feature, which explains the remaining weaknesses (i.e. 20.7%). Innovative aspects have been identified for future development of C and N models. They include the explicit representation of soil microbial biomass to drive soil organic matter turnover, the effect of N shortage on SOM decomposition, the improvements related to the production and consumption of gases and an adequate simulations of gas transport in soil. On these bases, the assessment of trends and gaps in the modelling approaches currently employed to represent biogeochemical cycles in crop and grassland systems appears an essential step for future research.

Hydro-climatic forcing of dissolved organic carbon in two boreal lakes of Canada

The boreal forest of the northern hemisphere represents one of the world's largest ecozones and contains nearly one third of the world's intact forests and terrestrially stored carbon. Long-term variations in temperature and precipitation have been implied in altering carbon cycling in forest soils, including increased fluxes to receiving waters. In this study, we use a simple hydrologic model and a 40-year dataset (1971-2010) of dissolved organic carbon (DOC) from two pristine boreal lakes (ELA, Canada) to examine the interactions between precipitation and landscape-scale controls of DOC production and export from forest catchments to surface waters. Our results indicate that a simplified hydrologically-based conceptual model can enable the long-term temporal patterns of DOC fluxes to be captured within boreal landscapes. Reconstructed DOC exports from forested catchments in the period 1901-2012 follow largely a sinusoidal pattern, with a period of about 37years and are tightly linked to multi-decadal patterns of precipitation. By combining our model with long-term precipitation estimates, we found no evidence of increasing DOC transport or in-lake concentrations through the 20th century.

Key challenges and priorities for modelling European grasslands under climate change

Grassland-based ruminant production systems are integral to sustainable food production in Europe, converting plant materials indigestible to humans into nutritious food, while providing a range of environmental and cultural benefits. Climate change poses significant challenges for such systems, their productivity and the wider benefits they supply. In this context, grassland models have an important role in predicting and understanding the impacts of climate change on grassland systems, and assessing the efficacy of potential adaptation and mitigation strategies. In order to identify the key challenges for European grassland modelling under climate change, modellers and researchers from across Europe were consulted via workshop and questionnaire. Participants identified fifteen challenges and considered the current state of modelling and priorities for future research in relation to each. A review of literature was undertaken to corroborate and enrich the information provided during the horizon scanning activities. Challenges were in four categories relating to: 1) the direct and indirect effects of climate change on the sward 2) climate change effects on grassland systems outputs 3) mediation of climate change impacts by site, system and management and 4) cross-cutting methodological issues. While research priorities differed between challenges, an underlying theme was the need for accessible, shared inventories of models, approaches and data, as a resource for stakeholders and to stimulate new research. Developing grassland models to effectively support efforts to tackle climate change impacts, while increasing productivity and enhancing ecosystem services, will require engagement with stakeholders and policy-makers, as well as modellers and experimental researchers across many disciplines. The challenges and priorities identified are intended to be a resource 1) for grassland modellers and experimental researchers, to stimulate the development of new research directions and collaborative opportunities, and 2) for policy-makers involved in shaping the research agenda for European grassland modelling under climate change.

Testing the interaction between analytical modules: an example with Roundup Ready soybean line GTS 40-3-2

BACKGROUND

The modular approach to analysis of genetically modified organisms (GMOs) relies on the independence of the modules combined (i.e. DNA extraction and GM quantification). The validity of this assumption has to be proved on the basis of specific performance criteria.

RESULTS

An experiment was conducted using, as a reference, the validated quantitative real-time polymerase chain reaction (PCR) module for detection of glyphosate-tolerant Roundup Ready(R) GM soybean (RRS). Different DNA extraction modules (CTAB, Wizard and Dellaporta), were used to extract DNA from different food/feed matrices (feed, biscuit and certified reference material [CRM 1%]) containing the target of the real-time PCR module used for validation. Purity and structural integrity (absence of inhibition) were used as basic criteria that a DNA extraction module must satisfy in order to provide suitable template DNA for quantitative real-time (RT) PCR-based GMO analysis. When performance criteria were applied (removal of non-compliant DNA extracts), the independence of GMO quantification from the extraction method and matrix was statistically proved, except in the case of Wizard applied to biscuit. A fuzzy logic-based procedure also confirmed the relatively poor performance of the Wizard/biscuit combination.

CONCLUSIONS

For RRS, this study recognises that modularity can be generally accepted, with the limitation of avoiding combining highly processed material (i.e. biscuit) with a magnetic-beads system (i.e. Wizard).

Use of pJANUS-02-001 as a calibrator plasmid for Roundup Ready soybean event GTS-40-3-2 detection: an interlaboratory trial assessment

Owing to the labelling requirements of food and feed products containing materials derived from genetically modified organisms, quantitative detection methods have to be developed for this purpose, including the necessary certified reference materials and calibrator standards. To date, for most genetically modified organisms authorized in the European Union, certified reference materials derived from seed powders are being developed. Here, an assessment has been made on the feasibility of using plasmid DNA as an alternative calibrator for the quantitative detection of genetically modified organisms. For this, a dual-target plasmid, designated as pJANUS-02-001, comprising part of a junction region of genetically modified soybean event GTS-40-3-2 and the endogenous soybean-specific lectin gene was constructed. The dynamic range, efficiency and limit of detection for the soybean event GTS-40-3-2 real-time quantitative polymerase chain reaction (Q-PCR) system described by Terry et al. (J AOAC Int 85(4):938-944, 2002) were shown to be similar for in house produced homozygous genomic DNA from leaf tissue of soybean event GTS-40-3-2 and for plasmid pJANUS-02-001 DNA backgrounds. The performance of this real-time Q-PCR system using both types of DNA templates as calibrator standards in quantitative DNA analysis was further assessed in an interlaboratory trial. Statistical analysis and fuzzy-logic-based interpretation were performed on critical method parameters (as defined by the European Network of GMO Laboratories and the Community Reference Laboratory for GM Food and Feed guidelines) and demonstrated that the plasmid pJANUS-02-001 DNA represents a valuable alternative to genomic DNA as a calibrator for the quantification of soybean event GTS-40-3-2 in food and feed products.

Fuzzy-logic based strategy for validation of multiplex methods: example with qualitative GMO assays

This paper illustrates the advantages that a fuzzy-based aggregation method could bring into the validation of a multiplex method for GMO detection (DualChip GMO kit, Eppendorf). Guidelines for validation of chemical, bio-chemical, pharmaceutical and genetic methods have been developed and ad hoc validation statistics are available and routinely used, for in-house and inter-laboratory testing, and decision-making. Fuzzy logic allows summarising the information obtained by independent validation statistics into one synthetic indicator of overall method performance. The microarray technology, introduced for simultaneous identification of multiple GMOs, poses specific validation issues (patterns of performance for a variety of GMOs at different concentrations). A fuzzy-based indicator for overall evaluation is illustrated in this paper, and applied to validation data for different genetically modified elements. Remarks were drawn on the analytical results. The fuzzy-logic based rules were shown to be applicable to improve interpretation of results and facilitate overall evaluation of the multiplex method.

Modelling vegetation greenness responses to climate variability in a Mediterranean terrestrial ecosystem

This work presents a modelling study where monthly-based climate data are used to estimate the Normalized Difference Vegetation Index (NDVI). The latter is a measure of vegetation greenness, usually derived from satellite-driven information. A model was developed to link NDVI data to rainfall and temperature measures. The test area was a 3 x 3 km grid centred to the top of Monte Pino hill (Southern Italy), for which multi-year (from 1996 to 2004) climate and satellite-derived NDVI data were available. The simulated NDVI data compared well with the remote-sensed measurements (e.g. modelling efficiency approximately 0.80), thus showing a strong linking between vegetation greenness and climate patterns in spite of the many disturbances exerted from farming. The model was used to reconstruct an extended series of monthly NDVI values for a period antecedent 1996 (1972-1995). The analysis of long-term anomalies indicated a positive trend of NDVI over time, consistent with the air temperature increase registered in the same period.

Analytical Method Performance Evaluation (AMPE)--a software tool for analytical method validation

A Windows-based software tool [Analytical Method Performance Evaluation (AMPE)] was developed to support the validation of analytical methods. The software implements standard statistical approaches commonly adopted in validation studies to estimate analytical method performance (limits of detection and quantitation, accuracy, specificity, working range, and linearity of responses) according to ISO 5725. In addition, AMPE proposes the application of innovative and unique approaches for the assessment of analytical method performance. Specifically, AMPE proposes the use of difference-based indexes to quantify the agreement between measurements and reference values, the use of pattern indexes to quantify methods bias with respect to specific external variables, and the application of fuzzy logic to aggregate into synthetic indicators the information collected independently via the different performance statistics traditionally estimated in validation studies. Aggregated measures are particularly useful for methods comparison, when more than one method is available for a specific analysis and it may be of interest to identify the best performing one taking into account, simultaneously, the information available from different performance statistics. Illustrative examples of the type of outputs expected from AMPE-based validation sessions are given. The extensive data handling capabilities and the wide range of statistics supplied in the software package makes AMPE suitable for specific needs that may arise in different validation studies. The installation package, complete with a fully documented help file, is distributed free of charge to interested users along with input files exemplary of the type of entry data required to run validation data analyses.