The research farm of Kassel University in Witzenhausen, Germany converted from conventional management to organic practices. The authors were concerned with documenting any changes in soil quality parameters including nutrient levels, soil organic matter, and microbial processes. To make these measurements more robust permanent sites where the soil samples would be removed were determined with the assistance of GIS. Spatial information about the farm including geography, relief-morphology, land use, soil maps, and intensive auger-borehole records were evaluated using geographic-statistical methods in a geographical information system. The total size of the research farm is 315 ha. Two sites were selected that are 1000 m2 and soil type was determined by digging pits at the corners of each site. Sampling procedures are for 18 single auger holes along two diagonals at an angle of 90 degrees. Each following year the diagonal will be turned 22.5 degrees.
This study was conducted on farm in a center-pivot conventional system near Wiggins, CO. Efficient nitrogen management is difficult because fertilization rates are often overestimated because they are based on high yields and do not take into account all sources of nitrogen supply, mainly mineralization from soil organic matter. If nitrogen can be added during the season as needed in an available form, reduced rates of fertilization may occur increasing nitrogen efficiency. Ground-based spectral measurements and SPAD chlorophyll meters were used to determine nitrogen content of the corn in a wedge-shaped portion of the field. During the 2000 and 2001 growing seasons, 112 kg/ha was saved each season by using ground-based remote sensing. The yields in the study area were not reduced with less N applied.
The basic principles of GIS application to soil and water quality are reviewed in this article. This new field is termed precision conservation. The authors discuss map analysis, spacial analysis, buffering, spacial statistics, surface modeling, and spatial data mining, and how they can be used in a soil and water conservation approach. Many recent papers were reviewed to give examples of current GIS applications. Some of the topics of these papers include: carbon and nitrogen management, the development of erosion probability maps, irrigation water conservation, managing nitrate leaching by monitoring irrigation practices, nitrate spacial variability, and spatial assessments of BMPs with a watershed. The authors conclude that GIS can enable scientists to identify landscape risk areas and then use the information to help make management decisions to implement conservation practices.
Phosphorous is a leading cause of aquatic system pollution. The ability to identify areas within a watershed that are providing the most influx of phosphorous into the waterways is an important tool to reduce pollution. This paper developed a multicomponent decision support system that consisted of a Phosphorous Index from the USDA, a diagnostic Expert System incorporating GIS, a prescription Expert System, and a hydrologic-nonpoint-source pollution model (GLEAMS) in order to identify critical source areas within the watershed, make a diagnosis of the probable causes, prescribe the appropriate best management practice, and test the effects of the prescribed best management practice. The watershed studied was the Vemmenhog (900 ha), situated in southwestern Sweden. Approximately 95% of the land use in the watershed is agricultural producing four main crops of sugar beet, spring barley, winter rape, and winter wheat. The purpose of studying this particular watershed was of its influence on the nearby Baltic Sea. The researchers concluded that the decision support system they developed was useful to the situation but noted some of the limitations as being the GLEAMS model they used and a lack of a software interface that would allow them to connect different parts of the support system.
This study compared the suitability of the soils for septic systems with the location of more than 1100 existing septic systems. The soils in the watershed were categorized into 4 classes of suitability and then mapped. The principle factors the researchers used to separate the classes of soils were (i) useable soil depth to bedrock, fragipan, or water table and (ii) soil surface slopes either less or greater than 15%. Septic tank locations were estimated from records and then placed as points on the map. While reviewing data for the septic systems some patterns of types of systems installed became apparent. In 1987 97% of all absorption systems were conventional trench and seepage pits, by 1992 these types were installed only 10% of the time. This change reflects new regulations on depth of soil appropriate for these systems with an emphasis on new shallower designs. Data on septic system failures was also reviewed. Points were created for the reported failures and then analyzed for which class of soil they were located on. 64% of the failures found were located on the worst two classes of soils, class 3 and 4. Even with the lack of suitable soils and potential for lateral flow of phosphates in the soil, it was concluded that the risk of substantial P transport appears to be small.
In order to ascertain the amount of highly productive soils that have been converted from agricultural to urban land use, the authors used GIS to analyze data layers. To determine the extent of urbanization, a GIS layer from the U.S. Air Force DMSP/OLS nighttime imagery was used. Soil productivity ratings were modeled using a soil productivity model SRPG taking data from the State Soil Geographic Database STATSGO. The ratings were then weighted by percentage composition to determine a single landscape rating value for each STATSGO mapping unit. The ratings were then groups into four categories: high, moderately high, moderate, and low. Totals for soil productivity classes show that relative percentages are 3, 26, 38, and 33% for high, moderately high, moderate, and low respectively for all of the lower 48 states. Results show that the degree of urbanization of USA land increases with increasing soil productivity with 5, 4, 2, and 1% for classes high, moderately high, moderate, and low respectively. The results of this study carry some pretty heavy limitations and the original paper should be consulted for further study on this matter.
Levels of soil organic carbon (SOC) and soil inorganic carbon (SIC) were studied using existing data from the State Soil Geographic Database (STATSGO.) Overlays of national land cover data (NLCD), topography, mean annual precipitation (MAP), and mean annual temperature (MAT) were made to examine the effect of these individual factors on soil C distribution in the landscape. In addition, changing climatic factors like temperature were examined to see their effects on soil carbon. The NLCD was produced by the USGS and the USEPA to obtain a consistent land-cover data layer for the conterminous USA based on 30 m resolution Landsat Thematic Mapper data acquired in the early 90’s. GTOPO30 was used to extract the spatial extent of each elevation gradient zone. Slope was obtained from this data by using the Arcview Spatial Analysis software by ESRI. MAP and MAT grid data sets with a resolution of 2 km were obtained from the Parameter-Elevation Regression on Independent Slopes Model (PRISM) from Oregon State University. The DEM projection and PRISM data were transformed to the projection of the NLCD data using Arcinfo software. A SOC and SIC content attribute table was created by joining the GIS base map with the table of SOC and SIC content of map units from STATSGO polygons. The STATSGO data was in vector form and converted to a grid with 30m cells to match the NLCD database. This converted grid was used as a base map to calculate SOC and SIC storage by terrestrial ecosystems, terrain classes, and climate zones based on an assumption that the SOC and SIC content was the same throughout the map unit. Some interesting findings from the analysis include: wetlands store the most SOC of the land classes, most of the SOC is sequestrated below 600 m, SOC content increases as MAP increases up to 700 to 850 mm, and 54.2% of the SOC is located in the 3 to 12 degree C MAT zones although a nonlinear relationship between SOC and MAT was found and an exponential model was a better fit for all zones with MAO <1000mm. This article proved that there is a large amount of untapped analyses possible using the STATSGO database.
A new computer software system was designed for making environmental farm-based management decisions. It marries ArcView with the erosion-productivity impact calculator known as EPIC. The new software carries the name EPIC-View and has a novel graphic user interface that guides the operator through a series of options describing their farming operations and results in a quantified estimate of the parameters modeled. Thematic maps and tables are produced using this system. This was designed as a decision making tool that can use site-specific input data or data already stored in a database. A simulation comparing various sources and sinks nitrogen in a conventional and minimum tillage system was carried out for a projected five years to help demonstrate the abilities of the new software package. The authors make clear that the simulation was not to test the model but only to demonstrate its use. This project was undertaken to help combine environmental models and an easy-to-use interface. The power of GIS to help illustrate georeferenced data is currently underutilized in natural resource decision making.
GIS layers were created from the topography, soil type, and yield of a corn field in Iowa. The researchers were interested if an overlay analysis could be used to explain the spatio-temporal variability in the yield pattern. Another research objective was to test the Tilth Index value distribution in the field to see if it could explain the variation in yield. The results showed no relationship and so it will not be discussed further. However, the topography and soil type did show some limited relationship to yield. Since a fertilization study was also taking place in the field at the same time the yield data across treatments was normalized for comparative purposes. The point data coverage was generalized for the whole field using a kriging technique in ARC. This was made into a contour coverage and then converted into a polygon coverage. The yield polygons were classified as either -1, 0, or 1 based on being more than one standard deviation away from the yield median. Results of the analysis showed that areas of lower yield were consistent from year to year for corn but not soybeans. Topography was the only variable that could explain some of the areas of higher and lower yield, with soils only revealing areas of lower yield.
An iso-group puts similar crop production fields into a like category based upon intrinsic physical properties of that field. The common parameters for determining which iso-group a farmer’s field belongs in are soil texture, drain class, slope, and exposure. GIS can help determine some of the physical parameters (elevation, slope, aspect) for the determination of iso-group. Once a field is placed within a particular iso-group a ranking based on yield and quality is determined. Other variables to create the ranking could be entertained such as financial return or environmental pressure. These ranking variables are then analyzed as to identify factors influencing their result. The factors can then be scrutinized and recommendations can be made as to improve the result. In this way, farmers operating within similar growing conditions can see how they compare to other farms and possibly improve their standing within their iso-group.