Consumer trust and willingness to pay for certified animal-friendly products

Increasing animal welfare standards requires changes along the supply chain which involve several stakeholders: scientists, farmers and people involved in transportation and slaughtering. The majority of researchers agree that compliance with these standards increases costs along the livestock value chain, especially for monitoring and certifying animal-friendly products. Knowledge of consumer willingness to pay (WTP) in such a decision context is paramount to understanding the magnitude of market incentives necessary to compensate all involved stakeholders. The market outcome of certification programs is dependent on consumer trust. Particularly, there is a need to understand to what extent consumers believe that stakeholders operating in the animal-friendly supply chain will respect certification standards. We examine these issues using a contingent valuation survey administered in five economically dominant EU countries. The implied WTP estimates are found to be sensitive to robust measures of consumer trust for certified animal-friendly products. Significant differences across countries are discussed

Target-adaptive CNN-based pansharpening

We recently proposed a convolutional neural network (CNN) for remote sensing image pansharpening obtaining a significant performance gain over the state of the art. In this paper, we explore a number of architectural and training variations to this baseline, achieving further performance gains with a lightweight network which trains very fast. Leveraging on this latter property, we propose a target-adaptive usage modality which ensures a very good performance also in the presence of a mismatch w.r.t. the training set, and even across different sensors. The proposed method, published online as an off-the-shelf software tool, allows users to perform fast and high-quality CNN-based pansharpening of their own target images on general-purpose hardware

Do Consumers Want Public or Private Bodies to Monitor Animal Friendly Production and Marketing Schemes? And Does Trust Matter?

Producing according to enhanced farm animal welfare (FAW) standards increases costs along the livestock value chain, especially for monitoring certified animal friendly products. In the choice between public or private bodies for carrying out and monitoring certification, consumer preferences and trust play a role. We explore this issue by applying logit analysis involving socio-economic and psychometric variables to survey data from Italy. Results identify marked consumer preferences for public bodies and trust in stakeholders a key determinant.animal welfare standards, certification, consumer trust, monitoring, stated choice, Food Consumption/Nutrition/Food Safety, Livestock Production/Industries,

A doubly nonlinear Cahn-Hilliard system with nonlinear viscosity

In this paper we discuss a family of viscous Cahn-Hilliard equations with a non-smooth viscosity term. This system may be viewed as an approximation of a "forward-backward" parabolic equation. The resulting problem is highly nonlinear, coupling in the same equation two nonlinearities with the diffusion term. In particular, we prove existence of solutions for the related initial and boundary value problem. Under suitable assumptions, we also state uniqueness and continuous dependence on data.Comment: Key words and phrases: diffusion of species; Cahn-Hilliard equations; viscosity; non-smooth regularization; nonlinearities; initial-boundary value problem; existence of solutions; continuous dependenc

Farm Animal Welfare, Consumer Willingness to Pay, and Trust: Results of a Cross-National Survey

Higher animal welfare standards increase costs along the supply chain of certified animal-friendly products (AFP). Since the market outcome of certified AFP depends on consumer confidence toward supply chain operators complying with these standards, the role of trust in consumer willingness-to-pay (WTP) for AFP is paramount. Results from a contingent valuation survey administered in five European Union countries show that WTP estimates were sensitive to robust measures of consumer trust for certified AFP. Deriving the WTP effect of a single food category on total food expenditure is difficult for survey respondents; hence, a budget approach was employed to facilitate this process.Animal welfare, certification, consumer trust, WTP, budget approach, Crop Production/Industries, Environmental Economics and Policy, Farm Management, Livestock Production/Industries, C81, Q13, Q18,

Guided patch-wise nonlocal SAR despeckling

We propose a new method for SAR image despeckling which leverages information drawn from co-registered optical imagery. Filtering is performed by plain patch-wise nonlocal means, operating exclusively on SAR data. However, the filtering weights are computed by taking into account also the optical guide, which is much cleaner than the SAR data, and hence more discriminative. To avoid injecting optical-domain information into the filtered image, a SAR-domain statistical test is preliminarily performed to reject right away any risky predictor. Experiments on two SAR-optical datasets prove the proposed method to suppress very effectively the speckle, preserving structural details, and without introducing visible filtering artifacts. Overall, the proposed method compares favourably with all state-of-the-art despeckling filters, and also with our own previous optical-guided filter

Comparative analysis of resonant phonon THz quantum cascade lasers

We present a comparative analysis of a set of GaAs-based THz quantum cascade lasers, based on longitudinal-optical phonon scattering depopulation, by using an ensemble Monte Carlo simulation, including both carrier-carrier and carrier-phonon scattering. The simulation shows that the parasitic injection into the states below the upper laser level limits the injection efficiency and thus the device performance at the lasing threshold. Additional detrimental effects playing an important role are identified. The simulation results are in reasonable agreement with the experimental findings.Comment: 3 pages, 3 figure

Band-wise Hyperspectral Image Pansharpening using CNN Model Propagation

Hyperspectral pansharpening is receiving a growing interest since the last few years as testified by a large number of research papers and challenges. It consists in a pixel-level fusion between a lower-resolution hyperspectral datacube and a higher-resolution single-band image, the panchromatic image, with the goal of providing a hyperspectral datacube at panchromatic resolution. Thanks to their powerful representational capabilities, deep learning models have succeeded to provide unprecedented results on many general purpose image processing tasks. However, when moving to domain specific problems, as in this case, the advantages with respect to traditional model-based approaches are much lesser clear-cut due to several contextual reasons. Scarcity of training data, lack of ground-truth, data shape variability, are some such factors that limit the generalization capacity of the state-of-the-art deep learning networks for hyperspectral pansharpening. To cope with these limitations, in this work we propose a new deep learning method which inherits a simple single-band unsupervised pansharpening model nested in a sequential band-wise adaptive scheme, where each band is pansharpened refining the model tuned on the preceding one. By doing so, a simple model is propagated along the wavelength dimension, adaptively and flexibly, with no need to have a fixed number of spectral bands, and, with no need to dispose of large, expensive and labeled training datasets. The proposed method achieves very good results on our datasets, outperforming both traditional and deep learning reference methods. The implementation of the proposed method can be found on https://github.com/giu-guarino/R-PN

Graph-based analysis of textured images for hierarchical segmentation

International audienceThe Texture Fragmentation and Reconstruction (TFR) algorithm has been recently introduced to address the problem of image segmentation by textural properties, based on a suitable image description tool known as the Hierarchical Multiple Markov Chain (H-MMC) model. TFR provides a hierarchical set of nested segmentation maps by first identifying the elementary image patterns, and then merging them sequentially to identify complete textures at different scales of observation. In this work, we propose a major modification to the TFR by resorting to a graph based description of the image content and a graph clustering technique for the enhancement and extraction of image patterns. A procedure based on mathematical morphology will be introduced that allows for the construction of a color-wise image representation by means of multiple graph structures, along with a simple clustering technique aimed at cutting the graphs and correspondingly segment groups of connected components with a similar spatial context. The performance assessment, realized both on synthetic compositions of real-world textures and images from the remote sensing domain, confirm the effectiveness and potential of the proposed method