## 1 Introduction  
Over the last decades, economies of scale have been achieved by using machines with increasing working width and speed. However, the technical optimization of machines for crop production is increasingly exhausted. Nevertheless, management processes in agriculture become more complex by, e.g., implementing Precision Farming (PF) technologies, machinery sharing or contracting [1]. Furthermore, machinery operation has a remarkable effect on the total cost of crop production [2] and affects other cost factors such as time and farm inputs. Thus, there is an increasing demand for ways to detect and minimize inefficiencies within the whole processes. [PERSON] et al. [3] emphasized that performance analyses are also very valuable for tractor-implement combinations and commercial telematics solutions for implements are already on the market, e.g., CLAAS telematics on implements (TONI). The efficiency of an agricultural process can be assessed with regard to several parameters, e.g., time, cost and single machine-related parameters [2; 3; 4; 5; 6; 7; 8; 9; 10]. Beyond those, area is a very important indicator, because plant production processes are always strongly linked with a spatial component. Area optimization, in terms of minimizing the overlapped areas, combined with time optimization, by minimizing the non-working time [11], could lead to efficient agricultural processes. Easily available and detailed area-related information could help farmers, contractors or machinery rings facilitate documentation and invoicing. Earlier studies (e.g., [5]) have used machine data to derive basic area-related information. Meanwhile, similar functionalities are implemented in commercial solutions. However, common hectare counters perform a rather indirect calculation without really examining the actually covered area. Commercial PF terminals can set the estimated cultivated area in relation to deposited values for field boundaries and, thus, give a rough estimation of the overlapping. Recent scientific works have already encompassed an automated field recognition [6; 9]. However, there is still a lot of manual effort necessary for farmers and contractors to define and then deposit the field boundaries in digital field records. These tasks are usually inconvenient for farmers and contractors because they are time-consuming and error-prone and partly demand ever new familiarization with the operation. First approaches to facilitate these processes are represented by commercial solutions for an automated derivation of field boundaries from GNSS data, e.g., FARMDOK [12]. Past research works have also dealt with area-related analyses within a field. First examples are studies with the aim to detect the effective cutting width of combine harvesters and, thus, enable more accurate yield measurements [13; 14]. The efficiency of a harvesting operation by means of coverage analyses was examined by [PERSON] et al. [2]. The presented information was assumed to deliver valuable information for the improvement of traffic patterns through optimization of the harvester route during non-harvest portions of the operation. However, a method that allows differentiated area-related analysis is still lacking. Especially for soil tillage, overlapping is still causing additional wear as well as fuel and time consumption. Meanwhile, lateral overlapping can be minimized with automatic steering systems. However, the accuracy of such systems is usually limited. Since the working width of a moldboard plow is usually low in comparison to other tillage implements, an evaluation of the lateral overlapping is still of interest. Furthermore, the overlapping between the headland and the main cropping area is still highly influenced by the operator and the field geometry. This interface area is of special interest for plowing with a moldboard plow: When it is elevated and lowered in the headland, the plow forms an inconsistent tillage operation and undesirable triangular shapes of unplowed segments.