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The present paper discusses studies related to the preparation of a hydrogeological model of groundwater flow and nitrate transport in an area where a precision farming system is applied. Components of water balance were determined using the UnSat Suite Plus software (HELP model), while the average infiltration rate calculated for the study area equalled 20 per cent. The Visual MODFLOW software was used for the purpose of modelling in the saturated zone. Hydrogeological parameters of the model layers, inclusive of hydraulic conductivity, were defined on the basis of results of column tests that were carried out under laboratory conditions (column experiment). Related to the dose of mineral nitrogen used in precision fertilisation (80 kg N/ha), scenarios of the spread of nitrates in the soil-water environment were worked out. The absolute residual mean error calculated for nitrate concentrations obtained from laboratory and modelling studies equalled 0.188 mg/L, the standard error of the estimate equalling 0.116 mg/L. Results obtained were shown graphically in the form of hydroisohypse maps and nitrate isolines. Conclusions were drawn regarding the possibility of using numerical modelling techniques in predicting transport and fate of nitrates from fertilisers applied in precision agriculture systems.


The main priorities in crop production are increasing the yield and decreasing the cost of production. Precision farming is the best practice to approach these goals. For real time measurement of sugar beet yield, a yield monitor was developed, and installed on the exterior side of the harvester’s chassis. The advantage of this arrangement over similar systems is the location of the load cell and system’s frame which prevents blockage by trash, mud or plant roots. For measurement of weight, one load cell on each side of the harvester chassis was used. Conveyor and ground speeds were measured using two proximity sensors. Because vibrations of the harvester can affect the output signals, it is necessary to find the main bandwidth associated with the weights moving on the conveyor. For this purpose, three different masses were placed on the moving conveyor and this bandwidth was determined using signal processing. Then, a suitable filter was designed and undesirable frequencies acting as noise were attenuated. After calibrating all the sensors, final evaluation of the system was performed in the field and the mean and standard deviation of error were 6.48% and 1.52, respectively. Although the error may seem to be somewhat high but the low of standard deviation indicates that there is a similar error in all tests. These negative errors indicate that the weight is systematically overestimated by the monitor. Thus, the error can be reduced by minor changes in conveyor shape or modified by software means. By software modification, the systematic error was alleviated. The median sugar beet yield was thus obtained to be 42.7 t/ha. Comparing this with the actual mean yield of 41.8 t/ha, it differs by only about 2%.


EDAS (EGNOS Data Access Service) is the EGNOS internet broadcast service, which provides free of charge access to the data collected and generated by the EGNOS infrastructure. EDAS disseminates over the Internet, both in real time and via an FTP archive, the raw data of the GPS, GLONASS (no commitment on GLONASS data is provided (1)) and EGNOS GEO satellites collected by the receivers located at the EGNOS reference stations, which are mainly distributed over Europe and North Africa. The EDAS services offer several types of GNSS data in various protocols and formats, such as DGNSS corrections. This paper reports on the results of some in-field tests conducted by ESSP and Topcon Agriculture to confirm the suitability of EDAS DGNSS corrections for precision farming in Europe.

The European Commission (EC) is the owner of EGNOS system (including EDAS) and has delegated the exploitation of EGNOS to the European GNSS Agency (GSA). EDAS service provision is performed by ESSP, as EGNOS Services Provider, under contract with the GSA, the EGNOS program manager.

In the ENC 2018 article “EDAS (EGNOS Data Access Service): Differential GPS corrections performance test with state-of-the-art precision agriculture system”, ESSP and Topcon Agriculture presented the results of the first in-field test conducted in a dynamic and real-life environment in the summer of 2017. The test results indicated that the EDAS DGNSS corrections could enable a reliable pass-to-pass accuracy performance for a wide range of precision agriculture applications and become an attractive solution for cereal farms, when the farm is located in the vicinity of an EGNOS reference station. In particular, Topcon Agriculture acknowledged that the observed performance was sufficient to support the following precision agriculture applications: spraying and spreading of any crop type, tilling and harvesting of cereal.

Then, ESSP and Topcon Agriculture engaged in additional testing activities to further characterise the EDAS DGPS performance in different scenarios (i.e. at various European locations and with a variety of distances between the designated farm and the target EGNOS reference station).

In each test, multiple runs with the rover tractors have been performed over the reference patterns predefined in the Topcon guidance systems. Data recorded during the tests has been analysed in detail, looking at the key performance indicators (e.g. cross track error and pass-to-pass performance) that characterize the EDAS DGPS performance for precision agriculture applications. Different techniques for the computation of the pass-to-pass accuracy performance have been used, including a procedure to measure live in the field and a post-processing alternative. The diversity of scenarios available allows drawing conclusions on the applicability of EDAS DGPS corrections (in terms of maximum distance from the target EGNOS station) for precision agriculture and also understanding the impact of operationally relevant aspects such as the quality of the mobile internet coverage (highly variable across Europe).

The EDAS system and its architecture, the main types of data disseminated through EDAS services and the online information available to the EDAS users are introduced in this paper. In particular, the EDAS Ntrip service is described in detail, since it provides the differential corrections to the GPS and GLONASS satellites at the EGNOS reference stations in RTCM format, which are the basis for the present study.

The article also reports on the results of the latest tests, which have been performed using Topcon receivers, vehicles and auto-steering systems. In all cases, two different Topcon guidance systems on board tractors were running simultaneously to assess the EDAS DGPS positioning performance with respect to a the reference provided by a top-performing RTK-based Topcon solution.

The objective of this paper is to draw conclusions on the use of EDAS DGPS corrections as a reliable free-of-charge alternative for precision farming in Europe (especially for cereal farms), based on the available performance results from the testing campaign and the feedback from the involved precision agriculture experts.

International Conference on Precision Agriculture 20–23 July 2014 Sacramento, California International Society of Precision Agriculture Ristorto, G., Gallo, R., Gasparetto, A., Scalera, L., Vidoni, R. and F. Mazzetto (2017): A mobile laboratory for orchard health status monitoring in precision farming. Chemical Engineering Transactions 58, 661–666. Ristorto G. Gallo R. Gasparetto A. Scalera L. Vidoni R. Mazzetto F. 2017 A mobile laboratory for orchard health status monitoring in precision farming Chemical Engineering Transactions 58 661 666 Rosell, J.R, Llorens, j., Sanz, R

, D. 1988. Computer Use in Agriculture: Evidence from Tulare County, California. ROBERTS, K. – ENGLISH, B. – LARSON, J. – COCHRAN, R. – GOODMAN, W. – LARKIN, S. – MARRA, M. – MARTIN, S. – SHURLEY, W. – REEVES, M. 2004. Adoption of site-specific information and variable-rate technologies in cotton precision farming. In Journal of Agricultural and Applied Economics, vol. 36. no. 1, pp. 143–158. SAGHAFI, F. – MOGHADDAM, E. N. – ASLANI, A. 2017. Examining effective factors in initial acceptance of high-tech localized technologies: Xamin, Iranian localized operating


Determining the production zones of field is an important analysis in the precision farming technology as these may be used to control field operations in site-specific application. The aim of this paper was to evaluate the potential to identify the yield potential zones based on historical yield maps and to evaluate the procedure over the growing extent of input data. Standardized yield values from six growing seasons were considered. Suitable datasets were created, and hierarchical and non-hierarchical clustering methods were applied to create clusters. Results showed that using the data from commercial combine monitoring systems enables determining the zones. Multiple yield data are recommended as the values of analyses increase with the increased number of input datasets. However, commercial data have limitations in terms of complexity.

no. 93-1506. St. Joseph, MI : ASAE. BIRREL, S. J. - SUDDUTH, K. A. - BORGELT, S. C. 1996. Comparison of sensors and techniques for crop yield mapping. In Computers and Electronics in Agriculture, vol. 14, 1996, pp. 215-223. BLACKMOORE, S. 2003. The role of yield maps in precision farming. [PhD thesis by papers]. Silsoe : Cranfield University, 2003. BURKS, T. F. - SHEARER, S. A. - FULTON, J. P. - SOBOLIK, C. J. 2002. Effects of timevarying inflow rates on combine yield monitor accuracy. In Applied Engineering in Agriculture, vol. 20, 2002, no. 3, pp. 269-275. COLVIN

, T., Bouma, J. (2005). Future directions for fungal plant pathogens. Pest Management Science, 59 .129-142. Pedersen, S.M., Fountas, S., Blackmore, B.S., Gylling, M., Pedersen, J.L. (2004). Adoption and perspectives of precision farming in Denmark. Acta Agriculturae Scandinavica , Section B-Soil & Plant Science, 54 , 4-8. Piskier, T., Mładanowicz, R. (2003). Efektywność nawożenia mineralnego w rolnictwie precyzyjnym. Inżynieria Rolnicza , 10 (52), 221-227. Stafford, J. A. (2007). Briew History of Precision Agriculture. Book of Abstracts: 2 nd Conference on

] Childers N.F. - „Pomicultura modernă”, Rutgers University - U.S.A.,1976. [7] Gabriela Teodorescu, Aurelia Corina Cosac, 2014 - „The application of precision farming principles on the quantitative and qualitative parameters of apple production, in the Voinesti-Dambovita”, Lucrări Stiinţifice Management Agricol, Seria I, vol.XVI, nr.2, pg. 11-16, Ed. Agroprint Timisoara, [8] Gabriela Teodorescu, Virgil Moise, Aurelia Corina Cosac, 2012 - „Evolution of starch indicator in ripeness apple fruits in Voinesti”, The Annals of Valahia University of Targoviste

mit zielverfolgenden Tachymetern”, Ingenieurvermessung 2000. XIII International Course on Engineering Surveying: 144-154, 2000. 21. W. Stempfhuber, “The Integration of Kinematic Measuring Sensors For Precision Farming System Calibration”, The 3rd International Symposium on Mobile Mapping Technology. FIG Symposium Cairo, 2001. 22. W. Stempfhuber, “Verification of the Trimble Universal Total Station (UTS) performance for kinematic applications”, Proceedings of Optical 3-D measurement techniques: applications in GIS, mobile mapping, manufacturing, quality control