Corn Fungicide ROI Calculator

Information on the Corn Fungicide ROI Calculator

The purpose of this calculator is to share results from university uniform corn fungicide trials conducted in the United States and Canada, and allow farmers and others in the agricultural industry to calculate the potential return on investment (ROI) for corn fungicide application across a variety of user-defined factors, which is based on research data included in this calculator.

The treatment cost, expected benefits, and breakeven probability values shown in the calculator are estimates based on data and not guaranteed values. Also, values are derived from data collected in trials designed to test specific product comparisons, and data are not available for all labeled fungicides on corn. Data are not comprehensive and represent only the treatments tested in these trials.

How to cite information from this resource:

Crop Protection Network. 2024. Corn Fungicide Return On Investment Calculator. Corn Disease Working Group. Https://cropprotectionnetwork.org/fungicide-roi-calculator. Doi.org/10.31274/cpn-20240724-0.

How were fungicide products selected for inclusion in the calculator?

The Corn Disease Working Group (CDWG) develops annual uniform testing protocols based on feedback from university extension specialists. Trials typically test between five and 10 fungicide products per year, per trial. Products are selected based on availability and market share, and typically represent products available to and used by a majority of farmers. Focus is also placed on newer products where efficacy data might be lacking across the corn production belt of the United States.

How were treatment costs determined?

The CDWG solicits information on fungicide product pricing and application costs from university extension specialists annually. Methods for collecting these costs vary by state or province and year. Specialists may collect pricing information from surveys or direct feedback from university Extension, industry, farmers, and other agricultural personnel. Product and application costs are averaged, and a national mean and median are determined for each product based on submitted data. The treatment costs for each product listed can be changed to tailor the expected benefit estimates to local pricing.

How are expected net benefits/acre calculated?

The expected net benefit is an estimate of ROI when considering several variable factors that can be measured or estimated. These factors include the proportional yield benefit (yield of the treated plots vs. non-treated) for each fungicide across a range of disease levels. This is then combined with total treatment cost (cost of the fungicide plus application cost) and expected commodity sale price per bushel with both parameters fit to a function that estimates the expected net benefit per acre. This function is only a best estimate based on the available data and reported, real-world treatment costs. Unaccountable error and uncertainty exists in the data and actual ROI may occasionally be significantly different from the estimates reported here.

How are expected breakeven probabilities calculated?

Thousands of simulations were performed to estimate the expected ROI of fungicide usage application across a range of inputs used to calculate expected net benefits. The expected breakeven probabilities represent the proportion of total simulations that had an expected net benefit of zero or greater, given a fungicide and set of crop characteristic inputs.

What does disease severity mean?

“Low” indicates simulations made using a final end-of-season disease severity level of 1%. “High” indicates simulations made using end-of-season disease severity level of 5% or above. The levels were chosen based on previous research demonstrating detectable yield loss from disease at 5% or above. You may also choose to explore a different end-of-season severity by ticking the box next to “I’d like to enter a custom disease severity %.” Disease severity levels should be set at what is expected for a particular hybrid in a particular field at the end of the season.

Important notes about data

Currently data available in the calculator are from university uniform corn fungicide trials conducted across 19 states and Ontario, Canada between 2019 and 2022. Primary diseases in this data set were tar spot and southern rust. Diseases, such as gray leaf spot, northern corn leaf blight, and others, were observed at lower frequencies in this dataset.

Disclaimer

This information is only a guide, but is based on multi-year research across multiple locations. Contributors and data managers assume no liability resulting from the use of these estimates.

References to products in this resource are not intended to be an endorsement to the exclusion of others that may be similar. Individuals using such products assume responsibility for their use in accordance with current directions of the manufacturer.

Find out More

The Crop Protection Network (CPN) is a multi-state and international collaboration of university and provincial extension specialists, and public and private professionals who provide unbiased, research-based information to farmers and agricultural personnel. Our goal is to communicate relevant information that will help professionals identify and manage field crop diseases.

Find more crop protection resources at the Crop Protection Network.

Acknowledgments

Data compilation:

Maria Oros, Isaac Baumann, and Jason Lo, Data Science Institute, University of Wisconsin-Madison

Contributors:

Protocols for testing fungicides developed by the Corn Disease Working Group (CDWG). Data submitted by: Tom Allen, Mississippi State University; Mandy Bish and Kaitlyn Bissonnette, University of Missouri; Emmanuel Byamukama, South Dakota State University; Martin Chilvers, Michigan State University; Alyssa Collins, Pennsylvania State University; Travis Faske, University of Arkansas; Andrew Friskop, North Dakota State University; Austin Hagan, Auburn University; Tamra Jackson-Ziems, University of Nebraska-Lincoln; Heather Kelly, University of Tennessee; Nathan Kleczewski, University of Illinois; Alyssa Koehler, University of Delaware; David Langston, Virginia Tech; Daren Mueller, Iowa State University; Rodrigo Onofre, Kansas State University; Paul Price III, LSU AgCenter; Alison Robertson, Iowa State University; Edward Sikora, Auburn University; Damon Smith, University of Wisconsin-Madison; Darcy Telenko, Purdue University; Albert Tenuta, Ontario Ministry of Agriculture, Food and Rural Affairs; and Kiersten Wise, University of Kentucky.

In addition to support from USDA-NIFA, this project was funded in part through Growing Forward 2 (GF2), a federal-provincial territorial initiative. The Agricultural Adaptation Council assists in the delivery of GF2 in Ontario.

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