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Soybeans, one of the most valuable crops worldwide, are annually decimated by the soybean cyst nematode (SCN), Heterodera glycines, resulting in massive losses in soybean yields and economic revenue. Conventional agricultural pesticides are generally effective in the short term; however, they pose growing threats to human and environmental health; therefore, alternative SCN management strategies are urgently needed. Preliminary findings show that phenolic acids are significantly induced during SCN infection and exhibit effective nematocidal activities in vitro. However, it is unclear whether these effects occur in planta or elicit any negative effects on plant growth traits. Here, we employed a phytochemical-based seed coating application on soybean seeds using phenolic acid derivatives (4HBD; 2,3DHBA) at variable concentrations and examined SCN inhibition against two SCN types. Moreover, we also examined plant growth traits under non-infected or SCN infected conditions. Notably, 2,3DHBA significantly inhibited SCN abundance in Race 2-infected plants with increasingly higher chemical doses. Interestingly, neither compound negatively affected soybean growth traits in control or SCN-infected plants. Our findings suggest that a phytochemical-based approach could offer an effective, more environmentally friendly solution to facilitate current SCN management strategies and fast-track the development of biopesticides to sustainably manage devastating pests such as SCN.
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Robotic agricultural instrument for automated extraction of nematode cysts and eggs from soil to improve integrated pest management
Abstract Soybeans are an important crop for global food security. Every year, soybean yields are reduced by numerous soybean diseases, particularly the soybean cyst nematode (SCN). It is difficult to visually identify the presence of SCN in the field, let alone its population densities or numbers, as there are no obvious aboveground disease symptoms. The only definitive way to assess SCN population densities is to directly extract the SCN cysts from soil and then extract the eggs from cysts and count them. Extraction is typically conducted in commercial soil analysis laboratories and university plant diagnostic clinics and involves repeated steps of sieving, washing, collecting, grinding, and cleaning. Here we present a robotic instrument to reproduce and automate the functions of the conventional methods to extract nematode cysts from soil and subsequently extract eggs from the recovered nematode cysts. We incorporated mechanisms to actuate the stage system, manipulate positions of individual sieves using the gripper, recover cysts and cyst-sized objects from soil suspended in water, and grind the cysts to release their eggs. All system functions are controlled and operated by a touchscreen interface software. The performance of the robotic instrument is evaluated using soil samples infested with SCN from two farms at different locations and results were comparable to the conventional technique. Our new technology brings the benefits of automation to SCN soil diagnostics, a step towards long-term integrated pest management of this serious soybean pest.
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- Award ID(s):
- 1556370
- PAR ID:
- 10220133
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 11
- Issue:
- 1
- ISSN:
- 2045-2322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1038/s41598-021-82261-w
