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Simple Summary: The environmental impact of livestock production is an important concern of modern societies. In the case of grazing cattle, the accumulation of feces in some areas within paddocks (e.g., around water troughs) may lead to soil degradation. Current precision technologies can monitor grazing animals in (near) real-time to detect and eventually avoid environmental damage. In this paper, we proved that commercial GPS trackers can provide meaningful data on animal distribution and behavior, which can be used to model dung distribution. Model estimates are improved when contextual data (e.g., terrain slope) are considered. The automatic monitoring of dung distribution is an opportunity to improve grazing management and land fertilization, reducing the environmental footprint of cattle production. The sustainability of agrosilvopastoral systems, e.g., dehesas, is threatened. It is necessary to deepen the knowledge of grazing and its environmental impact. Precision livestock farming (PLF) technologies pose an opportunity to monitor production practices and their effects, improving decision-making to avoid or reduce environmental damage. The objective of this study was to evaluate the potential of the data provided by commercial GPS collars, together with information about farm characteristics and weather conditions, to characterize the distribution of cattle dung in paddocks, paying special attention to the identification of hotspots with an excessive nutrient load. Seven animals were monitored with smart collars on a dehesa farm located in Cordoba, Spain. Dung deposition was recorded weekly in 90 sampling plots (78.5 m2) distributed throughout the paddock. Grazing behavior and animal distribution were analyzed in relation to several factors, such as terrain slope, insolation or distance to water. Animal presence in sampling plots, expressed as fix, trajectory segment or time counting, was regressed with dung distribution. Cattle showed a preference for flat terrain and areas close to water, with selection indices of 0.30 and 0.46, respectively. The accumulated animal presence during the experimental period explained between 51.9 and 55.4% of the variance of dung distribution, depending on the indicator used, but other factors, such as distance to water, canopy cover or ambient temperature, also had a significant effect on the spatiotemporal dynamics of dung deposition. Regression models, including GPS data, showed determination coefficients up to 82.8% and were able to detect hotspots of dung deposition. These results are the first step in developing a decision support tool aimed at managing the distribution of dung in pastures and its environmental effects. [ABSTRACT FROM AUTHOR]