Browsing by Author "Runck, Bryan"

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    GeoComputational Approaches to Evaluate the Impacts of Communication on Decision-making in Agriculture
    (2018-12) Runck, Bryan
    This dissertation proposes a new geocomputational approach to evaluate how communication-based interventions impact outcomes in agriculture. The decisions that people make in agriculture over the next ten to fifteen years will have long-term global consequences because agriculture is going to need to broadly change in order to meet the needs of the future. Many of the technical requirements and economic demands needed to enhance agriculture’s sustainability have been articulated with relative clarity. What remains opaque are the details of who should change, when, where, and how. A growing number of organizations are turning to communication-based interventions to answer these questions with people who will be impacted by changes. Evaluating these interventions is difficult because they are qualitative, affective, meaning-oriented, and discursive. This dissertation builds on existing trends in geocomputation around qualitative geographic information systems and incorporates new methods from machine learning into spatial agent-based modeling. Doing so allows for largely automated creation of agents from natural language text. The dissertation expands on these new tools in each chapter and applies them to the challenge of evaluating communication- based interventions focused on Midwest agriculture. Results suggest that novel insights can be gained into the inner workings of communication-based interventions for improving decision-making using the approaches described in this dissertation.
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    USDA-ARS Phenocart RGB Imagery Collected in Brookings, SD in 2021
    (2024-06-13) Ewing, Patrick; Runck, Bryan; patrick.ewing@usda.gov; Ewing, Patrick; Real-time Geoinformation Systems Lab, GEMS Informatics Center
    Data were collected from an experimental field in 2021 at the Eastern South Dakota Soil and Water Research Farm in Brookings, SD, USA (44.351 N, 96.805 W). The experiment consisted of a number of oat (Avena sativa L.) variety-by-seeding-rate treatments that were further divided into medium red clover planting treatments in a strip-block design with four replicates and a plot size of 6 m by 6 m. Oat treatments crossed variety (Reins, Natty, Sumo) and target oat population (140, 220, and 320 plants m-2) in 19 cm, drilled rows; red clover showed no responses to these oat treatments. Red clover treatments compared clover planted concurrently with oats (“underseeded”) on April 28, 2021; planted after oat harvest (“post-harvest”) on August 12, 2021; or no clover (“fallow”). Red clover was drilled at 1.25 cm depth at a rate of 8.2 kg ha-1 at a row spacing of 19 cm. An herbicide application of 210 g ha-1 sethoxydim (Poast, BASF Crop Protection, Research Triangle Park, North Carolina, USA), which selectively targets monocots, was applied on August 20, 2021, to control volunteer oats. A total of 720 RGB JPEG images were collected over six dates. The dates span the emergence of the post-harvest red clover planting to the first killing frost: August 21st, September 9th, September 29th, October 5th, October 15th, and October 25th. Images were collected by a Canon PowerShot ELPH 190 IS at a height of 2.5 m in the center of each plot using a phenotyping cart (White & Conley, 2013). To mirror the simplest use by researchers and practitioners, the camera was configured in full default, automatic mode, including ISO (a standard setting for controlling image darkness) and white balance and a 74-degree horizontal field of view. One image was taken from a representative location per plot on each date.