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Precision Farming Using GIS Technology
Precision Agriculture



Using PRECISION FARMING for Whaeat in Egypt
Site-specific information technologies help wheat farmers make decisions to improve nitrogen (NPK) fertilizer efficiency. Various information technologies, as well as farm and farmer characteristics, could affect fertilizer decisions differently. Knowing these differences could assist the targeting of specific groups of farmers for the adoption of various site-specific information technologies to improve NPK fertilizer efficiency and reduce negative environmental impacts. Ordered logit analysis was used to identify the information technologies and farm and farmer characteristics that influence the importance farmers place on precision farming (PF) technologies in improving the efficiency of NPK fertilization of wheat

Rice in kafr El-Sheikh, Egypt by Using PRECISION FARMING
Paddy rice is a very intensive crop, in terms of both inputs and labors. The fields are relatively small (less than 1ha.), flood irrigated and highly productive (6-7 t/ha). Most managers are also owners and know their fields intimately. African culture has a high regard for technology and most farms are already highly mechanized. Yield maps can be produced by fitting DGPS and yield monitors to the small efficient, head feeding combines. The optimum size for the treatment unit may be the current field or it may prove to be something smaller. Treatment maps can be implemented by applying spatially variable controllers to existing equipment. As the Egyptian farmer operates within a protected market (getting five times the world price for the rice), the main driver for PF is environmental protection.

Increasing Dates production in siwa oasis, Egypt as a result of Using PRECISION FARMING
Dates are a high value, culturally important crop in many Arab countries. Many date groves are well established and, like wheat, are highly structured. Once the trees have been surveyed and uniquely labeled, yield maps could be produced by recording the amount and quality of dates from each tree. Again, no extra cost is incurred apart from recording this information at the time of harvest. Treatment areas could well be blocks and performance of individual trees could be monitored. Traditionally, fertilizer is applied by hand and can therefore be easily adapted to being varied spatially. A special consideration is that there are consistent labor shortages due to the dangers involved in climbing the trees and the cultural importance of the dates may outweigh the economics.

Benefits of Precision agricultural engineering

Benefits of Precision agricultural engineering

Precision agricultural makes use of information technologies in agriculture. With the satellite positioning system and electronic communication standards, position and time may be integrated into all procedures connected to farming. Today, precision farming (PF) is primarily geared towards site-specific application of fertilizers with the resulting cost advantages being quite small. Thus, precision farming will likely gain in importance only when viable additional benefits, such as reduced environmental burdens and increased flow of information, are recognized and evaluated and become part of the reward itself.

The remote sensing and photogrammetric techniques, combined to the GIS capabilities, enabled us to develop a large database of geomorphological features, retaining the best accuracy and flexibility. Even through fieldwork, GPS equipment was ensuring high accuracy positioning and GIS data entry.

Yield maps, however, were not a useful basis for determining a variable nitrogen application strategy. It was shown that the spatial variation in canopy development with a field can be effectively determined using aerial digital photography for ‘real-time’ management.

As a result of using precision farming by making more informed management decisions and improving input allocation, farmers can become more efficient, lower production costs, and, potentially, increase profits. However, little is currently known about how farmers use PF technologies to support managerial decision making, or about the relative magnitude of benefits and costs of PF technologies on individual farms. Additional research on PF technology is needed to assist the agricultural community in finding answers to questions surrounding the adoption, uses, and the potential management benefits of PF technology.
In response to the question do you think that there are problems with the uptake of ICT in agriculture? 52.3% indicated in the affirmative (Gelb et al., 2001). When asked specifically about PF, 47.6% felt that this technology had unique characteristics that restricted adoption by farmers. Sixty percent of the countries in attendance had at least one representative who felt that there were characteristics unique to PF that restricted its adoption. When asked to identify those factors limiting the use of ICT by farmers, the factors suggested most frequently (in decreasing order of incidence) were cost of technology, too hard to use/unfriendly, no perceived economic or other benefits, do not understand the value of ICT, and lack of training.

Profitability of PF continues to be difficult to predict (Atherton et al., 1999). A study of nine field research sites by Swinton and Lowenberg-DeBoer (1998), found variable rate fertilizer application to be unprofitable on wheat and barley, sometimes profitable on corn, and profitable on sugarbeets. They concluded that because PF practices are site-specific, their profitability potential too is site-specific. Other studies have recognized that the profitability of PF depends heavily on the degree of spatial variability of soil attributes (e.g. soil types, fertility and organic matter) and yield response ( Roberts et al., 2000). These researchers conclude that economic returns of variable rate NPK application can only be determined on a field-by-field basis because returns depend on the specific attributes of each field.
Atherton et al., 1999. B.C. Atherton, M.T. Morgan, S.A. Shearer, T.S. Stombaugh and A.D. Ward , Site-specific farming: a perspective on information needs benefits and limitations. Journal of Soil and Water Conservation 54 2 (1999), pp. 455–460.
Roberts et al., 2000. R.K. Roberts, B.C. English and S.B. Mahajanashetti , Evaluating the returns to variable rate nitrogen application. Journal of Agricultural and Applied Economics 32 1 (2000), pp. 133–143.
Precision farming using GIS to improve Soil variability

For the part of precision farming as a productive tool, but environmentally friendly agriculture, in addition to current state surveys on Plant population, above all, the local site characteristics required (mapping Approach). At the field scale, this means the knowledge of Pedotopmusters. Past maps such as bottom estimate or mittelmabstabige soil maps (1:25,000), these not provide high spatial resolution information. Is the basic idea of our approach
collection of planar podenrelevanter information? The creation of a soil or Map this location does not require any interpolations, as in the transmission of (Drilling) point information are essential in the area. Rather, only the determine soil science content of designated sub-areas (holes).
Soil variability on all scales can be used in a predictable and a random Share split (HALL & Olson, 1991; SUMMER & SCHLICHTINO, 1997). The predictable Part is controlled by the soil-forming factors (Jenny, 1941; BIRKELAND, 1999). At Knowledge of the factors and their spatial variability of the properties of soils in the room with a blow to the divisional management sufficient accuracy be predicted. Under the terms of the 'tertiary hill country' should include four Pedotopmuster factors in the experimental farm and the wider catchment area of control: Starting material, relief, hydrological conditions and human activity.

A quantitative analysis of relief units and properties is done with the
"Automatic system for terrain analysis" (SARA) on the basis of DEM 5 (Kleefisch & KOTHE, 1993). SUMMER & Murschel (1999) have already succeeded in creating a forecast colluvial soils in a loess landscape using terrain analysis. Based on an elevation model (DHM 50) they succeeded in getting about 70% of Kolluvisols alluvial soils, and spatial accuracy with the help of Terrain analysis to predict. Since the resolution in the FAM-study area significantly higher (DHM is 5), are also significantly more accurate forecasts possible. Thus, there is adistinction founded hope, hill slopes, knolls and parietal areas further. An impression of the possible relief of differentiation provides Fig.1. The relationship between the relief features or units and soil types to develop by local expertise. Thus, for example, steep, convex relief elementsa stronger result in soil erosion and thus have less well-developed soil on. On the other hand used for agricultural purposes, converged landscape elements are by Sedimentation and thus Kolluvien characterized. From the terrain analysis can therefore corresponding differences in water and nutrient balances shown on the field scale will

Figure 1: Relief with SARA analysis based on a DEM 5, Metoki Scheyern, and dark gray: concave
Landscape elements (convergent flow, light gray: convex landscape elements) (divergent flow).

Fig.2: Daedalusaufnahme a stroke A5 (in the monastery Scheyern winter wheat, 11.7.95, channels 3, 5, 6); dark areas = low reflections = higher biomass

n addition, previous work in the FAM promising indications have shown that remote sensing of vegetation cover (bio-indication) Site characteristics can be mapped surface wise and high resolution (LENZ ETAL., 1997): soils with low water storage show low biomass and a chronologically earlier water stress during drought. This is v.a. for culture" Winter Wheat". The water storage capacity is essentially determined by the Substrate structure determined. Scanner images of winter wheat (and grassland) are used to the spatial mapping of the factor 'substrate'. Differences in the substrate structure result in the remote sensing images to patterns at the field scale (see Fig.2).
Local is a calibration of remote sensing data (gray values of individual Spectral regions) with vegetation parameters (eg, above-ground biomass).For operational use of remote sensing are both methods to derive
the land surface parameters from remote sensing data as well as customized math Develop methods for image analysis (see the article by KURZ ET AL.) This band. The found links between relief, vegetation and soils are in a Rules as "if-then" conditions "entered. Further investigationse.g. geophysical surveys (EM 38) or GIS information anthropogenic interference (gravel, clay mining, management, history, complementary, etc.), the Rules. Holes in the Experimental Farm and the wider catchment area to serve the Calibration and validation of the rules and regulations. Final product for the user, its high-resolution digital map of the floor and location of units.

BIRKELAND, P. (1999): Soils and Geomorphology. Oxford University Press, Oxford.
HALL, G.F.; OLSON, C.G. (1991): Predicting variability of soils from landscape models. Chapter 2., P.9-24. In: M.J. Mausbach & L.P. Wilding (Ed): Spatial variabilities of soils and landforms. SSSA Spec. Publ. 28, Madison, WI.
JENNY, H. (1941): Factors of Soil Formation. McGraw Hill, New York. Kleefisch, B.; KOTHE, R. (1993): Pathways to the computerized interpretation of digitalRelief data. Geol Jb, F 27: 59-122.
SHORT, F.; EHRICH, S.; HINZ, S. (2000): Possibilities and limitations of vision in the characterization of heterogeneity of vegetation using remote sensing methods.
GIL-Tagung 2000, this volume. LENZ, R.; BLESSED, T; TRACKED Strauch, J.; Wehrhan, M. (1997): Characterization of Site characteristics using remote sensing methods. FAM Annual Report 1997: 157 --167.
SOMMER, M.; Murschel, B. (1999): Erosion and Nährstoffabtrag. In: Dabbert, S., Herrmann, S., Kaul, G., Sommer, M.: Landscape Modeling for environmental planning. P.68-79. Springer Verlag, Berlin. SOMMER, M.; SCHLICHTINO, E. (1997): Archetypes of Catena in respect to matter - a conceptfor structuring and grouping Catenas. Geoderma 76: 1-33.

Precision farming is now having an impact on agriculture throughout the world. It is clear that the underlying principles remain the same but the implementation changes between crops and countries. This paper sets out to identify the underlying principles of Precision Farming to enable researchers and practitioners to adapt them for their own conditions.
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