Considerable progress has been made in developing physically based, distributed parameter, hydrologic/water quality (H/WQ) models for planning and control of nonpoint‐source pollution. The widespread use of these models is often constrained by the excessive and time‐consuming input data demands and the lack of computing efficiencies necessary for iterative simulation of alternative management strategies. Recent developments in geographic information systems (GIS) provide techniques for handling large amounts of spatial data for modeling nonpoint‐source pollution problems. Because a GIS can be used to combine information from several sources to form an array of model input data and to examine any combinations of spatial input/output data, it represents a highly effective tool for H/WQ modeling. This paper describes the integration of a distributed‐parameter model (AGNPS) with a GIS (ARC/INFO) to examine nonpoint sources of pollution in an agricultural watershed. The ARC/INFO GIS provided the tools to generate and spatially organize the disparate data to support modeling, while the AGNPS model was used to predict several water quality variables including soil erosion and sedimentation within a watershed. The integrated system was used to evaluate the effectiveness of several alternative management strategies in reducing sediment pollution in a 417‐ha watershed located in southern Iowa. The implementation of vegetative filter strips and contour buffer (grass) strips resulted in a 41 and 47% reduction in sediment yield at the watershed outlet, respectively. In addition, when the integrated system was used, the combination of the above management strategies resulted in a 71% reduction in sediment yield. In general, the study demonstrated the utility of integrating a simulation model with GIS for nonpoint‐source pollution control and planning. Such techniques can help characterize the diffuse sources of pollution at the landscape level.

Precision farming application requires better understanding of variability in yield patterns in order to determine the cause-effect relationships. This field study was conducted to investigate the relationship between soil attributes and corn (Zea mays L.)-soybean (Glycine max L.) yield variability using four years yield data from a 22-ha field located in central Iowa. Corn was grown in this field during 1995, 1996, and 1998, and soybean was grown in 1997. Yield data were collected on nine east-west transects, consisting of 25-yield blocks per transect. To compare yield variability among crops and years, yield data were normalized based on N-fertilizer treatments. The soil attributes of bulk density, cone index, organic matter, aggregate uniformity coefficient, and plasticity index were determined from data collected at 42 soil sampling sites in the field. Correlation and stepwise regression analyses over all soil types in the field revealed that Tilth Index, based upon soil attributes, did not show a significant relationship with the yield data for any year and may need modifications. The regression analysis showed a significant relationship of soil attributes to yield data for areas of the field with Harps and Ottosen soils. From a geographic information system (GIS) analysis performed with ARC/INFO, it was concluded that yield may be influenced partly by management practices and partly by topography for Okoboji and Ottosen soils. Map overlay analysis showed that areas of lower yield for corn, at higher elevation, in the vicinity of Ottosen and Okoboji soils were consistent from year to year; whereas, areas of higher yield were variable. From GIS and statistical analyses, it was concluded that interaction of soil type and topography influenced yield variability of this field. These results suggest that map overlay analysis of yield data and soil attributes over longer duration can be a useful approach to delineate subareas within a field for site specific agricultural inputs by defining the appropriate yield classes. RightsWorks produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted.This article is available at Iowa State University Digital Repository: http://lib.dr.iastate.edu/abe_eng_pubs/517 T oday's scientists are facing many challenges in developing strategies for sustainable crop production systems. The focus of earlier efforts in the 1960s increased crop yield by twofold or more by applying high-yield agricultural inputs (Bottrell and Weil, 1995). These inputs were comprised of biological inputs (crop varieties), mechanical inputs (farm mechanization), water inputs (irrigation systems), and chemical inputs. Use of chemical inputs such as herbicides, insecticides, fungicides, and fertilizers have become an integral part of the high-yield package despite some of their negative effects on the environment. This high-input strategy has been successful in narrowing the gap between food and fib...

An integrated approach coupling water quality computer simulation modeling with a geographic information system (GIS) was used to delineate critical areas of nonpoint source (NPS) pollution at the watershed level. Two simplified pollutant export models were integrated with the Virginia Geographic Information System (VIrGIS) to estimate soil erosion, sediment yield, and phosphorus (1') loading from the Nomini Creek watershed located in Westmoreland County, Virginia.On the basis of selected criteria for soil erosion rate, sediment yield, and P loading, model outputs were used to identily watershed areas which exhibit three categories (low, medium, high) of nonpoint source pollution potentials. The percentage of the watershed area in each category, and the land area with critical pollution problems were also identified. For the 1505-ha Nomini Creek watershed, about 15, 16, and 21 percent of the watershed area were delineated as sources of critical soil erosion, sediment, and phosphorus pollution problems, respectively. In general, the study demonstrated the usefulness of integrating GIS with simulation modeling for nonpoint source pollution control and planning. Such techniques can facilitate making priorities and targeting nonpoint source pollution control programs. (KEY TERMS: nonpoint source pollution; water quality modeling; geographic information system.)

Non-point source pollution cuntinues to be an important environmental and water quality management problem. For the moat part, analysis of non-point souree pollution in watersheds has depended on the use of distributed models to identify potential problem areas and to assess the effectiveness of alternative management practices. To effectively use these models for watershed water quality management, users depend on integrated geographic information systems (GIS)-based interfaces for input/output data management. However, existing interfaces are ad-hoc and the utility of GIS is limited to organization of input data and display of output data. A highly interactive water quality modeling interface that utilizes the functional components and analytical capability of GIS is highly desirable. This paper describes the tight coupling of the Agricultural Non-point Source (AGNPS) water quality model and ARC/INFO GIS software to provide an interactive hybrid modeling environment for evaluation of non-point source pollution in a watershed. The modeling environment is designed to generate AGNPS input parameters from user-specified GIS coverages, create AGNPS input data files, control AGNPS model simulations, and extract and organize AGNPS model output data for display. An example application involving the estimation of pesticide loading in a southern Iowa agricultural watershed demonstrates the capability of the modeling environment. Compared with traditional methods of watershed water quality modeling using the AGNPS model or other ad-hoc interfaces between a distributed model and GIS, the interactive modeling environment system is efficient and significantly reduces the task of watershed analysis using tightly coupled GIS databases and distributed models. (KEY TERMS: pollution modeling; water quality; non-point source pollution; GIS.) disparate data sets related to chemicals, soils, climate, topography, land cover, and land use. These 'Paper No.

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