About

John Fulton is a Professor in the Department of Food, Agricultural and Biological Engineering at The Ohio State University. He holds a B.A. in Physics from Wittenberg University, an M.S. in Agricultural Engineering from the University of Kentucky, and a Ph.D. in Biosystems & Agricultural Engineering from the University of Kentucky. His research and extension focus on machinery automation and the use of spatial data to improve farm business and in-season decisions. He specializes in developing and evaluating technology or automated components related to application equipment to more accurately meet site-specific crop and soil needs. His work addresses the challenges of increasing yields while maintaining farm profitability, emphasizing the integration of technology and data in agricultural production.

Research topics

• Engineering
• Geography
• Computer Science
• Artificial Intelligence
• Environmental science
• Geometry
• Composite material
• Archaeology
• Mathematics
• Agricultural engineering
• Mechanics
• Physics
• Environmental planning
• Agronomy
• Materials science
• Telecommunications
• Business
• Economics
• Environmental resource management

Selected publications

• Developments in variable seeding systems for precision agriculture

  2025-12-09

  book-chapter1st authorCorresponding

  Variable rate seeding (VRS) systems continue to be an innovative area of precision agriculture, enabling farmers to optimize seed distribution based on specific field conditions. This approach contrasts with traditional uniform seeding methods, which often fail to account for the spatial variability that exists within fields. VRS systems utilize data from soil sensors, precision soil sampling, remotely sensed imagery, high-resolution soil maps, elevation or topography maps, yield maps, and GNSS technology to create detailed maps of field variability. In essence, most VRS systems utilize extensive data, such as soil properties (Šarauskis et al., 2022), environmental conditions (Šarauskis et al., 2022), past crop yields, and current weather conditions, to offer tailored recommendations. Bullock et al. (1998) observed that implementing VRS profitably demands costly and detailed data on site characteristics, production inputs, and random factors. These spatial maps guide the seeding equipment to adjust the seeding rate in real time, ensuring that each area of the field receives the optimal amount of seed. This precision not only maximizes yield potential but also reduces input costs and minimizes environmental impact by avoiding over-seeding in less productive areas and under-seeding in more fertile zones.

  PublisherDOI

• Survey analysis of on‐farm research influence on farmer decision‐making

  Agronomy Journal · 2025-09-01 · 1 citations

  articleOpen access

  Abstract Farmers are faced with many production decisions each season that greatly impact the profitability of their operations. Conducting on‐farm trials can generate valuable insights regarding the benefits of new production practices or technologies, but the quality and reliability of the results hinge on trial design and analysis. Additionally, the impact of these findings is constrained by how widely the information is disseminated. In 2017, a team of Extension professionals at The Ohio State University launched the eFields program to enhance both the quantity and quality of farmer‐participatory on‐farm research in Ohio. In 2024, an evaluation tool was designed to understand how the eFields program has impacted the ways that farmers, agriculture professionals, and others engage in on‐farm research and the information derived and shared from it. Growth in the program has been substantial, with a 478% increase in the number of trials completed each season and a 269% increase in farmer participation. Exposure to the program increased survey respondents’ likelihood on using on‐farm research‐based information for decision‐making in their farm operations with 47.2% reporting they increased their use of these type of data. Participation in the program increased as farm size increased. Participating in on‐farm research had a positive influence on farmer behavior changes; farmers who had conducted trials through the program were more likely to adopt new management or technology than farmers who only received information from the program. The information shared through the annual report is recognized as valuable to the target audience of farmers and agriculture professionals.

  PublisherOA PDFDOI

• Regional drivers of soybean farmers’ yield, seed protein, and oil concentration

  Field Crops Research · 2025-10-08 · 1 citations

  article
  PublisherDOI

• Digitalization of agriculture for sustainable crop production: a use-case review

  Frontiers in Environmental Science · 2024-07-25 · 55 citations

  reviewOpen access

  The digitalization of agriculture is rapidly changing the way farmers do business. With the integration of advanced technology, farmers are now able to increase efficiency, productivity, and precision in their operations. Digitalization allows for real-time monitoring and management of crops, leading to improved yields and reduced waste. This paper presents a review of some of the use cases that digitalization has made an impact in the automation of open-field and closed-field cultivations by means of collecting data about soils, crop growth, and microclimate, or by contributing to more accurate decisions about water usage and fertilizer application. The objective was to address some of the most recent technological advances that are leading to increased efficiency and sustainability of crop production, reduction in the use of inputs and environmental impacts, and releasing manual workforces from repetitive field tasks. The short discussions included at the end of each case study attempt to highlight the limitations and technological challenges toward successful implementations, as well as to introduce alternative solutions and methods that are rapidly evolving to offer a vast array of benefits for farmers by influencing cost-saving measures. This review concludes that despite the many benefits of digitalization, there are still a number of challenges that need to be overcome, including high costs, reliability, and scalability. Most of the available setups that are currently used for this purpose have been custom designed for specific tasks and are still too expensive to be implemented on commercial scales, while others are still in their early stages of development, making them not reliable or scalable for widespread acceptance and adoption by farmers. By providing a comprehensive understanding of the current state of digitalization in agriculture and its impact on sustainable crop production and food security, this review provides insights for policy-makers, industry stakeholders, and researchers working in this field.

  PublisherOA PDFDOI

• Understanding the limitations of grain yield monitor technology to inform on‐farm research

  Agronomy Journal · 2024-09-21 · 4 citations

  articleOpen access

  Abstract Yield monitoring technology (YM) is a valuable tool to evaluate crop performance in on‐farm research (OFR). However, limited information exists on utilizing this technology to accurately inform OFR. The objectives of this study were to evaluate the ability of grain yield monitor mass flow sensors to detect changes in corn ( Zea mays L.) yield for different plot lengths, provide a recommended minimum plot length to utilize YM in OFR, and determine if differences in estimating yield existed between YMs. Six treatment lengths that varied in distance of intentional yield differences (7.6, 15.2, 30.5, 61.0, 121.9, and 243.8 m) were created by alternating high‐yield (202 kg N/ha application) and low‐yield (0 kg N/ha application) plots. A total of four grain YMs with impact‐style mass flow sensors were used within two commercially available combines. Yield comparisons were made between the plot combine and YMs to evaluate the accuracy of each technology for detecting the magnitude of yield change across lengths using analysis of variance and exponential regression curves. Results indicated that the mass flow sensors were not sensitive enough to detect quickly changing flow rates for alternating yield changes in small plot lengths. Minimum plot lengths ranged from 43 to 107 m depending on YM. Significant differences were observed between grain YMs from different manufacturers. Future work could evaluate the influence additional crops or smaller yield differences have on the optimum OFR plot length.

  PublisherOA PDFDOI

• Generating High-Definition As-Applied Maps for Pneumatic Fertilizer Application Equipment

  VDI Verlag eBooks · 2022-01-01

  book-chapter1st authorCorresponding
  PublisherDOI

• Potential of fertilizer segregation during application using spinner disc spreader

  Precision Agriculture · 2021-06-11 · 12 citations

  article
  PublisherDOI

• 94. Using virtual reality field demonstrations to increase farmer engagement

  2021-06-25 · 3 citations

  articleSenior author

  The Ohio State University Digital Agriculture Team piloted a virtual field demonstration approach during the virtual Farm Science Review (FSR) held in September 2020. The goal was to develop educational content that offered participants an immersive field demonstration experience that imparts information about precision agriculture technologies that practitioners would find useful for decision making. The virtual field demonstrations included tillage, harvest, aerial application and scouting field operations. Twenty-five videos featuring various views and data were created and shared via the FSR virtual platform and the Digital Ag website, YouTube, Facebook and Twitter. Across all platforms, the virtual reality videos exhibited a 43% increase in viewership and engagement compared to traditional (2D) video types.

  PublisherDOI

• 72. High definition fertilizer as-applied maps for pneumatic applicators

  2021-06-25

  article1st authorCorresponding

  As-applied maps are generated by variable-rate technology (VRT) and are intended to reflect the applied amount of product across a field and serve as key data layers for fertilizer records. The objective of this study was to develop sensing technology to measure fertilizer mass flow on pneumatic application equipment. Machine vision and image processing techniques were implemented. The machine vision system consisted of a camera and strobe light plus a small container mounted around the tube used on pneumatic application equipment. The prototype sensor was lab tested to develop the proper synchronization of the camera shutter timing and strobe light for measuring individual fertilizer particles. Lab work was used to develop filtering techniques to minimize the effect of dust and noise. Lab results indicated good performance at application rates above 224 kg/ha. Field validation indicated that the sensing technology estimated fertilizer flow on a row-by-row basis and generated a high definition (HD) as-applied fertilizer maps.

  PublisherDOI

• Soil and terrain properties that predict differences in local ideal seeding rate for soybean

  Agronomy Journal · 2020-02-20 · 4 citations

  articleOpen access

  Abstract The agronomic optimum seeding rate (AOSR) of soybean [ Glycine max (L.) Merr.] varies based on environment. Understanding where AOSR varies within a field is useful for farmers utilizing variable rate seeding technology. An AOSR representing an area smaller than a whole field is referred to as local ideal seeding rate (LISR). The objective of this study was to identify soil and terrain properties that were most predictive of differences in LISR. Seeding rate trials were established at four fields in 2017 and three fields in 2018. Yield data were used to estimate LISR 33–68 times per field. Soil properties were estimated at the same scale as LISR using 0.2‐ha grid samples and terrain properties were calculated from 0.76‐m digital elevation model developed using light detection and ranging data. Random forest analysis was performed to identify which soil and terrain properties were predictive of LISR within each site‐year. At all site‐years, terrain properties were generally more predictive of LISR compared to soil properties. Valley depth and general curvature were in the top‐five most predictive properties at four of seven site‐years. Moving from the lowest valley to the highest ridge was associated with an increase in LISR of 76,000 seeds ha −1 . Moving from the lowest relative slope position to the highest relative slope position was associated with a 38,000 seeds ha −1 increase in LISR. Terrain properties may be appealing to farmers because they relate to LISR, are publicly available data, and stable over time.

  PublisherDOI

Frequent coauthors

• Timothy McDonald

  30 shared

• S. A. Shearer

  The Ohio State University

  25 shared

• Ajay Sharda

  Kansas State University

  22 shared

• Randal K. Taylor

  12 shared

• Mark Dougherty

  11 shared

• Simerjeet Virk

  10 shared

• Wesley C. Zech

  University of Alabama at Birmingham

  10 shared

• Oladiran Fasina

  Auburn University

  9 shared