On-Farm Experimentation to transform global agriculture.

Restructuring farmer-researcher relationships and addressing complexity and uncertainty through joint exploration are at the heart of On-Farm Experimentation (OFE). OFE describes new approaches to agricultural research and innovation that are embedded in real-world farm management, and reflects new demands for decentralized and inclusive research that bridges sources of knowledge and fosters open innovation. Here we propose that OFE research could help to transform agriculture globally. We highlight the role of digitalization, which motivates and enables OFE by dramatically increasing scales and complexity when investigating agricultural challenges.

Life cycle assessment of forecasting scenarios for urban water management: A first implementation of the WaLA model on Paris suburban area.

A framework and an associated modeling tool to perform life cycle assessment (LCA) of urban water system, namely the WaLA model, has been recently developed. In this paper, the WaLA model is applied to a real case study: the urban water system of the Paris suburban area, in France. It aims to verify the capacity of the model to provide environmental insights to stakeholder's issues related to future trends influencing the system (e.g., evolution of water demand, increasing water scarcity) or policy responses (e.g., choices of water resources and technologies). This is achieved by evaluating a baseline scenario for 2012 and several forecasting scenarios for 2022 and 2050. The scenarios are designed through the modeling tool WaLA, which is implemented in Simulink/Matlab: it combines components representing the different technologies, users and resources of the UWS. The life cycle inventories of the technologies and users components include water quantity and quality changes, specific operation (electricity, chemicals) and infrastructures data (construction materials). The methods selected for the LCIA are midpoint ILCD, midpoint water deprivation impacts at the sub-river basin scale, and endpoint Impact 2002+. The results of the baseline scenario show that wastewater treatment plants have the highest impacts compared to drinking water production and distribution, as traditionally encountered in LCA of UWS. The results of the forecasting scenarios show important changes in water deprivation impacts due to water management choices or effects of climate change. They also enable to identify tradeoffs with other impact categories and to compare several scenarios. It suggests the capacity of the model to deliver information for decision making about future policies.

WaLA, a versatile model for the life cycle assessment of urban water systems: Formalism and framework for a modular approach.

The emphasis on the sustainable urban water management has increased over the last decades. In this context decision makers need tools to measure and improve the environmental performance of urban water systems (UWS) and their related scenarios. In this paper, we propose a versatile model, named WaLA (Water system Life cycle Assessment), which reduces the complexity of the UWS while ensuring a good representation of water issues and fulfilling life cycle assessment (LCA) requirements. Indeed, LCAs require building UWS models, which can be tedious if several scenarios are to be compared. The WaLA model is based on a framework that uses a "generic component" representing alternately water technology units and water users, with their associated water flows, and the associated impacts due to water deprivation, emissions, operation and infrastructure. UWS scenarios can be built by inter-operating and connecting the technologies and users components in a modular and integrated way. The model calculates life cycle impacts at a monthly temporal resolution for a set of services provided to users, as defined by the scenario. It also provides the ratio of impacts to amount of services provided and useful information for UWS diagnosis or comparison of different scenarios. The model is implemented in a Matlab/Simulink interface thanks to object-oriented programming. The applicability of the model is demonstrated using a virtual case study based on available life cycle inventory data.

Improvement of the chemical content prediction of a model powder system by reducing multiple scattering using polarized light spectroscopy.

Near-infrared spectroscopy (NIRS) is a powerful non-destructive analytical method used to analyze major compounds in bulk materials and products and requiring no sample preparation. It is widely used in routine analysis and also in line in industries, in vivo with biomedical applications, or in field for agricultural and environmental applications. However, highly scattering samples subvert Beer-Lambert law's linear relationship between spectral absorbance and the concentration. Instead of spectral pre-processing, which is commonly used by NIR spectroscopists to mitigate the scattering effect, we put forward an optical method, i.e., coupling polarized light with NIR spectrometry, to free spectra from scattering effect. This should allow us to retrieve linear and steady conditions for spectral analysis. When tested in visible-NIR (Vis-NIR) range (400-800 nm) on model media, mixtures of scattering and absorbing particles, the setup provided significant improvements in absorber concentration estimation precision as well as in the quality and robustness of the calibration model.

Combining linear polarization spectroscopy and the Representative Layer Theory to measure the Beer-Lambert law absorbance of highly scattering materials.

Visible and Near Infrared (Vis-NIR) Spectroscopy is a powerful non destructive analytical method used to analyze major compounds in bulk materials and products and requiring no sample preparation. It is widely used in routine analysis and also in-line in industries, in-vivo with biomedical applications or in-field for agricultural and environmental applications. However, highly scattering samples subvert Beer-Lambert law's linear relationship between spectral absorbance and the concentrations. Instead of spectral pre-processing, which is commonly used by Vis-NIR spectroscopists to mitigate the scattering effect, we put forward an optical method, based on Polarized Light Spectroscopy to improve the absorbance signal measurement on highly scattering samples. This method selects part of the signal which is less impacted by scattering. The resulted signal is combined in the Absorption/Remission function defined in Dahm's Representative Layer Theory to compute an absorbance signal fulfilling Beer-Lambert's law, i.e. being linearly related to concentration of the chemicals composing the sample. The underpinning theories have been experimentally evaluated on scattering samples in liquid form and in powdered form. The method produced more accurate spectra and the Pearson's coefficient assessing the linearity between the absorbance spectra and the concentration of the added dye improved from 0.94 to 0.99 for liquid samples and 0.84-0.97 for powdered samples.

Life cycle assessments of urban water systems: a comparative analysis of selected peer-reviewed literature.

Water is a growing concern in cities, and its sustainable management is very complex. Life cycle assessment (LCA) has been increasingly used to assess the environmental impacts of water technologies during the last 20 years. This review aims at compiling all LCA papers related to water technologies, out of which 18 LCA studies deals with whole urban water systems (UWS). A focus is carried out on these 18 case studies which are analyzed according to criteria derived from the four phases of LCA international standards. The results show that whereas the case studies share a common goal, i.e., providing quantitative information to policy makers on the environmental impacts of urban water systems and their forecasting scenarios, they are based on different scopes, resulting in the selection of different functional units and system boundaries. A quantitative comparison of life cycle inventory and life cycle impact assessment data is provided, and the results are discussed. It shows the superiority of information offered by multi-criteria approaches for decision making compared to that derived from mono-criterion. From this review, recommendations on the way to conduct the environmental assessment of urban water systems are given, e.g., the need to provide consistent mass balances in terms of emissions and water flows. Remaining challenges for urban water system LCAs are identified, such as a better consideration of water users and resources and the inclusion of recent LCA developments (territorial approaches and water-related impacts).

Assessing water deprivation at the sub-river basin scale in LCA integrating downstream cascade effects.

Physical water deprivation at the midpoint level is assessed in water-related LCIA methods using water scarcity indicators (e.g., withdrawal-to-availability and consumption-to-availability) at the river basin scale. Although these indicators represent a great step forward in the assessment of water-use-related impacts in LCA, significant challenges still remain in improving their accuracy and relevance. This paper presents a methodology that can be used to derive midpoint characterization factors for water deprivation taking into account downstream cascade effects within a single river basin. This effect is considered at a finer scale because a river basin must be split into different subunits. The proposed framework is based on a two-step approach. First, water scarcity is defined at the sub-river basin scale with the consumption-to-availability (CTA) ratio, and second, characterization factors for water deprivation (CFWD) are calculated, integrating the effects on downstream sub-river basins. The sub-river basin CTA and CFWD were computed based on runoff data, water consumption data and a water balance for two different river basins. The results show significant differences between the CFWD in a given river basin, depending on the upstream or downstream position. Finally, an illustrative example is presented, in which different land planning scenarios, taking into account additional water consumption in a city, are assessed. Our work demonstrates how crucial it is to localize the withdrawal and release positions within a river basin.

Environmental assessment of a territory: an overview of existing tools and methods.

In order to reduce our environmental impact, methods for environmental assessment of human activities are urgently needed. In particular in the case of assessment of land planning scenarios, there is presently no consensual and widely adopted method although it is strongly required by the European Directive (2001/42/EC) on Strategic Environmental Assessment. However, different kinds of tools and methods are available such as human and environmental risk assessment, the ecological footprint, material flow analysis, substance flow analysis, physical input-output table, ecological network analysis, exergy, emergy or life cycle assessment. This review proposes a discussion on these tools and methods specifically applied to territories. After the meaning of territory is clarified, these approaches are presented and analyzed based on "key features" such as formalization, system modeling, inventoried flows, indicators provided and usability. This comparison highlights the strengths and weaknesses of each tool. It also emphasizes that the approach of life cycle assessment could provide a relevant framework for the environmental assessment of territories as it is the only method which can avoid burden shifting between life cycle stages, environmental impacts and territories.

Least-squares support vector machines modelization for time-resolved spectroscopy.

By use of time-resolved spectroscopy it is possible to separate light scattering effects from chemical absorption effects in samples. In the study of propagation of short light pulses in turbid samples the reduced scattering coefficient and the absorption coefficient are usually obtained by fitting diffusion or Monte Carlo models to the measured data by use of numerical optimization techniques. In this study we propose a prediction model obtained with a semiparametric modeling technique: the least-squares support vector machines. The main advantage of this technique is that it uses theoretical time dispersion curves during the calibration step. Predictions can then be performed by use of data measured on different kinds of sample, such as apples.

Pattern analysis techniques to process fermentation curves: application to discrimination of enological alcoholic fermentations.

In fermentation processes, kinetic curves are generally aimed at control purposes. However, these curves could also contain information about inherent features of the product (such as origin, quality, etc.). This article presents several pattern analysis techniques used to classify fermentation curves. An application to alcoholic fermentation is presented as an illustration: it aims at retrieving the origin of a must from its fermentation curve. The fermentation kinetics of five vineyard musts, harvested over 9 years on the same parcels, were recorded. From these curves two sets of variables were generated: The first (p(1)) gathers all the kinetic curve points. The second (p(2)) contains a restrained number of variables, generated by the expert knowledge of the enologist. The set p(2) was processed by two very different techniques: a linear one (factorial discriminant analysis) and a nonlinear one (artificial neural networks). The set p(1) was processed by a new chemometric technique, the discriminant partial least-squares regression. For all the sets and the techniques used the selection of variables was studied. The interest in the latter is largely demonstrated both by theoretical and practical discussions. The discrimination results (up to 94% of good predictions) enhance the interest of the on-line measurements and their use in such pattern analysis tools.