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Analyzing efficacy of nitrogen-fixing biological amendments in maize using agronomic metrics and IoT LiDAR

Rebecca Caldbeck, University of Kentucky, Agricultural and Medical Biotechnology & Natural Resources and Environmental Science

Dr. Tony Vyn, Purdue University, Department of Agronomy

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This material is based upon work supported by the IoT4Ag Engineering Research Center funded by the National Science Foundation (NSF) under NSF Cooperative Agreement Number EEC-1941529.  Any opinions, findings and conclusions, or recommendations expressed in this material are those of the author(s), and do not necessarily reflect those of the NSF.

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Introduction and Problem Statement

  • Rising population= food production strain= increased stress on agricultural land
  • Nitrogen (N) is essential for plant growth
    • N-fixing bacteria (ex. legumes)
      • atmospheric N → usable form for plant uptake
    • Synthetic N application (required in maize, local agronomic requirement= 200 lbs N/ac)
  • Pivot Bio’s ‘Proven’: new, commercially available N-fixing microbial amendment, associates symbiotically with maize roots, applied at planting
    • Claim: reduce synthetic N input by 20-30lbs N/ac
    • Reduce negative environmental impact of N
  • Can the efficacy of N-fixing biological amendments in maize be conveyed using agronomic metrics and IoT LiDAR?

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1. N application

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Overview of the Technical Approach

  • Examine phenotypic responses of maize to biological amendment at varying N rates
  • Apply traditional quantitative metrics and IoT LiDAR (Light Detection and Ranging)
  • Agronomic metrics- ground-truthing
    • Height, relative leaf greenness, stalk diameter, biomass, nutrient analysis
  • LiDAR metrics- anecdotal, preliminary point cloud data
    • Used drones before, now experimenting with ground robot
    • Height, canopy density

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AgBot 2.0: Jackal ground robot

0N

Proven

Control

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Results- LiDAR

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0N-Proven

0N-Control

255N-Proven

255N-Control

A

D

C

B

H

F

G

E

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Results- Agronomic Metrics

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3. 95N: Proven vs Control

4. Extended leaf height

5. Digital Vernier caliper

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Results- Agronomic Metrics

                  • Based on agronomic and LiDAR data, Proven appeared to boost plant height and stalk diameter.
                  • SPAD results indicated Proven benefit in low N plots.
                  • Pending yield data and tissue analysis.
                  • LiDAR collected via ground robot successfully detected differences in canopy architecture between plots.

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2. SPAD reader

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Summary

  • Positive trends in agronomic/IoT LiDAR data support efficacy of N-fixing biological amendment in maize at varying N rates
    • Positive trend response to increased N rates
    • Positive trend response to N-fixing biological amendment
      • most significant at low-to-moderate N rates
  • N-fixing biological: reduce dependency on synthetic N inputs, mitigate environmental harm, provide net economic benefit to farmers
  • AgBot 2.0 LiDAR: reduce manual labor requirements, provide invaluable data, boost efficiency of agricultural research- support global population growth
  • Next steps: RGB camera, leaf cutter, process point cloud data from LiDAR

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Acknowledgements

  • Dr. Tony Vyn for his mentorship and guidance.
  • Garrett Verhagen and Brendan Hanson for their mentorship and support with data collection.
  • Natalie Duvanenko, Alyson Godwin, Garett Morrison, and Emma Kiewitt for their invaluable support with data collection.
  • Dr. David Cappelleri and Brian Huang for their support with LiDAR.
  • NSF, SURF, IoT4Ag for supporting student research.

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  • Questions? Rebecca.Caldbeck@gmail.com

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Sources

  1. https://www.deere.com/en/campaigns/ag-turf/production-system/applying-anhydrous-ammonia/
  2. https://www.specmeters.com/nutrient-management/chlorophyll-meters/chlorophyll/spad502p/
  3. Dr. Vyn’s personal repertoire
  4. PivotBio protocol
  5. https://www.amazon.com/Digital-Electronic-Vernier-Micrometer-Measuring/dp/B01IJA9O5E

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