Social Enterprise in Europe: At the Crossroads of Market, Public Policies and Third Sector

Peer reviewe

Tiny but mighty : bacterial membrane vesicles in food biotechnological applications

Membrane vesicle (MV) production is observed in all domains of life. Evidence of MV production accumulated in recent years among bacterial species involved in fermentation processes. These studies revealed MV composition, biological functions and properties, which made us recognize the potential of MVs in food applications as delivery vehicles of various compounds to other bacteria or the human host. Moreover, MV producing strains can deliver benefits as probiotics or starters in fermentation processes. Next to the natural production of MVs, we also highlight possible methods for artificial generation of bacterial MVs and cargo loading to enhance their applicability. We believe that a more in-depth understanding of bacterial MVs opens new avenues for their exploitation in biotechnological applications.</p

A near real-time water surface detection method based on HSV transformation of MODIS multi-Spectral time series data

In the face of global population growth and the uneven distribution of water supply, a better knowledge of the spatial and temporal distribution of surface water resources is critical. Remote sensing provides a synoptic view of ongoing processes, which addresses the intricate nature of water surfaces and allows an assessment of the pressures placed on aquatic ecosystems. However, the main challenge in identifying water surfaces from remotely sensed data is the high variability of spectral signatures, both in space and time. In the last 10 years only a few operational methods have been proposed to map or monitor surface water at continental or global scale, and each of them show limitations. The objective of this study is to develop and demonstrate the adequacy of a generic multi-temporal and multi-spectral image analysis method to detect water surfaces automatically, and to monitor them in near-real-time. The proposed approach, based on a transformation of the RGB color space into HSV, provides dynamic information at the continental scale. The validation of the algorithm showed very few omission errors and no commission errors. It demonstrates the ability of the proposed algorithm to perform as effectively as human interpretation of the images. The validation of the permanent water surface product with an independent dataset derived from high resolution imagery, showed an accuracy of 91.5% and few commission errors. Potential applications of the proposed method have been identified and discussed. The methodology that has been developed 27 is generic: it can be applied to sensors with similar bands with good reliability, and minimal effort. Moreover, this experiment at continental scale showed that the methodology is efficient for a large range of environmental conditions. Additional preliminary tests over other continents indicate that the proposed methodology could also be applied at the global scale without too many difficultie

Compilation and validation of SAR and optical data products for a complete and global map of inland/ocean water tailored to the climate modeling community

Accurate maps of surface water extent are of paramount importance for water management, satellite data processing and climate modeling. Several maps of water bodies based on remote sensing data have been released during the last decade. Nonetheless, none has a truly (90°N/90°S) global coverage while being thoroughly validated. This paper describes a global, spatially-complete (void-free) and accurate mask of inland/ocean water for the 2000–2012 period, built in the framework of the European Space Agency (ESA) Climate Change Initiative (CCI). This map results from the synergistic combination of multiple individual SAR and optical water body and auxiliary datasets. A key aspect of this work is the original and rigorous stratified random sampling designed for the quality assessment of binary classifications where one class is marginally distributed. Input and consolidated products were assessed qualitatively and quantitatively against a reference validation database of 2110 samples spread throughout the globe. Using all samples, overall accuracy was always very high among all products, between 98% and 100%. The CCI global map of open water bodies provided the best water class representation (F-score of 89%) compared to its constitutive inputs. When focusing on the challenging areas for water bodies’ mapping, such as shorelines, lakes and river banks, all products yielded substantially lower accuracy figures with overall accuracies ranging between 74% and 89%. The inland water area of the CCI global map of open water bodies was estimated to be 3.17 million km2 ± 0.24 million km2. The dataset is freely available through the ESA CCI Land Cover viewer

Social innovation: worklessness, welfare and well-being

The UK Government has recently implemented large-scale public-sector funding cuts and substantial welfare reform. Groups within civil society are being encouraged to fill gaps in service provision, and ‘social innovation’ has been championed as a means of addressing social exclusion, such as that caused by worklessness, a major impediment to citizens being able to access money, power and resources, which are key social determinants of health. The aim of this article is to make the case for innovative ‘upstream’ approaches to addressing health inequalities, and we discuss three prominent social innovations gaining traction: microcredit for enterprise; social enterprise in the form of Work Integration Social Enterprises (WISEs); and Self Reliant Groups (SRGs). We find that while certain social innovations may have the potential to address health inequalities, large-scale research programmes that will yield the quality and range of empirical evidence to demonstrate impact, and, in particular, an understanding of the causal pathways and mechanisms of action, simply do not yet exist

Scenario trees and policy selection for multistage stochastic programming using machine learning

We propose a hybrid algorithmic strategy for complex stochastic optimization problems, which combines the use of scenario trees from multistage stochastic programming with machine learning techniques for learning a policy in the form of a statistical model, in the context of constrained vector-valued decisions. Such a policy allows one to run out-of-sample simulations over a large number of independent scenarios, and obtain a signal on the quality of the approximation scheme used to solve the multistage stochastic program. We propose to apply this fast simulation technique to choose the best tree from a set of scenario trees. A solution scheme is introduced, where several scenario trees with random branching structure are solved in parallel, and where the tree from which the best policy for the true problem could be learned is ultimately retained. Numerical tests show that excellent trade-offs can be achieved between run times and solution quality

Why Social Enterprises Are Asking to Be Multi-stakeholder and Deliberative: An Explanation around the Costs of Exclusion.

The study of multi-stakeholdership (and multi-stakeholder social enterprises in particular) is only at the start. Entrepreneurial choices which have emerged spontaneously, as well as the first legal frameworks approved in this direction, lack an adequate theoretical support. The debate itself is underdeveloped, as the existing understanding of organisations and their aims resist an inclusive, public interest view of enterprise. Our contribution aims at enriching the thin theoretical reflections on multi-stakeholdership, in a context where they are already established, i.e. that of social and personal services. The aim is to provide an economic justification on why the governance structure and decision-making praxis of the firm needs to account for multiple stakeholders. In particular with our analysis we want: a) to consider production and the role of firms in the context of the “public interest” which may or may not coincide with the non-profit objective; b) to ground the explanation of firm governance and processes upon the nature of production and the interconnections between demand and supply side; c) to explain that the costs associated with multi-stakeholder governance and deliberation in decision-making can increase internal efficiency and be “productive” since they lower internal costs and utilise resources that otherwise would go astray. The key insight of this work is that, differently from major interpretations, property costs should be compared with a more comprehensive range of costs, such as the social costs that emerge when the supply of social and personal services is insufficient or when the identification of aims and means is not shared amongst stakeholders. Our model highlights that when social costs derived from exclusion are high, even an enterprise with costly decisional processes, such as the multistakeholder, can be the most efficient solution amongst other possible alternatives

Estimating smallholder crops production at village level from Sentinel-2 time series in Mali's cotton belt

In Mali's cotton belt, spatial variability in management practices, soil fertility and rainfall strongly impact crop productivity and the livelihoods of smallholder farmers. To identify crop growth conditions and hence improve food security, accurate assessment of local crop production is key. However, production estimates in heterogeneous smallholder farming systems often rely on labor-intensive surveys that are not easily scalable, nor exhaustive. Recent advances in high-resolution earth observation (EO) open up new possibilities to work in heterogeneous smallholder systems. This paper develops a method to estimate individual crop production at farm-to-community scales using high-resolution Sentinel-2 time series and ground data in the commune of Koningue, Mali. Our estimation of agricultural production relies on (i) a supervised, pixel-based crop type classification inside an existing cropland mask, (ii) a comparison of yield estimators based on spectral indices and derived leaf area index (LAI), and (iii) a Monte Carlo approach combining the resulting unbiased crop area estimate and the uncertainty on the associated yield estimate. Results show that crop types can be mapped from Sentinel-2 data with 80% overall accuracy (OA), with best performances observed for cotton (Fscore 94%), maize (88%) and millet (83%), while peanut (71%) and sorghum (46%) achieve less. Incorporation of parcel limits extracted from very high-resolution imagery is shown to increase OA to 85%. Obtained through inverse radiative transfer modeling, Sen2-Agri estimates of LAI achieve better prediction of final grain yield than various vegetation indices, reaching R2 of 0.68, 0.62, 0.8 and 0.48 for cotton, maize, millet and sorghum respectively. The uncertainty of Monte Carlo production estimates does not exceed 0.3% of the total production for each crop type

Spatial fields’ dispersion as a farmer strategy to reduce agro-climatic risk at the household level in pearl millet-based systems in the Sahel: A modeling perspective

The rainfall pattern in the Sahel is very erratic with a high spatial variability. We tested the often reported hypothesis that the dispersion of farmers’ fields around the village territory helps mitigate agro-climatic risk by increasing yield stability from year to year. We also wished to evaluate whether this strategy had an effect on the yield disparity among households in a village. Based on a network of approximately 60 rain gauges spread over 500 km2 in the Fakara region (Southwest Niger), daily rainfall was interpolated at 300 m × 300 m resolution over a 12-year period. This data was used to compute, by means of the APSIM crop simulation model, millet biomass and grain yields at the pixel scale. Simulated yields were combined with the land tenure map of the Banizoumbou village in a GIS to assess millet yield at field and household level. Agro-climatic risk analysis was performed using linear regression between a spatial dispersion index of household fields and the inter-annual (instability) and inter-household (disparity) millet yield variability of 107 households in the village territory. We find that the spatial variability of annual rainfall induces an even higher spatial variability of millet production at pixel, field and household levels. The dispersion of farm fields reduces moderately but significantly the disparity of millet yield between households each year and increases the inter-annual yield stability of a given household. The less the household fields are scattered, the more the presence of a fertility gradient around the village enhances the inter-annual stability but also the disparity between households. Our results provide evidence that field dispersion is an effective strategy to mitigate agro-climatic risk, as claimed by farmers in the Sahelian Niger. Although the results should be confirmed by further research on longer term rainfall spatial data, it is clearly advisable that any land reforms in the area take into account the benefits of field dispersion to mitigate climatic risk