About Me

I'm a graduate student passionate about sustaining agricultural productivity.
My work focuses on evaluating remote sensing and other precision agriculture tools as a means to reduce fertilizer loss and keep our soil and water resources clean and productive.

I believe there is work to be done to make the implementation of the 4Rs of nutrient stewardship (i.e., Right source, Right rate, Right time, and Right Place) easier and more effective for growers and other stakeholders. As a society, we must be more proactive in establishing a balance among the economic, environmental, and social ecosystems that are influenced by agriculture. My work involves conducting research and developing tools to establish a working balance among these ecosystems.


Here you'll find a sample of some of my past and current projects. Please get in touch if you have questions, feedback, or would like to just start a discussion!

EONR Python Package
A Python tool for computing the optimum nitrogen rate and its confidence intervals from agricultural research data
30 March 2019

EONR is a Python package for computing the economic optimum nitrogen fertilizer rate using data from agronomic field trials under economic conditions defined by the user (i.e., grain price and fertilizer cost), and it can be used for any crop (e.g., corn, wheat, potatoes, etc.).

Please see the following for more information:
1) EONR Documention/API
2) Methodology and use case in Computers and Electronics in Agriculture (peer-reviewed)

Feel free to reach out if you'd like help getting started! I think you'll find it rather easy to use, even if you're just beginning with Python.
ASA International Conference, Baltimore, MD - Nigon et al. (2018)
Independent calibration of the EPIC model using in-season aerial imagery
5 November 2018

I presented this poster at the Agronomy and Crops International Annual Meetings in Baltimore, MD.
  • I used remote sensing to estimate plant biomass at the early growth stages.
  • Using the estimated biomass from remote sensing, I performed a calibration on the EPIC model by fitting the EPIC leaf development parameters.
  • Results indicate that this method provides an opportunity to account for within-field spatial variability to make improved in-season nitrogen fertilizer decisions.
  • Future work will focus more attention on validating simulation results with ground truth measurements, including nitrogen uptake and grain yield.
  • 1st place recognition in the graduate student poster competition (of 7 posters).
47th North Central Extension-Industry Soil Fertility Conference, Des Moines, IA - Nigon et al. (2017)
Active and passive spectral sensing for predicting the optimum nitrogen rate and timing in corn
15 - 16 November 2017

I presented this poster at the North Central Extension-Industry Soil Fertility Conference organized by the International Plant Nutrition Institute in Des Moines, IA.
  • I looked at the ability of remote sensing to predict early season corn nitrogen uptake.
  • If nitrogen uptake can be predicted via remote sensing prior to sidedress nitrogen fertilizer application, producers can vary the fertilizer rate spatially to better match the rate required to achieve maximum profit and reduce nutrient losses that contribute to water pollution.
  • Results indicate that remote sensing can predict nitrogen uptake reasonably well by the V10 growth stage (when crop height is ~3'), but sensor type and calibration are important considerations.
  • However, until there is an economic value placed on pollution caused by nitrogen fertilizer loss, it is difficult to justify both in-season and variable rate N application from an economic perspective.
  • 2nd place recognition in the graduate student poster competition (of 29 posters).
More Projects
SSSA/ASA International Conference, Tampa, FL - Nigon et al. (2017)
Spectral imagery to estimate leaf area index and above-ground biomass in maize
23 October 2017

This is a poster I presented at the Agronomy, Crops, and Soils International Annual Meetings in Tampa, FL.
  • I looked at the relationships among maize height, above-ground biomass, leaf area index, and spectral reflectance during early growth stages (V5 - V10).
  • The approach used is a viable option for reliably informing remote sensing algorithms and crop models.
  • However, variability of such estimations should be considered when interpreting final precision nitrogen fertilizer recommendations.
  • 3rd place recognition in the graduate student poster competition (of 16 posters).
Weather station
Building a low-cost weather station that publishes data to the internet in real-time
April - June 2017

I built a weather station that measures rainfall, temperature, humidity, soil moisture, wind speed and gust, then publishes data to the cloud over a 3G network via the Particle Electron microcontroller. This was my first microcontroller project and was certainly a learning experience for me. See how I built it!
SSSA/ASA International Conference, Phoenix, AZ - Nigon et al. (2016)
Tetrapy: A Python package for cleaning and preprocessing Tetracam multispectral imagery
May - November 2016

This is a poster I presented at the Agronomy, Crops, and Soils International Annual Meetings in Phoenix, AZ.
  • It describes a Python project I've been working on that allows Tetracam users to decode RAW images and correct images for vignetting and radial distortion correction.
  • The Tetrapy program provides the ability to perform preprocessing tasks on Tetracam images in a more robust way than Tetracam's proprietary software (i.e., PixelWrench2).
  • Please get in touch if you'd like me to share the source code for use on your own imagers.
Tour of Nigon-View Dairy
Tour of Nigon-View Dairy - where I grew up and my parents currently operate their dairy farm
Fall 2015

I made a story map for a Web GIS course I completed. Story maps allow you to combine maps and geographic information with narrative text and photos to tell a story.
SSSA/ASA International Conference, Cincinnati, OH - Nigon et al. (2012)
Plant-based approaches for in-season detection of nitrogen stress in potato
24 October 2012

This is a poster I presented at the Agronomy, Crops, and Soils International Annual Meetings in Cincinnati.
  • It explains how total nitrogen and nitrate-nitrogen concentrations collected from in-season potato tissue samples (i.e., petioles and/or leaflets) change throughout the course of the growing season.
  • Total nitrogen and nitrate-nitrogen were correlated to non-invasive remote sensing techniques (i.e., SPAD relative chlorophyll and narrowband reflectance).
  • Results illustrate the ability of remote sensing techniques to predict nitrogen uptake in potato
41st North Central Extension-Industry Soil Fertility Conference, Des Moines, IA - Nigon et al. (2011)
Hyperspectral and thermal imagery for the detection of nitrogen and water stress
16-17 November 2011

This is a poster I presented at the 41st North Central Extension-Industry Soil Fertility Conference in Des Moines, IA.
  • The main objective of this research was to evaluate the ability of canopy level hyperspectral and thermal imagery to detect nitrogen and water status of a potato crop.
  • The specific wavelengths and/or spectral indices that best predict nitrogen stress are highlighted.