Traditionally, many agricultural purposes, mainly financial and accounting decision support software are available for the farmers. These general characteristic is that the ecological circumstances and particular their spatial variability is practically ignored. The most advanced decision support models, next to the economic assessment provide implicitly particularly strong analysis opportunities in this area. With the growth of the information technology, a more satisfactory development, which performs the special needs, is expected to be in
Cropping technology of precision agriculture
were linked to a digital graphical table map or maps interactively, which can be different thematically (e.g. yield map, airborne image). Looking its analysis possibilities, it can be considered to a modern digital field registration book, which is also prepared for the graphical and numerical following of GPS signals.
On the base of the " Future Farm" EU-(FP7) project we concluded that in the future European farmers will have to effectively manage information on and off their farms to improve economic viability and to reduce environmental impact. All three levels, in which agricultural activities need to be harmonized with economical and environmental constraints, require integrated ICT adoption: (i) improvement of farm efficiency; (ii) integration of public goods provided by farming into management strategies; (iii) relating to the environmental and cultural diversity of Europe’s agriculture by addressing the region-farm interaction. In addition, the communication between agriculture and other sectors needs improvement. Crop products for the value added chains must show their provenance through a transparent and certified management strategy and farmers receiving subsidies are requested to respect the environment through compliance of standards. To this end, an integration of information systems is needed to advise managers of formal instructions, recommended guidelines and implications resulting from different scenarios at the point of decision making during the crop cycle. Precision Farming as well as robotics are very data intensive and provide a wealth of information that helps to improve crop management and documentation. Based on these technologies a new Farm Information Management Systems (FMIS) will be developed. As most relevant farm data will be readily available in the proposed information system, or may be automatically integrated using standardised services and documentation in the form of instructions to operators, the certification of crop production process and cross compliance of standards can be generated more easily than with present systems. With the integrated PROGIS technology approach many activities on the farm can be measured when someone wants to trace back till to farms accordin the EU "farm to fork" slogans and legislations.
(http://www.esri.com). With this program, any map can be downloaded directly from the territory of our economy and data can be collected immediately with GPS or modem connection (Figure 56).
In the above figure, soil genetic map layer of the boundary of Tedej and above that the terrain contours are visualized. The software is suitable for the receiving of DGPS signals, so one part of the "office" GIS works can be done already in the field. In the spatial analysis capabilities, however, the so-called desktop mapping GIS programs are beyond the scope of the above software capabilities. In this environment complex operation may be carried out, done on more map layers.
The most common analysis means site selection carried out according to an aspect, a few typical examples:
Which culture where to be cultivated – production site selection; which areas are threatened by inland water, flooding; during the seed production the sowing structure and the clearance; in which areas can the sewage sludge be placed with a minimum of risk Tamás J.,1995; Tamás J., Lénárt Cs., 1995, 1997); in which areas is the lowest the risk of the heavy metal contamination of products (Tamás J., 1995); in which areas has the groundwater level and nitrate content changed in time (Tamás J., Juhász Cs., 1997).
During the analysis, first a so-called logic geographical model must be formed. In this, data sources that are needed towards the ambition are being determined. It is also being determined, what map layers are being dissociated and what types of objects will be placed in the individual layers.
The necessary data integration will be performed for the creation of the single vector or raster environment. The modeling particularly requires that the individual layers of the model can be used by appropriate mathematical combinations. For example, we can determine the erosion risk of an area with an equation, respectively the magnitude of erosion, which contain the slope categories, rainfall intensity, and aspect of the area. For this, the initial mapping values should be modified or transformed with operations corresponding to the mathematical equation. The map algebra tools typically include three different types of operations possibilities. First:
arithmetic operation performed by the constant (e.g. scalar mathematics). Second: possibility of the completion of standard mathematical operations (e.g. application of trigonometric functions or perform logarithmic
Cropping technology of precision agriculture
meaning of distance and thus combined effect of cost-distance can be modeled in the resistance surfaces. In this case, resistance surface corresponds effectively to a cost surface. In this case, the main factor is to achieve the lowest cost level, i.e. the lowest cost requirement transportation route must be found between two points. Of course, this cost surface will be very heterogeneous and will give different costs in each direction. In a given area, it is very important whether the production interventions have to be done mainly in the slope direction or opposite to the slope direction. The cost surface, which can be considered to a resistance surface, combined with another test can evaluate the lowest costs between two points (least cost path analysis). Allocation tests give further analysis possibility of cost distances. During the allocation tests it is assumed that the closest places to the designated property can be found. For example, we are interested in that how the harvest works can we distribute accordingly within one area in the district of the dryers. In this case, the nearest linear distance is understood under the nearest, or perhaps the cost distance values, such as travel time.
Geographical information systems provide a wide range of neighborhood tests (neighborhood operations, local context operators). In these tests such a new layer is created, which is based on some of the existing map layers.
The simplest example for this is the range of surface analyses, where for example the slope categories may be produced from a digital terrain model from an existing map, where elevation values of the certain neighborhood places, as pixel values are given. Another similarly frequent inquiry is the analysis of the various aspects, for example, determination of the direction of the maximum slope in a digital terrain model. These tests can be used optimally for example in case of topography and fruit planting localities that are sensitive for climatic conditions, such as peach, grape. Application of the different types of digital filters also belongs to this toolbar.
Character of the certain neighborhood values changes using the digital filters. For example, elevation values of a digital terrain model can be smoothed with an appropriate filter value, or in the opposite effect, highlight the edges. The most common application of digital filters is typical during the GIS and remote sensing, since in this case, the certain image noises and objects (e.g. weed recognition) have to be filtered from the values of the processes. In the traditional disciplines the certain complex phenomena had to be examined separately, while the GIS can analyze the certain phenomena in these complex interactions. The GIS models can very well support the understanding and simulation of the various decision-making processes. As a result of a simple database query the decision-maker has often not sufficient information for the preparation of the decision. However, in a complex decision-making system, where multi-factor criteria relevant to the decision-making aspects, harder and less hard boundary conditions and acceptable risk levels can be incorporated into the environmental model, resulting a more valid decision alternative and the decision will have a much lower risk than through a simple query. Nevertheless, the process-modeling is a fairly new field of GIS in both areas, so in the environment analysis processes and the decision-making, decision support processes. However, this area is very dynamic, newer and newer models are building in the GIS toolbar, newer test and analysis capabilities are used for the more accurate, more extensive process analysis, respectively decision preparation. The crop production models can particularly effectively be applied in GIS environment. J. T. Ritchie created the CERES model in 1972, in the Blackland Research Station belonging to the United States ARS, in Texas. The purpose was the yield forecast by the weather, soil and properties of the plant species, varieties. Ritchie dealt with soil physics and water management, and led an interdisciplinary group for the work. After a decade, with the dissolution of the original group, the CERES Centre was get into the Michigan State University with the led of Ritchie, Jones and Kiniry (1986) edited the CERES maize info book. Jones then made his seat to University of Florida, where he continued the construction of models in the name of GRO, essentially with the same principals. There he finished the soybean, peanut and bean models. These were subsequently summed up by G. Hoogenboom into a general leguminous model at the University of Georgia. The above models are taken up by IBSNAT (1990) project at the University of Hawaii. The so-called DSSAT decision support system (Decision Support System for Agrotechnology Transfer) was made in the frame of this. DSSAT aggregated to one system the above models by a common input, and output format and database, and placed into such environment that is suitable for the store of the results of the runs and graphic display. Another widely used model is the EPIC that have
CERES has conquered the world for today, prevalent on every continent (CERES, 1989). In Hungary, the domestic adoption of the model is coordinated by Kovács G. (1989) in the Research Institute for Soil Science and Agricultural Chemistry of the Hungarian Academy of Sciences. The model integrated to ArcView environment is directly suitable for the performation of GIS precision testing, production and also for farm management inspections. Pakurár et al. (1999b) conducted simulations using the DSSAT version 3 Decision Support System - on the basis of the maize longterm field experiment set in the pilot site of the University of Debrecen in Látókép - and recommends the application of the software for yield estimation, determination of N doses, timing of N fertilization, and determination of lack of water.