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Enhancing prediction of soybean cyst nematode spatial distribution through geostatistical optimization: A comparison of manual and automated sampling methods

Gonzalez Aquino, Richard Stefano
http://rave.ohiolink.edu/etdc/view?acc_num=osu1721310953439732

Abstract Details

2024, Master of Science, Ohio State University, Plant Pathology.
Soybean cyst nematode (SCN), Heterodera glycines, remains the most damaging soybean pathogen in North America. Management decisions of SCN begins with accurate soil sampling to assess current population densities. Although widely used, manual sampling is time-consuming, less effective over large areas, and may overlook spatial aggregation. This project aimed to optimize and compare the accuracy and efficiency of an automated soil sampling approach using an automated precision soil sampler (APSS) against manual soil sampling method. Three SCN-infested fields, one from Clark County (WARS) and two from Fulton (-1 and -2) County in Ohio, were selected for this study. Both manual and automated methods were used to collect soil samples following a grid pattern. The two sampling methods were evaluated using two approaches: direct SCN egg count and pixel-based counting derived from surface interpolation predicted by inverse distance weighting (IDW) model. Lin’s concordance correlation coefficient (ρc) was used to assess agreement between the two methods. The egg count approach showed positive ρc values in WARS (ρc = 0.53) and Fulton-1 (ρc = 0.77), indicating agreement between manual and automated sampling methods. However, a lower correlation was observed for Fulton-2 (ρc = 0.47). Using the IDW interpolation approach, which accounts for spatial aggregation, both sampling methods detected similar SCN values (ρc = 0.99) for all fields. Moreover, removing 50% of the automated sampling points resulted in similar spatial resolution of SCN distribution across the fields, offering a significant cost-benefit advantage. We also demonstrated the feasibility of conducting on-farm trials using this automated approach. This research demonstrates that integrating automated sampling with a geospatial approach can enhance our understanding of SCN distribution, streamline soil sampling, and identify high-risk areas of infestation, thereby assisting growers in making informed decisions to manage this pest.
Horacio Lopez-Nicora (Advisor)
Christopher Taylor (Committee Member)
Pierce Paul (Committee Member)
97 p.
Plant Pathology
Heterodera glycines, automated soil sampling approach, inverse distance weighting (IDW), sampling optimization, spatial distribution

Recommended Citations

Citations

  • Gonzalez Aquino, R. S. (2024). Enhancing prediction of soybean cyst nematode spatial distribution through geostatistical optimization: A comparison of manual and automated sampling methods [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1721310953439732

    APA Style (7th edition)

  • Gonzalez Aquino, Richard. Enhancing prediction of soybean cyst nematode spatial distribution through geostatistical optimization: A comparison of manual and automated sampling methods. 2024. Ohio State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1721310953439732.

    MLA Style (8th edition)

  • Gonzalez Aquino, Richard. "Enhancing prediction of soybean cyst nematode spatial distribution through geostatistical optimization: A comparison of manual and automated sampling methods." Master's thesis, Ohio State University, 2024. http://rave.ohiolink.edu/etdc/view?acc_num=osu1721310953439732

    Chicago Manual of Style (17th edition)

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© 2024, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.