Precision agriculture aims at improving the management of spatial and temporal variability within agricultural fields, by applying the right amount of farm input (fertilisers,water for irrigation, pesticides, seeds, tillage etc.) into the right place in the right time by using of the right technologies and practices. In crop production the scale of management of variability is down to within field or subfield scale.
The final target of precision agriculture is successful management of within field variability to maximise yield at reduced input cost, and reduced environmental impacts and waste. The final farm output is increased profit and farming production efficiency, whereas a reduced risk for pollution can be achieved by applying less agrochemicals into the environment (e.g., into soil, water and air).
The implementation of precision agriculture requires the combination of several technologies into an integrated agricultural management system. These technologies often include global positioning systems, geographical information systems, remote sensing of crop, proximal soil and crop sensing, yield monitoring, geostatistical modelling and mapping, decision support tool (PA software), and variable rate technologies.

This course provides theoretical and practical insight into non-destructive methods to map, characterize and monitor spatial and temporal variations in the shallow layers of the subsurface. The course mainly focuses on geophysical and geochemical methods that provide high-resolution continuous information about the soil and its interaction with the bio- and lithosphere. The emphasis of the course is on providing practical solutions in a wide range of applications in environmental studies. These include pollutant research, precision agriculture, infrastructure works, hydrological and forensic applications and heritage studies. In addition to understanding the working principles and application limits of the discussed methods, the development of prospecting strategies with integrated validation/calibration schemes is also discussed. Finally, relevant linear and non-linear modeling methods are discussed, together with the integration of data from different types of sensors.