CONTRIBUTORS: David Clay, Gregg Carlsen, Sharen Clay, Douglas Malo, Lee Vierling, Kurt Cogswell and Kevin Dalsted


This first draft from the ag/bio/geo team will focus on the contribution of cropland agriculture to the ecosystem dynamics in a closed drainage system. Numerous data/research gaps contribute to our lack of knowledge in this setting, which is represented by many, but not all, landscapes in eastern South Dakota. An interdisciplinary research approach is the best means of addressing the major research areas, which will be discussed later.

The geographic location of South Dakota provides unique advantages for using remote sensing technologies for evaluation of changes in surficial and subsurface phenomenon. First, there is great variability within a single season and over multiple years. Second, the topography associated with closed drainage systems provides a dynamical physical framework that is highly sensitive to land and atmospheric variability regardless of the time scales addressed. Furthermore, the changes are directly reflected in productivity in the both the natural and agriculture elements of the biosphere. The great difficulty of characterizing the climate by point measurements is quite likely the most important reason the measurement data necessary to validate numerical model and spatial observation techniques have not been collected in the past. Only with the advent of reliable and reasonably priced remote data collection technologies will this activity be considered feasible.

When we consider land use in eastern SD, we find that there is no typical setting for the closed drainage systems. Depending upon the scale of observation we may find a few to many types of wetlands within the closed drainage system and some which receive runoff and/or groundwater linkages from croplands in the system. Land cover in a closed basin system can include cropped fields, pasture and hayland, CRP land, permanent and temporary wetlands, farm lands and urban areas, roads, various native and planted grasslands/woodlands. Landuse can include agriculture, wildlife habitat, recreations and hunting, among others. The agriculture group will focus on the cropped field component of this system.


Rural communities are undergoing a period of profound change. Increasingly, opportunities for U S farmers lie in reaching overseas markets or creating differentiated crops that serve niche markets at home. In some areas, run-off from herbicides and fertilizers has contaminated lakes, streams, and drinking water supplies. Many communities have been depopulated by the efficiency of modern production agriculture. Site specific management has the potential to improve international competitiveness of US farmers, develop valuable skills and higher paying jobs in rural communities, and improve water quality in agricultural areas. Public investment is needed to turn this potential into reality. By working in partnership with growers, agribusiness, and the aerospace industry scientifically sound practical information that farm managers can use will be created


Our main objectives are (1) to gain a better understanding of how remote sensing observations can be tied to ground/atmospheric measurements within agricultural systems (2) to better quantify cropland agriculture’s contribution to the closed drainage system dynamics and (3) to develop new analysis tools for better incorporating remote sensing and ground/atmospheric measurement data into decision models.


Some of the research issues of interest are listed/discussed below:

1.      Remote sensing

         Geometry—accurate area measurements, automated registration,

         Radiometry—calibration of multidate for data change detection

         Digital elevation models—accurate models, contribution of landscape position to system dynamics

2.      Ground/Atmosphere-based measurements

         Nutrient cycling—denitrification, greenhouse gases, N modeling (mass spect.), "macro look"

         Carbon sequestration—crop management influence, large area quantification

         Weeds—distribution, population/density, variety

          Soils—EM, wind erosion, tillage effects

Hydrologic cycle—soil moisture, runoff (erosion), percolation, and ET all with respect to fertilizer, fates of pesticides, crop health and crop growth/yield modeling

Crop health (stress)—fertility, moisture, disease, insects, weeds, yield loss, Insurance measurements (crop area planted by field, crop loss continuum—drown out and other causes)

Other—irrigation, crop yields, harvest logistics (variable crop dry down)

Atmospheric variables—improved measurement of heat and moisture fluxes:

The measurements and analyses discussed above will be the baseline for understanding a geographic area that here-to-fore has defied rational explanation at levels that have become expected in other areas in the North American continent. In other words, the quantification of flux has been done in other areas, but it has never been done in an integrated framework that anticipates the interactive integration of point measurements with several remote sensing technologies that have resolutions at the 1km (or better) spatial scale and time scales of hours or minutes.

3.                  Analyses

3.1 Crop modeling, dynamical systems, geospatial statistics

3.2 Digital image classification, statistical evaluations—integration of remote sensing data with ground-based/atmospheric measurements leading to products

3.3 Modern Mathematical Methods as Tools for Analyzing Agricultural Systems:

As one component of the EPSCoR proposal, we will study the effectiveness of using relatively recently developed mathematical methods to describe and predict the behavior of agricultural systems. The methods we will investigate include, but are not limited to:

3.3.1  Non-linear time series data analysis techniques derived from the theory of chaotic dynamical systems. The standard statistical techniques employed for data analysis are primarily based on linear models, which may not be appropriate for the complex processes observed in agricultural systems. Non-linear time series analysis may serve as a useful additional tool in this regard.

3.3.2  Fractal modeling. Nature abounds with systems possessing fractal properties. There is no reason to think that agricultural systems should not have some of these same features.

3.3.3  Self-organized criticality modeling. Large collections of simple interacting systems can give rise to complicated behavior organized on a large scale that could not be predicted from the dynamics of the individual systems.

3.3.4  Wavelet (as opposed to Fourier transform) analysis of data. This is closely linked to 1 and 2 above, and could give new insights into phenomena operating at several length scales.

3.3.5  Game theory. The Noble Prize for Economics in 1994 went to three Game Theorists. Evidently, game theory has something to say about economics, hence about agricultural economics.

It is unknown at this time if these or any other modern mathematical developments could yield tools that would improve farm profitability. Given the record of success of more conventional, well-established mathematical tools, it seems worthwhile to investigate the question.

4. Products/End Results

- educational tools
- insurance industry products including actual measurements and models that predict crop yield loss
- precision agriculture tools (profitability) and information management
- cost-benefit analysis of the measurements for operational use of crop model outputs (made with and without flux verification) and c/b analysis of other developed tools for use in precision agriculture.


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