The aim of this paper is to study the behavior of an earth fill dam, analyzing the deformations determined by high precision geodetic techniques and those obtained by the Finite Element Method (FEM). A large number of control points were established around the area of the dam, and the measurements of their displacements took place during several periods. In this study, high-precision leveling and GNSS (Global Navigation Satellite System) techniques were used to monitor vertical and horizontal displacements respectively. Seven surveys were carried out: February and July 2008, March and July 2013, August 2014, September 2015 and September 2016. Deformations were predicted, taking into account the general characteristics of an earth fill dam. A comparative evaluation of the results derived from predicted (FEM) and observed deformations shows the differences on average being 20 cm for vertical displacements, and 6 cm for horizontal displacements at the crest. These differences are probably due to the simplifications assumed during the FEM modeling process: critical sections are considered homogeneous along their longitude, and the properties of the materials were established according to the general characteristics of an earth fill dam. These characteristics were taken from the normative and similar studies in the country. This could also be due to the geodetic control points being anchored in the superficial layer of the slope when the construction of the dam was finished.

Predictive systems are a crucial tool in management and decision-making in any productive sector. In the case of agriculture, it is especially interesting to have advance information on the profitability of a farm. In this sense, depending on the time of the year when this information is available, important decisions can be made that affect the economic balance of the farm. The aim of this study is to develop an effective model for predicting crop yields in advance that is accessible and easy to use by the farmer or farm manager from a web-based application. In this case, an olive orchard in the Andalusia region of southern Spain was used. The model was estimated using spatio-temporal training data, such as yield data from eight consecutive years, and more than twenty meteorological parameters data, automatically charged from public web services, belonging to a weather station located near the sample farm. The workflow requires selecting the parameters that influence the crop prediction and discarding those that introduce noise into the model. The main contribution of this research is the early prediction of crop yield with absolute errors better than 20%, which is crucial for making decisions on tillage investments and crop marketing.

In the production sector, the usefulness of predictive systems as a tool for management and decision-making is well known. In the agricultural sector, a correct economic balance of the farm depends on making the right decisions. For this purpose, having information in advance on crop yields is an extraordinary help. Numerous predictive models infer accurate crop yield data from aerobiological variables and pollen analysis; this is around spring, in the middle stage of the crop year, when planning and investments are already done. The aim of this study is to anticipate accurate crop yield data at an early stage of the cropping season. In the case of olive groves in Spain, this period is in February. This work is developed for an entire province, Jaen, belonging to the region of Andalusia, in Southern Spain. The methodology uses Machine Learning algorithms together with an exhaustive analysis of predictor variables. Temporal data come from public web services, such as spatial data infrastructures of some state agencies. The processing of the satellite imagery is carried out by the geospatial processing service Google Earth Engine. The result is the early prediction of kilograms of both olive crop and olive oil, eight months prior to the beginning of the first harvesting campaigns of the year, with an average absolute error of prediction better than 26%. The relevance of this work is the early availability of predicted crop yield data together with the multi-scale applicability of the predictive models. This makes this model a useful tool for all the agents involved in olive grove management. From farmers, to agricultural technicians, researchers and scientists dedicated to the study of the olive tree, to governmental institutions and agricultural associations that provide technical support, advice and regulation to ensure responsible practices and the long-term viability of the olive industry.

The Campo de Dalias is an area with relevant seismicity associated to the active tectonic deformations of the southern boundary of the Betic Cordillera. A non-permanent GPS network was installed to monitor, for the first time, the fault- and fold-related activity. In addition, two high precision levelling profiles were measured twice over a one-year period across the Balanegra Fault, one of the most active faults recognized in the area. The absence of significant movement of the main fault surface suggests seismogenic behaviour. The possible recurrence interval may be between 100 and 300 y. The repetitive GPS and high precision levelling monitoring of the fault surface during a long time period may help us to determine future fault behaviour with regard to the existence (or not) of a creep component, the accumulation of elastic deformation before faulting, and implications of the fold-fault relationship.