Supporting decision making under uncertainty: Development of a participatory integrated model for water management in the middle Guadiana river basin.

Following the Integrated Water Resources Management approach, the European Water Framework Directive demands Member States to develop water management plans at the catchment level. Those plans have to integrate the different interests and must be developed with stakeholder participation. To face these requirements, managers need tools to assess the impacts of possible management alternatives on natural and socio-economic systems. These tools should ideally be able to address the complexity and uncertainties of the water system, while serving as a platform for stakeholder participation. The objective of our research was to develop a participatory integrated assessment model, based on the combination of a crop model, an economic model and a participatory Bayesian network, with an application in the middle Guadiana sub-basin, in Spain. The methodology is intended to capture the complexity of water management problems, incorporating the relevant sectors, as well as the relevant scales involved in water management decision making. The integrated model has allowed us testing different management, market and climate change scenarios and assessing the impacts of such scenarios on the natural system (crops), on the socio-economic system (farms) and on the environment (water resources). Finally, this integrated assessment modelling process has allowed stakeholder participation, complying with the main requirements of current European water laws.

A multi-criteria optimization and decision-making approach for improvement of food engineering processes.

The objective of this study was to propose a multi-criteria optimization and decision-making technique to solve food engineering problems. This technique was demostrated using experimental data obtained on osmotic dehydratation of carrot cubes in a sodium chloride solution. The Aggregating Functions Approach, the Adaptive Random Search Algorithm, and the Penalty Functions Approach were used in this study to compute the initial set of non-dominated or Pareto-optimal solutions. Multiple non-linear regression analysis was performed on a set of experimental data in order to obtain particular multi-objective functions (responses), namely water loss, solute gain, rehydration ratio, three different colour criteria of rehydrated product, and sensory evaluation (organoleptic quality). Two multi-criteria decision-making approaches, the Analytic Hierarchy Process (AHP) and the Tabular Method (TM), were used simultaneously to choose the best alternative among the set of non-dominated solutions. The multi-criteria optimization and decision-making technique proposed in this study can facilitate the assessment of criteria weights, giving rise to a fairer, more consistent, and adequate final compromised solution or food process. This technique can be useful to food scientists in research and education, as well as to engineers involved in the improvement of a variety of food engineering processes.

Exploring the economic value of personal information from firms' financial statements

Currently personal data gathering in online markets is done on a far larger scale and much cheaper and faster than ever before. Within this scenario, a number of highly relevant companies for whom personal data is the key factor of production have emerged. However, up to now, the corresponding economic analysis has been restricted primarily to a qualitative perspective linked to privacy issues. Precisely, this paper seeks to shed light on the quantitative perspective, approximating the value of personal information for those companies that base their business model on this new type of asset. In the absence of any systematic research or methodology on the subject, an ad hoc procedure is developed in this paper. It starts with the examination of the accounts of a number of key players in online markets. This inspection first aims to determine whether the value of personal information databases is somehow reflected in the firms’ books, and second to define performance measures able to capture this value. After discussing the strengths and weaknesses of possible approaches, the method that performs best under several criteria (revenue per data record) is selected. From here, an estimation of the net present value of personal data is derived, as well as a slight digression into regional differences in the economic value of personal information.

The Economic Value of Residential Land in Historical Areas: An Application of the Residual Method to the Secondary Market

Land value bears significant weight in house prices in historical town centers. An essential aim for regulating the mortgage market, particularly in the financial and property crisis that countries such as Spain are undergoing, is to have at hand objective procedures for its valuation, whatever the conditions (location, construction, planning). Of all the factors contributing to house price make-up, the land is the only one whose value does not depend on acquisition cost, but rather on the location-time binomial. That is to say, the specific circumstances at that point and at the exact moment of valuation. For this reason, the most commonly applied procedure for land valuation in town centers is the use of the residual method: once the selling price of new housing in a district is known, the other necessary costs and expenses of development are deducted, including those of building and the developer’s profit. The value left is that of the land. To apply these procedures it is vital to have figures such as building costs, technical fees, tax costs, etc. But, above all, it is essential to obtain the selling price of the new housing. This is not always feasible, on account of the lack of newbuild development in this location. This shortage of information occurs in historical town cities, where urban renewal is slight due to the heritage-protection policies, and where, nevertheless there is substantial activity in the secondary market. In these circumstances, as an alternative for land valuation in consolidated urban areas, we have the adaptation of the residual method to the particular characteristics of the secondary market. To these ends, there is the proposal for the appreciation of the dwelling which follows, in a backwards direction, the application of traditional depreciation methods proposed by the various valuation manuals and guidelines. The reliability of the results obtained is analyzed by contrasting it with published figures for newly-built properties, according to different rules applied in administrative appraisals in Spain and the incidence of an eventual correction due to conservation state.

Composition, potential emissions and agricultural value of pig slurry from Spanish commercial farms

Pig slurry is a valuable fertilizer for crop production but at the same time its management may pose environmental risks. Slurry samples were collected from 77 commercial farms of four animal categories (gestating and lactating sows, nursery piglets and growing pigs) and analyzed for macronutrients, micronutrients, heavy metals and volatile fatty acids. Emissions of ammonia (NH3) and biochemical methane potential (BMP) were quantified. Slurry electrical conductivity, pH, dry matter content and ash content were also determined. Data analysis included an analysis of correlations among variables, the development of prediction models for gaseous emissions and the analysis of nutritional content of slurries for crop production. Descriptive information is provided in this work and shows a wide range of variability in all studied variables. Animal category affected some physicochemical parameters, probably as a consequence of different slurry management and use of cleaning water. Slurries from gestating sows and growing pigs tended to be more concentrated in nutrients, whereas the slurry from lactating sows and nursery piglets tended to be more diluted. Relevant relationships were found among slurry characteristics expressed in fresh basis and gas emissions. Predictive models using on-farm measurable parameters were obtained for NH3 (R2 = 0.51) and CH4