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Range Roundup: Precision Agriculture Range Project With Producer Participation

Figure 1: Forage quality and quantity predictions using a Random Forest algorithm for two sites in South Dakota using metrics derived from Google Earth Engine and Planet Imagery APIs. For assistance reading this graphic and data set, please call SDSU Extension at 605-688-6729.
Figure 1. Forage quality and quantity predictions using a Random Forest algorithm for two sites in South Dakota using metrics derived from Google Earth Engine and Planet Imagery APIs.

Written collaboratively by Jamie Brennan, Krista Ehlert, Josh Leffler, Hossein Moradi and Sandy Smart.

A group of scientists from SDSU are starting a new precision agriculture range project using remote sensing, machine learning, ground-collected vegetation samples, and web app development to build a user-friendly phone or computer website application to measure forage quality and quantity in near real-time. In addition, the project will have the ability to make predictions using forecasted climate data for drought preparation. The scientists include Doctors Jamie Brennan (project leader), Krista Ehlert, Josh Leffler, Hossein Moradi and Sandy Smart. Our team has collected preliminary data from the SDSU Cottonwood Field Station near Philip and at the SDSU Cow-Calf Unit in Brookings (Figure 1). Hand clipped samples were collected every two weeks in the summer of 2020 at both sites. According to our modeling efforts, we were able to verify that we could estimate forage quality (Acid Detergent Fiber, ADF; Neutral Detergent Fiber, NDF; Crude protein, CP) and forage quantity (Dry Matter Weight) quite effectively (predicted vs actual in each graph in Figure 1).

Data Collection Hubs

Map showing the location of four hub study sites in South Dakota. Two of the sites (Brookings and Cottonwood) are SDSU research facilities; the other two are working ranches run by producers. For assistance reading this graphic and data set, please call SDSU Extension at 605-688-6729.
Figure 2. Location of the four hub study sites in South Dakota. Two of the sites (Brookings and Cottonwood) are SDSU research facilities; the other two are working ranches run by producers. Source: PRISM mean annual precipitation 1981-2010

The next step is to expand our data collection efforts across South Dakota. We chose four intensive data collection sites (hubs) and will collect hand-clipped samples every two weeks during the growing season from five areas and two different plant community types. Around each hub we would like to find two additional ranches for a total of eight “satellite” ranches to help expand our model prediction efforts by feeding real ground-truth data once a month during the growing season (see Figure 2). All sites exist along the gradient from over 600 mm (23.6 inches) to fewer than 400 mm (15.7 inches) of annual precipitation from east to west in South Dakota.

Custom Prediction Models

NDVI (normalized difference vegetation index) – or a measure of greenness – for three different years: 2002 (drought), 2009 (average), 2019 (wet). Julian day 1 corresponds to January 1st. Note that NDVI in 2002 started out with a flatter path compared with 2009 and 2019. This kind of graph allows us to make predictions of forage quantity depending on the steepness of the NDVI curve. For assistance reading this graphic and data set, please call SDSU Extension at 605-688-6729.
Figure 3. NDVI (normalized difference vegetation index) – or a measure of greenness – for three different years: 2002 (drought), 2009 (average), 2019 (wet). Julian day 1 corresponds to January 1st.

The uniqueness of our approach is that we intend to develop customized prediction models based on individual rancher-derived data for that specific rancher – instead of relying on a universal model with data that may not adequately represent your location. In addition, we are very fortunate to have long-term data to help us develop growing season forage quantity predictions. Figure 3 shows the greenness index (NDVI) for three very different years (drought, average, and wet). Note that NDVI in 2002 started out with a flatter path compared with 2009 and 2019. This kind of graph allows us to make predictions of forage quantity depending on the steepness of the NDVI curve.

The research and Extension team is excited about the impact that this tool will have when fully developed and tested. Please reach out to Krista Ehlert, SDSU Extension Range Specialist, krista.ehlert@sdstate.edu, if you are interested in becoming a satellite ranch.