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Precision Conservation With Geospatial Technologies
T.G. Mueller, S. A. Shearer
Department of Plant and Soil Sciences
The NRCS provides substantial payments through the conservation reserve program (CRP) to land owners who have conservation buffers installed and maintained. This is because these structures have been shown to prevent ephemeral gully erosion which accounts for a substantial amount of the total water erosion that occurs in agricultural fields.
To determine whether CRP-eligible grassed waterways can be established in agricultural fields, an NRCS conservationist must first make an on-farm site assessment. This involves walking across fields in order to identify areas where there is evidence of erosion resulting from concentrated water flow. This is a slow and time-consuming process and eroded channels scattered across large fields can easily be missed. Given contracting budgets and growing responsibilities of NRCS conservationists, they have less time to make these field visits. Tools are needed that will help them rapidly and accurately identify areas that are eligible to receive CRP payments for these conservation structures.
We are developing neural network and regression models to predict the erosion from concentrated flow. Terrain attributes are used as predictor variables such as LS (length slope factor), WET (topographic wetness index), and PLAN (Plan curvature) are terrain attributes derived from precision GPS measurements. Cost effective mapping procedures will be developed in order to help NRCS conservationists more rapidly identify and prioritize potential locations for grassed waterways and buffer strips for enrollment in CRP. These maps will be similar to the one presented in Fig.1. , but in addition to estimating the potential erosion associated with each feature, we will also estimate the potential for delivering sediment beyond the edge-of-field and will estimate the potential for sequestration of carbon.
Furthermore, we will we will evaluate the accuracy of prediction maps created using elevation data sources that differ in spatial resolution: USGS digital elevation models (DEMs) and light detecting and ranging (LIDAR). LIDAR data is much less expensive than surveys created from precision GPS measurements on a per hectare basis. The USGS DEMs are freely available on the internet. However, the adequacy with which prediction maps can be created from these datasets is unknown but preliminary results suggests that USGS data will be adequate in some cases. We will determine how these models should be parameterized across the different physiographic regions in Kentucky.
We expect this work to lead to the development of regional models that predict where waterways will be needed. We hope that these maps of erosion potential will be on the internet and available to government personnel, farmers, and citizens.
2011 Project Description
We updated our analyses to automate the calculations of terrain attributes with TauDEM for ArcGIS 10.0 using Model Builder. We wrote code in Python to model model buffers up slopes. This work is being used to conduct analyses for two book chapters. We are also working with the University to develop cloud computing capabilities so that these analyses can be conducted on-line by practitioners in the field.
The work from this grant was incorporated into two courses: BAE 599 (Precision agriculture) and PLS 468G (Soil Use and Management). We have used results of this work to develop two NIFA proposals and one preproposal in 2011. These were not funded but the comments were very positive. We will revise and resubmit in 2012.
Neelakantan, S., T.G. Mueller, B. Lee, B. Lee, P. Finnell, V. Bumgardner, and D. Carey. 2011. Web 2.0 spatial data browser for visualizing land-use assessment information from soil surveys. J. of Soil and Water Conservation and Management. 66:37A-39A.
Delgado, J.A., R. Khosla, and T.G. Mueller. 2011. Recent advances in precision (target) conservation. Journal of Soil and Water Conservation. 513-518.