Prediction of Indirect Indicators of a Grass-Based Diet by Milk Fourier Transform Mid-Infrared Spectroscopy to Assess the Feeding Typologies of Dairy Farms.

This research aims to develop a predictive model to discriminate milk produced from a cattle diet either based on grass or not using milk mid-infrared spectrometry and the month of testing (an indirect indicator of the feeding ration). The dataset contained 3,377,715 spectra collected between 2011 and 2021 from 2449 farms and 3 grazing traits defined following the month of testing. Records from 30% of the randomly selected farms were kept in the calibration set, and the remaining records were used to validate the models. Around 90% of the records were correctly discriminated. This accuracy is very good, as some records could be erroneously assigned. The probability of belonging to the GRASS modality allowed confirmation of the model's ability to detect the transition period even if the model was not trained on this data. Indeed, the probability increased from the spring to the summer and then decreased. The discrimination was mainly explained by the changes in the milk fat, mineral, and protein compositions. A hierarchical clustering from the averaged probability per farm and year highlighted 12 groups illustrating different management practices. The probability of belonging to the GRASS class could be used in a tool counting the number of grazing days.

LCA Capability Roadmap: Product System Model Description and Revision.

Life cycle assessment (LCA) practitioners face many challenges in their efforts to describe, share, review, and revise their product system models; and to reproduce the models and results of others. Current Life cycle inventory modeling techniques have weaknesses in the areas of describing model structure; documenting the use of proxy or non-ideal data; specifying allocation; and including modeler's observations and assumptions -- all affecting how the study is interpreted and limiting the reuse of models. Moreover, LCA software systems manage modeling information in different and sometimes non-compatible ways. Practitioners must also deal with licensing, privacy / confidentiality of data, and other issues around data access which impact how a model can be shared. The aim of this SETAC North America working group is to define a roadmap of the technical advances needed to achieve easier LCA model sharing and improve replicability of LCA results among different users in a way that is independent of the LCA software used to compute the results and does not infringe on any licensing restrictions or confidentiality requirements.

Machine learning for toxicity characterization of organic chemical emissions using USEtox database: Learning the structure of the input space.

Toxicity characterization of chemical emissions in Life Cycle Assessment (LCA) is a complex task which usually proceeds via multimedia (fate, exposure and effect) models attached to models of dose-response relationships to assess the effects on target. Different models and approaches do exist, but all require a vast amount of data on the properties of the chemical compounds being assessed, which are hard to collect or hardly publicly available (especially for thousands of less common or newly developed chemicals), therefore hampering in practice the assessment in LCA. An example is USEtox, a consensual model for the characterization of human toxicity and freshwater ecotoxicity. This paper places itself in a line of research aiming at providing a methodology to reduce the number of input parameters necessary to run multimedia fate models, focusing in particular to the application of the USEtox toxicity model. By focusing on USEtox, in this paper two main goals are pursued: 1) performing an extensive exploratory analysis (using dimensionality reduction techniques) of the input space constituted by the substance-specific properties at the aim of detecting particular patterns in the data manifold and estimating the dimension of the subspace in which the data manifold actually lies; and 2) exploring the application of a set of linear models, based on partial least squares (PLS) regression, as well as a nonlinear model (general regression neural network--GRNN) in the seek for an automatic selection strategy of the most informative variables according to the modelled output (USEtox factor). After extensive analysis, the intrinsic dimension of the input manifold has been identified between three and four. The variables selected as most informative may vary according to the output modelled and the model used, but for the toxicity factors modelled in this paper the input variables selected as most informative are coherent with prior expectations based on scientific knowledge of toxicity factors modelling. Thus the outcomes of the analysis are promising for the future application of the approach to other portions of the model, affected by important data gaps, e.g., to the calculation of human health effect factors.

Applying consequential LCA to support energy policy: land use change effects of bioenergy production.

Luxembourg aims at complying with the EU objective of attaining a 14% use of bioenergy in the national grid by 2020. The increase of biomethane production from energy crops could be a valuable option in achieving this objective. However, the overall environmental benefit of such option is yet to be proven. Consequential Life Cycle Assessment (CLCA) has shown to be a useful tool to evaluate the environmental suitability of future energy scenarios and policies. The objective of this study was, therefore, to evaluate the environmental consequences of modifying the Luxembourgish agricultural system to increase maize production for biomethane generation. A total of 10 different scenarios were modelled using a partial equilibrium (PE) model to identify changes in land cultivation based on farmers' revenue maximisation, which were then compared to the baseline scenario, i.e. the state of the agricultural sector in 2009. The results were divided into three different consequential decision contexts, presenting differing patterns in terms of land use changes (LUCs) but with minor shifts in environmental impacts. Nevertheless, energy from maize production would imply substantially higher environmental impacts when compared with the current use of natural gas, mainly due to increases in climate change and agricultural land occupation impacts. The results are discussed based on the consequences they may generate on the bioenergy policy, the management of arable land, the changes in import-export flows in Luxembourg and LUCs in the domestic agricultural system. In addition, the specific PE+LCA method presented intends to be of use for other regional studies in which a high level of site-specific data is available.

Emergy evaluation using the calculation software SCALE: case study, added value and potential improvements.

This paper reports the emergy-based evaluation (EME) of the ecological performance of four water treatment plants (WTPs) using three different approaches. The results obtained using the emergy calculation software SCALE (EMESCALE) are compared with those achieved through a conventional emergy evaluation procedure (EMECONV), as well as through the application of the Solar Energy Demand (SED) method. SCALE's results are based on a detailed representation of the chain of technological processes provided by the lifecycle inventory database ecoinvent®. They benefit from a higher level of details in the description of the technological network as compared to the ones calculated with a conventional EME and, unlike the SED results, are computed according to the emergy algebra rules. The analysis delves into the quantitative comparison of unit emergy values (UEVs) for individual technospheric inputs provided by each method, demonstrating the added value of SCALE to enhance reproducibility, accurateness and completeness of an EME. However, SCALE cannot presently include non-technospheric inputs in emergy accounting, like e.g. human labor and ecosystem services. Moreover, SCALE is limited by the approach used to build the dataset of UEVs for natural resources. Recommendations on the scope and accuracy of SCALE-based emergy accounting are suggested for further steps in software development, as well as preliminary quantitative methods to account for ecosystem services and human labor.

POEMS: a case study of an Italian wine-producing firm.

Over the last decade, researchers paid much attention to concepts such as Design for Environment, Extended Producer Responsibility, Responsible Chain Management, and Eco-design. Many management tools and standards (such as EMAS, ISO 14001, LCA, EPD, Ecolabel) have been developed to support companies in the evaluation and management of their environmental performance and to pursue continual environmental improvement. The more recent development of the aforesaid fields looks at interorganizational environmental management. Such an approach can complement the more traditional intraorganizational corporate environmental management approaches and tools. A typical example of this new trend is the Product Oriented Environmental Management System (POEMS), which represents the natural evolution of the above-mentioned tools, combining the features of EMS, EPD and Ecolabel. Although the structure of the POEMS is still not standardized, many experimental applications have yet been carried out in Europe. In developing a POEMS, a company needs to determine all of the environmental impacts caused at all life-cycle stages of the product and, ideally, to reduce all of them through a continual commitment. The aim of the present study was to perform a survey of the developed POEMS models and to analyze their peculiarities and drawbacks in the application to Small and Medium Enterprises. A case study regarding an Italian winery company is presented. The study analyzes the structure and the activities of the examined firm, in order to estimate direct and indirect environmental impacts following a life-cycle approach. The chosen functional unit is a 0.75-L bottle of red wine. The article also suggests some solutions to improve the environmental performances of the firm's products.